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使用计算和系统生物学方法鉴定与耐药性乳腺癌相关的关键溶酶体相关基因。

Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach.

作者信息

Shiralipour Aref, Khorsand Babak, Jafari Leila, Salehi Mohammad, Kazemi Mahsa, Zahiri Javad, Jajarmi Vahid, Kazemi Bahram

机构信息

Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

出版信息

Iran J Pharm Res. 2022 Oct 15;21(1):e130342. doi: 10.5812/ijpr-130342. eCollection 2022 Dec.

Abstract

BACKGROUND

Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches.

OBJECTIVES

We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer.

METHODS

All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes.

RESULTS

Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer.

CONCLUSIONS

The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance.

摘要

背景

乳腺癌中的耐药性是治疗患者时一个尚未解决的问题。最近有讨论称溶酶体与癌细胞的侵袭和血管生成有关。有证据表明溶酶体也可导致多药耐药。我们通过计算和系统生物学方法分析了乳腺癌中这个新出现的概念。

目的

我们旨在鉴定与耐药性乳腺癌相关的关键溶酶体相关基因。

方法

通过人类溶酶体基因数据库查询所有对溶酶体结构和功能有贡献的基因。从作为优先级排序工具的Endeavour、ToppGene和GPSy提供的列表中选择优先级最高的前51个基因。比对所有溶酶体基因和12个乳腺癌相关基因以鉴定与乳腺癌相关基因最相似的基因。应用不同的中心性对每个人类蛋白质进行评分,以计算人类蛋白质 - 蛋白质相互作用(PPI)网络中最核心的溶酶体基因。从上述方法的结果中提取共同基因作为选定基因集。对于基因本体富集分析,通过WebGestalt、DAVID和KOBAS对选定基因集进行分析。通过STRING数据库构建PPI网络。利用Cytoscape分析PPI网络的拓扑网络相互作用,并利用CytoHubba提取枢纽基因。

结果

基于生物学研究、文献综述以及比较所有上述分析方法,六个基因被确定为在乳腺癌中至关重要。这种针对所有溶酶体相关基因的计算方法表明候选基因包括PRF1、TLR9、CLTC、GJA1、AP3B1和RPTOR。对这六个基因的分析表明它们可能在乳腺癌发展中起关键作用,而这很少被评估。这些基因对化疗耐药性乳腺癌的新药发现具有潜在的治疗意义。

结论

本研究聚焦于所有与乳腺癌相关的功能性和结构性溶酶体相关基因。它揭示了六个可能作为耐药性乳腺癌治疗靶点的溶酶体枢纽基因。由于这些基因在溶酶体的结构和功能中起关键作用,靶向它们可以有效克服耐药性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5826/10007991/ffe9eec5240a/ijpr-21-1-130342-i001.jpg

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