Identification of a Potential Biomarker PDGFRA for Endometriosis Based on Comprehensive Analysis of Multiple Omics Data
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This study identified PDGFRA as a core gene overexpressed in endometriosis tissues, particularly in stromal cells, and demonstrated its role in cell differentiation, migration, invasion, and immune responses, suggesting it as a potential biomarker and therapeutic target.
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Using multiple GEO datasets, the study integrated bulk RNA-seq and scRNA-seq analyses with DEG identification, WGCNA, and machine learning to pinpoint candidate genes involved in endometriosis, reporting PDGFRA as a core gene. PDGFRA was overexpressed in endometriosis tissues, increased with more advanced disease stage, was mainly expressed in endometrial stromal cells, and was found elevated in the center of ectopic lesion regions by spatial transcriptomics. Virtual knockout of PDGFRA altered mesenchymal cell differentiation, focal-adhesion and apoptosis-related processes in stromal cells and was associated with hypoxia, p53 signaling, and changes in multiple immune-cell populations, while basic experiments showed that inhibiting PDGFRA reduced stromal-cell migration and invasion; a stated caveat is that the analysis depends on publicly available GEO datasets. This paper is centrally about endometriosis — it identifies PDGFRA as a potential biomarker linked to lesion biology and stromal-cell functional changes in endometriosis.
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Abstract
Endometriosis (EMs) is a common chronic inflammatory gynecological disorder. But the exact pathogenetic mechanism of the disease is not clear, with some theories proposing that the disease is caused by the interaction of endocrine, immune, and genetic factors. Based on the GEO databases, we conducted an analysis employing DEGs, WGCNA, and machine learning. Using bulk RNA-seq and scRNA-seq data, we identified PDGFRA as a core gene. We found that PDGFRA was overexpressed in EMs tissues, and its expression increases with advanced stages of the disease. Interestingly, PDGFRA is predominantly expressed in the stromal cells of the endometrium, and the spatial transcriptome analysis revealed PDGFRA overexpression at the center of ectopic lesions regions. And the virtual knockout of PDGFRA affected the mesenchymal cell differentiation, focal adhesion and apoptosis of stromal cells. Additionally, PDGFRA influenced hypoxia, p53 signaling, and various immune cells. Moreover, basic experiments showed that inhibiting PDGFRA expression resulted in suppressed migration and invasion of stromal cells. In summary, PDGFRA emerges as a potential biomarker favoring cell growth, proliferation, differentiation, and anti-apoptotic, suggesting its promise as a therapeutic target in EMs.
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Abstract
Endometriosis (EMs) is a common chronic inflammatory gynecological disorder. But the exact pathogenetic mechanism of the disease is not clear, with some theories proposing that the disease is caused by the interaction of endocrine, immune, and genetic factors. Based on the GEO databases, we conducted an analysis employing DEGs, WGCNA, and machine learning. Using bulk RNA-seq and scRNA-seq data, we identified PDGFRA as a core gene. We found that PDGFRA was overexpressed in EMs tissues, and its expression increases with advanced stages of the disease. Interestingly, PDGFRA is predominantly expressed in the stromal cells of the endometrium, and the spatial transcriptome analysis revealed PDGFRA overexpression at the center of ectopic lesions regions. And the virtual knockout of PDGFRA affected the mesenchymal cell differentiation, focal adhesion and apoptosis of stromal cells. Additionally, PDGFRA influenced hypoxia, p53 signaling, and various immune cells. Moreover, basic experiments showed that inhibiting PDGFRA expression resulted in suppressed migration and invasion of stromal cells. In summary, PDGFRA emerges as a potential biomarker favoring cell growth, proliferation, differentiation, and anti-apoptotic, suggesting its promise as a therapeutic target in EMs.
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The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We thank all the GEO databases.
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This study was supported in part by a grant from the National Natural Science Foundation of China (#81873826).
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Shengnan Chen: Conceptualization, Data curation, Writing – original draft, Writing – review and editing. Ying Jiang: Validation. Xianqing Wu: Funding acquisition, Writing – original draft. Xiaoshan Chai: Data curation, Software, Writing – original draft.
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The study was approved by the Medical Ethics Committee of the Second Xiangya Hospital of Central South University (#2020–065). All participants provided written informed consent to participate in the study and for their data to be published.
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Chen, S., Jiang, Y., Chai, X. et al. Identification of a Potential Biomarker PDGFRA for Endometriosis Based on Comprehensive Analysis of Multiple Omics Data. Reprod. Sci. 33, 358–371 (2026). https://doi.org/10.1007/s43032-025-02049-5
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DOI: https://doi.org/10.1007/s43032-025-02049-5
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