microRNAs (miRNAs) are small RNA molecules that represent the top of the pyramid of many tumorigenesis cascade pathways as they have the ability to affect multiple, intricate, and still undiscovered downstream targets. Understanding how miRNA molecules serve as master regulators in these important networks involved in cancer initiation and progression open up significant innovative areas for therapy and diagnosis that have been sadly lacking for deadly female reproductive tract cancers. This review will highlight the recent advances in the field of miRNAs in epithelial ovarian cancer, endometrioid endometrial cancer and squamous-cell cervical carcinoma focusing on studies associated with actual clinical information in humans. Importantly, recent miRNA profiling studies have included well-characterized clinical specimens of female reproductive tract cancers, allowing for studies correlating miRNA expression with clinical outcomes. This review will summarize the current thoughts on the role of miRNA processing in unique miRNA species present in these cancers. In addition, this review will focus on current data regarding miRNA molecules as unique biomarkers associated with clinically significant outcomes such as overall survival and chemotherapy resistance. We will also discuss why specific miRNA molecules are not recapitulated across multiple studies of the same cancer type. Although the mechanistic contributions of miRNA molecules to these clinical phenomena have been confirmed using in vitro and pre-clinical mouse model systems, these studies are truly only the beginning of our understanding of the roles miRNAs play in cancers of the female reproductive tract. This review will also highlight useful areas for future research regarding miRNAs as therapeutic targets in cancers of the female reproductive tract.

INTRODUCTION

According to the World Health Organization (WHO) International Agency for Research on Cancer (GLOBOCAN 2012), over 1 million women worldwide were diagnosed with female reproductive tract cancers including ovarian, cervical and endometrial cancers in 2012 [1,2]. Squamous-cell cervical carcinoma (SCCC) accounts for over half of these diagnoses, whereas ovarian (~20%) and endometrial (~30%) cancers account for the remaining half together. More than half of these 1 million women will die from their disease, with SCCC accounting for a disproportionate number of deaths among female reproductive tract cancers worldwide. This SCCC cancer death burden is seen predominantly in less-developed nations with limited resources for screening and disease prevention. Furthermore, SCCC deaths are twice as prevalent in younger women (<65 years) than older women (>65 years) whereas ovarian and endometrial cancer deaths are nearly equally distributed in younger (<65 years) and older women (>65 years). In developed nations, ovarian cancer is the most deadly female reproductive tract cancer due to its lack of screening modalities, poor understanding of initiating lesions and frequent metastatic disease at time of diagnosis [1,2]. These health disparities between less-developed and developed nations highlight the need for research into the discovery of inexpensive early diagnostic tools, biomarkers, prognostic factors and initiating lesions to understand the pathogenesis of these diseases and improve therapy for these deadly cancers in both developed and less-developed nations.

An area of translational interest on these fronts has been microRNAs (miRNAs). miRNAs are single-stranded RNA molecules that are 18–24 nucleotides in length that potentially regulate large gene networks [35]. Over the last 20 years since these small RNA molecules were originally described [6], the number of unique miRNAs has exponentially increased with the latest version of miRBase (www.miRBase.org), the online resource dedicated to the miRNA world [7], describing the sequence of over 30000 mature miRNA species. To date (miRBase v20), 2578 of these are expressed in human [7]. miRNAs represent the top of the pyramid of many tumorigenesis cascade pathways as they have the ability to affect multiple, intricate and yet undiscovered downstream targets [8]. Understanding how miRNA molecules serve as master regulators in these important networks involved in cancer initiation and progression open up significant innovative areas for therapy and diagnosis that have been sadly lacking for deadly female reproductive tract cancers. Although still in the discovery phase, miRNA molecules as diagnostic markers or therapeutic applications have potential for significant clinical impact in the field of female reproductive tract cancers.

Multiple studies have profiled miRNAs in ovarian, cervical, and endometrial cancer tissues and cell lines using either microarray technology or next-generation sequencing (NGS). NGS offers depth of sequencing giving novel miRNA molecules, unique isomiRs, and both miR-5p and miR-3p forms [915], whereas microarrays are good for characterizing expression of known miRNAs usually with less expense. Importantly, many investigators have studied individual miRNAs in depth to push those molecules forward as biomarkers or therapeutic modulators based on in vitro studies or clinical association studies. Additionally, bioinformatics approaches integrating multiple platforms and datasets have allowed investigators to ask clinically important questions and get biologically plausible answers. This review will highlight the recent advances in the field of miRNAs in epithelial ovarian cancer (EOC), SCCC and endometrioid endometrial cancer (EEC), focusing on studies associated with clinical information in humans since our last reviews [3,16]. This review will summarize the current thoughts on the role of miRNA processing in unique miRNA species present in these cancers. This review will then focus on current data regarding miRNA molecules in early diagnosis, initiating lesions, pathogenesis, prognostic factors and therapy in each of these cancers. Additionally, potential reasons for why specific miRNA molecules are not replicated across specific cancer types will be included. Although the mechanistic contributions of miRNA molecules to these clinical phenomena have been confirmed using in vitro and pre-clinical mouse model systems, these studies are truly only the beginning of our understanding of the role miRNAs play in cancers of the female reproductive tract. This review will also contain hopeful areas for future research regarding miRNAs as therapeutic targets in cancers of the female reproductive tract.

OVERVIEW OF miRNA PROCESSING AND ITS IMPORTANCE IN FEMALE REPRODUCTIVE TRACT CANCERS

The role of DICER processing (Figure 1) in regulation of miRNA expression is only beginning to be understood within the complexities of novel miRNAs, isomiRs and expression of both complementary miRNA forms (i.e., miR-3p forms). NGS has allowed for discovery of uniquely processed miRNAs, called isomiRs, which differ from mature miRNA sequence being 1–2 nucleotides longer [915]. Differential excision by DICER may lead to these variations in the 5′ and 3′ ends of mature miRNAs, altering affinity to Argonaute proteins, and changing target specificity [1720]. Moreover, additions of adenosine residues may be catalysed by nucleotidyltransferases [14,18,21]. The unique functions of these isomiRs such as adenylated forms that may be degraded faster [22,23] or have alternate functions are not yet understood in female reproductive tract cancers. From a translational standpoint, the biological and clinical association of alternately processed miRNAs such as isomiRs and less abundant mature miR-3p forms needs to be fully characterized in well-characterized clinical specimens of female reproductive cancers using NGS to identify miRNAs that are associated with these deadly cancers.

Schema for miRNA genesis

Figure 1
Schema for miRNA genesis

(A) Primary miRNA (pri-miRNA) molecules are transcribed from DNA loci by RNA polymerase II. The pri-miRNA transcript, roughly 200 bp in length, has a characteristic stem-loop structure. The pri-miRNA transcript is further processed by DROSHA, an RNAse, removing the ‘arms’ and leaving the looped structure, roughly 70 bp in length precursor miRNA (pre-miRNA). Exportin 5 transports the pre-miRNA to the cytoplasm where DICER, another RNAse, processes it into two reverse complementary single-stranded mature miRNA molecules, each roughly 22 bp in length. These mature miRNA molecules are incorporated into and Argonaut protein in the RNA induced silencing complex (RISC) [196,197]. The seed sequence, nucleotides 2–8 of the mature miRNA, determines the complementary targets in the 3′UTR of target genes. Within the RISC, the miRNA binds to complementary target sites in the 3′UTR mRNA, leading to mRNA degradation if perfect complementary binding or translational repression if imperfect complementary binding [3,198200]. In general, the mRNA degradation and subsequent decrease in protein levels are the main post-transcriptional functions of miRNAs. Thus, an overexpressed miRNA will lead to decreased expression of its target genes. Loss of miRNA will lead to overexpression of its target genes [198200]. Furthermore, each miRNA can target multiple genes. Thus, miRNAs regulate large networks of genes in a coordinated fashion, serving as master regulators and, as such, some have been termed oncomiRs or tumour suppressors [201]. (B) In the cytoplasm, DICER processes pre-miRNA molecules to two molecules, the miR-5p strand and the miR-3p strand (also known as the * form). Each of these molecules can be loaded independently into RISC. Although miRNAs with the same seed sequence have similar targets and are grouped into miRNA families [198200], miR-3p and miR-5p molecules may not target the same 3′UTRs as they have different seed sequence. The more abundant form is typically the miR-5p form that is likely the most active. The miR-3p form (also known as the miRNA * form) is typically less abundant due to degradation with little known activity. Currently, the function of many miR-3p forms on gene expression is not yet known

Figure 1
Schema for miRNA genesis

(A) Primary miRNA (pri-miRNA) molecules are transcribed from DNA loci by RNA polymerase II. The pri-miRNA transcript, roughly 200 bp in length, has a characteristic stem-loop structure. The pri-miRNA transcript is further processed by DROSHA, an RNAse, removing the ‘arms’ and leaving the looped structure, roughly 70 bp in length precursor miRNA (pre-miRNA). Exportin 5 transports the pre-miRNA to the cytoplasm where DICER, another RNAse, processes it into two reverse complementary single-stranded mature miRNA molecules, each roughly 22 bp in length. These mature miRNA molecules are incorporated into and Argonaut protein in the RNA induced silencing complex (RISC) [196,197]. The seed sequence, nucleotides 2–8 of the mature miRNA, determines the complementary targets in the 3′UTR of target genes. Within the RISC, the miRNA binds to complementary target sites in the 3′UTR mRNA, leading to mRNA degradation if perfect complementary binding or translational repression if imperfect complementary binding [3,198200]. In general, the mRNA degradation and subsequent decrease in protein levels are the main post-transcriptional functions of miRNAs. Thus, an overexpressed miRNA will lead to decreased expression of its target genes. Loss of miRNA will lead to overexpression of its target genes [198200]. Furthermore, each miRNA can target multiple genes. Thus, miRNAs regulate large networks of genes in a coordinated fashion, serving as master regulators and, as such, some have been termed oncomiRs or tumour suppressors [201]. (B) In the cytoplasm, DICER processes pre-miRNA molecules to two molecules, the miR-5p strand and the miR-3p strand (also known as the * form). Each of these molecules can be loaded independently into RISC. Although miRNAs with the same seed sequence have similar targets and are grouped into miRNA families [198200], miR-3p and miR-5p molecules may not target the same 3′UTRs as they have different seed sequence. The more abundant form is typically the miR-5p form that is likely the most active. The miR-3p form (also known as the miRNA * form) is typically less abundant due to degradation with little known activity. Currently, the function of many miR-3p forms on gene expression is not yet known

