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pmid:36628439

Screening and verification of genes related to polycystic ovary syndrome.

Xuebing Li · Chunxia Wang · Heng Yang · Dongxu Pei · Yuchun Liu · Sha Yan · Yongwei Li

The Journal of international medical research · 2023 doi:10.1177/03000605221147444

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:36628439
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science-record
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title
Screening and verification of genes related to polycystic ovary syndrome.
doi
10.1177/03000605221147444
pmid
36628439
authors array · 7 items
[0]
Xuebing Li
[1]
Chunxia Wang
[2]
Heng Yang
[3]
Dongxu Pei
[4]
Yuchun Liu
[5]
Sha Yan
[6]
Yongwei Li
journal
The Journal of international medical research
year
2023
abstract
OBJECTIVE: To identify key genes involved in occurrence and development of polycystic ovary syndrome (PCOS). METHODS: By downloading the GSE85932 dataset from the GEO database, we used bioinformatical analysis to analyse differentially expressed genes (DEGs) from blood samples of eight women with PCOS and eight matched controls. Following bioinformatic analysis, we performed a cross-sectional study of serum samples taken from 79 women with PCOS and 36 healthy controls. RESULTS: From the 178 DEGs identified by bioinformatical analysis, 15 genes were identified as significant, and of these, ORM1 and ORM2 were selected for further verification as potential biomarkers for PCOS. Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value. ORM1 and ORM2 were positively correlated with testosterone, cholesterol, and triglycerides. ORM1 levels were negatively correlated with high density lipoprotein (HDL) while ORM2 levels showed no significant correlation. CONCLUSIONS: ORM may be an effective biomarker for the diagnosis of PCOS and its monitoring may be a useful therapeutic strategy.
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Receiver operating characteristic (ROC) curve analysis was applied to define optimal diagnostic cutoffs and diagnostic performance given by the area under the curve (AUC), evaluating sensitivity and specificity. Pearson’s correlation analysis was used to analyse correlations between serum ORM levels and several laboratory parameters.
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Receiver operating characteristic (ROC) curve analysis was applied to define optimal diagnostic cutoffs and diagnostic performance given by the area under the curve (AUC), evaluating sensitivity and specificity. Pearson’s correlation analysis was used to analyse correlations between serum ORM levels and several laboratory parameters.
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By downloading the GSE85932 dataset from the GEO database, (https://www.ncbi.nlm.nih.gov/gds) using the GEO2R tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/) bioinformatical analysis was used to analyse differentially expressed genes (DEGs) from the blood of eight women with PCOS and eight matched controls.
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By downloading the GSE85932 dataset from the GEO database, (https://www.ncbi.nlm.nih.gov/gds) using the GEO2R tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/) bioinformatical analysis was used to analyse differentially expressed genes (DEGs) from the blood of eight women with PCOS and eight matched controls.
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Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value.
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Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value.
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Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value.
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By downloading the GSE85932 dataset from the GEO database, (https://www.ncbi.nlm.nih.gov/gds) using the GEO2R tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/) bioinformatical analysis was used to analyse differentially expressed genes (DEGs) from the blood of eight women with PCOS and eight matched controls.
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title
The peripheral blood transcriptome identifies dysregulation of inflammatory response genes in polycystic ovary syndrome
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Receiver operating characteristic (ROC) curve analysis was applied to define optimal diagnostic cutoffs and diagnostic performance given by the area under the curve (AUC), evaluating sensitivity and specificity. Pearson’s correlation analysis was used to analyse correlations between serum ORM levels and several laboratory parameters.
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By downloading the GSE85932 dataset from the GEO database, (https://www.ncbi.nlm.nih.gov/gds) using the GEO2R tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/) bioinformatical analysis was used to analyse differentially expressed genes (DEGs) from the blood of eight women with PCOS and eight matched controls.
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Complete file
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Following bioinformatic analysis, we performed a cross-sectional study of serum samples taken from 79 women with PCOS and 36 healthy controls.\nRESULTS: From the 178 DEGs identified by bioinformatical analysis, 15 genes were identified as significant, and of these, ORM1 and ORM2 were selected for further verification as potential biomarkers for PCOS. Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value. ORM1 and ORM2 were positively correlated with testosterone, cholesterol, and triglycerides. ORM1 levels were negatively correlated with high density lipoprotein (HDL) while ORM2 levels showed no significant correlation.\nCONCLUSIONS: ORM may be an effective biomarker for the diagnosis of PCOS and its monitoring may be a useful therapeutic strategy.", "claims": [{"id": "claim-method", "statement": "Receiver operating characteristic (ROC) curve analysis was applied to define optimal diagnostic cutoffs and diagnostic performance given by the area under the curve (AUC), evaluating sensitivity and specificity. 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