肺癌中枢纽生物标志物和通路的鉴定及预后评估

The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.

作者信息

Yin Yi, Li Dong, He Muqun, Wang Jianfeng

机构信息

Department of Medical Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China.

Cancer Institute, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China.

出版信息

Transl Cancer Res. 2022 Aug;11(8):2622-2635. doi: 10.21037/tcr-22-245.

DOI:10.21037/tcr-22-245

PMID:36093542

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9459535/

Abstract

BACKGROUND

Lung cancer is the most frequently diagnosed malignant tumor and the highest mortality worldwide, and can be divided into two differential histologic subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). However, there are significant differences in diagnosis and prognosis between NSCLC and SCLC. We aimed to identify hub diﬀerentially expressed genes (DEGs) and pathways for diagnostic and prognostic prediction in NSCLC and SCLC.

METHODS

Three expression profiles (GSE43346, GSE40275 and GSE18842) were obtained through GEO2R tools from Gene Expression Omnibus (GEO) database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to investigate functional enrichment of the DEGs. The protein-protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Kaplan-Meier analysis was performed using Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA).

RESULTS

We have identified 84 overlap DEGs that may play an important role in SCLC & NSCLC. However, we also found some genes were only significantly differential expressed in SCLC or NSCLC. There were 87 DEGs unique to SCLC tissues and 28 DEGs unique to NSCLC ones. Functional analysis results indicated that these DEGs had different biological functions and were significantly enriched in different pathways. Hub DEGs were identified via protein-protein interaction network and cross-validated using Kaplan-Meier plotter and GEPIA. The 14 hub DEGs were highly correlated with the overall survival of NSCLC. Kyoto Encyclopedia of Genes and Genome (KEGG) re-analysis of 14 hub DEGs showed that , and enriched in the p53 signaling pathway, and enriched in pyrimidine metabolism pathway maybe play a key role in SCLC&NSCLC and were significantly related to overall survival in patients with NSCLC.

CONCLUSIONS

, , and , which are mainly enriched in the p53 signaling pathway and pyrimidine metabolism pathway, were significantly associated with the overall survival of NSCLC patients. These genes could serve as potential prognostic markers in NSCLC and therapeutic target in lung cancer for personalized oncology.

摘要

背景

肺癌是全球最常被诊断出的恶性肿瘤且死亡率最高，可分为两种不同的组织学亚型，即非小细胞肺癌（NSCLC）和小细胞肺癌（SCLC）。然而，NSCLC和SCLC在诊断和预后方面存在显著差异。我们旨在鉴定核心差异表达基因（DEGs）及相关通路，用于NSCLC和SCLC的诊断及预后预测。

方法

通过GEO2R工具从基因表达综合数据库（GEO）获取了三个表达谱（GSE43346、GSE40275和GSE18842）。利用注释、可视化和综合发现数据库（DAVID）研究DEGs的功能富集情况。通过检索相互作用基因的搜索工具（STRING）和Cytoscape构建蛋白质 - 蛋白质相互作用网络。使用Kaplan - Meier绘图仪和基因表达谱交互式分析（GEPIA）进行Kaplan - Meier分析。

结果

我们鉴定出84个重叠的DEGs，它们可能在SCLC和NSCLC中起重要作用。然而，我们也发现一些基因仅在SCLC或NSCLC中显著差异表达。SCLC组织特有的DEGs有87个，NSCLC组织特有的DEGs有28个。功能分析结果表明，这些DEGs具有不同的生物学功能，且在不同通路中显著富集。通过蛋白质 - 蛋白质相互作用网络鉴定出核心DEGs，并使用Kaplan - Meier绘图仪和GEPIA进行交叉验证。这14个核心DEGs与NSCLC的总生存期高度相关。对14个核心DEGs进行京都基因与基因组百科全书（KEGG）再分析表明，、和在p53信号通路中富集，和在嘧啶代谢通路中富集，可能在SCLC和NSCLC中起关键作用，并与NSCLC患者的总生存期显著相关。