mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning
Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA s...
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| Format: | Article |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.950782/full |
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| author | Meilin Weng Meilin Weng Ting Li Ting Li Jing Zhao Miaomiao Guo Miaomiao Guo Wenling Zhao Wenling Zhao Wenchao Gu Wenchao Gu Caihong Sun Caihong Sun Ying Yue Ying Yue Ziwen Zhong Ziwen Zhong Ke Nan Ke Nan Qingwu Liao Qingwu Liao Minli Sun Minli Sun Di Zhou Di Zhou Changhong Miao Changhong Miao |
| author_facet | Meilin Weng Meilin Weng Ting Li Ting Li Jing Zhao Miaomiao Guo Miaomiao Guo Wenling Zhao Wenling Zhao Wenchao Gu Wenchao Gu Caihong Sun Caihong Sun Ying Yue Ying Yue Ziwen Zhong Ziwen Zhong Ke Nan Ke Nan Qingwu Liao Qingwu Liao Minli Sun Minli Sun Di Zhou Di Zhou Changhong Miao Changhong Miao |
| author_sort | Meilin Weng |
| collection | DOAJ |
| description | Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy. |
| first_indexed | 2024-04-11T21:22:27Z |
| format | Article |
| id | doaj.art-a7b28b4de4034bb98b73466f243d670f |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-11T21:22:27Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-a7b28b4de4034bb98b73466f243d670f2022-12-22T04:02:35ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-08-011310.3389/fimmu.2022.950782950782mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learningMeilin Weng0Meilin Weng1Ting Li2Ting Li3Jing Zhao4Miaomiao Guo5Miaomiao Guo6Wenling Zhao7Wenling Zhao8Wenchao Gu9Wenchao Gu10Caihong Sun11Caihong Sun12Ying Yue13Ying Yue14Ziwen Zhong15Ziwen Zhong16Ke Nan17Ke Nan18Qingwu Liao19Qingwu Liao20Minli Sun21Minli Sun22Di Zhou23Di Zhou24Changhong Miao25Changhong Miao26Department of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, JapanDepartment of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, JapanDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaDepartment of Anesthesiology, Zhongshan hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan hospital, Fudan University, Shanghai, ChinaColorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.https://www.frontiersin.org/articles/10.3389/fimmu.2022.950782/fullcolorectal cancermRNAsistemnessrisk score modelimmunotherapymetabolism |
| spellingShingle | Meilin Weng Meilin Weng Ting Li Ting Li Jing Zhao Miaomiao Guo Miaomiao Guo Wenling Zhao Wenling Zhao Wenchao Gu Wenchao Gu Caihong Sun Caihong Sun Ying Yue Ying Yue Ziwen Zhong Ziwen Zhong Ke Nan Ke Nan Qingwu Liao Qingwu Liao Minli Sun Minli Sun Di Zhou Di Zhou Changhong Miao Changhong Miao mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning Frontiers in Immunology colorectal cancer mRNAsi stemness risk score model immunotherapy metabolism |
| title | mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning |
| title_full | mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning |
| title_fullStr | mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning |
| title_full_unstemmed | mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning |
| title_short | mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning |
| title_sort | mrnasi related metabolic risk score model identifies poor prognosis immunoevasive contexture and low chemotherapy response in colorectal cancer patients through machine learning |
| topic | colorectal cancer mRNAsi stemness risk score model immunotherapy metabolism |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.950782/full |
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