scholarly journals Identification of key gene in colorectal cancer using bioinformatics analysis.

2020 ◽  
Author(s):  
Rongrong Xiao ◽  
Ping Wang ◽  
Tian Xia ◽  
Chun-Yi Li ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Tumor microenvironment plays important roles in the development of cancer. The aim of our study was to examine the expression of genes in colorectal cancer and also to evaluate the association value between expression level of these genes and clinical features. Methods We combined The Cancer Genome Atlas (TCGA) datasets to identify differentially expressed genes in colon cancer. Using these differentially expressed genes, we constructed protein-protein interaction network and conducted functional enrichment analysis. Genes with degree beyond 10 in the PPI network were regarded as hub genes. Then, we verified of the expression of molecules in Oncomine datasets and conducted Kaplan-Meier curve and log-rank test and functional enrichment analysis on these hub genes. Finally, we analyzed the relationship clinicopathological features analysis with the key gene. Results There were 719 differentially expressed genes identified to be associated with colon cancer microenvironment. We screened out 10 hub genes by construction of PPI network. The functions of these hub genes were enriched in cytokine-cytokine receptor interaction, alcoholism and systemic lupus erythematosus, which provided further insight into the roles of these genes in the tumor microenvironment. GNG4, with the highest degrees in the PPI network, were highly exprepressed in metastasis(P = 9.5-05) ,N1(P = 0.0025) and N2(,0.037).It was a relationship with stage. It was significantly different between with stage I and IV, II and III, II and IV,III and IV (P = 0.0015,0.029,3.9-05,0.00074,0.01,respectively) Conclusions We identified GNG4 can be regarded as a prognostic biomarker in colon cancer.

scholarly journals Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

2021 ◽  
Author(s):  
Mingyi Yang ◽  
Yani Su ◽  
Yao Ma ◽  
Yirixiati Aihaiti ◽  
Peng Xu
Keyword(s):  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Hub Gene ◽  
Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

scholarly journals Analysis of core genes for colorectal cancer prognosis based on immune and stromal scores

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12452
Author(s):  
Yi Zhu ◽  
Yuan Zhou ◽  
HongGang Jiang ◽  
ZhiHeng Chen ◽  
BoHao Lu
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Differentially Expressed Genes ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
The Core ◽  
Regulatory Effects ◽  
Core Genes

Background Colorectal cancer (CRC) is one of the most common malignancies.An early diagnosis and an accurate prognosis are major focuses of CRC research. Tumor microenvironment cells and the extent of infiltrating immune and stromal cells contribute significantly to the tumor prognosis. Methods Immune and stromal scores were calculated based on the ESTIMATE algorithm using the sample expression profile of the The Cancer Genome Atlas (TCGA) database. GSE102479 was used as the validation database. Differentially expressed genes whose expression was significantly associated with the prognosis of CRC patients were identified based on the immune matrix score. Survival analysis was conducted on the union of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed using the STRING database to identify the closely connected modules. To conduct functional enrichment analysis of the relevant genes, GO and KEGG pathway analyses were performed with Cluster Profiler. Pivot analysis of the ncRNAs and TFs was performed by using the RAID2.0 database and TRRUST v2 database. TF-mRNA regulatory relationships were analyzed in the TRRUST V2 database. Hubgene targeting relationships were screened in the TargetScan, miRTarBase and miRDB databases. The SNV data of the hub genes were analyzed by using the R maftools package. A ROC curve was drawn based on the TCGA database. The proportion of immune cells was estimated using CIBERSORT and the LM22 feature matrix. Results The results showed that the matrix score was significantly correlated with colorectal cancer stage T. A total of 789 differentially expressed genes and 121 survival-related prognostic genes were identified. The PPI network showed that 22 core genes were related to the CRC prognosis. Furthermore, four ncRNAs that regulated the core prognosis genes, 11 TFs with regulatory effects on the core prognosis genes, and two drugs, quercetin and pseudoephedrine, that have regulatory effects on colorectal cancer were also identified. Conclusions We obtained a list of tumor microenvironment-related genes for CRC patients. These genes could be useful for determining the prognosis of CRC patients. To confirm the function of these genes, additional experiments are necessary.

scholarly journals Genomic Expression Profiling of Colorectal Cancer in Silico

2021 ◽  
Author(s):  
Feifei Liu ◽  
Yu Wang ◽  
Wenxue Li ◽  
Diancheng Li ◽  
Yuwei Xin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Mrna Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Analysis Tool ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.

scholarly journals Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

2021 ◽  
Author(s):  
Nana Yang ◽  
Qianghua Wang ◽  
Biao Ding ◽  
Yinging Gong ◽  
Yue Wu ◽  
...  
Keyword(s):  
Iron Homeostasis ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

scholarly journals Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

2019 ◽  
Vol 48 (5) ◽  
pp. 030006051988726
Author(s):  
Yuting Zhang ◽  
Bo Shen ◽  
Liya Zhuge ◽  
Yong Xie
Keyword(s):  
Inflammatory Bowel Disease ◽  
Differentially Expressed Genes ◽  
Bowel Disease ◽  
Gene Expression Pattern ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Inflammatory Bowel

Objective We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD). Methods RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein–protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug–gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test. Results DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug–gene network identified these hub genes as potential drug targets. Conclusion Nineteen DEG were identified between the colon and ileum of IBD patients.

