ArticleDiscover oncology2025
Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.
Article in Discover oncology, 2025. 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated with higher risk for cancer development and worse prognosis in certain types of malignancies.
methodsAn integrative approach incorporating 10 machine learning methods was employed to develop an obesity and aging-related signature (ORS) using data from the TCGA, GSE15459, GSE26253, GSE62254, and GSE84437 datasets. To assess the predictive value of ORS for immunotherapy benefits, we utilized several indicating scores and three immunotherapy datasets (GSE91061, GSE78220, and IMvigor210).
resultsThe predictive model developed using the LASSO method achieved the highest average C-index and was identified as the optimal ORS. This ORS served as an independent risk factor for the clinical outcomes of STAD patients, demonstrating robust performance in predicting overall survival rates. In the TCGA cohort, the area under the curve values for the 1-, 3-, and 5-year receiver operator characteristic curves were 0.871, 0.803, and 0.768, respectively. Patients with low ORS score exhibited higher gene set scores for immune-activated cells, increased cytolytic activity, and enhanced T cell co-stimulation. Additionally, low ORS score was associated with a reduced tumor immune dysfunction and exclusion score, decreased immune escape score, elevated PD1 and CTLA4 immunophenoscore, higher tumor mutation burden, improved response rates, and better prognosis in STAD. Conversely, the IC50 values for common chemotherapy and targeted therapy regimens were lower in the high ORS score group.
conclusionThe current study developed an optimal ORS in STAD, which could be used for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.
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.