Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning

TLDR

Non‑small cell lung cancer is a leading cause of cancer mortality worldwide, and accurate prognostic stratification is essential for guiding therapeutic decisions. The study aimed to develop a deep neural network that integrates gene expression and clinical data to predict overall survival in NSCLC patients. Using microarray data from 614 patients, the authors identified 15 prognostic biomarkers (seven established and eight novel selected via prognosis relevance values) and trained a bimodal deep neural network that combines these biomarkers with clinical variables to predict 5‑year survival. The integrative model achieved high predictive performance, with an AUC of 0.8163 and 75.44% accuracy for 5‑year survival, suggesting its potential as a precision‑medicine tool.

Abstract

Abstract Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.