Identification of Hub Genes Associated With Development and Microenvironment of Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis and Differential Gene Expression Analysis

Bai, Qingquan; Liu, Haoling; Guo, Hongyu; Lin, Han Chieh; Song, Xuan; Jin, Yongfeng; Liu, Yao; Guo, Hongrui; Liang, Shuhang; Song, Ran; Wang, Jiabei; Qu, Zhibo; Guo, Huaxin; Jiang, Hongchi; Liu, Lianxin; Yang, Haiyan

doi:10.3389/fgene.2020.615308

Cited by 22 publications

(16 citation statements)

References 33 publications

(39 reference statements)

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“…CSN1S1 is an effective molecular marker for litter size in goat breeding [ 51 ]. STAP1 [ 52 ] and DUSP1 [ 53 , 54 ] are significantly associated with anti-inflammatory responses and immune infiltration in human autoimmune diseases. We also identified nine candidate genes in the WECPs, of which six ( CDH13 , PROX1 , EGR2 , CCNT2 , SETD2 , and MYL3 ) were related to muscular development, and three ( FLVCR1 , FGF18 , and FGF20 ) were involved in skeletal morphogenesis.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Jiang

Liu

et al. 2022

BMC Genomics

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Background Runs of homozygosity (ROH) are continuous homozygous regions typically located in the DNA sequence of diploid organisms. Identifications of ROH that lead to reduced performance can provide valuable insight into the genetic architecture of complex traits. Here, we systematically investigated the population genetic structure of five Anhui indigenous pig breeds (AHIPs), and compared them to those of five Western commercial pig breeds (WECPs). Furthermore, we examined the occurrence and distribution of ROHs in the five AHIPs and estimated the inbreeding coefficients based on the ROHs (FROH) and homozygosity (FHOM). Finally, we identified genomic regions with high frequencies of ROHs and annotated candidate genes contained therein. Results The WECPs and AHIPs were clearly differentiated into two separate clades consistent with their geographical origins, as revealed by the population structure and principal component analysis. We identified 13,530 ROHs across all individuals, of which 4,555 and 8,975 ROHs were unique to AHIPs and WECPs, respectively. Most ROHs identified in our study were short (< 10 Mb) or medium (10–20 Mb) in length. WECPs had significantly higher numbers of short ROHs, and AHIPs generally had longer ROHs. FROH values were significantly lower in AHIPs than in WECPs, indicating that breed improvement and conservation programmes were successful in AHIPs. On average, FROH and FHOM values were highly correlated (0.952–0.991) in AHIPs and WECPs. A total of 27 regions had a high frequency of ROHs and contained 17 key candidate genes associated with economically important traits in pigs. Among these, nine candidate genes (CCNT2, EGR2, MYL3, CDH13, PROX1, FLVCR1, SETD2, FGF18, and FGF20) found in WECPs were related to muscular and skeletal development, whereas eight candidate genes (CSN1S1, SULT1E1, TJP1, ZNF366, LIPC, MCEE, STAP1, and DUSP) found in AHIPs were associated with health, reproduction, and fatness traits. Conclusion Our findings provide a useful reference for the selection and assortative mating of pig breeds, laying the groundwork for future research on the population genetic structures of AHIPs, ultimately helping protect these local varieties.

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Section: Discussionmentioning

confidence: 99%

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Jiang

Liu

et al. 2022

BMC Genomics

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“… 126 , 127 Subsequently, the hub genes were identified by cytoHubba using four methods such as DEGREE, maximum clique centrality (MCC), maximum neighborhood component (MNC), and density of maximum neighborhood component (DMNC). 128 − 130 …”

Section: Materials and Methodologymentioning

confidence: 99%

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Nayak

2022

ACS Omega

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Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein–protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM–receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial–mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

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“…The minimum interaction score was set as greater than 0.4, and unconnected nodes in the network were removed. Further, key nodes within the PPI network were selected as hub genes using cytohubba plugin in Cytoscape software [ 15 ]. Hub genes were selected mainly based on their Maximal Clique Centrality (MCC) algorithm, which indicates essentiality of nodes in biological network [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach

et al. 2022

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Objective Polycystic ovary syndrome (PCOS) is a common endocrine disorder with high incidence. Recently it has been implicated as a significant risk factor for endometrial cancer (EC). Our study aims to detect shared gene signatures and biological mechanism between PCOS and EC by bioinformatics analysis. Methods Bioinformatics analysis based on GEO database consisted of data integration, network construction and functional enrichment analysis was applied. In addition, the pharmacological methodology and molecular docking was also performed. Results Totally 10 hub common genes, MRPL16, MRPL22, MRPS11, RPL26L1, ESR1, JUN, UBE2I, MRPL17, RPL37A, GTF2H3, were considered as shared gene signatures for EC and PCOS. The GO and KEGG pathway analysis of these hub genes showed that “mitochondrial translational elongation”, “ribosomal subunit”, “structural constituent of ribosome” and “ribosome” were highly correlated. Besides, associated transcription factors (TFs) and miRNAs network were constructed. We identified candidate drug molecules including fenofibrate, cinnarizine, propanil, fenthion, clindamycin, chloramphenicol, demeclocycline, hydrochloride, azacitidine, chrysene and artenimol according to these hub genes. Molecular docking analysis verified a good binding interaction of fenofibrate against available targets (JUN, ESR1, UBE2I). Conclusion Gene signatures and regulatory biological pathways were identified through bioinformatics analysis. Moreover, the molecular mechanisms of these signatures were explored and potential drug molecules associated with PCOS and EC were screened out.

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Identification of Hub Genes Associated With Development and Microenvironment of Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis and Differential Gene Expression Analysis

Cited by 22 publications

References 33 publications

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Genome-wide detection of genetic structure and runs of homozygosity analysis in Anhui indigenous and Western commercial pig breeds using PorcineSNP80k data

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach

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