Identification Of Core Genes Research Articles

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific mechanisms of IDD remain unclear. This study aims to identify key genes and pathways associated with IDD using bioinformatics and machine learning algorithms. Gene expression profiles, including those from 35 LDH patients and 43 healthy volunteers, were downloaded from the GEO database (GSE124272, GSE150408, GSE23130, GSE153761). After merging four microarray datasets, differentially expressed genes (DEGs) were selected for GO and KEGG pathway enrichment analysis. Weighted Gene Co-expression Network Analysis (WGCNA) was then applied to the merged dataset to identify relevant modules and intersect with DEGs to discover candidate genes with diagnostic value. A LASSO model was established to select appropriate genes, and ROC curves were drawn to elucidate the diagnostic value of genetic markers. A Protein-Protein Interaction (PPI) network was constructed and visualized to determine central genes, followed by external validation using qRT-PCR. Differential analysis of the preprocessed dataset identified 244 genes, including 183 upregulated and 61 downregulated genes. WGCNA analysis revealed the most relevant module intersecting with DEGs, yielding 9 candidate genes. The lasso-cox method was used for regression analysis, ultimately identifying 6 genes: ASPH, CDC42EP3, FOSL2, IL1R1, NFKBIZ, TCF7L2. A Protein-Protein Interaction (PPI) network created with GENEMANIA identified IL1R1 and TCF7L2 as central genes. Our study shows that IL1R1 and TCF7L2 are the core genes of IDD, offering new insights into the pathogenesis and therapeutic development of IDD.

Co-expression network analysis and identification of core genes in the interaction between wheat and Puccinia striiformis f. sp. tritici.

The epidemic of stripe rust, caused by the pathogenPuccinia striiformisf. sp.tritici(Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.

Identification of core genes, critical signaling pathways, and potential drugs for countering BPA-induced hippocampal neurotoxicity in male mice

Although the neurotoxicity of the common chemical bisphenol A (BPA) to the mouse hippocampus has been often reported, the mechanism underlying BPA-induced depression-like behavior in mice remains unclear. We evaluated BPA's role in inducing depressive-like behavior by exposing male mice to different BPA concentrations (0, 0.01, 0.1, and 1 μg/mL) and using the forced swimming test (FST) and tail suspension test (TST). We aimed to identify critical gene and anti-BPA-neurotoxicity compounds using RNA sequencing combined with bioinformatics analysis. Our results showed that 1 μg/mL BPA exposure increased mouse immobility during the FST and TST. Based on BPA-induced hippocampal transcriptome changes, we identified NADH: ubiquinone oxidoreductase subunit AB1 (Ndufab1) as a critical and potential therapeutic target gene, and Ndufab1 mRNA and protein levels were downregulated in the BPA-exposed groups. Furthermore, molecular docking identified phenelzine as a compound that could counteract BPA-related neurotoxicity. Conclusively, our analyses confirmed that BPA triggers depressive behavior in male mice by downregulating Ndufab1 expression and suggested that phenelzine might reduce BPA-induced neurotoxicity.

Identification of potential biomarkers associated with meat tenderness in Hanwoo (Korean cattle): An expression quantitative trait loci analysis.

Meat tenderness is considered the most important trait contributing to beef quality, level of consumer satisfaction, willingness to pay premium prices and industry profit. Genomic selection method would be helpful for genetic improvement of traits with low heritability and that are difficult to measure. The identification of core genes can aid genomic selection for complex traits with low heritability that are difficult to measure. We performed statistical analysis of associations between longissimus dorsi muscle tenderness and gene expression in 20 Hanwoo cattle, using Warner-Bratzler shear force and RNAseq data, respectively. We found a total of 166 core genes, from which six (ASAP1, CAPN5, ELN, SUMF2, TTC8 and MGAT4A) were regulated by 16 cis-expression quantitative trait loci (eQTL) SNPs. Notably, we found that a cis-eQTL SNP of the ELN gene contained an MFZ-1 binding site in its putative promoter region. These findings provide useful information for genomic prediction of beef tenderness in Hanwoo cattle.

