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用于结肠癌预后评估的免疫基因组风险评分构建

Construction of an Immunogenomic Risk Score for Prognostication in Colon Cancer.

作者信息

Zhang Han, Qin Chuan, Gan Hua, Guo Xiong, Zhang Li

机构信息

First Clinical Medical College, Chongqing Medical University, Chongqing, China.

Department of Digestive Oncology, Three Gorges Hospital, Chongqing University, Chongqing, China.

出版信息

Front Genet. 2020 May 21;11:499. doi: 10.3389/fgene.2020.00499. eCollection 2020.

DOI:10.3389/fgene.2020.00499
PMID:32508884
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7253627/
Abstract

Immune-related genes (IRGs) play regulatory roles in the immune system and are involved in the initiation and progression of colon cancer. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among colon cancer patients. We analyzed the expressions of IRGs in colon specimens and discovered 484 differentially expressed IRGs when we compared specimens from colon cancer and adjacent normal tissue. Univariate Cox regression analyses were performed to identify 26 IRGs that were associated with survival. A Cox proportional hazards model with a lasso penalty identified five optimal IRGs for constructing the immunogenomic risk score (, and ). The risk score had good performance in predicting overall survival among patients with colon cancer and was correlated with the amount of tumor-infiltrating immune cells. Our findings suggest that the immunogenomic risk score may be useful for prognostication in colon cancer cases. Furthermore, the five IRGs included in the risk score might be useful targets for investigating the initiation of colon cancer and designing personalized treatments.

摘要

免疫相关基因(IRGs)在免疫系统中发挥调节作用,并参与结肠癌的发生和发展。本研究旨在开发一种免疫基因组风险评分,用于预测结肠癌患者的生存结果。我们分析了结肠癌标本中IRGs的表达,在比较结肠癌标本与相邻正常组织标本时发现了484个差异表达的IRGs。进行单变量Cox回归分析以鉴定与生存相关的26个IRGs。采用带有套索惩罚的Cox比例风险模型确定了五个用于构建免疫基因组风险评分的最佳IRGs( 、 和 )。该风险评分在预测结肠癌患者的总生存方面表现良好,并且与肿瘤浸润免疫细胞的数量相关。我们的研究结果表明,免疫基因组风险评分可能有助于结肠癌病例的预后评估。此外,风险评分中包含的五个IRGs可能是研究结肠癌发生和设计个性化治疗的有用靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/86193538b220/fgene-11-00499-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/a1bd1924e8b9/fgene-11-00499-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/6cd3a6f75a67/fgene-11-00499-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/6f1828c6cd77/fgene-11-00499-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/d9dd28ba7d7a/fgene-11-00499-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/86193538b220/fgene-11-00499-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/a1bd1924e8b9/fgene-11-00499-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/6585f775fd54/fgene-11-00499-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/490d54a085f3/fgene-11-00499-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/dfae6e514a2c/fgene-11-00499-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/d365c4ea9cef/fgene-11-00499-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/6cd3a6f75a67/fgene-11-00499-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/6f1828c6cd77/fgene-11-00499-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/d9dd28ba7d7a/fgene-11-00499-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/196a/7253627/86193538b220/fgene-11-00499-g009.jpg

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