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Updated: May 23, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Comprehensive analysis of autophagy-related prognostic genes in breast cancer using bulk and single-cell RNA
Yong Li1,2, Chunmei Chen1, Weiwen Li1
1Department of Breast, Jiangmen Central Hospital Jiangmen, Guangdong, P. R. China.
Objective:
This study aimed to utilize single-cell RNA sequencing (scRNA-seq) to elucidate the autophagic landscape in breast cancer and to develop a prognostic model for breast cancer patients based on traditional high-throughput RNA sequencing (bulk RNA-seq).
Methods:
We analyzed scRNA-seq data from the GSE75688 dataset to explore the expression patterns of autophagy-related genes (ARGs) across distinct cellular clusters. ARGs were retrieved from the GeneCards database, and bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA). Cox proportional hazards regression was employed to construct a prognostic risk model based on ARGs. Patients were subsequently stratified into high-risk and low-risk groups according to their risk scores. For external validation, we used gene expression data from the GSE20685 and GSE48390 datasets. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the 3-gene signature.
Results:
Using the FindClusters function in Seurat, all cells were grouped into four distinct clusters, highlighting the intratumoral heterogeneity within the samples. Significant differences in autophagy scores were observed among the clusters. Fifteen differentially expressed autophagy-related genes were identified, and a prognostic signature consisting of three autophagy-related genes - FEZ1, STX11, and ADAMTSL1 - was developed. Based on this model, patients were classified into high- and low-risk groups, with a statistically significant difference in survival between the two groups (log-rank test, P = 0.0011). The model demonstrated robust predictive performance with an AUC of 0.761 in the external validation dataset. A nomogram incorporating the 3-gene signature and clinical factors showed strong prognostic discrimination.
Conclusion:
This study uncovered significant variation in autophagy levels among different breast cancer cell clusters. Furthermore, we established a novel 3-gene autophagy-related prognostic model that effectively stratifies patient risk and provides a potential tool for personalized prognosis in breast cancer.
