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。

IF 2.1 Q3 ONCOLOGY
World Journal of Oncology Pub Date : 2023-10-01 Epub Date: 2023-09-20 DOI:10.14740/wjon1700
Takashi Takeshita, Hirotaka Iwase, Rongrong Wu, Danya Ziazadeh, Li Yan, Kazuaki Takabe
{"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}
引用次数: 0

摘要

背景:确定激素受体阳性(HR+)乳腺癌症(BC)的预后对于改善生存结果至关重要,该癌占所有BC的80%。对BC相关死亡率高风险人群进行分层并改善预后是十多年来研究的重点。然而,这些工具并不通用,因为它们仅限于临床因素。我们假设可以使用人工智能开发一个预测HR+BC患者预后的新框架。方法:从癌症国际联合会(METABRIC)、癌症基因组图谱(TCGA)和基因表达综合(GEO)队列中分析2338例HR+人表皮生长因子受体2阴性(HER2-)BC病例。然后利用复发预测模型(RPM)将各组分为高风险组和低风险组。RPM是通过使用逻辑进展模型从18000多个基因中提取9个预后相关基因而创建的。结果:在发现队列和验证队列中,RPM的风险分类都有显著的分层。在与时间相关的曲线下区域分析中,根据队列的不同,存在一些变化,但发现大约10年后准确性显著下降。通过基因集富集分析,细胞周期相关基因集、MYC和PI3K-AKT-mTOR信号在高危肿瘤中富集。高危肿瘤与高水平的淋巴和髓系免疫细胞和免疫细胞溶解活性以及低水平的干细胞和基质细胞有关。高危肿瘤也与化疗和内分泌治疗效果不佳有关。结论:该模型能够对多个队列的预后进行分层。这是因为该模型反映了BC的主要治疗靶点途径和肿瘤免疫微环境,并得到了化疗和内分泌治疗的治疗效果的进一步支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
15.40%
发文量
37
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信