ArticleTranslational cancer research2026
Integrative machine learning of hypoxia and centrosome-related gene signatures enables prognostic stratification and therapeutic insights 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer (NSCLC), exhibits significant clinical heterogeneity and commonly observed therapeutic resistance. Although hypoxia-driven tumor adaptation and centrosome-mediated genomic instability are established microenvironmental drivers, their synergistic molecular contributions to LUAD progression remain poorly characterized. Therefore, this study aims to develop an integrative machine learning (ML) model based on hypoxia and centrosome-related genes to enable prognostic stratification and provide therapeutic insights for LUAD. Methods: We developed an integrative multi-omics framework that combines weighted gene co-expression network analysis (WGCNA) to identify key regulatory modules and single-sample gene set enrichment analysis (ssGSEA) for assessing the hypoxia and-centrosome pathway. Differential expression analysis of The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohorts identified hypoxia-centrosome-associated genes, which were refined via univariate Cox regression and ML to construct a prognostic signature. Clinical relevance was validated through nomogram development, tumor microenvironment (TME) profiling, mutational burden assessment, and therapeutic response prediction. Results: A 16-gene prognostic signature was established using 306 differentially expressed genes linked to hypoxia and centrosome dysregulation. Stratification of LUAD patients into high- and low-risk groups demonstrated longer overall survival (OS) in the low-risk cohort. High-risk patients demonstrated elevated tumor mutational burden (TMB) and immunosuppressive microenvironment features, including reduced infiltration of eosinophils, immature dendritic cells, and mast cells. Risk scores were correlated with sensitivity to targeted therapy and chemotherapy. Conclusions: Our integrative ML model uncovers hypoxia-centrosome crosstalk as a critical driver of LUAD progression. The hypoxia and centrosome score-related genes (HCSRGs) signature enables robust risk stratification and identifies actionable targets for precision oncology, providing a framework for personalized therapeutic strategies in LUAD.
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.