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Transcriptomic characterization reveals prognostic molecular signatures of sorafenib resistance in hepatocellular carcinoma.

ABSTRACT: Sorafenib is the first-line treatment for patients with advanced unresectable hepatocellular carcinoma (HCC); however, only a small number of patients benefit from sorafenib, and many develop sorafenib resistance (SR) and severe side effects. To identify biomarkers for SR, we systematically analyzed the molecular alterations in both sorafenib-resistant HCC specimens and cultured cells. By combining bioinformatics tools and experimental validation, four genes (C2orf27A, insulin-like growth factor 2 receptor, complement factor B, and paraoxonase 1) were identified as key genes related to SR in HCC and as independent prognostic factors significantly associated with clinical cancer stages and pathological tumor grades of liver cancer. These genes can affect the cytotoxicity of sorafenib to regulate the proliferation and invasion of Huh7 cells in vitro. Additionally, immune-cell infiltration according to tumor immune dysfunction and exclusion, a biomarker integrating the mechanisms of dysfunction and exclusion of T cells showed good predictive power for SR, with an AUC of 0.869. These findings suggest that immunotherapy may be a potential strategy for treating sorafenib-resistant HCC. Furthermore, the results enhance the understanding of the underlying molecular mechanisms of SR in HCC and will facilitate the development of precision therapy for patients with liver cancer.

SUBMITTER: Yuan W

PROVIDER: S-EPMC7906139 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Transcriptomic characterization reveals prognostic molecular signatures of sorafenib resistance in hepatocellular carcinoma.

Yuan Wei W Tao Ran R Huang Da D Yan Weiming W Shen Guanxin G Ning Qin Q

Aging 20210120 3

Sorafenib is the first-line treatment for patients with advanced unresectable hepatocellular carcinoma (HCC); however, only a small number of patients benefit from sorafenib, and many develop sorafenib resistance (SR) and severe side effects. To identify biomarkers for SR, we systematically analyzed the molecular alterations in both sorafenib-resistant HCC specimens and cultured cells. By combining bioinformatics tools and experimental validation, four genes (<i>C2orf27A</i>, insulin-like growth ...[more]

PMID: 33495404

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Project description:Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes. Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples. Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages. Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

Dataset Information

Transcriptomic characterization reveals prognostic molecular signatures of sorafenib resistance in hepatocellular carcinoma.

Publications

Transcriptomic characterization reveals prognostic molecular signatures of sorafenib resistance in hepatocellular carcinoma.

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