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利用计算机模拟方法寻找乳腺腺癌的生物标志物

Biomarkers for Breast Adenocarcinoma Using In Silico Approaches.

作者信息

Pandi Jhansi, Arulprakasam Ajucarmelprecilla, Dhandapani Ranjithkumar, Ramanathan Saikishore, Thangavelu Sathiamoorthi, Chinnappan Jayaprakash, Vidhya Rajalakshmi V, Alghamdi Saad, Shesha Nashwa Talaat, Prasath S

机构信息

Medical Microbiology Unit, Department of Microbiology, Alagappa University, Karaikudi, Tamil Nadu, India.

Chimertech Private Limited, Chennai, India.

出版信息

Evid Based Complement Alternat Med. 2022 Mar 3;2022:7825272. doi: 10.1155/2022/7825272. eCollection 2022.

DOI:10.1155/2022/7825272
PMID:35280505
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8913068/
Abstract

This work elucidates the idea of finding probable critical genes linked to breast adenocarcinoma. In this study, the GEO database gene expression profile data set (GSE70951) was retrieved to look for genes that were expressed variably across breast adenocarcinoma samples and healthy tissue samples. The genes were confirmed to be part of the PPI network for breast cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. For correlation analysis, the predictive biomarker of these hub genes, as well as GEPIA, was used. A total of 155 (85 upregulated genes and 70 downregulated genes) were identified. By integrating the PPI and CytoHubba data, the major key/hub genes were selected from the results. The KM plotter is employed to find the prognosis of those major pivot genes, and the outcome shows worse prognosis in breast adenocarcinoma patients. Further experimental validation will show the predicted expression levels of those hub genes. The overall result of our study gives the consequences for the identification of a critical gene to ease the molecular targeting therapy for breast adenocarcinoma. It could be used as a prognostic biomarker and could lead to therapy options for breast adenocarcinoma.

摘要

这项工作阐明了寻找与乳腺腺癌相关的潜在关键基因的想法。在本研究中，检索了基因表达综合数据库（GEO）中的基因表达谱数据集（GSE70951），以寻找在乳腺腺癌样本和健康组织样本中表达存在差异的基因。这些基因被证实是乳腺癌发病机制和预后的蛋白质-蛋白质相互作用（PPI）网络的一部分。在Cytoscape软件中，使用CytoHubba模块来发现枢纽基因。对于相关性分析，使用了这些枢纽基因以及基因表达谱交互分析（GEPIA）的预测生物标志物。总共鉴定出155个基因（85个上调基因和70个下调基因）。通过整合PPI和CytoHubba数据，从结果中选择了主要的关键/枢纽基因。使用KM绘图工具来研究那些主要枢纽基因的预后情况，结果显示乳腺腺癌患者的预后较差。进一步的实验验证将展示那些枢纽基因的预测表达水平。我们研究的总体结果为鉴定关键基因以促进乳腺腺癌的分子靶向治疗提供了依据。它可以用作预后生物标志物，并可能为乳腺腺癌带来治疗选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/d0c0e7c68f4c/ECAM2022-7825272.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/8ee071f94391/ECAM2022-7825272.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/27c260034fe8/ECAM2022-7825272.002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/70d22f91b6fe/ECAM2022-7825272.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/d28eef628e35/ECAM2022-7825272.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/1bf831cb90b6/ECAM2022-7825272.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/4bb30a6a2434/ECAM2022-7825272.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/01a4a8f372d3/ECAM2022-7825272.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/d0c0e7c68f4c/ECAM2022-7825272.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/8ee071f94391/ECAM2022-7825272.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/27c260034fe8/ECAM2022-7825272.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/3c9cbd1add1f/ECAM2022-7825272.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/70d22f91b6fe/ECAM2022-7825272.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/d28eef628e35/ECAM2022-7825272.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/1bf831cb90b6/ECAM2022-7825272.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/4bb30a6a2434/ECAM2022-7825272.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/01a4a8f372d3/ECAM2022-7825272.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a40d/8913068/d0c0e7c68f4c/ECAM2022-7825272.009.jpg

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