From a basic mechanistic standpoint, the role of DICER as a critical miRNA-processing enzyme in the formation and stability of these isomiRs and miR-3p forms is not fully understood. Mutations in one allele of DICER were discovered in 30% of non-EOCs [24]. Further studies showed that this haploinsufficient mutation in one of the two RNAse III domains led to significant changes in the processing of precursor miRNAs, giving altered processing of miR-5p and miR-3p forms (Figure 1B). In vitro experiments with DICER haploinsufficiency showed loss of processing of the more abundant mature miR-5p forms but normal processing of the less abundant miR-3p form [25]. Other in vitro experiments showed that mutations in the RNAse IIIA domain disrupted miR-3p processing completely and decreased miR-5p processing whereas mutations in RNAse IIIB domain disrupted miR-5p processing of let-7 [26]. In additional studies, mutations in the two Mg2+ binding sites in the RNAse IIIB domain of DICER have recently been reported in endometrial cancer [27]. Mg2+ ions help DICER bind and cleave RNA, which is essential for miRNA biogenesis [28]. Typically, the miR-5p and miR-3p forms of the same mature miRNA have unique seed sequences, belong to different miRNA families and target different genes. Given that mature miR-5p forms are the most abundant and the most widely studied, the switch to mature miR-3p forms (Figure 1B) being more abundant may have drastic downstream effects on a large number of pathways important in cancer. However, the relative expression of the miR-5p and miR-3p species is still unknown in well-characterized clinical specimens. Future studies will be important to determine the expression patterns of these miRNA forms as they may have significant clinical impact into disease prognosis.

From a mouse model standpoint, genetically engineered mouse models support a role of Dicer haploinsufficiency in non-female reproductive tract cancers [2932]. However, complete deletion of Dicer alone in female reproductive tract of mice does not lead to cancer [3,16,3336], and cancer studies in female reproductive tract with Dicer haploinsufficiency have not been published. In both endometrial cancer and EOC, low DICER expression is associated with poor prognosis [3739]. However, the specific mechanism for reduced DICER transcript in these cancers is not clear. Several miRNAs reported to be up-regulated in endometrial cancer have been predicted to target DICER by in silico algorithms and include miR-103, miR-107, miR-200a, miR-141, miR-9 and let-7c [38,40]. However, none of these miRNAs have been confirmed to directly regulate DICER expression in vitro. On the other hand, miR-130b has been shown to directly target DICER in human endometrial cancer cells, which in turn leads to abnormal expression of epithelial to mesenchymal transition (EMT)-related genes [41]. Critical studies are needed to determine if DICER haploinsufficiency or loss of DICER expression is related to miRNA processing (i.e., miR-5p to miR-3p switching or isomiR production) or other mechanisms in these female reproductive tract cancers.

CLINICAL ASSOCIATIONS OF miRNA MOLECULES IN EPITHELIAL OVARIAN CANCER

Brief overview of miRNAs in epithelial ovarian cancer

Using 2012 data, 238,719 women worldwide were diagnosed with ovarian cancer and over 151,905 women died from this disease [42], mostly from disease recurrence or progression on therapy. Although 70% of women with advanced-stage EOC initially respond to surgical debulking and standard chemotherapy, the 5-year survival rate is still poor, probably due to a subset of cells being resistant to chemotherapy [43]. These data are not surprising considering that the survival rates for EOC have not improved significantly in 40 years due to lack of early diagnostic markers [44]. In stark contrast with breast cancer, which uses personalized therapy based on molecular markers (i.e., ER, PR, Her2-neu) [4548], EOC treatment does not yet use molecular markers to personalize therapy. Just as oestrogen affects multiple gene networks in breast tissue [49], individual miRNAs affect large network of genes in ovarian cancer [16,5052]. Multiple studies have profiled miRNAs in EOC compared with normal tissues or cell lines to generate lists of differentially expressed miRNAs [12,5260]. Many of these miRNA molecules from profiling studies have in vitro or in vivo xenograft data to support the biological plausibility of the miRNA action (Table 1) [3,16]. Many in vitro studies in EOC have focused on the role of miRNAs in chemotherapy sensitivity, tumour growth or invasion that are all clinically important cellular properties. Thus, miRNAs have the potential to play significant roles in early diagnosis, initiating lesion formation, pathogenesis, prognosis and therapy.

Table 1
In vitro studies of miRNA molecules in ovarian cancer
miRNAEffect in vitroGenes, pathways regulatedReference(s)
let-7, let-7e, let-7g Growth (xenograft) EZH2, CCND1 [202204
 Chemotherapy sensitivity IMP1, MDR1  
miR-100 Proliferation PLK1 [205
miR-101 Growth (xenograft) EZH2 [206
 Apoptosis   
 Migration   
miR-106a Chemotherapy sensitivity PDCD4, MCL1, BCL10, Caspase-7, RBL2 [207210
 Apoptosis   
 Proliferation   
 Putative stem cell population   
 Growth (xenograft)   
miR-124, miR-124a Migration, invasion SPHK1 [211,212
 Proliferation   
 Cell cycle   
miR-128 Motility and adhesion CSF1 (affects large networks of genes associated with cell cycle control) [213,214
miR-130a,b Chemotherapy sensitivity XIAP, CSF1, MDR1 [215217
 Apoptosis   
miR-138 Invasion (xenograft) SOX4, HIF1α [218
miR-141 Chemotherapy sensitivity KEAP1, p38α [219,220
 Oxidative stress response   
 Growth (xenograft)   
miR-145 Growth (xenograft) P70S6K1 [221,222
 Invasion MUC1  
 Angiogenesis   
miR-148a Proliferation  [223
miR-152 Chemotherapy sensitivity DNMT1 [213,223,224
 Apoptosis CSF1  
 Metastasis (xenograft)   
 Adhesion and motility   
 Proliferation   
miR-155 Ovarian cancer initiating cells (CD44+CD117+CLDN1 [225
 Proliferation   
 Invasion (xenograft)   
miR-182 Chemotherapy sensitivity PDCD4 [226
 Growth and invasion   
miR-182 + miR-96 Leptin-mediated proliferation FOXO3, STAT5 [227
miR-185 Chemotherapy sensitivity DNMT1 [224
 Apoptosis   
miR-187 Migration DAB2 [228
 EMT   
miR-192 Proliferation  [212
 Cell cycle   
miR-193b*, miR-193a Chemotherapy sensitivity ARHGAP19, CCND1, ERBB4, KRAS, MCL1 [212,229
 Proliferation   
 Cell cycle   
 Apoptosis   
miR-199a Chemotherapy sensitivity (xenograft) mTOR, CD44+ [230,231
 Apoptosis   
 Cancer stem cell   
miR-199a + miR-125b Angiogenesis VEGF [232234
 Regulated by reactive oxygen species Akt/p70S6K1/HIF1α  
 Chemotherapy sensitivity HER2, HER3  
 Apoptosis ERBB2, ERBB3, Bcl-2  
miR-200a Chemotherapy sensitivity p38α [220
 Oxidative stress response   
 Growth (xenograft)   
miR-200c Putative stem cell population PTEN [235237
 EMT TrkB  
 Metastasis (xenograft)   
 Proliferation   
 Anoikus sensitivity   
 Chemotherapy sensitivity (xenograft)   
miR-20a Putative stem cell population PTEN [235
miR-21 Proliferation, apoptosis PCDC4 [238
 Migration, Invasion   
miR-214 Ovarian cancer stem cells P53, NANOG [239
miR-222 Proliferation p27KIP1 [240
miR-25 Apoptosis BIM [241
miR-27a/b Proliferation/DGCR8 Sprout2 [242,243
 Genistein sensitivity   
miR-29 Chemotherapy sensitivity COL1A1, ERK1/2, GSK3B [244
 Apoptosis   
miR-30-5p Chemotherapy sensitivity  [245
miR-302b Chemotherapy sensitivity (xenograft) HDAC4 [246
miR-30c-2* Proliferation BCL9 [247
miR-30d Proliferation CASP3 [248
 Apoptosis   
 Cellular senescence   
 Growth (xenograft)   
miR-31 Chemotherapy sensitivity MET [249
miR-335 Migration, invasion BCL2L2 [250
miR-367 Chemotherapy sensitivity  [245
miR-372 Proliferation  [212
miR-373 Proliferation  [212
miR-375 Chemotherapy modulation to RAWQ01 (xenograft)  [251
miR-520d-3p Proliferation EPHA2 [183
 Migration, invasion   
 Metastasis (xenograft)   
miR-591 Chemotherapy sensitivity ZEB1 [209
 Apoptosis   
miR-7 Proliferation Affects large networks of genes associated with EMT and proliferation [212,214
miR-9 Proliferation TLN1, RAB34 [83,252
 Migration, invasion   
miR-92a Peritoneal dissemination (xenograft) ITGA5 [253
miR-93 Chemotherapy sensitivity PTEN [254
 Apoptosis   
miR-498 Growth (xenograft) Telomerase [255
miRNAEffect in vitroGenes, pathways regulatedReference(s)
let-7, let-7e, let-7g Growth (xenograft) EZH2, CCND1 [202204
 Chemotherapy sensitivity IMP1, MDR1  
miR-100 Proliferation PLK1 [205
miR-101 Growth (xenograft) EZH2 [206
 Apoptosis   
 Migration   
miR-106a Chemotherapy sensitivity PDCD4, MCL1, BCL10, Caspase-7, RBL2 [207210
 Apoptosis   
 Proliferation   
 Putative stem cell population   
 Growth (xenograft)   
miR-124, miR-124a Migration, invasion SPHK1 [211,212
 Proliferation   
 Cell cycle   
miR-128 Motility and adhesion CSF1 (affects large networks of genes associated with cell cycle control) [213,214
miR-130a,b Chemotherapy sensitivity XIAP, CSF1, MDR1 [215217
 Apoptosis   
miR-138 Invasion (xenograft) SOX4, HIF1α [218
miR-141 Chemotherapy sensitivity KEAP1, p38α [219,220
 Oxidative stress response   
 Growth (xenograft)   
miR-145 Growth (xenograft) P70S6K1 [221,222
 Invasion MUC1  
 Angiogenesis   
miR-148a Proliferation  [223
miR-152 Chemotherapy sensitivity DNMT1 [213,223,224
 Apoptosis CSF1  
 Metastasis (xenograft)   
 Adhesion and motility   
 Proliferation   
miR-155 Ovarian cancer initiating cells (CD44+CD117+CLDN1 [225
 Proliferation   
 Invasion (xenograft)   
miR-182 Chemotherapy sensitivity PDCD4 [226
 Growth and invasion   
miR-182 + miR-96 Leptin-mediated proliferation FOXO3, STAT5 [227
miR-185 Chemotherapy sensitivity DNMT1 [224
 Apoptosis   
miR-187 Migration DAB2 [228
 EMT   
miR-192 Proliferation  [212
 Cell cycle   
miR-193b*, miR-193a Chemotherapy sensitivity ARHGAP19, CCND1, ERBB4, KRAS, MCL1 [212,229
 Proliferation   
 Cell cycle   
 Apoptosis   
miR-199a Chemotherapy sensitivity (xenograft) mTOR, CD44+ [230,231
 Apoptosis   
 Cancer stem cell   
miR-199a + miR-125b Angiogenesis VEGF [232234
 Regulated by reactive oxygen species Akt/p70S6K1/HIF1α  
 Chemotherapy sensitivity HER2, HER3  
 Apoptosis ERBB2, ERBB3, Bcl-2  
miR-200a Chemotherapy sensitivity p38α [220
 Oxidative stress response   
 Growth (xenograft)   
miR-200c Putative stem cell population PTEN [235237
 EMT TrkB  
 Metastasis (xenograft)   
 Proliferation   
 Anoikus sensitivity   
 Chemotherapy sensitivity (xenograft)   
miR-20a Putative stem cell population PTEN [235
miR-21 Proliferation, apoptosis PCDC4 [238
 Migration, Invasion   
miR-214 Ovarian cancer stem cells P53, NANOG [239
miR-222 Proliferation p27KIP1 [240
miR-25 Apoptosis BIM [241
miR-27a/b Proliferation/DGCR8 Sprout2 [242,243
 Genistein sensitivity   
miR-29 Chemotherapy sensitivity COL1A1, ERK1/2, GSK3B [244
 Apoptosis   
miR-30-5p Chemotherapy sensitivity  [245
miR-302b Chemotherapy sensitivity (xenograft) HDAC4 [246
miR-30c-2* Proliferation BCL9 [247
miR-30d Proliferation CASP3 [248
 Apoptosis   
 Cellular senescence   
 Growth (xenograft)   
miR-31 Chemotherapy sensitivity MET [249
miR-335 Migration, invasion BCL2L2 [250
miR-367 Chemotherapy sensitivity  [245
miR-372 Proliferation  [212
miR-373 Proliferation  [212
miR-375 Chemotherapy modulation to RAWQ01 (xenograft)  [251
miR-520d-3p Proliferation EPHA2 [183
 Migration, invasion   
 Metastasis (xenograft)   
miR-591 Chemotherapy sensitivity ZEB1 [209
 Apoptosis   
miR-7 Proliferation Affects large networks of genes associated with EMT and proliferation [212,214
miR-9 Proliferation TLN1, RAB34 [83,252
 Migration, invasion   
miR-92a Peritoneal dissemination (xenograft) ITGA5 [253
miR-93 Chemotherapy sensitivity PTEN [254
 Apoptosis   
miR-498 Growth (xenograft) Telomerase [255

