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TGF-ß Pathways Stratify Colorectal Cancer into Two Subtypes with Distinct Cartilage Oligomeric Matrix Protein (COMP) Expression-Related Characteristics.
Ding, Jia-Tong; Zhou, Hao-Nan; Huang, Ying-Feng; Peng, Jie; Huang, Hao-Yu; Yi, Hao; Zong, Zhen; Ning, Zhi-Kun.
Affiliation
  • Ding JT; Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
  • Zhou HN; The Second Clinical Medicine School, Nanchang University, Nanchang 330006, China.
  • Huang YF; Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
  • Peng J; Queen Mary School, Nanchang University, Nanchang 330006, China.
  • Huang HY; Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
  • Yi H; Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
  • Zong Z; The Second Clinical Medicine School, Nanchang University, Nanchang 330006, China.
  • Ning ZK; Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
Biomolecules ; 12(12)2022 12 14.
Article in En | MEDLINE | ID: mdl-36551305
ABSTRACT

BACKGROUND:

Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-ß pathway-inducing CRCs remains uncharacterized.

METHODS:

Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences in TGF-ß pathways. Weighted gene co-expression network analysis was applied to identify the key gene modules mediating the different characteristics between two subtypes. An algorithm integrating the least absolute shrinkage and selection operator (LASSO), XGBoost, and random forest regression was performed to narrow down the candidate genes. Further bioinformatic analyses were performed focusing on COMP-related immune infiltration and functions.

RESULTS:

The integrated machine learning algorithm identified COMP as the hub gene, which exhibited a significant predictive value for two subtypes with an area under the curve (AUC) value equaling 0.91. Further bioinformatic analysis revealed that COMP was significantly upregulated in various cancers, especially in advanced CRCs, and regulated the immune infiltration, especially M2 macrophages and cancer-associated fibroblasts in CRCs.

CONCLUSIONS:

Comprehensive immune analysis and experimental validation demonstrate that COMP is a reliable signature for subtype prediction. Our results could provide a new point for TGFß-targeted anticancer drugs and contribute to guiding clinical decision making for CRC patients.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colorectal Neoplasms / Transforming Growth Factor beta / Cartilage Oligomeric Matrix Protein / Cancer-Associated Fibroblasts Type of study: Prognostic_studies Limits: Humans Language: En Journal: Biomolecules Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colorectal Neoplasms / Transforming Growth Factor beta / Cartilage Oligomeric Matrix Protein / Cancer-Associated Fibroblasts Type of study: Prognostic_studies Limits: Humans Language: En Journal: Biomolecules Year: 2022 Document type: Article