Taiwanese journal of obstetrics & gynecology · 2025
Exploration of immune-related diagnostic biomarkers in unexplained infertility by bioinformatics analysis and machine learning.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ma Ran, Qin Jituan, Wang Sugai, Guan Sufen, Jia Fangjuan, Deng YingYing, Bai Jing, Wang Saili
Аннотация
OBJECTIVE: We aimed to discover the biomarkers associated with UI and their correlation with immune cell infiltration.
MATERIALS AND METHODS: The GSE165004 data set was extracted from the Gene Expression Omnibus and IRGs were obtained from Immport and InnateDB databases. Differential expression analysis, WGCNA, and three machine learning algorithms (LASSO, SVM, and random forest) were used to determine the immune-related hub biomarkers for UI. The diagnostic performance of these markers was evaluated in GSE165004 and validation set (GSE16532). Furthermore, single-sample GSEA was employed to analyze the infiltration level of immune cells and Spearman analysis was conducted to assess the correlation between biomarker and immune cells. The functional enrichment and potential drugs for each biomarker were explored. The biomarker genes were validated in clinical samples by real time PCR assay.
RESULTS: Six shared genes (ANXA2, CD300E, IL27RA, SEMA3F, GIPR, and WFDC2) were identified as diagnostic biomarkers by integration analysis. ROC analysis revealed that these markers had diagnostic value for UI both in training and validation sets. Moreover, these biomarkers are closely associated with immune cells, such as natural killer T cells and effector memory CD8 T cells. GSEA analysis showed that these genes were mainly involved in chromosome and mitochondria-related biological functions. Drug prediction indicated that all genes targeted Benzo(a)pyrene. All the biomarker genes, expect for GIPR were differentially expressed in endometrium tissues of UI patients, compared with controls.
CONCLUSION: This study identified immune-related diagnostic biomarkers in UI, providing new insights into understanding the molecular mechanisms and therapeutic targets of UI.
MATERIALS AND METHODS: The GSE165004 data set was extracted from the Gene Expression Omnibus and IRGs were obtained from Immport and InnateDB databases. Differential expression analysis, WGCNA, and three machine learning algorithms (LASSO, SVM, and random forest) were used to determine the immune-related hub biomarkers for UI. The diagnostic performance of these markers was evaluated in GSE165004 and validation set (GSE16532). Furthermore, single-sample GSEA was employed to analyze the infiltration level of immune cells and Spearman analysis was conducted to assess the correlation between biomarker and immune cells. The functional enrichment and potential drugs for each biomarker were explored. The biomarker genes were validated in clinical samples by real time PCR assay.
RESULTS: Six shared genes (ANXA2, CD300E, IL27RA, SEMA3F, GIPR, and WFDC2) were identified as diagnostic biomarkers by integration analysis. ROC analysis revealed that these markers had diagnostic value for UI both in training and validation sets. Moreover, these biomarkers are closely associated with immune cells, such as natural killer T cells and effector memory CD8 T cells. GSEA analysis showed that these genes were mainly involved in chromosome and mitochondria-related biological functions. Drug prediction indicated that all genes targeted Benzo(a)pyrene. All the biomarker genes, expect for GIPR were differentially expressed in endometrium tissues of UI patients, compared with controls.
CONCLUSION: This study identified immune-related diagnostic biomarkers in UI, providing new insights into understanding the molecular mechanisms and therapeutic targets of UI.