Sialic-acid-binding immunoglobulin-like lectin (siglec) regulates cell death, anti-proliferative effects and mediates a variety of cellular activities. Little was known about the relationship between siglecs and hepatocellular carcinoma (HCC) prognosis. Siglec gene expression between tumor and non-tumor tissues were compared and correlated with overall survival (OS) from HCC patients in GSE14520 microarray expression profile. Siglec-1 to siglec-9 were all down-regulated in tumor tissues compared with those in non-tumor tissues in HCC patients (all P < 0.05). Univariate and multivariate Cox regression analysis revealed that siglec-2 overexpression could predict better OS (HR = 0.883, 95%CI = 0.806–0.966, P = 0.007). Patients with higher siglec-2 levels achieved longer OS months than those with lower siglec-2 levels in the Kaplan–Meier event analysis both in training and validation sets (P < 0.05). Alpha-fetoprotein (AFP) levels in siglec-2 low expression group were significantly higher than those in siglec-2 high expression group using Chi-square analysis (P = 0.043). In addition, both logistic regression analysis and ROC curve method showed that siglec-2 down-regulation in tumor tissues was significantly associated with AFP elevation over 300 ng/ml (P < 0.05). In conclusion, up-regulation of siglec-2 in tumor tissues could predict better OS in HCC patients. Mechanisms of siglec-2 in HCC development need further research.

Introduction

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second most common cause of cancer-related deaths [1–3]. In the past two decades, a marked increase in HCC-related annual death rates was observed [2,4]. And, the incidence of HCC will continue to rise until 2030 based on a SEER registry projects study [5]. Previous research revealed that the prediction of prognosis plays a critical role in therapeutic options of HCC. But, little tumor markers have been externally validated in HCC survival prediction [6]. To find novel biomarkers for predicting HCC prognosis, and to reveal HCC target for treatment is urgently required.

As a characteristic of cancer, immune evasion is more prevalent in organs with high immune tolerance including the liver [7]. The sialic-acid-binding immunoglobulin-like lectins (siglecs), a novel family of immunoregulatory, have received more and more attention for their capacity to mediate cell death, anti-proliferative effects and to regulate a variety of cellular activities [8]. Currently, pharmacological strategies using siglec agonistic cross-linking therapeutics are discussed. Modulation of immune responses by targeting siglecs using agonistic or antagonistic therapeutics may have important clinical implications and may be a novel pharmacological strategy in tumor immunotherapy [8]. A recent research has revealed that high expression of siglec-10 on NK cells mediates impaired NK cell function, and siglec-10 expression in tumors is associated with poorer survival of HCC patients [9]. However, roles of siglec family in HCC development were little discussed.

According to the potential value of siglecs in HCC development, this study aimed to evaluate the associations between siglec family and outcomes from hepatitis B virus (HBV)-related HCC patients, hoping that the data may provide potential biomarker candidates and useful insights into the pathogenesis and progression of HCC.

Materials and methods

Patients

Using GSE14520 profile from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, 247 patients with HCC were identified. Twenty-seven patients were excluded for the unavailable siglec gene expression or insufficient clinical outcome data. Finally, 220 HCC cases were included in the analysis. All the HCC patients had a history of HBV infection or HBV-related liver cirrhosis; the diagnosis of HCC was made in all cases by two independent pathologists who had detailed information on clinical presentation and pathological characteristics as declared by Roessler et al. [10].

All liver tissue was obtained with informed consent from patients who underwent radical resection between 2002 and 2003 at the Liver Cancer Institute and Zhongshan Hospital, Fudan University. The study was approved by the Institutional Review Board of the participating institutes [10]. All participants provided written informed consent, as reported by Roessler et al. [10,11].

Data extraction and end points

We extracted the GSE14520 microarray expression profile. Tumor sample and microarray processing were reported by Roessler et al. [10,11] and are available at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520. The experiment protocols and data processing methods are available at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM362949. Siglec gene expression levels were calculated using the matchprobes package in the R program and the log2 RMA-calculated signal intensity was reported. Nine siglecs including siglec-1, siglec-2, siglec-3, siglec-4, siglec-5, siglec-6, siglec-7, siglec-8 and siglec-9 were searched and included in our analysis. Overall survival (OS) was defined as the time from surgery to death from any disease.

