Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Study on potential differentially expressed genes in stroke by bioinformatics analysis

Neurological Sciences ◽

10.1007/s10072-021-05470-1 ◽

2021 ◽

Author(s):

Xitong Yang ◽

Pengyu Wang ◽

Shanquan Yan ◽

Guangming Wang

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Normal Control ◽

Enrichment Analysis ◽

Normal Control Group ◽

Control Group ◽

Ppi Network ◽

Hub Genes ◽

Pathway Enrichment ◽

Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17031053 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1053

Author(s):

Chengzhang Li ◽

Jiucheng Xu

Keyword(s):

Gene Expression ◽

Target Genes ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Gene Expression Profiles ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Gene Differential Expression ◽

Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

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Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

BioMed Research International ◽

10.1155/2022/1776082 ◽

2022 ◽

Vol 2022 ◽

pp. 1-17

Author(s):

Md. Rakibul Islam ◽

Lway Faisal Abdulrazak ◽

Mohammad Khursheed Alam ◽

Bikash Kumar Paul ◽

Kawsar Ahmed ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Clinical Information ◽

Gene Expression Omnibus ◽

Hub Genes ◽

New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log fold change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

10.21203/rs.3.rs-580657/v1 ◽

2021 ◽

Author(s):

Zimeng Wei ◽

Min Zhao ◽

Linnan Zang

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Analysis Tool ◽

Hub Genes ◽

Kegg Analysis ◽

Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

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Comprehensive analysis of DNA methylation and gene expression profiles in rectal cancer

10.21203/rs.3.rs-29003/v1 ◽

2020 ◽

Author(s):

Cheng Zhang ◽

Di Meng ◽

Songjie Chao ◽

Chunlin Ge

Keyword(s):

Gene Expression ◽

Rectal Cancer ◽

Differentially Expressed Genes ◽

Cox Regression ◽

Expression Profiles ◽

Methylation Status ◽

Differentially Expressed ◽

Ppi Network ◽

Hub Genes ◽

Differentially Methylated Genes

Abstract BackgroundAbnormal hypomethylation of oncogenes and hypermethylation of tumor suppressor genes play important roles in human tumorigenesis and cancer progression, including those of rectal cancer (RC). However, conjoint analysis of RC involving both gene expression and methylation profiling datasets remains rare. This study aimed to identify methylation-regulated differentially expressed genes (MeDEGs) and to evaluate their prognostic value in RC through bioinformatics analysis.MethodsGene expression (GSE20842 and GSE68204) and gene methylation (GSE75546) profiling datasets were obtained from the Gene Expression Omnibus database. GEO2R was adopted to identify differentially expressed genes (DEGs) and differentially methylated genes (DMGs). MeDEGs were obtained by overlapping the DEGs and DMGs and then subjected to protein–protein interaction (PPI) network analysis using STRING. Modules and hub genes within the network were identified using MCODE and CytoHubba, respectively. Prognostic MeDEGs were selected by univariate Cox regression. Finally, our findings were validated based on The Cancer Genome Atlas (TCGA) database.ResultsIn total, 243 upregulated-hypomethylated and 51 downregulated-hypermethylated genes were identified as MeDEGs. A PPI network of MeDEGs was constructed with 290 nodes and 578 edges. Three modules and three hub genes—COL3A1, FPR1, and PLK1—within the network were identified. Three MeDEGs—NFE2, COMP, and LAMA1—were found to be survival-related. Furthermore, the expression and methylation status of two hub genes (excluding FPR1) and the three prognostic MeDEGs were also significantly altered in TCGA and were consistent with our findings.ConclusionsWe identified novel MeDEGs and explored their relationship with survival in RC. Our methodology may provide an effective bioinformatics basis for further understanding of the methylation-mediated regulatory mechanisms in RC.

