An ABCC1-based risk model is effective in the diagnosis of synchronous peritoneal metastasis in advanced colorectal cancer

Wenqing Xie ^1,2,3, Qianxin Luo ^1,4, Zhimei Ou ^1,2

¹Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
²Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³International Cancer Center, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
⁴Department of Intensive Care Unit, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
⁵Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. lanping@mail.sysu.edu.cn.
⁶Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. lanping@mail.sysu.edu.cn.
⁷Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. chendc3@mail.sysu.edu.cn.
⁸Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. chendc3@mail.sysu.edu.cn.

⁰Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

British Journal of Cancer +

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September 27, 2025

View abstract on PubMed

Summary

A new risk model using ABCC1 effectively predicts peritoneal metastasis (PM) in colorectal cancer (CRC) patients, aiding diagnosis and chemotherapy selection. This biomarker approach improves outcomes for advanced CRC.

Area Of Science

Oncology
Genomics
Proteomics
Biomarker Discovery

Background

Peritoneal metastasis (PM) signifies advanced colorectal cancer (CRC), posing diagnostic challenges and limiting treatment efficacy.
Current diagnostic methods for PM are often insufficient, necessitating the identification of novel biomarkers for early detection.
Understanding the mechanisms underlying PM is crucial for developing effective therapeutic strategies.

Purpose Of The Study

To identify reliable biomarkers for the accurate diagnosis of synchronous peritoneal metastasis in colorectal cancer patients.
To develop and validate a predictive risk model for PM in CRC.
To explore the potential of identified biomarkers as predictors of chemotherapy efficacy in patients with PM.

Main Methods

Label-free proteomic analysis of primary tumors from 31 CRC patients to identify differentially expressed genes between synchronous and non-synchronous PM groups.
Validation of gene expression using quantitative real-time PCR, multiplex, and conventional immunohistochemistry.
Construction and validation of a logistic regression risk model based on ABCC1 expression in independent training and validation cohorts.

Main Results

An ABCC1-based risk model demonstrated high accuracy in identifying patients with PM, even in imaging-negative or CEA-negative CRC cases (AUC ranging from 0.819 to 0.913).
The model's diagnostic performance was consistently validated in independent cohorts.
Low ABCC1 expression was identified as a potential predictive marker for chemotherapy efficacy in CRC patients with PM.

Conclusions

The ABCC1-based risk model provides an effective tool for predicting PM in colorectal cancer, complementing existing diagnostic approaches.
ABCC1 shows promise as a predictive biomarker for chemotherapy response in patients with peritoneal metastasis.
Further research into ABCC1 could lead to improved management strategies for advanced CRC with PM.