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胃癌中补体相关基因的预后模型及肿瘤免疫微环境分析

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer.

作者信息

Gu Xianhua, Shen Honghong, Zhu Guangzheng, Li Xinwei, Zhang Yue, Zhang Rong, Su Fang, Wang Zishu

机构信息

Department of Gynecology Oncology, First Affiliated Hospital of Bengbu Medical College, Bengbu, People's Republic of China.

Department of Medical Oncology, First Affiliated Hospital of Bengbu Medical College, Bengbu, People's Republic of China.

出版信息

J Inflamm Res. 2023 Oct 18;16:4697-4711. doi: 10.2147/JIR.S422903. eCollection 2023.

DOI:10.2147/JIR.S422903
PMID:37872955
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10590588/
Abstract

INTRODUCTION

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

METHODS

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

RESULTS

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably , and , according to an analysis of immune processes. The core gene in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of had lower 10-year overall survival (OS) rates.

CONCLUSION

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

摘要

引言

补体系统是先天性和适应性免疫反应的组成部分,有助于抗体清除病原体。然而,补体及其调节剂在胃癌(GC)肿瘤微环境(TME)中的潜在作用仍不清楚。

方法

本研究评估了来自癌症基因组图谱(TCGA)的GC中补体家族基因的表达、体细胞突变频率和拷贝数变异。进行套索回归和Cox回归分析,以基于补体基因家族建立一个预后模型,训练集和验证集分别取自TCGA-GC队列(n=371)和国际基因表达综合数据库(GEO)队列(n=433)。使用列线图评估模型来预测患者预后。此外,还研究了免疫检查点、免疫细胞与预后模型之间的联系。

结果

与低风险患者相比,高风险患者的预后较差。根据多变量Cox分析,预后模型得出的风险评分被证明可独立作为GC的预后标志物。列线图评估显示,该模型在预测GC患者1、3、5年生存率方面具有较高的可靠性。此外,根据免疫过程分析,风险评分与免疫检查点的表达呈正相关,尤其是 和 。发现预后模型中的核心基因 在GC组织中相对于相邻正常组织上调,且 表达水平升高的患者10年总生存率(OS)较低。

结论

我们的研究表明补体基因与TME的多样性和复杂性相关。补体预后模型有助于提高我们对TME浸润特征的理解,并使免疫治疗策略更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/7979a23fdcfb/JIR-16-4697-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/352df106c08d/JIR-16-4697-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/d7e7f86eb568/JIR-16-4697-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/7b705d289bf1/JIR-16-4697-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/1b666d5558ef/JIR-16-4697-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/d0b3c9c153e3/JIR-16-4697-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/f27c2084e182/JIR-16-4697-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/6b709b97a6c1/JIR-16-4697-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/7979a23fdcfb/JIR-16-4697-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/352df106c08d/JIR-16-4697-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/d7e7f86eb568/JIR-16-4697-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/7b705d289bf1/JIR-16-4697-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/1b666d5558ef/JIR-16-4697-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/d0b3c9c153e3/JIR-16-4697-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/f27c2084e182/JIR-16-4697-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/6b709b97a6c1/JIR-16-4697-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da85/10590588/7979a23fdcfb/JIR-16-4697-g0008.jpg

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