Abstract
BackgroundHepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC.MethodsUnivariate Cox regression analysis was performed to determine the prognosis-related AS events and gene set enrichment analysis (GSEA) was employed for functional annotation, followed by the development of prognostic signatures using univariate Cox, LASSO and multivariate Cox regression. K-M survival analysis, proportional hazards model, and ROC curves were conducted to validate prognostic value. ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed to uncover the context of TIME in HCC. Quantitative real-time polymerase chain reaction was implemented to detect ZDHHC16 mRNA expression. Cytoscape software 3.8.0 were employed to visualize AS-splicing factors (SFs) regulatory networks.ResultsA total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, eight AS prognostic signature with robust prognostic predictive accuracy were constructed. Furthermore, quantitative prognostic nomogram was developed and exhibited robust validity in prognostic prediction. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. ZDHHC16 presented promising prospect as prognostic factor in HCC. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs).ConclusionHerein, exploration of AS patterns may provide novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And pivotal players of AS events in context of TIME and immunotherapy efficiency were revealed, contributing to clinical decision-making and personalized prognosis monitoring of HCC.
Highlights
Hepatocellular carcinoma (HCC) is a malignant and aggressive disease marked with frequently diagnosed and high cancer-attributable mortality in the world [1,2,3]
Full list of author information is available at the end of the article
A great part of HCC was derived from inflammatory liver diseases, which suggested that infiltrating immune cells in tumor immune microenvironment (TIME) might serve as pivotal regulatory roles in tumorigenesis and progression in HCC [12,13,14]
Summary
Hepatocellular carcinoma (HCC) is a malignant and aggressive disease marked with frequently diagnosed and high cancer-attributable mortality in the world [1,2,3]. Xu et al Cancer Cell Int (2021) 21:190 anti-tumor therapy, clinical treatment result is still undesirable [4,5,6]. A great part of HCC was derived from inflammatory liver diseases, which suggested that infiltrating immune cells in tumor immune microenvironment (TIME) might serve as pivotal regulatory roles in tumorigenesis and progression in HCC [12,13,14]. The most effective strategy for precise prognostic predictions of how a given malignancy will respond to immunotherapy or tumor will progress may be one based on molecular risk distribution, identifying HCC patients on line with particular molecular signatures, enhancing prognostic precision and optimize immunotherapeutic benefit . This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC
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