SYNGR2 as a Multifaceted Biomarker in Hepatocellular Carcinoma Linking Prognosis,Immune Microenvironment and Therapeutic Response

Abstract

Introduction

Hepatocellular carcinoma (HCC) is characterized by a complex tumor microenvironment and limited therapeutic options. Synaptogyrin-2 (SYNGR2), a transmembrane protein, has been implicated in cancer but its comprehensive role in HCC prognosis, immune regulation, and treatment response remains unclear.

Methods

Multiomics bioinformatics analysis was performed via RNA-seq data from the TCGA-LIHC cohort. Prognostic value was assessed via Cox regression and Kaplan‒Meier analysis. Immune cell infiltration was estimated via CIBERSORT, and drug sensitivity was predicted via the oncoPredict R package. In vitro validation was conducted using Hep3B and Huh7 cell lines. To establish causality, isogenic gain- and loss-of-function experiments were performed, followed by RT–qPCR, and CCK-8 assays.

Results

SYNGR2 expression was significantly elevated in HCC tissues and correlated with advanced tumor stage, poor differentiation, and poor overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (all P<0.05). It served as an independent prognostic biomarker (AUC=0.923). High SYNGR2 expression was associated with an immunosuppressive microenvironment characterized by increased numbers of M0 macrophages and Tregs and strongly positively correlated with immune checkpoint genes (PD-1, CTLA-4, and PD-L1). Bioinformatic predictions revealed that SYNGR2-high tumors were more sensitive to MK-1775 (DNA damage inhibitor) and paclitaxel, whereas SYNGR2-low tumors were sensitive to JAK1/IAP inhibitors. In vitro, Hep3B cells (high endogenous SYNGR2) displayed a markedly lower IC50 for MK-1775 than Huh7 cells (low endogenous SYNGR2). Critically, plasmid-mediated overexpression of SYNGR2 in Huh7 cells significantly sensitized them to MK-1775, while siRNA-mediated knockdown of SYNGR2 in Hep3B cells conferred significant resistance, establishing a causal role for SYNGR2 in modulating sensitivity to this WEE1 inhibitor.

Conclusion

SYNGR2 has emerged as a multifaceted biomarker candidate in HCC, associated with prognosis, an immunosuppressive microenvironment, and differential therapeutic responses. Its integration into clinical models could enhance prognostic stratification and guide personalized treatment strategies, particularly in selecting patients for DNA damage-targeting agents.

Keywords

hepatocellular carcinoma SYNGR2 prognosis immune microenvironment therapeutic response

Introduction

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and its high incidence is closely associated with chronic hepatitis virus infection, metabolic syndrome, and cirrhosis.¹ Despite recent advancements in surgical resection, targeted therapy, and immunotherapy, the prognosis of HCC patients remains poor, with a 5-year postoperative recurrence rate as high as 70%.^2,3 Therefore, elucidating the molecular mechanisms underlying HCC pathogenesis and identifying effective prognostic biomarkers and therapeutic targets are critical directions in current research.

Genomic instability and dysregulated epigenetic mechanisms are hallmarks of HCC.⁴ An imbalance between oncogenes and tumor suppressor genes (e.g., mutations in TP53 and CTNNB1) drives malignant progression.⁵ Additionally, the immunosuppressive tumor microenvironment (TME), characterized by tumor-associated macrophage infiltration and exhausted effector T cells, further diminishes the response to immunotherapy.^6,7 Recent multiomics studies have revealed that specific gene expression patterns not only correlate with the clinical features of HCC patients but also influence therapeutic responses by modulating immune cell functions within the TME.^8,9 For example, inhibition of DOCK2 kinase activity via the SULT2B1-cholesterol sulfate axis leads to T-cell exhaustion, revealing a novel mechanism of immunotherapy resistance.¹⁰

Synaptogyrin-2 (SYNGR2), a transmembrane protein involved in vesicle trafficking and synaptic transmission, is poorly understood in cancer.¹¹ Emerging evidence suggests that SYNGR2, in combination with FKBP10 and CYP3A4, may serve as a predictive biomarker for postoperative recurrence risk in HCC patients, as its protein expression levels are significantly associated with 2-year and 5-year survival rates.¹² This finding implies that SYNGR2 may regulate tumor cell behavior or interact with the TME to influence HCC progression. However, the precise role of SYNGR2 in HCC, its relationship with immune infiltration, and its association with chemoresistance remain to be explored.

