Identification of key genes in relapsed multiple myeloma by weighted gene co-expression network analysis

Abstract Background Multiple myeloma is a hematologic disorder of abnormal plasma cell proliferation. Although there are some agents with different mechanisms in the clinic, the treatment of multiple myeloma is still challenging for the reason that its recurrence and progression are common. Therefore, it is critical to determine novel biomarkers to improve the prognosis of patients. Methods Firstly, raw data of GSE82307 was collected from the Gene Expression Omnibus database. Secondly, the top 50% of most variant genes were employed to construct a gene co-expression network in the R.WGCNA algorithm, and module significance and module membership were utilized to identify hub modules and hub genes respectively. The gene ontology enrichment and Kyoto encyclopedia of genes and genomes pathway analysis were carried out to assess biological characteristics. Then a protein-protein interaction network was conducted based on the STRING website and Cytoscape software. Next, differentially expressed genes were analyzed using the limma R package. Finally, survival analysis was performed by Kaplan–Meier plotter to evaluate prognosis. Results 10826 genes were used to construct a co-expression network. In this network, the blue module was identified as a hub module in which 68 genes were identified as hub genes. Furthermore, 46 differentially expressed genes were screened in samples of GSE82307. Integrating hub genes and differentially expressed genes, we determined 14 key genes. Finally, survival analysis revealed that ten genes CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 were related to the relapse and prognosis of multiple myeloma. Conclusion Our study suggested that CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 may be potential biomarkers for predicting the relapse and prognosis of multiple myeloma.

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Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Journal of Translational Medicine ◽

10.1186/s12967-021-02834-1 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqin Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract Background Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. Methods Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. Results GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. Conclusion ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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Identification of Key Methylation Differentially Expressed Genes in Posterior Fossa Ependymoma Based on Epigenomic and Transcriptome Analysis

10.21203/rs.3.rs-148618/v1 ◽

2021 ◽

Author(s):

Guanyi Wang ◽

Yibin Jia ◽

Yuqing Ye ◽

Enming Kang ◽

Huijun Chen ◽

...

Keyword(s):

Gene Expression ◽

Posterior Fossa ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Cpg Island ◽

Adaptive Immune Response ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Abstract BackgroundPosterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma(EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis. MethodsUsing epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database. ResultsGO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R=0.69, P=1 x 10-08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC. ConclusionssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

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SLC1A1, SLC16A9, and CNTN3 Are Potential Biomarkers for the Occurrence of Colorectal Cancer

BioMed Research International ◽

10.1155/2020/1204605 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Jie Zhou ◽

Zhiman Xie ◽

Ping Cui ◽

Qisi Su ◽

Yu Zhang ◽

...

Keyword(s):

Colorectal Cancer ◽

Ulcerative Colitis ◽

Differentially Expressed Genes ◽

Colorectal Adenoma ◽

Immune Cells ◽

Interaction Network ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes

Background. This study is aimed at identifying unknown clinically relevant genes involved in colorectal cancer using bioinformatics analysis. Methods. Original microarray datasets GSE107499 (ulcerative colitis), GSE8671 (colorectal adenoma), and GSE32323 (colorectal cancer) were downloaded from the Gene Expression Omnibus. Common differentially expressed genes were filtered from the three datasets above. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, followed by construction of a protein-protein interaction network to identify hub genes. Kaplan-Meier survival analysis and TIMER database analysis were used to screen the genes related to the prognosis and tumour-infiltrating immune cells of colorectal cancer. Receiver operating characteristic curves were used to assess whether the genes could be used as markers for the diagnosis of ulcerative colitis, colorectal adenoma, and colorectal cancer. Results. A total of 237 differentially expressed genes common to the three datasets were identified, of which 60 were upregulated, 125 were downregulated, and 52 genes that were inconsistently up- and downregulated. Common differentially expressed genes were mainly enriched in the cellular component of extracellular exosome and integral component of membrane categories. Eight hub genes, i.e., CXCL3, CXCL8, CEACAM7, CNTN3, SLC1A1, SLC16A9, SLC4A4, and TIMP1, were related to the prognosis and tumour-infiltrating immune cells of colorectal cancer, and these genes have diagnostic value for ulcerative colitis, colorectal adenoma, and colorectal cancer. Conclusion. Three novel genes, CNTN3, SLC1A1, and SLC16A9 were shown to have diagnostic value with respect to the occurrence of colorectal cancer and should be verified in future studies.

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

10.21203/rs.3.rs-349764/v1 ◽

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.

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Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

Current Pharmaceutical Design ◽

10.2174/1381612826666200311130133 ◽

2020 ◽

Vol 26 (29) ◽

pp. 3619-3630

Author(s):

Saumya Choudhary ◽

Dibyabhaba Pradhan ◽

Noor S. Khan ◽

Harpreet Singh ◽

George Thomas ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Gene ◽

Therapeutic Targets ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Keratinocyte Differentiation ◽

Hub Genes ◽

Lesional Skin ◽

Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

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Genomic Expression Profiling of Colorectal Cancer in Silico

10.21203/rs.3.rs-310514/v1 ◽

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.

