Integrated Single-Cell and Spatial Transcriptomics Coupled with Machine Learning Uncovers MORF4L1 as a Critical Epigenetic Mediator of Radiotherapy Resistance in Colorectal Cancer Liver Metastasis

<b>Background and Objective:</b> Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. <b>Methods and Results:</b> We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified <i>MORF4L1</i> (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the <i>MORF4L1</i>-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in <i>MORF4L1</i> expression. Furthermore, cell-cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of <i>MORF4L1</i>-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. <b>Conclusions:</b> Our comprehensive spatial and single-cell profiling establishes <i>MORF4L1</i> as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of <i>MORF4L1</i>, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM.

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Journal
Biomedicines
Published
2026-01-26
DOI
https://doi.org/10.3390/biomedicines14020273
Primary Topic
Ferroptosis and cancer prognosis
Type
article
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article

Integrated Single-Cell and Spatial Transcriptomics Coupled with Machine Learning Uncovers MORF4L1 as a Critical Epigenetic Mediator of Radiotherapy Resistance in Colorectal Cancer Liver Metastasis

X. Wang, Yuanyuan Zhang, Haitao Liu, Yan Xiang et al.
Biomedicines
Ferroptosis and cancer prognosis
article

Integrated Single-Cell and Spatial Transcriptomics Coupled with Machine Learning Uncovers MORF4L1 as a Critical Epigenetic Mediator of Radiotherapy Resistance in Colorectal Cancer Liver Metastasis

X. Wang, Yuanyuan Zhang, Haitao Liu, Yan Xiang, Le Yu
article en

Abstract

<b>Background and Objective:</b> Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. <b>Methods and Results:</b> We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified <i>MORF4L1</i> (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the <i>MORF4L1</i>-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in <i>MORF4L1</i> expression. Furthermore, cell-cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of <i>MORF4L1</i>-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. <b>Conclusions:</b> Our comprehensive spatial and single-cell profiling establishes <i>MORF4L1</i> as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of <i>MORF4L1</i>, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM.

BiomedicinesVol. 14(2)
Fudan University (CN), Jiangsu Cancer Hospital (CN), Nanjing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 78%
Ferroptosis and cancer prognosis
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