Weighted Gene Co-Expression Network Analysis (WGCNA) Discovered Novel Long Non-Coding RNAs for Polycystic Ovary Syndrome.
Roozbeh HeidarzadehpilehroodMaryam PirhoushiaranMalina OsmanHabibah Abdul HamidKing-Hwa LingPublished in: Biomedicines (2023)
Polycystic ovary syndrome (PCOS) affects reproductive-age women. This condition causes infertility, insulin resistance, obesity, and heart difficulties. The molecular basis and mechanism of PCOS might potentially generate effective treatments. Long non-coding RNAs (lncRNAs) show control over multifactorial disorders' growth and incidence. Numerous studies have emphasized its significance and alterations in PCOS. We used bioinformatic methods to find novel dysregulated lncRNAs in PCOS. To achieve this objective, the gene expression profile of GSE48301, comprising PCOS patients and normal control tissue samples, was evaluated using the R limma package with the following cut-off criterion: p -value < 0.05. Firstly, weighted gene co-expression network analysis (WGCNA) was used to determine the co-expression genes of lncRNAs; subsequently, hub gene identification and pathway enrichment analysis were used. With the defined criteria, nine novel dysregulated lncRNAs were identified. In WGCNA, different colors represent different modules. In the current study, WGCNA resulted in turquoise, gray, blue, and black co-expression modules with dysregulated lncRNAs. The pathway enrichment analysis of these co-expressed modules revealed enrichment in PCOS-associated pathways, including gene expression, signal transduction, metabolism, and apoptosis. In addition, CCT7 , EFTUD2 , ESR1 , JUN , NDUFAB1 , CTTNB1 , GRB2 , and CTNNB1 were identified as hub genes, and some of them have been investigated in PCOS. This study uncovered nine novel PCOS-related lncRNAs. To confirm how these lncRNAs control translational modification in PCOS, functional studies are required.
Keyphrases
- polycystic ovary syndrome
- network analysis
- insulin resistance
- poor prognosis
- long non coding rna
- genome wide identification
- genome wide
- metabolic syndrome
- gene expression
- adipose tissue
- high fat diet
- copy number
- skeletal muscle
- genome wide analysis
- type diabetes
- high fat diet induced
- oxidative stress
- dna methylation
- binding protein
- magnetic resonance imaging
- risk factors
- newly diagnosed
- transcription factor
- bioinformatics analysis
- single cell
- weight loss
- patient reported outcomes
- cell cycle arrest
- atomic force microscopy
- drug induced