Evidence map›Paper›PMID 36068422›Full record

ArticleEndocrine2022

Integrated bioinformatics analysis and screening of hub genes in polycystic ovary syndrome.

Gan Qiao, Jinshan Xing, Xin Luo, Chunxiang Zhang, Jingyan Yi

Abstract read
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In one paragraph

Article in Endocrine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 28% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

5 authors at 1 institution in 1 country.

Gan Qiao *Department of Pharmacology, School of Pharmacy, Nucleic Acid Medicine of Luzhou Key Laboratory, Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, 646000, Sichuan, China.
Jinshan Xing *Department of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Xin Luo *Department of Pharmacology, School of Pharmacy, Nucleic Acid Medicine of Luzhou Key Laboratory, Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, 646000, Sichuan, China.
Chunxiang ZhangNucleic Acid Medicine of Luzhou Key Laboratory, Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, Sichuan, China. zhangchx999@163.com.
Jingyan YiDepartment of Medical Cell Biology and Genetics, School of Basic Medical Sciences, Nucleic Acid Medicine of Luzhou Key Laboratory, Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, Sichuan, China. jingyany@swmu.edu.cn.ORCID 0000-0002-7146-1360
Southwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePolycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders, posing a serious threat to the health of women. Herein, we aimed to explore new biomarkers and potential therapeutic targets for PCOS by employing integrated bioinformatics tools.

methodsThree gene expression profile datasets (GSE138518, GSE155489, GSE106724) were obtained from the Gene Expression Omnibus database and the differentially expressed genes in PCOS and normal groups with an adjusted p-value < 0.05 and a |log fold change (FC) | > 1.2 were first identified using the DESeq package. The weighted correlation network analysis (WGCNA) R package was used to identify clusters of highly correlated genes or modules associated with PCOS. Protein-protein interaction (PPI) network analysis and visualization of genes in the key module were performed using the STRINGdb database and the NetworkX package (edge > 5), respectively. The genes overlapping among the key module genes and PCOS-associated genes were further analyzed. Ligand molecules with strong binding energy < -10 kJ/mol to GNB3 were screened in the drug library using MTiOpenScreen. AutoDock, ChimeraX, and BIOVIA Discovery Studio Visualizer were further used to elucidate the mechanism of ligand interaction with GNB3. Finally, the relationship between GNB3 and PCOS was verified using experimental models in vivo and in vitro.

resultsOf the 11 modules identified by WGCNA, the black module had the highest correlation with PCOS (correlation = 0.96, P = 0.00016). The PPI network of 351 related genes revealed that VCL, GNB3, MYH11, LMNA, MLLT4, EZH2, PAK3, and CHRM1 have important roles in PCOS. The hub gene GNB3 was identified by taking the intersection of PCOS-related gene sets. MTiOpenScreen revealed that five compounds interacted with GNB3. Of these five, compound 1 had the strongest binding ability and can bind amino acids in the WD40 motif of GNB3, which in turn affects the function of the G protein-coupled receptor β subunit. GNB3 was also significantly downregulated in PCOS models.

conclusionWe identified the hub gene GNB3 as the most important regulatory gene in PCOS. We suggest that compound 1 can target the WD40 motif of GNB3 to affect related functions and must be considered as a lead compound for drug development. This study will provide new insights into the development of PCOS-related drugs.

Indexed as

Computational BiologyPolycystic Ovary SyndromeFemaleGene Expression ProfilingGene Regulatory NetworksHumansLigandsp21-Activated KinasesReceptor, Muscarinic M1CHRM1 protein, humanLigandsp21-Activated KinasesPAK3 protein, humanReceptor, Muscarinic M1BioinformaticsGNB3Molecular dockingPCOSWGCNA

Identifiers

PMID36068422
OpenAlexW4294844929

What Socratic holds

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Read underepoch 390

Registered trials

None linked

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 Socratic graph.