The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma

• Published in: Journal of cancer research and clinical oncology Vol. 149; no. 9; pp. 5751 - 5760
• Main Authors: Zhao, Yu, Yin, Zhenjie, Huang, Kangming, Zhang, Fajing, Chen, Yun, Deng, Yinghan, Chen, Hongbin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023; Springer Nature B.V
• Subjects: Basement membranes; Cancer Research; Gene set enrichment analysis; Genes; Hematology; Hepatocellular carcinoma; Immunity; Infiltration; Internal Medicine; Liver cancer; Medical prognosis; Medicine; Medicine & Public Health; Metastases; Oncology; Risk groups; Hepatocellular carcinoma; Extracellular matrix; Tumor microenvironment; Prediction model; Basement membrane
• ISSN: 0171-5216; 1432-1335
• DOI: 10.1007/s00432-022-04549-2

Aims Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis. Methods All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity. Results Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS ( p  < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p  < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group. Conclusions Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.