A Model of Basement Membrane-Associated Gene Signature Predicts Liver Hepatocellular Carcinoma Response to Immune Checkpoint Inhibitors

• Published in: Mediators of inflammation Vol. 2023; pp. 7992140 - 20
• Main Authors: Shen, Jiajia, Wei, Zhihong, Lv, Lizhi, He, Jingxiong, Du, Suming, Wang, Fang, Wang, Ye, Ni, Lin, Zhang, Xiaojin, Pan, Fan
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28-04-2023; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Ablation; Algorithms; Analysis; Basement Membrane; Basement membranes; Cancer; Cancer therapies; Carcinoma, Hepatocellular - genetics; Collagen; Correlation analysis; Drug therapy; Gene expression; Genes; Genetic aspects; Health aspects; Hepatocellular carcinoma; Hepatoma; Humans; Immune Checkpoint Inhibitors; Immune response; Immunotherapy; Liver; Liver cancer; Liver Neoplasms - genetics; Medical prognosis; Medical research; Medicine, Experimental; Metabolism; Microenvironments; Patients; Physiological aspects; Prognosis; Risk groups; RNA Helicases; Statistical analysis; Survival analysis; Transcriptomes; Tumor Microenvironment; Tumors
• ISSN: 0962-9351; 1466-1861
• DOI: 10.1155/2023/7992140

Liver hepatocellular carcinoma (LIHC) is a highly lethal malignant tumor originating from the digestive system, which is a serious threat to human health. In recent years, immunotherapy has shown significant therapeutic effects in the treatment of LIHC, but only for a minority of patients. The basement membrane (BM) plays an important role in the occurrence and development of tumors, including LIHC. Therefore, this study is aimed at establishing a risk score model based on basement membrane-related genes (BMRGs) to predict patient prognosis and response to immunotherapy. First, we defined three patterns of BMRG modification (C1, C2, and C3) by consensus clustering of BMRG sets and LIHC transcriptome data obtained from public databases. Survival analysis showed that patients in the C2 group had a better prognosis, and Gene Set Variation Analysis (GSVA) revealed that the statistically significant pathways were mainly enriched in the C2 group. Moreover, we performed Weighted Correlation Network Analysis (WGCNA) on the above three subgroups and obtained 179 intersecting genes. We further applied function enrichment analyses, and the results demonstrated that they were mainly enriched in metabolism-related pathways. Furthermore, we conducted the LASSO regression analysis and obtained 4 BMRGs (MPV17, GNB1, DHX34, and MAFG) that were significantly related to the prognosis of LIHC patients. We further constructed a prognostic risk score model based on the above genes, which was verified to have good predictive performance for LIHC prognosis. In addition, we analyzed the correlation between the risk score and the tumor immune microenvironment (TIM), and the results showed that the high-risk scoring group tended to be in an immunosuppressed status. Finally, we investigated the relationship between the risk score and LIHC immune function. The results demonstrated that the risk score was closely related to the expression levels of multiple immune checkpoints. Patients in the low-risk group had significantly higher IPS scores, and patients in the high-risk group had lower immune escape and TIDE score. In conclusion, we established a novel risk model based on BMRGs, which may serve as a biomarker for prognosis and immunotherapy in LIHC.