Specific Regulatory Motifs Network in SARS-CoV-2-Infected Caco-2 Cell Line, as a Model of Gastrointestinal Infections

Ghani, Sepideh; Kalantari, Sima; Mirmotalebisohi, Seyed Amir; Sameni, Marzieh; Poursheykhi, Hossein; Dadashkhan, Sadaf; Abbasi, Maryam; Zali, Hakimeh

doi:10.1089/cell.2021.0055

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…developed a nine-gene signature based on GEO and TCGA datasets and constructed a nomogram combining the gene signature and clinical prognostic features to predict OS in PC [ 25 ]. Besides, Sima Kalantari et al used bioinformatics tools to recognize 10 key genes associated with SARS-CoV-2 infection in Caco-2 cell was significantly overexpressed in colon cancer [ 26 ]. Our present study provides a different approach to selecting candidate biomarkers and establishing a diagnosis and survival prediction model.…”

Section: Discussionmentioning

confidence: 99%

Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer

Zhou

et al. 2022

Heliyon

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Section: Discussionmentioning

confidence: 99%

Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer

Zhou

et al. 2022

Heliyon

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“…In recent decades, systems biology approaches, such as protein-protein interaction (PPI) and gene regulatory network (GRN) analyses, have revolutionized the understanding of molecular mechanisms underlying biological phenomena and pathologies [ 12 ]. These innovative approaches have been instrumental in unraveling the intricate molecular connections and relationships among various diseases [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deciphering molecular bridges: Unveiling the interplay between metabolic syndrome and Alzheimer’s disease through a systems biology approach and drug repurposing

Azizan,

Zali,

Mirmotalebisohi

et al. 2024

PLoS ONE

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The association between Alzheimer’s disease and metabolic disorders as significant risk factors is widely acknowledged. However, the intricate molecular mechanism intertwining these conditions remains elusive. To address this knowledge gap, we conducted a thorough investigation using a bioinformatics method to illuminate the molecular connections and pathways that provide novel perspectives on these disorders’ pathological and clinical features. Microarray datasets (GSE5281, GSE122063) from the Gene Expression Omnibus (GEO) database facilitated the way to identify genes with differential expression in Alzheimer’s disease (141 genes). Leveraging CoreMine, CTD, and Gene Card databases, we extracted genes associated with metabolic conditions, including hypertension, non-alcoholic fatty liver disease, and diabetes. Subsequent analysis uncovered overlapping genes implicated in metabolic conditions and Alzheimer’s disease, revealing shared molecular links. We utilized String and HIPPIE databases to visualize these shared genes’ protein-protein interactions (PPI) and constructed a PPI network using Cytoscape and MCODE plugin. SPP1, CD44, IGF1, and FLT1 were identified as crucial molecules in the main cluster of Alzheimer’s disease and metabolic syndrome. Enrichment analysis by the DAVID dataset was employed and highlighted the SPP1 as a novel target, with its receptor CD44 playing a significant role in the inflammatory cascade and disruption of insulin signaling, contributing to the neurodegenerative aspects of Alzheimer’s disease. ECM-receptor interactions, focal adhesion, and the PI3K/Akt pathways may all mediate these effects. Additionally, we investigated potential medications by repurposing the molecular links using the DGIdb database, revealing Tacrolimus and Calcitonin as promising candidates, particularly since they possess binding sites on the SPP1 molecule. In conclusion, our study unveils crucial molecular bridges between metabolic syndrome and AD, providing insights into their pathophysiology for therapeutic interventions.

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“…Topological examination of the node functions in gene regulatory and protein−protein interaction networks (PPI) (gene regulatory network [GRN] and PPIN) may illuminate critical molecular actors responsible for therapeutic effects 29–31 . In regulatory networks, motifs are the fundamental units, and the nodes that participate in motif subnetworks are presented as playing a crucial regulatory function in many therapeutic and disease‐related biological features 32–34 …”

Section: Introductionmentioning

confidence: 99%

“…[29][30][31] In regulatory networks, motifs are the fundamental units, and the nodes that participate in motif subnetworks are presented as playing a crucial regulatory function in many therapeutic and disease-related biological features. [32][33][34] The purpose of this research was to identify the defining factors and molecular processes that contribute to the subtype-specific effects of Rapamycin in BC. We carried out the joint analysis by examining profiles of mRNA expression.…”

Section: Introductionmentioning

confidence: 99%

A systems biology approach and in vitro experiment indicated Rapamycin targets key cancer and cell cycle‐related genes and miRNAs in triple‐negative breast cancer cells

Tafti,

Shojaei,

Zali

et al. 2023

Molecular Carcinogenesis

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An anticancer drug known as Rapamycin acts by inhibiting the mammalian target of the Rapamycin pathway. This agent has recently been investigated for its potential therapeutic benefits in sensitizing drug‐resistant breast cancer (BC) treatment. The molecular mechanism underlying these effects, however, is still a mystery. Using a systems biology method and in vitro experiment, this study sought to discover essential genes and microRNAs (miRNAs) targeted by Rapamycin in triple‐negative BC (TNBC) cells to aid prospective new medications with less adverse effects in BC treatment. We developed the transcription factor‐miRNA‐gene and protein−protein interaction networks using the freely accessible microarray data sets. FANMOD and MCODE were utilized to identify critical regulatory motifs, clusters, and seeds. Then, functional enrichment analyses were conducted. Using topological analysis and motif detection, the most important genes and miRNAs were discovered. We used quantitative real‐time polymerase chain reaction (qRT‐PCR) to examine the effect of Rapamycin on the expression of the selected genes and miRNAs to verify our findings. We performed flow cytometry to investigate Rapamycin's impact on cell cycle and apoptosis. Furthermore, wound healing and migration assays were done. Three downregulated (PTGS2, EGFR, VEGFA) and three upregulated (c‐MYC, MAPK1, PIK3R1) genes were chosen as candidates for additional experimental verification. There were also three upregulated miRNAs (miR‐92a, miR‐16, miR‐20a) and three downregulated miRNAs (miR‐146a, miR‐145, miR‐27a) among the six selected miRNAs. The qRT‐PCR findings in MDA‐MB‐231 cells indicated that c‐MYC, MAPK1, PIK3R1, miR‐92a, miR‐16, and miR‐20a expression levels were considerably elevated following Rapamycin treatment, whereas PTGS2, EGFR, VEGFA, miR‐146a, and miR‐145 expression levels were dramatically lowered (p < 0.05). These genes are engaged in cancer pathways, transcriptional dysregulation in cancer, and cell cycle, according to the top pathway enrichment findings. Migration and wound healing abilities of the cells declined after Rapamycin treatment, and the number of apoptotic cells increased. We demonstrated that Rapamycin suppresses cell migration and metastasis in the TNBC cell line. In addition, our data indicated that Rapamycin induces apoptosis in this cell line. The discovered vital genes and miRNAs affected by Rapamycin are anticipated to have crucial roles in the pathogenesis of TNBC and its therapeutic resistance.

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Specific Regulatory Motifs Network in SARS-CoV-2-Infected Caco-2 Cell Line, as a Model of Gastrointestinal Infections

Cited by 6 publications

References 31 publications

Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer

Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer

Deciphering molecular bridges: Unveiling the interplay between metabolic syndrome and Alzheimer’s disease through a systems biology approach and drug repurposing

A systems biology approach and in vitro experiment indicated Rapamycin targets key cancer and cell cycle‐related genes and miRNAs in triple‐negative breast cancer cells

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