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cohort·oncology, clinical trial, observational study, preclinical research, biomedical research·PMC10067095

A large‐scale targeted proteomics of plasma extracellular vesicles shows utility for prognosis prediction subtyping in colorectal cancer

Cancer Medicine · 10 authors, 9 centres

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TL;DR

It identified two novel patient subtypes with significantly different overall survival outcomes, with ORM1 protein being a key prognostic marker.

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Cohort 1 (59 patients, 59 healthy controls) identified 99 proteins associated with CRC. Cohort 2 (150 patients) used consensus clustering to identify two novel CRC subtypes with significantly different overall survival (OS), with a p-value of 0.017. Twelve proteins, including ORM1, were associated with the subtypes, and ORM1 was a significant contributor to OS. Limitations include the need for larger verification cohorts and further mechanistic studies to confirm the biological roles of identified proteins.

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cohort

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oncology

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ORIGINAL PAPER

PMC10067095

A large‐scale targeted proteomics of plasma extracellular vesicles shows utility for prognosis prediction subtyping in colorectal cancer

Cancer Medicine

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ABSTRACT

Abstract Purpose The pathogenesis of cancers depends on the molecular background of each individual patient. Therefore, verifying as many biomarkers as possible and clarifying their relationships with each disease status would be very valuable. We performed a large‐scale targeted proteomics analysis of plasma extracellular vesicles (EVs) that may affect tumor progression and/or therapeutic resistance. Experimental design Plasma EVs from 59 were collected patients with colorectal cancer (CRC) and 59 healthy controls (HC) in cohort 1, and 150 patients with CRC in cohort 2 for the large‐scale targeted proteomics analysis of 457 proteins as candidate CRC markers. The Mann–Whitney‐Wilcoxon test and random forest model were applied in cohort 1 to select promising markers. Consensus clustering was applied to classify patients with CRC in cohort 2. The Kaplan–Meier method and Cox regression analysis were performed to identify potential molecular factors contributing to the overall survival (OS) of patients. Results In the analysis of cohort 1, 99 proteins were associated with CRC. The analysis of cohort 2 revealed two clusters showing significant differences in OS ( p = 0.017). Twelve proteins, including alpha‐1‐acid glycoprotein 1 (ORM1), were suggested to be associated with the identified CRC subtypes, and ORM1 was shown to significantly contribute to OS, suggesting that ORM1 might be one of the factors closely related to the OS. Conclusions The study identified two novel subtypes of CRC, which exhibit differences in OS, as well as important biomarker proteins that are closely related to the identified subtypes. Liquid biopsy assessment with targeted proteomics analysis was proposed to be crucial for predicting the CRC prognosis. Keywords: colorectal cancer, exosome, extracellular vesicle, mass spectrometry, targeted proteomics

CITATION EXPORT

Keiko Kasahara, Ryohei Narumi, Satoshi Nagayama et al.. A large‐scale targeted proteomics of plasma extracellular vesicles shows utility for prognosis prediction subtyping in colorectal cancer. Cancer Medicine. (2022). https://doi.org/10.1002/cam4.5442

AUTHORS

10 · 9 CENTRES

Keiko Kasahara, Ryohei Narumi, Satoshi Nagayama, Keiko Masuda, Tsuyoshi Esaki, Kazutaka Obama, Takeshi Tomonaga, Yoshiharu Sakai, Yoshihiro Shimizu, Jun Adachi

1 Department of Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
2 Laboratory of Proteome Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
3 Laboratory of Proteomics for Drug Discovery, Center for Drug Design Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka, Japan
4 Laboratory of Clinical and Analytical Chemistry, Collaborative Research Center for Health and Medicine, National Institute of Biomedical Innovation, Health and Nutrition, Osaka, Japan
5 Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
6 Department of Surgery, Uji‐Tokusyukai Medical Center, Kyoto, Japan
7 Laboratory for Cell‐Free Protein Synthesis, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
8 The Center for Data Science Education and Research, Shiga University, Shiga, Japan
9 Japanese Red Cross Osaka Hospital, Osaka, Japan