Research on a Weighted Gene Co-expression Network Analysis method for mining pathogenic genes in thyroid cancer

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Thyroid cancer (TC) is a prevalent malignancy, particularly in women, with a growing incidence worldwide.
• Understanding the molecular pathogenesis of TC is crucial for identifying effective treatment targets.
• Current research seeks to uncover key biological macromolecules driving TC occurrence and progression.

Purpose of the Study:

• To investigate carcinogenic biological macromolecules in thyroid cancer.
• To identify potential prognostic markers and therapeutic targets for TC.
• To elucidate the occurrence and development mechanisms of TC.

Main Methods:

• Utilized The Cancer Genome Atlas (TCGA) for clinical and gene expression data.
• Employed Weighted Gene Co-expression Network Analysis (WGCNA) to cluster mRNA and long non-coding RNA (lncRNA).
• Applied differential gene expression analysis, univariate Cox regression, and constructed a hub competitive endogenous RNA (ceRNA) network.

Main Results:

• Identified modules highly correlated with clinical phenotypes using WGCNA.
• Screened differential microRNAs (miRNAs) and hub genes associated with overall survival (OS).
• Constructed a ceRNA network involving disease-associated lncRNAs, miRNAs, and messenger RNAs (mRNAs).

Conclusions:

• The identified hub RNAs are significantly associated with thyroid cancer development.
• These hub RNAs demonstrate potential as valuable prognostic markers for TC.
• The study highlights novel therapeutic targets for managing thyroid cancer.