ArticleInternational journal of cancer2021
A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.
Article in International journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Machine learning-based single-sample molecular classifier for cancer grading.Frontiers in oncology · 2025Article
- DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Evaluating Generative AI's Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets.Journal of personalized medicine · 2024Article
- Cancerous time estimation for interpreting the evolution of lung adenocarcinoma.Briefings in bioinformatics · 2024Article
- Article
- Ensemble methods of rank-based trees for single sample classification with gene expression profiles.Journal of translational medicine · 2024Article
- Article
- Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma.Nature communications · 2023Article
- Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor.Briefings in bioinformatics · 2021Article
- [DEPDC1 is Highly Expressed in Lung Adenocarcinoma and Promotes Tumor Cell Proliferation].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Article
- A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.International journal of cancer · 2021Article
- Establishment and Validation of a Ferroptosis-Related Gene Signature to Predict Overall Survival in Lung Adenocarcinoma.Frontiers in genetics · 2021Article
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Authors and funding
22 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.
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