Evidence map›Paper›PMID 42878948›Full record

ArticleJournal of molecular neuroscience : MN2026

Identification of Transcriptome-Derived Candidate Genes Associated with Post-Translational Modification Gene Sets in Neonatal Hypoxic-Ischemic Encephalopathy.

Baoying Zhang, Xueli Ding, Yang Hu, Yi Wei, Yu Tang, Maoqiong Chen, Jianwei Xu

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Article in Journal of molecular neuroscience : MN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Baoying ZhangGraduate School of Guizhou Medical University, Guiyang, 550004, China.
Xueli DingDepartment of Neonatology, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
Yang HuDepartment of Neonatology, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.
Yi WeiDepartment of Neonatology, Guilin Women's and Children's Hospital, Guilin, 541001, China.
Yu TangDepartment of Neonatology, Guilin Women's and Children's Hospital, Guilin, 541001, China.
Maoqiong ChenCenter for Tissue Engineering and Stem Cell Research, Guizhou Medical University, Gui'an New District, 550025, Guiyang, China. chenmaoqiong189@163.com.
Jianwei XuDepartment of Neonatology, Affiliated Hospital of Guizhou Medical University, 550004, Guiyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neonatal hypoxic-ischemic encephalopathy (HIE) is a major cause of neurological injury in newborns. Post-translational modifications (PTMs) precisely regulate protein activity, localization, and interactions during cellular stress. However, transcriptomic changes in genes associated with PTM pathways in HIE remain incompletely characterized. Gene expression data from two mouse datasets (GSE23317 and GSE144456) were acquired from GEO. Candidate screening combined differential expression, WGCNA, and overlap with 21 PTM-related gene sets. Further analyses included PPI networks, expression comparisons, receiver operating characteristic (ROC) analyses, functional enrichment, immune-cell deconvolution, miRNA prediction, molecular docking, and molecular dynamics (MD) simulation. A total of 113 time-consistent differentially expressed genes were identified, and 12 overlapped the HIE-associated WGCNA module and PTM-related gene sets. Five genes, Hk2, Lgals3, Sdc4, Upp1, and Vim, showed positive expression changes in the analysed contrasts (discovery log2 fold-change ranges, 0.271-1.027 at 3 h and 0.326-2.031 at 24 h). Individual-gene areas under the ROC curve (AUCs) ranged from 0.893 to 1.000 in GSE23317; the combined model yielded a leave-one-out cross-validation AUC of 0.929. Functional analyses highlighted glial and cytoskeletal processes, carbohydrate metabolism, and oxidative phosphorylation, and linked the five genes to estimated immune-cell signals. HK2-calcitriol and HK2-menogaril yielded the lowest docking scores, both - 8.9 kcal/mol. Integration of transcriptomic data with PTM-related gene sets identified five candidate genes and highlighted glial, cytoskeletal, metabolic, and immune-associated responses in neonatal HIE. These findings provide a basis for investigating their biological roles and potential biomarker relevance. Integration of transcriptomic data with PTM-related gene sets identified five candidate genes and highlighted glial, cytoskeletal, metabolic, and immune-associated responses in neonatal HIE. These findings provide a basis for investigating their biological roles and potential biomarker relevance.

Indexed as

Hypoxia-Ischemia, BrainProtein Processing, Post-TranslationalTranscriptomeAnimalsMiceMicroRNAsProtein Interaction MapsMicroRNAsBioinformatics analysisCandidate genesImmune-cell deconvolutionMolecular dockingNeonatal hypoxic-ischemic encephalopathyPost-translational modification-related gene sets

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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.