Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes
The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor...
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eLife Sciences Publications Ltd
2023-05-01
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| Online Access: | https://elifesciences.org/articles/82959 |
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| author | Aojia Zhuang Aobo Zhuang Yijiao Chen Zhaoyu Qin Dexiang Zhu Li Ren Ye Wei Pengyang Zhou Xuetong Yue Fuchu He Jianmin Xu Chen Ding |
| author_facet | Aojia Zhuang Aobo Zhuang Yijiao Chen Zhaoyu Qin Dexiang Zhu Li Ren Ye Wei Pengyang Zhou Xuetong Yue Fuchu He Jianmin Xu Chen Ding |
| author_sort | Aojia Zhuang |
| collection | DOAJ |
| description | The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC. |
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| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-13T08:02:19Z |
| publishDate | 2023-05-01 |
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| spelling | doaj.art-89d1d99875d441ca98ea17f8063b7a122023-06-01T13:47:17ZengeLife Sciences Publications LtdeLife2050-084X2023-05-011210.7554/eLife.82959Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodesAojia Zhuang0Aobo Zhuang1https://orcid.org/0000-0002-3958-7975Yijiao Chen2Zhaoyu Qin3Dexiang Zhu4Li Ren5Ye Wei6Pengyang Zhou7Xuetong Yue8Fuchu He9Jianmin Xu10Chen Ding11https://orcid.org/0000-0001-8673-3464State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Xiamen University Research Center of Retroperitoneal Tumor Committee of Oncology Society of Chinese Medical Association, College of Medicine, Xiamen University, Xiamen, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing, China; Research Unit of Proteomics Driven Cancer Precision Medicine, Chinese Academy of Medical Sciences, Beijing, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaState Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Institutes of Biomedical Sciences, Department of Colorectal Surgery, Colorectal Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Institute of Biomedical Science, Henan Normal University, Xinxiang, ChinaThe presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.https://elifesciences.org/articles/82959T1 colorectal cancerlymph nodes metastasisproteomicsmachine learning |
| spellingShingle | Aojia Zhuang Aobo Zhuang Yijiao Chen Zhaoyu Qin Dexiang Zhu Li Ren Ye Wei Pengyang Zhou Xuetong Yue Fuchu He Jianmin Xu Chen Ding Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes eLife T1 colorectal cancer lymph nodes metastasis proteomics machine learning |
| title | Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes |
| title_full | Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes |
| title_fullStr | Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes |
| title_full_unstemmed | Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes |
| title_short | Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes |
| title_sort | proteomic characteristics reveal the signatures and the risks of t1 colorectal cancer metastasis to lymph nodes |
| topic | T1 colorectal cancer lymph nodes metastasis proteomics machine learning |
| url | https://elifesciences.org/articles/82959 |
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