An extracellular matrix-based signature associated with immune microenvironment predicts the prognosis of patients with hepatocellular carcinoma

Exploring the Regulatory Role of XIST-microRNAs/mRNA Network in Circulating CD4+ T Cells of Hepatocellular Carcinoma Patients

Hepatocellular carcinoma (HCC) is one of the most common cancers and the main cause of cancer-related death globally. Immune dysregulation of CD4⁺ T cells has been identified to play a role in the development of HCC. Nevertheless, the underlying molecular pathways of CD4⁺ T cells in HCC are not completely known. Thus, a better understanding of the dysregulation of the lncRNA-miRNA/mRNA network may yield novel insights into the etiology or progression of HCC. In this study, circulating CD4⁺ T cells were isolated from the whole blood of 10 healthy controls and 10 HCC patients for the next-generation sequencing of the expression of lncRNAs, miRNAs, and mRNAs. Our data showed that there were different expressions of 34 transcripts (2 lncRNAs, XISTs, and MIR222HGs; 29 mRNAs; and 3 other types of RNA) and 13 miRNAs in the circulating CD4⁺ T cells of HCC patients. The expression of lncRNA-XIST-related miRNAs and their target mRNAs was confirmed using real-time quantitative polymerase chain reaction (qPCR) on samples from 100 healthy controls and 60 HCC patients. The lncRNA-miRNA/mRNA regulation network was created using interaction data generated from ENCORI and revealed there are positive correlations in the infiltration of total CD4⁺ T cells, particularly resting memory CD4⁺ T cells, and negative correlations in the infiltration of Th1 CD4⁺ T cells.

Development and validation of an ECM-related prognostic signature to predict the immune landscape of human hepatocellular carcinoma

Background

The global burden of hepatocellular carcinoma (HCC) is increasing, negatively impacting social health and economies. The discovery of novel and valuable biomarkers for the early diagnosis and therapeutic guidance of HCC is urgently needed.

Methods

Extracellular matrix (ECM)-related gene sets, transcriptome data and mutation profiles were downloaded from the Matrisome Project and The Cancer Genome Atlas (TCGA)-LIHC datasets. Coexpression analysis was initially performed with the aim of identifying ECM-related lncRNAs (r > 0.4, p < 0.001). The screened lncRNAs were subjected to univariate analysis to obtain a series of prognosis-related lncRNA sets, which were incorporated into least absolute selection and shrinkage operator (LASSO) regression for signature establishment. Following the grouping of LIHC samples according to risk score, the correlations between the signature and clinicopathological, tumour immune infiltration, and mutational characteristics as well as therapeutic response were also analysed. lncRNA expression levels used for modelling were finally examined at the cellular and tissue levels by real-time PCR. All analyses were based on R software.

Results

AL031985.3 and MKLN1-AS were ultimately identified as signature-related lncRNAs, and both were significantly upregulated in HCC tissue samples and cell lines. The prognostic value of the signature reflected by the AUC value was superior to that of age, sex, grade and stage. Correlation analysis results demonstrated that high-risk groups exhibited significant enrichment of immune cells (DCs, macrophages and Tregs) and increased expression levels of all immune checkpoint genes. Prominent differences in clinicopathological profiles, immune functions, tumour mutation burden (TMB) and drug sensitivity were noted between the two risk groups.

Conclusions

Our signature represents a valuable predictive tool in the prognostic management of HCC patients. Further validation of the mechanisms involved is needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10049-w.