Role of miRNAs in predisposition and early diagnosis of epithelial ovarian cancer

For EOC, two genome-wide association studies suggest that single nucleotide polymorphisms (SNPs) in miRNAs or 3′UTR are uncommon in EOC [61,62]. On the other hand, a large cohort study using SNPs in miRNAs suggests that 17q21.31 contains a susceptibility locus for EOC, although the candidate genes at this locus have not yet been studied for biological plausibility [63]. A candidate gene approach examined a polymorphism in a let-7 binding site in the 3′UTR of KRAS, finding no association with EOC in one population but association with poor outcome in another population [64,65] (Table 2). Given the multiple genomic changes noted in EOC by The Cancer Genome Atlas (TCGA) data and others [66,67], small polymorphisms may not play a significant role in advanced stage disease but may play a role in predisposition of normal tissues to malignant transformation.

Table 2
Clinically associated miRNA molecules in ovarian cancer
miRNAClinical indicationReference(s)
let-7 Genomic deletion in 44% of EOC [64,65,202
 KRAS let-7 binding site (LCS6):  
  Not associated with EOC outcomes  
  Associated with EOC outcomes, chemotherapy sensitivity  
let-7a Chemotherapy sensitivity to paclitaxel [256
let-7b Master regulator of networks of proteins associated with poor survival [257
let-7f (plasma) Low expression correlates with poor progression free survival [68
miR-100 Associated with overall survival [205
miR-138 Low miR-138/high SOX4 associated with poor prognostic features [218
miR-148 Expression not correlated with clinical features [223,258
miR-181d High expression associated with decrease disease free interval [88
miR-187 High expression associated with improved overall survival [228
miR-200a Biphasic expression with lowest expression associated with high grade and stage [220,259
 High miR-200a with low p38α associated with stress response and better prognosis  
miR-200b + miR-200c High expression in high-grade serous EOC [260
miR-200c Low expression associated with relapse in stage I EOC [261
 Low expression associated with poor overall survival and progression free survival in stage I EOC  
miR-205 (plasma), let-7f (plasma) High specificity as diagnostic marker [68
miR-21, miR-214 High expression in ascites compared with metastatic lesions [262
miR-221 (serum) Biomarker [263
 High expression correlates with poor prognosis  
miR-27a High expression in cancer stem cells (ALD+[264
 Associated with metastatic disease  
miR-30a* Low expression associated with poor survival in elderly EOC [85
miR-30c High expression associated with decreased disease-free interval [88
miR-30d High expression associated with decreased overall survival [88,248
 High expression associated with decreased disease-free interval  
 Amplified in 50% of EOC  
miR-30e* Low expression associated with poor survival in elderly EOC [85,88
 High expression associated with decrease disease free interval  
miR-31 Low expression correlates with poor survival [249
miR-484 Associated with tumour vessel density (IHC) [84
miR-502 Mutation in miR-503 binding site in SET8 correlates with EOC [265
miR-503 High expression in cancer stem cells (ALD+[264
 Associated with clinical stage  
miR-509-5p Low expression associated with poor overall survival [266
miR-510 Low expression associated with poor overall survival [266
miR-520d-3p High expression associated with improved overall survival [183
miR-9 High expression associated with overall survival [83
miR-92 Biomarker [267
miR-505* Low expression associated with poor survival in elderly EOC [85
miRNAClinical indicationReference(s)
let-7 Genomic deletion in 44% of EOC [64,65,202
 KRAS let-7 binding site (LCS6):  
  Not associated with EOC outcomes  
  Associated with EOC outcomes, chemotherapy sensitivity  
let-7a Chemotherapy sensitivity to paclitaxel [256
let-7b Master regulator of networks of proteins associated with poor survival [257
let-7f (plasma) Low expression correlates with poor progression free survival [68
miR-100 Associated with overall survival [205
miR-138 Low miR-138/high SOX4 associated with poor prognostic features [218
miR-148 Expression not correlated with clinical features [223,258
miR-181d High expression associated with decrease disease free interval [88
miR-187 High expression associated with improved overall survival [228
miR-200a Biphasic expression with lowest expression associated with high grade and stage [220,259
 High miR-200a with low p38α associated with stress response and better prognosis  
miR-200b + miR-200c High expression in high-grade serous EOC [260
miR-200c Low expression associated with relapse in stage I EOC [261
 Low expression associated with poor overall survival and progression free survival in stage I EOC  
miR-205 (plasma), let-7f (plasma) High specificity as diagnostic marker [68
miR-21, miR-214 High expression in ascites compared with metastatic lesions [262
miR-221 (serum) Biomarker [263
 High expression correlates with poor prognosis  
miR-27a High expression in cancer stem cells (ALD+[264
 Associated with metastatic disease  
miR-30a* Low expression associated with poor survival in elderly EOC [85
miR-30c High expression associated with decreased disease-free interval [88
miR-30d High expression associated with decreased overall survival [88,248
 High expression associated with decreased disease-free interval  
 Amplified in 50% of EOC  
miR-30e* Low expression associated with poor survival in elderly EOC [85,88
 High expression associated with decrease disease free interval  
miR-31 Low expression correlates with poor survival [249
miR-484 Associated with tumour vessel density (IHC) [84
miR-502 Mutation in miR-503 binding site in SET8 correlates with EOC [265
miR-503 High expression in cancer stem cells (ALD+[264
 Associated with clinical stage  
miR-509-5p Low expression associated with poor overall survival [266
miR-510 Low expression associated with poor overall survival [266
miR-520d-3p High expression associated with improved overall survival [183
miR-9 High expression associated with overall survival [83
miR-92 Biomarker [267
miR-505* Low expression associated with poor survival in elderly EOC [85

Unfortunately, most EOC are diagnosed after significant disease spread, as there are no highly specific or sensitive methods of early diagnosis [44]. Several studies have focused on circulating miRNAs in blood as a means of non-invasive diagnosis and early detection in EOC (Table 3). One study has discovered miRNAs that are potential diagnostic markers in the blood such as miR-205/let-7f [68], although the study did not have concrete data on the function of these miRNAs in EOC. Studies in prostate cancer showed that miR-205 was critical for activation of the RAS oncogene [69], which is a potential player in EOC [70]. The present study was well-designed having training and independent validation sets for calculation of sensitivity and specificity [68]. Recently, the miRNA profiles of pre-operative plasma from subjects with ovarian cancer, benign adnexal masses and no abnormal pathology were compared. From this dataset, miR-106b, miR-126, miR-158, miR-17, miR-20a and miR-92a were significantly differentially expressed in malignant compared with benign subjects, and miR-1290 showed significant association with disease prognosis using pre-surgery plasma. However, the present study did not use an independent validation set to test the miRNA signature and did not calculate specificity and sensitivity [71]. Work from whole blood showed a circulating signature of miRNAs associated with recurrent ovarian cancer after different chemotherapy agents. Although the present study did not use independent samples for validation, the study did discover several miRNAs such as miR-30-1 that were found to be important in other studies of ovarian cancer [72,73]. Profiling from circulating exosomes showed significant correlation in miRNA expression between tumour and circulating exosomes. However, a signature of eight exosomal-miRNAs was not associated with stage of disease [74]. Finally, a critical study examining miRNAs found in blood compared control women to women with endometriosis to women with EOC. They found that the number of miRNAs detected in the blood increased from control to endometriosis to cancer. Importantly, a minimal panel of miRNAs had reasonable sensitivity and specificity between control, endometriosis, clear-cell, endometrioid and serous EOC. Excitingly, these same miRNAs were tested in a mouse model of endometriosis-associated EOC and were found to closely replicate the human studies [75]. The use of miRNA signatures from blood as a means of non-invasive diagnosis seems promising but standardization regarding RNA isolation (i.e., whole blood, cellular components, plasma or exosomes) is needed. Additionally, the mechanism by which tumours release miRNAs (i.e., exosomes, secretion, cell lysis or tumour macrophages) still needs to be deciphered. Prudent studies would use pre-clinical mouse models of ovarian cancer for optimization of miRNA isolation from blood and determination of minimal signature of miRNA for diagnostic purposes.

