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

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.