肺鳞状细胞癌发生的早期标志物及肿瘤预测模型

选取癌症基因组图谱数据库的肺鳞状细胞癌(Lung Squamous Cell Carcinoma,LUSC)样本作为数据集,在全基因组的水平上研究肺鳞状细胞癌病人从正常到发病I期基因表达的变化,寻找与LUSC发病密切相关的早期标志物,并建立一种基于早期标志基因的肿瘤预测模型。方法 采用模式识别分类法和基因通路和功能分析相结合的筛选方法,对LUSC的早期标志物进行识别,并运用Fisher判别建立肿瘤预测模型。得到12个LUSC的早期标志物,分别是CLDN18, CD34, ESAM, JAM2, CDH5, F11, F8, CFD, MRC1, MARCO, SFTPA2 和 SFTPA1,机器学习建模后对LUSC早期癌症样本和正常肺组织样本的分类精度达到了98%以上。由基因SFTPA1和ESAM建立的LUSC早期肿瘤预测模型,对正常肺组织和LUSC肿瘤Ⅰ期样本的分类敏感性和特异性分别为99.18%和100%,并且独立验证集的分类准确率也在90%以上。结论 筛选出的12个早期分子标志物有望成为LUSC诊断的标志分子,并且建立的肿瘤预测模型具有极高的准确性,可以为LUSC的发生机理研究以及早期肿瘤预测提供帮助。     

NSCLC分类及生存分析预测的全基因组特征基因识别

基于NSCLC(非小细胞肺癌)子类分类在临床和生物医学研究方面的意义,利用全基因组基因表达水平(GE)和甲基化(ME)水平的微阵列数据对NSCLC子类分类进行全基因组特征基因识别分析。针对全基因组微阵列数据的高噪声、超高维小样本特性,利用弹性正交贝叶斯算法对全基因组基因进行递归筛选,识别分类精度最优的特征基因集。以TCGA的490的基因表达数据和378个甲基化数据为例,分别识别出52个GE特征基因和25个ME特征基因,相应的分类准确率分别为99%和98%。结合特征基因和临床数据建立的多变量Cox模型明确说明了特征基因在病人生存分析方面的重要作用:仅利用相应的基因表达数据和甲基化数据即可对病人样本的"高/低风险"进行正确分类,显著性水平均低于0.05。特征基因参与的代谢通路与p53、TGF-beta、Wnt等重要的癌症分类和发展的代谢通路的密切关系进一步证实了特征基因对NSCLC分类的重要性。     

肺鳞状细胞癌癌症发展模式识别分类模型及特征基因识别

本文利用先进的生物信息学方法,首次从全基因组水平综合基因表达、甲基化水平和拷贝数变异三类数据,寻找与肺鳞状细胞癌(LUSC)发生和发展密切相关的特征基因,为进一步解释其内在机理、开发新的靶向药物和治疗手段提供更加深入的理论依据.为克服全基因组数据超高维高噪声小样本特性对机器学习算法性能的影响,防止信息饱和现象的干扰,本文创新性地组合应用4种特征基因筛选方法,分别从特异性、相关性、生物学功能和对肿瘤分类模型的贡献等多个方面,通过迭代降维技术递归筛选真正的特征基因.研究中,我们以TCGA(The Cancer Genome Atlas project)数据库中的LUSCⅠ~Ⅲ期病人样本为例,对其基因表达数据(GE)、基因甲基化数据(ME)以及拷贝数变异数据(CNV)进行分析.结果筛选出67个GE特征基因,对3类样本分类的平均准确率达到86.29%,70个ME特征基因,相应的分类准确率为90.92%,31个CNV特征基因,相应的分类准确率为69.16%.KEGG(Kyoto Encyclopedia of Genes and Genomes)和IPA(Ingenuity Pathway Analysis)对上述3类特征基因集在代谢通路水平和基因调控网络水平上的分析,证明了其在调控水平上的密切关系.同时也表明,识别的特征基因与LUSC肿瘤进展之间有着重要的直接关系,这对了解肿瘤机理以及新靶向治疗的发展非常重要.     

联合TCGA和GEO数据库筛选乳腺癌易感基因

本研究是利用公共基因芯片数据库筛选乳腺癌的预后基因,预测和探索这些基因在乳腺癌进展中的可能机制和临床价值.首先,我们筛选了公共基因芯片数据库(gene expression omnibus,GEO)GSE22820和癌症基因组图谱(the cancer genome atlas,TCGA)乳腺癌数据库的重叠差异表达基因,联合R语言分析乳腺癌组织与癌旁正常组织差异表达的基因;其次,基于STRING数据库及Cytoscape软件构建蛋白质相互作用网络图,分析并识别了中枢基因和前3个模块;之后进行了更多的功能分析,包括基因本体(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以及基因集富集分析(gene set enrichment analysis,GSEA),以研究这些基因的作用以及潜在的潜在机制;最后进行了Kaplan-Meier分析和Cox比例风险分析,以阐明这些基因的诊断和预后效果.相关数据分析表明15个基因的表达水平与生存预后相关,高表达基因患者的总生存时间短于低表达患者(P＜0.05);Cox比例风险分析表明UBE2T、ER-CC6L和RAD51这3个基因是预后生存的独立因素(P＜0.05);GSEA分析表明在UBE2T、ERCC6L和RAD51基因中细胞周期、基础转录因子和卵母细胞减数分裂明显富集.最终,我们得出结论,这3种基因标志物的高表达是乳腺癌预后不良因素,可作为预测乳腺癌患者转移和预后的有效生物标志物.     

