Research Paper Volume 16, Issue 11 pp 10108—10131
Comprehensive evaluation of genes related to basement membrane in hepatocellular carcinoma
- 1 Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Southern Medical University, Guangzhou 510315, China
- 2 Cancer Center, Southern Medical University, Guangzhou 510315, China
- 3 Heshan Hospital of Traditional Chinese Medicine, Jiangmen 529000, China
Received: October 25, 2023 Accepted: May 3, 2024 Published: June 12, 2024
https://doi.org/10.18632/aging.205923How to Cite
Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In all mammals, the basement membrane serves as a pivotal extracellular matrix. Hepatocellular carcinoma (HCC) is a challenge among numerous cancer types shaped by basement membrane-related genes (BMGs). Our research established an innovative prognostic model that is highly accurate in its prediction of HCC prognoses and immunotherapy efficacy to summarize the crucial role of BMGs in HCC. We obtained HCC transcriptome analysis data and corresponding clinical data from The Cancer Genome Atlas (TCGA). To augment our dataset, we incorporated 222 differentially expressed BMGs identified from relevant literature. A weighted gene coexpression network analysis (WGCNA) of 10158 genes demonstrated four modules that were connected to HCC. Additionally, 66 genes that are found at the intersection of BMGs and HCC-related genes were designated as hub HCC-related BMGs. MMP1, ITGA2, P3H1, and CTSA comprise the novel model that was engineered using univariate and multivariate Cox regression analysis. Furthermore, the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) datasets encouraged the BMs model’s validity. The overall survival (OS) of individuals with HCC may be precisely predicted in the TCGA and ICGC databases utilizing the BMs model. A nomogram based on the model was created in the TCGA database at similar time, and displayed a favorable discriminating ability for HCC. Particularly, when compared to the patients at an elevated risk, the patients with a low-risk profile presented different tumor microenvironment (TME) and hallmark pathways. Moreover, we discovered that a lower risk score of HCC patients would display a greater response to immunotherapy. Finally, quantitative real-time PCR (qRT-PCR) experiments were used to verify the expression patterns of BMs model. In summary, BMs model demonstrated efficacy in prognosticating the survival probability of HCC patients and their immunotherapeutic responsiveness.
Abbreviations
HCC: hepatocellular carcinoma; TCGA: the Cancer Genome Atlas Database; ICGC: International Cancer Genome Consortium; BMGs: basement membrane-related genes; BMs: Basement membranes; TME: tumor micro-environment; LIHC: liver hepatocellular carcinoma; ssGSEA: single-sample gene set enrichment analysis; ROC: receiver operating characteristic; CNVs: copy number alterations; PCA: principal component analysis; GO: Gene ontology; KEGG: The Kyoto Encyclopedia of Genes and Genomes; OS: overall survival; APC: antigen presenting cell; MHC: major histocompatibility complex; HLA: human leukocyte antigen; AUC: area under curve; ICI: immune checkpoint inhibitors; IC50: the half-maximal inhibitory concentrations; t-SNE: t-distribution random neighborhood embedding; IPS: immunophenoscore; CC: cellular component; BP: biological processes; MF: molecular function; TMB: tumor mutation burden; CCR: chemokine receptors; MDSCs: myeloid-derived suppressor cells; CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease; IFN: interferon; TIME: tumor immune microenvironment; ECM: extracellular matrix.