ArticleFrontiers in oncology2021
Glycolysis-Based Genes Are Potential Biomarkers in Thyroid Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- The role and mechanism of CHST6-mediated lactation modification in lenvatinib resistance in hepatocellular carcinoma cells.American journal of translational research · 2026Article
- The Potential of Nuclear Pore Complexes in Cancer Therapy.Molecules (Basel, Switzerland) · 2024Review
- Nuclear pore protein POM121 regulates subcellular localization and transcriptional activity of PPARγ.Cell death & disease · 2024Article
- Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype.British journal of cancer · 2023Article
- Clinicopathological Significances and Prognostic Value ofJournal of Cancer · 2023Article
- Reprogramming of Cellular Metabolism and Its Therapeutic Applications in Thyroid Cancer.Metabolites · 2022Review
- Novel Hypoxia-Associated Gene Signature Depicts Tumor Immune Microenvironment and Predicts Prognosis of Colon Cancer Patients.Frontiers in genetics · 2022Article
- Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma.Frontiers in oncology · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and
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