Comparison of Clinical and Genetic Characteristics Between Younger and Older Lung Cancer Patients

ARCHIVOS DE BRONCONEUMOLOGIA(2024)

Cited 0|Views38
No score
Abstract
Introduction: The aim of this study was to analyze the clinical and genetic characteristics of young lung cancer cases, and to compare them with those of older cases. Methods: We used the Thoracic Tumors Registry (TTR) as a data source representative of lung cancer cases diagnosed in Spain, and included all cases registered until 9/01/2023 which had information on age at diagnosis or the data needed to calculate it. We performed a descriptive statistical analysis and fitted logistic regressions to analyze how different characteristics influenced being a younger lung cancer patient. Results: A total of 26,336 subjects were included. Lung cancer cases <50 years old had a higher probability of being women (OR: 1.38; 95% CI: 1.21-1.57), being in stage III or IV (OR: 1.32; 95% CI: 1.08-1.62), not having comorbidities (OR: 5.21; 95% CI: 4.59-5.91), presenting with symptoms at diagnosis (OR: 1.53; 95% CI: 1.29-1.81), and having ALK translocation (OR: 7.61; 95% CI: 1.25-46.32) and HER2 mutation (OR: 5.71; 95% CI: 1.34-24.33), compared with subjects >= 50 years. Among subjects <35 years old (n = 61), our study observed a higher proportion of women (59.0% vs. 26.6%; p < 0.001), never smokers (45.8% vs. 10.3%; p < 0.001), no comorbidities (21.3% vs. 74.0%; p < 0.001); ALK translocation (33.3% vs. 4.4%; p < 0.001) and ROS1 mutation (14.3% vs. 2.3%; p = 0.01), compared with subjects >= 35 years. Conclusions: Lung cancer displays differences by age at diagnosis which may have important implications for its clinical management. (c) 2023 The Author(s). Published by Elsevier Espan similar to a, S.L.U. on behalf of SEPAR. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
More
Translated text
Key words
Characteristics,Genetic alterations,Lung cancer,Young patients,Epidermal growth factor receptor,Anaplastic lymphoma kinase
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined