Prognostic Autophagy-Related Model Revealed by Integrating Single-Cell RNA Sequencing Data and Bulk Gene Profiles in Gastric Cancer

• Published in: Frontiers in cell and developmental biology Vol. 9; p. 729485
• Main Authors: Tong, Tianying, Zhang, Jie, Zhu, Xiaoqiang, Hui, Pingping, Wang, Zhimin, Wu, Qiong, Tang, Jiayin, Chen, Haoyan, Tian, Xianglong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10-01-2022
• Subjects: autophagy; Cell and Developmental Biology; gastric cancer; immune checkpoint; immunotherapy; single-cell RNA sequence; autophagy; gastric cancer; single-cell RNA sequence; immune checkpoint; immunotherapy
• ISSN: 2296-634X; 2296-634X
• DOI: 10.3389/fcell.2021.729485

Autophagy has been associated with tumor progression, prognosis, and treatment response. However, an autophagy-related model and their clinical significance have not yet been fully elucidated. In the present study, through the integrative analysis of bulk RNA sequencing and single-cell RNA sequencing, an autophagy-related risk model was identified. The model was capable of distinguishing the worse prognosis of patients with gastric cancer (GC), which was validated in TCGA and two independent Gene Expression Omnibus cohorts utilizing the survival analysis, and was also independent of other clinical covariates evaluated by multivariable Cox regression. The clinical value of this model was further assessed using a receiver operating characteristic (ROC) and nomogram analysis. Investigation of single-cell RNA sequencing uncovered that this model might act as an indicator of the dysfunctional characteristics of T cells in the high-risk group. Moreover, the high-risk group exhibited the lower expression of immune checkpoint markers (
and
) than the low-risk group, which indicated the potential predictive power to the current immunotherapy response in patients with GC. In conclusion, this autophagy-associated risk model may be a useful tool for prognostic evaluation and will facilitate the potential application of this model as an indicator of the predictive immune checkpoint biomarkers.