ArticleHuman genomics2022
A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer.
Article in Human genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 38 citations in OpenAlex.
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- The P4HA1/HIF1α feedback loop modulates endothelial dysfunction in pulmonary hypertension.Respiratory research · 2025Article
- Identification of glycolysis-related molecular subtypes and prognostic model in intrahepatic cholangiocarcinoma.Discover oncology · 2025Article
- Identification of a mitophagy-related gene signature for predicting overall survival and response to immunotherapy in rectal cancer.BMC cancer · 2025Article
- Construction of an Oxidative Stress Risk Model to Analyze the Correlation Between Liver Cancer and Tumor Immunity.Current cancer drug targets · 2025Article
- Exploring the Glycolytic Mechanisms in "Driver Gene-Negative" Lung Adenocarcinoma (LUAD): A Single-Cell RNA Sequencing Approach to Identify the MIF-HIF-1α Axis.Journal of Cancer · 2025Article
- Identification and validation of KIF20A for predicting prognosis and treatment outcomes in patients with breast cancer.Scientific reports · 2024Article
- PCK1 as a target for cancer therapy: from metabolic reprogramming to immune microenvironment remodeling.Cell death discovery · 2024Review
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- Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma.Oncology letters · 2024Article
- Hypoxia-induced P4HA1 overexpression promotes post-ischemic angiogenesis by enhancing endothelial glycolysis through downregulating FBP1.Journal of translational medicine · 2024Article
- A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.PeerJ · 2024Article
- MCT4 blockade increases the efficacy of immune checkpoint blockade.Journal for immunotherapy of cancer · 2023Article
- Seven bacterial response-related genes are biomarkers for colon cancer.BMC bioinformatics · 2023Article
- A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy.Frontiers in oncology · 2023Article
- The analysis of tumor-infiltrating immune cell and ceRNA networks in laryngeal squamous cell carcinoma.Medicine · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients.
resultsThree gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ.
conclusionsWe found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.
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