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Author Notes:

Ye Xu, Email: yexu@shmu.edu.cn

Sheng-Lin Huang, slhuang@fudan.edu.cn

T-AG, H-YL, Z-ZZ, H-BH, S-LH, and YX were responsible for the study concept and study design. T-AG, H-YL, and CL performed the data acquisition. H-YL, Y-TJ, YL, and Y-CL were responsible for the methodology, software, formal analysis, and visualization. T-AG and H-YL wrote the original draft. YX, S-LH, and Z-ZZ edited and revised the manuscript. All authors contributed to the article and approved the submitted version.

The authors would like to thank all the participants included in this study.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Research Funding:

This work was supported by the National Natural Science Foundation of China (82072694, 81872294), the Shanghai Science and Technology Innovation Action Plan (20JC1419000), and the Shanghai Committee of Science and Technology (20DZ1100101, 19511121202).

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Oncology
  • extracellular vesicle
  • long RNAs
  • colorectal cancer
  • colorectal adenoma
  • early detection
  • PROGNOSTIC BIOMARKER
  • FEATURE-SELECTION
  • GENE
  • EXPRESSION
  • COLONOSCOPY
  • MICROARRAY
  • EXOSOMES
  • SURVIVAL

Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer

Tools:

Journal Title:

FRONTIERS IN ONCOLOGY

Volume:

Volume 12

Publisher:

, Pages 829230-829230

Type of Work:

Article | Final Publisher PDF

Abstract:

Background: Early detection of colorectal cancer (CRC) is crucial to the treatment and prognosis of patients. Traditional screening methods have disadvantages. Methods: 231 blood samples were collected from 86 CRC, 56 colorectal adenoma (CRA), and 89 healthy individuals, from which extracellular vesicle long RNAs (exLRs) were isolated and sequenced. An CRC diagnostic signature (d-signature) was established, and prognosis-associated cell components were evaluated. Results: The exLR d-signature for CRC was established based on 17 of the differentially expressed exLRs. The d-signature showed high diagnostic efficiency of CRC and control (CRA and healthy) samples with an area under the curve (AUC) of 0.938 in the training cohort, 0.943 in the validation cohort, and 0.947 in an independent cohort. The d-signature could effectively differentiate early-stage (stage I–II) CRC from healthy individuals (AUC 0.990), as well as differentiating CEA-negative CRC from healthy individuals (AUC 0.988). A CRA d-signature was also generated and could differentiate CRA from healthy individuals both in the training (AUC 0.993) and validation (AUC 0.978) cohorts. The enrichment of class-switched memory B-cells, B-cells, naive B-cells, and mast cells showed increasing trends between CRC, CRA, and healthy cohorts. Class-switched memory B-cells, mast cells, and basophils were positively associated with CRC prognosis while natural killer T-cells, naive B-cells, immature dendritic cells, and lymphatic endothelial cells were negatively associated with prognosis. Conclusions: Our study identified that the exLR d-signature could differentiate CRC from CRA and healthy individuals with high efficiency and exLR profiling also has potential in CRA screening and CRC prognosis prediction.

Copyright information:

© 2022 Guo, Lai, Li, Li, Li, Jin, Zhang, Huang, Huang and Xu

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/rdf).
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