Identification of core genes in prefrontal cortex and hippocampus of Alzheimer's disease based on mRNA-miRNA network.

Alzheimer's disease (AD) is a neurodegenerative disorder with cognitive impairment and abnormal mental behaviour. There is currently no effective cure. The development of early diagnostic markers and the mining of potential therapeutic targets are one of the important strategies. This study aimed to explore potential biomarkers or therapeutic targets related to AD in the hippocampus and prefrontal cortex, two brain regions highly related to AD. Differentially expressed genes and miRNAs between AD patients and healthy controls were obtained from the Gene Expression Omnibus database. The mRNA-miRNA network was constructed and key genes involved in AD were screened out by protein-protein interaction analysis, and were subsequently verified by independent datasets and qPCR in an AD mouse model. Our findings showed that six hub genes including CALN1, TRPM7, ATR, SOCS3, MOB3A and OGDH were believed to be involved in the pathogenesis of AD. Western blot analysis further determined that CALN1, ATR and OGDH were the possible biomarkers and therapeutic targets for AD. In addition, 6 possible miRNAs biomarkers have also been verified by qPCR on AD animal models. Our findings may benefit clinical diagnosis and early prevention of AD.

Identification of core genes and pathways between geriatric multimorbidity and renal insufficiency: potential therapeutic agents discovered using bioinformatics analysis

BackgroundGeriatric people are prone to suffer from multiple chronic diseases, which can directly or indirectly affect renal function. Through bioinformatics analysis, this study aimed to identify key genes and pathways associated with renal insufficiency in patients with geriatric multimorbidity and explore potential drugs against renal insufficiency.MethodsThe text mining tool Pubmed2Ensembl was used to detect genes associated with the keywords including "Geriatric", "Multimorbidity" and "Renal insufficiency". The GeneCodis program was used to specify Gene Ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape. Module analysis was performed using CytoHubba and Molecular Complex Detection (MCODE) plugins. GO and KEGG analysis of gene modules was performed using the Database for Annotation, Visualization and Integrated Discover (DAVID) platform database. Genes clustered in salient modules were selected as core genes. Then, the functions and pathways of core genes were visualized using ClueGO and CluePedia. Finally, the drug-gene interaction database was used to explore drug-gene interactions of the core genes to identify drug candidates for renal insufficiency in patients with geriatric multimorbidity.ResultsThrough text mining, 351 genes associated with "Geriatric", "Multimorbidity" and "Renal insufficiency" were identified. A PPI network consisting of 216 nodes and 1087 edges was constructed and CytoHubba was used to sequence the genes. Five gene modules were obtained by MCODE analysis. The 26 genes clustered in module1 were selected as core candidate genes primarily associated with renal insufficiency in patients with geriatric multimorbidity. The HIF-1, PI3K-Akt, MAPK, Rap1, and FoxO signaling pathways were enriched. We found that 21 of the 26 selected genes could be targeted by 34 existing drugs.ConclusionThis study indicated that CST3, SERPINA1, FN1, PF4, IGF1, KNG1, IL6, VEGFA, ALB, TIMP1, TGFB1, HGF, SERPINE1, APOA1, APOB, FGF23, EGF, APOE, VWF, TF, CP, GAS6, APP, IGFBP3, P4HB, and SPP1 were key genes potentially involved with renal insufficiency in patients with geriatric multimorbidity. In addition, 34 drugs were identified as potential agents for the treatment and management of renal insufficiency.

Identification of core genes as potential biomarkers for predicting progression and prognosis in glioblastoma.