Statistical analysis

PASW Statistics software version 22.0 from SPSS Inc. (Chicago, IL, USA) was used for statistical analysis. Student’s t-test, Mann–Whitney U-test and Chi-squared test were used for normally distributed continuous data, non-normally distributed continuous data and categorical variables, respectively. Univariate analysis and multivariate Cox and logistic regression were assessed for identifying factors associated with OS and clinico-pathological features. The Kaplan–Meier curve by log rank method was used to compare OS between different groups. A two-tailed P < 0.05 were considered statistically significant.

Results

Siglec levels comparison between tumor and non-tumor tissues

Nine members of siglec family were identified, including siglec-1 to siglec-9. As shown in Figure 1, all siglecs were overexpressed in non-tumor tissues compared with those in tumor tissues (all P < 0.05, Figure 1).

Differential expression of siglecs between non-tumor and tumor tissues in HCC patients

Figure 1
Differential expression of siglecs between non-tumor and tumor tissues in HCC patients
Figure 1
Differential expression of siglecs between non-tumor and tumor tissues in HCC patients

Relationship between siglecs and HCC overall survival

As shown in Table 1, univariate analysis showed that siglec-2 and siglec-4 were potential factors associated with HCC OS (P = 0.065 and P = 0.061, respectively). When all siglecs were evaluated by a multivariate model using enter selection, up-regulation of siglec-2 in tumor tissues showed protective potentials for HCC OS (HR = 0.883, 95%CI = 0.806–0.966, P = 0.007). In contrast, siglec-4 overexpression was negatively associated with HCC OS (HR = 1.059, 95%CI = 1.025–1.094, P = 0.001).

Table 1
Univariate and multivariate Cox regression analysis of siglecs and HCC overall survival
Siglecs, per increase of 1 unit Univariate analysis Multivariate analysis 
 HR (95%CI) P value HR (95%CI) P value 
Siglec-1 0.988 (0.971–1.006) 0.18   
Siglec-2 0.932 (0.65–1.004) 0.065 0.883 (0.806–0.966) 0.007 
Siglec-3 1.005 (0.979–1.032) 0.708   
Siglec-4 1.028 (0.999–1.058) 0.061 1.059 (1.025–1.094) 0.001 
Siglec-5 1.025 (0.968–1.084) 0.397   
Siglec-6 0.995 (0.911–1.087) 0.917   
Siglec-7 1.003 (0.94–1.07) 0.939   
Siglec-8 1.018 (0.898–1.153) 0.783   
Siglec-9 1.004 (0.864–1.167) 0.957   
Siglecs, per increase of 1 unit Univariate analysis Multivariate analysis 
 HR (95%CI) P value HR (95%CI) P value 
Siglec-1 0.988 (0.971–1.006) 0.18   
Siglec-2 0.932 (0.65–1.004) 0.065 0.883 (0.806–0.966) 0.007 
Siglec-3 1.005 (0.979–1.032) 0.708   
Siglec-4 1.028 (0.999–1.058) 0.061 1.059 (1.025–1.094) 0.001 
Siglec-5 1.025 (0.968–1.084) 0.397   
Siglec-6 0.995 (0.911–1.087) 0.917   
Siglec-7 1.003 (0.94–1.07) 0.939   
Siglec-8 1.018 (0.898–1.153) 0.783   
Siglec-9 1.004 (0.864–1.167) 0.957   

Furthermore, we performed R software analysis to determine the cut-off values of siglec-2 and siglec-4 for the prediction of OS in the training set. Then, we transformed the continuous data above into dichotomous variables according to the determined cut-off values. Unfortunately, no statistical significance was found between siglec-4 and HCC OS in training set based on randomized sampling. According to R language analysis, we grouped siglec-2 using cut-off values of 11.6 into siglec-2 low group and siglec-2 high group. This demonstrated that patients in siglec-2 high group had better OS than those in siglec-2 low group, both in training set and validation set (log rank P = 0.041 and log rank P = 0.031, respectively, Figure 2A,B). When all HCC patients were included in the Kaplan–Meier event analysis, patients with higher siglec-2 levels achieved longer OS months than those with lower siglec-2 levels (mean survival months in siglec-2 high group = 50.9 ± 1.8 and in siglec-2 low group = 41.5 ± 3.9, respectively, log rank P = 0.01, Figure 2C).

Association between siglec-2 expression and OS in HCC patients

Figure 2
Association between siglec-2 expression and OS in HCC patients

Higher siglec-2 levels are associated with better OS in HCC patients, in training set (A), validation set (B) and total database (C).

