Abstract
Liver hepatocellular carcinoma (LIHC) is the seventh most commonly diagnosed malignancy and the third leading cause of all cancer death worldwide. The undifferentiated macrophages M0 can be induced into polarized M1 and M2 to exert opposite effects in tumor microenvironment. However, the prognostic value of macrophages M0 phenotype remains obscure in LIHC. The transcriptome data of LIHC was obtained from TCGA database and ICGC database. 365 LIHC samples from TCGA database and 231 LIHC samples from ICGC database were finally included. Macrophages M0-related genes (MRGs) were screened by Pearson correlation analysis and univariate Cox regression analysis based on the infiltration level of Macrophages M0. LASSO regression analysis was employed to construct a prognostic signature based on MRGs, and risk scores were accordingly calculated. Then we investigated the MRGs-based prognostic signature with respects to prognostic value, clinical significance, strengthened pathways, immune infiltration, gene mutation and drug sensitivity. Furthermore, the expression pattern of MRGs in the tumor microenvironment were also detected in LIHC. A ten-MRG signature was developed and clarified as independent prognostic predictors in LIHC. The risk score-based nomogram showed favorable capability in survival prediction. Several substance metabolism activities like fatty acid/amino acid metabolism were strengthened in low-risk group. Low risk group was deciphered to harbor TTN mutation-driven tumorigenesis, while TP53 mutation was dominant in high-risk group. We also ascertained that the infiltration levels of immune cells and expressions of immune checkpoints are significantly influenced by the risk score. Besides, we implied that patients in low-risk group may be more sensitive to several anti-cancer drugs. What's more important, single-cell analysis verified the expression of MRGs in the tumor microenvironment of LIHC. Multidimensional evaluations verified the clinical utility of the macrophages M0-related gene signature to predict prognosis, assist risk decision and guide treatment strategy for patients with LIHC.
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