Background: Glioblastoma is a common malignant neuroepithelial neoplasm with poor clinical outcomes and limited treatment options. It is extremely important to search and confirm diverse hub genes that are effective in the advance and prediction of glioblastoma. Methods: We analyzed GSE50161, GSE4290, and GSE68848, the three microarray datasets retrieved from the GEO database. GO function and KEGG pathway enrichment analyses for differentially expressed genes (DEGs) were performed using DAVID. The PPI network of the DEGs was analyzed using the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. Hub genes were identified through the PPI network and a robust rank aggregation method. The Cancer Genome Atlas (TCGA) and the Oncomine database were used to validate the hub genes. In addition, a survival curve analysis was conducted to verify the correlation between the expression of hub genes and patient prognosis. Human glioblastoma cells and normal cells were collected, and then RT-PCR, Western blot, and immunofluorescence were conducted to validate the expression of the NDC80 gene. A cell proliferation assay was used to detect the proliferation of glioma cells. The effects of NDC80 expression on migration and invasion of GBM cell lines were evaluated by conducting scratch and transwell assays. Results: A total of 716 DEGs were common to all three microarray datasets, which included 188 upregulated DEGs and 528 downregulated DEGs. Furthermore, we found that among the common DEGs, 10 hub genes showed a high degree of connectivity. The expression of the 10 hub genes in TCGA and the Oncomine database was significantly overexpressed in glioblastoma compared with normal genes. Additionally, the survival analysis showed that the patients with low expression of six genes (BIR5C, CDC20, NDC80, CDK1, TOP2A, and MELK) had a significantly favorable prognosis (p < 0.01). We discovered that NDC80, which has been shown to be important in other cancers, also has an important role in malignant gliomas. The RT-PCR, Western blot, and immunofluorescence results showed that the expression level of NDC80 was significantly higher in human glioblastoma cells than in normal cells. Moreover, we identified that NDC80 increased the proliferation and invasion abilities of human glioblastoma cells. Conclusion: The six genes identified here may be utilized to form a panel of disease progression and predictive biomarkers of glioblastoma for clinical purposes. NDC80, one of the six genes, was discovered to have a potentially important role in GBM, a finding that needs to be further studied.

Identification of Core Genes and Prognostic Models of Laryngeal Cancer by Autophagy Related Biomarkers

Background: The aims of our article were to identify the core genes of the autophagy-related genes (ARGs) which abnormally expressed in laryngeal cancer (LC) and constructed a risk prognostic models with these genes. Methods: In this study, we identified genes with abnormally expressed in LC, and they were mainly involved in some cancer-related gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG). Statistical analysis were conducted to identify the powerful independent prognostic factors associated with clinical factors and survival. Results: A total of 35 DEGs were identified in our research. The risk prediction model was constructed with three potential prognostic genes (VEGFA, SPNS1 and CCL2) of autophagy by lasso regression analysis that can successfully predict the prognosis in LC. We applied ROC curve to evaluate the effectiveness of the risk prognostic model, and found that AUC was 0.693 below the curve. Risk prediction model was only related to survival status (P &lt; 0.01), and was not related to clinicopathological factors. Furthermore, the genes (VEGFA and CCL2) were considered as core genes not only because they were the highly connected genes but also they were the composed genes of risk prognostic model. Conclusions: Taken together, ARGs were considered as important roles in the progression of LC and the prognostic model can help to identification of new targets to guide the diagnosis and therapy.

High-quality pan-genome of Escherichia coli generated by excluding confounding and highly similar strains reveals an association between unique gene clusters and genomic islands.

The pan-genome analysis of bacteria provides detailed insight into the diversity and evolution of a bacterial population. However, the genomes involved in the pan-genome analysis should be checked carefully, as the inclusion of confounding strains would have unfavorable effects on the identification of core genes, and the highly similar strains could bias the results of the pan-genome state (open versus closed). In this study, we found that the inclusion of highly similar strains also affects the results of unique genes in pan-genome analysis, which leads to a significant underestimation of the number of unique genes in the pan-genome. Therefore, these strains should be excluded from pan-genome analysis at the early stage of data processing. Currently, tens of thousands of genomes have been sequenced for Escherichia coli, which provides an unprecedented opportunity as well as a challenge for pan-genome analysis of this classical model organism. Using the proposed strategies, a high-quality E. coli pan-genome was obtained, and the unique genes was extracted and analyzed, revealing an association between the unique gene clusters and genomic islands from a pan-genome perspective, which may facilitate the identification of genomic islands.

