Bioinformatics approach to analyze gene expression profile and comorbidities of gastric cancer

Datta, Ratri; Podder, Nitun Kumar; Rana, Humayan Kabir; Islam, Khaled Ben; Moni, Mohammad Ali

doi:10.1109/iccit51783.2020.9392587

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…There must be several DEGs in the two diseases for them to be associated [ 43 ]. Therefore, we performed a cross-comparison analysis on the identified DEGs among the CVD and the risk factors.…”

Section: Resultsmentioning

confidence: 99%

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Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Barua

Omit

Rana

et al. 2022

Cardiovascular Therapeutics

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Background. Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors. Methods. We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET. Results. We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging. Conclusions. Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.

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Section: Resultsmentioning

confidence: 99%

“…In T2D, 233 up-and 339 downregulated genes are identified where total DEGs are 572. 1195 DEGs were identified for HCL including 538 up- There must be several DEGs in the two diseases for them to be associated [43]. Therefore, we performed a crosscomparison analysis on the identified DEGs among the CVD and the risk factors.…”

Section: Resultsmentioning

confidence: 99%

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Barua

Omit

Rana

et al. 2022

Cardiovascular Therapeutics

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“…Therefore, we executed gene ontology analysis to understand the correlation between COVID-19 and each of the comorbidities. We used the commonly altered DEGs of COVID-19 and hypertension, diabetes, obesity, and lung cancer in GO Biological Process [ 33 ] and GO Cellular Component [ 34 ] databases of Enrichr and collected ontologies. We employed the same parameters for the significant ontologies identification that we used in the pathway analysis.…”

Section: Methods and Analysesmentioning

confidence: 99%

“…Mathematical and graphical illustration for the structural organization of proteins in a cellular unit is known as protein-protein interaction. To understand cellular/molecular physiology formed by complex biochemical functions and also for drug development, PPI is crucial [ 26 , 34 ]. Therefore, we performed PPI analysis using the commonly altered DEGs between COVID-19 and its comorbidities for the possible linkage among them, utilizing the STRING ( https://string-db.org/ ) database.…”

Section: Methods and Analysesmentioning

confidence: 99%

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Omit

Akhter

Rana

et al. 2023

BioMed Research International

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Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard’s similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

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Statistical and Bioinformatics Model to Identify the Influential Genes and Comorbidities of Glioblastoma

Podder

Shill

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Bioinformatics approach to analyze gene expression profile and comorbidities of gastric cancer

Cited by 7 publications

References 7 publications

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Statistical and Bioinformatics Model to Identify the Influential Genes and Comorbidities of Glioblastoma

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