Metastasis and basement membrane-related signature enhances hepatocellular carcinoma prognosis and diagnosis by integrating single-cell RNA sequencing analysis and immune microenvironment assessment

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and second leading cause of cancer-related deaths worldwide. The heightened mortality associated with HCC is largely attributed to its propensity for metastasis, which cannot be achieved without remodeling or loss of the basement membrane (BM). Despite advancements in targeted therapies and immunotherapies, resistance and limited efficacy in late-stage HCC underscore the urgent need for better therapeutic options and early diagnostic biomarkers. Our study aimed to address these gaps by investigating and evaluating potential biomarkers to improve survival outcomes and treatment efficacy in patients with HCC.

Method: In this study, we collected the transcriptome sequencing, clinical, and mutation data of 424 patients with HCC from The Cancer Genome Atlas (TCGA) and 240 from the International Cancer Genome Consortium (ICGC) databases. We then constructed and validated a prognostic model based on metastasis and basement membrane-related genes (MBRGs) using univariate and multivariate Cox regression analyses. Five immune-related algorithms (CIBERSORT, QUANTISEQ, MCP counter, ssGSEA, and TIMER) were then utilized to examine the immune landscape and activity across high- and low-risk groups. We also analyzed Tumor Mutation Burden (TMB) values, Tumor Immune Dysfunction and Exclusion (TIDE) scores, mutation frequency, and immune checkpoint gene expression to evaluate immune treatment sensitivity. We analyzed integrin subunit alpha 3 (ITGA3) expression in HCC by performing single-cell RNA sequencing (scRNA-seq) analysis using the TISCH 2.0 database. Lastly, wound healing and transwell assays were conducted to elucidate the role of ITGA3 in tumor metastasis.

Results: Patients with HCC were categorized into high- and low-risk groups based on the median values, with higher risk scores indicating worse overall survival. Five immune-related algorithms revealed that the abundance of immune cells, particularly T cells, was greater in the high-risk group than in the low-risk group. The high-risk group also exhibited a higher TMB value, mutation frequency, and immune checkpoint gene expression and a lower tumor TIDE score, suggesting the potential for better immunotherapy outcomes. Additionally, scRNA-seq analysis revealed higher ITGA3 expression in tumor cells compared with normal hepatocytes. Wound healing scratch and transwell cell migration assays revealed that overexpression of the MBRG ITGA3 enhanced migration of HCC HepG2 cells.

Conclusion: This study established a direct molecular correlation between metastasis and BM, encompassing clinical features, tumor microenvironment, and immune response, thereby offering valuable insights for predicting clinical outcomes and immunotherapy responses in HCC.

Keywords: Basement membrane; Hepatocellular carcinoma; Immunotherapy Response; Metastasis; Prognostic model; ScRNA-seq.

Fig. 1

Workflow chart of the study, created with BioRender. Abbreviations: BM, basement membrane; CNV, copy number variable; GO, Gene Ontology; EMT, epithelial-mesenchymal transition; GSEA, gene set enrichment analysis; ICGC, International Cancer Genome Consortium; ITGA3, integrin subunit alpha 3; KEGG, Kyoto Encyclopedia of Genes and Genomes; MBRGs, metastasis and basement membrane-related genes; PCA, principal component analysis; ROC, receiver operating characteristic; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TIDE, tumor immune dysfunction and exclusion; TMB, tumor mutation burden; t-SNE, t-distributed stochastic neighbor embedding

Fig. 2

Prognosis-related DEGs screened in TCGA-LIHC cohort. (A) Venn diagram illustrating the overlap of datasets. (B) Volcano plot of DEGs related to prognosis in metastasis and BM between tumor and normal tissues. (C) Comparative expression levels of 12 DEGs related to prognosis in metastasis and BM in tumor tissues versus normal tissues. (D) Forest plot demonstrating the prognostic value of 12 DEGs related to prognosis in metastasis and BM for HCC. (E) MAF tool analysis of 12 DEGs related to prognosis in metastasis and BM. (F) CNV analysis of 12 DEGs related to prognosis in metastasis and BM. Abbreviations: BM, basement membrane; CNV, copy number variable; DEG, differentially expressed gene; HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; MAF, mutation annotation format; TCGA, The Cancer Genome Atlas

Fig. 3

Construction and validation of the MBRGs prognostic model. (A) Kaplan-Meier curve displaying OS of TCGA dataset. (B) ROC curve for TCGA dataset. (C) Risk curve and survival status for TCGA dataset. (D) PCA and t-SNE graph for TCGA dataset. (E) Kaplan-Meier curve displaying OS of the ICGC dataset. (F) ROC curve for the ICGC dataset. (G) Risk curve and survival status for the ICGC dataset. (H) PCA and t-SNE graph for the ICGC dataset. Abbreviations: AUC, area under the curve; ICGC, International Cancer Genome Consortium; MBRGs, metastasis and basement membrane-related genes; OS, overall survival; PCA, principal component analysis; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; t-SNE, t-distributed stochastic neighbor embedding