This study aimed to systematically analyze the relationship between SYNGR2 gene expression and the clinical characteristics/prognosis of HCC patients via bioinformatics approaches and evaluate its value as an independent prognostic biomarker. Furthermore, by integrating TME immune features, we investigated the correlations between SYNGR2 expression and immune cell infiltration and predicted its potential impact on responses to chemotherapy and immunotherapy (e.g., PD-1 inhibitors). To substantiate the computational findings, we performed in vitro experiments to confirm the differential drug sensitivity associated with SYNGR2 expression, thereby moving beyond correlation toward potential clinical applicability. These findings may provide novel targets for personalized HCC treatment and establish a theoretical foundation for developing SYNGR2-based prognostic models to optimize clinical decision-making.

Method

Data Acquisition

RNA sequencing (RNAseq) data from 33 cancer projects processed via the STAR pipeline were downloaded and organized from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/). Transcripts per million (TPM) values were extracted and normalized via the log2(TPM + 1) method to analyze SYNGR2 expression patterns across tumors. For the TCGA-LIHC cohort, which included 373 hepatocellular carcinoma (HCC) samples and 49 normal samples, HCC patients were stratified into high- and low-expression groups on the basis of the median SYNGR2 expression value. No additional batch correction was performed beyond the standard TCGA normalization pipeline, and potential batch effects were considered negligible for this intra-cohort analysis.

Prognostic Value Analysis

To evaluate the prognostic significance of SYNGR2 in HCC, univariate and multivariate Cox regression analyses were performed to assess the impact of patient characteristics (e.g., gender, age, T/N/M stage, histologic grade) and SYNGR2 mRNA expression levels on overall survival (OS). A prognostic nomogram was developed to predict the 1-, 3-, and 5-year survival probabilities of HCC patients. Calibration curves were constructed to compare the predicted versus observed survival rates at different time points, ensuring model accuracy. Kaplan‒Meier survival analysis with log-rank tests was applied to statistically evaluate differences in OS, disease-specific survival (DSS), and progression-free interval (PFI) between patient groups stratified by the optimal SYNGR2 expression cutoff. Multivariate Cox regression models incorporated SYNGR2 expression along with baseline clinical variables (e.g., tumor stage, histologic grade, age) to test independent prognostic value.

Protein‒protein Interaction (PPI) and Functional Enrichment Analysis

The STRING database (https://www.string-db.org/) was used to identify SYNGR2-interacting proteins with a confidence threshold of 0.15 and a maximum of 50 interactions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these proteins, with the top 10 enriched pathways/functions visualized via bubble plots.

Immune Infiltration and Immune Checkpoint-Related Gene Analysis

To assess the relationship between SYNGR2 expression and immune cell infiltration in HCC, the CIBERSORT (Based on the e1071 (v1.7-13), parallel (v4.3.0) and preprocessCore (v1.62.1) R packages)algorithm was used to estimate the proportions of 22 immune cell types.¹³ Spearman correlation analysis was used to quantify the strength of the associations between SYNGR2 expression and immune cell infiltration. Differences in immune cell enrichment between the SYNGR2 high- and low-expression groups were analyzed. Additionally, correlations between SYNGR2 and immune checkpoint genes were evaluated.

