Research Paper Volume 14, Issue 6 pp 2775—2792

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

Jin Ouyang1,2,3, , Zhijian Hu4, , Jianlin Tong4, , Yong Yang3, , Juan Wang3, , Xi Chen3, , Ting Luo1, , Shiqun Yu1, , Xin Wang1, , Shaoxin Huang1,3,5, ,

  • 1 Laboratory of Precision Preventive Medicine, Medical School, Jiujiang University, Jiujiang, Jiangxi 332000, PR China
  • 2 Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang 330006, PR China
  • 3 SpecAlly Life Technology Co. Ltd., Wuhan, Hubei 430075, PR China
  • 4 Laboratory Department, Jiujiang University Clinical Medical College, Jiujiang University Hospital, Jiujiang, Jiangxi 332000, PR China
  • 5 School of Public Health, Qingdao University, Qingdao 266100, PR China

Received: October 11, 2021       Accepted: February 28, 2022       Published: March 23, 2022
How to Cite

Copyright: © 2022 Ouyang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

Methods: A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

Results: Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61–3.53) in the validation set I, 1.35 (95% CI, 1.06–1.71) in the validation set II, and 2.71 (95% CI, 1.77–4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

Conclusions: In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.


LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; PGSs: progression gene signatures; AUC: area under the curve; SCLC: squamous cell lung cancer; GEO: gene expression omnibus; OS: overall survival; PRS: prognosis-related risk score; TCGA: The Cancer Genome Atlas; DEGs: Differentially expressed genes; GSEA: Gene Set Enrichment Analysis; K-M: Kaplan-Meier; CI: confidence interval; NSCLC: non-small cell lung carcinoma; tROC: time-dependent ROC.