Xue Li, Meng Wu, Min Wu, Jie Liu, Li Song, Jiasi Wang, Jun Zhou, Shilin Li, Hang Yang, Jun Zhang, Xinwu Cui, Zhenyu Liu, Fanxin Zeng
{"title":"用于预测结直肠癌转移和预后的放射组学和基因组学衍生模型","authors":"Xue Li, Meng Wu, Min Wu, Jie Liu, Li Song, Jiasi Wang, Jun Zhou, Shilin Li, Hang Yang, Jun Zhang, Xinwu Cui, Zhenyu Liu, Fanxin Zeng","doi":"10.1093/carcin/bgad098","DOIUrl":null,"url":null,"abstract":"<p><p>Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.</p>","PeriodicalId":9446,"journal":{"name":"Carcinogenesis","volume":" ","pages":"170-180"},"PeriodicalIF":3.3000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer.\",\"authors\":\"Xue Li, Meng Wu, Min Wu, Jie Liu, Li Song, Jiasi Wang, Jun Zhou, Shilin Li, Hang Yang, Jun Zhang, Xinwu Cui, Zhenyu Liu, Fanxin Zeng\",\"doi\":\"10.1093/carcin/bgad098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.</p>\",\"PeriodicalId\":9446,\"journal\":{\"name\":\"Carcinogenesis\",\"volume\":\" \",\"pages\":\"170-180\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Carcinogenesis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/carcin/bgad098\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carcinogenesis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/carcin/bgad098","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer.
Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.
期刊介绍:
Carcinogenesis: Integrative Cancer Research is a multi-disciplinary journal that brings together all the varied aspects of research that will ultimately lead to the prevention of cancer in man. The journal publishes papers that warrant prompt publication in the areas of Biology, Genetics and Epigenetics (including the processes of promotion, progression, signal transduction, apoptosis, genomic instability, growth factors, cell and molecular biology, mutation, DNA repair, genetics, etc.), Cancer Biomarkers and Molecular Epidemiology (including genetic predisposition to cancer, and epidemiology), Inflammation, Microenvironment and Prevention (including molecular dosimetry, chemoprevention, nutrition and cancer, etc.), and Carcinogenesis (including oncogenes and tumor suppressor genes in carcinogenesis, therapy resistance of solid tumors, cancer mouse models, apoptosis and senescence, novel therapeutic targets and cancer drugs).