Prediction of Immunotherapy Response and Potential Drug Sensitivity in SYNGR2 Expression Subgroups

To identify patients more likely to respond to immune checkpoint inhibitors, immunophenotype scores (IPSs) for TCGA-HCC samples were downloaded from The Cancer Immunome Atlas (TCIA) database. Wilcoxon tests were used to compare the IPS between SYNGR2 expression subgroups, predicting responsiveness to anti-PD-1 and anti-CTLA-4 therapies. Drug sensitivity for each patient was predicted via the “calcPhenotype” function in the “oncoPredict” R package (v1.2), which leverages bulk RNA-seq data. The ridge regression model incorporated datasets from the Genomics of Drug Sensitivity in Cancer (GDSC2) and CTRP V2 databases (https://osf.io/c6tfx/), which are maintained and regularly updated by the “oncoPredict” development team. Gene expression input for oncoPredict was normalized using the identical log2(TPM+1) values derived from the TCGA-LIHC cohort to ensure compatibility with the GDSC/CTRP model training data.

Experimental Validation of Drug Sensitivity

To experimentally validate the bioinformatics predictions regarding SYNGR2 expression and drug sensitivity, in vitro assays were performed using hepatocellular carcinoma cell lines.

Cell culture: Human liver cancer cell lines (Hep3B and Huh7) were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Gibco). All the cells were maintained at 37°C in a humidified incubator with 5% CO₂.

Quantitative real-time PCR (RT-qPCR): Total RNA was extracted from cells using TRIzol reagent (Invitrogen) and reverse-transcribed into cDNA using the PrimeScript RT reagent Kit (Takara). RT-qPCR was performed on a QuantStudio 5 Real-Time PCR System (Applied Biosystems) using SYBR Green Premix (Takara). The relative expression level of SYNGR2 was calculated using the 2−ΔΔCt method, with GAPDH serving as the internal control. The primer sequences are provided in Supplementary Table 1

Cell viability and drug sensitivity assay: The sensitivity of Hep3B and Huh7 cells to MK-1775 (Selleckchem) was determined using the Cell Counting Kit-8 (CCK-8) assay. Cells were seeded in 96-well plates and treated with a gradient concentration of MK-1775 (0, 0.0625, 0.125, 0.25, 0.5, 1 μM) for 48 hours. Subsequently, 10 μL of CCK-8 reagent was added to each well and incubated for 2 hours. The absorbance at 450 nm was measured using a microplate reader. The half-maximal inhibitory concentration (IC50) was calculated using GraphPad Prism software via a non-linear regression model.

Functional Validation of SYNGR2 Causal Role in MK-1775 Sensitivity

To establish causality, gain- and loss-of-function experiments were performed. For overexpression, Huh7 cells were transiently transfected with a pcDNA3.1-SYNGR2-FLAG plasmid (or empty vector) (GenePharma) using Lipofectamine 3000 (Invitrogen). For knockdown, Hep3B cells were transfected with two independent SYNGR2-specific siRNAs (si-SYNGR2#1, si-SYNGR2#2) (GenePharma) or a non-targeting scramble control using Lipofectamine RNAiMAX (Invitrogen). At 48 h post-transfection, overexpression and knockdown efficiencies were verified by RT-qPCR. CCK-8 assays were then conducted under identical conditions to determine IC50 values. The siRNA sequences are provided in Supplementary Table 1.

Analytical Framework

All computations were performed via the R programming language (v4.3.0). Intergroup differences were evaluated via the Wilcoxon test for comparisons between two groups or the Kruskal‒Wallis test for multigroup comparisons.