Identification of Core Genes and Screening of Potential Targets in Intervertebral Disc Degeneration Using Integrated Bioinformatics Analysis.

Background: Intervertebral disc degeneration (IDD), characterized by diverse pathological changes, causes low back pain (LBP). However, prophylactic and delaying treatments for IDD are limited. The aim of our study was to investigate the gene network and biomarkers of IDD and suggest potential therapeutic targets. Methods: Differentially expressed genes (DEGs) associated with IDD were identified by analyzing the mRNA, miRNA, and lncRNA expression profiles of IDD cases from the Gene Expression Omnibus (GEO). The protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis as well as miRNA–lncRNA–mRNA networks were conducted. Moreover, we obtained 71 hub genes and performed a comprehensive analysis including GO, KEGG, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), Disease Ontology (DO), methylation analysis, receiver operating characteristic (ROC) curve analysis, immune infiltration analysis, and potential drug identification. We finally used qRT-PCR to verify 13 significant DEGs in normal and degenerative nucleus pulposus cells (NPCs). Results: We identified 305 DEGs closely related to IDD. The GO and KEGG analyses indicated that changes in IDD are significantly associated with enrichment of the inflammatory and immune response. GSEA analysis suggested that cell activation involved in the inflammatory immune response amide biosynthetic process was the key for the development of IDD. The GSVA suggested that DNA repair, oxidative phosphorylation, peroxisome, IL-6-JAK-STAT3 signaling, and apoptosis were crucial in the development of IDD. Among the 71 hub genes, the methylation levels of 11 genes were increased in IDD. A total of twenty genes showed a high functional similarity and diagnostic value in IDD. The result of the immune cell infiltration analysis indicated that seven genes were closely related to active natural killer cells. The most relevant targeted hub genes for potential drug or molecular compounds were MET and PIK3CD. Also, qRT-PCR results showed that ARHGAP27, C15orf39, DEPDC1, DHRSX, MGAM, SLC11A1, SMC4, and LINC00887 were significantly downregulated in degenerative NPCs; H19, LINC00685, mir-185-5p, and mir-4306 were upregulated in degenerative NPCs; and the expression level of mir-663a did not change significantly in normal and degenerative NPCs. Conclusion: Our findings may provide new insights into the functional characteristics and mechanism of IDD and aid the development of IDD therapeutics.

Identification of Core Genes and Key Pathways in Gastric Cancer using Bioinformatics Analysis

Identification of Core Genes and Key Pathways in Gastric Cancer using Bioinformatics Analysis

Identification of core genes and clinical outcomes in tumors originated from endoderm (gastric cancer and lung carcinoma) via bioinformatics analysis.

During last decade, bioinformatics analysis has provided an effective way to study the relationship between various genes and biological processes. In this study, we aimed to identify potential core candidate genes and underlying mechanisms of progression of lung and gastric carcinomas which both originated from endoderm. The expression profiles, GSE54129 (gastric carcinoma) and GSE27262 (lung carcinoma), were collected from GEO database. One hundred eleven patients with gastric carcinoma and 21 health people were included in this research. Meanwhile, there were 25 lung carcinoma patients. Then, 75 differentially expressed genes were selected via GEO2R online tool and Venn software, including 31 up-regulated genes and 44 down-regulated genes. Next, we used Database for Annotation, Visualization, and Integrated Discovery and Metascpe software to analyze Kyoto Encyclopedia of Gene and Genome pathway and gene ontology. Furthermore, Cytoscape software and MCODE App were performed to construct complex of these differentially expressed genes . Twenty core genes were identified, which mainly enriched in extracellular matrix-receptor interaction, focal adhesion, and PI3K-Akt pathway (P < .01). Finally, the significant difference of gene expression between cancer tissues and normal tissues in both lung and gastric carcinomas was examined by Gene Expression Profiling Interactive Analysis database. Twelve candidate genes with positive statistical significance (P < .01), COMP CTHRC1 COL1A1 SPP1 COL11A1 COL10A1 CXCL13 CLDN3 CLDN1 matrix metalloproteinases 7 ADAM12 PLAU, were picked out to further analysis. The Kaplan-Meier plotter website was applied to examine relationship among these genes and clinical outcomes. We found 4 genes (ADAM12, SPP1, COL1A1, COL11A1) were significantly associated with poor prognosis in both lung and gastric carcinoma patients (P < .05). In conclusion, these candidate genes may be potential therapeutic targets for cancer treatment.

Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis.

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

Identification of core genes in the progression of endometrial cancer and cancer cell-derived exosomes by an integrative analysis

Endometrial cancer is one of the most prevalent tumors of the female reproductive system causing serious health effects to women worldwide. Although numerous studies, including analysis of gene expression profile and cellular microenvironment have been reported in this field, pathogenesis of this disease remains unclear. In this study, we performed a system bioinformatics analysis of endometrial cancer using the Gene Expression Omnibus (GEO) datasets (GSE17025, GSE63678, and GSE115810) to identify the core genes. In addition, exosomes derived from endometrial cancer cells were also isolated and identified. First, we analyzed the differentially expressed genes (DEGs) between endometrial cancer tissues and normal tissues in clinic samples. We found that HAND2-AS1, PEG3, OGN, SFRP4, and OSR2 were co-expressed across all 3 datasets. Pathways analysis showed that several pathways associated with endometrial cancer, including “p53 signaling pathway”, “Glutathione metabolism”, “Cell cycle”, and etc. Next, we selected DEGs with highly significant fold change and co-expressed across the 3 datasets and validated them in the TCGA database using Gene Expression Profiling Interactive Analysis (GEPIA). Finally, we performed a survival analysis and identified four genes (TOP2A, ASPM, EFEMP1, and FOXL2) that play key roles in endometrial cancer. We found up-regulation of TOP2A and ASPM in endometrial cancer tissues or cells, while EFEMP1 and FOXL2 were down-regulated. Furthermore, we isolated exosomes from the culturing supernatants of endometrial cancer cells (Ishikawa and HEC-1-A) and found that miR-133a, which regulates expression of FOXL2, were present in exosomes and that they could be delivered to normal endometrial cells. The common DEGs, pathways, and exosomal miRNAs identified in this study might play an important role in progression as well as diagnosis of endometrial cancer. In conclusion, our results provide insights into the pathogenesis and risk assessment of endometrial cancer. Even so, further studies are required to elucidate on the precise mechanism of action of these genes in endometrial cancer.

Identification of Core Genes and Pathways in Medulloblastoma by Integrated Bioinformatics Analysis.

Medulloblastoma (MB) is one of the most common intracranial malignancies in children. The present study applied integrated bioinformatics to identify potential core genes associated with the pathogenesis of MB and reveal potential molecular mechanisms. Through the integrated analysis of multiple data sets from the Gene Expression Omnibus (GEO), 414 differentially expressed genes (DEGs) were identified. Combining the protein-protein interaction (PPI) network analysis with gene set enrichment analysis (GSEA), eight core genes, including CCNA2, CCNB1, CCNB2, AURKA, CDK1, MAD2L1, BUB1B, and RRM2, as well as four core pathways, including "cell cycle", "oocyte meiosis", "p53 pathway" and "DNA replication" were selected. In independent data sets, the core genes showed superior diagnostic values and significant prognostic correlations. Moreover, in the pan-caner data of the cancer genome atlas (TCGA), the core genes were also widely abnormally expressed. In conclusion, this study identified core genes and pathways of MB through integrated analysis to deepen the understanding of the molecular mechanisms underlying the MB and provide potential targets and pathways for diagnosis and treatment of MB.

Identification of Core Genes and Potential Drugs for Castration-Resistant Prostate Cancer Based on Bioinformatics Analysis.