Figure 2
Association between siglec-2 expression and OS in HCC patients

Higher siglec-2 levels are associated with better OS in HCC patients, in training set (A), validation set (B) and total database (C).

Relationship between siglecs and HCC clinico-pathological features

We grouped HCC patients with siglec-2 cut-off of 11.6 and compared differences of clinico-pathological features between these two groups. As shown in Table 2, more patients had higher alpha-fetoprotein (AFP) levels in siglec-2 low group than those in siglec-2 high group (60% vs. 41.7%, P = 0.043). Additionally, no differences were found in patients’ clinico-pathological features including HBV virus status, ALT levels, tumor size, multinodular, cirrhosis and tumor staging (all P > 0.05).

Table 2
Clinico-pathological features based on siglec-2 expression in HCC patients
Clinico-pathological features High siglec-2 group (n = 180) Low siglec-2 group (n = 40) P value 
Gender (male/female), n 156/24 34/6 0.781 
Age (>50 years/<50 years), n 99/81 25/15 0.387 
HBV viral status (AVR-CC/no/NA), n 47/128/5 9/27/4 0.111 
ALT (>50/<50/NA), U/l 76/104 14/26 0.401 
Main tumor size (>5/<5/NA), cm 66/114/0 14/25/1 0.104 
Multinodular (yes/no), n 37/143 7/33 0.662 
Cirrhosis (yes/no), n 163/17 39/1 0.147 
TNM staging (I–II/III/NA), n 138/40/2 31/8/1 0.763 
BCLC staging (0-A/B-C/NA), n 138/41/1 30/9/1 0.503 
CLIP staging (0/1/2/3/4/5/NA), n 81/61/25/8/2/1/2 15/13/9/1/1/0/1 – 
AFP (>300/<300/NA), ng/ml 75/102/3 24/16/0 0.043 
Clinico-pathological features High siglec-2 group (n = 180) Low siglec-2 group (n = 40) P value 
Gender (male/female), n 156/24 34/6 0.781 
Age (>50 years/<50 years), n 99/81 25/15 0.387 
HBV viral status (AVR-CC/no/NA), n 47/128/5 9/27/4 0.111 
ALT (>50/<50/NA), U/l 76/104 14/26 0.401 
Main tumor size (>5/<5/NA), cm 66/114/0 14/25/1 0.104 
Multinodular (yes/no), n 37/143 7/33 0.662 
Cirrhosis (yes/no), n 163/17 39/1 0.147 
TNM staging (I–II/III/NA), n 138/40/2 31/8/1 0.763 
BCLC staging (0-A/B-C/NA), n 138/41/1 30/9/1 0.503 
CLIP staging (0/1/2/3/4/5/NA), n 81/61/25/8/2/1/2 15/13/9/1/1/0/1 – 
AFP (>300/<300/NA), ng/ml 75/102/3 24/16/0 0.043 

AFP, alpha-fetoprotein; ALT, alanine aminotransferase; AVR-CC, active viral replication chronic carrier; NA, not available.

We performed logistic regression analysis to identify the relationship between siglecs and HCC clinico-pathological features. This was summarized in Table 3. Univariate analysis showed that siglec-2 was a potential factor associated with AFP levels in HCC patients (P = 0.012). When all siglecs were evaluated by a multivariate model using enter selection, siglec-2 overexpression is negatively associated with HCC patients’ AFP level (OR = 0.822, 95%CI = 0.724–0.934, P = 0.003). To evaluate the predictive accuracy of siglec-2 and siglec-4 for AFP levels in HCC patients, we analyzed ROCs and found that elevated siglec-2 significantly and accurately predicted lower AFP level (AUC = 0.607, P = 0.007, Figure 3).

