Integrating RNA-seq and ScRNA-seq to Explore the Prognostic Features and Immune Landscape of Exosome-related Genes in Breast Cancer Metastasis

Overview

Journal Ann Med

Publisher Informa Healthcare

Date 2025 Jan 23

PMID 39847423

Authors

Guanyou Huang

Yong Yu

Heng Su

Hongchuan Gan

Liangzhao Chu

Affiliations

Soon will be listed here.

Abstract

Objective: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.

Methods: Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data.

Results: M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns.

Conclusions: This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model's clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.

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