Prostate cancer (PCa) is a common malignant tumor in elderly men worldwide. Most primary PCas inevitably progress into castration-resistant prostate cancer (CRPC) after androgen deprivation therapy. The mechanisms contributing to this progression are still controversial. In this study, functional module genes, DNA methylations, core regulators, and potential drugs in primary PCa and CRPC were explored by integrating a series of bioinformatics analyses. First, 588 differentially expressed genes (DEGs) were identified. Combined with related genes, protein-protein interaction networks were constructed, and 22 and 14 significant modules were identified in primary PCa and CRPC, respectively. More DEGs were identified in differentially methylated genes in CRPC modules. The hub genes in CRPC included CDC20 and CDK1. Moreover, core noncoding RNAs and transcription factors that significantly regulate CRPC modules were identified, including TUG1, MALAT1, E2F3, and MED1. Finally, the prediction of potential drugs for primary PCa and CRPC was also performed. Exisulind and phosphodiesterase-4 inhibitors were predicted as potential drugs for CRPC. The results of this study provide a new way for biologists and pharmacists to understand the potential molecular mechanisms of CRPC and also provide valuable references for drug redirection and new drug development for PCa.

Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis.

Type 2 diabetes mellitus (T2DM) is a metabolic disorder. Numerous proteins have been identified that are associated with the occurrence and development of T2DM. This study aimed to identify potential core genes and pathways involved in T2DM, through exhaustive bioinformatic analyses using GSE20966 microarray profiles of pancreatic β-cells obtained from healthy controls and patients with T2DM. The original microarray data were downloaded from the Gene Expression Omnibus database. Data were processed by the limma package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out to identify potential biological functions and pathways of the DEGs. Key transcription factors were identified using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) and Enrichr. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to establish a protein-protein interaction (PPI) network for the DEGs. In total, 329 DEGs were involved in T2DM, with 208 upregulated genes enriched in pancreatic secretion and the complement and coagulation cascades, and 121 downregulated genes enriched in insulin secretion, carbohydrate digestion and absorption, and the Toll-like receptor pathway. Furthermore, hepatocyte nuclear factor 1-alpha (HNF1A), signal transducer and activator of transcription 3 (STAT3) and glucocorticoid receptor (GR) were key transcription factors in T2DM. Twenty important nodes were detected in the PPI network. Finally, two core genes, serpin family G member 1 (SERPING1) and alanyl aminopeptidase, membrane (ANPEP), were shown to be associated with the development of T2DM. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of T2DM and provide potential targets for further research.

Identification of core genes and clinical roles in pregnancy-associated breast cancer based on integrated analysis of different microarray profile datasets

More women are delaying child-birth. Thus, the diagnosis of pregnancy-associated breast cancer (PABC) will continue to increase. The aim of this study was to identify core candidate genes of PABC, and the relevance of the genes on the prognosis of PABC. GSE31192 and GSE53031 microarray profile datasets were downloaded from the Gene Expression Omnibus database and differentially expressed genes were analyzed using the R package and GEO2R tool. Then, Gene Ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Moreover, the Search Tool for the Retrieval of Interacting Genes and the Molecular Complex Detection Cytoscape software plug-in were utilized to visualize protein–protein interactions and to screen candidate genes. A total of 239 DEGs were identified in PABC, including 101 up-regulated genes mainly enriched in fatty acid activation and the fibroblast growth factor signaling pathway, while 138 down-regulated genes particularly involved in activation of DNA fragmentation factor and apoptosis-induced DNA fragmentation. Fourteen hub genes with a high degree of connectivity were selected, including CREB1, ARF3, UBA5, SIAH1, KLHL3, HECTD1, MMP9, TRIM69, MEX3C, ASB6, UBE2Q2, FBXO22, EIF4A3, and PXN. Overall survival (OS) analysis of core candidate genes was performed using the Gene Expression Profiling Interactive Analysis and UALCAN websites. High ASB6 expression was associated with worse OS of PABC patients. Molecular subtypes and menopause status were also associated with worse OS for PABC patients. In conclusion, ASB6 could be a potential predictor and therapeutic target in patient with PABC.

Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis

BackgroundBreast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer. MethodsThe gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein–protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan–Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples. ResultsA total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan–Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted. ConclusionThese results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis.

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.