Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of hepatocellular carcinoma (HCC)

Growing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the “edgeR” package of the R software. Functional annotation and pathway enrichment were performed using “ggplots2” and “clusterProfiler” packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients’ prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients’ prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.     

基于GEO数据库分析糖尿病心肌病的差异表达基因

目的 通过对GEO数据库中有关糖尿病心肌病的mRNA芯片进行分析,筛选出导致糖尿病心肌病的关键基因。方法 使用GEO数据库的在线分析工具GEO2R对数据集GSE4745和GSE5606进行差异基因分析后,在R中绘制差异表达基因火山图,利用在线韦恩图绘制工具分析两个数据集共同表达的差异基因,在R中利用clusterProfile包将差异表达基因进行基因本体和京都基因与基因组百科全书富集分析,GSEA软件进行基因集富集分析,同时使用STRING在线数据库构建差异基因对应蛋白的蛋白互作网络,利用Cytoscape的插件Cytohubba中最大集团中心算法计算出排名前10的基因,在原代大鼠心肌细胞中观察比较筛选出的关键基因在正常组和高糖组的表达情况。结果 Pdk4、Ucp3、Hmgcs2、Asl6、Slc2a4与芯片分析结果相符,Pdk4、Ucp3、Hmgcs2在高糖(25 mmol/L)刺激72 h后的心肌细胞中表达增加,Acsl6、Slc2a4在高糖刺激后的心肌细胞中表达下降。结论 Pdk4、Ucp3、Hmgcs2、Asl6、Slc2a4可能与糖尿病心肌病的发生发展相关,可能是糖尿病心肌病...     

弥漫性大B细胞淋巴瘤预后相关microRNA筛选及其预后价值

目的 识别与弥漫性大B细胞淋巴瘤(diffuse large B-cell lymphoma ,DLBCL)预后相关的microRNA,并结合临床数据评估其预后价值。方法 从GEO (gene expression omnibus)数据库中获取116例DLBCL患者的MicroRNA表达谱数据与临床数据。采用lasso - logistic回归模型识别与DLBCL预后相关的miRNAs,卡方和Fisher确切概率检验筛选与DLBCL患者总生存期(overall survival,OS)相关的临床指标,再利用logistic逐步回归法筛选出预测因子并构建其单因素及多因素Cox比例风险模型。采用时依ROC (time - dependent receiver operating characteristic)曲线评价各模型的预测能力,各模型预测准确性用一致性统计量C评价。结果 Hsa - miR - 23a (HR = 0.20, 95%CI: 0.10～0.39, P＜0.001)和hsa - miR - 566 (HR = 6.69, 95%CI: 2.40～18.65, P＜0.001)与DLBCL患者的OS相关。单因素Cox回归模型与多因素Cox回归模型相比,C统计量差异具有统计学意义(IPI与IPI + hsa - miR - 23a + hsa - miR - 566: Z = 36.2, P＜0.001, hsa - miR - 23a + hsa - miR - 566与IPI +hsa - miR - 23a + hsa - miR - 566: Z = 7.0, P＜ 0.001)。结论 Hsa - miR - 23a高表达是DLBCL患者预后的保护因素,hsa - miR - 566高表达是DLBCL患者预后的危险因素,IPI、Hsa - miR - 23a和hsa - miR - 566这3个指标联合建模可提高DLBCL患者5年生存预测的准确性。     

利用GEO及TCGA数据集分析SMARCA1在胃癌中的表达及其临床意义#br#

目的探讨基因表达汇编（GEO）及癌症和肿瘤基因表达图谱（TCGA）数据集中胃癌组织的SMARCA1表达情况及其与胃癌临床病理特征和预后的关系。 方法利用GEO和TCGA数据集汇总胃癌相关数据,下载胃癌组织SMARCA1表达数据及临床病理参数。分析SMARCA1表达与胃癌临床病理学特征和总生存期（OS）的关系;采用基因集富集分析（GSEA）预测SMARCA1相关的基因通路。 结果在GSE62254数据集中,SMARCA1表达与T分期有关（P=0022）,而与TNM分期、性别、年龄和N分期均无关（P＞005）。在TCGA数据集中,SMARCA1表达与T分期、性别、年龄、N分期、TNM分期和组织学分级均无关（P＞005）。GSE62254数据集中,SMARCA1高、中及低表达组胃癌患者的中位OS分别为364个月、894个月和未达（P=0001）;TCGA数据集中,SMARCA1高、中及低表达组胃癌患者的中位OS分别为237个月、294个月和562个月（P=0033）。GSEA结果显示,SMARCA1高表达样本富集了K Ras、Akt、BMI 1和mTOR通路等基因集。 结论胃癌组织中SMARCA1的表达与T分期有关,高表达患者的预后不良,可作为潜在评估胃癌预后的分子标志物。     

