An analysis of lung cancer survival using multi-omics neural networks

Abstract

A key goal of precision health medicine is to improve cancer prognosis. Despite the fact that numerous models can forecast differential survival from data, progressive algorithms that can assemble and select important predictors from progressively complex data inputs are urgently required. As a result, these models should be capable to provide more information about which types of data are most significant for improving prediction. Because they are adaptable and account for data density in a non-linear manner, deep learning-based neural networks may be a feasible solution for both difficulties. In this study, we use Deep Learning-based networks to get how gene expression data predicts Cox regression survival in lung cancer. SALMON (Survival Analysis Learning with Multi-Omics Neural Networks) is an algorithm that collects and simplifies gene expression data and cancer biomarkers in order to enable prognosis prediction. When more omics data was comprised in model construction, the results (concordance index = 0.635 and log-rank test p-value = 0.00881) showed that performance improved. We employ eigengene modules from the results of gene co-expression network analysis as model inputs in its place of raw gene expression principles. This algorithm verified specific mRNA-seq co-expression modules and clinical information, which show crucial roles in lung cancer prognosis, revealing various biological functions by exploring how each contributes to the hazard ratio. SALMON also performed well compared to other Deep Learning Survival prognosis models.