ArticlePeerJ2025
A comprehensive analysis identified an autophagy-related risk model for predicting recurrence and immunotherapy response in stage I lung adenocarcinoma.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma (LUAD) is characterized by early recurrence and poor prognosis. Autophagy is a double-edged sword in tumor development and anti-tumor therapy resistance. However, the prediction of relapse and therapeutic response in LUAD patients with stage I based on the signature of autophagy remains unclear. Methods: Gene expression data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. Autophagy-associated genes were extracted from the Human Autophagy Moderator Database. The autophagy score was established by Least Absolute Shrinkage and Selection Operator (LASSO) regression. Real-time PCR was used to detect gene expression of hub genes in LUAD patients. Protein-protein interaction (PPI) was analyzed to identify crucial genes. Gene set enrichment analysis (GSEA) was used to reveal the molecular features of patients. ESTIMATE algorithm was applied to estimate the tumor immune infiltration. TIDE score and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to assess therapeutic response. Results: We established an autophagy score based on 19 autophagy genes. Among these genes, MAP1LC3B played a crucial role in PPI network and was down-regulated in tumor tissues both in TCGA and local cohort. Receiver operating characteristic (ROC) curve showed that the risk model effectively predict RFS of stage I LUAD (area under the curve (AUC) at 1, 2, 3 years = 0.701, 0.836, and 0.818, respectively). Multivariate regression analysis indicated that the autophagy score was an independent predictor for relapse ( Conclusion: We constructed an effective recurrence risk predictive model for stage I LUAD patients based on autophagy related genes. High autophagy score predicted a higher recurrence risk and suppressing 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.