ROC curve of siglec-2 for AFP > 300 ng/ml

Figure 3
ROC curve of siglec-2 for AFP > 300 ng/ml
Figure 3
ROC curve of siglec-2 for AFP > 300 ng/ml
Table 3
Relationship between siglecs and HCC clinico-pathological characteristics by logistic regression analysis
Siglecs, per increase of 1 unit AFP > 300 ng/ml 
 Univariate analysis Multivariate analysis 
 OR (95%CI) P value OR (95%CI) P value 
Siglec-1 1.001 (0.984–1.018) 0.936   
Siglec-2 0.891 (0.815–0.975) 0.012 0.822 (0.724–0.934) 0.003 
Siglec-3 1.0 (0.967–1.035) 0.992   
Siglec-4 1.034 (0.969–1.102) 0.313   
Siglec-5 1.028 (0.944–1.12) 0.523   
Siglec-6 1.045 (0.932–1.173) 0.449   
Siglec-7 1.044 (0.959–1.137) 0.316   
Siglec-8 1.063 (0.908–1.245) 0.448   
Siglec-9 0.861 (0.714–1.038) 0.117   
Siglecs, per increase of 1 unit AFP > 300 ng/ml 
 Univariate analysis Multivariate analysis 
 OR (95%CI) P value OR (95%CI) P value 
Siglec-1 1.001 (0.984–1.018) 0.936   
Siglec-2 0.891 (0.815–0.975) 0.012 0.822 (0.724–0.934) 0.003 
Siglec-3 1.0 (0.967–1.035) 0.992   
Siglec-4 1.034 (0.969–1.102) 0.313   
Siglec-5 1.028 (0.944–1.12) 0.523   
Siglec-6 1.045 (0.932–1.173) 0.449   
Siglec-7 1.044 (0.959–1.137) 0.316   
Siglec-8 1.063 (0.908–1.245) 0.448   
Siglec-9 0.861 (0.714–1.038) 0.117   

Siglec-2 coexpression genes and pathways enrichment

Using the GSE14520 microarray database, coexpressed genes of siglec-2 in HCC were searched in HCC. As shown in Table 4, 137 genes were found to be positively coexpressed with siglec-2. On the other hand, 352 genes were negatively coexpressed with siglec-2 as shown in Table 5.