Results

Expression Characteristics and Clinical Relevance of SYNGR2 in HCC

Pancancer analysis of TCGA data revealed that SYNGR2 expression is elevated in the majority of tumor tissues compared with the corresponding normal tissues (Figure S1). In HCC, SYNGR2 expression was significantly higher in tumor tissues than in adjacent nontumor tissues (Figure 1A), a finding validated in paired HCC samples (Figure 1B). These findings suggest that the upregulation of SYNGR2 may contribute to HCC initiation and progression. Further analysis of the TCGA-LIHC cohort demonstrated that SYNGR2 expression significantly correlated with pathological grade, T stage, histologic grade, and survival status (Figure 1C–H). Specifically, patients with grade III pathology, T2 or higher stage, G2 or higher histologic grade, or deceased status presented markedly elevated SYNGR2 expression, indicating its potential role in driving malignant progression and poor prognosis.

Diagnostic and Prognostic Value of SYNGR2 in HCC

ROC curve analysis revealed that SYNGR2 had an AUC of 0.923 (95% CI: 0.891–0.954) for HCC diagnosis (Figure 2A), indicating high diagnostic accuracy. Survival analysis revealed that patients in the SYNGR2 high-expression group had significantly worse OS (Figure 2B), with AUC values of 0.629, 0.604, and 0.598 for predicting 1-year, 3-year, and 5-year OS, respectively (Figure 2C). Additionally, PFI and DSS were significantly lower in the high-expression group than in the low-expression group (Figures 2D–2E). Subgroup analyses stratified by clinicopathological features such as age, gender, and tumor stage consistently confirmed that SYNGR2 serves as an independent prognostic factor that is unaffected by other variables (Figure 2F). To increase its clinical utility, a nomogram integrating clinical parameters and SYNGR2 expression was constructed to predict the 1-, 3-, and 5-year survival probabilities (Figure 2G). The calibration curves demonstrated strong concordance between the predicted and observed survival rates (Figure 2H).

Identification of SYNGR2-Interacting Partners and Coexpressed Genes

STRING database analysis revealed 36 proteins that interact with SYNGR2, including ADGRE5, ADGRG5, AKIRIN2, ANKRD22, and C9orf78 (Figure 3A). Functional enrichment analysis revealed that these proteins are associated primarily with pathways such as neuroactive ligand‒receptor interaction, calcium signaling, and adenylate cyclase-modulating G protein‒coupled receptor signaling (Figure 3B). The 13 SYNGR2-interacting genes were differentially expressed in the HCC samples (Figure 3C). Among these 13 interacting genes, ANKRD22, CCNF and FPR2 were significantly positively correlated with SYNGR2 in terms of expression, whereas SLCO1B1, TMEM47 and VIPR1 were negatively correlated (Figure 3D). Univariate Cox analysis indicated that CCNF, SLCO1B1, and VIPR1 expression levels were significantly associated with HCC prognosis (P < 0.05) (Figure 3E). K‒M analysis further revealed that high CCNF expression was correlated with poor survival (a risk factor), whereas high SLCO1B1 and VIPR1 expression was linked to improved outcomes (protective factors) (Figure 3F).

Correlation Between SYNGR2 Expression and Immune Cell Infiltration

CIBERSORT-based analysis of immune cell infiltration revealed distinct patterns between the SYNGR2 high- and low-expression groups. The high-expression group presented significantly elevated proportions of plasma cells, activated memory CD4+ T cells, follicular helper T cells, M0 macrophages, and eosinophils (Figure 4A). In contrast, the low-expression group displayed higher proportions of resting memory CD4+ T cells, monocytes, M2 macrophages, and resting mast cells (Figure 4A). Correlation analysis revealed that SYNGR2 expression was positively correlated with M0 macrophages, activated memory CD4+ T cells, regulatory T cells (Tregs), eosinophils, and resting dendritic cells but negatively correlated with resting mast cells, M2 macrophages, resting memory CD4+ T cells, and monocytes (Figure 4B-C).

