What question did this study set out to answer?

This research aims to create a predictive model for the risk of liver metastasis in primary colorectal cancer.

June 10, 2026Open Access

An epithelial cell fate-driven predictive model for liver metastasis risk in primary colorectal cancer through single-cell and multi-omics integration

MYMin YinDalian Medical University XBXiaochen BoShanghai Jiao Tong University YLYaoyao LiYangzhou University

Key Points

This research aims to create a predictive model for the risk of liver metastasis in primary colorectal cancer.
Integrated single-cell RNA sequencing and multi-omics analyses to identify gene signatures,
Utilized consensus clustering on a large reference cohort (TCGA-COAD/READ, N = 433) to establish a malignancy signature,
Validated findings through immunohistochemistry in a clinical cohort of 45 patients.
Identified three distinct risk trajectories: Aggressive-Metastasis-Enhanced (AMECRLM), Cell Cycle-Active (CCACRLM), and Growth-Inhibited (GICRLM),
Found that GICRLM has the best prognosis while AMECRLM has the worst,
Demonstrated that HSPA1A knockout significantly reduces tumor invasion and metastasis in experimental models.

Abstract

Colorectal cancer (CRC) is the third most prevalent form of cancer worldwide, with colorectal cancer liver metastases (CRLM) representing a principal cause of CRC-related mortality. However, a lack of molecular subgroups based on the differentiation states of diverse cell types in CRLM poses a significant barrier to progress in precision therapy. We integrated single-cell RNA sequencing (scRNA-seq, GSE178318) of paired CRLM tissues to define an epithelial cell fate gene signature. To evaluate the intrinsic “malignant seed” potential, this signature was projected onto a large-scale primary reference cohort (TCGA-COAD/READ, N = 433) using consensus clustering to establish the Malignant Development Signature of CRLM (MDSCRLM). Comprehensive multi-omics analyses—encompassing somatic mutations, copy number variations, tumor mutational burden (TMB), and ATAC-seq chromatin accessibility ( N = 81)—alongside immune microenvironment deconvolutions and proteomic data integration were performed. The clinical relevance of the MDSCRLM model was externally validated using immunohistochemistry in an independent clinical cohort of 45 patients. Furthermore, the biological function of HSPA1A , a core marker, was experimentally verified through in vitro assays and in vivo metastasis models. The MDSCRLM system discriminates three distinctmetastatic risk trajectories: Cluster 1—Aggressive-Metastasis-Enhanced CRLM (AMECRLM), Cluster 2—Cell Cycle-Active CRLM (CCACRLM), and Cluster 3—Growth-Inhibited CRLM (GICRLM). Survival analyses in the primary reference cohort validated the biological plausibility and prognostic impact of this model, revealing that GICRLM is associated with the most favorable prognosis while AMECRLM confers the poorest outcome. Our findings delineate distinct patterns of cellular heterogeneity, genomic instability ( TP53 mutations), and gene expression across CRLM developmental stages. Prognostic validation in the clinical cohort and functional validation demonstrating that HSPA1A knockout attenuates tumor invasion and metastasis robustly supported the robustness of the MDSCRLM risk stratification. We established a novel, clinically relevant CRLM risk stratification model with strong diagnostic and prognostic potential. By identifying pre-existing malignant features and stage-specific therapeutic vulnerabilities within the primary tumor, this model bridges the gap between biological discovery and precision medicine in advanced colorectal cancer.

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