Evidence map›Paper›PMID 42098401›Full record

ArticleMolecular neurobiology2026

Mitochondrial Gene Signature Reveals Novel Diagnostic Biomarkers for Autism Spectrum Disorder.

Jieyu Wang, Zeyu Cheng, Mingyuan Liu, Yuting Zhang, Yi Jiang, Tianyu Liu, Junyu Ren, Lijie Wu, Mingyang Zou, Caihong Sun

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular neurobiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jieyu WangDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Zeyu ChengDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Mingyuan LiuDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Yuting ZhangDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Yi JiangDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Tianyu LiuDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Junyu RenDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Lijie WuDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China.
Mingyang ZouDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China. mingyangshine@sina.com.
Caihong SunDepartment of Children's and Adolescent Health, School of Public Health, Harbin Medical University, Harbin, 150081, China. suncaihong2003@163.com.

Funding

National Natural Science Foundation of China U24A20770
6 · The paper itself

Abstract

Autism Spectrum Disorder (ASD) pathogenesis remains unclear, with mitochondrial dysfunction implicated as a key contributor. Reliable mitochondrial-related diagnostic biomarkers are lacking, hindering early detection and mechanistic studies. This study integrated transcriptomic data from postmortem ASD cortical tissues (GSE28521 for training; GSE64018 for validation) with mitochondrial-related genes (MRGs) from MitoCarta3.0. Mitochondrial pathways were investigated using gene set enrichment analysis (GSEA). Candidate ASD-mitochondria (ASD-MIT) genes were identified by combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), and MRGs. Machine learning algorithms (LASSO, Random Forest, and SVM-RFE) were applied to screen hub genes. Diagnostic performance was evaluated using a linear predictive model, an artificial neural network (ANN), and a nomogram. Single-sample GSEA (ssGSEA) was used to assess associations between hub genes and mitochondrial pathway activity. Biological validation included qPCR in BTBR mice and protein localization analysis using the Human Protein Atlas (HPA). GSEA revealed significant downregulation of mitochondrial pathways in ASD. 22 candidate ASD-MIT genes were identified, from which three hub genes-IDH3A, MRPL2, and CHCHD4-were consistently selected by all three machine learning models. The three-gene panel demonstrated strong diagnostic ability (AUC = 0.910), confirmed by the ANN model (AUC = 0.903). The nomogram achieved excellent predictive accuracy (C-index = 0.964). Importantly, ssGSEA analysis showed that these genes were strongly associated with mitochondrial pathway activity, particularly mitochondrial calcium ion transport. qPCR validated significant downregulation of Idh3a and Mrpl2 in BTBR mice, and HPA confirmed mitochondrial localization and brain expression. This study identifies a mitochondrial gene signature associated with ASD and highlights IDH3A, MRPL2, and CHCHD4 as promising diagnostic biomarkers. These findings advance understanding of mitochondrial dysfunction in ASD pathogenesis and further suggest that disruption of mitochondrial Ca

Indexed as

Autism Spectrum DisorderGenes, MitochondrialMitochondriaTranscriptomeAnimalsBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningMiceBiomarkersAutism spectrum disorderBiomarkersMachine learningMitochondrial

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.