ArticleBioMed research international2020
Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- Identification and assessment ofOncology letters · 2026Article
- Integrated bioinformatics and molecular docking identify novel key genes, pathways, and potential candidate drugs in papillary thyroid carcinoma.Journal, genetic engineering & biotechnology · 2026Article
- Integrative Bioinformatics Analysis Reveals COL13A1 and COL23A1 as Potential Diagnostic and Prognostic Biomarkers in Thyroid Cancer.Health science reports · 2026Article
- Unveiling a new SORBS2::BRAF fusion in papillary thyroid carcinoma: insights from molecular diagnostics.Virchows Archiv : an international journal of pathology · 2026Article
- Prognostic Stratification of Highly Differentiated Thyroid Cancer Based on Molecular Genetic Studies.Asian Pacific journal of cancer prevention : APJCP · 2026Review
- Integrated transcriptomics approach identifies the upregulated SLC7A5, and associated pathways across different thyroid cancer cell types.Frontiers in genetics · 2026Article
- Determination of protein markers of inflammasome formation and ferroptosis in thyroid cancer based on gender.Journal of Cancer · 2026Article
- Proliferation-cycle gene signatures predict immune landscape and prognosis in lung adenocarcinoma.Discover oncology · 2025Article
- Gene Expression Analysis and Validation of a Novel Biomarker Signature for Early-Stage Lung Adenocarcinoma.Biomolecules · 2025Article
- Comprehensive pan-cancer analysis of CHRDL1 and experimental validation of its role in lung adenocarcinoma.BMC cancer · 2025Article
- A comprehensive analysis to reveal the underlying molecular mechanisms of natural killer cell in thyroid carcinoma based on single-cell RNA sequencing data.Discover oncology · 2025Article
- Chorordin-like 1 inhibits pancreatic cancer cell migration and invasion: involvement of the BMP4/SMAD pathway.Frontiers in oncology · 2025Article
- Identification of urinary metabolites correlated with tacrolimus levels through high-precision liquid chromatography-mass spectrometry and machine learning algorithms in kidney transplant patients.Medicine and pharmacy reports · 2025Article
- DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.BMC medical informatics and decision making · 2024Article
- Article
- Role of prognostic gene DKK1 in oral squamous cell carcinoma.Oncology letters · 2024Article
- Exosomal noncoding RNAs: decoding their role in thyroid cancer progression.Frontiers in endocrinology · 2024Review
- Prediction of immune infiltration and prognosis for patients with gastric cancer based on the immune-related genes signature.Heliyon · 2023Article
- Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis.Molecular and cellular biochemistry · 2023Article
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Abstract
backgroundThe molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms.
methodsThe GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression.
resultsBy integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database.
conclusionThe identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.
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