Table 3
miRNAs in blood for use as diagnostic markers of female reproductive tract cancers

NC, not calculated.

miRNACancer compared with comparison group (sample studied)ROCSensitivitySpecificityReference(s)
let-7f Ovarian cancer compared with controls (plasma) 0.780 66.9% 84.2% [68
miR-205 Ovarian cancer compared with controls (plasma) 0.609 30.1% 94.2% [68
let-7f + miR-205 Independent validation set of ovarian cancer compared with controls (plasma) 0.813 71.3% 81.2% [68
miR-106b, miR-126, miR-158, miR-17, miR-20a, miR-92a Ovarian cancer compared with benign mass compared with control (plasma) NC NC NC [71
miR-1290 Ovarian cancer compared with benign mass compared with control (plasma) NC NC NC [71
miR-21, miR-141, miR-200a, miR-200c, miR-200b, miR-203, miR-205, miR-214 Ovarian cancer compared with benign disease (circulating exosomes) NC NC NC [74
miR-16, miR-191, miR-21 Endometrioid or clear cell ovarian cancer compared with healthy controls (plasma) 0.96 86% 85% [75
miR-16, miR-191, miR-4284 Serous ovarian cancer compared with healthy controls (plasma) 0.90 90% 55% [75
miR-342-3p Relapsed ovarian cancer compared with healthy controls (whole blood) 0.86 NC NC [72
miR-221 Ovarian cancer compared with healthy controls (serum) NC NC NC [263
miR-92 Ovarian cancer compared with healthy controls (serum) 0.803 80.7% 75% [267
miR-203 Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.658 65% 62.5% [128
miR-20a Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.734 75% 72.5% [128
miR-1246, miR-20a, miR-2392, miR-3147, miR-3162-5p, miR-4484 Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.992 85.6% 85% [129
miR-218 SCCC compared with healthy controls (germline DNA) NC NC NC [111,268
miR-27a SCCC compared with healthy controls (germline DNA) NC NC NC [269
miR-222, miR-223, miR-186, miR-204 Endometrial cancer to healthy controls (serum) 0.927 87.5% 91.7% [157
miR-9, miR-1228 Endometrial cancer to healthy controls (plasma) 0.909 73% 100% [138
miR-9, miR92a Endometrial cancer to healthy controls (plasma) 0.913 79% 100% [138
miR-200b, miR-200c, miR-203, miR-449a Endometrial cancer with myometrial invasion >0.5 compared with myometrial invasion <0.5 (plasma) 0.851 NC NC [138
miR-99a, miR-199b Endometrial cancer to healthy controls (plasma) 0.903 88% 93% [158
miRNACancer compared with comparison group (sample studied)ROCSensitivitySpecificityReference(s)
let-7f Ovarian cancer compared with controls (plasma) 0.780 66.9% 84.2% [68
miR-205 Ovarian cancer compared with controls (plasma) 0.609 30.1% 94.2% [68
let-7f + miR-205 Independent validation set of ovarian cancer compared with controls (plasma) 0.813 71.3% 81.2% [68
miR-106b, miR-126, miR-158, miR-17, miR-20a, miR-92a Ovarian cancer compared with benign mass compared with control (plasma) NC NC NC [71
miR-1290 Ovarian cancer compared with benign mass compared with control (plasma) NC NC NC [71
miR-21, miR-141, miR-200a, miR-200c, miR-200b, miR-203, miR-205, miR-214 Ovarian cancer compared with benign disease (circulating exosomes) NC NC NC [74
miR-16, miR-191, miR-21 Endometrioid or clear cell ovarian cancer compared with healthy controls (plasma) 0.96 86% 85% [75
miR-16, miR-191, miR-4284 Serous ovarian cancer compared with healthy controls (plasma) 0.90 90% 55% [75
miR-342-3p Relapsed ovarian cancer compared with healthy controls (whole blood) 0.86 NC NC [72
miR-221 Ovarian cancer compared with healthy controls (serum) NC NC NC [263
miR-92 Ovarian cancer compared with healthy controls (serum) 0.803 80.7% 75% [267
miR-203 Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.658 65% 62.5% [128
miR-20a Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.734 75% 72.5% [128
miR-1246, miR-20a, miR-2392, miR-3147, miR-3162-5p, miR-4484 Stage I–IIA SCCC lymph node metastasis+ compared with lymph node metastasis- (serum) 0.992 85.6% 85% [129
miR-218 SCCC compared with healthy controls (germline DNA) NC NC NC [111,268
miR-27a SCCC compared with healthy controls (germline DNA) NC NC NC [269
miR-222, miR-223, miR-186, miR-204 Endometrial cancer to healthy controls (serum) 0.927 87.5% 91.7% [157
miR-9, miR-1228 Endometrial cancer to healthy controls (plasma) 0.909 73% 100% [138
miR-9, miR92a Endometrial cancer to healthy controls (plasma) 0.913 79% 100% [138
miR-200b, miR-200c, miR-203, miR-449a Endometrial cancer with myometrial invasion >0.5 compared with myometrial invasion <0.5 (plasma) 0.851 NC NC [138
miR-99a, miR-199b Endometrial cancer to healthy controls (plasma) 0.903 88% 93% [158

The role of miRNAs in initiating lesions in epithelial ovarian cancer

The initiating lesion is hotly debated as to whether high-grade serous EOC, the most common histotype of EOC, begins in the fimbria of the fallopian tube or the ovarian surface epithelium [76,77]. Serous tubal intraepithelial carcinoma (STIC) lesions in fimbria may be the precursor lesions for high-grade serous EOC. Studies in humans have shown increased expression of miR-182 in STIC lesions over normal fallopian tube. Forced overexpression of miR-182 in vitro lead to increased cellular transformation, likely through dysregulation of DNA repair pathways involving BRCA1, MTSS1 and HMGA2 [78]. Additionally studies support the role of overexpression of miR-182a in suppression of FOXO3A in fallopian tube cells leading to high-grade serous EOC [79]. Recently, a genetically engineered mouse model, containing conditional deletions of Dicer and Pten in the mesenchymal cells of the female reproductive tract (Amhr2cre; Ptenf/f; Dicerf/f), showed that high-grade serous EOC originates in the fallopian tube [80]. A different genetically engineered mouse model, containing an oncogenic Kras with Pten deletion in the mesenchymal cells of the female reproductive tract (Amhr2cre; Ptenf/f; KrasG12D/+), suggested that low-grade serous EOC originates in the ovarian surface epithelium with dysregulation of p53 and miR-34c [81]. These few studies highlight the use of miRNAs as future biomarkers for the origins of EOC. Further studies are needed to decipher the role of miRNAs in cancer initiation in female reproductive tract cancers including the endometriosis-associated histotypes of EOC, specifically clear-cell and endometrioid EOC. Again, the use of pre-clinical mouse models with well-defined genetics offers a unique advantage if validated by human tissue profiles that are available.

miRNAs as prognostic markers in epithelial ovarian cancer

Recent profiling datasets have focused on well-defined clinical characteristics to determine specific miRNAs or miRNA signatures associated with important clinical features such as EOC histotype, lesion location, prognosis or chemotherapy resistance (Table 2) [8288]. Looking at miRNA profiles, a recent study showed the histotypes of stage I ovarian tumours are molecularly different [82]. Importantly, miR-30a and miR-30a* were markers of clear-cell tumours whereas miR-192 and miR-194 were markers of mucinous tumours. Each of these miRNAs had functional targets leading to unique networks affected in clear-cell or mucinous tumours at the mRNA level [82]. Previous studies have shown unique gene signatures for EOC histotypes [8992]. Therefore, miRNA play critical roles in distinguishing EOC histotypes and underlying biological networks. Thus, miRNA play a significant role in determining which molecular markers in tumours will respond to specific therapies in the future.

Although individual miRNA molecules may be important, a minimal signature of miRNAs in the tumours may increase sensitivity and specificity of prognosis or treatment effects. Focusing on miRNA profiles from metastatic lesions, one study recently profiled miRNAs from primary tumours compared with omental metastatic lesions. The authors found that miR-146a and miR-150 were highly expressed in omental lesions with significant chemotherapy sensitization effects of these 2 miRNAs in vitro [86]. Thus, miRNAs may allow better differentiation by location of lesion that affects clinically important factors such as chemotherapy resistance. For prognostic studies, analysis of miRNA datasets from early relapsing compared with late relapsing tumours revealed low expression of miRNAs on chromosome Xq27.3. In vitro expression of these miRNAs was associated with chemotherapy sensitivity [93]. Similarly, miRNA profiling studies focusing on serous EOC with chemotherapy resistance showed a minimal signature of miR-484, miR-642 and miR-217 associated with chemotherapy resistance. This resistance was attributed to regulation of angiogenic factors by miR-484 in human ovarian tissue samples using vessel density and xenograft models. This landmark study may have identified three miRNAs that may determine which patients will benefit from anti-angiogenesis therapy in combination with standard therapy [84]. In support of this, a large-scale bioinformatics study showed an anti-angiogenesis miRNA signature that correlated with overall survival [94]. Additional translational studies like these may allow stratification of therapy, based on need for angiogenesis modulation, to determine which subjects would benefit from expensive and still experimental anti-angiogenesis therapy.

