AUTHOR=Chen Lei , Zhang Dafang , Zheng Shengmin , Li Xinyu , Gao Pengji 

TITLE=Stemness analysis in hepatocellular carcinoma identifies an extracellular matrix gene–related signature associated with prognosis and therapy response

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.959834

DOI=10.3389/fgene.2022.959834

ISSN=1664-8021

ABSTRACT=Background: Tumor stemness is the stem-like phenotype of cancer cells, as a hallmark for multiple process in the development of hepatocellular carcinoma (HCC). However, the comprehensive functions of the regulators of tumor cell stemness in HCC remain unclear.
Methods: Gene expression data and clinical information of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) dataset as the training set, and three validation datasets were derived from Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Patients were dichotomized according to the median mRNA expression-based stemness index (mRNAsi) scores, and differentially expressed genes  were further screened out. Functional enrichment analysis of these DEGs was performed to identify candidate extracellular matrix (ECM) related genes in key pathways. A prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) to candidate ECM genes. Kaplan-Meier curve and receiver operating characteristic (ROC) curve were used to evaluate prognostic value of the signature. Correlations between signatures and genomic profiles, tumor immune microenvironment, and treatment response were also explored using multiple bioinformatic methods.
Results: A prognostic prediction signature was established based on ten ECM genes, including TRAPPC4, RSU1, ILK, LAMA1, LAMB1, FLNC, ITGAV, AGRN, ARHGEF6, and LIMS2, which could effectively distinguish patients with different outcomes in the training and validation sets, showing a good prognostic prediction ability. Across different clinicopathological parameter stratifications, the ECMs signature still retains its robust efficacy in discriminating patient with different outcomes. Based on the risk score, vascular invasion, α-fetoprotein (AFP), T stage and N stage, we further constructed a nomogram (C-index=0.70; AUCs at 1-, 3- and 5- survival =0.71, 0.75 and 0.78) which is more practical for clinical prognostic risk stratification. The infiltration abundance of macrophages M0, mast cells and Treg cells was significantly higher in the high-risk group, which also had up-regulated level of immune checkpoints PD-1 and CTLA-4. More importantly, the ECMs signature was able to distinguish patients with superior responses to immunotherapy, transarterial chemoembolization and sorafenib.
Conclusion: In this study, we constructed an ECMs signature, which is an independent prognostic biomarker for HCC patients and has a potential guiding role in treatment selection.