Establishment of a novel glycolysis-immune-related diagnosis gene signature for endometriosis by machine learning

Qizhen Chen, Yufan Jiao, Zhe Yin, Xiayan Fu, Shana Guo, Yuhua Zhou, Yanqiu Wang
Journal of assisted reproduction and genetics · 2023 · vol. 40(5) , pp. 1147–1161 · doi:10.1007/s10815-023-02769-0 · PMID:36930359 · PMC10239430 · W4327675642
article OA: gold CC0 ⤵ 5 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study identified five glycolysis-related genes and established a predictive model for endometriosis diagnosis, revealing differences in the immune environment between endometriosis and control groups.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-09 ⓘ

This study used publicly available GEO transcriptomic datasets of eutopic endometrium from women with endometriosis and healthy controls, focusing on glycolysis-related genes and their links to immune-cell infiltration. Using LASSO regression and random forest, the authors identified five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, and ADH6) to build a machine-learning diagnostic model, which showed ROC AUC values around 0.77–0.82 in training and test sets; they also reported immune-environment differences between endometriosis and controls and verified the five genes with RT-qPCR. A key caveat is that the model and immune-cell estimates are derived from in silico analyses of bulk gene-expression datasets with normalization across batches and array platforms. This paper is centrally about endometriosis — it develops a glycolysis-immune-related gene signature and diagnostic prediction model for endometriosis using machine learning and immune infiltration analysis.

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Abstract

PURPOSE: The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model. METHODS: A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed. RESULTS: The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR. CONCLUSION: The glycolysis-immune-based predictive model was established to forecast EMS patients' diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Area Under Curve Area Under Curve Area Under Curve Area Under Curve Area Under Curve Area Under Curve Area Under Curve Area Under Curve

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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Cited by (5)

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