Table 4
Siglec-2 positive coexpressed genes (n = 137)
ACADS TG PLCB2 NNAT LCAT GNAO1 VIPR1 CD79A GPR162 MYLPF 
RIN1 ESR1 RCE1 SULT2B1 TCP11L1 MYOM2 CD33 LLGL1 WNT10B PRKCG 
ADCYAP1 NPHP1 ELAVL3 SCN2A CACNG3 PDE3A KLKB1 INSL4 F11 MYOD1 
UMOD CUBN NAT2 ADRB3 NGF STATH IL11 HTR6 AKAP4 CHRND 
LTK SLC6A13 NOS1 KCNS1 POU6F2 CRYGD SLC28A1 FOXH1 CRYBB3 CACNB4 
PRMT8 CD160 SCN7A BMP8B MYBPC3 PSD GIPR OSBPL7 RASGRP2 BMP3 
CYP2A13 GLP1R SLC14A2 GJA8 EYA2 CORO2B PDE6G CHRNA3 NR6A1 CLEC4M 
TACR1 GRIN1 ADRA1D BMP7 DSCAM TUBB7P CAMK2A SH3BP1 GPD1 MYOZ3 
PRSS53 FSHB GPR182 PLAC4 TOM1L2 EMX1 CFAP74 DNAH2 CFAP70 MYCNOS 
CYP2A7P1 LOC101929073 DDR1-AS1 KLK1 LINC01482 GRIK5 FUT7 CNPY4 TTC38 ECHDC2 
A4GALT MYOZ1 NLGN3 CPLX3 SLC13A4 RNF122 RETN CARD14 KCNQ1DN NOX5 
LINC00652 PLA2G3 THEG CTNNA3 GABRQ CHST8 GSN-AS1 C7orf69 CLDN17 HOXC8 
ZNF717 FGF17 TAS2R7 IL36A OR1D2 MYL10 LZTS1 CLEC4A KIAA1644 LRCH4 
DMWD ADRBK1 PNPLA2 ACACB CACNG4 LOC100505915 NPEPL1    
ACADS TG PLCB2 NNAT LCAT GNAO1 VIPR1 CD79A GPR162 MYLPF 
RIN1 ESR1 RCE1 SULT2B1 TCP11L1 MYOM2 CD33 LLGL1 WNT10B PRKCG 
ADCYAP1 NPHP1 ELAVL3 SCN2A CACNG3 PDE3A KLKB1 INSL4 F11 MYOD1 
UMOD CUBN NAT2 ADRB3 NGF STATH IL11 HTR6 AKAP4 CHRND 
LTK SLC6A13 NOS1 KCNS1 POU6F2 CRYGD SLC28A1 FOXH1 CRYBB3 CACNB4 
PRMT8 CD160 SCN7A BMP8B MYBPC3 PSD GIPR OSBPL7 RASGRP2 BMP3 
CYP2A13 GLP1R SLC14A2 GJA8 EYA2 CORO2B PDE6G CHRNA3 NR6A1 CLEC4M 
TACR1 GRIN1 ADRA1D BMP7 DSCAM TUBB7P CAMK2A SH3BP1 GPD1 MYOZ3 
PRSS53 FSHB GPR182 PLAC4 TOM1L2 EMX1 CFAP74 DNAH2 CFAP70 MYCNOS 
CYP2A7P1 LOC101929073 DDR1-AS1 KLK1 LINC01482 GRIK5 FUT7 CNPY4 TTC38 ECHDC2 
A4GALT MYOZ1 NLGN3 CPLX3 SLC13A4 RNF122 RETN CARD14 KCNQ1DN NOX5 
LINC00652 PLA2G3 THEG CTNNA3 GABRQ CHST8 GSN-AS1 C7orf69 CLDN17 HOXC8 
ZNF717 FGF17 TAS2R7 IL36A OR1D2 MYL10 LZTS1 CLEC4A KIAA1644 LRCH4 
DMWD ADRBK1 PNPLA2 ACACB CACNG4 LOC100505915 NPEPL1    
Table 5
Siglec-2 negative coexpressed genes (n = 352)
EIF4G2 RPS5 CBX3 ZNF146 ILF2 RPL30 RPL37 HNRNPU NCL CLTC PTGES3 YWHAZ 
PHB DYNLL1 MAPRE1 CAPRIN1 RPS27 GNB1 RAN HNRNPC CALU RPLP1 LAMC1 XRCC6 
SNRPD2 ZNF207 CCT4 SSR1 CCT3 DEK IPO7 ACTR3 YWHAH EIF5B RPS18 TUBA1B 
ARF4 CSE1L ACLY SSB UBA2 PSMD1 PCNA CAPZA2 PSMC4 RPS16 SRP9 TOP2A 
PPIA CCT6A UBE2D2 YME1L1 TPD52L2 PPP1CB BUB3 VBP1 RRM1 RCN2 TOMM70A CBX1 
UBE2N RPA1 TRIP12 MCM3 NME1 SEC23B PPP4R1 ZC3H15 PWP1 ACP1 ITGA6 ARL1 
SMC4 MARCKS PSMC6 TUBG1 CDC123 WSB2 ADNP VPS26A NET1 HDAC2 RRM2 CKS1B 
UBE2A MCM6 CPD CCT2 RSU1 KIF5B MORF4L2 LANCL1 DPF2 PRPF4B PPP1R2 VEZF1 
NUP133 SRPK1 STT3A EIF3M PSMB4 CDK4 VPS72 STAG1 SMARCA5 ACBD3 UBE2K PSMD12 
USP1 CPSF6 H2AFV KIAA0101 GMFB HSPA13 TYMS SSBP1 HTATSF1 TOPBP1 NRAS LPGAT1 
ACTL6A GTF2A2 SNRPD1 UBE2S PIGC CDC20 SRSF3 HLTF TXNDC9 DNM1L HAT1 SRPK2 
CDK1 MAPK9 HS2ST1 SNRPE PPP2R5E RBBP8 EZH2 PSMA4 MFAP1 SUCO RPP30 SEC61G 
STAM PTTG1 CD2AP RTCA COIL RFC2 UTP18 TRIP4 C5orf22 TDG BUB1B SNRPF 
RFC4 ZWINT CKS2 DBF4 CEP350 PPM1D IARS FEN1 EEF1E1 VRK2 HNRNPA2B1 SRP19 
PFDN4 SNRPG KIN SLBP GINS1 NUP155 MFN1 NIPBL CAND1 NCKAP1 NUP62 RBM3 
CLIC1 RPN2 RPS3 PRKDC ARPC3 YWHAB NAP1L1 HNRNPR PSMD11 MRPL3 HMGB2 PTK2 
POLE3 CANX STK24 TXN ILF3 PRCC SEPHS1 BECN1 DNAJB6 ABI1 SF3B4 GLRX3 