scholarly journals Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

2020 ◽  
Vol 9 (2) ◽  
pp. LMT30
Author(s):  
Chuanli Ren ◽  
Weixiu Sun ◽  
Xu Lian ◽  
Chongxu Han
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Kaplan Meier ◽  
Key Genes ◽  
Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

scholarly journals Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine–cytokine receptor interaction pathway related to health status

Genome ◽  
2017 ◽  
Vol 60 (12) ◽  
pp. 1021-1028 ◽  
Author(s):  
M.H. Ye ◽  
H. Bao ◽  
Y. Meng ◽  
L.L. Guan ◽  
P. Stothard ◽  
...  
Keyword(s):  
Health Status ◽  
Differentially Expressed Genes ◽  
Peripheral Blood ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cytokine Receptor ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Marker Genes

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine–cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

scholarly journals Xiaoyu Xiezhuo Drink Protects against Ischemia-Reperfusion Acute Kidney Injury in Aged Mice through Inhibiting the TGF-β1/Smad3 and HIF1 Signaling Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Qingqing Ye ◽  
Hongbo Chen ◽  
Hongzhen Ma ◽  
Xiaojun Xiang ◽  
Shouci Hu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Ischemia Reperfusion ◽  
Kidney Injury ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Aged Mice ◽  
Tgf Β1

Acute kidney injury (AKI) is responsible for significant mortality among hospitalized patients that is especially troubling aged people. An effective self-made Chinese medicine formula, Xiaoyu Xiezhuo Drink (XXD), displayed therapeutic effects on AKI. However, the compositions and underlying mechanisms of XXD remain to be elucidated. In this study, we used the ultra-high-performance liquid chromatography method coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to investigate the chemical components in XXD. Then, the absorbable components of XXD were identified based on the five principles and inputted into the SwissTargetPrediction and STITCH databases to identify the drug targets. AKI-related targets were collected from the GenCLiP 3, GeneCards, and DisGeNET databases. The crossover genes of XXD and AKI were identified for functional enrichment analysis. The protein-protein interaction (PPI) network of crossover genes was constructed, followed by the identification of hub genes. Subsequently, the effects and potential mechanisms of XXD on AKI predicted by the network pharmacology and bioinformatics analyses were experimentally validated in ischemia-reperfusion (I/R) injury-induced AKI aged mouse models. A total of 122 components in XXD were obtained; among them, 58 components were found that could be absorbed in the blood. There were 800 potential drug targets predicted from the 58 absorbable components in AKI which shared 36 crossover genes with AKI-related targets. The results of functional enrichment analysis indicated that crossover genes mostly associated with the response to oxidative stress and the HIF1 signaling pathway. In the PPI network analysis, 12 hub genes were identified, including ALB, IL-6, TNF, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, and HMOX1. In AKI aged mice, XXD prominently alleviated I/R injury-induced renal dysfunction, abnormal renal pathological changes, and cellular senescence, inflammation, and oxidative damage with a reduction in the expression level of the inflammatory mediator, α-SMA, collagen-1, F4/80, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, ICAM-1, TGF-β1, Smad3, and p-Smad3 and an increase of nephridial tissue p-H3, Ki67, HMOX1, MMP-9, and Smad7 levels. In summary, our findings suggest that XXD has renoprotective effects against AKI in aged mice via inhibiting the TGF-β1/Smad3 and HIF1 signaling pathways.

scholarly journals Construction of Circular RNA–MicroRNA–Messenger RNA Regulatory Network of Recurrent Implantation Failure to Explore Its Potential Pathogenesis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiahuan Luo ◽  
Li Zhu ◽  
Ning Zhou ◽  
Yuanyuan Zhang ◽  
Lirong Zhang ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Implantation Failure ◽  
Ppi Network ◽  
Recurrent Implantation Failure ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

scholarly journals Bioinformatics analysis of differentially expressed genes and their potentially interacting miRNAs in oral lichen planus

2021 ◽  
Author(s):  
Churen Zhang ◽  
Ruoran Sun
Keyword(s):  
Lichen Planus ◽  
Differentially Expressed Genes ◽  
Oral Mucosa ◽  
Oral Lichen Planus ◽  
Kegg Pathway ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Oral Lichen

Abstract Background. Among the diseases of oral mucosa, oral lichen planus (OLP) is characterized by chronic autoimmune/autoinflammation. However, the etiology and pathogenesis of OLP were still limited. The aim of this research was to identify the differentially expressed genes and their potentially interacted miRNAs in OLP to provide a possible explanation of the pathogenesis of OLP and therapeutic biomarkers.Methods. The OLP microarray dataset (GSE52130) was download from the Gene Expression Omnibus (GEO) database. R software was used to identify differentially expressed genes between the OLP samples and normal oral mucosa. Functional enrichment analysis of DEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, were conducted. Protein–protein interaction (PPI) network analysis was performed in the STRING database. CytoHubba in the Cytoscape software was applied to determining the top 10 hub genes, whose relative miRNA was identified through RNA Interactome Database.Results. Overall, 627 DEGs was identified in OLP samples, including 351 highly expressed genes and 276 lowly expressed genes. GO analysis indicated that the epidermal differentiation was mostly enriched. For the KEGG pathway, the DEGs in OLP samples were mostly involved in Staphylococcus aureus infection, Estrogen signaling pathway, Serotonergic synapse and Histidine metabolism. Top 10 hub genes including LOR, LCE3D, LCE3E, LCE1B, LCE2B, SPRR2E, SPRR2G, LCE2A, RPTN and CDSN were identified from the PPI network. The miRNA (hsa-miR-98-5p) was regarded as the mostly possible miRNA involved in OLP.

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