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基于计算机整合生物信息学分析皮肤黑色素瘤微环境中的预后关键基因

Analyzing Prognostic Hub Genes in the Microenvironment of Cutaneous Melanoma by Computer Integrated Bioinformatics.

机构信息

Affiliated Hospital of Jiujiang University, No. 57 Xunyang East Road, Jiujiang 332000, Jiangxi Province, China.

First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China.

出版信息

Comput Intell Neurosci. 2022 Mar 8;2022:4493347. doi: 10.1155/2022/4493347. eCollection 2022.

DOI:10.1155/2022/4493347
PMID:35300397
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8923759/
Abstract

Cutaneous melanoma (CM) is attracting increasing attention due to high mortality. In response to this, we synthetically analyze the CM dataset from the TCGA database and explore microenvironment-related genes that effectively predict patient prognosis. Immune/stromal scores of cases are calculated using the ESTIMATE algorithm and are significantly associated with overall patient survival. Then, differentially expressed genes are identified by comparing the immune score and stromal score, also prognostic genes are subsequently screened. Functional analysis shows that these genes are enriched in different activities of immune system. Moreover, 19 prognosis-related hub genes are extracted from the protein-protein interaction network, of which four unreported genes (IL7R, FLT3, C1QC, and HLA-DRB5) are chosen for validation. A significant negative relationship is found between the expression levels of the 4 genes and pathological stages, notably grade. Furthermore, the K-M plots and TIMER results show that these genes have favorable value for CM prognosis. In conclusion, these results give a novel insight into CM and identify IL7R, FLT3, C1QC, and HLA-DRB5 as crucial roles for the diagnosis and treatment of CM.

摘要

皮肤黑色素瘤（CM）由于其高死亡率而引起了越来越多的关注。针对这一情况，我们综合分析了 TCGA 数据库中的 CM 数据集，并探索了与微环境相关的有效预测患者预后的基因。使用 ESTIMATE 算法计算病例的免疫/基质评分，并与总体患者生存率显著相关。然后，通过比较免疫评分和基质评分来鉴定差异表达基因，随后筛选出预后相关基因。功能分析表明，这些基因在免疫系统的不同活动中富集。此外，从蛋白质-蛋白质相互作用网络中提取了 19 个与预后相关的枢纽基因，其中选择了四个未报道的基因（IL7R、FLT3、C1QC 和 HLA-DRB5）进行验证。发现这 4 个基因的表达水平与病理阶段呈显著负相关，尤其是分级。此外，K-M 图和 TIMER 结果表明，这些基因对 CM 预后有良好的预测价值。总之，这些结果为 CM 提供了新的见解，并确定了 IL7R、FLT3、C1QC 和 HLA-DRB5 作为 CM 诊断和治疗的关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/26a349e71ca2/CIN2022-4493347.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/a8434e88b847/CIN2022-4493347.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/bb47d0bd33c5/CIN2022-4493347.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/aa46686608a7/CIN2022-4493347.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/fb22586fc425/CIN2022-4493347.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/95402494f6cb/CIN2022-4493347.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/72789389547e/CIN2022-4493347.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/fd6045045961/CIN2022-4493347.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/e17f1ebfb3aa/CIN2022-4493347.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/713f0c44350e/CIN2022-4493347.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/26a349e71ca2/CIN2022-4493347.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/a8434e88b847/CIN2022-4493347.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/bb47d0bd33c5/CIN2022-4493347.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/aa46686608a7/CIN2022-4493347.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/fb22586fc425/CIN2022-4493347.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/95402494f6cb/CIN2022-4493347.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/72789389547e/CIN2022-4493347.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/fd6045045961/CIN2022-4493347.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/e17f1ebfb3aa/CIN2022-4493347.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/713f0c44350e/CIN2022-4493347.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ed1/8923759/26a349e71ca2/CIN2022-4493347.010.jpg

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