UFD1L DR1 FAM208A SWAP70 SLC35A2 POLR3C BAG2 MSH2 EED MRPL9 SOCS5 CHUK 
PRKCI CDKN3 PHTF2 HMGN4 CNPY2 UBE2E3 TPX2 NOL7 HSP90AA1 PSMD4 CACYBP PDCD10 
MCM7 HSPA4 CDK7 COX11 TUBA1C KPNA2 HSPA5 ITGB1 SMARCE1 RPL7 U2SURP LSM14A 
RBM12 ANKLE2 NUP205 WAPL SERPINB1 MAPK1 PSMD14 CLASP2 GNS DESI2 KIAA0368 SNRNP27 
AVL9 UBE2E1 NEK7 AQR MAPK1IP1L KDM3A NUP160 ATF2 TRIM37 DNAJC9 SP3 SNRPB 
RHEB TUBB3 H2AFZ HSP90AB1 GMPS RALA H2AFY SUB1 RIF1 CCNB1 SNW1 SUMO4 
CLTA MIR1244-3 PDIA6 HN1 ALDH18A1 UFC1 ENAH SYNCRIP PRELID3B CDC27 DYNLRB1 MRPL42 
SAE1 CNOT6 MORF4L1 ASNSD1 PRC1 NUP85 NUSAP1 PRPF40A AGFG1 MRPS10 ARMC1 GOLT1B 
TMEM258 GTPBP4 MEX3C CKAP2 MAP4K3 FAM208B PFDN2 GMNN RIOK2 MRS2 LYRM4 DUSP12 
CDC73 DTL HEATR1 NUP37 NXT1 IFT52 CNIH4 NUP107 RPAP3 PPP2R3C RPS6KC1 TMEM106B 
TPRKB RRP15 HSPA14 TMEM185B OLA1 PSMD10 UXS1 ECT2 UCHL5 SAP130 NAA35 ARID4B 
LYRM2 TBL1XR1 ARPP19 ANP32E DENR MED17 PRPF18 METTL5 DDX50 ADSS SEH1L NOL11 
PAPOLA MCM4 RACGAP1 THOC2         
EIF4G2 RPS5 CBX3 ZNF146 ILF2 RPL30 RPL37 HNRNPU NCL CLTC PTGES3 YWHAZ 
PHB DYNLL1 MAPRE1 CAPRIN1 RPS27 GNB1 RAN HNRNPC CALU RPLP1 LAMC1 XRCC6 
SNRPD2 ZNF207 CCT4 SSR1 CCT3 DEK IPO7 ACTR3 YWHAH EIF5B RPS18 TUBA1B 
ARF4 CSE1L ACLY SSB UBA2 PSMD1 PCNA CAPZA2 PSMC4 RPS16 SRP9 TOP2A 
PPIA CCT6A UBE2D2 YME1L1 TPD52L2 PPP1CB BUB3 VBP1 RRM1 RCN2 TOMM70A CBX1 
UBE2N RPA1 TRIP12 MCM3 NME1 SEC23B PPP4R1 ZC3H15 PWP1 ACP1 ITGA6 ARL1 
SMC4 MARCKS PSMC6 TUBG1 CDC123 WSB2 ADNP VPS26A NET1 HDAC2 RRM2 CKS1B 
UBE2A MCM6 CPD CCT2 RSU1 KIF5B MORF4L2 LANCL1 DPF2 PRPF4B PPP1R2 VEZF1 
NUP133 SRPK1 STT3A EIF3M PSMB4 CDK4 VPS72 STAG1 SMARCA5 ACBD3 UBE2K PSMD12 
USP1 CPSF6 H2AFV KIAA0101 GMFB HSPA13 TYMS SSBP1 HTATSF1 TOPBP1 NRAS LPGAT1 
ACTL6A GTF2A2 SNRPD1 UBE2S PIGC CDC20 SRSF3 HLTF TXNDC9 DNM1L HAT1 SRPK2 
CDK1 MAPK9 HS2ST1 SNRPE PPP2R5E RBBP8 EZH2 PSMA4 MFAP1 SUCO RPP30 SEC61G 
STAM PTTG1 CD2AP RTCA COIL RFC2 UTP18 TRIP4 C5orf22 TDG BUB1B SNRPF 
RFC4 ZWINT CKS2 DBF4 CEP350 PPM1D IARS FEN1 EEF1E1 VRK2 HNRNPA2B1 SRP19 
PFDN4 SNRPG KIN SLBP GINS1 NUP155 MFN1 NIPBL CAND1 NCKAP1 NUP62 RBM3 
CLIC1 RPN2 RPS3 PRKDC ARPC3 YWHAB NAP1L1 HNRNPR PSMD11 MRPL3 HMGB2 PTK2 
POLE3 CANX STK24 TXN ILF3 PRCC SEPHS1 BECN1 DNAJB6 ABI1 SF3B4 GLRX3 
UFD1L DR1 FAM208A SWAP70 SLC35A2 POLR3C BAG2 MSH2 EED MRPL9 SOCS5 CHUK 
PRKCI CDKN3 PHTF2 HMGN4 CNPY2 UBE2E3 TPX2 NOL7 HSP90AA1 PSMD4 CACYBP PDCD10 
MCM7 HSPA4 CDK7 COX11 TUBA1C KPNA2 HSPA5 ITGB1 SMARCE1 RPL7 U2SURP LSM14A 
RBM12 ANKLE2 NUP205 WAPL SERPINB1 MAPK1 PSMD14 CLASP2 GNS DESI2 KIAA0368 SNRNP27 
AVL9 UBE2E1 NEK7 AQR MAPK1IP1L KDM3A NUP160 ATF2 TRIM37 DNAJC9 SP3 SNRPB 
RHEB TUBB3 H2AFZ HSP90AB1 GMPS RALA H2AFY SUB1 RIF1 CCNB1 SNW1 SUMO4 
CLTA MIR1244-3 PDIA6 HN1 ALDH18A1 UFC1 ENAH SYNCRIP PRELID3B CDC27 DYNLRB1 MRPL42 
SAE1 CNOT6 MORF4L1 ASNSD1 PRC1 NUP85 NUSAP1 PRPF40A AGFG1 MRPS10 ARMC1 GOLT1B 
TMEM258 GTPBP4 MEX3C CKAP2 MAP4K3 FAM208B PFDN2 GMNN RIOK2 MRS2 LYRM4 DUSP12 
CDC73 DTL HEATR1 NUP37 NXT1 IFT52 CNIH4 NUP107 RPAP3 PPP2R3C RPS6KC1 TMEM106B 
TPRKB RRP15 HSPA14 TMEM185B OLA1 PSMD10 UXS1 ECT2 UCHL5 SAP130 NAA35 ARID4B 
LYRM2 TBL1XR1 ARPP19 ANP32E DENR MED17 PRPF18 METTL5 DDX50 ADSS SEH1L NOL11 
PAPOLA MCM4 RACGAP1 THOC2         

