Integrative analysis of ferroptosis regulators for clinical prognosis based on deep learning and potential chemotherapy sensitivity of prostate cancer.

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL Precision Clinical Medicine Pub Date : 2023-03-01 DOI:10.1093/pcmedi/pbad001
Tuanjie Guo, Zhihao Yuan, Tao Wang, Jian Zhang, Heting Tang, Ning Zhang, Xiang Wang, Siteng Chen
{"title":"Integrative analysis of ferroptosis regulators for clinical prognosis based on deep learning and potential chemotherapy sensitivity of prostate cancer.","authors":"Tuanjie Guo,&nbsp;Zhihao Yuan,&nbsp;Tao Wang,&nbsp;Jian Zhang,&nbsp;Heting Tang,&nbsp;Ning Zhang,&nbsp;Xiang Wang,&nbsp;Siteng Chen","doi":"10.1093/pcmedi/pbad001","DOIUrl":null,"url":null,"abstract":"<p><p>Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice. We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score (DLF<sub>score</sub>) for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer. Based on this prognostic model, there was a statistically significant difference in the disease-free survival probability between patients with high and low DLF<sub>score</sub> in the The Cancer Genome Atlas (TCGA) cohort (<i>P</i> < 0.0001). In the validation cohort GSE116918, we also observed a consistent conclusion with the training set (<i>P</i> = 0.02). Additionally, functional enrichment analysis showed that DNA repair, RNA splicing signaling, organelle assembly, and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis. Meanwhile, the prognostic model we constructed also had application value in predicting drug sensitivity. We predicted some potential drugs for the treatment of prostate cancer through AutoDock, which could potentially be used for prostate cancer treatment.</p>","PeriodicalId":33608,"journal":{"name":"Precision Clinical Medicine","volume":"6 1","pages":"pbad001"},"PeriodicalIF":5.1000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/10/40/pbad001.PMC9982702.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Clinical Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/pcmedi/pbad001","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 1

Abstract

Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice. We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score (DLFscore) for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer. Based on this prognostic model, there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas (TCGA) cohort (P < 0.0001). In the validation cohort GSE116918, we also observed a consistent conclusion with the training set (P = 0.02). Additionally, functional enrichment analysis showed that DNA repair, RNA splicing signaling, organelle assembly, and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis. Meanwhile, the prognostic model we constructed also had application value in predicting drug sensitivity. We predicted some potential drugs for the treatment of prostate cancer through AutoDock, which could potentially be used for prostate cancer treatment.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度学习和前列腺癌潜在化疗敏感性的铁下垂调节因子对临床预后的综合分析。
探索有用的预后标志物和发展一个强大的预后模型的前列腺癌患者是至关重要的临床实践。我们应用深度学习算法构建预后模型,并提出基于深度学习的铁下垂评分(DLFscore)来预测前列腺癌的预后和潜在的化疗敏感性。基于该预后模型,在the Cancer Genome Atlas (TCGA)队列中,高、低dlf评分患者的无病生存率差异有统计学意义(P P = 0.02)。此外,功能富集分析表明,DNA修复、RNA剪接信号、细胞器组装和中心体周期途径的调节可能通过铁下垂调节前列腺癌。同时,所构建的预后模型在预测药物敏感性方面也具有应用价值。我们通过AutoDock预测了一些治疗前列腺癌的潜在药物,这些药物有可能用于前列腺癌的治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
期刊最新文献
Global distribution of Klebsiella pneumoniae producing extended-spectrum β-lactamases in neonates. Application of metagenomic next-generation sequencing with brain tissue biopsy for diagnosing intracranial lesions in people with HIV. Revisiting ecological fallacy: are single-case experimental study designs even more relevant in the era of precision medicine? Targeted nuclear degranulation of neutrophils promotes the progression of pneumonia in ulcerative colitis. The relationship between contact lens ultraviolet light transmittance and myopia progression: a large-scale retrospective cohort study.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1