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Background <p>Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aimed to identify and characterize key biomarkers associated with progression and treatment response in patients with RCC.</p> Methods <p>Primary location-related genes (PLGs), including MX1, were identified through weighted gene coexpression network analysis (WGCNA) to construct a primary location signature prognostic risk score (PLriskScore) in the TCGA RCC dataset. The prognostic performance of PLriskScore was validated using four GEO datasets. Tumor microenvironment scores and chemotherapy responses across PLriskScore subgroups were assessed in bulk RNA sequencing datasets. The key target gene was then identified using machine learning algorithms. Its expression and functional roles were examined in single-cell RNA sequencing (scRNA-seq) datasets. Expression patterns were validated by quantitative real-time PCR (qRT-PCR) and immunohistochemical (IHC) staining of clinical samples, while functional characteristics were confirmed using Cell Counting Kit-8 (CCK-8) and transwell migration assays.</p> Results <p>Seven PLGs (MX1, MLLT6, PEDS1, STK4, TOMM34, SLC35A2, and LZTS3) were used to establish the PLriskScore. A higher PLriskScore was significantly associated with poorer prognosis, advanced TNM stages, and increased stromal scores. Notably, the VEGF pathway was enriched in the high-PLriskScore group. Machine learning algorithms identified MX1 as a novel key target, closely related to chemoresistance and tumor microenvironment dysregulation. scRNA-seq analyses revealed that MX1 was predominantly expressed in epithelial cells and promoted angiogenesis through interactions with endothelial cells via the VEGFA-VEGFR2 pathway. Finally, in vitro experiments demonstrated that MX1 was highly expressed in RCC and enhanced proliferation and migration in two CRC cell lines.</p> Conclusion <p>A prognostic model based on primary location-related genes was developed to predict prognosis and chemotherapy response in RCC. MX1 was identified as a pivotal target that promotes angiogenesis and chemoresistance, thereby providing a potential therapeutic strategy for patients with RCC.</p>

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MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Yingdong Hou,
  • Hubin Xia,
  • Chenghao Ji,
  • Wencheng Kong,
  • Yifeng Zhou,
  • Xiaofeng Zhang

摘要

Background

Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aimed to identify and characterize key biomarkers associated with progression and treatment response in patients with RCC.

Methods

Primary location-related genes (PLGs), including MX1, were identified through weighted gene coexpression network analysis (WGCNA) to construct a primary location signature prognostic risk score (PLriskScore) in the TCGA RCC dataset. The prognostic performance of PLriskScore was validated using four GEO datasets. Tumor microenvironment scores and chemotherapy responses across PLriskScore subgroups were assessed in bulk RNA sequencing datasets. The key target gene was then identified using machine learning algorithms. Its expression and functional roles were examined in single-cell RNA sequencing (scRNA-seq) datasets. Expression patterns were validated by quantitative real-time PCR (qRT-PCR) and immunohistochemical (IHC) staining of clinical samples, while functional characteristics were confirmed using Cell Counting Kit-8 (CCK-8) and transwell migration assays.

Results

Seven PLGs (MX1, MLLT6, PEDS1, STK4, TOMM34, SLC35A2, and LZTS3) were used to establish the PLriskScore. A higher PLriskScore was significantly associated with poorer prognosis, advanced TNM stages, and increased stromal scores. Notably, the VEGF pathway was enriched in the high-PLriskScore group. Machine learning algorithms identified MX1 as a novel key target, closely related to chemoresistance and tumor microenvironment dysregulation. scRNA-seq analyses revealed that MX1 was predominantly expressed in epithelial cells and promoted angiogenesis through interactions with endothelial cells via the VEGFA-VEGFR2 pathway. Finally, in vitro experiments demonstrated that MX1 was highly expressed in RCC and enhanced proliferation and migration in two CRC cell lines.

Conclusion

A prognostic model based on primary location-related genes was developed to predict prognosis and chemotherapy response in RCC. MX1 was identified as a pivotal target that promotes angiogenesis and chemoresistance, thereby providing a potential therapeutic strategy for patients with RCC.