CLINICAL ASSOCIATIONS OF miRNAs IN SQUAMOUS-CELL CERVICAL CARCINOMA

Brief overview of miRNAs in squamous-cell cervical carcinoma

According to GLOBOCAN 2012, worldwide 527624 women were diagnosed with cancer of the uterine cervix and over 265653 died from this disease [1,2]. Worldwide, uterine cervix cancer is the fourth most common cancer in women. Sadly, nine out of ten deaths from cervical cancer occur in less-developed countries. Overall, less-developed areas have five times the incidence of cervical cancer and eight times the mortality [1,2]. In more developed countries, screening for early cytological abnormalities with Pap testing and human papilloma virus (HPV) co-testing affords early identification and treatment of pre-cancerous lesions, cervical intraepithelial neoplasia (CIN) [9597]. Additionally, early age immunization for HPV high-risk subtypes should be protective [98]. However, these screening tests lead to expensive diagnostic procedures such as colposcopy that are frequently not available in resource poor nations [99]. Therefore, additional inexpensive means of early triage for abnormal cervical cytology are needed to determine which women need invasive procedures and which do not. miRNAs may play a role in the early initiating lesion of SCCC, thus making them an excellent avenue for research. Multiple studies have profiled miRNAs in cervical cancers, CIN lesions and cell lines [100106]. Many of the miRNA molecules from profiling studies have in vitro or in vivo xenograft data to support the biological plausibility of miRNA function (Table 4) [3]. Thus, miRNAs may play significant roles in predisposition, initiating lesions and prognosis of SCCC.

Table 4
In vitro studies of miRNA molecules in cervical cancer
miRNAEffect in vitroGenes, pathways regulatedReference(s)
let-7 Cell survival HAS2 [270
 Invasion   
miR-100 Proliferation PLK1 [271
 Apoptosis   
miR-101 Proliferation Cox-2 [272
 Apoptosis   
 Migration, invasion   
miR-10a Proliferation CHL1 [273
 Migration, invasion   
miR-125b Apoptosis BAK1, PIK3CD AKT pathway [274,275
 Proliferation   
miR-129-5p Proliferation E6, E7, SP1 [276
 Apoptosis   
miR-133b Proliferation MST2, CDC42, RHOA AKT, ERK1 [277
 Growth and metastasis (xenograft)   
miR-143 Growth (xenograft) Bcl-2 [278
miR-145 Cortisol-mediated chemotherapy sensitivity P53 [279
miR-15-3p Apoptosis BCL2L1 [280
miR-155 Autophagy activity mTOR pathway: RHEB, RICTOR, RPS6K2 SMAD2, CCND1 [281,282
 EGF-mediated EMT   
 Chemotherapy sensitivity   
miR-17-5p Apoptosis TP53INP1 [283
miR-181a,b Proliferation AC9 PRKCD [284,285
 Apoptosis   
 cAMP production   
 Chemotherapy sensitivity   
 Growth (xenograft)   
miR-182 Growth (xenograft) Cell cycle and apoptosis pathways [286
 Apoptosis FOXO1  
miR-19a/b Proliferation CUL5 [287
 Invasion   
miR-203 Proliferation VEGFA [126,128
 Growth (xenograft)   
 Angiogenesis   
miR-205 Proliferation CYR61 [288
 Migration CTGF  
miR-20a Proliferation TNSK2 [128,289
 Migration, invasion   
miR-21 Proliferation Migration, invasion CCL20 [290
miR-214 Proliferation GALNT7 [127,291
 Migration, invasion BCL2L2  
 Chemotherapy sensitivity   
 Apoptosis   
miR-218 Proliferation LAMB3 [292,293
 Chemotherapy sensitivity mTOR/AKT  
 Apoptosis   
 Growth (xenograft)   
miR-223 Proliferation FOXO1 [294
miR-29a/c Migration, invasion HSP47, YY1, CDK6 [295,296
 HPV-mediated proliferation and apoptosis   
miR-302-367 Proliferation CCND1 [297
  AKT1  
miR-361-5p Proliferation E-cadherin [298
 Apoptosis   
 Migration   
 EMT   
miR-375 Chemotherapy sensitivity SP1 [299301
 Apoptosis   
 Growth (xenograft)   
 Migration, invasion   
miR-424 Proliferation CHK1 [302
 Apoptosis   
 Migration, invasion   
miR-497 Proliferation IGF1R [303
 Apoptosis   
 Migration, invasion   
miR-590-5p Growth CHL1 [304
 Migration   
miR-630 Radiotherapy sensitivity  [305
miR-7 Growth XIAP [306
 Apoptosis   
miR-9 Growth  [307
 Proliferation   
 Migration   
miR-99 Proliferation TRIB2 [308
 Apoptosis   
miRNAEffect in vitroGenes, pathways regulatedReference(s)
let-7 Cell survival HAS2 [270
 Invasion   
miR-100 Proliferation PLK1 [271
 Apoptosis   
miR-101 Proliferation Cox-2 [272
 Apoptosis   
 Migration, invasion   
miR-10a Proliferation CHL1 [273
 Migration, invasion   
miR-125b Apoptosis BAK1, PIK3CD AKT pathway [274,275
 Proliferation   
miR-129-5p Proliferation E6, E7, SP1 [276
 Apoptosis   
miR-133b Proliferation MST2, CDC42, RHOA AKT, ERK1 [277
 Growth and metastasis (xenograft)   
miR-143 Growth (xenograft) Bcl-2 [278
miR-145 Cortisol-mediated chemotherapy sensitivity P53 [279
miR-15-3p Apoptosis BCL2L1 [280
miR-155 Autophagy activity mTOR pathway: RHEB, RICTOR, RPS6K2 SMAD2, CCND1 [281,282
 EGF-mediated EMT   
 Chemotherapy sensitivity   
miR-17-5p Apoptosis TP53INP1 [283
miR-181a,b Proliferation AC9 PRKCD [284,285
 Apoptosis   
 cAMP production   
 Chemotherapy sensitivity   
 Growth (xenograft)   
miR-182 Growth (xenograft) Cell cycle and apoptosis pathways [286
 Apoptosis FOXO1  
miR-19a/b Proliferation CUL5 [287
 Invasion   
miR-203 Proliferation VEGFA [126,128
 Growth (xenograft)   
 Angiogenesis   
miR-205 Proliferation CYR61 [288
 Migration CTGF  
miR-20a Proliferation TNSK2 [128,289
 Migration, invasion   
miR-21 Proliferation Migration, invasion CCL20 [290
miR-214 Proliferation GALNT7 [127,291
 Migration, invasion BCL2L2  
 Chemotherapy sensitivity   
 Apoptosis   
miR-218 Proliferation LAMB3 [292,293
 Chemotherapy sensitivity mTOR/AKT  
 Apoptosis   
 Growth (xenograft)   
miR-223 Proliferation FOXO1 [294
miR-29a/c Migration, invasion HSP47, YY1, CDK6 [295,296
 HPV-mediated proliferation and apoptosis   
miR-302-367 Proliferation CCND1 [297
  AKT1  
miR-361-5p Proliferation E-cadherin [298
 Apoptosis   
 Migration   
 EMT   
miR-375 Chemotherapy sensitivity SP1 [299301
 Apoptosis   
 Growth (xenograft)   
 Migration, invasion   
miR-424 Proliferation CHK1 [302
 Apoptosis   
 Migration, invasion   
miR-497 Proliferation IGF1R [303
 Apoptosis   
 Migration, invasion   
miR-590-5p Growth CHL1 [304
 Migration   
miR-630 Radiotherapy sensitivity  [305
miR-7 Growth XIAP [306
 Apoptosis   
miR-9 Growth  [307
 Proliferation   
 Migration   
miR-99 Proliferation TRIB2 [308
 Apoptosis   

Role of miRNAs in predisposition to squamous-cell cervical carcinoma

Although the cause of SCCC is derived from HPV, genetic pre-disposition studies have attempted to find a genetic link as to which women clear HPV and other women have progressive disease, looking for SNPs in miRNA target genes or miRNAs [107]. A few SNPs have been found to be protective in cervical cancer (Table 5), although the underlying biological plausibility for protection has not yet been clearly deciphered. One of the most widely studied SNP is rs2910164 in miR-146a. Several meta-analysis have reviewed these datasets and found that this SNP is associated with decreased risk of SCCC but mostly in Asian population [108], but other meta-analyses found no protection [109,110]. Interestingly, a variant in the pri-miR-218 was found to decrease risk of SCCC in Eastern Chinese women, possibly through alteration of the secondary structure of the primary miRNA molecule [111]. For SCCC, studies suggest that copy number changes affect DROSHA, a key enzyme for miRNA genesis [102,112,113]. However, SNPs in miRNA genesis genes are not associated with cervical cancer [114]. Thus, genetic predisposition to infection from HPV from a miRNA perspective does not seem to have a high clinical relevance.

