An exosome-related long non-coding RNAs risk model could predict survival outcomes in patients with breast cancer

Abstract

Breast cancer (BC) is one of the most frequent malignancies among women worldwide. Accumulating evidence indicates that long non-coding RNA (lncRNA) may affect BC progression. Exosomes, a class of small membrane vesicles, have been reported to promote tumor progression through transporting proteins, mRNAs, lncRNAs and some other small molecules. However, the interaction between exosome-related lncRNAs and the microenvironment of malignancies is unclear. Hence, we proceeded to investigate the relationship between exosome-related lncRNAs and BC microenvironment. 121 exosome-associated genes were extracted from ExoBCD database. Then, the Pearson analysis was used to screened out the exosome-related lncRNAs. After that, 15 exosome-related differentially expressed lncRNAs were identified by the correlation with BC prognosis. According to the sum of the expression of these 15 lncRNAs, extracted from The Cancer Genome Atlas, and the regression coefficients, an exosome-related lncRNAs signature was developed by using Cox regression analysis. With the median risk score of the training set, the patients in training and validation sets were separated to low-risk group and high-risk group. Subsequently, the lncRNA–mRNA co-expression network was constructed. The distinct enrichment pathways were compared among the different risk groups by using the R package clusterProfiler. The ESTIMATE method and ssGESA database were adopted to study the ESTIMATE Score and immune cell infiltration. Eventually, the expression of immune checkpoint associated genes, microsatellite instable and the immunophenoscore were further analyzed between different risk groups. Different risk groups exhibited different prognosis, with lower survival rate in the high-risk group. The differentially expressed genes between the different risk groups were enriched in biological processes pathways as well as immune responses. BC patients in high-risk group were identified with lower scores of ESTIMATE scores. Subsequently, we noticed that the infiltrating levels of aDCs, B cells, CD8+ T cells, iDCs, DCs, Neutrophils, macrophages, NK cells, pDCs, Tfh, T helper cells, TIL and Tregs were obvious elevated with the decreased risk score in training and validation cohorts. And some immune signatures were significantly activated with the decreased risk score in both cohorts. Eventually, the exosome-associated lncRNAs risk model was demonstrated to accurately predict immunotherapy response in patients with BC. The results of our study suggest that exosome-related lncRNAs risk model has close relationship with prognosis and immune cells infiltration in BC patients. These findings could make a great contribution to improving BC immunotherapy.

Spatial-timely Quantitative Network Analysis for TGF-β pathway of Tumor Infiltrating Lymphocytes

“…Cancer immunotherapy mainly exerts either active stimulation of the immune system by vaccination or adoptive immunotherapy by cell therapy such as TIL, nature killer cells, cytokine-induced peripheral blood mononuclear cells (lymphokine-activated killer, LAK; cytokine-induced killer cells, CIK; dendritic cells and cytokine-induced killer cells, DC-CIK), neutrophil and macrophage [56][57][58][59][60] . According to published evidence, TILs obtained from tumor tissues with a higher population of CD8 cells, but they also have other immune cells, such as T-cell, B-cell, NK cells, macrophage, and neutrophil [61][62][63][64][65][66][67][68] .…”

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

“…In comparison, a distinct study [ 64 ] focused on short-term outcomes reported AUCs of 0.68, 0.745, and 0.714 for 100, 200, and 300 days, emphasizing the variability in predictive timelines. Another research [ 65 ] showed promising long-term predictions for 2, 3, and 5 years with AUCs of 0.727, 0.691, and 0.695, while a further study [ 66 ] validated its risk score for breast cancer survival over similar periods, achieving AUCs of 0.714, 0.676, and 0.729. Our model's focus on longer-term predictions aligns better with clinical needs in breast cancer, emphasizing its utility in providing meaningful prognostic insights.…”

Extracellular Vesicles in Breast Cancer: From Biology and Function to Clinical Diagnosis and Therapeutic Management

“…Serum exosomal lncRNA XIST has been described as a potential biomarker to diagnose TNBC recurrence [ 477 ]. Fifteen exosome-related differentially expressed lncRNAs were recently identified to be correlated with BC prognosis [ 478 ].…”