Association of SYNGR2 With Immune Checkpoint Genes and Therapeutic Implications

SYNGR2 expression was significantly positively correlated with immune checkpoint genes, including HAVCR2, CD86, CD80, CTLA4, PD-1 (PDCD1), TNFRSF9, TIGIT, LAG3, PD-L1 (CD274), and IDO1 (Figure 5A), suggesting enhanced immune evasion in SYNGR2-high-expressing tumors. Analysis of the IPS indicated that SYNGR2-high patients were less likely to respond to anti-PD-1 and anti-CTLA-4 therapies (Figure 5B). Drug sensitivity predictions via the oncoPredict algorithm revealed that SYNGR2-high tumors were more sensitive to MK-1775, paclitaxel, lapatinib, YK-4-279, and ML323, whereas SYNGR2-low tumors were more sensitive to SB505124, doramapimod, JAK1 inhibitors, IAP inhibitors, and AZD1208 (Figures 6A–C). These findings underscore the potential for personalized therapeutic strategies tailored to SYNGR2 expression levels.

SYNGR2 Causally Modulates Sensitivity to MK-1775 in HCC Cells

RT-qPCR confirmed that endogenous SYNGR2 mRNA was significantly higher in Hep3B than in Huh7 cells (Figure 6D). Consistent with the bioinformatic prediction, Hep3B cells exhibited a markedly lower IC50 for MK-1775 (0.1128 µM) compared with Huh7 cells (0.6896 µM) (Figure 6E), suggesting an association between high SYNGR2 and MK-1775 sensitivity.

To test causality, we overexpressed SYNGR2 in the low-SYNGR2 Huh7 cell line. Transient transfection of pcDNA3.1-SYNGR2-FLAG led to a robust >8-fold increase in SYNGR2 mRNA (Figure 6F). Notably, SYNGR2 overexpression reduced the IC50 of MK-1775 from 0.6516 µM (vector) to 0.3439 µM (Figure 6G), indicating that increased SYNGR2 directly sensitizes cells to the drug.

Conversely, we knocked down SYNGR2 in the high-SYNGR2 Hep3B cells using two specific siRNAs. Both siRNAs achieved >70% reduction in SYNGR2 mRNA (Figure 6H). This loss of SYNGR2 significantly increased the IC50 of MK-1775 from 0.1211 µM (NC) to 0.4144 µM (si-SYNGR2#1) and 0.3291 µM (si-SYNGR2#2) (Figure 6I). The concordant effects from two independent siRNAs exclude off-target artifacts and confirm that SYNGR2 expression levels causally determine MK-1775 sensitivity in HCC cells.

Taken together, these isogenic gain- and loss-of-function experiments establish a direct causal relationship between SYNGR2 expression and response to the WEE1 inhibitor MK-1775.

Discussion

The present study systematically investigated the clinical significance, prognostic value, and immune-related roles of SYNGR2 in HCC through comprehensive bioinformatics analyses. Our findings highlight SYNGR2 as a promising biomarker for HCC prognosis and a potential modulator of the TME, offering novel insights into personalized therapeutic strategies.

The elevated expression of SYNGR2 in HCC tissues compared with adjacent normal tissues, coupled with its strong association with advanced pathological stages (grade III, T2+), poor histologic differentiation, and unfavorable survival outcomes, underscores its critical role in HCC progression. ROC analysis further validated its diagnostic potential (AUC=0.923), suggesting that SYNGR2 could serve as a noninvasive diagnostic marker. Importantly, survival analysis confirmed SYNGR2 as an independent prognostic factor, independent of traditional clinicopathological variables. These results align with emerging evidence implicating SYNGR2 in cancer recurrence and metastasis, although its mechanistic contributions remain underexplored. The constructed nomogram integrating SYNGR2 expression and clinical parameters provides a practical tool for predicting survival probabilities, which may aid clinicians in risk stratification and treatment planning. Moreover, the prognostic significance of CCNF, SLCO1B1, and VIPR1, coupled with their expression correlation with SYNGR2, suggests a potential cooperative network influencing HCC biology. For instance, CCNF (Cyclin F) regulates the cell cycle and may link SYNGR2-high states to genomic instability, hypothetically sensitizing cells to DNA damage inhibitors. Integrating these interaction partners into a mechanistic framework warrants future systems biology approaches.