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Dysfunctional network and mutation genes of hypertrophic cardiomyopathy

10.21203/rs.2.22781/v1 ◽

2020 ◽

Author(s):

Yunwen Cui ◽

Cheng Liu ◽

Jian Luo ◽

Jie Liang

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Differential Analysis ◽

Ppi Network ◽

Roc Curve Analysis ◽

Study Gene Expression

Abstract Background Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous diseases that affect the myocardium. It is also a common familial disease. The symptoms are not common and easy to find. Methods In this study, gene expression profiles of 37 samples (GSE130036) were downloaded from GEO database. Differential analysis was used to identify the related dysregulated genes in patients with HCM. Enrichment analysis identified the biological function and signal pathway of these differentially expressed genes. Then, we build PPI network and verify it in GSE36961 dataset. Finally, the gene of single nuclear variants (SNVs) in HCM samples was screened by means of maftools. Results Herein, we obtained 920 differentially expressed genes, and found that these genes are mainly related to metabolic related signaling pathways. 187 interacting genes were identified by PPI network analysis, and the expression trends of C1QB, F13A1, CD163, FCN3, PLA2G2A and CHRDL2 were verified by another dataset. ROC curve analysis showed that they had certain clinical diagnostic ability, and they were the potential key dysfunctional genes of HCM. In addition, we found that PRMT5 mutation was the most frequent in HCM samples, which may affect the pathogenesis of HCM. Conclusions Therefore, the key genes and enrichment results identified by our analysis may provide a reference for the occurrence and development mechanism of HCM. In addition, mutations in PRMT5 may be a useful therapeutic and diagnostic target for HCM.

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Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

10.21203/rs.3.rs-402557/v1 ◽

2021 ◽

Author(s):

Gang Chen ◽

Mingwei Yu ◽

Jianqiao Cao ◽

Huishan Zhao ◽

Yuanping Dai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Kegg Pathway ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Molecular Therapy ◽

Hub Genes

Abstract Background: Breast cancer (BC) is a malignancy with a high incidence among women in the world, and it is very urgent to identify significant biomarkers and molecular therapy methods.Methods: Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic values and molecular mechanism of these hub genes Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). Results: 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. GO and KEGG pathway analysis revealed that DEGs were mainly related to cell cycles and cell proliferation. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree of genes were selected, among which, 11 hub gene were significantly correlated with the prognosis of patients with BC. From GSEA reviewed correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. Conclusion: this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

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Identification of differentially expressed genes in Cushing's disease by integrated bioinformatics analysis

10.21203/rs.3.rs-29947/v1 ◽

2020 ◽

Author(s):

Tingting Lv ◽

Haoying Yu ◽

Shuyue Ren ◽

Jingrong Wang ◽

Lan Sun ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Cushing’S Disease ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Cushing's Disease ◽

Age Group ◽

Ppi Network ◽

Hub Genes ◽

Differential Genes

Abstract Background Cushing's disease is a rare and little-known disease, and the individualization of drug treatment varies greatly. Studies have shown that the gene expression profile of Cushing's disease is related to its clinical characteristics. Therefore, the study aims to identify key differential genes between the age and size of tumors through bioinformatics technology, thus providing a theoretical basis for personalized targeted therapy of Cushing's disease. Methods Downloading the gene expression microarray (GSE93825) data from the Gene Expression Omnibus (GEO) database and obtaining differentially expressed genes (DEGs) of different tumor sizes and ages through GEO2R. The DAVID database, Cytoscape and String platforms were utilized for functional enrichment analysis and protein-protein interaction (PPI) network analysis on selected differential genes. Results First, 96 DEGs were identified between macroadenoma (MAC) and microadenoma (MIC), which initially proved the different gene expression characteristics between them. Second, a total of 2128 DEGs were identified in MAC age group. The top five hub genes of the PPI network were GNGT2, LPAR3, PDYN, GRM3, and HTR1D. A total of 16 DEGs were identified in MIC age group. In addition, 88 DEGs were identified in younger MAC and MIC groups. The top five hub genes included LEP, PTGS2, STAT6, CXCL12, and ITPKB. 299 DEGs were identified in senior MAC and MIC groups. The first five hub genes were CCR7, LPAR2, CXCR5, ADCY3, and TAS2R14. By virtue of DAVID and Cytoscape software, the function enrichment analysis and core module analysis were performed successfully. Conclusions In summary, our research shows through bioinformatics analysis that different gene expression profiles of Cushing's disease are related to the size and age of the tumor, which may provide new insights into the molecular pathogenesis of Cushing's disease. These hub genes may be used for accurate diagnosis and treatment of Cushing's disease.

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