Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC.

Material and methods: The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups.

Results: The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups.

Conclusion: Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.

Keywords: BMR; Basement membrane; Drug sensitivity; Hepatocellular carcinoma; Immunotherapy.

Fig. 1

Flowchart of overall study design

Fig. 2

Data processing before the construction of BMR signature A Differential expression of basement membrane-related genes in HCC tissues versus normal tissues in the TCGA-LIHC cohort. B The correlation network of candidate genes in the TCGA-LIHC cohort. C Unsupervised classification of candidate genes in the TCGA-LIHC cohort. D Comparison of OS between A and B clusters in the TCGA-LIHC cohort. E Comparison of DSS between A and B clustersin the TCGA-LIHC cohort. F Comparison of DFI between A and B clusters in the TCGA-LIHC cohort. G Comparison of PFI between A and B clusters in the TCGA-LIHC cohort. H Comparison of the expression levels of candidate genes in cluster A and cluster B in the TCGA-LIHC cohort

Fig. 3

A Lasso coefficient profiles. B Candidate basement membrane-related genes were filtered by the Lasso algorithm. C Comparison of the expression levels of ADAM9, CTSA, and P3H1 between HCC and normal tissues in the ICGC-JP dataset. D Comparison of the expression levels of ADAM9, CTSA, and P3H1 between HCC and normal tissues in the GSE14520 dataset. E Identification of the expression levels of ADAM9, CTSA, and P3H1 in JHH-2 and SNU-387 cells in the CCLE dataset

Fig. 4

Assessment of the prognostic signature (BMR) in the TCGA-LIHC cohort. A Survival status distribution of HCC patients in low- and high-risk groups. B PCA analysis of low- and high-risk groups; t-NSE analysis of low- and high-risk groups. C ROC curve of age, gender, stage, and risk score. D timeROC curve of risk score. E C-index curve of age, gender, stage, and risk score. F Comparison of OS between low- and high-risk groups. G Comparison of DSS between low- and high-risk groups. H Comparison of PFI between low- and high-risk groups. I Univariate Cox analysis of risk score, gender, stage, and age. J Multivariate Cox analysis of risk score and stage

Fig. 5

Validation of the prognostic signature (BMR) in the ICGC-JP cohort. A Survival status distribution of HCC patients in low- and high-risk groups. B PCA analysis of low- and high-risk groups; t-NSE analysis of low- and high-risk groups. C ROC curve of age, gender, stage, and risk score. D timeROC curve of risk score. E C-index curve of age, gender, stage, and risk score. F Comparison of OS between low- and high-risk groups. G Univariate Cox analysis of risk score, gender, stage, and age. H Multivariate Cox analysis of risk score and stage

Fig. 6

The correlation between BMR and clinical indicators. A The correlation heatmap about BMR and common clinical indicators. B Comparison of tumor grade between the low- and high-risk groups. C Comparison of tumor stage between low- and high-risk groups. D Comparison of T-stage between low- and high-risk groups. E Comparison of gender between low- and high-risk groups. (* and ** representing p < 0.05 and p < 0.01,respectively.)

Fig. 7

A C-index curve of BMR and other studies. B–H Decision curve analysis of BMR with other gene signatures

Fig. 8

Nomogram based on BMR, stage, gender, and stage for prediction of OS in TCGA-LIHC cohort. A Nomgram based on BMR, stage, gender, and stage in TCGA-LIHC cohort. B timeROC curve of the nomogram. C C-index curve of the nomogram, age, gender, stage, and risk score. D Calibration curve of the nomogram. E PCA plot of the nomogram. F DCA curve of BMR and nomogram

Fig. 9

Somatic mutation and mRNAsi analyses in TCGA-LIHC cohort. A Matfools of the high-risk group. B Matfools of the low-risk group. C Comparison of TMB between high- and low-risk groups. D Comparsion of OS between the low-TMB and high-TMB groups. E Comparsion of OS between low-TMB + low-risk, low-TMB + high-risk, high-TMB + low-risk, and high-TMB + high-risk groups. F Comparison of mRNAsi between high- and low-risk groups. G The correlation between risk score and mRNAsi

Fig. 10

Functional analysis of DEGs between high- and low-risk groups based on TCGA-LIHC cohort. A Go analysis of DEGs between high- and low-risk groups. B KEGG analysis of DEGs between high- and low-risk groups. C Comparison of tumor-related pathways between low- and high-risk groups

Fig. 11

The analyses of immune features in the TCGA-LIHC cohort. A Comparison of immune cell infiltration between high- and low-risk groups with ssGSEA algorithm. B Comparison of immune function between high- and low-risk groups with ssGSEA algorithm. C Comparison of ESTIMATE score, stromal score, and immune score. D Comparison of immune cell infiltration between high- and low-risk groups with CIBERSORT algorithm. E Comparison of expression levels of immune checkpoints between the high- and low-risk groups. (*, **, *** representing p < 0.05, p < 0.01 and p < 0.001,respectively.)

Fig 12

The analysis of response to immunotherapy and TACE. A Comparison of TIDE score between high- and low-risk groups. B Comparison of IPS score (negative PD-1 and negative CTLA-4) between high- and low-risk groups. C Comparison of TIS between the low- and high- risk groups. D Comparison of NFAG between low- and high-risk groups. E Comparison of risk scores between responders and non-responders to TACE therapy group. (*, **, *** representing p < 0.05, p < 0.01 and p < 0.001,respectively.)

Fig 13

Drug sensitivity analyses based on the TCGA-LIHC cohort. A Comparison of drug sensitivity between high- and low-risk groups. B The correlation between risk score and drug sensitivity