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Comprehensive Analysis of Differentially Expressed Genes and Key Pathways in Neuropathic Pain by Spinal Nerve Ligation

10.21203/rs.3.rs-74125/v1 ◽

2020 ◽

Author(s):

Chao Xu ◽

HuiFang Li ◽

YunPeng Zhang ◽

TianYu Liu ◽

Yi Feng

Keyword(s):

Functional Analysis ◽

Neuropathic Pain ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Spinal Nerve ◽

Spinal Nerve Ligation ◽

Differentially Expressed ◽

Hub Genes ◽

Long Term Treatment

Abstract Background: Neuropathic pain can cause significant physical and economic burden to people, and there are no effective long-term treatment methods for this condition. We conducted a bioinformatics analysis of microarray data to identify related mechanisms to determine strategies for more effective treatments of neuropathic pain.Methods: GSE24982 and GSE63442 microarray datasets were extracted from the Gene Expression Omnibus (GEO) database to analyze transcriptome differences of neuropathic pain in the dorsal root ganglions caused by spinal nerve ligation. We filtered the differentially expressed genes (DEGs) in the two datasets and Webgestalt was applied to conduct GeneOntology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the shared DEGs. String Database and Cytoscape software were used to construct the Protein-Protein Interaction (PPI) network to determine the hub genes, which were subsequently verified in the GSE30691 dataset. Finally, miRDB and miRWalk Databases were used to predict potential miRNA of the selected DEGs.Results: A total of 182 overlapped DEGs were found between GSE24982 and GSE63442 datasets. The GO functional analysis and KEGG enrichment analysis showed that the selected DEGs were mainly enriched in infection, transmembrane transport of ion channels, and synaptic transmission. Combining the results of PPI analysis and the verification of the GSE30691 dataset, we identified seven hub genes related to neuropathic pain (Atf3, Aif1, Ctss, Gfap, Scg2, Jun, and Vgf). Predicted miRNA targeting each selected hub genes were identified.Conclusion: Seven hub genes related to the pathogenesis of neuropathic pain and potential targeting miRNA were identified, expanding understanding of the mechanism of neuropathic pain and facilitating treatment development.

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Identification of key genes, pathways and potential therapeutic agents for IgA nephropathy using an integrated bioinformatics analysis

Journal of the Renin-Angiotensin-Aldosterone System ◽

10.1177/1470320320919635 ◽

2020 ◽

Vol 21 (2) ◽

pp. 147032032091963

Author(s):

Xiaoxue Chen ◽

Mindan Sun

Keyword(s):

Differentially Expressed Genes ◽

Immunoglobulin A ◽

Therapeutic Targets ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Signal Pathways ◽

Immunoglobulin A Nephropathy ◽

Hub Genes ◽

Limma Package

Purpose: This study aims to identify immunoglobulin-A-nephropathy-related genes based on microarray data and to investigate novel potential gene targets for immunoglobulin-A-nephropathy treatment. Methods: Immunoglobulin-A-nephropathy chip data was obtained from the Gene Expression Omnibus database, which included 10 immunoglobulin-A-nephropathy and 22 normal samples. We used the limma package of R software to screen differentially expressed genes in immunoglobulin-A-nephropathy and normal glomerular compartment tissues. Functional enrichment (including cellular components, molecular functions, biological processes) and signal pathways were performed for the differentially expressed genes. The online analysis database (STRING) was used to construct the protein-protein interaction networks of differentially expressed genes, and Cytoscape software was used to identify the hub genes of the signal pathway. In addition, we used the Connectivity Map database to predict possible drugs for the treatment of immunoglobulin-A-nephropathy. Results: A total of 348 differentially expressed genes were screened including 107 up-regulated and 241 down-regulated genes. Functional analysis showed that up-regulated differentially expressed genes were mainly concentrated on leukocyte migration, and the down-regulated differentially expressed genes were significantly enriched in alpha-amino acid metabolic process. A total of six hub genes were obtained: JUN, C3AR1, FN1, AGT, FOS, and SUCNR1. The small-molecule drugs thapsigargin, ciclopirox and ikarugamycin were predicted therapeutic targets against immunoglobulin-A-nephropathy. Conclusion: Differentially expressed genes and hub genes can contribute to understanding the molecular mechanism of immunoglobulin-A-nephropathy and providing potential therapeutic targets and drugs for the diagnosis and treatment of immunoglobulin-A-nephropathy.

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

Lung Cancer Management ◽

10.2217/lmt-2020-0009 ◽

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.

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Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

Frontiers in Genetics ◽

10.3389/fgene.2021.668702 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guofeng Zhou ◽

Shaoyan Sun ◽

Qiuyue Yuan ◽

Run Zhang ◽

Ping Jiang ◽

...

Keyword(s):

Adipose Tissue ◽

Differentially Expressed Genes ◽

Complex Disease ◽

Cerebral Arteries ◽

Interaction Network ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Tissue Specific ◽

Gene Pairs

Heart failure with preserved ejection fraction (HFpEF) is a complex disease characterized by dysfunctions in the heart, adipose tissue, and cerebral arteries. The elucidation of the interactions between these three tissues in HFpEF will improve our understanding of the mechanism of HFpEF. In this study, we propose a multilevel comparative framework based on differentially expressed genes (DEGs) and differentially correlated gene pairs (DCGs) to investigate the shared and unique pathological features among the three tissues in HFpEF. At the network level, functional enrichment analysis revealed that the networks of the heart, adipose tissue, and cerebral arteries were enriched in the cell cycle and immune response. The networks of the heart and adipose tissues were enriched in hemostasis, G-protein coupled receptor (GPCR) ligand, and cancer-related pathway. The heart-specific networks were enriched in the inflammatory response and cardiac hypertrophy, while the adipose-tissue-specific networks were enriched in the response to peptides and regulation of cell adhesion. The cerebral-artery-specific networks were enriched in gene expression (transcription). At the module and gene levels, 5 housekeeping DEGs, 2 housekeeping DCGs, 6 modules of merged protein–protein interaction network, 5 tissue-specific hub genes, and 20 shared hub genes were identified through comparative analysis of tissue pairs. Furthermore, the therapeutic drugs for HFpEF-targeting these genes were examined using molecular docking. The combination of multitissue and multilevel comparative frameworks is a potential strategy for the discovery of effective therapy and personalized medicine for HFpEF.

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