{"title":"使用Nine基因表达特征的基于机器学习的激素受体阳性乳腺癌症预后模型的开发。","authors":"Takashi Takeshita, Hirotaka Iwase, Rongrong Wu, Danya Ziazadeh, Li Yan, Kazuaki Takabe","doi":"10.14740/wjon1700","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Determining the prognosis of hormone receptor positive (HR<sup>+</sup>) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR<sup>+</sup> BC patients can develop using artificial intelligence.</p><p><strong>Methods: </strong>A total of 2,338 HR<sup>+</sup> human epidermal growth factor receptor 2 negative (HER2<sup>-</sup>) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.</p><p><strong>Results: </strong>Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.</p><p><strong>Conclusions: </strong>This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.</p>","PeriodicalId":46797,"journal":{"name":"World Journal of Oncology","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5f/16/wjon-14-406.PMC10588506.pdf","citationCount":"0","resultStr":"{\"title\":\"Development of a Machine Learning-Based Prognostic Model for Hormone Receptor-Positive Breast Cancer Using Nine-Gene Expression Signature.\",\"authors\":\"Takashi Takeshita, Hirotaka Iwase, Rongrong Wu, Danya Ziazadeh, Li Yan, Kazuaki Takabe\",\"doi\":\"10.14740/wjon1700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Determining the prognosis of hormone receptor positive (HR<sup>+</sup>) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR<sup>+</sup> BC patients can develop using artificial intelligence.</p><p><strong>Methods: </strong>A total of 2,338 HR<sup>+</sup> human epidermal growth factor receptor 2 negative (HER2<sup>-</sup>) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.</p><p><strong>Results: </strong>Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.</p><p><strong>Conclusions: </strong>This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.</p>\",\"PeriodicalId\":46797,\"journal\":{\"name\":\"World Journal of Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5f/16/wjon-14-406.PMC10588506.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14740/wjon1700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14740/wjon1700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/20 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Development of a Machine Learning-Based Prognostic Model for Hormone Receptor-Positive Breast Cancer Using Nine-Gene Expression Signature.
Background: Determining the prognosis of hormone receptor positive (HR+) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR+ BC patients can develop using artificial intelligence.
Methods: A total of 2,338 HR+ human epidermal growth factor receptor 2 negative (HER2-) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.
Results: Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.
Conclusions: This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.
期刊介绍:
World Journal of Oncology, bimonthly, publishes original contributions describing basic research and clinical investigation of cancer, on the cellular, molecular, prevention, diagnosis, therapy and prognosis aspects. The submissions can be basic research or clinical investigation oriented. This journal welcomes those submissions focused on the clinical trials of new treatment modalities for cancer, and those submissions focused on molecular or cellular research of the oncology pathogenesis. Case reports submitted for consideration of publication should explore either a novel genomic event/description or a new safety signal from an oncolytic agent. The areas of interested manuscripts are these disciplines: tumor immunology and immunotherapy; cancer molecular pharmacology and chemotherapy; drug sensitivity and resistance; cancer epidemiology; clinical trials; cancer pathology; radiobiology and radiation oncology; solid tumor oncology; hematological malignancies; surgical oncology; pediatric oncology; molecular oncology and cancer genes; gene therapy; cancer endocrinology; cancer metastasis; prevention and diagnosis of cancer; other cancer related subjects. The types of manuscripts accepted are original article, review, editorial, short communication, case report, letter to the editor, book review.