A novel defined basement membrane-related genes signature for predicting the prognosis of Hepatocellular carcinoma

Abstract

Abstract Background. Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with poor prognosis, making the prediction of the prognosis much challenges. Basement membrane-related genes (BMRGs) play an important role in the progression of cancer. Thus, they are often used as targets to inhibit tumor progression. However, the value of BMRGs in predicting prognosis of HCC still remains to be further elucidated. This study aimed to find the relationship between BMRGs and HCC and the value of BMRGs in predicting the prognosis of HCC. Methods. We acquired transcriptome and clinical data of HCC from The Cancer Genome Atlas (TCGA) and randomly divided the data into training and test sets in order to develop a reliable prognostic signature of BMRGs for HCC. The BMRGs model was built using multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), and univariate Cox regression. The risk signature was further validated and assessed using the principal component analysis (PCA), Kaplan-Meier analysis, and time-dependent receiver operating characteristics (ROC). To forecast the overall survival, a nomogram and calibration curves were created (OS). Functional enrichment analysis was used to evaluate the potential biological pathways. We also conducted immunological research and a pharmacological comparison between the high- and low-risk groups in this study. Results. We identified 16 differentially expressed genes and constructed a risk model of four BMRGs, including COL2A1, CTSA, LAMB1,P3H1. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. Patients in the low-risk group showed significantly better outcome compared with patients in the high-risk group. Receiver operating characteristic (ROC) curve analysis show predictive capacity. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for HCC. Furthermore, analysis of clinical characteristics and tumor microenvironment (TME) between risk groups showed significant difference. Functional analysis revealed different immune-related pathways were enriched, and immune status were different between two risk groups. Mediation analysis with IC50 revealed that the two risk group were significantly different, which could be a guidance of systemic treatment. Finally, we further verified in clinical samples that the mRNA and protein expression levels of the four genes in this model are significantly higher in liver cancer tissues than in adjacent tissues. Conclusion. A novel BMRGs signature can be used for prognostic prediction in HCC. This provide us with a potential progression trajectory as well as predictions of therapeutic response.