Table 5
Clinically associated miRNA molecules in cervical cancer
miRNAClinical indicationReference(s)
miR-25/miR-92a and miR-22/miR-29a Increased expression ratio associated with SCCC [122
miR-100 Decreased expression associated with invasive disease over CIN [271
miR-133b Expression associated with invasive disease over CIN [277
miR-146a SNP associated with decreased cervical cancer risk [108
miR-181a Chemotherapy resistance [309
miR-203 Circulating levels predict lymph node metastasis [128
miR-20a Circulating levels predict lymph node metastasis, diameter of tumour [128,310
miR-21 Expression associated with invasive disease over CIN [311
miR-218 Serum levels predict stage, lymph node metastasis SNP associated with decreased cervical cancer risk [111,268
miR-224 Microvascular invasion, HPV infection, overall survival, stage, lymph node metastasis [312
miR-27a SNP associated with decreased cervical cancer risk [269
miR-375 Decreased expression associated with lymph node metastasis [300
miR-424 Stage, lymph node metastasis, tumour grade, microvascular invasion [302
miR-497 Low tissue levels associated with worse stage and lymph node metastasis [303
miRNAClinical indicationReference(s)
miR-25/miR-92a and miR-22/miR-29a Increased expression ratio associated with SCCC [122
miR-100 Decreased expression associated with invasive disease over CIN [271
miR-133b Expression associated with invasive disease over CIN [277
miR-146a SNP associated with decreased cervical cancer risk [108
miR-181a Chemotherapy resistance [309
miR-203 Circulating levels predict lymph node metastasis [128
miR-20a Circulating levels predict lymph node metastasis, diameter of tumour [128,310
miR-21 Expression associated with invasive disease over CIN [311
miR-218 Serum levels predict stage, lymph node metastasis SNP associated with decreased cervical cancer risk [111,268
miR-224 Microvascular invasion, HPV infection, overall survival, stage, lymph node metastasis [312
miR-27a SNP associated with decreased cervical cancer risk [269
miR-375 Decreased expression associated with lymph node metastasis [300
miR-424 Stage, lymph node metastasis, tumour grade, microvascular invasion [302
miR-497 Low tissue levels associated with worse stage and lymph node metastasis [303

The role of miRNAs in initiating lesions to squamous-cell cervical carcinoma

The role of the persistent precancerous lesion CIN is apparent prior to SCCC [115117]. Predicting which CIN lesions regress compared with those lesions which progress to invasive cancer is an important clinical question as treatment of CIN lesions can result in significant morbidity from excisional procedures (i.e., cold knife cone, loop electrosurgical excision procedure) that may affect future reproductive outcomes such as incompetent cervix and preterm labour [118]. Studies have shown that miRNAs are encoded by HPV genes and loss of miRNA binding sites within the HPV genome is relatively common although the clinical significance remains to be determined [119,120]. Profiling of high-grade CIN lesions found a minimal signature associated with CIN that is apparent in invasive disease. Thus, miRNAs may play a significant role in the initiation of pre-cancerous lesion [121]. Recently, a study found that miRNA pairs could predict which lesions would progress to SCCC. An expression ratio of miR-25/miR-92a and miR-22/miR-29a could be a useful diagnostic marker for HPV infection leading to SCCC, possibly through HPV E6 and E7 [122]. Importantly, one of the miRNAs that was found to be regulated by methylation and important for higher grade lesion, miR-124 [123], has been under testing for a quantitative methylation-specific PCR assay on Pap specimens [124]. Recent work in the miRNA field has focused on epigenetic changes that affect miRNA expression in the progression of CIN to invasive disease using human tissue samples and treated in vitro cultures [125]. For example, miR-203, which affects angiogenesis through VEGFA, is regulated by methylation of its promoter [126]. Additionally, miR-214, which affects apoptosis through BCL2L2, is regulated by methylation and acetylation [127]. More work is needed to determine a minimal miRNA profile in CIN that predicts progression to invasive disease and the epigenetic changes that regulate those miRNAs.

miRNAs as prognostic markers in squamous-cell cervical cancer

Recent miRNA profiling datasets have focused on well-defined clinical characteristics to determine specific miRNAs or miRNA signatures associated with important clinical features of SCCC such as chemotherapy resistance, lymph node metastasis, invasive disease and overall survival (Table 5). One of the most difficult clinical features of cervical cancer to determine pre-operatively can be the presence of lymph node metastasis. Clinical suspicion of distant disease by lymph node metastasis changes management from radical hysterectomy to leaving the uterus in situ and combined chemotherapy with radiotherapy. However, determination of lymph node metastasis typically requires biopsy for actual diagnosis. Thus, non-invasive tests to determine extent of disease are critical for reducing surgical risk and optimizing therapy. Serum miR-20a had a sensitivity and specificity greater than 70% in detecting lymph node metastasis preoperatively [128]. More promisingly, a minimal serum miRNA signature containing miR-1246, miR-20a, miR-2392, miR-3147, miR-3162-5p and miR-4484 has reasonable sensitivity and specificity with similar expression in tissues [129] (Table 3). In addition, miRNA may play a role in chemotherapy sensitivity. A randomized-controlled trial suggests that neoadjuvant chemotherapy may increase the p53:miR-34c:E2F1 and p53:miR-605:MDM2 pathways in stage IIB SCCC which may be protective and improve outcomes [130]. Similar to EOC, none of these miRNAs are used clinically, and future studies are needed.

CLINICAL USES OF miRNAs IN ENDOMETRIAL CANCER

Brief overview of miRNAs in endometrial cancer

According to GLOBOCAN 2012, over 319000 women were diagnosed with endometrial cancer and 76155 died from this disease [1,2]. Endometrial cancers can be classified into Type I and Type II tumours. Type I tumours are low grade and early stage tumours that are routinely cured by surgery, are hormonally sensitive, and encompass mainly EECs. Type II tumours are usually poorly differentiated, frequently recur, are hormonally insensitive, and encompass clear-cell, serous and high-grade EEC [131]. Many groups have performed expression-profiling studies to identify miRNAs aberrantly expressed in endometrial cancer [40,132143]. Many of these individual miRNAs are supported by in vitro studies (Table 6). For example, both miR-302 and miR-503 have been shown to inhibit tumorigenicity of endometrial cancer cells by targeting the cell cycle-associated oncogene, cyclin D1 (CCND1) [144,145]. In contrast, miR-125b acts as an oncogene by directly targeting the tumour suppressor gene, TP53INP1, and promoting proliferation and migration of type II endometrial cancer cells [146]. Given that miR-125 expression is significantly higher in type II non-EEC than type I EEC [132], miR-125 may serve as an important prognostic biomarker for disease progression. Other miRNA molecules are associated with clinical outcomes (Table 7) and discussed in further detail below.

Table 6
In vitro studies of miRNA molecules in endometrial cancer
miRNAEffect in vitroGenes, pathways regulatedReference(s)
BCM-173 Proliferation Vinculin [139
let-7a Proliferation AURORA B [306
miR-103 Proliferation TIMP-3 [313
 Invasion   
miR-106b Invasion TWIST1 [171,314
 EMT p21  
 Proliferation   
miR-125b Proliferation TP53INP1 [146,315
 Invasion ERRB2  
 Migration   
 Growth (xenograft)   
miR-130b Proliferation DICER1 [41,316,317
 Invasion ZEB1  
 EMT SNAIL  
 Growth (xenograft) E-cadherin, KLF4, NANOG, MDR1  
miR-138 Migration NGAL [318
 Growth (xenograft)   
miR-145 Differentiation OCT4 [319
 Tumorigenesis (xenograft)   
miR-148a Migration WNT10B [320
miR-152 Proliferation E2F3 [165
 Apoptosis MET  
 Growth (xenograft) RICTOR  
miR-155 Proliferation AGTR1 [321
miR-17-5p Bortezomib cell killing p21 [322
miR-193a-5p Tumour growth (xenograft) YY1 [323
miR-194 Invasion EMT BMI-1 [170
miR-199a-3p Proliferation mTOR [193
miR-200b, miR-200c, miR-429 family Chemotherapy sensitivity AP-2α [324
miR-200b MMP2 activity TIMP2 [325
miR-200c Proliferation ZEB1 [326
 Apoptosis VEGFA, FLT1, IKKβ, KLF9, BRD7  
miR-204 Migration FOXC1 [327
 Invasion   
 Extracellular matrix formation   
miR-204-5p Proliferation TrkB [172
 Invasion   
 Migration   
 Anchorage-independent growth   
 Growth (xenograft)   
miR-205 Proliferation ESRRG [328
 Migration   
 Invasion   
miR-206 Proliferation ERα [329
 Invasion   
miR-21 Proliferation PTEN [156
miR-25 Apoptosis BCL2L11 [314
miR-302 Proliferation CCND1 [145
 Migration CDK1  
 Growth (xenograft)   
miR-30c Proliferation MTA1 [166
 Migration   
 Invasion   
miR-34b Migration MET [166
 Invasion   
miR-429 Proliferation  [134
 Chemotherapy sensitivity   
miR-503 Proliferation CCND1 [144
 Cell cycle   
 Anchorage-independent growth   
 Growth (xenograft)   
miR-93 Proliferation [314 
 Cell cycle   
miR-98 Proliferation PGRMC1, CYP19A1 [330
miR-181a  PGR, DDX3X, TIMP3 [330
miRNAEffect in vitroGenes, pathways regulatedReference(s)
BCM-173 Proliferation Vinculin [139
let-7a Proliferation AURORA B [306
miR-103 Proliferation TIMP-3 [313
 Invasion   
miR-106b Invasion TWIST1 [171,314
 EMT p21  
 Proliferation   
miR-125b Proliferation TP53INP1 [146,315
 Invasion ERRB2  
 Migration   
 Growth (xenograft)   
miR-130b Proliferation DICER1 [41,316,317
 Invasion ZEB1  
 EMT SNAIL  
 Growth (xenograft) E-cadherin, KLF4, NANOG, MDR1  
miR-138 Migration NGAL [318
 Growth (xenograft)   
miR-145 Differentiation OCT4 [319
 Tumorigenesis (xenograft)   
miR-148a Migration WNT10B [320
miR-152 Proliferation E2F3 [165
 Apoptosis MET  
 Growth (xenograft) RICTOR  
miR-155 Proliferation AGTR1 [321
miR-17-5p Bortezomib cell killing p21 [322
miR-193a-5p Tumour growth (xenograft) YY1 [323
miR-194 Invasion EMT BMI-1 [170
miR-199a-3p Proliferation mTOR [193
miR-200b, miR-200c, miR-429 family Chemotherapy sensitivity AP-2α [324
miR-200b MMP2 activity TIMP2 [325
miR-200c Proliferation ZEB1 [326
 Apoptosis VEGFA, FLT1, IKKβ, KLF9, BRD7  
miR-204 Migration FOXC1 [327
 Invasion   
 Extracellular matrix formation   
miR-204-5p Proliferation TrkB [172
 Invasion   
 Migration   
 Anchorage-independent growth   
 Growth (xenograft)   
miR-205 Proliferation ESRRG [328
 Migration   
 Invasion   
miR-206 Proliferation ERα [329
 Invasion   
miR-21 Proliferation PTEN [156
miR-25 Apoptosis BCL2L11 [314
miR-302 Proliferation CCND1 [145
 Migration CDK1  
 Growth (xenograft)   
miR-30c Proliferation MTA1 [166
 Migration   
 Invasion   
miR-34b Migration MET [166
 Invasion   
miR-429 Proliferation  [134
 Chemotherapy sensitivity   
miR-503 Proliferation CCND1 [144
 Cell cycle   
 Anchorage-independent growth   
 Growth (xenograft)   
miR-93 Proliferation [314 
 Cell cycle   
miR-98 Proliferation PGRMC1, CYP19A1 [330
miR-181a  PGR, DDX3X, TIMP3 [330
Table 7
Clinically associated miRNA molecules in endometrial cancer
miRNAClinical indicationReference(s)
miR-100 Down-regulation correlates with poor overall survival [158
miR-130b Expression correlates with myometrial invasion, stage, and expression of oestrogen receptor [41,317
 Higher expression associated with longer survival  
miR-145 Low expression and DNMTB3 overexpression correlates with short survival [331
miR-143   
miR-194 Increased expression associated with longer overall survival [168
miR-199b Tissue biomarker for EEC [158
miR-205, miR-200a Expression predicts relapse [138
miR-205 High expression correlates with shorter overall survival [332
miR-206 Expression lower in grade1 and 2 tumours compared with grade 3 [329
miR-214*, miR-221, miR-222 Lower expression and higher VEGFA associated with stage IB over stage IA tumours [174
miR-503 Expressed expression associated with longer survival [144
miR-99a, miR-199b Plasma biomarker with high sensitivity and specificity [158
miRNAClinical indicationReference(s)
miR-100 Down-regulation correlates with poor overall survival [158
miR-130b Expression correlates with myometrial invasion, stage, and expression of oestrogen receptor [41,317
 Higher expression associated with longer survival  
miR-145 Low expression and DNMTB3 overexpression correlates with short survival [331
miR-143   
miR-194 Increased expression associated with longer overall survival [168
miR-199b Tissue biomarker for EEC [158
miR-205, miR-200a Expression predicts relapse [138
miR-205 High expression correlates with shorter overall survival [332
miR-206 Expression lower in grade1 and 2 tumours compared with grade 3 [329
miR-214*, miR-221, miR-222 Lower expression and higher VEGFA associated with stage IB over stage IA tumours [174
miR-503 Expressed expression associated with longer survival [144
miR-99a, miR-199b Plasma biomarker with high sensitivity and specificity [158

