Construction of a Novel Clinical Stage-Related Gene Signature for Predicting Outcome and Immune Response in Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors. However, there were no studies to create a clinical stage-related gene signature for HCC patients. Differentially expressed genes (DEGs) associated with clinical stage of HCC were analyzed based on TCGA datasets. Functional enrichment analysis was carried out by the use of stage-related DEGs. Then, the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox regression were performed to reduce the overfit and the number of genes for further analysis. Next, survival and ROC assays were carried out to demonstrate the model using TCGA. Functional analysis and immune microenvironment analysis related to stage-related DEGs were performed. Reverse transcriptase polymerase chain reaction (RT-PCR) and Cell Counting Kit-8 (CCK-8) assays were applied to examine the expression and function of PNCK in HCC. In this research, there were 21 DEGs between HCC specimens with stage (I-II) and HCC specimens with stage (III-IV), including 20 increased genes and 1 decreased genes. A novel seven-gene signature (including PITX2, PNCK, GLIS1, SCNN1G, MMP1, ZNF488, and SHISA9) was created for the prediction of outcomes of HCC patients. The ROC curves confirmed the prognostic value of the new model. Cox assays demonstrated that the seven-gene signature can independently forecast overall survival. The immune analysis revealed that patients with low risk score exhibited more immune activities. Moreover, we confirmed that PNCK expressions were distinctly increased in HCC, and its silence suppressed the proliferation of HCC cells. Overall, our research offered a robust and reliable gene signature which displayed an important value in the prediction of overall survival of HCC patients and might deliver more effective personalized therapies.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

“…The rest of the BP categories were also enriched with common DEDGs, which also coincided with existing studies, like cellular response to cadmium ion 42,57,70 , cellular response to copper ion 36,70 , and steroid catabolic process 42 . The top 5 GO terms were significantly enriched with common DEDGS, which were also consistent with previous results, such as extra cellular region 35,37,38,57 , extracellular exosome 37,38 , extracellular space 37,38,57 , high-density lipoprotein particle 57 , and apical plasma membrane 53 . In the case of MFs, common DEDGs were also enriched with top five GO terms.…”

“…Top five items were selected. et al30 , 13 from Jiang et al31 , 20 from Zhang et al32 , 12 from Wu et al33 , 5 from Nguyen et al34 , 15 from Zhou et al35 , 6 from Yu et al36 , 10 from Kakar et al37 , 10 from Ji et al38 , 11 from Chen et al39 , 10 from Qiang et al40 , 10 from Wang et al41 , 10 from Zhang et al42 , 14 from Kim et al43 , 10 from Zhang et al44 , 14 from Sha et al45 , 10 from Chen et al46 , 4 from He et al47 , 10 from Zhang et al48 , 4 from Hu et al49 , 9 from Zhang et al50 , 15 from Li et al51 , 5 from Cao et al52 , 7 from Yang et al53 , 5 from Wang et al54 , 9 from Jiang et al55 , 16 from Li et al56 , 15 from Xing et al…”