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Molecular Diagnostics

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

British Journal of Cancer (2025)Cite this article

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Abstract

Background

The presence of peritoneal metastasis (PM) in colorectal cancer (CRC) patients indicates an advanced CRC stage. Prompt diagnosis and early PM detection are difficult, and the underlying mechanisms are unclear, resulting in limited treatment options and unsatisfactory therapeutic effects. We aimed to identify applicable biomarkers for accurately diagnosing synchronous PM in CRC patients.

Methods

Differentially expressed genes between synchronous and non-synchronous PM groups were identified via label-free proteomic analysis of primary tumors from 31 CRC patients. Quantitative real-time PCR, multiplex and conventional immunohistochemistry were used to validate gene expression. We attempted to construct a logistic regression risk model for the diagnosis of PM, which was tested in a training cohort and validated in an independent cohort.

Results

Utilizing the results from multi-omics, we established an ABCC1-based risk model. In CRC patients with imaging-negative diagnoses, the model identified patients with metastases including PM (AUC = 0.892, 95% CI: 0.840–0.944) or those with PM only (AUC = 0.859, 95% CI: 0.794–0.924); these results were validated in an independent cohort of patients with metastases including PM (AUC = 0.831, 95% CI: 0.729–0.933) or PM only (AUC = 0.819, 95% CI: 0.702–0.936). In CRC patients with CEA-negative, this model more effectively distinguishes those with exclusive peritoneal involvement, with consistent results across both training (AUC = 0.913, 95% CI: 0.854–0.972) and validation (AUC = 0.869, 95% CI: 0.795–0.943) cohorts. Additionally, in CRC patients with PM, low ABCC1 may serve as a predictive marker for chemotherapy efficacy.

Conclusions

The ABCC1-based risk model effectively predicts PM in CRC, complementing traditional diagnostics. ABCC1 may serve as a predictive marker for chemotherapy efficacy in PM.

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Fig. 1
Fig. 2: Proteomic profiling to identify differential expression genes between PM and NPM patients.
Fig. 3: Multiplex immunohistochemistry staining to validate DEGs.
Fig. 4: Performance of the ABCC1-based risk model in the training and validation cohorts.
Fig. 5: Association between expression of ABCC1/chemotherapy and survival.

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Data availability

The Proteomics datasets supporting the conclusions of this article have been uploaded to the iProx Consortium with the project ID IPX0006751000. All other data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

The authors would like to express their sincere gratitude to Dr. Lishuo Shi, Huanmiao Zhan, and Hongfei Liang for their invaluable contributions in statistical consultation and follow-up update. Moreover, With the greatest respect, this article is dedicated to and in memorial of Professor Lei Wang, who was the formal vice president of our hospital.

Funding

This work was financially supported by the Natural Science Foundation of China (Grant No. 31970703), Natural Science Foundation of Guangdong Province (Grant No. 2022A1515012472 and 2025A1515010593), and supported by National Key Clinical Discipline and the Program.

Author information

Author notes

  1. Wenqing Xie

    Present address: International Cancer Center, Shenzhen University Medical School, Shenzhen University, Shenzhen, China

  2. These authors contributed equally: Wenqing Xie, Qianxin Luo, Zhimei Ou.

Authors and Affiliations

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

    Wenqing Xie, Qianxin Luo, Zhimei Ou, Wanjun Liu, Minghan Huang, Qian Wang, Ping Lan & Daici Chen

  2. Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Wenqing Xie, Zhimei Ou, Wanjun Liu, Minghan Huang & Daici Chen

  3. Department of Intensive Care Unit, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Qianxin Luo & Qian Wang

  4. Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Ping Lan

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Contributions

Conception or design of the work: DC Chen and P Lan; data collection: WQ Xie, QX Luo, Q Wang, ZM Ou; data analysis and interpretation: WQ Xie and ZM Ou; drafting the article: WQ Xie, QX Luo, and DC Chen; critical revision of the article: DC Chen, P Lan, WQ Xie, QX Luo, ZM Ou, WJ Liu, MH Huang; final approval of the version to be published: all the authors.

Corresponding authors

Correspondence to Ping Lan or Daici Chen.

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All procedures were conducted in accordance with the Declaration of Helsinki and approved by the Human Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (approval number: 2022ZSLYEC-469). This study is a retrospective study and the information are anonymous. The authors had no access to any identifying information during the analysis of the data. In accordance with the relevant guidelines and regulations, the requirement for informed consent was waived.

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Xie, W., Luo, Q., Ou, Z. et al. An ABCC1-based risk model is effective in the diagnosis of synchronous peritoneal metastasis in advanced colorectal cancer. Br J Cancer (2025). https://doi.org/10.1038/s41416-025-03203-1

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