基于TCGA数据库挖掘甲状腺乳头状癌淋巴结转移相关的突变基因及其机制探讨北大核心

目的:挖掘并筛选与甲状腺乳头状癌(papillary thyroid carcinoma,PTC)淋巴结转移相关的突变基因及其潜在的机制。方法:从TCGA数据库获得377例PTC患者的测序数据及完整的临床资料,并分为淋巴结转移组(LM,n=212)与无淋巴结转移组(NLM,n=165)。利用R语言(v3.6.2)对转移组特有的突变基因进行富集分析。采用String在线软件绘制蛋白互作网络,Cytoscape软件筛选网络中的核心基因。在UALCAN网站上验证基因表达量与淋巴结转移之间的关系。利用荧光实时定量PCR测定候选基因在细胞系中mRNA的表达量。结果:一共筛选出1197个仅在LM组发生突变的基因,它们主要富集在黏着连接、细胞黏附分子(cell adhesion molecules,CAMs)通路。CAMs通路的核心基因ITGB1与VCAN的表达量与淋巴结转移相关。与正常甲状腺细胞系相比,VCAN基因在PTC细胞系TPC-1和B-CPAP中mRNA的表达量较高,尤其是B-CPAP细胞系。结论:CAMs通路可能是PTC淋巴结转移相关机制之一,其中VCAN基因可能是潜在的分子标志物。     

基于TCGA数据库分析肿瘤突变负荷在肌层浸润性膀胱癌预后评估中的价值

目的：探讨肿瘤突变负荷（TMB）在肌层浸润性膀胱癌（MIBC）预后评估中的价值。方法：从TCGA数据库下载MIBC测序数据，结合临床数据分析TMB在MIBC中的临床意义，从TMB分组中识别出差异表达的免疫相关基因进行预后分析；另外采用非负矩阵分解CIBERSORT算法确定免疫细胞与TMB亚型之间的相关性。结果：纳入的375例MIBC患者样品中单核苷酸多态性（SNP）和C > T是最常见的错义突变；TP53、TTN、KMT2D、MUC16、ARID1A基因的突变率较高；与低TMB组MIBC患者相比，高TMB组的患者预后较好（P < 0.01）；以KIR2DL4、IL1RL1、SSTR5构建的COX回归模型中低风险组MIBC患者较高风险组预后更佳，曲线下面积（ROC）为0.71；与正常膀胱组织相比，高TMB组的CD8⁺ T细胞、活化的CD4⁺ T细胞、嗜酸性粒细胞表达较高，而在低TMB组中记忆B细胞及未活化的肥大细胞表达比例较高（P < 0.05）。结论：TMB较高的MIBC患者可能在免疫治疗中获得较好的预后，TMB具有预测肿瘤免疫治疗疗效的潜在应用价值；还发现了不同组分的免疫细胞在TMB分组的MIBC肿瘤微环境中存在表达差异。     

基于TCGA数据库乳头状甲状腺癌miRNA预后风险模型的建立与分析CSCD

目的构建基于微小RNA(miRNA)表达的预测乳头状甲状腺癌(papillary thyroid carcinoma,PTC)患者预后的生存模型。方法从TCGA数据库官方网站上下载PTC miRNA测序数据和患者的临床资料,利用R3.6.0软件中的edgeR包筛选表达失调的miRNA。利用单因素Cox及Lasso回归分析筛选出与患者预后相关的miRNA(P<0.05),进一步使用多因素Cox回归分析建立预后模型的风险评分方程risk score,构建生存预后模型,使用受试者工作特征曲线(ROC)来评价模型的敏感度和特异性。结果与正常甲状腺组织相比,PTC组织中失调表达的miRNA共有75个(|log foldchange|≥2,FDR<0.05),多因素Cox回归分析最终得到基于8个miRNA(hsa-mir-6730、hsa-mir-4709、hsa-mir-196a-2、hsa-mir-146b、hsa-mir-6860、hsa-mir-509-3、hsa-mir-513c、hsa-mir-515-1)的预测患者预后的风险模型。ROC曲线下面积(AUC)分析显示,该模型具有较好的敏感度和特异性(AUC>0.8)。结论成功构建了基于miRNA表达的风险预测模型,该模型可有效预测PTC患者的预后。     

A prognostic signature based on immune-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is the fourth commonest female malignancy worldwide. CESC progresses in immune-microenvironment mainly composed of infiltrating immune and stromal cells. Here, we performed an integrated analysis incorporating the expression profiles from the Cancer Genome Atlas (TCGA) database and scores of immune and stromal cells calculated by Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm. A two-gene signature (CD1C and CD6 genes) was established to predict the prognosis of CESC. Based on this signature, patients were divided into the high- and low-risk groups, and this signature showed good prognostic performance according to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis in train set and two validation sets. A nomogram was built for evaluating the clinical applicability of this signature. In addition, based on Tumor Immune Estimation Resource (TIMER) database, 2 hub genes showed negative correlations with tumor purity and positive correlations with infiltrating levels of immune filtrating cells. What’s more, we propose new treatment strategies for the two prognostic subtypes. Low- risk patients were found presenting with a higher level of immune checkpoint molecules and showing higher immunogenicity in immunophenoscore (IPS) analysis, which indicated a better response for immunotherapy. Meanwhile, estimated by Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk patients showed sensitive responses to five chemotherapy drugs. Finally, 10 candidate small-molecule drugs for CESC were defined. In summary, the CD1C-CD6 signature can accurately predict the prognosis of CESC.     

