Identification of the hub and prognostic genes in liver hepatocellular carcinoma via bioinformatics analysis

Abstract

Hepatocellular carcinoma (HCC) is a common malignancy. However, the molecular mechanisms of the progression and prognosis of HCC remain unclear. In the current study, we merged three Gene Expression Omnibus (GEO) datasets and combined them with The Cancer Genome Atlas (TCGA) dataset to screen differentially expressed genes. Furthermore, protein‒protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to identify key gene modules in the progression of HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that the terms were associated with the cell cycle and DNA replication. Then, four hub genes were identified (AURKA, CCNB1, DLGAP5, and NCAPG) and validated via the expression of proteins and transcripts using online databases. In addition, we established a prognostic model using univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression. Eight genes were identified as prognostic genes, and four genes (FLVCR1, HMMR, NEB, and UBE2S) were detrimental gens. The areas under the curves (AUCs) at 1, 3 and 5 years were 0.622, 0.69, and 0.684 in the test dataset, respectively. The effective of prognostic model was also validated using International Cancer Genome Consortium (ICGC) dataset. Moreover, we performed multivariate independent prognostic analysis using multivariate Cox proportional hazards regression. The results showed that the risk score was an independent risk factor. Finally, we found that all prognostic genes had a strong positive correlation with immune infiltration. In conclusion, this study identified the key hub genes in the development and progression of HCC and prognostic genes in the prognosis of HCC, which was significant for the future diagnosis and prognosis of HCC.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

“…Gao S. et al developed a prognostic model utilizing 10 genes, with corresponding AUC values of 0.67 and 0.66 for 3-and 5-year time points, respectively 43 . Moreover, Gao Q. et al built a prognostic model with 8 genes, achieving AUC values of 0.645 and 0.630 at 3and 5-year, respectively 44 . According to multivariate Cox repression analyses results, a nomogram model with c-index value of 0.748 was further established to predict 1-, 3-, and 5-year survival of HCC patients (Fig.…”

Identification of oncogenes and tumor-suppressor genes with hepatocellular carcinoma: A comprehensive analysis based on TCGA and GEO datasets

“…There is an urgent need to find new targets for the diagnosis and treatment of liver cancer in the direction of a breakthrough in the current treatment limitations. Some previous studies used TCGA and GEO databases to screen oncogenes and identify prognostic or immune-related genes ( Chen et al, 2020 ; Gao et al, 2021 ; Yan et al, 2021 ; Shen et al, 2022a ; Shen et al, 2022b ; Ding et al, 2022 ; Gao et al, 2022 ; Long et al, 2022 ; Yang et al, 2022 ). However, in this study, four GSE datasets, GSE36376, GSE102079, GSE54236, and GSE45267, were summarized for analysis, hoping to find the mechanism of cancer-promoting and tumor-suppressor factors in liver cancer from the perspective of improving the accuracy of the research results.…”

“…Currently, public data and bioinformatic analysis methods have provided us with invaluable resources to find HCC-related oncogenes and tumor-suppressor genes. Some studies searched cancer-related target genes through public databases, whether prognostic oncogene identification ( Gao et al, 2021 ; Yan et al, 2021 ; Shen et al, 2022a ; Gao et al, 2022 ), single-gene research studies ( Ding et al, 2022 ; Yang et al, 2022 ), or immune-related oncogenes ( Chen et al, 2020 ; Shen et al, 2022b ; Long et al, 2022 ), all of which provided a strong basis for targeted therapy and precise treatment of liver cancer. However, many studies only consider the differential expression of oncogenes in tumors, ignoring the important role and potential association of tumor-suppressor genes.…”

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

“…The top 10 genes filtered by each method were selected, and those that overlapped between these methods were identified as core genes. [ 17 , 18 ]. MCODE is another plugin within Cytoscape software for the purpose of module analysis and hub gene identification [ 18 ].…”