Hub Genes Of Differentially Expressed Genes Research Articles

Expression and clinical significance of PD-1 in UCEC and its Impact on tumor.

Programmed death 1 (PD-1) plays an important role in the immune escape, occurrence and development of tumors by inhibiting the function of immune cells. However, its prognostic value in uterine corpus endometrial carcinoma (UCEC) and its impact on the tumor microenvironment remain to be further explored. Transcriptional datasets were retrieved from the GEPIA, TIMER and TCGA databases. "edgeR" package was used for the identification of differentially expressed genes (DEGs) between two groups of patients (PD1-high and PD1-low group). Gene set enrichment analysis (GSEA) was performed to identify underlying pathways between betweenPD1-high and PD1-low groups functioning in UCEC. Gene Correlation Analysis was used to further confirm the associations of PD1 expression with T-cell-related genes. Cytoscape software was used to identify hub genes in DEGs. Kaplan-Meier analysis was performed to validate the prognostic value of hub genes. Mutational characteristics of UCEC patients according to PD1 levels were depicted and analyzed. We found that the transcriptional expression of the PD1 gene in UCEC tumor tissues markedly increased in cohorts from the GEPIA and TCGA databases. PD1 expression was negatively correlated with gene signatures associated with the T-cell receptor signaling pathway and primary immunodeficiency. GESA confirmed that PD1 expression was negatively correlated with gene signatures associated with the T-cell receptor signaling pathway. T-cell receptor complex-related genes, ZAP70, TRAC, CD3D, CD3E, CD8A, TRBC2, TRBV28 and CD247, showed significant positive associations with PD1 expression. The results of the Kaplan-Meier OS analysis indicated that PD1, TIGIT, FASLG, ICOS and TNFRSF9 are the protective factor for patients with UCEC. The top 5 genes of mutations in the low expression group, included PTEN (56%), PIK3CA (43%), TP53 (41%), TTN (39%), and ARID1A (37%). The genes with a higher proportion of mutations in the PD1-high group are PTEN (67%), TTN (62%), PIK3CA (53%), ARID1A (52%), and MUC16 (12%). The prognosis of UCEC patients with PD1 overexpression phenotype is worse than that of the PD1 low group, which is due to the involvement of the PD1 gene in the T-cell receptor signaling pathway. This study provides a further theoretical basis and reference for targeted therapy against PD1.

EGR2 is a hub-gene in myocardial infarction and aggravates inflammation and apoptosis in hypoxia-induced cardiomyocytes

BackgroundMyocardial infarction (MI) is characterized by coronary artery occlusion, ischemia and hypoxia of myocardial cells, leading to irreversible myocardial damage. Therefore, it is urgent to explore the potential mechanism of myocardial injury during the MI process to develop effective therapies for myocardial cell rescue.MethodsWe downloaded the GSE71906 dataset from GEO DataSets, and the R software was used to identify the differentially expressed genes (DEGs) in mouse heart tissues of MI and sham controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed to understand the significantly activated signaling pathways in MI. Protein–protein interaction (PPI) network was constructed to highlight the hub genes in DEGs. The Western Blot, qRT-PCR and TUNEL staining were used to explore the function of hub gene in hypoxia-induced cardiomyocytes in vitro.ResultsA total of 235 DEGs were identified in GSE71906 dataset. Functional enrichment analysis revealed that the upregulated genes were primarily associated with the inflammatory response and apoptosis. 20 hub genes were identified in PPI network, and the early growth response 2 (EGR2) was highlighted. In vitro. We confirmed the EGR2 was upregulated induced by hypoxia and revealed the upregulated EGR2 aggravates pro-inflammation and pro-apoptotic genes expression. In addition, EGR2 knockout mitigates hypoxia-induced inflammation and apoptosis in cardiomyocytes.ConclusionThe present study identified the EGR2 was a hub gene in myocardial damage during MI process, the excessive EGR2 aggravates hypoxia-induced myocardial damage by accelerating inflammation and apoptosis in vitro. Therefore, targeting EGR2 offers a potential pharmacological strategy for myocardial cell rescue in MI.

Identification of key regulators associated with colon cancer prognosis and pathogenesis.

Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BackgroundSteroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH.MethodsThe GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve.ResultsNine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14.ConclusionsOur study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Melanoma

Melanoma is the deadliest skin tumor and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma. We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553, and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples. Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL), and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL, and EGFR were identified in the TCGA database and melanoma tissues. The results suggested that FLG, DSG1, DSG3, IVL, and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma. These hub genes might well have clinical significance as diagnostic markers.

Identification of Potential Gene Interactions in Heart Failure Caused by Idiopathic Dilated Cardiomyopathy.

BackgroundMany heart failure (HF) cases are caused by idiopathic dilated cardiomyopathy (iDCM). This study explored the mechanisms of the development and progression of HF caused by iDCM.Material/MethodsThe gene expression profiles of 102 samples were downloaded from the GEO database (GSE5406). Differentially expressed genes (DEGs) were identified through GO analysis and a KEGG pathway analysis, respectively. A protein–protein interaction (PPI) network was constructed and analyzed to screen potential regulatory proteins. In addition, MCODE and a cytoHubba plugin were used to identify the module and hub genes of DEGs. Finally, transcription factors (TFs) were predicted using PASTAA. We did not perform whole-exome sequencing (WES) for detecting mitochondrial DNA (mtDNA).ResultsA total of 197 DEGs were screened, and 3 modules, and 4 upregulated and 11 downregulated hub genes were screened. The GO analysis focused on the terms and 12 KEGG pathways were enriched. The FOS, TIMP1, and SERPINE1 hub genes, as well as some key TFs, demonstrated important roles in the progression of HF caused by iDCM. CEBPD, CEBOB, CDC37L1, and SRGN may be new targets for HF in iDCM patients.ConclusionsThe identified DEGs and their enriched pathways provide references for exploring the mechanisms of the development and progression of HF patients with iDCM. Moreover, modules, hub genes, and TFs may be useful in the treatment and diagnosis of HF patients with iDCM. However, mtDNA was not investigated.