Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Song, Hongjiang; Ding, Na; Li, Shang; Liao, Jianlong; Xie, Aimin; Yu, Youtao; Zhang, Chunlong; Ni, Caifang

doi:10.3389/fgene.2020.00895

Cited by 10 publications

(11 citation statements)

References 68 publications

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“…In accordance with our findings, previous studies have also identified hub genes that participate in HCC ( Hua et al, 2020 ; Song et al, 2020 ). Shengni et al constructed a PPI network based on 176 DEGs.…”

Section: Discussionsupporting

confidence: 93%

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Zhao

Zhang

Shao

et al. 2021

PeerJ

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Background Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. Methods A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). Results Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). Conclusion MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

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Section: Discussionsupporting

confidence: 93%

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Zhao

Zhang

Shao

et al. 2021

PeerJ

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“…These ROC curves described the diagnostic efficiency of the mRNA levels of 10 key genes for HCC and normal tissues. This figure was drawn with IBM SPSS Statistics 25 . …”

Section: Resultsmentioning

confidence: 99%

“…Previous studies focused on the specific genes in the initiation and progression of HCC 22 – 24 . Although some bioinformatics research on HCC has been reported 9 , 25 , but the precise molecular mechanisms underlying HCC progression was not clear. Therefore, the GCNA-Kpca algorithm was used to analyze the gene expression profiles of HCC and more accurately identify the gene modules and key genes in HCC, so as to further understand the pathogenesis of HCC.…”

Section: Discussionmentioning

confidence: 99%

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

Zhang

Lin

et al. 2021

Sci Rep

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To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.

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“…TOP2A encoded for DNA topoisomerase II protein, which controls DNA topology during DNA replication [35]. Recently, the TOP2A gene was reported as a hub gene in HCC [36] with an inference value of 143.13 from the gene-disease association dataset in the Comparative Toxicogenomics Database, and it is currently being considered as a potential drug target for the treatment of HCC [37][38][39]. TOP2A has been shown to directly interact with P53, a well-known tumor suppressor protein [40].…”

Section: Discussionmentioning

confidence: 99%

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Nguyen

Ngoc

Nguyễn-Thanh

et al. 2021

Biology

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Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein–protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2, NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

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Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Cited by 10 publications

References 68 publications

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

Identification of hub genes and biological pathways in hepatocellular carcinoma by integrated bioinformatics analysis

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

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