The role of miRNAs in initiating lesions in endometrial cancer

For endometrial cancer, clearly, unopposed oestrogen, either from exogenous hormone consumption (oestrogen-only hormone replacement therapy), endogenous hormone production (obesity) or progesterone resistance (polycystic ovarian syndrome), can lead to abnormal epithelial cell proliferation in the uterus. Clinically, for EEC, the classic precursor lesion is complex hyperplasia with atypia from over proliferation of the endometrium [147151]. Given the number of oestrogen responsive miRNAs [152], these small molecules probably play a significant role. The second major mechanism of endometrial cancer involves activation of the phosphoinositide 3-kinase (PI3K) pathway, through the loss of the tumour suppressor PTEN. PTEN acts as a phosphatase for AKT. Phospho-AKT is the active form, and removal of phosphorylation by PTEN decreases AKT downstream effects. Loss of PTEN through loss of protein function or mutations leads to loss of this repression, and subsequent activation of the PI3K-AKT pathway, and abnormal endometrial proliferation (Figure 2) [153]. Somatic mutations in PTEN have been reported in 34–83% of EEC cases along with a 50–83% frequency of loss of PTEN protein [133,154,155]. Several groups have investigated the relationship between dysregulated miRNAs and PTEN expression in endometrial cancer [132,133,156]. Lee et al. [133] found that miR-200c expression was significantly higher in PTEN-negative endometrial tumours compared with PTEN-positive tumours. In addition, miR-200c and miR-183 have both been predicted to target PTEN in silico [132,133], whereas miR-21 has been shown to directly target PTEN in endometrial cancer cells [156]. Although these studies shed some light into the mechanism for reduced PTEN protein in PTEN wild-type endometrial cancers, they lack in vitro and in vivo functional studies that describe the functional roles of miRNAs in PTEN-deficient endometrial carcinogenesis.

The molecular mechanisms leading to endometrial cancer

Figure 2
The molecular mechanisms leading to endometrial cancer

This simplified diagram depicts the critical pathways in endometrial cancer. Arrows indicate stimulation. Block lines indicate inhibition. ER and PR are likely all nuclear effects, but ER effects could be membrane bound effects.

Figure 2
The molecular mechanisms leading to endometrial cancer

This simplified diagram depicts the critical pathways in endometrial cancer. Arrows indicate stimulation. Block lines indicate inhibition. ER and PR are likely all nuclear effects, but ER effects could be membrane bound effects.

Role of miRNAs in diagnosis of endometrial cancer

Although endometrial cancer can be diagnosed using an office-based endometrial biopsy, many women do not tolerate this office procedure due to stenotic cervical os, low pain thresholds and anxiety. Thus, the idea of plasma diagnosis may allow for easier screening, especially for Type II EEC that do not present as frequently with postmenopausal or abnormal uterine bleeding [131]. Similar to EOC and SCCC, studies have attempted to use serum or plasma to detect endometrial cancer (Table 3). Using serum samples, a panel containing miR-222, miR-223, miR-186 and miR-204 was found to be a useful serum biomarker with sensitivity and specific over 90%, but these data were not validated in independent samples [157]. Additionally, a study profiled miRNAs from both endometrial cancer tissues and plasma. They found that miR-9 with miR-1228 and miR-9 with miR-92a were good plasma markers for diagnosis. Additionally, a plasma signature containing miR-200b, miR-200c, miR-203 and miR-449a could distinguish myometrial invasion [138]. In another study, a plasma panel containing miR-99a/miR-100/miR-199b was able to accurate classify endometrial cancer based on mTOR status [158]. Given that these miRNAs target members of the PI3K-AKT pathway, these miRNA may be useful plasma biomarkers (Table 3). In addition, a tissue biomarker panel of four miRNAs (miR-182, miR-183, miR-200a, miR-200c) had 95% sensitivity and 91% specificity at distinguishing complex hyperplasia from endometrial cancer in formalin-fixed paraffin-embedded specimens [133]. As many more young obese women are diagnosed with complex hyperplasia and want to maintain child bearing potential, the accurate diagnosis of complex hyperplasia and endometrial cancer is critical [148,149,159164]. miRNAs may be useful biomarkers for this in the future.

Similar to SCCC, aberrant DNA hypermethylation of CpG islands is another mechanism for transcriptional silencing of tumour suppressor miRNAs in endometrial cancer that may improve biomarker ability. Tsuruta et al. [165] identified miR-152 as a tumour suppressor that is frequently inhibited by DNA hypermethylation in endometrial cancer. This inhibition was reversed after restoring miR-152 expression in endometrial cancer cells. Similarly, Hiroki et al. [166] showed that miR-34b is down-regulated in serous endometrial carcinoma due to promoter hypermethylation, and ectopic expression of miR-34b inhibits cell growth, migration and invasion of endometrial cancer cells. Similar to SCCC, the future of endometrial cancer diagnosis may lie in epigenetic modifications in endocervical fluid on office-based Pap test. Studies suggest that miRNAs may be released via exosomes and may exhibit epithelial-stromal cross talk within the uterus [33,167], making serum, plasma or endocervical detection exciting avenues for future diagnostic studies.

miRNAs as prognostic markers in endometrial cancer

Similar to EOC and SCCC, recent miRNA profiling datasets have focused on well-defined clinical characteristics to determine specific miRNAs or miRNA signatures associated with important clinical features of EEC such as relapse, myometrial invasion and overall survival (Table 7). Clinically, miR-194 was found to be significantly lower in patients with advanced stage (stage III and IV) type I EEC than in patients with type II endometrial cancer [168]. Furthermore, patients with higher miR-194 levels were shown to have a better prognosis, with a median survival time of 85 months compared with patients with low miR-194 levels who have median survival time of 14 months [168]. EMT is a key mechanism in many cancers including endometrial cancer [169], with miR-106b and miR-194 inhibiting EMT through TWIST1 and BMI1 [170,171]. In addition, the oncogenic neurotrophic receptor kinase B (TrkB) has been shown to promote EMT and anoikus resistance in endometrial cancer [172] through a novel regulatory loop involving TrkB- signal transducer and activator of transcription 3 (STAT3)-miR-204-5p [173]. The present study showed that overexpression of TrkB leads to activation of the JAK2/STAT3 pathway, which in turn constitutively represses miR-205-5p in endometrial cancer cells [173]. Functionally, reestablishment of miR-204-5p suppressed migration and invasion of endometrial cancer cells and tumorigenicity in vitro and in vivo. Furthermore, lower miR-205-5p expression was correlated with tumour stage and lymph node metastasis in endometrial cancer patients [172]. Finally, miR-1228 with miR-200c and miR-429 were associated with overall survival as a tissue biomarker [138]. Taken together, these findings suggest that miR-194 and miR-204-5p may be prognostic biomarkers in tissues and may be used to augment the activities of endogenous miRNAs in endometrial cancer.

Similar to studies in EOC, miRNAs that function in angiogenesis pathways are dysregulated in endometrial cancer. In one EEC study, miR-15b, miR-17-5p, miR-125a, miR-214, miR-221, miR-222 and miR-424 were significantly down-regulated whereas miR-200b and miR-210 were up-regulated in endometrial cancers. Importantly, those miRNAs that were down-regulated were inversely correlated with VEGFA protein levels. Although there were no clinical associations, the present study suggests that these miRNAs may regulate VEGFA expression and opens up avenues for personalized therapy based on angiogenesis targets and miRNAs that target angiogenesis pathway genes [174]. Overall, the studies on the clinical function of miRNAs in endometrial cancers suggest specific miRNAs play a clinically significant role. Future clinical trials need to be aimed at discerning the role of molecular players such as miRNAs in treatment failures or recurrent disease.

