A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer

Abstract

ABSTRACT Background Early‐stage non–small cell lung cancer (NSCLC) is being diagnosed increasingly, and in 30% of diagnosed patients, recurrence will develop within 5 years. Thus, it is urgent to identify recurrence‐related markers to optimize the management of patient‐tailored therapeutics. Methods The eligible datasets were downloaded from TCGA and GEO. In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. The recurrence‐associated signature was developed by penalized Cox regression. The nomogram was constructed and further tested via other independent cohorts. Results In this retrospective study, 14 eligible datasets and 7 published signatures were included. A 13‐gene based signature was generated by penalized Cox regression categorized training cohort into high‐risk and low‐risk subgroups (HR = 8.873, 95% CI: 4.228–18.480 p < 0.001). Furthermore, a nomogram integrating the recurrence‐related signature, age, and histology was developed to predict the recurrence‐free survival in the training cohort, which performed well in the two external validation cohorts (concordance index: 0.737, 95% CI: 0.732–0.742, p < 0.001; 0.666, 95% CI: 0.650–0.682, p < 0.001; 0.651, 95% CI: 0.637–0.665, p < 0.001, respectively). The nomogram was further performed well in the Jiangsu cohort enrolled 163 patients (HR = 2.723, 95% CI: 1.526–4.859, p = 0.001). Post‐operative adjuvant therapy achieved evaluated disease‐free survival in high and intermediate risk groups (HR = 4.791, 95% CI: 1.081–21.231, p = 0.039). Conclusions The proposed nomogram is a promising tool for estimating recurrence‐free survival in stage I NSCLC, which might have tremendous value in management of early stage NSCLC and guiding adjuvant therapy strategies.

Keywords

• nomogram
• recurrence
• stage I non–small cell lung cancer
• survival