Identification of potential common molecular factors of pancreatic cancer and diabetes mellitus using microarray data analysis combined with bioinformatics techniques and experimental validation

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Abstract

Background and Aim: Detection of early-stage pancreatic cancer (PaC) patients can decrease the mortality rate through surgical resection thanks to a screening approach of high-risk and asymptomatic individuals. Up to 80% of PaC patients are either hyperglycemic or diabetic at the time of diagnosis. Diabetes mellitus (DM) identification as an early manifestation of PaC may lead to detection of this malignancy at early and presymptomatic stages. Therefore, the current study aims to identify common molecular factors between DM and PaC to shed light on a potential screening for DM as a diagnostic approach for PaC. Methods: The study was divided into three steps including dataset selection and identification of key genes, quantitative real-time polymerase chain reaction (qRT-PCR) to validate these genes, and enrichment analysis of their target genes. Results: Using GEO2R analysis, conducted on microarray dataset (GSE15932), SPI1 and Yin Yang 1 (YY1) genes were found to be expressed in peripheral blood mononuclear cells of PaC, DM, and PaC + DM patients. Moreover, qRT-PCR results in validation of microarray data showed a significant increment of these two genes among all patient groups. Furthermore, enrichment analyses of SPI1 and YY1 target genes revealed that they are mainly expressed in hematopoietic cells and associated with immune responses as well as immune signaling pathways. Discussion: We speculate that this study on SPI1 and YY1 genes and their targets can result in a successful strategy to investigate diabetes as a screening step for better management of PaC screening using standard serologic tests.

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Kalantari, S., Pourshams, A., Roudi, R., Zali, H., Bandehpour, M., Kalantari, A., … Madjd, Z. (2021). Identification of potential common molecular factors of pancreatic cancer and diabetes mellitus using microarray data analysis combined with bioinformatics techniques and experimental validation. Biomedical and Biotechnology Research Journal, 5(3), 286–294. https://doi.org/10.4103/bbrj.bbrj_122_21

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