Developing high-resolution metastasis signatures for improved cancer prognosis using single-cell RNA sequencing data：A case study in lung adenocarcinoma

Abstract

Abstract Background Metastasis remains the reason for high cancer mortality and it is a valuable predictive factor in cancer prognosis. Single-cell RNA sequencing (scRNA-seq) can reveal cellular heterogeneity in metastasis microenvironment and capture high-resolution signatures for improved cancer prediction. Methods An integrated analysis framework was designed for metastatic lung adenocarcinoma (LUAD) scRNA-seq profiles and we identified 9 key prognostic genes (KPGs) that were trained and validated in 407 internal and external patient cohorts using Lasso-Cox method and Receiver Operating Characteristic (ROC) curves. To ensure the predictive stability of the KPGs signatures, 10 random samples of data from the TCGA cohort were taken. Correlation analysis revealed the strong association between KPGs signatures and several clinical characteristics such as gender, T-stage, and N-stage. We incorporated these risk clinical variables into a KPGs nomogram model. Results The results based on ROC curves and calibration curves show that the KPGs nomogram model with superior accuracy for overall survival (OS) prediction. We also found that high risk group with high nomogram scores had poorer prognosis accompanied by a higher tumor mutation burden (TMB) and it was associated with the upregulation of cell cycle, DNA replication, ECM receptor interaction, P53 signaling pathway, spliceosome and proteasome pathway. Conclusions Mining single-cell resolution metastatic features from scRNA-seq data to improve cancer prognosis is a viable strategy that would be a useful tool in risk gene discovery and targeted therapy in metastatic cancers.