USE OF INTEGRATED ANALYSIS TO DETERMINE CLINICALLY IMPORTANT NETWORKS

With the requirement for resource sharing of data by journals and funding agencies, large amounts of data are available for multi-platform integration studies. Integrated analysis of miRNA profiles with other datasets (i.e., mRNA, methyl-DNA, protein) allows for improved triangulation of biologically plausible networks or characterization of specific miRNA molecules associated with clinical outcomes. Additionally, examination of miRNA target genes allows for examination of those genes for additional association with clinical outcomes. Table 8 lists clinically important molecules from integrated analysis in EOC and to a lesser extent SCCC. Future work should focus on integration of multiple datasets with high quality clinical data so that entire networks modulated by miRNAs will be better understood in the context of clinical outcomes. In the future, the resources poured into TCGA for genomic profiling and data sharing should propel answers to important clinical questions in female reproductive tract cancers forward quickly.

Table 8
Integrated datasets with miRNA molecules
Datasets analysedResultsReference
miRNA + mRNA + transcription factors in EOC Multiple overlapping feed forward networks focusing on oncogenic networks, including cellular differentiation, EMT, cellular proliferation, cell cycle regulation, apoptosis [333337
 miR-521 associated with overall survival  
miRNA + mRNA   
 in EOC, breast, lung, prostate Common pathways across multiple cancer types with connectivity weight based on thermodynamics [338
 in renal, EOC, GM miR-17 and miR-221/miR-222 common across all 3 cancers [339
miRNA + mRNA in EOC miR-506, miR-141, miR-200c target mesenchymal phenotype [51,340,341
 miR-506 expression associated with progression free survival  
 integrated analysis allows for ease of biologically plausible validation studies  
 miR-29a affects proliferation and chemosensitivity  
miRNA + mRNA + miRNA targeting by sequence + protein interactions in EOC Protein interaction based miRNA molecules (TCGA datasets) [342
miRNA + mRNA + DNA methylation in EOC Lower levels of miR-502-5p, miR-128, miR-215/625 with higher levels of targets CCND1, PPARG, RUNX1 had worse overall survival [343
 Significant functional and transcriptional enrichment using multiple platforms and datasets [344,345
miRNA + mRNA in SCCC Intricate regulatory networks affected by miRNAs [346
miRNA + aCGH in SCCC Functionally relevant miRNAs are affected by chromosomal aberrations [307
Datasets analysedResultsReference
miRNA + mRNA + transcription factors in EOC Multiple overlapping feed forward networks focusing on oncogenic networks, including cellular differentiation, EMT, cellular proliferation, cell cycle regulation, apoptosis [333337
 miR-521 associated with overall survival  
miRNA + mRNA   
 in EOC, breast, lung, prostate Common pathways across multiple cancer types with connectivity weight based on thermodynamics [338
 in renal, EOC, GM miR-17 and miR-221/miR-222 common across all 3 cancers [339
miRNA + mRNA in EOC miR-506, miR-141, miR-200c target mesenchymal phenotype [51,340,341
 miR-506 expression associated with progression free survival  
 integrated analysis allows for ease of biologically plausible validation studies  
 miR-29a affects proliferation and chemosensitivity  
miRNA + mRNA + miRNA targeting by sequence + protein interactions in EOC Protein interaction based miRNA molecules (TCGA datasets) [342
miRNA + mRNA + DNA methylation in EOC Lower levels of miR-502-5p, miR-128, miR-215/625 with higher levels of targets CCND1, PPARG, RUNX1 had worse overall survival [343
 Significant functional and transcriptional enrichment using multiple platforms and datasets [344,345
miRNA + mRNA in SCCC Intricate regulatory networks affected by miRNAs [346
miRNA + aCGH in SCCC Functionally relevant miRNAs are affected by chromosomal aberrations [307

LACK OF REPRODUCIBILITY ACROSS DIFFERENT STUDIES

From the tables above, many studies have identified unique miRNAs associated with the same clinical feature that do not overlap with results from other studies. Reasons for lack of reproducibility across the miRNA studies described above are numerous. First, each platform uses different technology. Mature miRNAs are formed from longer precursor species (Figure 1). Detection methods of hybridization (i.e., microarray or real-time reverse transcription PCR) may detect both the mature miRNA and the longer precursor species. Additionally, the small size of miRNAs and high sequence similarity–sometimes differing by only 1 nucleotide–makes primer design and hybridization conditions tricky and can limit the number of mature miRNAs detected by a platform [175,176]. On the other hand, NGS approaches will only select what is prepared in the library usually based on a cut-off size for the mature miRNA species. Secondly, each patient's tumour is derived from a unique individual. Although most studies try to get the most homogenous population as possible based on race, age and ethnicity, some variability will be present based on other underlying co-morbidities, medications or unknown genetic admixture. Work in cervical cancer has analysed different platforms but found little overlap across studies, suggesting that a minimal requirement for tissue characterization and clinical features needs to be reported [177]. Finally, tumours are genetically diverse. TCGA datasets have shown the differences across large sample sizes for endometrial cancer and EOC [66,178,179]. As a last note, the climate of the current peer review system does not give impact to studies that replicate already published studies or studies with negative data. Thus, replicative studies may have been done and not published.

THE FUTURE OF miRNA MOLECULES IN PERSONALIZED CANCER TREATMENT

Although in vitro, in vivo and clinical association studies suggest that miRNAs play a role in female reproductive tract cancers, delivery of miRNA molecules to targeted cancer cells remains a challenge. Recent advances in EOC have used aptamers that target MUC1, a marker of ovarian cancer, as chimeras to miR-29b or let-7i. These chimeras are cleaved by DICER, allowing miR-29b and let-7i to act on cells, decreasing tumour volume and stem cell populations in xenograft studies and increasing apoptosis and chemotherapy sensitivity in vitro [180182]. However, low DICER expression is associated with poor prognostic ovarian and endometrial tumours [3739]. Recent headway in therapy for EOC was made combining miRNA with small interfering RNAs (siRNAs). Nanoliposomes loaded with siRNA EPHA2 and miR-520-5p synergistically affected in vitro migration and invasion. Importantly, the combination revealed synergistic decrease in tumour burden and angiogenesis features [183]. Given that nanoliposomes loaded with siRNA EPHA2 are being evaluated for use in actual human clinical trials [184,185], these results are very encouraging for personalized therapy options. Finally, given the angiogenesis network targeted by specific miRNAs, this signature may allow for selection for personalized therapy with biological inhibitors such as bevcizumab based on miRNA signature.

Although most cases of endometrial cancer are diagnosed at an early stage and can be cured with surgery and adjuvant therapy, patients with hormone-resistant or recurrent endometrial cancer have a less favourable prognosis and limited effective treatment options. As a result, current research is focused on developing drugs that target specific molecular pathways in endometrial carcinogenesis [153]. Over activation of the PI3K/AKT/mTOR pathway, through loss of PTEN, is responsible for initiating ~80% of endometrial cancers [186]. There are several inhibitors to various components of this pathway that are currently in clinical trials [153]. However, targeting most signalling pathways with a single therapeutic agent can lead to compensatory activation of other molecular pathways and tumour resistance, rendering them ineffective [187192]. Thus, combining inhibitors of the PI3K/AKT/mTOR pathway with other therapeutic options may improve the efficacy of the drugs and quality of life of the patient [153]. Recently miR-199a-3p was shown to regulate endometrial cancer cell proliferation by directly targeting mTOR and combined treatment with rapamycin showed synergistic effects [193]. Furthermore, miRNAs (miR-100, miR-99 and miR-199b) targeting mTOR kinase are significantly decreased in EEC patients [158]. Use of miRNA molecules may be a novel way to modulate mTOR and allow for innovative therapy. In other cancer types, silencing of miR-21 conferred radiosensitivity through inhibition of the PI3K/AKT pathway in malignant glioma cells [194], whereas overexpression of miR-175 inhibited colorectal cancer growth by targeting this same pathway [195]. These studies highlight the need to identify and exploit miRNAs as therapeutic targets of these signalling pathways in female reproductive tract cancers.

SUMMARY OF miRNAs IN FEMALE REPRODUCTIVE TRACT CANCERS

Use of miRNA sequencing data with metadata containing clinical and tumour information offers opportunities for discovery of novel therapies, innovative prognostic molecules and clinically useful biomarkers. The key to implementing this translational science is the research team. A good research team will include many people. The clinician is critical for obtaining clinical samples that are well characterized over extended longitudinal time periods, incorporating many data points. The bench scientist is critical for isolating miRNAs, in vitro studies and pre-clinical in vivo studies. The bioinformatician is critical to data analysis. The clinician–scientist is critical to interpreting the data into useful information that can be presented to clinicians. One last opportunity for advancement will be a system or app that allows busy clinicians to access the best science of innovative therapy that is personalized for the one particular patient in the room at a time. Each month hundreds of manuscripts are published regarding the use of biomarkers and innovative therapy. As the system is now, a busy clinician is lost in a sea of manuscripts. A system of informing clinicians regarding the quality of the data and implications for personalized therapy needs to be developed as the final component of this team.

Abbreviations

     
  • bp

    base pair

  •  
  • CCND1

    cyclin D1

  •  
  • CIN

    cervical intraepithelial neoplasia

  •  
  • EEC

    endometrioid endometrial cancer

  •  
  • EMT

    epithelial to mesenchymal transition

  •  
  • EOC

    epithelial ovarian cancer

  •  
  • HPV

    human papilloma virus

  •  
  • miRNA

    microRNA

  •  
  • NGS

    next-generation sequencing

  •  
  • PI3K

    phosphoinositide 3-kinase

  •  
  • RISC

    RNA-induced silencing complex

  •  
  • SCCC

    squamous-cell cervical carcinoma

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • STAT3,signal

    transducer and activator of transcription 3

  •  
  • STIC

    serous tubal intraepithelial carcinoma

  •  
  • TCGA

    The Cancer Genome Atlas

  •  
  • WHO

    World Health Organization

FUNDING

Our own work was supported by The Liz Tilberis Scholarship Ovarian Cancer Research Fund through the Estate of Agatha Fort (to S.M.H.) and a Postdoctoral Training Grant from the Cancer Prevention Research Institute of Texas [grant number RP140102 (to M.L.)].

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