Fig. 4

Analysis of the correlation between the MBRGs prognostic model and clinical features. (A) Correlation analysis between tumor grade and risk score. (B) Correlation analysis between overall cancer stage and risk score. (C) Correlation analysis between T stage and risk score. (D) Analysis of vascular invasion. (E) Correlation analysis between M stage and risk score. (F) Correlation analysis between N stage and risk score. *p < 0.05, **p < 0.01, ***p < 0.001. Abbreviations: M, metastasis (stage); MBRGs, metastasis and basement membrane-related genes; N, node (stage); T, tumor (stage)

Fig. 5

Independent prognostic analyses of risk scores and construction of a predictive nomogram model. (A) Univariate analysis of TCGA cohort. (B) Multivariate analysis of TCGA cohort. (C) Nomogram for predicting survival. (D) Calibration curve of the nomogram to assess accuracy. (E) Multivariate ROC curve for tumor grade, age, stage, gender, nomogram and risk score. (F) Concordance index in the model performance evaluation. Abbreviations: AUC, area under the curve; M, metastasis (stage); N, node (stage); OS, overall survival; ROC, receiver operating characteristic; T, tumor (stage); TCGA, The Cancer Genome Atlas

Fig. 6

Functional enrichment analyses and correlation of MBRGs expression levels with the EMT pathway. (A) GO enrichment pathway analysis plot. (B) KEGG enrichment pathway analysis plot. (C) GSEA. (D) Scatterplots illustrating the expression correlation between the 10 signature genes and the EMT pathway in the hallmark gene set. Abbreviations: EMT, epithelial-mesenchymal transition; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MBRGs, metastasis and basement membrane-related genes

Fig. 7

Immunological analysis of LIHC patients in respective risk groups. (A, B) ssGSEA scores for immune cells and immune function in TCGA cohort. (C) Correlation between ITGA3 expression and enrichment of immune cells. (D) Correlation between ITGA3 expression and macrophage enrichment. (E) Correlation between ITGA3 expression and Treg enrichment. ns = p > 0.05 (not significant), *p < 0.05, **p < 0.01, ***p < 0.001. Abbreviations: APC, antigen-presenting cell; aDC, activated dendritic cell; CCR, chemokine receptor; DC, dendritic cell; HLA, human leukocyte antigen; iDC, immature dendritic cell; INF, interferon; ITGA3, integrin subunit alpha 3; LIHC, liver hepatocellular carcinoma; MHC, major histocompatibility complex; NK, natural killer; pDC, plasmacytoid dendritic cell; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; Tfh, T follicular helper; TIL, tumor-infiltrating; Treg, regulatory T cell

Fig. 8

Mutations in MBRGs and immunotherapeutic response. (A) TMB analysis. (B) TIDE analysis. (C, D) Differences in mutation incidence between the high- and low-risk groups. (E) Differences in the expression of immune checkpoint genes between the high- and low-risk groups. *p < 0.05, **p < 0.01, ***p < 0.001. Abbreviations: DEL, deletion; INS, insertion; TIDE, tumor immune dysfunction and exclusion; TMB, tumor mutation burden

Fig. 9

Drug sensitivity analysis. (A) Sensitivity analyses of axitinib, erlotinib, sorafenib, and sunitinib in the high- and low-risk patient groups. (B) Differences in target gene expression between the low- and high-risk groups. *p < 0.05, **p < 0.01, ***p < 0.001. Abbreviations: IC50, half-maximal inhibitor concentration

Fig. 10

Single cell RNA sequencing analysis and construction of a PPI network. (A-C) T-SNE projection of all cells and ITGA3 expression from LIHC-GSE146409. (D-F) T-SNE projection of all cells and ITGA3 expression from LIHC-GSE166635. (G) PPI network centered on ITGA3.Abbreviations: ITGA3, integrin subunit alpha 3; PPI, protein-protein interaction;

Fig. 11

ITGA3 plays a crucial role in the migration and invasion of HCC cells. (A)ITGA3 mRNA expression profile in HCC cells from the HPA database. (B)ITGA3 expression after transfection with ITGA3-OE lentivirus. (C) Results of the CCK8 assay. (D, E) Results of wound healing assay. (G, F) Results of the transwell migration assay. (H) Representative immunohistochemical results of ITGA3 expression in HCC and normal liver tissues from the HPA database. *p < 0.05, **p < 0.01, ***p < 0.001. Abbreviations: CCK-8, cell counting kit-8; HCC, hepatocellular carcinoma; HPA, Human Protein Atlas; ITGA3, integrin subunit alpha 3; OE, overexpression; RT-qPCR, reverse transcription-quantitative PCR