基于TCGA数据库分析ASPM在肺腺癌中的表达及临床意义

目的：探讨异常纺锤体样小头畸形相关基因（ASPM）在肺腺癌中的表达水平，及其与患者临床病理指标和预后的关系。方法：从癌症基因组图谱（TCGA）数据库下载肺腺癌组织和癌旁组织mRNA表达水平和临床病例资料，比较ASPM mRNA在癌组织和癌旁组织中的表达差异，统计学分析ASPM表达水平与肺腺癌患者临床病理指标及预后关系。通过R语言ballgown和ggplot包筛选高低表达ASPM组间差异基因并绘制火山图；利用DAVID工具对差异基因进行GO分析，采用GSEA预测ASPM可能调控的信号通路；STRING和Cytoscape分析关键基因及差异表达基因间的相互作用。结果：ASPM mRNA在肺腺癌中表达水平显著高于癌旁组织（P < 0.05）；ASPM表达水平与生存期相关，高表达ASPM肺腺癌患者预后较差（P < 0.05），是肺腺癌患者预后的独立危险因素，R语言ballgown包筛选出ASPM高低表达组间的差异表达基因共183个，差异表达基因富集到生物过程（BP）、细胞组分（CC）、分子功能（MF）在3个类别共19个亚类和18条KEGG通路；Cytoscape软件发现4个枢纽蛋白。结论：ASPM mRNA在肺腺癌患者中呈高表达且高表达组预后差，可作为判断肺腺癌预后的标志物。     

基于TCGA数据初步探查肝内胆管癌预后相关基因

目的 分析tCGA数据库中肝内胆管癌（ICC）高通量测序数据，寻找其预后相关基因，构建风险模型，并研究其在ICC组织中表达及作用通路。方法 下载tCGA数据库中33例ICC组织和8例癌旁组织中的RNA-seq表达矩阵数据和患者临床资料信息，利用edgeR软件包进行基因差异表达分析，通过单因素Cox回归分析筛选出预后相关差异基因，对差异基因绘制生存曲线，筛选出具有临床意义的基因，经多因素Cox回归分析并构建风险模型，通过京都基因与基因组百科全书（KEGG）通路富集分析了解预后相关基因的作用通路。结果 通过edgeR分析后得到6 617个差异基因（筛选标准为|log2 Fold Change|＞1，P＜0.05），其中高表达组4 094个，低表达组2 523个。通过功能富集发现，这些基因主要集中在化学物致癌作用、药物代谢-细胞色素P450系统、细胞色素P450对异生物质的代谢影响以及视黄醇代谢通路。经单因素Cox回归、R软件“survival”包生存曲线分析显示，UCN2、CST1、PROS1、SLC35E4、PEMT五个基因对ICC患者预后存在显著性影响。通过多因素Cox回归分析，CST1、PEMT、PROS1构建的风险模型对ICC患者预后具有判断作用。结论 UCN2、CST1、PROS1、SLC35E4、PEMT基因可能成为ICC预后判断指标，为后续临床试验提供数据支持。     

基于TCGA、THPA、GEO数据库挖掘PTTG1在乳腺癌中的表达及预后意义

目的:通过对癌症基因组图谱(The Cancer Genome Atlas，TCGA)、人类蛋白组图谱(The Human Protein Atlas，THPA)和人类肿瘤相关的基因表达汇编(Gene Expression Omnibus，GEO)数据库的数据挖掘，探索垂体肿瘤转化基因1（pituitary tumor-transforming gene 1，PTTG1）在乳腺癌中的表达及预后意义。方法:从TCGA、THPA和GEO数据库提取PTTG1在乳腺癌及正常组织中mRNA和蛋白水平的变化和患者临床病理资料，GEO在线预后分析数据库Kaplan-Meier Plotter(http://kmplot.com)检索PTTG1乳腺癌预后的相关性。结果:在数据库中，PTTG1 mRNA和蛋白水平在乳腺癌队列中表达显著高于正常乳腺组织(P<0.01)；PTTG1 mRNA在不同的分子亚型中表达存在显著差异(P<0.05)；PTTG1 mRNA在ER和PR阳性乳腺癌患者组织中表达显著低于ER和PR阴性乳腺癌患者(P<0.05)；PTTG1 mRNA在进展期乳腺癌患者组织中表达显著高于早期乳腺癌患者(P<0.05)；PTTG1 mRNA随着乳腺癌患者组织学分级程度升高而降低(P<0.05)。PTTG1低表达乳腺癌患者的预后显著好于其高表达患者(P<0.001)；PTTG1高表达的ER和PR阳性、HER-2阴性乳腺癌患者的预后均显著差于其低表达者(P<0.05)。PTTG1高表达乳腺癌样本富集到细胞因子受体相互作用、趋化因子信号通路、细胞周期、自然杀伤细胞介导细胞毒性、Toll样受体信号通路和T细胞受体信号通路等相关基因集。结论:PTTG1 mRNA和蛋白在乳腺癌组织中高表达，PTTG1 mRNA表达与乳腺癌预后相关且在不同分子分型的乳腺癌中具有不同的预后监测意义，可以作为乳腺癌预后判断的标志物。     

Tumor Mutation Burden,Immune Cell Infiltration,and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors.Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model.Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis.Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635.Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.