肺腺癌患者のシングルセルシーケンスにおける擬似時間解析に基づく腫瘍予後リスク層別化

Accurate prognostic risk stratification for lung adenocarcinoma (LUAD) remains a critical challenge. This study employs single-cell pseudo-time series analysis to identify pseudo-time differential genes (PTDGs) associated with LUAD development and constructs tumor progression-related prognostic risk stratification for patients with LUAD.

This study conducted quality control for single-cell RNA sequencing (RNA-seq) data firstly. Then, malignant tumor cells of the epithelium were identified using cell-cluster markers and InferCNV. Trajectory analysis with Monocle identified PTDGs, further refined to 2,597 candidate genes. The prognostic signature of PTDGs was constructed by using Cox, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses. In addition, multicenter datasets and immunohistochemistry analysis were used to evaluate the expression of the identified PTDGs between the two groups.

The developmental trajectory of the LUAD epithelium was plotted, and 13 PTDGs were identified to be highly associated with the prognosis of LUAD. Based on the prognostic risk score, The Cancer Genome Atlas (TCGA)-LUAD patients were well stratified into low- and high-risk groups. The PTDGs-based risk model demonstrated good performance in the training, internal and external validation datasets, as well as being compared with existing risk score formulas. In addition, multi-center datasets and immunohistochemistry revealed a significant up-regulation of DDIT4, FURIN, PTTG1 and RIPK2 at transcriptional and protein expression levels in LUAD tissues compared to normal tissues.

PTDGs are potential biological markers for prognostic risk stratification of patients with LUAD.