ArticleHeliyon2024
A novel signature of seven aging-related genes for risk stratification, prognosis prediction and benefit evaluation of chemotherapy, and immunotherapy in elderly patients with lung adenocarcinoma.
Article in Heliyon, 2024. 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.
- Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer.Discover oncology · 2024Article
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: Aging, a multifaceted biological process, is thought to be associated with lung adenocarcinoma (LUAD) development and progression. However, it is unclear whether aging-related genes (ARGs) can predict tumor risk, chemotherapy and immunotherapy benefits, and prognosis in LUAD patients at different ages. Methods: Gene expression datasets and clinical information of LUAD patients were downloaded from TCGA and GEO database. Univariate and multivariate Cox regression, and lasso algorithm were employed to identify the ARG signatures. Patients were stratified into high-risk and low-risk groups to evaluate the predictive accuracy using Kaplan-Meier curves, ROC curves, and time-dependent AUC. A nomogram was established to predict the survival probability. GSEA revealed potential pathways, and CIBERSORT indicated different immunologic status. TIDE score was used to predict the potential tumor response to immune checkpoint inhibitors, and GDSC was employed to evaluate the sensitivity of chemotherapeutic drugs. The correlation of TIDE score and patient age, as well as that of ARGs and patient age was investigated. And cell Culture and RT-qPCR for external validation for key gene. Results: A novel gene signature based on seven ARGs was established, including BMP15, CD79A, CDKN3, CDX2, COL1A1, DKK1, and GRIK2. Our model demonstrated exceptional prediction accuracy for elderly LUAD patients of 71-90 years old. A nomogram model was constructed to predict the survival probability, and the C-index value was 0.737, indicating our prognostic nomogram model has high accuracy. Through external RT-qPCR validation, we found that CD79A expression in H1299 was higher than that of BEAS-2B. And novel immunotherapy and chemotherapy regimens were accordingly proposed for the elderly LUAD patients. Conclusion: We identified a novel gene signature based on seven ARGs for risk stratification, prognosis prediction and benefit evaluation of immunotherapy and chemotherapy in elderly LUAD patients.
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.