Additionally, gene set enrichment analysis (GSEA) was used for identification of putative KEGG pathways associated with siglec-2 coexpressed genes. Consequently, pathways including MAPK signaling pathway and calcium signaling pathway, which have been proved in liver cancer, were significantly enriched with siglec-2 positively coexpressed genes (FDR < 0.05, Figure 4), While siglec-2 with its negatively coexpressed genes contributed to tumor cell phenotype including cell cycle, spliceosome, DNA replication, ubiquitin-mediated proteolysis, proteasome, oocyte meiosis, mismatch repair, ribosome, pathways in cancer and pathogenic Escherichia coli infection (FDR < 0.05, Figure 5).

KEGG functional enrichment of siglec-2 with its positive coexpressed genes

Figure 4
KEGG functional enrichment of siglec-2 with its positive coexpressed genes
Figure 4
KEGG functional enrichment of siglec-2 with its positive coexpressed genes

KEGG functional enrichment of siglec-2 with its negative coexpressed genes

Figure 5
KEGG functional enrichment of siglec-2 with its negative coexpressed genes
Figure 5
KEGG functional enrichment of siglec-2 with its negative coexpressed genes

Discussion

Immunotherapy for HCC has shown some success [7]. However, in most HCC patients or animal models, tumors progressed in spite of tumor-specific immune responses [12]. Thus, to find new immune markers of HCC development is still of significant importance. Functionally, siglecs participate in regulating the innate and adaptive immune responses through the recognition of their glycan ligands [13]. They have been demonstrated to be involved in a series of inhibitory processes, cell–cell interaction processes and endocytosis [8,14–16]. In our analysis, we found that all siglecs including siglec-1 to siglec-9 were significantly suppressed in HCC tumors, which may serve as anti-oncogenes. Recently, several studies revealed that siglec deficiencies contributed to the potential for generation of malignancy like lymphomas and leukemias [17,18]. As reviewed by Macauley et al., siglecs played a role in regulating of immune surveillance of cancer by keeping with their roles aiding immune cells in distinguishing between self and non-self [13]. They concluded that siglecs effectively reduce innate immune responses against cancer cells by down-regulating immune cells that express them through recognition of sialoside ligands on the cancer cell itself or soluble mucins produced by the cancer cell [13].