Our immune infiltration analysis revealed that tumors with high SYNGR2 expression have unique immune cell composition characteristics, including increased numbers of M0 macrophages, activated CD4+ T cells and Tregs, whereas antitumor immune populations, such as resting memory CD4+ T cells and monocytes, decrease. Many studies have revealed that Tregs mediate the malignant progression of HCC by promoting an immunosuppressive TME.^14-16 Importantly, the observed correlation between high SYNGR2 and an immunosuppressed TME does not determine causality; SYNGR2 overexpression could merely be a passenger event in advanced, pro-inflammatory tumors that naturally harbor high M0 macrophage and Treg infiltration. Functional studies dissecting this relationship are warranted. Notably, the strong positive correlation between SYNGR2 and immune checkpoint genes (such as PD-1, PD-L1, and CTLA-4) suggests that tumors with high SYNGR2 expression may exhibit enhanced immune escape, which explains the observed resistance to anti-PD-1/CTLA-4 therapy.^17,18 This finding is consistent with recent studies indicating that immunosuppressive TME features reduce the efficacy of immunotherapy for HCC. Mechanistically, as a vesicle-trafficking protein, SYNGR2 may facilitate the exocytosis of immunosuppressive cytokines or the recycling of immune checkpoint molecules to the cell surface, thereby actively shaping an immunosuppressive TME. Moreover, its potential role in intracellular drug sequestration or efflux could explain the differential sensitivity to DNA-damaging agents observed here. While direct functional evidence is pending, these mechanisms provide testable hypotheses for future studies.

The differential drug sensitivity profiles between SYNGR2 expression subgroups offer actionable insights for personalized therapy. SYNGR2-high tumors showed heightened sensitivity to DNA damage response inhibitors (e.g., MK-1775) and microtubule-targeting agents (e.g., paclitaxel),^19,20 whereas SYNGR2-low tumors were more responsive to JAK1 and IAP inhibitors. These patterns may reflect SYNGR2-associated pathways, such as neuroactive ligand‒receptor interactions and calcium signaling, which could modulate chemoresistance. Targeting these pathways in SYNGR2-stratified patients might improve therapeutic outcomes. To experimentally validate these predictions, we first compared the sensitivity of Hep3B (high endogenous SYNGR2) and Huh7 (low endogenous SYNGR2) cell lines to MK-1775. Consistent with our bioinformatics analysis, Hep3B cells exhibited a markedly lower IC50 value than Huh7 cells. However, recognizing that these two cell lines differ in numerous genetic and molecular characteristics beyond SYNGR2 expression, we performed isogenic gain- and loss-of-function experiments to directly test causality. Plasmid-mediated overexpression of SYNGR2 in Huh7 cells significantly reduced the IC50 of MK-1775 from 0.6516 μM (empty vector) to 0.3439 μM, demonstrating that elevating SYNGR2 in a low-SYNGR2 background directly sensitizes cells to this WEE1 inhibitor. Conversely, siRNA-mediated knockdown of SYNGR2 in Hep3B cells using two independent siRNAs increased the IC50 from 0.1211 μM (scramble control) to approximately 0.4 μM, confirming that loss of SYNGR2 confers resistance to MK-1775. The consistent phenotype observed with two independent siRNAs excludes off-target effects and establishes a direct causal relationship between SYNGR2 expression and MK-1775 sensitivity in HCC cells. These isogenic functional experiments provide robust evidence that SYNGR2 is not merely a correlative biomarker but a causal determinant of therapeutic response to DNA damage-targeting agents. Notably, a recent pan-cancer analysis of SYNGR2 by Liu et al. also reported its upregulation in HCC and association with immune infiltration, reinforcing our observations.²¹ Our study extends these findings by integrating drug sensitivity predictions and, importantly, providing in vitro validation for SYNGR2-guided therapeutic response, representing a step forward in translational relevance. However, the biological link between a synaptic vesicle-trafficking protein and sensitivity to a WEE1 kinase inhibitor (MK-1775) remains unclear. Hypotheses include SYNGR2’s indirect impact on cell-cycle checkpoint fidelity through membrane trafficking-dependent signaling, or alterations in drug uptake and intracellular distribution. Elucidating this connection is a critical next step.

