scholarly journals Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

2021 ◽  
Author(s):  
Varun Alur ◽  
Varshita Raju ◽  
Basavaraj Mallikarjunayya Vastrad ◽  
Anandkumar Revanasiddappa Tengli ◽  
Chanabasayya Vastrad ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Small Molecules ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Therapeutic Agents ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Transcription Profiling ◽  
Hub Gene

Gestational diabetes mellitus (GDM) is the metabolic disorder appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up regulated and 430 down regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

scholarly journals Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Anandkumar Tengli
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Small Molecules ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Transcription Profiling ◽  
Hub Gene ◽  
Gene Regulatory

AbstractGestational diabetes mellitus (GDM) is one of the metabolic diseases during pregnancy. The identification of the central molecular mechanisms liable for the disease pathogenesis might lead to the advancement of new therapeutic options. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non GDM samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network by the miRNet database and NetworkAnalyst database. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up regulated and 430 down regulated genes. Biological process analysis of GO enrichment analysis showed these DEGs were mainly enriched in reproduction, nuclear outer membrane-endoplasmic reticulum membrane network, identical protein binding, cell adhesion, supramolecular complex and signaling receptor binding. Signaling pathway enrichment analysis indicated that these DEGs played a vital in cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA in the center of the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. Our results indicated that HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA could be the potential novel biomarkers for GDM diagnosis, prognosis and the promising therapeutic targets. The current might be essential to understanding the molecular mechanism of GDM initiation and development.

scholarly journals Identification of potential prognostic markers for osteosarcoma by integrated analysis

2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  
Keyword(s):  
Prognostic Markers ◽  
Bone Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Modules

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

scholarly journals Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Integrated Bioinformatics Analysis and Molecular Docking Experiments

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Anandkumar Tengli
Keyword(s):  
Molecular Docking ◽  
Regulatory Network ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Docking Experiment ◽  
Gene Regulatory

AbstractThe current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Limma package of R was used to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) networks of the DEGs were constructed. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Furthermore, consistent hub genes were further validated. Molecular docking experiment was conducted. A total of 463 DEGs (232 up regulated and 231 down regulated genes) were identified between very early PDAC and normal control samples. The results of Functional enrichment analysis revealed that the DEGs were significantly enriched in vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis and adaptive immune system. The PPI network and gene regulatory network of up regulated genes and down regulated genes were established, and hub genes were identified. The expression of hub genes (CCNB1, FHL2, HLA-DPA1 and TUBB1) were also validated to be differentially expressed among PDAC and normal control samples. Molecular docking experiment predicted the novel inhibitory molecules for CCNB1 and FHL2. The identification of hub genes in PDAC enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.

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 RBBP4 Enhances Platinum Chemo Resistance in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Nianwu Wang ◽  
Wei Wang ◽  
Wenli Mao ◽  
Nazuke Kuerbantayi ◽  
Nuan Jia ◽  
...  
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Primary Treatment ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Hub Gene ◽  
Prognostic Analysis ◽  
Platinum Based Chemotherapy ◽  
Gene Modules

Background. The majority of lung cancers are adenocarcinomas, with the proportion being 40%. The patients are mostly diagnosed in the middle and late stages with metastasis and easy recurrence, which poses great challenge to the treatment and prognosis. Platinum-based chemotherapy is a primary treatment for adenocarcinoma, which frequently causes drug resistance. As a result, it is important to uncover the mechanisms of the chemoresponse of adenocarcinoma to platinum-based chemotherapy. Methods. The genes from the dataset GSE7880 were gathered into gene modules with the assistance of weighted gene coexpression network analysis (WGCNA), the gene trait significance absolute value (|GS|), and gene module memberships (MM). The genes from hub gene modules were calculated with a protein-protein interaction (PPI) network analysis in order to obtain a screening map of hub genes. The hub genes with both a high |GS| and MM and a high degree were selected. Furthermore, genes in the hub gene modules also went through a Gene Ontology (GO) functional enrichment analysis. Results. 11 hub genes in four hub gene modules (LY86, ACTR2, CDK2, CKAP4, KPNB1, RBBP4, SMAD4, MYL6, RPS27, TSPAN2, and VAMP2) were chosen as the significant hub genes. Through the GO function enrichment analysis, it was indicated that four modules were abundant in immune system functions (floralwhite), amino acid biosynthetic process (lightpink4), cell chemotaxis (navajowhite2), and targeting protein (paleturquoise). Four hub genes with the highest |GS| were verified by prognostic analysis.

scholarly journals Identification of potential prognostic markers for osteosarcoma by integrated analysis

2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  
Keyword(s):  
Prognostic Markers ◽  
Bone Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Modules

Abstract Background: Osteosarcoma (OS) is the most common type of musculoskeletal malignant tumor, accounting for over 30% of primary skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. In this study, we aim to identify gene modules and representative candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to construct a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

scholarly journals Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Eryou Feng ◽  
Haiyang Wang ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Common Chronic Disease ◽  
Drug Candidates ◽  
Therapeutic Modalities ◽  
Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

scholarly journals Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11321
Author(s):  
Di Zhang ◽  
Pengguang Yan ◽  
Taotao Han ◽  
Xiaoyun Cheng ◽  
Jingnan Li
Keyword(s):  
Ulcerative Colitis ◽  
Mitochondrial Dysfunction ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junction ◽  
Biological Processes ◽  
Hub Genes ◽  
Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

scholarly journals Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuqiang Zu ◽  
Peng Liu ◽  
Huaitao Wang ◽  
Ting Zhu ◽  
Jian Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Tumor Formation ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network

Abstract Background It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression. Methods We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC. Results A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC. Conclusion Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

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