Serum AFP levels increase by 20–80% in HCC patients and are strongly associated with tumor aggressiveness [19–21]. High level of AFP is correlated with tumor size, vascular invasion and poorly differentiated HCC [19,22,23]. In our analysis, we found that siglec-2 expression in tumor tissues was significantly negatively associated with AFP elevation. Although the immunogenicity of AFP is weak, it could induce the immune escapes through inhibiting the function of dendritic cells, natural killer cells and T lymphocytes [24,25]. Several studies demonstrated that AFP is involved in immunosuppression [25,26]. It can impair the function of macrophages leading to decreased phagocytosis and impaired antigen-presenting abilities [27]. AFP-modified immune cell vaccine or peptide vaccine has displayed the specific antitumor immunity against AFP-positive tumor cells [28,29]. Hence, siglec-2 could play antitumor effects via enhancing immune responses by inhibition AFP levels. Although the proportion of patients with elevated AFP in siglec-2 low expression group was significantly higher than that in siglec-2 high expression group (60.0% vs. 41.7%), the biologic value is not strong. Further research with larger samples are needed.

Our results also showed that siglec-2 elevation predicts better survival in HCC. Siglecs including siglec-2 have been reported to regulate cell growth and survival, by both inhibition of proliferation and/or induction of apoptosis [13]. Throughout the last decade, several novel therapeutic agents that target siglec-2 are being developed as an alternative approach for cancer treatment [17,18,30]. Previous reports showed that siglec-2 as a B-cell-associated adhesion protein appeared to play a critical role in establishing signaling thresholds for B-cell activation, mediating normal antibody response to thymus-independent antigens and regulating the lifespan of mature B cells [31,32]. Therefore, down-regulating of siglec-2 in tumor tissues might risk the tumor progress by reducing innate immune response and mature B cells proliferation in HCC patients. Recently, it is gradually recognized that some B-cell subpopulations including regulatory B cells can impair CD4+ T cell activation or produce cytokines promote tumor progression [33–35], Leading to dramatically suppress antibody and inhibit antitumor effector T cells [34,36]. Lymphotoxin secreted from tumor-infiltrating B cells also promotes tumor growth [37]. Therefore, serves as B cell receptor inhibitor, siglec-2 might suppress tumor progress and development, contributing to a prolonging survival in HCC patients. Additionally, we enriched coexpressed genes of siglec-2 and its functional pathways. Siglec-2 and its coexpressed genes participant in the tumor cell phenotype including cell cycle, spliceosome, DNA replication, ubiquitin mediated proteolysis, proteasome, mismatch repair and pathways in cancer like MAPK signaling pathway and calcium signaling pathway, which should be the main research directions of siglec-2 mechanism in HCC in future.

Although siglec-4 levels in tumor tissues might associate with HCC OS in our Cox regression analysis, no significance was found in log-rank methods. Known as myelin-associated glycoprotein (MAG), siglec-4 is selectively localized in periaxonal Schwann cell and oligodendroglial membranes of myelin sheaths [38] and plays a role in axon-myelin stabilization and inhabitation of axon regeneration after injury [39,40]. Since siglec-4 is only found in the nervous system, even though siglec-4 showed some significance for HCC OS in our analysis, deep research of this gene in HCC development should be cautious and well-designed.

The present study has some limitations: First, our research was a preliminary analysis from GEO database, no further mechanism data were shown. Second, we included siglecs as a continuous variable in the logistic and Cox regression process, leading to a small HRs of the siglecs biomarker candidates. Third, only siglec-1 to siglec-9 were included in this analysis, other siglec family members like siglec-10 to siglec-15 were not available in this gene database. Fourth, we did not conduct mechanism research in siglec-2 protein level. Even with these limitations, the results might provide useful insights for HCC research in therapeutic strategy.

This work was not supported by any pharmaceutical company or government agency or grants from other sources.

Author contribution

X.Q. and X.R. conceived and designed the study. X.R. wrote the manuscript. X.R and Y.J. analyzed and interpreted the data. X.J. helped to draft the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Abbreviations

     
  • AFP

    alpha-fetoprotein

  •  
  • GEO

    Gene Expression Omnibus

  •  
  • HBV

    hepatitis B virus

  •  
  • HCC

    hepatocellular carcinoma

  •  
  • OS

    overall survival

  •  
  • siglec

    sialic-acid-binding immunoglobulin-like lectin

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