ArticleTranslational cancer research2021
Identification of candidate genes associated with papillary thyroid carcinoma pathogenesis and progression by weighted gene co-expression network analysis.
Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explore the possible influence of Sjogren's syndrome on thyroid cancer: A literature data mining and meta-analysis.PloS one · 2025Pooled it
- Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas · 2026Article
- GDF15 is associated with thyroid cancer progression and may modulate thyroid cancer cell senescence in a p53-dependent manner.Frontiers in endocrinology · 2025Article
- Identification of LINC02454-related key pathways and genes in papillary thyroid cancer by weighted gene coexpression network analysis (WGCNA).Thyroid research · 2024Article
- Growth differentiation factor 15 (GDF-15) in endocrinology.Endocrine · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe genes and genetic mechanisms underlying the occurrence and progression of papillary thyroid carcinoma (PTC) are still unknown. This study aimed to find candidate genes related to the pathogenesis and progression of PTC.
methodsRNA sequencing (RNA-seq) data of PTC and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database with clinical stage data to form a test, validation, and clinical-stage data matrix. We used the test data set to analyze differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to find those gene clusters highly correlated with PTC. We then verified the expression of genes in the interested modules using the validation matrix. The quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the reliability of the expression of selected genes. Five key genes (
resultsWe used WGCNA to find two modules of interest, the yellow module, which was positively associated with PTC, and the blue module, which was negatively correlated with PTC. Four genes (
conclusionsFour candidate genes (
Indexed as
Identifiers
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Registered trials
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