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Identification of the collagen family as prognostic biomarkers in papillary thyroid carcinoma

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Abstract

Objective

The aim of this study was to construct a collagen-related prognostic model for thyroid cancer and to investigate prognostic value of collagen family genes for thyroid cancer.

Methods

A LASSO Cox regression model for thyroid cancer was developed based on the expression profiles of collagen-related genes. Kaplan–Meier survival analysis was performed for high and low risk groups. The ROC method was used to assess its predictive performance. Predictive independence was verified by multivariate Cox regression analysis. The relationship between this feature and immune cell infiltration was analyzed by tumor microenvironment. COL18A1 was validated by immunohistochemistry and RT-PCR in thyroid cancer tissues. The effect of COL18A1 on cell proliferation, migration and invasion ability of tumor cells were further valuated by CCK-8 assay and transwell assay. The effect of COL18A1 on the immune escape ability of tumor cells was further valuated by cytotoxicity assays.

Results

A model including 4 collagen family genes was developed to predict thyroid cancer prognosis. Patients with high-risk score had a poorer prognosis than those with low-risk scores for 1-, 2-, 3-, and 5- year survival. The model independently predicted prognosis after adjusting for other prognostic factors. A nomogram combining risk score and age was constructed with high sensitivity and specificity. This feature was significantly associated with immune cell infiltration. COL18A1 was aberrantly over-expressed in thyroid cancer compared with control tissues and significantly increased proliferative capacity, migration capacity, invasion capacity, and immune escape ability of tumor cells.

Conclusion

Our findings establish a signature associated with collagen family genes that can be a promising tool to predict the prognosis of thyroid cancer. High COL18A1 expression significantly correlates with the poor prognosis of patients and enhances the immune escape ability of tumor cells.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was partially supported by grants from National Natural Science Foundation of China (Grant No. 81872079), Clinical Research Project of the Second Hospital of Tianjin Medical University (Grant No. KY2019K086-02).

Author information

Authors and Affiliations

  1. Department of Otorhinolaryngology, Head and Neck Surgery, the Second Hospital, Tianjin Medical University, Tianjin, China

    Juntao Niu & Wenyu Guo

  2. Department of Pharmaceutics, School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China

    Yu-Zhou Chen

  3. Department of Urology, Tianjin Institute of Urology, the Second Hospital, Tianjin Medical University, Tianjin, China

    Ning Jiang

Authors
  1. Juntao Niu
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  2. Wenyu Guo
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  3. Yu-Zhou Chen
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  4. Ning Jiang
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Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by J.N., W.G., and N.J. The experiments in vitro were performed by J.N. and Y.C. The first draft of the manuscript was written by J.N. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ning Jiang.

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The authors declare no competing interests.

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The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. And this study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Second Hospital of Tianjin Medical University.

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Niu, J., Guo, W., Chen, YZ. et al. Identification of the collagen family as prognostic biomarkers in papillary thyroid carcinoma. Endocrine 78, 491–506 (2022). https://doi.org/10.1007/s12020-022-03175-9

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  • DOI: https://doi.org/10.1007/s12020-022-03175-9

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