ArticleCell biology and toxicology2026
Harnessing machine learning-driven multiomics integration: deciphering programmed cell death networks for prognostication and immunotherapy prediction in lung adenocarcinoma.
Article in Cell biology and toxicology, 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
Abstract
backgroundProgrammed cell death (PCD) patterns play important roles in lung adenocarcinoma (LUAD) development as well as treatment resistance, and in-depth study of PCD is beneficial for improving the therapeutic paradigm for LUAD.
methodsFourteen PCD-related patterns were integrated and multiple datasets from TCGA and GEO were collected to develop a PCD signature using 101 machine learning algorithm combinations. Prognosis, immune cell infiltration, and sensitivity to chemotherapy and immunotherapy were compared between different risk groups and validated by multiple bulk RNA-seq and scRNA-seq datasets of patients receiving immunotherapy. CellChat was used to analyze the cellular interactions between patients with different PCD groups. Immune cell infiltration in the tumor tissues of 38 LUAD patients treated with anti-PD-1 therapy was validated by multiplex immunohistochemistry (mIHC).
resultsA PCD signature containing 7 genes was constructed using 101 machine learning algorithm combinations and validated across multiple datasets. High PCD scores in patients are associated with poorer prognosis, lower immune cell infiltration, and reduced responsiveness to immunotherapy. In addition, the PCD signature were comprehensively analyzed by scRNA-seq, and the results showed that the high PCD signature was concentrated mainly in advanced LUAD. Moreover, pathways associated with tumor progression and immune resistance were more strongly promoted in the high PCD signature group. The expression of the key gene NAPSA correlated with immune cell infiltration and immunotherapy response, as confirmed by IHC and mIHC.
conclusionThe PCD signature confers significant potential to predict prognosis of LUAD in patients, and NAPSA is promising as a new marker for predicting the efficacy of immunotherapy.
Indexed as
Identifiers
What Socratic 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 Socratic graph.