Dataset Information

Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

ABSTRACT: A large number RNAs are enriched and stable in extracellular vesicles (EVs), and they can reflect their tissue origins and are suitable as liquid biopsy markers for cancer diagnosis and treatment efficacy prediction. In this study, we used extracellular vesicle long RNA (exLR) sequencing to characterize the plasma-derived exLRs from 112 breast cancer patients, 19 benign patients and 41 healthy participants. The different exLRs profiling was found between the breast cancer and non-cancer groups. Thus, we constructed a breast cancer diagnostic signature which showed high accuracy with an area under the curve (AUC) of 0.960 in the training cohort and 0.900 in the validation cohort. The signature was able to identify early stage BC (I/II) with an AUC of 0.940. Integrating the signature with breast imaging could increase the diagnosis accuracy for breast cancer patients. Moreover, we enrolled 58 patients who received neoadjuvant treatment and identified an exLR (exMSMO1), which could distinguish pathological complete response (pCR) patients from non-pCR with an AUC of 0.790. Silencing MSMO1 could significantly enhance the sensitivity of MDA-MB-231 cells to paclitaxel and doxorubicin through modulating mTORC1 signaling pathway. This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

SUBMITTER: Su Y

PROVIDER: S-EPMC8664804 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

Su Yonghui Y Li Yuchen Y Guo Rong R Zhao Jingjing J Chi Weiru W Lai Hongyan H Wang Jia J Wang Zhen Z Li Lun L Sang Yuting Y Hou Jianjing J Xue Jingyan J Shao Zhimin Z Chi Yayun Y Huang Shenglin S Wu Jiong J

NPJ breast cancer 20211210 1

A large number RNAs are enriched and stable in extracellular vesicles (EVs), and they can reflect their tissue origins and are suitable as liquid biopsy markers for cancer diagnosis and treatment efficacy prediction. In this study, we used extracellular vesicle long RNA (exLR) sequencing to characterize the plasma-derived exLRs from 112 breast cancer patients, 19 benign patients and 41 healthy participants. The different exLRs profiling was found between the breast cancer and non-cancer groups. ...[more]

PMID: 34893642

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Project description:BackgroundExtracellular vesicles (EVs) are essential for cell-to-cell communication because they transport functionally active molecules, including proteins, RNA, and lipids, from secretory cells to nearby or distant target cells. Seminal plasma contains a large number of EVs (sEVs) that are phenotypically heterogeneous. The aim of the present study was to identify the RNA species contained in two subsets of porcine sEVs of different sizes, namely small sEVs (S-sEVs) and large sEVs (L-sEVs). The two subsets of sEVs were isolated from 54 seminal plasma samples by a method combining serial centrifugations, size exclusion chromatography, and ultrafiltration. The sEVs were characterized using an orthogonal approach. Analysis of RNA content and quantification were performed using RNA-seq analysis.ResultsThe two subsets of sEVs had different size distributions (P < 0.001). They also showed differences in concentration, morphology, and specific protein markers (P < 0.05). A total of 735 RNAs were identified and quantified, which included: (1) mRNAs, rRNAs, snoRNAs, snRNAs, tRNAs, other ncRNAs (termed as "all RNAs"), (2) miRNAs and (3) piRNAs. The distribution pattern of these RNA classes differed between S-sEVs and L-sEVs (P < 0.05). More than half of "all RNAs", miRNAs and piRNAs were found to be differentially abundant between S- and L-sEVs (FDR < 0.1%). Among the differentially abundant RNAs, "all RNAs" were more abundant in L- than in S-sEVs, whereas the most of the miRNAs were more abundant in S- than in L-sEVs. Differentially abundant piRNAs were equally distributed between S- and L-sEVs. Some of the all RNAs and miRNAs found to be differentially abundant between S- and L-sEVs were associated with sperm quality and functionality and male fertility success.ConclusionsSmall and large sEVs isolated from porcine seminal plasma show quantitative differences in RNA content. These differences would suggest that each sEV subtype exerts different functional activities in the targeted cells, namely spermatozoa and functional cells of the female reproductive tract.

Project description:IntroductionThe introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs).MethodsWe enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature).ResultsIn the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort.DiscussionThis study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

Dataset Information

Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

Publications

Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

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