While this study provides valuable insights, several limitations warrant consideration. First, the reliance on retrospective TCGA data limits causal inference. All bioinformatic analyses were conducted on the single TCGA-LIHC cohort without replication in an external dataset, which limits the generalizability of our prognostic model and necessitates external validation. Experimental validation of SYNGR2’s functional roles in HCC progression and immune regulation is also needed. Second, the immune cell proportions were estimated algorithmically (CIBERSORT), necessitating validation through single-cell sequencing, flow cytometry or multiplex immunohistochemistry. Third, the in vitro validation of MK-1775 sensitivity was confined to two cell lines (Hep3B and Huh7). Confirmation in a broader panel of HCC lines with varying SYNGR2 levels is necessary to solidify the predictive value. Of the predicted candidate drugs, only the sensitivity to the Wee1 inhibitor MK-1775 was experimentally tested; thus, the bioinformatic predictions for paclitaxel and other agents remain unvalidated hypotheses. Moreover, the predicted sensitivity of SYNGR2-low tumors to JAK1/IAP inhibitors remains unverified. In vivo studies are imperative to validate these differential drug responses in physiologically relevant models before considering clinical application. Collectively, these drug sensitivity predictions should be regarded as preliminary and hypothesis-generating, supported only by initial in vitro evidence. Their translational potential hinges on rigorous validation—including mechanistic interrogation of SYNGR2 function, independent in vivo confirmation, and prospective assessment in well-annotated clinical cohorts—before any integration into therapeutic decision-making frameworks.

Conclusion

In summary, SYNGR2 has emerged as a multifaceted biomarker candidate in HCC, associated with prognosis, an immunosuppressive TME, and differential therapeutic responses. SYNGR2 has emerged as a multifaceted biomarker candidate in HCC, associated with prognosis, an immunosuppressive TME, and differential therapeutic responses. Its integration into investigational prognostic and therapeutic frameworks may ultimately enhance precision oncology, pending further mechanistic and clinical validation.

Supplemental Material

Supplemental Material - SYNGR2 as a multifaceted biomarker in hepatocellular carcinoma linking prognosis, immune microenvironment and therapeutic response

Supplemental Material for SYNGR2 as a multifaceted biomarker in hepatocellular carcinoma linking prognosis, immune microenvironment and therapeutic response by Rong Wang, Dongmei Chen, Qiu Du, Shuqi Wu, Peng Wu, Junyao Jiang in Technology in Cancer Research & Treatment

Footnotes

ORCID iD

Junyao Jiang

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by Health Commission of Sichuan Province Medical Science and Technology Program (24WSXT041).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

References

Vogel

Meyer

Sapisochin

Salem

Saborowski

. Hepatocellular carcinoma. Lancet. 2022;400:1345–1362. doi:10.1016/s0140-6736(22)01200-4.

Brown

Tsilimigras

Ruff

, et al. Management of Hepatocellular Carcinoma: A Review. JAMA surgery. 2023;158:410–420. doi:10.1001/jamasurg.2022.7989.

Llovet

Pinyol

Yarchoan

, et al. Adjuvant and neoadjuvant immunotherapies in hepatocellular carcinoma. Nat Rev Clin Oncol. 2024;21:294–311. doi:10.1038/s41571-024-00868-0.

Braghini

Lo Re

Romito

, et al. Epigenetic remodelling in human hepatocellular carcinoma. J Exp Clin Cancer Res. 2022;41:107. doi:10.1186/s13046-022-02297-2.

