CTHRC1 promotes colorectal cancer progression by recruiting tumor-associated macrophages via up-regulation of CCL15

Liu, Yixin; Chen, Xiangzheng; Xu, Ying; Yang, Tinghan; Wang, Haichuan; Wang, Ziqiang; Hu, Zhangyong; Chen, Longqi; Zhang, Zheng; Wu, Yangping

doi:10.1007/s00109-023-02399-0

Cited by 7 publications

(7 citation statements)

References 55 publications

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“…CTHRC1 - Collagen triple helix repeat containing 1 (CTHRC1) is involved in tissue repair and is highly expressed in various malignant tumors including colorectal cancer ( Liu et al, 2023 ). CTHRC1 activates Wnt signaling as well as the PI3K/ERK pathway ( Liu et al, 2023 ). Activation of Wnt is associated with chemotherapy resistance.…”

Section: Discussionmentioning

confidence: 99%

“…Activation of Wnt is associated with chemotherapy resistance. Activation of PI3K and ERK is associated with cell growth and proliferation ( Liu et al, 2023 ). In vivo analysis showed that knocking down of CTHRC1 from CRC cell line inhibits the formation of tumor ( Liu et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

“…Activation of PI3K and ERK is associated with cell growth and proliferation ( Liu et al, 2023 ). In vivo analysis showed that knocking down of CTHRC1 from CRC cell line inhibits the formation of tumor ( Liu et al, 2023 ). The reduced expression of CTHRC1 in FOLFIRI responders would make these patients’ cancer more sensitive to chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

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High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Amniouel,

Jafri

2024

Front. Physiol.

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Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed.Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models.Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Amniouel,

Jafri

2024

Front. Physiol.

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“…By utilizing WGCNA and machine learning, we ultimately constructed a BGN + Fib derived risk signature (BGNFRS) consisting of 17 risk-associated genes that exhibit stable prognostic prediction for CRC. Among these 17 genes, studies have shown that 15 genes are associated with CRC progression, including COMP, GPC1, POSTN, SLC2A3, CTHRC1, TNS1, INHBA, TIMP1, CAV1, AEBP1, CRYAB, THBS2, WNT5A, SPARCL1, and CALB2 [47,[51][52][53][54][55][56][57][58][59][60][61][62][63]. CPXM2 and CHPF have not been reported in CRC, but have been found to promote gastric cancer progression [64,65].…”

Section: Discussionmentioning

confidence: 99%

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

Hu,

Xiao,

Gao

et al. 2024

BMC Cancer

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Background Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC. Methods The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models. Results Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis. Conclusion In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

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“… 10 It has recently gained significant attention in cancer research. 11 , 12 The CTHRC1 is a highly conserved secreted glycoprotein that was identified as a differentially expressed gene during arterial injury. 13 It shows transient high expression in the outer fibrous cells and inner smooth muscle cells involved in the repair of injured blood vessels, whereas its expression is not detected in normal arteries.…”

Section: Introductionmentioning

confidence: 99%

Expression, Prognostic Value, and Biological Function of CTHRC1 in Different Types of Gliomas: A Bioinformatic Analysis and Experiment Validation

Shi,

Zeng,

Jiao

et al. 2024

Clin Med Insights Oncol

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Background: In recent years, abnormal expression of collagen triple helix repeat containing 1 (CTHRC1) has been found in some tumors, closely related to the poor prognosis of cancer patients. However, the clinical significance of CTHRC1 in gliomas is not completely understood. Methods: We investigated the expression, prognostic value, and potential biological function of CTHRC1 in different types of gliomas through bioinformatics analysis and experimental verification. Results: Bioinformatics analysis revealed several key findings regarding the expression and clinical significance of CTHRC1 in gliomas. First, the analysis demonstrated a positive correlation between CTHRC1 expression and the World Health Organization (WHO) grading of gliomas, a relationship that was validated through immunohistochemistry experiments. In addition, a trend was observed in which CTHRC1 expression increased with the extent of glioma invasion, as supported by Western blot experiments. Subsequent bioinformatics analysis identified the mesenchymal subtype of gliomas as having the highest levels of CTHRC1 expression, a finding reinforced by immunohistochemical staining. Moreover, high CTHRC1 expression was associated with poor prognosis in gliomas and emerged as an independent prognostic factor, with varying impacts on prognosis between low-grade gliomas (LGGs) and glioblastoma (GBM) subgroups. Notably, comparative analysis unveiled distinct patterns of immune infiltration of CTHRC1 in LGG and GBM. Furthermore, alterations in copy number variations and DNA methylation were identified as potential mechanisms underlying elevated CTHRC1 levels in gliomas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that CTHRC1 and its associated genes mainly function in the extracellular matrix and participate in tumor-related signaling pathways. Conclusions: The CTHRC1 has shown significant clinical utility as a prognostic marker and mesenchymal subtype marker of glioma.

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CTHRC1 promotes colorectal cancer progression by recruiting tumor-associated macrophages via up-regulation of CCL15

Cited by 7 publications

References 55 publications

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

Expression, Prognostic Value, and Biological Function of CTHRC1 in Different Types of Gliomas: A Bioinformatic Analysis and Experiment Validation

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