A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer

Abstract

Background: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. Methods: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions.We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. Results: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOB mRNA , exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOB mRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOB mRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. Conclusions: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

ALKBH family members as novel biomarkers and prognostic factors in human breast cancer

“…CXCL12 expression and activated hepatic stellate cells (aHSCs) abundance were associated with breast cancer patients with liver metastases, the interplay between NK cells and aHSCs master switched breast cancer dormancy to outgrowth [ 39 ]. MARCKS from plasma extracellular vesicles is also considered to diagnose breast cancer [ 40 ]. But there was a lack of directing proof to definite whether the connection between ALKBH7, NK CD56 bright cells, and CXCL12 or MARCKS or not.…”

Role of TIM-1 in the development and treatment of tumours

“…One study demonstrated higher expression of neutrophil gelatinase-associated lipocalin and TIM-1 in breast cancer with in situ components ( Diniz et al, 2022 ). Cai et al (2021) developed a high-powered predictive classifier for the diagnosis of breast cancer via eight mRNAs from plasma extracellular vesicles (including TIM-1), which could provide a new approach for the early diagnosis of breast cancer. The study also showed that the relevance of interrogating the levels of TIM-1 protein as a diagnostic/prognostic biomarker of high-prevalence breast and lung cancers by using an amperometric disposable magnetic microparticles-assisted immunoplatform ( Quinchia et al, 2024 ).…”

Extracellular vesicles in the treatment and diagnosis of breast cancer: a status update

“…noted significantly higher phosphorylation of proteins such as RALGAPA2, PKG1, and TJP2 in plasma EVs of patients vs. healthy controls. In addition, these phosphorylated proteins were considered to be viable markers for breast cancer and could assist in tumor screening and surveillance ( 9 , 121 ). These results implied that certain cargoes of EVs can be used as biomarkers for breast cancer, with high clinical value in the diagnosis, treatment, and even survival outcomes.…”