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本文引用的文献

1
Relating mutational signature exposures to clinical data in cancers via signeR 2.0.通过 signeR 2.0 将突变特征暴露与癌症临床数据相关联。
BMC Bioinformatics. 2023 Nov 22;24(1):439. doi: 10.1186/s12859-023-05550-3.
2
Analysis of CD74 Occurrence in Oncogenic Fusion Proteins.分析致癌融合蛋白中 CD74 的出现情况。
Int J Mol Sci. 2023 Nov 5;24(21):15981. doi: 10.3390/ijms242115981.
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Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.在 cBioPortal 中分析和可视化 AACR 项目 GENIE 生物制药协作的纵向基因组和临床数据。
Cancer Res. 2023 Dec 1;83(23):3861-3867. doi: 10.1158/0008-5472.CAN-23-0816.
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Recent advances in non-small cell lung cancer targeted therapy; an update review.非小细胞肺癌靶向治疗的最新进展;综述更新
Cancer Cell Int. 2023 Aug 11;23(1):162. doi: 10.1186/s12935-023-02990-y.
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DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update).DAVID:一个用于基因列表功能富集分析和功能注释的网络服务器(2021 更新)。
Nucleic Acids Res. 2022 Jul 5;50(W1):W216-W221. doi: 10.1093/nar/gkac194.
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Database resources of the national center for biotechnology information.国家生物技术信息中心数据库资源。
Nucleic Acids Res. 2022 Jan 7;50(D1):D20-D26. doi: 10.1093/nar/gkab1112.
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Mutational signature analysis in non-small cell lung cancer patients with a high tumor mutational burden.高肿瘤突变负荷的非小细胞肺癌患者的突变特征分析。
Respir Res. 2021 Nov 24;22(1):302. doi: 10.1186/s12931-021-01871-0.
8
Therapeutic and prognostic insights from the analysis of cancer mutational signatures.从癌症突变特征分析中获得的治疗和预后见解。
Trends Genet. 2022 Feb;38(2):194-208. doi: 10.1016/j.tig.2021.08.007. Epub 2021 Sep 2.
9
Protein domain identification methods and online resources.蛋白质结构域鉴定方法及在线资源。
Comput Struct Biotechnol J. 2021 Feb 2;19:1145-1153. doi: 10.1016/j.csbj.2021.01.041. eCollection 2021.
10
The repertoire of mutational signatures in human cancer.人类癌症中的突变特征谱。
Nature. 2020 Feb;578(7793):94-101. doi: 10.1038/s41586-020-1943-3. Epub 2020 Feb 5.

通过对基因相互作用和特征的新型分析深入了解非小细胞肺癌。

Insight into NSCLC through novel analysis of gene interactions and characteristics.

作者信息

Pan Eric, Bai Yongsheng

机构信息

Debakey High School Houston, TX 77030, USA.

Next-Gen Intelligent Science Training Ann Arbor, MI 48105, USA.

出版信息

Am J Clin Exp Immunol. 2024 Apr 25;13(2):58-67. doi: 10.62347/ANLV4963. eCollection 2024.

DOI:10.62347/ANLV4963
PMID:38765019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11101995/
Abstract

Around 80 to 85% of all lung cancers are non-small cell lung cancer (NSCLC). Previous research has aimed at exploring the genetic basis of NSCLC through individual approaches, but studies have yet to investigate the results of combining them. Here we show that analyzing NSCLC genetics through three approaches simultaneously creates unique insights into our understanding of the disease. Through a combination of previous research and bioinformatics tools, we determined 35 NSCLC candidate genes. We analyzed these genes in 3 different approaches. First, we found the gene fusions between these candidate genes. Second, we found the common superfamilies between genes. Finally, we identified mutational signatures that are possibly associated with NSCLC. Each approach has its individual, unique results. Fusion relationships identify specific gene fusion targets, common superfamilies identify possible avenues to determine novel target genes, and identifying NSCLC associated mutational signatures has diagnostic and prognostic benefits. Combining the approaches, we found that gene CD74 has significant fusion relationships, but it has no association with the other two approaches, suggesting that CD74 is associated with NSCLC mainly because of its fusion relationships. Targeting the gene fusions of CD74 may be an alternative NSCLC treatment. This genetic analysis has indeed created unique insight into NSCLC genes. Both the results from each of the approaches separately and combined allow pursuit of more effective treatment strategies for this cancer. The methodology presented can also apply to other cancers, creating insights that current analytical methods could not find.

摘要

所有肺癌中约80%至85%为非小细胞肺癌(NSCLC)。以往的研究旨在通过个体方法探索NSCLC的遗传基础,但尚未有研究调查将这些方法结合起来的结果。在此我们表明,通过同时采用三种方法分析NSCLC遗传学,能为我们对该疾病的理解带来独特见解。通过结合以往研究和生物信息学工具,我们确定了35个NSCLC候选基因。我们用3种不同方法分析了这些基因。首先,我们发现了这些候选基因之间的基因融合。其次,我们发现了基因之间的共同超家族。最后,我们确定了可能与NSCLC相关的突变特征。每种方法都有其独特的结果。融合关系确定了特定的基因融合靶点,共同超家族确定了确定新靶点基因的可能途径,而确定与NSCLC相关的突变特征具有诊断和预后价值。将这些方法结合起来,我们发现基因CD74具有显著的融合关系,但与其他两种方法无关,这表明CD74与NSCLC相关主要是因为其融合关系。针对CD74的基因融合可能是一种NSCLC的替代治疗方法。这种遗传分析确实为NSCLC基因带来了独特见解。每种方法单独以及结合后的结果都有助于为这种癌症寻求更有效的治疗策略。所提出的方法也可应用于其他癌症,带来现有分析方法无法发现的见解。