Developing High-Resolution Metastasis Signatures for Improved Cancer Prognosis and Drug Sensitivity Prediction using Single-Cell RNA Sequencing Data: A Case Study in Lung Adenocarcinoma

Abstract

Metastasis remains the reason for chemoresistance and high cancer mortality. It is hence a valuable predictive factor in cancer prognosis and drug sensitivity. Single-cell RNA sequencing (scRNA-seq) can reveal cellular heterogeneity in metastasis microenvironment and capture high-resolution signatures for improved cancer prediction. As a case study, an integrated analysis framework was designed for metastatic lung adenocarcinoma (LUAD) scRNA-seq profiles and we identified nine key prognostic genes (KPGs) that were trained and validated in 407 internal and external patient cohorts using machine learning and other methods. Correlation analysis revealed the strong association between KPGs signatures and several clinical characteristics such as gender, [Formula: see text]-stage and [Formula: see text]-stage. We incorporated these risk clinical variables into a 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 was more sensitive to chemotherapy and targeted agents, which was associated with the upregulation of DNA replication, ECM receptor interaction, P53 signaling pathway, spliceosome and proteasome pathway. Collectively, we proposed a simple and feasible strategy to mine single-cell resolution metastasis signatures from scRNA-seq data for improved cancer prognosis and drug sensitivity prediction, which will be a useful tool in risk gene discovery and targeted therapy in metastatic cancers.