Glycosylation has been demonstrated to be involved in tumorigenesis, progression, and immunoregulation, and to present specific profiles in different tumors. In this study, we aimed to explore the specific glycosylation-related gene (GRG) signature and its potential immunological roles and prognostic implications in hepatocellular carcinoma (HCC). Patients and Methods: The GRG expression profile was defined using the transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and the least absolute shrinkage and selection operator Cox analyses were performed to develop a GRG-based risk score model. A nomogram was subsequently established and validated. Its correlation with cancer immune microenvironment and drug susceptibility was further analyzed. The role and immunological correlation of ST6GALNAC4 were further experimentally validated at the tissue and cellular levels in HCC. Results: A total of 87 GRGs were identified to be significantly dysregulated in HCC, and a novel risk score model was constructed using eight critical GRGs, which demonstrated superior prognostic discrimination and predictive power in both training and validation groups. High risk scores in HCC patients were associated with lower OS. The model was also identified as an independent risk factor for HCC, and a novel nomogram was subsequently constructed and validated. Notably, significant correlations were found in risk scores with immune cells infiltration, tumor immunophenotyping, immune checkpoint genes' expression, and sensitivities to multiple drugs. Furthermore, we validated in local HCC samples that ST6GALNAC4 was significantly upregulated and its knockdown significantly inhibited the tumor proliferation, migration and invasion ability and affected the expression of immune checkpoints on hepatoma cells. Conclusion:We identified a novel GRG-based model which showed significant prognostic and immunological correlations in HCC, and the oncogenic role of ST6GALNAC4 has been validated and may serve as a potential drug target.

Background Due to its high recurrence rate, hepatocellular carcinoma (HCC) has a poor prognosis after hepatectomy. An effective model to predict postoperative recurrence and identify high-risk patients is essential. Recent studies have revealed the important role of cancer-associated fibroblasts (CAFs) in predicting HCC prognosis. However, the prognostic value of CAFs-related gene signature in HCC recurrence remains unknown. According to the BIOSTORM study, adjuvant sorafenib efficacy data may help to predict the recurrence in HCC. Therefore, we aimed to create a novel CAFs-related gene signature based on adjuvant sorafenib efficacy to predict HCC recurrence. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to obtain the transcriptomic gene expression profiles and corresponding clinical data of HCC patients. The CAFs-related genes based on adjuvant sorafenib efficacy were identified using EPIC and weighted gene co-expression network analysis (WGCNA) algorithm. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a novel risk model. Univariate and multivariate COX analyses were used to identify independent prognostic factors for disease-free survival (DFS), and a nomogram was developed. The CIBERSORT and ESTIMATE algorithms were used to assess the tumor microenvironment components. Tumor immune dysfunction and exclusion (TIDE) score was used to predict immunotherapy response. Results A novel risk model was created using ten CAFs-related genes based on adjuvant sorafenib efficacy (DCLRE1C, DDX11, MAP4K2, SHCBP1, ADAM12, PAQR4, BEND3, ADAMTSL2, NUP93 and MPP2). Survival analyses revealed that high-risk patients had worse DFS, and the risk model was found as an independent prognostic factor for DFS in both the training and validation groups. A novel nomogram combined with pathologic stage and risk score status was developed. In the high-risk group, the stromal and immune cell content was found significantly lower while the tumor purity was significantly higher. In addition, immune checkpoints genes were highly expressed in the high-risk group and a higher risk score may predict a better response to immunotherapy. Conclusions The novel risk model comprised of ten CAFs-related genes based on adjuvant sorafenib efficacy may accurately predict recurrence and immunotherapy response in HCC patients after hepatectomy.