一个用于预测胃癌预后、免疫特征和药物敏感性的新型血小板相关基因特征。

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer.

机构信息

Department of Medical Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China.

出版信息

Front Immunol. 2024 Nov 13;15:1477427. doi: 10.3389/fimmu.2024.1477427. eCollection 2024.

DOI:10.3389/fimmu.2024.1477427

PMID:39606245

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11599260/

Abstract

BACKGROUND

Platelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.

METHODS

We downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).

RESULTS

A ten-gene (, , , , , , , , , and ) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.

CONCLUSION

The novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

摘要

背景

血小板可以动态调节肿瘤的发生和发展。然而，关于血小板在胃癌（GC）中的预测价值和具体作用的研究有限。本研究旨在建立一个具有预后和治疗意义的 GC 中预测血小板相关基因特征。

方法

我们从癌症基因组图谱（TCGA）数据库中下载了 378 例 GC 患者的转录组数据和临床资料。通过单因素 Cox 回归筛选出与血小板相关的预后基因，纳入最小绝对值收缩和选择算子（LASSO）分析，构建风险模型。在 TCGA 队列和三个独立验证队列中进行 Kaplan-Meier 曲线和受试者工作特征曲线（ROC）分析。构建整合风险评分和临床病理特征的列线图。进行功能富集和肿瘤微环境（TME）分析。通过癌症治疗反应门户（CTRP）数据库进行药物敏感性预测。最后，通过定量实时 PCR（qRT-PCR）验证十个特征基因的表达。

结果

最终构建了一个由十个基因（、、、、、、、、和）组成的预测风险模型。患者的风险评分中位数作为界值分为高低风险组。训练队列中 1、2、3 年总生存（OS）的 ROC 曲线下面积（AUC）值分别为 0.670、0.695 和 0.707。生存分析表明，在训练和验证队列中，低风险组的 OS 更好。预测 1、2、3 年 OS 的列线图 AUC 值分别为 0.708、0.763 和 0.742。TME 分析显示，高危组 M2 巨噬细胞浸润和免疫抑制性 TME 更高。此外，高危患者对沙利度胺、MK-0752 和 BRD-K17060750 更敏感。