Identifying Biomarkers for Predicting Lung Cancer Risk from Clinical Data: Discovery and Validation of the Rheumatoid Arthritis Disease Duration Threshold

Jianbin Li^1*Suihuang Li¹Xiaoqing Wang²Wei Liu^1*

• ¹First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Nankai District, China
• ²Henan Provincial Orthopedic Hospital, Henan, China

Objective: Patients with rheumatoid arthritis (RA) are at an increased risk of lung cancer, yet the association between RA duration and lung cancer risk is not well-established. This study aims to investigate the time-dependent association between RA duration and lung cancer risk, identify potential nonlinear associations, and determine clinical risk thresholds. Methods: This single-center, retrospective cohort study included 4,736 RA patients who met the 2010 ACR/EULAR criteria and were treated at the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine from January 2014 to December 2024. A restricted cubic spline (RCS) analysis was initially used to identify potential inflection points for risk. Based on this analysis, patients were divided into two groups: disease duration <9.3 years and ≥9.3 years. Propensity score matching (PSM) at a 1:1 ratio was used to control for potential confounding factors, resulting in 3,778 matched patients included in the final analysis. The primary outcome was the diagnosis of lung cancer, confirmed by histopathology or imaging. Multivariate logistic regression was employed to identify independent risk factors, and RCS analysis was used to assess the nonlinear relationship between RA disease duration, age, and lung cancer risk. Results: After matching, 53 patients (1.40%) developed lung cancer. In the ≥9.3-year disease duration group, 34 patients (1.80%) developed lung cancer, compared to 19 patients (1.01%) in the <9.3-year group (P=0.038). Multivariate analysis indicated that a disease duration ≥9.3 years was an independent risk factor for lung cancer (adjusted odds ratio [aOR] 2.154, 95% confidence interval [CI] 1.211-3.832, P=0.009). RCS analysis demonstrated a significant nonlinear relationship between RA disease duration and lung cancer risk, with a marked increase in risk around 9.3 years of disease duration. Age also exhibited a nonlinear association with lung cancer risk, with the risk accelerating after the age of 65. Conclusion: A disease duration of ≥9.3 years in RA patients is significantly associated with an increased risk of lung cancer, exhibiting a clear nonlinear growth pattern. This finding suggests that clinical management of RA patients with a disease duration ≥9.3 years should include intensified lung cancer screening, particularly in the elderly population.