Abstract
Cancer-associated fibroblasts (CAFs) are key components in tumor microenvironment (TME). The secreted products of CAFs play important roles in regulating tumor cells and further impacting clinical prognosis. This study aims to reveal the relationship between CAF-secreted cytokines and breast cancer (BC) by constructing the risk signature. We performed three algorithms to reveal CAF-related cytokines in the TCGA BC dataset and identified five prognosis-related cytokines. Then we used single-cell RNA sequencing (ScRNA-Seq) datasets of BC to confirm the expression level of these five cytokines in CAFs. METABRIC and other independent datasets were utilized to validate the findings in further analyses. Based on the identified five-cytokine signature derived from CAFs, BC patients with high-risk score (RS) had shorter overall survival than low-RS cases. Further analysis suggested that the high-RS level correlated with cell proliferation and mast cell infiltration in BCs of the Basal-like subtype. The results also indicated that the level of RS could discriminate the high-risk BC cases harboring driver mutations (i.e., PI3KCA, CDH1, and TP53). Additionally, the status of five-cytokine signature was associated with the frequency and molecular timing of whole genome duplication (WGD) events. Intratumor heterogeneity (ITH) analysis among BC samples indicated that the high-RS level was associated with the increase of tumor subclones. This work demonstrated that the prognostic signature based on CAF-secreted cytokines was associated with clinical outcome, tumor progression, and genetic alteration. Our findings may provide insights to develop novel strategies for early intervention and prognostic prediction of BC.
Highlights
Breast cancer (BC) is one of the most common cancers of women and remains a major cause of cancer-associated death worldwide [1]
Candidate cytokines were filtered by correlation analyses using Cancer-associated fibroblasts (CAFs) proportion scores (Figure S1B and Table S3), and a total of 106 cytokines were indicated by all three algorithms (Figure 1C and Figure S1C)
Among these CAF-related cytokines, we found five of them were associated with clinical outcomes (Figure 1D, Figure S2A and Table S4)
Summary
Breast cancer (BC) is one of the most common cancers of women and remains a major cause of cancer-associated death worldwide [1]. Based on the high-throughput transcriptional data, analysis of molecular typing is often performed to indicate differential pathological features and clinical prognosis among BC patients, for which PAM50 subtyping was most widely used [2, 3]. These molecular subtypes were derived from the mathematic clustering, the prognosis among BC cases within each subtype still vary widely. Cancer-associated fibroblasts (CAFs) are one of the most dominant components in the tumor stroma and have a tremendous influence on remodeling the extracellular matrix (ECM) structure [5]. Utilizing CAF-secreted cytokines to predict therapeutic effect and clinical prognosis is worth further investigation
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