Integrative Transcriptomic and Network Analyses Reveal Common Molecular Signatures and Turmeric‐Derived Common Therapeutic Candidates for Neurological Diseases
- 1Department of Mathematics Faculty of Science Jashore University of Science and Technology Jashore Bangladesh just.edu.bd
- 2High Performance Computing (HPC) Laboratory Department of Mathematics Faculty of Science Jashore University of Science and Technology Jashore Bangladesh just.edu.bd
- 3Department of Nursing and Health Science Faculty of Health Science Jashore University of Science and Technology Jashore Bangladesh just.edu.bd
Abstract
Background: Neurological diseases (NDs), including Alzheimer′s disease (AD), Parkinson′s disease (PD), stroke, and brain tumors, impose a substantial global health burden. Although these disorders have distinct clinical manifestations and etiologies, accumulating evidence suggests that they share certain molecular mechanisms. However, their common molecular signatures and the therapeutic potential of turmeric‐derived bioactive compounds have not been systematically investigated.
Objectives: This study is aimed at identifying shared molecular mechanisms and key hub genes across AD, PD, stroke, and glioblastoma (GBM) using an integrative bioinformatics framework and at investigating turmeric‐derived compounds as potential multitarget common therapeutic candidates.
Materials and Methods: Gene expression datasets (GSE22491, GSE5281, GSE122709, and GSE4290) were retrieved from the Gene Expression Omnibus (GEO) database. Common host differentially expressed genes (cHDEGs) were identified through integrated bioinformatics analysis. Protein–protein interaction (PPI) networks were subsequently constructed to identify common hub genes (cHubGs), followed by functional enrichment and transcription factor (TF) and miRNA‐mediated regulatory network analyses. The interactions between the identified cHubGs and turmeric‐derived compounds were further investigated using molecular docking and 500‐ns molecular dynamics simulations (MDS).
Results: A total of 81 cHDEGs and four cHubGs (CCNB1, CDK1, CYCS, and STAT3) were identified. Functional enrichment and regulatory network analysis revealed shared biological processes and signaling pathways, as well as key TFs and miRNAs potentially associated with ND pathogenesis. Among the 77 turmeric‐derived compounds that passed the toxicity screening, demethoxycurcumin, cyclocurcumin, and (1E,4E)‐1‐(4‐hydroxy‐3‐methoxyphenyl)‐5‐(4‐hydroxyphenyl)‐1,4‐pentadien‐3‐one exhibited the strongest predicted binding affinities. The selected compound–target complexes also showed stable behavior during the MDS.
Conclusion: This study provides insights into the shared molecular features across AD, PD, stroke, and GBM and prioritizes turmeric‐derived compounds as potential multitarget therapeutic candidates. These computational findings require further validation through independent datasets and experimental studies.
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BibTeX
@article{bayragi2026integrative,
title = {{Integrative Transcriptomic and Network Analyses Reveal Common Molecular Signatures and Turmeric‐Derived Common Therapeutic Candidates for Neurological Diseases}},
author = {Sumi Rani Bayragi and Riday Hossain and Md. Zohurul Islam and Sharmin Akter Sumi and Fee Faysal Ahmed},
journal = {BioMed Research International},
year = {2026},
month = nov,
doi = {10.1155/bmri/7855459},
url = {https://doi.org/10.1155/bmri/7855459},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrative Transcriptomic and Network Analyses Reveal Common Molecular Signatures and Turmeric‐Derived Common Therapeutic Candidates for Neurological Diseases
AU - Sumi Rani Bayragi
AU - Riday Hossain
AU - Md. Zohurul Islam
AU - Sharmin Akter Sumi
AU - Fee Faysal Ahmed
PY - 2026
DA - 2026/11/04
JO - BioMed Research International
DO - 10.1155/bmri/7855459
UR - https://doi.org/10.1155/bmri/7855459
AB - Background: Neurological diseases (NDs), including Alzheimer′s disease (AD), Parkinson′s disease (PD), stroke, and brain tumors, impose a substantial global health burden. Although these disorders have distinct clinical manifestations and etiologies, accumulating evidence suggests that they share certain molecular mechanisms. However, their common molecular signatures and the therapeutic potential of turmeric‐derived bioactive compounds have not been systematically investigated. Objectives: This study is aimed at identifying shared molecular mechanisms and key hub genes across AD, PD, stroke, and glioblastoma (GBM) using an integrative bioinformatics framework and at investigating turmeric‐derived compounds as potential multitarget common therapeutic candidates. Materials and Methods: Gene expression datasets (GSE22491, GSE5281, GSE122709, and GSE4290) were retrieved from the Gene Expression Omnibus (GEO) database. Common host differentially expressed genes (cHDEGs) were identified through integrated bioinformatics analysis. Protein–protein interaction (PPI) networks were subsequently constructed to identify common hub genes (cHubGs), followed by functional enrichment and transcription factor (TF) and miRNA‐mediated regulatory network analyses. The interactions between the identified cHubGs and turmeric‐derived compounds were further investigated using molecular docking and 500‐ns molecular dynamics simulations (MDS). Results: A total of 81 cHDEGs and four cHubGs (CCNB1, CDK1, CYCS, and STAT3) were identified. Functional enrichment and regulatory network analysis revealed shared biological processes and signaling pathways, as well as key TFs and miRNAs potentially associated with ND pathogenesis. Among the 77 turmeric‐derived compounds that passed the toxicity screening, demethoxycurcumin, cyclocurcumin, and (1E,4E)‐1‐(4‐hydroxy‐3‐methoxyphenyl)‐5‐(4‐hydroxyphenyl)‐1,4‐pentadien‐3‐one exhibited the strongest predicted binding affinities. The selected compound–target complexes also showed stable behavior during the MDS. Conclusion: This study provides insights into the shared molecular features across AD, PD, stroke, and GBM and prioritizes turmeric‐derived compounds as potential multitarget therapeutic candidates. These computational findings require further validation through independent datasets and experimental studies.
SN - 2314-6133
SN - 2314-6141
ER -