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Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by elevated androgen levels and impaired follicular development. A hallmark of PCOS is the excessive proliferation of thecal cells (TCs), which are critical for androgen production. However, the molecular mechanisms underlying this aberrant cellular expansion remain incompletely understood.
Methods: A DHEA-induced mouse model was used to recapitulate the hormonal and ovarian features of human PCOS. Spatial transcriptomics was employed to profile gene expression in ovarian tissue at cellular resolution. Differential expression analysis, pathway enrichment, and spatial co-localization were performed to identify regulatory networks. Functional assays were conducted in cultured TCs using siRNA-mediated knockdown of target genes, and cell proliferation and cell cycle progression were evaluated using EdU incorporation and flow cytometry.
Results: Spatial transcriptomic profiling revealed widespread transcriptional changes in the ovaries of PCOS mice, including a marked expansion of a TCs subpopulation with high Lrp2 expression. This subset exhibited enhanced activity in genes involved in androgen synthesis and cell cycle regulation. A signaling axis comprising Inhba, Smad2, and E2f4 was identified as a key regulator of this proliferative response, with all three genes co-expressed in the affected regions. Knockdown of any component of this axis significantly suppressed TCs proliferation , with the greatest effect observed upon Inhba silencing.
Conclusion: The Inhba/Smad2/E2f4 axis contributes to thecal cell hyperplasia and androgen excess in PCOS, and may serve as a mechanistic entry point for further investigation into the regulation of TCs proliferation in this disorder.
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http://dx.doi.org/10.3389/fcell.2025.1633254 | DOI Listing |
NPJ Precis Oncol
September 2025
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
School of Life Sciences, Anhui Medical University, Hefei, 230032, China; Translational Research Institute of Henan Provincial People's Hospital, Henan International Joint Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial Key Laboratory of Long Non-coding RNA and Cancer Metaboli
Melanoma is the most aggressive and lethal form of skin cancer, posing significant challenges for prognosis assessment and treatment. Recently, metabolic reprogramming and epigenetic regulation have gained attention for their roles in cancer progression. The role of the key metabolic enzyme dihydrolipoic acid succinyltransferase (DLST) in cancer is currently unclear.
View Article and Find Full Text PDFCell Syst
September 2025
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:
Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFBioinformatics
September 2025
Department of Mathematical Sciences, The University of Texas at Dallas, TX United States.
Motivation: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes that elucidate the clustering result, referred to as spatial domain-specific discriminating genes (DGs).
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