An integrated systems biology and machine learning framework for identifying potential biomarkers and pathways in autism spectrum disorder.

Overview

Authors: Sara Hosseinpoor¹, Hakimeh Zali², Hassan Zohrevand³, Seyed Amir Mirmotalebisohi^4,5, Fariba Khodagholi¹, Maryam Bazrgar⁶, Sareh Asadi⁶, Abolhassan Ahmadiani¹

ORCID iDs: Sareh Asadi

Neuroscience Research Center, Institute of Neuroscience and Cognition, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Student Research Committee, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Science, Tehran, Iran
Student Research Committee, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Neurobiology Research Center, Institute of Neuroscience and Cognition, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Institutions: Shahid Beheshti University (Iran); Shahid Beheshti University of Medical Sciences (Iran)

Journal: PloS one, volume 21, issue 8, article e0355984

Dates: received 19 July 2025; accepted 28 July 2026; published online 24 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0355984 · PMID 42636188 · PMCID PMC13502595 · OpenAlex W7204133958

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: genetics / omics (modality), human (organism), rat (organism), autism (population)

Methods: Connectivity, Statistics, Machine learning

MeSH: Autism Spectrum Disorder*, Biomarkers*, Machine Learning*, Systems Biology*, Animals, Disease Models, Animal, Gene Expression Profiling, Gene Regulatory Networks, Humans, Protein Interaction Maps, Rats (* major topic)

Journal subjects: Biology and Life Sciences, Psychology, Developmental Psychology, Pervasive Developmental Disorders, Autism Spectrum Disorder, Social Sciences, Genetics, Gene Expression, Biochemistry, Nucleic acids, RNA, Non-coding RNA, Natural antisense transcripts, MicroRNAs, Gene regulation, Immunology, Immune System Proteins, Immune Receptors, Toll-like Receptors, Medicine and Health Sciences, Proteins, Cell Biology, Signal Transduction, Anatomy, Body Fluids, Blood, Physiology, Autism, Medical Conditions, Neurodevelopmental Disorders, Neuroscience, Developmental Neuroscience, Neurology, DNA-binding proteins, Brain, Hippocampus

Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Shahid Beheshti University of Medical Sciences (#43013354-3-1)

Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Background: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders whose underlying molecular mechanisms and biological processes remain incompletely understood. In this study, we used a multi-layered systems biology approach to prioritize candidate genes and regulatory factors associated with ASD.

Method: Gene expression data from peripheral blood samples were obtained from the Gene Expression Omnibus (GEO) database (GSE18123). Using analyses performed in R software, differentially expressed genes (DEGs) in patients with ASD were identified (p-value < 0.05 and |log2FC| > 0.5). These DEGs were used to perform weighted gene co-expression network analysis (WGCNA) and construct a protein–protein interaction (PPI) network. By integrating the results of these network analyses with feature selection techniques (LASSO and random forest feature importance), candidate genes associated with ASD were prioritized and evaluated using qRT-PCR in the valproic acid (VPA)-induced rat model of autism. Furthermore, a gene regulatory network (GRN) was constructed to identify the regulatory factors associated with DEGs.

Result: TLR8 and CASP4 were prioritized as candidate genes that may be associated with ASD, because they were located within the co-expression module that showed the strongest correlation with ASD, were identified as key nodes of the PPI network, and were selected by feature selection algorithms. Our experimental validation showed increased expression of TLR8 and CASP4 in the autism model compared with controls; TLR8 was upregulated in both the hippocampus and peripheral blood, whereas CASP4 was upregulated only in the hippocampus. Furthermore, GRN analysis identified miR-891b and miR-627-3p as potential regulators of TLR8, and miR-26b-5p as associated with CASP4.

Conclusion: These findings indicate that CASP4 and TLR8, together with their associated regulatory miRNAs, may represent promising biomarkers and potential therapeutic targets for future ASD research and contribute to a better understanding of the pathophysiological mechanisms underlying ASD.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Data links

ncbi.nlm.nih.gov/geo, NCBI; found in the text, “2.2.1. Transcriptomic data preprocessing and…”

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 11 MeSH terms, 1 funder, 56 references.

Cite

This paper

Hosseinpoor, S., Zali, H., Zohrevand, H., Mirmotalebisohi, S. A., Khodagholi, F., Bazrgar, M., Asadi, S., & Ahmadiani, A. (2026). An integrated systems biology and machine learning framework for identifying potential biomarkers and pathways in autism spectrum disorder. PloS one, 21(8), e0355984. https://doi.org/10.1371/journal.pone.0355984

BibTeX

@article{hosseinpoor2026integrated, author = {Hosseinpoor, Sara and Zali, Hakimeh and Zohrevand, Hassan and Mirmotalebisohi, Seyed Amir and Khodagholi, Fariba and Bazrgar, Maryam and Asadi, Sareh and Ahmadiani, Abolhassan}, title = {{An integrated systems biology and machine learning framework for identifying potential biomarkers and pathways in autism spectrum disorder}}, journal = {PloS one}, year = {2026}, month = aug, volume = {21}, number = {8}, pages = {e0355984}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0355984}, url = {https://doi.org/10.1371/journal.pone.0355984}, pmid = {42636188}, pmcid = {PMC13502595} }

RIS

TY - JOUR AU - Hosseinpoor, Sara AU - Zali, Hakimeh AU - Zohrevand, Hassan AU - Mirmotalebisohi, Seyed Amir AU - Khodagholi, Fariba AU - Bazrgar, Maryam AU - Asadi, Sareh AU - Ahmadiani, Abolhassan TI - An integrated systems biology and machine learning framework for identifying potential biomarkers and pathways in autism spectrum disorder T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/08/24 VL - 21 IS - 8 SP - e0355984 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0355984 UR - https://doi.org/10.1371/journal.pone.0355984 LA - en ER -

CSL-JSON

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