Abstract
Immune-related genes (IRGs) have been identified as critical drivers of the initiation and progression of hepatocellular carcinoma (HCC). This study is aimed at constructing an IRG signature for HCC and validating its prognostic value in clinical application. The prognostic signature was developed by integrating multiple IRG expression data sets from TCGA and GEO databases. The IRGs were then combined with clinical features to validate the robustness of the prognostic signature through bioinformatics tools. A total of 1039 IRGs were identified in the 657 HCC samples. Subsequently, the IRGs were subjected to univariate Cox regression and LASSO Cox regression analyses in the training set to construct an IRG signature comprising nine immune-related gene pairs (IRGPs). Functional analyses revealed that the nine IRGPs were associated with tumor immune mechanisms, including cell proliferation, cell-mediated immunity, and tumorigenesis signal pathway. Concerning the overall survival rate, the IRGPs distinctly grouped the HCC samples into the high- and low-risk groups. Also, we found that the risk score based on nine IRGPs was related to clinical and pathologic factors and remained a valid independent prognostic signature after adjusting for tumor TNM, grade, and grade in multivariate Cox regression analyses. The prognostic value of the nine IRGPs was further validated by forest and nomogram plots, which revealed that it was superior to the tumor TNM, grade, and stage. Our findings suggest that the nine-IRGP signature can be effective in determining the disease outcomes of HCC patients.
Highlights
Hepatocellular carcinoma (HCC) is a common cancer of the liver and one of the leading causes of cancer-associated mortality worldwide [1]
Because T, stage, and grade were significantly associated with the prognosis of HCC patients based on the nineIRGP signature, we investigated the overall survival (OS) for the T, stage, and grade
We constructed a nine-immunerelated gene pairs (IRGPs) signature for HCC patients and validated its accuracy and effectiveness in an independent data set through multidimensional bioinformatics methods
Summary
Hepatocellular carcinoma (HCC) is a common cancer of the liver and one of the leading causes of cancer-associated mortality worldwide [1]. Given the lack of specific symptoms in the early stage of the disease, patients are often diagnosed when the disease has advanced to middle and late stages This leads to a low 5-year survival rate of 40%~50% if patients do not receive radical treatment. It is urgent to find a novel clinical signature that is closely associated with the occurrence and development of HCC for better prediction of the recurrence, metastasis, and prognosis of patients. This will ensure early diagnosis timely and treatment of the condition
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