ArticleWorld journal of surgical oncology2020
Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma.
Article in World journal of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prognostic determinants in cancer survival: a multidimensional evaluation of clinical and genetic factors across 10 cancer types in the participants of Genomics England's 100,000 Genomes Project.Discover oncology · 2024Article
- LINC02126 is a potential diagnostic, prognostic and immunotherapeutic target for lung adenocarcinoma.BMC pulmonary medicine · 2022Article
- Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma.World journal of surgical oncology · 2022Article
- UCHL1 acts as a potential oncogene and affects sensitivity of common anti-tumor drugs in lung adenocarcinoma.World journal of surgical oncology · 2022Article
- Circular RNA circVAPA contributes to non-small-cell lung cancer progression via miR-342-3p-dependent regulation of ZEB2.World journal of surgical oncology · 2021Article
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7 authors.
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Abstract
backgroundIntegrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data.
methodsWe enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk.
resultsThe model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells.
conclusionsWe developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS.
trial registrationThis study was based on public open data from TCGA and hence the study objects were retrospectively registered.
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