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免疫相关基因预后模型在胃腺癌中的建立和验证。

Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma.

机构信息

Medical College, Guizhou University, Guiyang, China.

Department of Medicine Emergency, Guizhou Provincial People's Hospital, Guiyang, China.

出版信息

Biosci Rep. 2020 Oct 30;40(10). doi: 10.1042/BSR20201012.

DOI:10.1042/BSR20201012
PMID:33112406
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7593539/
Abstract

PURPOSE

Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis.

METHODS

RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested.

RESULTS

A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group.

CONCLUSIONS

Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

摘要

目的

胃腺癌（STAD）是最常见的恶性肿瘤之一，其发生和预后与炎症密切相关。本研究旨在通过生物信息学分析，鉴定 STAD 中的基因特征，并构建一个免疫相关基因（IRG）预后模型。

方法

分析来自健康样本和 STAD 样本的 RNA 测序数据、IRGs 和转录因子。使用单变量和多变量 Cox 回归分析鉴定核心 IRGs。使用核心 IRGs 构建 IRG 预后模型。测试 IRG 预后模型与临床数据之间的关系。

结果

筛选出 289 个差异表达的 IRGs 和 20 个预后 IRGs，阈值为 P<0.05。通过多变量逐步 Cox 回归分析，我们基于 7 个 IRGs 开发了一个预后模型。使用 GEO 数据集（GSE84437）验证了该预后模型。IRGs 与 STAD 患者的临床结局（年龄、组织学分级、N 和 M 分期）显著相关。高风险组的树突状细胞和巨噬细胞浸润丰度高于低风险组。

结论

我们的研究结果为 STAD 的发病机制提供了新的见解。基于 7 个 IRGs 的 IRG 预后模型具有预测价值，在临床决策和个体化治疗中具有潜在的应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/012b34def844/bsr-40-bsr20201012-g11.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/0756470cfd38/bsr-40-bsr20201012-g1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/87b22479c277/bsr-40-bsr20201012-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/199222188bce/bsr-40-bsr20201012-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/5b9db956268e/bsr-40-bsr20201012-g7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/1abd95745fc8/bsr-40-bsr20201012-g8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/b800b09f291d/bsr-40-bsr20201012-g9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/8dd57969f9df/bsr-40-bsr20201012-g10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b48d/7593539/012b34def844/bsr-40-bsr20201012-g11.jpg

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