Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Alur, Varun; Raju, Varshita; Vastrad, Basavaraj; Vastrad, Chanabasayya; Kavatagimath, Satish; Kotturshetti, Shivakumar

doi:10.1177/11795514231155635

Cited by 15 publications

(13 citation statements)

References 263 publications

(207 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, understanding the pathological changes and molecular mechanisms of MS is essential for clinical diagnosis and treatment. Efficient NGS and bioinformatics analyses help us understand the molecular mechanisms of the disease onset and advancement, which is invaluable for biomarkers and therapeutic target identification [51–52]. In our study, 959 DEGs were identified between MS patients and normal controls based on NGS dataset.…”

Section: Discussionmentioning

confidence: 96%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Multiple sclerosis (MS) is the main reason for disability caused by autoimmunity. However, effective biomarkers for MS have not been identified. This study explored the molecular mechanisms and potential therapeutic targets for MS occurrence and progression using bioinformatics and next generation sequencing (NGS) data analysis. NGS data GSE138614, including patients with MS and 25 normal control samples, were obtained from Gene Expression Omnibus (GEO) database Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were built by Cytoscape to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. A total of 959 DEGs (479 up regulated genes and 480 down regulated genes) were detected. The GO terms and pathways of DEGs involve immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). The network analysis revealed that hub genes include LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3 might be involved in the development of MS. The present investigation illustrates a characteristic gene profile in MS, which might contribute to the interpretation of the progression of MS and provide novel biomarkers and therapeutic targets for MS.

show abstract

Section: Discussionmentioning

confidence: 96%

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For diagnosis and treatment, it is necessary to further understand the molecular pathogenesis resulting in occurrence and advancement. Due to the advancement of NGS technology, the genetic modifications due to disease progression can be detected, indicating gene targets for diagnosis, therapy and prognosis of specific diseases [38-39].…”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

View full text Add to dashboard Cite

Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

show abstract

“…In recent years, next generation sequencing (NGS) technology has been applied to the research on various bioinformatics, which can screen and identify genes and signalizing pathways of various diseases [19–20]. NGS is an emerging molecular biology technology based on a high-throughput platform, which is widely used in COPD [21].…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Identification of accurate biomarkers is still particularly urgent for improving the poor survival of chronic obstructive pulmonary disease (COPD) patients. In this investigation, we aimed to identity the potential biomarkers in COPD via bioinformatics and next generation sequencing (NGS) data analysis. In this investigation, the differentially expressed genes (DEGs) in COPD were identified using NGS dataset (GSE239897) from Gene Expression Omnibus (GEO) database. Subsequently, gene ontology (GO) and pathway enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of COPD. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to determine the hub genes, miRNAs and TFs. The receiver operating characteristic (ROC) analysis was performed to determine the diagnostic value of hub genes. A total of 956 overlapping DEGs (478 up regulated and 478 down regulated genes) were identified in the NGS dataset. DEGs were mainly associated with GO functional terms and pathways in cellular response to stimulus. response to stimulus, immune system and neutrophil degranulation. There were 10 hub genes (MYC, LMNA, VCAM1, MAPK6, DDX3X, SHMT2, PHGDH, S100A9, FKBP5 and RPS6KA2) identified by PPI, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis. In conclusion, the DEGs, relative GO terms, pathways and hub genes identified in the present investigation might aid in understanding of the molecular mechanisms underlying COPD progression and provide potential molecular targets and biomarkers for COPD.

show abstract

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus

Cited by 15 publications

References 263 publications

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Identification of key genes and biological pathways in chronic obstructive pulmonary disease using bioinformatics and next generation sequencing data analysis

Contact Info

Product

Resources

About