Single extracellular vesicle surface protein‐based blood assay identifies potential biomarkers for detection and screening of five cancers

Author:

Min Yuxin1,Deng Wenjiang2,Yuan Huangbo3,Zhu Dongliang1,Zhao Renjia3,Zhang Pengyan1,Xue Jiangli4,Yuan Ziyu4,Zhang Tiejun145,Jiang Yanfeng34,Xu Kelin6,Wu Di7,Cai Yanling8ORCID,Suo Chen149ORCID,Chen Xingdong4510ORCID

Affiliation:

1. Department of Epidemiology, School of Public Health Fudan University Shanghai China

2. Department of Medical Epidemiology and Biostatistics Karolinska Institute Stockholm Sweden

3. State Key Laboratory of Genetic Engineering, School of Life Science Human Phenome Institute, Fudan University Shanghai China

4. Fudan University Taizhou Institute of Health Sciences Taizhou China

5. Yiwu Research Institute of Fudan University China

6. Department of Biostatistics, School of Public Health Fudan University Shanghai China

7. Vesicode AB Stockholm Sweden

8. Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Key Laboratory of Genitourinary Tumor, Department of Urology The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen Institute of Translational Medicine Shenzhen China

9. Shanghai Institute of Infectious Disease and Biosecurity Shanghai China

10. State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and National Clinical Research Center for Aging and Medicine, Huashan Hospital Fudan University Shanghai China

Abstract

Extracellular vesicles (EVs) and EV proteins are promising biomarkers for cancer liquid biopsy. Herein, we designed a case–control study involving 100 controls and 100 patients with esophageal, stomach, colorectal, liver, or lung cancer to identify common and type‐specific biomarkers of plasma‐derived EV surface proteins for the five cancers. EV surface proteins were profiled using a sequencing‐based proximity barcoding assay. In this study, five differentially expressed proteins (DEPs) and eight differentially expressed protein combinations (DEPCs) showed promising performance (area under curve, AUC > 0.900) in pan‐cancer identification [e.g., TENM2 (AUC = 0.982), CD36 (AUC = 0.974), and CD36‐ITGA1 (AUC = 0.971)]. Our classification model could properly discriminate between cancer patients and controls using DEPs (AUC = 0.981) or DEPCs (AUC = 0.965). When distinguishing one cancer from the other four, the accuracy of the classification model using DEPCs (85–92%) was higher than that using DEPs (78–84%). We validated the performance in an additional 14 cancer patients and 14 controls, and achieved an AUC value of 0.786 for DEPs and 0.622 for DEPCs, highlighting the necessity to recruit a larger cohort for further validation. When clustering EVs into subpopulations, we detected cluster‐specific proteins highly expressed in immune‐related tissues. In the context of colorectal cancer, we identified heterogeneous EV clusters enriched in cancer patients, correlating with tumor initiation and progression. These findings provide epidemiological and molecular evidence for the clinical application of EV proteins in cancer prediction, while also illuminating their functional roles in cancer physiopathology.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Wiley

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