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是一种有前景的预后生物标志物,可介导胃癌中的细胞焦亡。

is a promising prognostic biomarker that mediates pyroptosis in gastric cancer.

作者信息

Cui Ming, Wang Xiaowu, Qiao Haiyan, Wu Shixi, Shang Bingbing

机构信息

The Second Hospital of Dalian Medical University, Dalian, Liaoning province, China.

The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang Province, China.

出版信息

Heliyon. 2024 Jul 15;10(14):e34360. doi: 10.1016/j.heliyon.2024.e34360. eCollection 2024 Jul 30.

DOI:10.1016/j.heliyon.2024.e34360
PMID:39130462
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11315173/
Abstract

BACKGROUND

Gastric cancer (GC) is a typical malignant tumor and the main cause of cancer-related deaths. Its pathogenesis involves multiple steps, including pyroptosis, although these steps are still uncertain. Pyroptosis, also known as gasdermin-mediated programmed necrosis, participates in various pathological processes in tumors, including GC. , which encodes neutrophil elastase, is closely associated with GC. Additionally, has been implicated in GC cell pyroptosis, but this has not been confirmed. Therefore, investigating the link between and pyroptosis in GC is warranted. This research uses bioinformatics and experiments to examine the relationship between , pyroptosis, and GC prognosis.

METHODS

The GEO and TCGA databases, along with pyroptosis-related genes, were applied to identify pyroptosis-related differentially expressed genes (DEGs). was selected via primary screening. Using the median expression level of as the threshold, pyroptosis-related DEGs were divided into low- and high- groups. Based on the DEGs in these two groups, GO, KEGG and GSEA analyses were conducted to elucidate the mechanisms of in GC. Furthermore, we plotted ROC and Kaplan-Meier curves to analyze the clinical and pathological features of expression. The Nomograms tool was applied to calculate the predictive value of for the clinical outcomes of GC cases. Immunohistochemical analysis was performed to detect the level of in GC tissues and to validate whether was involved in pyroptosis in GC cells through cell experiments. Finally, the immune infiltration of was investigated, and interaction networks (proteins-, microRNA-, and small-molecule drug-) were constructed.

RESULTS

We aimed to investigate the expression of the gene in GC and study the relationship among , pyroptosis, and the prognosis of patients with GC. Differential expression analysis of gene-expression datasets from TCGA-STAD and GSE49051 revealed that the expression of the gene was significantly up-regulated in GC. Using STRING network analysis, we identified multiple proteins involved in the occurrence and development of GC, including interactions between and GSDMC, a member of the gasdermin protein family. Survival analysis showed that expression levels significantly affected overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in patients with GC. Additionally, ROC analysis demonstrated that was effective in distinguishing GC patients from normal controls (AUC = 0.812). Immunohistochemical analysis showed that was highly expressed in gastric cancer tissues and was closely related to age, tumor grade, and stage. The cell experiments further confirmed that the high expression of in gastric cancer cells was associated with pyroptosis. Comprehensive analysis indicated that could be used as a potential prognostic marker for GC and plays an important role in pyroptosis.

CONCLUSION

High expression is related to poor survival and prognosis of patients with GC. It participates in pyroptosis and immune infiltration in GC. Therefore, is a promising prognostic biomarker for pyroptosis in GC.

摘要

背景

胃癌(GC)是一种典型的恶性肿瘤,也是癌症相关死亡的主要原因。其发病机制涉及多个步骤,包括细胞焦亡,尽管这些步骤仍不明确。细胞焦亡,也称为gasdermin介导的程序性坏死,参与肿瘤的各种病理过程,包括胃癌。编码中性粒细胞弹性蛋白酶的[基因名称未给出]与胃癌密切相关。此外,[基因名称未给出]已被认为与胃癌细胞焦亡有关,但尚未得到证实。因此,有必要研究[基因名称未给出]与胃癌细胞焦亡之间的联系。本研究采用生物信息学和实验方法来研究[基因名称未给出]、细胞焦亡与胃癌预后之间的关系。

方法

利用基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)数据库以及细胞焦亡相关基因,来鉴定细胞焦亡相关的差异表达基因(DEGs)。通过初步筛选选择[基因名称未给出]。以[基因名称未给出]的中位表达水平作为阈值,将细胞焦亡相关的DEGs分为低表达组和高表达组。基于这两组中的DEGs,进行基因本体(GO)、京都基因与基因组百科全书(KEGG)和基因集富集分析(GSEA),以阐明[基因名称未给出]在胃癌中的作用机制。此外,我们绘制了受试者工作特征(ROC)曲线和Kaplan-Meier曲线,以分析[基因名称未给出]表达的临床和病理特征。应用列线图工具来计算[基因名称未给出]对胃癌病例临床结局的预测价值。进行免疫组织化学分析以检测胃癌组织中[基因名称未给出]的水平,并通过细胞实验验证[基因名称未给出]是否参与胃癌细胞的焦亡。最后,研究了[基因名称未给出]的免疫浸润情况,并构建了相互作用网络(蛋白质-、微小RNA-和小分子药物-)。

结果

我们旨在研究[基因名称未给出]基因在胃癌中的表达,并研究[基因名称未给出]、细胞焦亡与胃癌患者预后之间的关系。对来自TCGA-STAD和GSE49051的基因表达数据集进行差异表达分析,结果显示[基因名称未给出]基因在胃癌中显著上调。使用STRING网络分析,我们鉴定了多个参与胃癌发生发展的蛋白质,包括[基因名称未给出]与gasdermin蛋白家族成员GSDMC之间的相互作用。生存分析表明,[基因名称未给出]表达水平显著影响胃癌患者的总生存期(OS)、无病生存期(DFS)和无进展生存期(PFS)。此外,ROC分析表明,[基因名称未给出]在区分胃癌患者与正常对照方面是有效的(曲线下面积[AUC]=0.812)。免疫组织化学分析表明,[基因名称未给出]在胃癌组织中高表达,且与年龄、肿瘤分级和分期密切相关。细胞实验进一步证实,胃癌细胞中[基因名称未给出]的高表达与细胞焦亡有关。综合分析表明,[基因名称未给出]可作为胃癌的潜在预后标志物,并在细胞焦亡中起重要作用。

结论

[基因名称未给出]高表达与胃癌患者的不良生存和预后相关。它参与胃癌的细胞焦亡和免疫浸润。因此,[基因名称未给出]是胃癌细胞焦亡的一个有前景的预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/a012462e7a3c/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/201373faab13/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/82073a8cce65/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/ccbea89ac47b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/8866fad6ff9d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/0a3f44f20cb1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/9e5c1961643d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/db8d8a886ae7/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/7e67b04608e0/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/b183b8317647/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/cd5a04232d8e/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/a012462e7a3c/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/201373faab13/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/82073a8cce65/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/ccbea89ac47b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/8866fad6ff9d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/0a3f44f20cb1/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/9e5c1961643d/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/db8d8a886ae7/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/7e67b04608e0/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/b183b8317647/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/cd5a04232d8e/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/11315173/a012462e7a3c/mmcfigs1.jpg

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