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

Zhao, Qian; Zhang, Yan; Shao, Shichun; Sun, Yeqing; Lin, Zhengkui

doi:10.7717/peerj.10594

Cited by 10 publications

(4 citation statements)

References 59 publications

(49 reference statements)

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“…Drawing on the method of gene co-expression network construction [26,27], our distance between two genes was defined based on the Pearson correlation coefficient (Formula 1): where, g 1i denotes the expression level of gene 1 in the i th sample, g 1 denotes the mean expression level of gene 1 in all samples, and m denotes the number of samples. We ran the PAM with a range of different values (from 8 to 20) of cluster number k , and the k that optimized the Silhouette Score (Formula 2 ~ 3) was determined.…”

Section: Partitioning Around Medoidsmentioning

confidence: 99%

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

2023

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Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

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Section: Partitioning Around Medoidsmentioning

confidence: 99%

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

2023

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“…[10,11] Moreover, as a hub gene or candidate gene, NCAPD2 is involved in the invasion process of a variety of cancers and is a potential therapeutic target in hepatocellular carcinoma, gastric cancer, and ovarian cancer. [12][13][14] However, at present, the clinical application and functional mechanisms of NCAPD2 in LUAD remain unclear.…”

Section: Introductionmentioning

confidence: 99%

NCAPD2 is a novel marker for the poor prognosis of lung adenocarcinoma and is associated with immune infiltration and tumor mutational burden

Zheng

et al. 2023

Medicine

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Lung adenocarcinoma (LUAD) is at present the most prevalent subtype of lung cancer worldwide. Non-SMC condensin I complex subunit D2 (NCAPD2) is one of the 3 non-SMC subunits in condensin I. Previous studies have confirmed that NCAPD2 plays a critical role in chromosome cohesion and segregation. NCAPD2 may be involved in tumorigenesis and progression by participating in abnormal cell cycle division, but the prognostic value of NCAPD2 in LUAD remains unclear. We investigated differences in the expression levels of NCAPD2 and determined their association with clinical features, as well as their diagnostic and prognostic value using the cancer genome atlas database. The function of NCAPD2 was analyzed using gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis. CIBERSORT, single-sample gene set enrichment analysis, and ESTIMATE were used to analyze the immune microenvironment of tumor patients. Tumor mutational burden (TMB) and immune checkpoints were analyzed, while hub genes were identified using weighted gene coexpression network analysis and were used to construct prognostic models. Subsequently, the competing endogenous RNAs network of NCAPD2 in LUAD was explored. Finally, we performed qPCR to verify differences in NCAPD2 expression between the tumor and normal tissues. The expression of NCAPD2 in LUAD was significantly upregulated compared with normal lung tissues. NCAPD2 has been linked to the T stage, N stage, and tumor stage. The elevated expression of NCAPD2 in LUAD can predict a poor prognosis. Functional enrichment analysis indicated that the main function of NCAPD2 was in cell cycle regulation. Moreover, NCAPD2 was also associated with immune cell infiltration and TMB. NCAPD2 is a novel prognostic marker in LUAD and is associated with immune infiltration and TMB.Abbreviations: ceRNA = competing endogenous RNA, DEGs = differential expression genes, GO = gene ontology, GSEA = gene set enrichment analysis, KEGG = Kyoto encyclopedia of genes and genomes, LUAD = lung adenocarcinoma, NCAPD2 = Non-SMC condensin I complex subunit D2, PPI = protein-protein interaction, ssGSEA = single-sample gene set enrichment analysis, TCGA = the cancer genome atlas, TMB = tumor mutational burden, WGCNA = weighted gene coexpression network analysis.

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“…Zhang et al screened the genes and pathways associated with HCC development and prevalence through a series of bioinformatics observations, such as DEG recognition, functional enrichment analysis, PPI network and module analysis, and weighted network correlation analysis [ 24 ]. Zhou et al identified HCC critical genes and microRNAs through raw data processing by using Gene Ontology (GO), GEO2R, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment processing and PPI network creation [ 33 , 34 ]. Li et al identified 89 out of 320 consistent differentially expressed genes in HCC patients.…”

Section: Introductionmentioning

confidence: 99%

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

Kakar

Mehboob

Akram

et al. 2022

BioMed Research International

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Objective. The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Methods. GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression. Results. We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

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

Cited by 10 publications

References 59 publications

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

NCAPD2 is a novel marker for the poor prognosis of lung adenocarcinoma and is associated with immune infiltration and tumor mutational burden

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

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