ArticleBMC cancer2026
Identification of glycolysis -correlative features for predicting prognosis and investigating immune landscape in prostate cancer.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer (PCa) is characterized not merely as a malignant tumor, but also as a metabolic disorder encompassing dysregulation of glycolysis. This study was purposed to develop a new effective prognostic model correlated with glycolysis-related genes (GRGs) and investigate its potential mechanisms in PCa.
methodsWe compared the expression differences of GRGs. A glycolysis-associated prognostic model was then developed to categorize PCa patients into different risk subgroups. The diagnostic accuracy and predictive efficacy of the models were assessed. Furthermore, a comprehensive nomogram was developed, incorporating the risk score feature, T and N stage, Gleason score, and age, which was further calibrated for accuracy. Risk groups were analyzed for correlation with tumor-infiltrating immune cells (TIICs), immune function, and immunotherapy. In addition, we performed functional enrichment analyses.
resultsThrough constructing Cytoscape regulatory networks, 10 hub genes were identified, and their significance was evaluated and validated. As a result, we confirmed 12 genes (B3GALT6, ANKZF1, IDUA, ENO2, ALDH1A3, GUSB, AURKA, CDK1, LDHB, ALDH3B2, GALM, and ADH1C) for prognostic modeling and calculation of risk scores. Mutations, TIICs, and drug sensitivity were also analyzed. Furthermore, the PTTG1, associated with glycolysis and tumor immunity, was confirmed in vivo.
conclusionsOverall, these findings underscore the prognostic relevance of glycolysis-related genes in prostate cancer and provide novel insights into their association with disease progression and the tumor immune microenvironment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.