Integrated bioinformatics analysis reveals fatty acid metabolism subtypes and immune landscape in recurrent implantation failure.

Huiqi Liao, Honglu Diao, Libing Liang, Ziwei Ai, Ying Zhang, Zhiling Li

Abstract read

In one paragraph

Article in Scientific reports, 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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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Huiqi LiaoDepartment of Reproductive Center, First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou City, 515041, Guangdong, China.

Honglu DiaoReproductive Medicine Center, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei Province, China.

Libing LiangDepartment of Reproductive Center, First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou City, 515041, Guangdong, China.

Ziwei AiReproductive Medicine Center, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei Province, China.

Ying ZhangReproductive Medicine Center, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei Province, China.

Zhiling LiDepartment of Reproductive Center, First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou City, 515041, Guangdong, China. stlizhiling@126.com.

Funding

the Guangdong Provincial Science and Technology Project Grant Number 2016A020218015the Hubei Provincial Natural Science Foundation and Shiyan of China Grant Number 2025AFD17the National Natural Science Foundation of China Grant Numbers 81871223, 81671536, and 81471522the Natural Science Foundation of Guangdong Province of China Grant Number 2014A030313482

6 · The paper itself

Abstract

Microarray and RNA-sequencing datasets for recurrent implantation failure (RIF) were retrieved from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were subsequently integrated with lipid metabolism-related gene sets to identify candidate biomarkers. Cross-analysis of these approaches yielded seven candidate genes, which were then subjected to four machine learning algorithms. This led to the identification of five hub genes: PRKAG2, CPT1A, PPARGC1A, PIK3C2G, and PTGS2. Logistic regression further validated these genes as robust biomarkers, enabling the construction of a diagnostic nomogram. Molecular docking using CB-Dock Tools subsequently demonstrated that peucedanin binds favorably to all five hub gene products. Collectively, these findings highlight PRKAG2, CPT1A, PPARGC1A, PIK3C2G, and PTGS2 as promising diagnostic biomarkers for RIF and offer new perspectives for therapeutic intervention.

Indexed as

Computational BiologyEmbryo ImplantationFatty AcidsBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansLipid MetabolismMachine LearningMolecular Docking SimulationBiomarkersFatty AcidsImmune infiltrationLipid metabolismMachine learningMolecular clusterMolecular dockingRecurrent implantation failureWGCNA

Identifiers

PMID42393159

DOI10.1038/s41598-026-57894-4

PMCPMC13558650

What Socratic 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 Socratic graph.