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Cai R, Ke L, Zhao Y, Zhao J, Zhang H, Zheng P, Xin L, Ma C, Lin Y. NMR-based metabolomics combined with metabolic pathway analysis reveals metabolic heterogeneity of colorectal cancer tissue at different anatomical locations and stages. Int J Cancer 2025; 156:1644-1655. [PMID: 39629979 PMCID: PMC11826128 DOI: 10.1002/ijc.35273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 10/15/2024] [Accepted: 10/23/2024] [Indexed: 02/16/2025]
Abstract
Colorectal cancer (CRC) still remains the leading cause of cancer death worldwide. This study aimed to profile the metabolic differences of colorectal cancer tissues (CCT) at different stages and sites, as compared with their distant noncancerous tissues (DNT), to investigate the temporal and spatial heterogeneities of metabolic characterization. Our NMR-based metabolomics fingerprinting revealed that many of the metabolite levels were significantly altered in CCT compared to DNT and esophageal cancer tissues, indicating deregulations of glucose metabolism, one-carbon metabolism, glutamine metabolism, amino acid metabolism, fatty acid metabolism, TCA cycle, choline metabolism, and so forth. A total of five biomarker metabolites, including glucose, glutamate, alanine, valine and histidine, were identified to distinguish between early and advanced stages of CCT. Metabolites that distinguish the different anatomical sites of CCT include glucose, glycerol, glutamine, inositol, succinate, and citrate. Those significant metabolic differences in CRC tissues at different pathological stages and sites suggested temporal and spatial heterogeneities of metabolic characterization in CCT, providing a metabolic foundation for further study on biofluid metabolism in CRC early detection.
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Affiliation(s)
- Rongzhi Cai
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - LiXin Ke
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - Yan Zhao
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - Jiayun Zhao
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - Huanian Zhang
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - Peie Zheng
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
| | - Lijing Xin
- Animal Imaging and Technology Core, Center for Biomedical ImagingEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Changchun Ma
- Radiation Oncology DepartmentCancer Hospital of Shantou University Medical CollegeShantouGuangdongChina
| | - Yan Lin
- Radiology Department, Second Affiliated HospitalShantou University Medical CollegeShantou CityGuangdong ProvinceChina
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Cai M, Zhao K, Wu L, Huang Y, Zhao M, Hu Q, Chen Q, Yao S, Li Z, Fan X, Liu Z. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis. Chin Med J (Engl) 2024; 137:421-430. [PMID: 38238158 PMCID: PMC10876244 DOI: 10.1097/cm9.0000000000002964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
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Affiliation(s)
- Ming Cai
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Ke Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Lin Wu
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
| | - Minning Zhao
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qingru Hu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, Guangdong 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
| | - Zhenhui Li
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, Yunnan 650118, China
| | - Xinjuan Fan
- Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510655, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China
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Boyarskikh U, Kechin A, Khrapov E, Fedyanin M, Raskin G, Mukhina M, Kravtsova E, Tsukanov A, Achkasov S, Filipenko M. Detecting Microsatellite Instability in Endometrial, Colon, and Stomach Cancers Using Targeted NGS. Cancers (Basel) 2023; 15:5065. [PMID: 37894432 PMCID: PMC10605658 DOI: 10.3390/cancers15205065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023] Open
Abstract
PURPOSE To develop a method for testing the MSI based on targeted NGS. METHODS Based on the results of previous studies, 81 microsatellite loci with high variability in MSI-H tumors were selected, and a method for calculating the MSI score was developed. Using the MSI score, we defined the MSI status in endometral (162), colon (153), and stomach (190) cancers. Accuracy of the MSI scores was evaluated by comparison with MMR immunohistochemistry for 137 endometrium (63 dMMR and 74 pMMR), 76 colon (29 dMMR and 47 pMMR), and 81 stomach (8 dMMR and 73 pMMR) cancers. RESULTS Classification of MSS and MSI-H tumors was performed with AUC (0.99), sensitivity (92%), and specificity (98%) for all tumors without division into types. The accuracy of MSI testing in endometrial cancer was lower than for stomach and colon cancer (0.98, 87%, and 100%, respectively). The use of 27 loci only, the most informative for endometrial cancer, increased the overall accuracy (1.00, 99%, and 99%). Comparison of MSI score values in 505 tumors showed that MSI score is significantly higher in colon (p < 10-5) and stomach (p = 0.008) cancer compared with endometrial cancer. CONCLUSION The MSI score accurately determines MSI status for endometrial, colon, and stomach cancers and can be used to quantify the degree of MSI.
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Affiliation(s)
- Ulyana Boyarskikh
- Institute of Chemical Biology and Fundamental Medicine, Siberian Division of the Russian Academy of Sciences, 630090 Novosibirsk, Russia (E.K.); (M.F.)
| | - Andrey Kechin
- Institute of Chemical Biology and Fundamental Medicine, Siberian Division of the Russian Academy of Sciences, 630090 Novosibirsk, Russia (E.K.); (M.F.)
| | - Evgeniy Khrapov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Division of the Russian Academy of Sciences, 630090 Novosibirsk, Russia (E.K.); (M.F.)
| | - Mikhail Fedyanin
- State Budgetary Institution of Health Care of Moscow (Moscow Multidisciplinary Clinical Center “Kommunarka”), 142770 Moscow, Russia
| | - Grigory Raskin
- Dr. Berezin Medical Institute, 197758 St. Petersburg, Russia; (G.R.); (M.M.)
| | - Marina Mukhina
- Dr. Berezin Medical Institute, 197758 St. Petersburg, Russia; (G.R.); (M.M.)
| | - Elena Kravtsova
- Dr. Berezin Medical Institute, 197758 St. Petersburg, Russia; (G.R.); (M.M.)
| | - Aleksey Tsukanov
- Ryzhikh National Medical Research Center of Coloproctology, 123423 Moscow, Russia
| | - Sergey Achkasov
- Ryzhikh National Medical Research Center of Coloproctology, 123423 Moscow, Russia
| | - Maksim Filipenko
- Institute of Chemical Biology and Fundamental Medicine, Siberian Division of the Russian Academy of Sciences, 630090 Novosibirsk, Russia (E.K.); (M.F.)
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