Multi-platform analysis of methylation-regulated genes in human lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most frequent pathological type of lung cancer that has a poor prognosis and high mortality rate. DNA methylation plays a critical role in various biological processes during development, while dysregulation results in pathological consequences. Thus, this study aimed to identify DNA methylation-regulated genes involved in LUAD occurrence. Initially, 300 downregulated and 168 upregulated mRNA expression levels were identified in two databases: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. In addition, GEO was utilized to detect 243 DNA hyper-methylated sites. Based on our observations, it was possible to correlate downregulation of mRNA expression and DNA hyper-methylation of six genes (ABCA3, COX7A1, HOXA5, SLIT3, SOX17, and SPARCL1). Functional analysis of the six genes indicated that these genes are predominantly enriched in cancer-related pathways and may promote carcinogenesis by regulating epithelialmesenchymal transition processes. In conclusion, our study identified a panel of DNA methylation-regulated genes involved in LUAD and may serve as potential epigenetic markers for this type of carcinoma.     

Microenvironment Analysis of Prognosis and Molecular Signature of Immune-Related Genes in Lung Adenocarcinoma

There is growing evidence on the clinical significance of tumor microenvironment (TME) cells in predicting prognosis and therapeutic effects. However, cell interactions in tumor microenvironments have not been thoroughly studied or systematically analyzed so far. In this study, 22 immune cell components in the lung adenocarcinoma (LUAD) TME were analyzed using gene expression profile from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TME-based molecular subtypes of LUAD were defined to evaluate further the relationship between molecular subtypes, prognosis, and clinical characteristics. A TME risk score model was constructed by using the differentially expressed genes (DEGs) of molecular subtypes. The relationship between the TME score and clinical characteristics and genomic mutations was compared to identify the genes that have significant associations with the TME. The comprehensive analysis of the TME characteristics may be helpful in revealing the response of LUAD patients to immunotherapy, providing a new strategy for immunotherapy.     

Identification of Differentially Expressed Plasma lncRNAs As Potential Biomarkers for Breast Cancer

BackgroundBreast cancer is the most common malignant tumor in women and is not easy to diagnose. Increasing evidence has underscored that long non-coding RNAs (lncRNAs) play important regulatory roles in the occurrence and progression of many cancers, including breast cancer. We aimed to identify lncRNAs in plasma as potential biomarkers for breast cancer.Patients and MethodsWe analyzed the Gene Expression Omnibus (GEO) datasets GSE22820, GSE42568, and GSE65194 to identify the common differential genes between cancer tissues and adjacent tissues. Then 14 lncRNAs were identified among the common differential genes and validated by using real-time quantitative polymerase chain reaction in 92 patients with breast cancer and 100 healthy controls. Receiver operating characteristic (ROC) curves were constructed to evaluate their diagnostic value for breast cancer.ResultsIntegrated analysis of the GEO datasets identified three significantly upregulated and 11 downregulated lncRNAs in breast cancer tissues. Compared with healthy controls, MIAT was significantly upregulated in breast cancer patient plasma, and LINC00968 and LINC01140 were significantly downregulated. ROC curve analysis suggested that these three lncRNAs can discriminate breast cancer from healthy individual with high specificity and sensitivity.ConclusionThis research identified three differentially expressed lncRNAs in breast cancer patient plasma. Our data suggest that these three lncRNAs can be used as potential diagnostic biomarkers of breast cancer.     

CRISP3在宫颈癌分泌物与组织中的表达及临床意义

目的:筛选宫颈癌诊断标志物,检测其在宫颈癌组织中的表达及临床意义。方法:从基因表达数据库(gene expression omnibus,GEO)中下载宫颈癌相关数据集,利用R语言对差异表达基因(differentially expressed genes,DEGs)进行筛选,酶联免疫法检测其在宫颈癌血清中表达水平,PCR法检测其在宫颈癌分泌物中的表达情况,组织芯片上检测其在宫颈癌组织和正常组织中的表达情况,统计学分析其与临床特征的关系。结果:GEO数据库分析筛选出差异基因富含半胱氨酸分泌蛋白3(cystein-rich secretory protein 3,CRISP3),血清中CRISP3的表达,宫颈癌组与对照组相比无显著差异（P＞0.05）,宫颈分泌物中CRISP3表达在宫颈癌组显著降低（P＜0.01）,组织芯片中CRISP3在宫颈癌组中呈低表达（P＜0.01）,宫颈癌不同分期之间比较,Ⅰ期和Ⅱ-Ⅲ期差异有统计学意义（P＜0.05）。结论:宫颈分泌物中CRISP3有可能作为宫颈癌诊断的潜在生物标志物之一。