Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Md Bayazid Hossen; Md Ariful Islam; Md Selim Reza; Md Kaderi Kibria; Md Abu Horaira; Khanis Farhana Tuly; Md Omar Faruqe; Firoz Kabir; Md Nurul Haque Mollah

doi:10.1016/j.compbiomed.2022.106411

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Comput Biol Med. 2023 Jan:152:106411. doi: 10.1016/j.compbiomed.2022.106411. Epub 2022 Dec 5.

Authors

Md Bayazid Hossen¹, Md Ariful Islam¹, Md Selim Reza¹, Md Kaderi Kibria¹, Md Abu Horaira¹, Khanis Farhana Tuly¹, Md Omar Faruqe², Firoz Kabir³, Md Nurul Haque Mollah⁴

Affiliations

¹ Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
² Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205, Bangladesh.
³ Department of Ophthalmology and Visual Sciences, School of Medicine, University of Maryland, Baltimore, MD, USA.
⁴ Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: mollah.stat.bio@ru.ac.bd.

PMID: 36502691
DOI: 10.1016/j.compbiomed.2022.106411

Abstract

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Keywords: Drug targets and agents; Gene expression profiles; Integrated robust statistics and bioinformatics approaches; Key genes; Pancreatic cancer.

MeSH terms

Biomarkers, Tumor / genetics
Computational Biology
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Genomics
Humans
Molecular Docking Simulation
Pancreatic Neoplasms* / diagnosis
Pancreatic Neoplasms* / drug therapy
Pancreatic Neoplasms* / genetics
Transcriptome*

Substances

Collagen Type I, alpha2 Subunit
Biomarkers, Tumor