Identification of Prognostic Stromal-Immune Score–Based Genes in Hepatocellular Carcinoma Microenvironment

Abstract

A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score–based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein–protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. MMP9 (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment–associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.

A pyrimidine metabolism-related signature for prognostic and immunotherapeutic response prediction in hepatocellular carcinoma by integrating analyses

“…These populations of cells often engage in cooperative or competitive interactions that facilitate tumor growth, progression, metastasis, as well as evasion from the host immune system [ 38 , 39 ]. The stromal score is derived from the expression levels of genes associated with non-cancerous stromal cells surrounding tumors [ 40 ]. A higher stromal score typically indicates a greater amount of stromal cell infiltration within the tumor microenvironment, which may suggest a better prognosis due to an increased presence of immune cells that can target cancer cells [ 41 ].…”

Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy

“…Accumulating studies have employed the ESTIMATE algorithm to infer proportion of infiltrating immune and stromal cells in the tumor samples, which highlighted reliability and effectiveness of prognosis prediction according to TME signatures and the predictive advantage of TME scores than other single biomarkers. 19 , 20 Based on methodology of WGCNA and a machine-learning method LASSO-Cox, we conducted a dimension-reduced screening for gene expression matrix from TCGA-LUSC samples and a simplified model containing 6 gene signatures (TGM2, C11orf96, PLAAT4, PNCK, KLF5 and C4BPA) was generated.…”

Effect of Tertiary Lymphoid Structures on Prognosis of Patients with Hepatocellular Carcinoma and Preliminary Exploration of Its Formation Mechanism

“…The results showed that HCC patients with high expression of LCK had higher immune scores. The higher the immune score is, the higher the objective response rate of HCC patients to ICIs [ 61 , 62 ]. Activation and maturation of T cells can increase the sensitivity of HCC patients to ICIs [ 63 , 64 ].…”