Prognostic prediction of lung adenocarcinoma based on transcriptomic data and stacked supervised autoencoder

Abstract

Objective To build a stacked supervised autoencoder (SSAE) model based on transcriptomic data, so as to improve the prognostic prediction of lung adenocarcinoma (LUAD). Methods Transcriptomic data (475 samples and 25 481 genes) from the Cancer Genome Atlas (TCGA) database were collected, and the survival prognosis and gene differential expression analyses were performed in LUAD patients, using SSAE, random survival forest (RSF), and DeepSurv methods, respectively. Concordance index (CI) and P-value of Log-rank test were adopted to evaluate the performance of each method. Results SSAE had a higher concordance index (CI=0.58) and a lower Log-rank test P value (P=0.05) than RSF (CI=0.54, P=0.15) and DeepSurv (CI=0.55, P=0.10). There were significant differences in survival outcomes between the high-risk and low-risk groups in the survival analysis (HR=2.841; 95%CI: 1.907~4.232; Log-rank test P < 0.001). Biogenic analysis identified 21 representative differentially upregulated genes, including IGFBP1, ANXA13, MUC2, CIDEC, NTSR1 and DSG3. Conclusion SSAE with omics data significantly improves the prognostic prediction of LUAD. The cross-fusion of deep learning and omics research provides a novel scheme for cancer-related research of diagnosis, treatment, and prognosis.

Keywords

• lung adenocarcinoma
• transcriptomics
• prognostic prediction
• cox regression
• autoencoder