ArticleTranslational cancer research2026
A pathomics model for predicting lactate-related prognostic risk groups and their biological mechanisms in lung adenocarcinoma.
Article in Translational cancer research, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity in postoperative survival, underscoring the need for robust prognostic biomarkers. Given the emerging role of lactate metabolism in cancer progression, this study aimed to develop a pathomics model for prognostic stratification by predicting lactate-related prognostic risk groups. Methods: Using data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project, we performed non-negative matrix factorization to stratify patients based on lactate-related genes. A lactate-related prognostic model was constructed via least absolute shrinkage and selection operator (LASSO)-Cox regression. Patients were then randomly split into training and validation sets to develop and evaluate an XGBoost-based pathomics model for prognostic risk groups prediction. Gene set variation analysis (GSVA), immune cell infiltration analysis, and mutational landscape analysis, were further conducted to explore the underlying mechanisms. Results: A four-gene lactate-related prognostic signature ( Conclusions: We developed a pathomics model for predicting lactate-related prognostic risk groups in LUAD. The model showed moderate predictive performance, suggesting potential utility for prognostic stratification and treatment planning, though further external validation is needed.
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.