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Integrated Single-Cell and Spatial Transcriptomic Analysis Identifies Putative Metabolic Crosstalk Between SPP1+ TAMs and SLC6A20+ Epithelial Cells in Colorectal Cancer
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School of Life Sciences, Faculty of Medicine, Tianjin University, Tianjin 300072, China
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HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou 310022, China
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College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
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College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
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Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
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Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
*
Authors to whom correspondence should be addressed.
Cancers 2026, 18(11), 1755; https://doi.org/10.3390/cancers18111755
Submission received: 25 April 2026
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Revised: 22 May 2026
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Accepted: 24 May 2026
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Published: 27 May 2026
(This article belongs to the Special Issue Single-Cell and Spatial Data Analytics in Cancers)
Simple Summary
Colorectal cancer cells do not grow alone; they interact with nearby immune cells and can change how nutrients are used inside tumors. These interactions may help tumors grow, avoid immune attack, and lead to worse outcomes, but they are not fully understood. In this study, we examined how cancer epithelial cells and tumor-associated macrophages communicate through metabolic programs using single-cell and spatial analyses. We identified a closely linked pair of cell populations, SLC6A20+ epithelial cells and SPP1+ TAMs, that shared altered energy, vitamin B6, and amino acid metabolism. Their signals were found in the same tumor regions and were associated with poorer prognosis. Based on these findings, we developed a gene-based model that may help classify patients into different risk groups and support future research on metabolism-related treatment strategies.
Abstract
Background: Colorectal cancer (CRC) progression is associated with tumor metabolic reprogramming and an immunosuppressive tumor microenvironment, yet coordinated metabolic interactions between malignant epithelial and immune cells remain unclear. This study aimed to characterize metabolic crosstalk in CRC, validate spatial organization, and develop a metabolism-based prognostic model. Methods: Six CRC single-cell RNA sequencing datasets were integrated to identify cell populations, evaluate metabolic pathway activity, and infer cell–cell communication. Spatial transcriptomics was used to assess regional co-enrichment of key cell-subset signatures and metabolic activities. Bulk transcriptomic cohorts and targeted metabolomics data were analyzed for pathway-level support. Patients were stratified using metabolic features of selected subsets, followed by protein–protein interaction analysis and elastic net modeling. Results: Across six scRNA-seq datasets comprising 431,217 cells from 173 samples (107 tumor, 60 normal, and 6 border), we identified a metabolically reprogrammed malignant epithelial subset (SLC6A20+ epithelial cells) and an immunosuppressive SPP1+ tumor-associated macrophage (TAM) subset. Both exhibited elevated glycolysis, vitamin B6 metabolism, and aromatic amino acid metabolism. Spatial transcriptomics supported regional co-enrichment of their signatures and shared metabolic activities within the same tumor regions. Independent bulk transcriptomic cohorts and targeted metabolomics further supported these pathway alterations. Cell–cell communication analysis revealed extensive bidirectional ligand-receptor interactions. Based on metabolic features of these subsets, patients were stratified into two prognostic groups. A 14-gene elastic net signature predicted the high-risk subtype with consistent performance across independent cohorts. Conclusions: SLC6A20+ epithelial cells and SPP1+ TAMs showed coordinated, transcriptome-inferred metabolic programs and predicted bidirectional communication in CRC. These features provide candidate biologically interpretable biomarkers and a metabolism-based prognostic model for patient stratification.
