PDF(Pubmed)
Abstract：
UNASSIGNED: Small cell lung cancer (SCLC) is a highly malignant and aggressive neuroendocrine tumor. With the rise of immunotherapy, it has provided a new direction for SCLC. However, due to the lack of prognostic biomarkers, the median overall survival of SCLC is still to be improved. This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC.
UNASSIGNED: Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein-protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan-Meier plotter in 18 patients with extensive-stage SCLC.
UNASSIGNED: A total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan-Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC.
UNASSIGNED: Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.
UNASSIGNED: Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein-protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan-Meier plotter in 18 patients with extensive-stage SCLC.
UNASSIGNED: A total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan-Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC.
UNASSIGNED: Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.
摘要：
■小细胞肺癌（SCLC）是一种高度恶性和侵袭性的神经内分泌肿瘤。随着免疫疗法的兴起,它为SCLC提供了新的方向。然而,由于缺乏预后生物标志物，SCLC的中位总生存期仍有待改善.本研究旨在探索新的生物标志物和肿瘤浸润性免疫细胞特征，可能作为SCLC的潜在诊断和预后标志物。
■SCLC患者的基因表达谱从基因表达综合（GEO）数据库下载，和肿瘤微环境（TME）浸润谱数据使用CIBERSORT获得。利用稳健秩聚集(RRA)方法整合从GEO数据库下载的三个SCLC微阵列数据集,并鉴定正常和肿瘤组织样品之间的稳健差异表达基因(DEG)。进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析以探索稳健DEG的功能。随后，通过Cytoscape构建蛋白质-蛋白质相互作用网络和关键模块，使用插件cytoHubba从整个网络中选择集线器基因。通过Kaplan-Meier绘图仪对18例广泛期SCLC患者进行了hub基因的生存分析。
■总共312个鲁棒DEG，包括55个上调基因和257个下调基因，从129个SCLC组织样本和44个正常组织样本中筛选。GO和KEGG富集分析显示，强大的DEGs主要参与人类T细胞白血病病毒1感染，病灶粘连,补体和凝血级联，肿瘤坏死因子（TNF）信号通路，和ECM-受体相互作用，与SCLC的发展密切相关。随后，三个DEGs模块和六个hub基因(ITGA10、DUSP12、PTGS2、FOS、TGFBR2和ICAM1）通过使用Cytoscape插件MCODE和cytoHubba进行筛选来鉴定，分别。通过CIBERSORT算法进行的免疫细胞浸润分析显示，静息记忆CD4T细胞是SCLC中主要的浸润免疫细胞。此外,Kaplan-Meier绘图仪显示，前列腺素-内过氧化物合酶2（PTGS2）基因是SCLC的潜在预后生物标志物。
■Hub基因和肿瘤浸润免疫细胞可能是SCLC发展的分子机制，这一发现可能有助于制定针对SCLC的个体化免疫治疗策略.
■SCLC患者的基因表达谱从基因表达综合（GEO）数据库下载，和肿瘤微环境（TME）浸润谱数据使用CIBERSORT获得。利用稳健秩聚集(RRA)方法整合从GEO数据库下载的三个SCLC微阵列数据集,并鉴定正常和肿瘤组织样品之间的稳健差异表达基因(DEG)。进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析以探索稳健DEG的功能。随后，通过Cytoscape构建蛋白质-蛋白质相互作用网络和关键模块，使用插件cytoHubba从整个网络中选择集线器基因。通过Kaplan-Meier绘图仪对18例广泛期SCLC患者进行了hub基因的生存分析。
■总共312个鲁棒DEG，包括55个上调基因和257个下调基因，从129个SCLC组织样本和44个正常组织样本中筛选。GO和KEGG富集分析显示，强大的DEGs主要参与人类T细胞白血病病毒1感染，病灶粘连,补体和凝血级联，肿瘤坏死因子（TNF）信号通路，和ECM-受体相互作用，与SCLC的发展密切相关。随后，三个DEGs模块和六个hub基因(ITGA10、DUSP12、PTGS2、FOS、TGFBR2和ICAM1）通过使用Cytoscape插件MCODE和cytoHubba进行筛选来鉴定，分别。通过CIBERSORT算法进行的免疫细胞浸润分析显示，静息记忆CD4T细胞是SCLC中主要的浸润免疫细胞。此外,Kaplan-Meier绘图仪显示，前列腺素-内过氧化物合酶2（PTGS2）基因是SCLC的潜在预后生物标志物。
■Hub基因和肿瘤浸润免疫细胞可能是SCLC发展的分子机制，这一发现可能有助于制定针对SCLC的个体化免疫治疗策略.
