Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches

Arnob Sarker; Md Abdul Aziz; Md Bayazid Hossen; Md Manir Hossain Mollah; Al-Amin; Md Nurul Haque Mollah

doi:10.1038/s41598-024-79391-2

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches

Sci Rep. 2024 Nov 11;14(1):27545. doi: 10.1038/s41598-024-79391-2.

Authors

Arnob Sarker^{1

2}, Md Abdul Aziz¹, Md Bayazid Hossen^{2

3}, Md Manir Hossain Mollah⁴, Al-Amin⁵, Md Nurul Haque Mollah⁶

Affiliations

¹ Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh.
² Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
³ Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
⁴ Department of Physical Sciences, Independent University, Bangladesh (IUB), Dhaka, Bangladesh.
⁵ Department of Zoology, University of Rajshahi, Rajshahi, 6205, Bangladesh.
⁶ Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh. mollah.stat.bio@ru.ac.bd.

Abstract

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Keywords: Bioinformatics and machine learning approaches; Drug repurposing; Gene expression profiles; Glioblastoma; Key genes.

MeSH terms

Biomarkers, Tumor / genetics
Brain Neoplasms* / diagnosis
Brain Neoplasms* / drug therapy
Brain Neoplasms* / genetics
Brain Neoplasms* / therapy
DNA Methylation
Gene Expression Profiling
Gene Expression Regulation, Neoplastic*
Gene Regulatory Networks*
Glioblastoma* / diagnosis
Glioblastoma* / drug therapy
Glioblastoma* / genetics
Glioblastoma* / therapy
Humans
Molecular Docking Simulation
Supervised Machine Learning
Transcriptome
Unsupervised Machine Learning*

Substances

Biomarkers, Tumor