Evidence map›Paper›PMID 41388545›Full record

ArticleJournal of ovarian research2025

Integrative multi-omics analysis of druggable genes for therapeutic target identification in polycystic ovary syndrome.

Dan Xu, Dan Jia, Xiaohui Fang, Wansu Chen, Ying Liu, Qingxia Song, Xiudao Song

Abstract read
In one paragraph

Article in Journal of ovarian research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Integrative Multi-omics Analysis for Prioritization of Candidate Genes in Polycystic Ovary Syndrome.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Dan Xu *Department of Gynecology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Dan Jia *Department of Gynecology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Xiaohui FangClinical Laboratory, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Wansu ChenDepartment of Gynecology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Ying LiuDepartment of Gynecology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Qingxia SongDepartment of Gynecology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China. songqx72@163.com.
Xiudao SongChinese Medicine Technology Transfer Center, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China. fsyy00530@njucm.edu.cn.

Funding

Gusu Health Talents Program Training Project in Suzhou GSWS2022082Gusu Health Talents Program Training Project in Suzhou GSWS2023116National Natural Science Foundation of China 82305297National Natural Science Foundation of China 82405457Suzhou Key Medical Discipline SZXK202518Suzhou Science and Technology Development Plan (Basic Research-Medical Application Basic Research) Project SKY2023218Suzhou Science and Technology Development Plan (Basic Research-Medical Application Basic Research) Project SYW2024126
6 · The paper itself

Abstract

backgroundPolycystic ovary syndrome (PCOS) is a common endocrine disorders in women of reproductive age with limited targeted therapies. This study aimed to identify and prioritize potential druggable genes for PCOS through a purely computational, multi-omics approach.

methodWe conducted an integrated in silico analysis leveraging publicly available datasets. A multi-omics Mendelian randomization (MR) framework was applied to 2,888 druggable genes. Our workflow incorporated two-sample MR for initial discovery of gene-PCOS associations with false discovery rate (FDR) correction, followed by validation using summary-data-based MR (SMR) and colocalization. Tissue-specific effects were assessed via cis-expression quantitative trait loci (cis-eQTL) MR in ovary, uterus, and blood, and epigenetic regulation via methylation QTL (mQTL) MR. The ovarian microenvironment was characterized using CIBERSORT and single-cell RNA sequencing (scRNA-seq). We also assessed whether immune cell traits mediate the causal pathway between cis-eQTLs and PCOS. Finally, we predicted potential drug targets using the Comparative Toxicogenomics Database and thereby performed molecular docking simulations.

resultsWe identified seven FDR-significant druggable genes associated with PCOS risk: NRBP1, LGR6, GHRL, and IPP as risk-enhancing genes, and CBLN3, VIPR1, and TFRC as protective. SMR analysis confirmed that genetically predicted NRBP1 (OR = 1.5383, p = 8.67E-04), LGR6 (OR = 1.3442, p = 0.0107), GHRL (OR = 1.3391, p = 0.0413) increased PCOS risk, while CBLN3 (OR = 0.8605, p = 4.09E-04) decreased PCOS risk. Colocalization analysis prioritized CBLN3 (PP.H4 = 86.65%) and NRBP1 (PP.H4 = 78.46%) as high-confidence targets. CBLN3 expression was protective across tissues (blood: OR = 0.8586; ovary: OR = 0.8926; uterus: OR = 0.8713), while blood NRBP1 expression increased PCOS risk (OR = 2.1003). mQTL MR analysis revealed significant locus-specific causal effects for CBLN3 and NRBP1. Immune infiltration analysis revealed CBLN3 correlated positively with resting dendritic cells (r = 0.80), and inversely with eosinophils (r=-0.72) and neutrophils (r=-0.66), while NRBP1 associated positively with activated dendritic cells (r = 0.67) and inversely with resting CD4 T cells (r=-0.77). scRNA-seq identified four subtypes of PCOS theca cells, and the expression levels of CBLN3 and NRBP1 in four subtypes were explored. Mediation analysis suggested that the protective effect of CBLN3 may be partially mediated through NKT %lymphocytes. Primary compound screening identified nine high-affinity compounds binding both CBLN3 and NRBP1 (ΔG≤-5 kcal/mol).

conclusionsUsing a comprehensive bioinformatic framework, we prioritize CBLN3 as a novel protective target and NRBP1 as a risk-associated target with multi-omics validation, thus providing mechanistically informed candidates for the development of urgently needed PCOS therapeutics.

Indexed as

Polycystic Ovary SyndromeFemaleGenetic Predisposition to DiseaseHumansMolecular Docking SimulationMultiomicsQuantitative Trait LociBioinformaticDruggable genesGWASMendelian randomizationPCOS

Identifiers

PMID41388545
PMCPMC12699805

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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 OpenQuestion graph.