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TGF-β Pathways Stratify Colorectal Cancer into Two Subtypes with Distinct Cartilage Oligomeric Matrix Protein (COMP)
Jia-Tong Ding1,2, Hao-Nan Zhou1,3, Ying-Feng Huang1
1Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China.
Background:
Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-β pathway-inducing CRCs remains uncharacterized.
Methods:
Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences in TGF-β 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 TGFβ-targeted anticancer drugs and contribute to guiding clinical decision making for CRC patients.
Insights
Cartilage oligomeric matrix protein (COMP) is a key gene for predicting colorectal cancer (CRC) subtypes. This finding offers new avenues for TGFβ-targeted therapies and clinical decision-making in CRC patients.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Colorectal cancers (CRCs) are a major global cause of cancer mortality.
- The molecular characteristics of TGF-β pathway-inducing CRCs are not fully understood.
Purpose of the Study:
- To identify molecular features distinguishing TGF-β pathway-inducing colorectal cancer subtypes.
- To discover novel biomarkers for CRC subtype prediction and potential therapeutic targets.
Main Methods:
- Unsupervised hierarchical clustering and weighted gene co-expression network analysis were used to stratify CRC samples.
- A machine learning algorithm integrating LASSO, XGBoost, and random forest regression identified key genes.
- Bioinformatic analyses focused on COMP-related immune infiltration and functions.
Main Results:
- The integrated machine learning approach identified COMP as a crucial hub gene.
- COMP demonstrated significant predictive value for CRC subtypes (AUC = 0.91).
- COMP is upregulated in various cancers, particularly advanced CRCs, and influences immune infiltration (M2 macrophages, cancer-associated fibroblasts).
Conclusions:
- COMP serves as a reliable signature for CRC subtype prediction.
- These findings offer a new target for TGFβ-targeted anticancer drugs.
- The results can aid in clinical decision-making for CRC patients.
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