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生物信息学分析揭示巨噬细胞标志物基因特征在胃腺癌中的预后意义。

Bioinformatics Analysis Reveals Prognostic Significance of the Macrophage Marker Gene Signature in Gastric Adenocarcinoma.

作者信息

Li Zhipeng, Chen Hui, Chen Zhongqing, Xie Lihe, Pan Dun

机构信息

Department of Gastrointestinal Surgery, The First Affiliated Hospital, Fujian Medical University, 350000 Fuzhou, Fujian, China.

Department of Gastrointestinal Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 350212 Fuzhou, Fujian, China.

出版信息

Front Biosci (Landmark Ed). 2024 May 6;29(5):172. doi: 10.31083/j.fbl2905172.

Abstract

BACKGROUND

Gastric adenocarcinoma (GAC) is a malignant tumor with the highest incidence in the digestive system. Macrophages have been proven to play important roles in tumor microenvironment.

METHODS

Herein, single-cell RNA sequencing (scRNA-seq) profiles from the Gene Expression Omnibus (GEO) and bulk RNA-seq data from the Cancer Genome Atlas (TCGA) database were utilized to construct a macrophage marker gene signature (MMGS) to predict the prognosis of GAC patients. Subsequently, a risk score model based on the MMGS was built to predict the prognosis of GAC patients; further, this was validated in the GEO cohort. The risk score categorized patients into the high- and low-risk groups. A nomogram model based on the risk score and clinic-pathological characteristics was developed.

RESULTS

Seven genes, , , , , , , and , were included in the risk score model. Patients with a low-risk score showed a better prognosis. The MMGS had good sensitivity and specificity for predicting the prognosis inGAC patients. The risk score was an independent prognostic factor. The constructed nomogram exhibited favorable predictability and reliability for predicting GAC prognosis.

CONCLUSION

In conclusion, the risk score model based on the seven MMGSs performed well in the predicting prognosis of GAC patients. Our study may provide new insights into clinical decision-making for the personalized treatment of patients with gastric cancer (GC).

摘要

背景

胃腺癌(GAC)是消化系统中发病率最高的恶性肿瘤。巨噬细胞已被证明在肿瘤微环境中发挥重要作用。

方法

本文利用基因表达综合数据库(GEO)的单细胞RNA测序(scRNA-seq)数据和癌症基因组图谱(TCGA)数据库的批量RNA-seq数据构建巨噬细胞标志物基因特征(MMGS),以预测GAC患者的预后。随后,建立基于MMGS的风险评分模型来预测GAC患者的预后;此外,在GEO队列中对其进行验证。风险评分将患者分为高风险组和低风险组。基于风险评分和临床病理特征建立了列线图模型。

结果

风险评分模型纳入了7个基因,分别为[此处原文缺失基因名称]。低风险评分的患者预后较好。MMGS在预测GAC患者预后方面具有良好的敏感性和特异性。风险评分是一个独立的预后因素。构建的列线图在预测GAC预后方面表现出良好的预测性和可靠性。

结论

总之,基于7个MMGS的风险评分模型在预测GAC患者预后方面表现良好。我们的研究可能为胃癌(GC)患者的个性化治疗临床决策提供新的见解。

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