文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期，新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

采用机器学习方法筛选造釉细胞瘤型颅咽管瘤的新诊断特征并探索个性化治疗策略。

Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies.

作者信息

Wu Ji, Qin Chengjian, Fang Guoxing, Shen Lei, Li Muhua, Lu Bimin, Li Yanghong, Yao Xiaomin, Fang Dalang

机构信息

Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.

Department of Breast and Thyroid Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.

出版信息

Transl Pediatr. 2023 May 30;12(5):947-966. doi: 10.21037/tp-23-152. Epub 2023 May 10.

DOI:10.21037/tp-23-152
PMID:37305719
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10248946/
Abstract

BACKGROUND: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. METHODS: Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. RESULTS: Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. CONCLUSIONS: Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP.

摘要

背景：颅咽管瘤（ACP）是一种发病机制不明的非恶性肿瘤，常见于儿童，具有恶性潜能。目前主要的治疗选择是手术切除和放疗。这些治疗可能导致严重并发症，极大地影响患者的总体生存率和生活质量。因此，利用生物信息学探索ACP发生发展的机制并识别新分子具有重要意义。 方法：从综合基因表达数据库下载ACP的测序数据，用于差异表达基因鉴定，并通过基因本体论、京都基因和基因集富集分析（GSEA）进行可视化。加权基因共表达网络分析用于识别与ACP关联最紧密的基因。使用GSE94349作为训练集，采用机器学习算法筛选出5个诊断标志物，并使用受试者工作特征（ROC）曲线评估诊断准确性，同时使用GSE68015作为验证集进行验证。 结果：I型细胞角蛋白15（KRT15）、滤泡树突状细胞分泌肽（FDCSP）、Rho相关GTP结合蛋白RhoC（RHOC）、调节角质形成细胞中TGFB1信号负调节因子（CD109）和II型细胞角蛋白6A（KRT6A）（训练集和验证集的受试者工作特征曲线下面积均为1），使用这5个标志物构建的列线图可预测ACP患者的病情进展。而激活的T细胞表面糖蛋白CD4、γδT细胞、嗜酸性粒细胞和调节性T细胞在ACP组织中的表达水平高于正常组织，这可能有助于ACP的发病机制。根据CellMiner数据库（肿瘤细胞与药物相关数据库工具）分析，高CD109水平显示对右丙亚胺具有显著的药物敏感性，右丙亚胺有可能成为ACP的治疗药物。 结论：我们的研究结果扩展了对ACP分子免疫机制的认识，并为ACP的靶向精准治疗提出了可能的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/f329c566fba8/tp-12-05-947-f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/26202d882042/tp-12-05-947-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/97713033837d/tp-12-05-947-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/7cde2faaf7a7/tp-12-05-947-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/3d3b3a0f2e17/tp-12-05-947-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/a0c8e04f45e6/tp-12-05-947-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/6a9bad3d01eb/tp-12-05-947-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/8486d4a34037/tp-12-05-947-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/165d2b2fb357/tp-12-05-947-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/c0f2fd4c0304/tp-12-05-947-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/f5779db601b2/tp-12-05-947-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/dd410b67ee6f/tp-12-05-947-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/60e88b5ce33f/tp-12-05-947-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/f329c566fba8/tp-12-05-947-f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/26202d882042/tp-12-05-947-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/97713033837d/tp-12-05-947-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/7cde2faaf7a7/tp-12-05-947-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/3d3b3a0f2e17/tp-12-05-947-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/a0c8e04f45e6/tp-12-05-947-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/6a9bad3d01eb/tp-12-05-947-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/8486d4a34037/tp-12-05-947-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/165d2b2fb357/tp-12-05-947-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/c0f2fd4c0304/tp-12-05-947-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/f5779db601b2/tp-12-05-947-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/dd410b67ee6f/tp-12-05-947-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/60e88b5ce33f/tp-12-05-947-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a0/10248946/f329c566fba8/tp-12-05-947-f13.jpg

相似文献

[1]
Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies.

Transl Pediatr. 2023-5-30

[2]
Integrative Analysis of Biomarkers and Mechanisms in Adamantinomatous Craniopharyngioma.

Front Genet. 2022-3-30

[3]
Identification and Functional Enrichment Analysis of Potential Diagnostic and Therapeutic Targets in Adamantinomatous Craniopharyngioma.

J Comput Biol. 2020-1

[4]
Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

Cancer Gene Ther. 2018-4-23

[5]
Integrative analyses identify HIF-1α as a potential protective role with immune cell infiltration in adamantinomatous craniopharyngioma.

Front Immunol. 2022

[6]
Bioinformatics analysis and validation of the critical genes associated with adamantinomatous craniopharyngioma.

Front Oncol. 2022-10-27

[7]
MMP12 is a potential therapeutic target for Adamantinomatous craniopharyngioma: Conclusions from bioinformatics analysis and experiments.

Oncol Lett. 2021-7

[8]
Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

Neuro Oncol. 2024-6-3

[9]
Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies.

Bioengineered. 2021-12

[10]
Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.

Front Med (Lausanne). 2022-9-29

引用本文的文献

[1]
Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.

Front Immunol. 2025-5-1

[2]
Molecular subtypes of adamantinomatous craniopharyngiomas.

Neuro Oncol. 2025-2-3

[3]
Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment.

Front Neurol. 2024-11-27

[4]
Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records.

BioData Min. 2024-1-5

本文引用的文献

[1]
Machine learning screening for Parkinson's disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes.

Ann Transl Med. 2023-1-15

[2]
Rational design of poly-L-glutamic acid-palbociclib conjugates for pediatric glioma treatment.

J Control Release. 2023-3

[3]
Identifying and validating key genes mediating intracranial aneurysm rupture using weighted correlation network analysis and exploration of personalized treatment.

Ann Transl Med. 2022-10

[4]
Rb Tumor Suppressor in Small Cell Lung Cancer: Combined Genomic and IHC Analysis with a Description of a Distinct Rb-Proficient Subset.

Clin Cancer Res. 2022-11-1

[5]
Characterization of novel CTNNB1 mutation in Craniopharyngioma by whole-genome sequencing.

Mol Cancer. 2021-12-18

[6]
Intraventricular Craniopharyngiomas-Overcoming Their Relative Inaccessibility: Institutional Experience With a Review of Literature.

Front Neurol. 2021-11-15

[7]
Management of Childhood-onset Craniopharyngioma in Italy: A Multicenter, 7-Year Follow-up Study of 145 Patients.

J Clin Endocrinol Metab. 2022-2-17

[8]
A Phase 0 Trial of Ceritinib in Patients with Brain Metastases and Recurrent Glioblastoma.

Clin Cancer Res. 2022-1-15

[9]
CD109-GP130 interaction drives glioblastoma stem cell plasticity and chemoresistance through STAT3 activity.

JCI Insight. 2021-5-10

[10]
Variation in relative biological effectiveness for cognitive structures in proton therapy of pediatric brain tumors.

Acta Oncol. 2021-2

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索