Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Al-Mustanjid, Md.; Mahmud, Sakib; Royel, Md. Rejaul Islam; Rahman, Md. Habibur; Islam, Tania; Rahman, Rezanur; Moni, Mohammad Ali

doi:10.1016/j.ygeno.2020.06.001

Cited by 48 publications

(45 citation statements)

References 66 publications

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“…The network was visualized using the NetworkAnalyst web-based framework. As the need for gene expression-based datasets grow, NetworkAnalyst has been used as a leading bioinformatics method for system-level data understanding [52] , [53] . We used the Network repository’s TF-RNAseq coregulatory interactions to find regulatory TFs that control DEGs of interest at the transcriptional and post-transcriptional levels.…”

Section: Introductionmentioning

confidence: 99%

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Hasan

Rahman

Islam

et al. 2022

Informatics in Medicine Unlocked

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

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Section: Introductionmentioning

confidence: 99%

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Hasan

Rahman

Islam

et al. 2022

Informatics in Medicine Unlocked

Self Cite

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“…The mutation frequency of 10 genes, including ASAP1, AGTPBP1, FZD7 etc., differed when RIPK2 differed in expression. ASAP1 and RIPK2 were reported as hub proteins of in ammatory bowel disease and colorectal cancer; and ASAP1 expression might be associated with pulmonary and bladder neoplasm diseases (36). Therefore, our research suggested that ASAP1 mutation might be related to RIPK2 alteration and thus be associated with taxol resistance of ovarian cancer.…”

Section: Discussionmentioning

confidence: 64%

High Expression of RIPK2 is Associated with Taxol Resistance in Serous Ovarian Cancer

Shen¹,

Lin²,

Chen³

et al. 2021

Preprint

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BackgroundTaxol resistance of serous ovarian cancer is responsible for its poor prognosis, yet the underlying mechanism was still poorly understood. Thus, we probed the mechanism of taxol resistance in serous ovarian cancer with multiple bioinformatic methods to provide novel insights for potential therapy. MethodsThe differentially expressed genes (DEGs) and their relationship with overall survival (OS) and progress-free interval (PFI) of ovarian cancer patients were analyzed using gene expression datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The role of receptor interacting serine/threonine kinase 2 (RIPK2) was validated by identifying its co-expressed genes, making function analysis and generating protein-protein network (PPI). Single-sample GSEA (ssGSEA) method was used to explore the immune infiltration and genomic alterations of RIPK2 was also analyzed via cBio Cancer Genomics Portal (cBioProtal).ResultsRIPK2 was highly expressed in taxol resistant ovarian cancer cell lines, while its high expression was also linked with OS and PFI in serous ovarian cancer patients. The PPI network analysis and pathway analysis demonstrated that RIPK2 might take part in positive regulation of NF-κB transcription factor activity. Different expression level of RIPK2 was related to tumor microenvironment alteration, which might participate in formation of taxol resistance.ConclusionsOur studies suggested that high expression of RIPK2 was related to taxol resistance in serous ovarian cancer, while RIPK2 induced taxol resistance through NOD1/RIPK2/NF-κB inflammatory pathway activation and tumor microenvironment changes.

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“…The mutation frequency of 10 genes, including ASAP1 , AGTPBP1 and FZD7 etc., differed when RIPK2 expression differed. ASAP1 and RIPK2 were reported as hub proteins of inflammatory bowel disease and colorectal cancer; and ASAP1 expression might be associated with pulmonary and bladder neoplasms [ 40 ]. Therefore, our research suggests that ASAP1 mutation might be related to RIPK2 alteration and thus be associated with Taxol resistance in ovarian cancer.…”

Section: Discussionmentioning

confidence: 99%

High expression of RIPK2 is associated with Taxol resistance in serous ovarian cancer

et al. 2022

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Background Taxol resistance in serous ovarian cancer is responsible for its poor prognosis, yet the underlying mechanism is still poorly understood. Thus, we probed the mechanism of Taxol resistance in serous ovarian cancer with multiple bioinformatic methods to provide novel insights into potential therapies. Methods The differentially expressed genes (DEGs) in Taxol-sensitive and Taxol-resistant cell lines and their relationship with the overall survival (OS) and progression-free interval (PFI) of ovarian cancer patients were analyzed using gene expression datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The role of receptor interacting serine/threonine kinase 2 (RIPK2) was validated via identification of its coexpressed genes, functional analysis and generation of a protein-protein interaction (PPI) network. The single sample gene set enrichment analysis (ssGSEA) was used to explore immune infiltration, and genomic alterations of RIPK2 were also analyzed via cBio Cancer Genomics Portal (cBioProtal). Results RIPK2 was highly expressed in Taxol resistant ovarian cancer cell lines, and its high expression was also linked with shorter OS and PFI in serous ovarian cancer patients. The PPI network analysis and pathway analysis demonstrated that RIPK2 might participate in the positive regulation of NF-κB transcription factor activity. RIPK2 expression was related to tumor microenvironment alterations, which might participate in the formation of Taxol resistance. Conclusions Our studies suggested that high expression of RIPK2 is related to Taxol resistance in serous ovarian cancer, and that RIPK2 induces Taxol resistance through NOD1/RIPK2/NF-κB inflammatory pathway activation and tumor microenvironment changes.

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Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Cited by 48 publications

References 66 publications

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

High Expression of RIPK2 is Associated with Taxol Resistance in Serous Ovarian Cancer

High expression of RIPK2 is associated with Taxol resistance in serous ovarian cancer

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