Gao

Zhu

Dong

, et al. Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell. 2019;179:561–577.e522. doi:10.1016/j.cell.2019.08.052.

Shen

Zhu

Xie

Qin

. Immunosuppressive tumor microenvironment and immunotherapy of hepatocellular carcinoma: current status and prospectives. J Hematol Oncol. 2024;17:25. doi:10.1186/s13045-024-01549-2.

Chen

Wang

Ding

Qin

. Tumor microenvironment-mediated immune evasion in hepatocellular carcinoma. Frontiers in immunology. 2023;14:1133308. doi:10.3389/fimmu.2023.1133308.

Liu

Yang

Liu

, et al. Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma. Frontiers in immunology. 2024;15:1371829. doi:10.3389/fimmu.2024.1371829.

Wang

Guo

Zhang

, et al. Disulfidptosis classification of hepatocellular carcinoma reveals correlation with clinical prognosis and immune profile. Int Immunopharmacol. 2023;120:110368. doi:10.1016/j.intimp.2023.110368.

10.

Wang

, et al. SULT2B1-CS-DOCK2 axis regulates effector T-cell exhaustion in HCC microenvironment. Hepatology (Baltimore, Md. 2023;78:1064–1078. doi:10.1097/hep.0000000000000025.

11.

Ren

Chen

, et al. SYNGR2 serves as a prognostic biomarker and correlates with immune infiltrates in esophageal squamous cell carcinoma. The journal of gene medicine. 2022;24:e3441. doi:10.1002/jgm.3441.

12.

Aihua

Fuchu

Yushan

A predictive kit for the risk of recurrence after resection of primary hepatocellular carcinoma and its application. CN116930498B.

13.

Newman

Liu

Green

, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature methods. 2015;12:453–457. doi:10.1038/nmeth.3337.

14.

Gao

You

, et al. Intratumoral stem-like CCR4+ regulatory T cells orchestrate the immunosuppressive microenvironment in HCC associated with hepatitis B. Journal of hepatology. 2022;76:148–159. doi:10.1016/j.jhep.2021.08.029.

15.

Dou

Sun

Wang

Liu

. Regulatory T cells and immune escape in HCC: understanding the tumor microenvironment and advancing CAR-T cell therapy. Frontiers in immunology. 2024;15:1431211. doi:10.3389/fimmu.2024.1431211.

16.

Zhao

Yao

. Roles of Tregs in development of hepatocellular carcinoma: a meta-analysis. World journal of gastroenterology. 2014;20:7971–7978. doi:10.3748/wjg.v20.i24.7971.

17.

Han

Yang

Chen

. PD-1/PD-L1 checkpoint inhibitors in advanced hepatocellular carcinoma immunotherapy. Frontiers in immunology. 2022;13:1070961. doi:10.3389/fimmu.2022.1070961.

18.

Rimassa

Finn

Sangro

. Combination immunotherapy for hepatocellular carcinoma. Journal of hepatology. 2023;79:506–515. doi:10.1016/j.jhep.2023.03.003.

19.

Bridges

Hirai

Buser

, et al. MK-1775, a novel Wee1 kinase inhibitor, radiosensitizes p53-defective human tumor cells. Clin Cancer Res. 2011;17:5638–5648. doi:10.1158/1078-0432.Ccr-11-0650.

20.

Alqahtani

Aleanizy

El Tahir

Alkahtani

AlQuadeib

Paclitaxel. Profiles of drug substances, excipients, and related methodology. 2019;44:205–238, doi:10.1016/bs.podrm.2018.11.001.

21.

Liu

Zhao

Wang

Zhan

Zhang

. Pan-cancer analysis of SYNGR2 with a focus on clinical implications and immune landscape in liver hepatocellular carcinoma. BMC Bioinformatics. 2023;24:192. doi:10.1186/s12859-023-05323-y.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.16 MB