基于基因组关联数据识别阿尔茨海默病相关通路

阿尔茨海默病又称老年性痴呆,是一种复杂的中枢神经系统退行性疾病,本文选取一套阿尔茨海默病全基因组关联分析(GWAS)数据,利用Proxy Gene LD软件进行基因水平上的检验,利用Web Gestalt数据库进行遗传通路分析,识别出320个显著(P0.05)的阿尔茨海默病相关基因、8个显著的KEGG通路和41个显著的GO功能类,这些研究结果对进一步揭示阿尔茨海默病潜在的发病机制具有重要意义。     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。     

NF2基因甲基化及其mRNA表达与乳腺癌发病的关系

目的:探讨乳腺癌中NF2基因启动子甲基化状态及其mRNA水平与乳腺癌发病的关系.方法:应用甲基化特异性聚合酶链反应(MSP)和逆转录-聚合酶链反应(RT-PCR)技术,检测47例乳腺癌组织及相应的癌旁组织和15例乳腺良性病变组织,分析NF2基因的甲基化与某些临床参数及mRNA表达的关系.结果:NF2基因启动子区在乳腺癌、癌旁和乳腺良性病变组织中的甲基化频率分别为57.4%(27/47)、23.4%(23/47)和0%(0/15).且乳腺癌组明显高于其余两组(P＜0.05).NF2基因发生甲基化与发病年龄、组织分型、转移和组织分级无相关性.乳腺癌组NF2基因mRNA的相对表达量(0.16±0.11)明显低于相应的癌旁组(0.27±0.14)及乳腺良性病变组(0.64±0.17)(P＜0.05).NF2基因启动子区甲基化频率与其mRNA表达呈负相关(Spearman's r=-0.314,P＜0.05).结论:NF2基因发生甲基化与乳腺癌的发生密切相关,NF2mRNA表达与NF2基因启动子高甲基化呈负相关.     

动态增强磁共振扫描与超声弹性成像对乳腺癌的诊断价值探讨

目的:探讨动态增强磁共振成像扫描与超声弹性成像对乳腺癌良恶性肿瘤的诊断价值,为临床诊断提供影像学依据。方法:回顾性分析2009年10月至2013年5月在我院经穿刺或手术病理证实为乳腺癌的59例患者的临床资料,患者术前均行超声与动态增强MR检查。依据病理组织活检和临床随访分别评价动态增强MR和UE对乳腺癌诊断的准确性。结果:DCE-MRI检测共发现病灶59个,55个初步诊断乳腺恶性肿瘤(BI-RADS 4-5),4个诊断为良性(BI-RADS 3),诊断准确率为93.22%(55/59)。UE对59个病灶进行评分,54个评分为乳腺恶性肿瘤,5个评分为良性,诊断率为91.53%(54/59)。UE检测乳腺癌的敏感性明显低于DCE-MRI及DCE-MRI+UE,DCE-MRI检测乳腺癌的特异性明显低于UE及DCE-MRI+UE,差异具有统计学意义(P0.05)。DCE-MRI+UE诊断乳腺癌的准确率为96.61%(57/59),明显高于DCE-MRI或UE单独检测的准确率(P0.05)。结论:动态增强MR诊断乳腺癌的敏感性较高,而超声弹性成像的特异性较好,两者联合可提高诊断准确率,对乳腺癌的早期诊断具有重要的临床应用价值。     

中医痰湿体质基因表达谱研究

中医体质理论与个体化医学理念相通,在疾病防治个体化中发挥着重要作用.痰湿体质作为体质类型的一种,存在其独特的特征及发病倾向性.对比痰湿体质与平和体质(健康人)外周血单个核细胞全基因组表达谱,在FDR0.05且FC≥1.5条件下,共获得355个差异基因.GO注释和信号通路富集分析显示,差异基因主要与代谢紊乱密切相关.通过qRT-PCR验证,发现ELOVL7,PRKAR1A,SOCS3,ACSL4,CLU及ABCG1等6个基因在痰湿体质中表达与平和体质相比存在显著差异.该研究表明,基于体质分类的个体差异存在其生物学基础.痰湿体质的特征基因识别为该体质的分子机制研究及相关疾病探索奠定了基础.     

TNRC9基因rs12443621A/G多态性与中国女性乳腺癌易感性及临床病理关系的研究

目的：研究TNRC9/LOC643714基因rs12443621A/G多态性与乳腺癌易感性及临床病理之间的关系。方法：DNA试剂盒提取321例乳腺癌患者和340例正常女性静脉血全基因组DNA,PCR扩增目的基因片段,提取扩增样本进行DNA测序检测分析rs12443621多态性。应用SPSS17.0软件对实验结果进行统计学分析。结果：应用SPSS17.0软件对TNRC9/LOC643714基因rs12443621A/G多态性AA、AG、GG进行卡方检验分析,结果显示三种基因型分布在病例组及对照组中无统计学意义（X2=1.43,P〉0.05）,与乳腺癌易感性无关,与乳腺癌病理分型、ER、PR、HER-2状态以及淋巴结是否转移无相关性（X2=2.90,P〉0.05;X2=2.25,P〉0.05;X2=1.671,P〉0.05;X2=1.34,P〉0.05;X2=3.24,P〉0.05）。结论：TNRC9基因rs12443621A/G多态性与乳腺癌易感性及临床病理特征无关,不能作为独立的基因标志物对乳腺癌进行早期检测和诊断。     

A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients

Introduction

The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures.

Methods

We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes).

Results

The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis.

Conclusion

This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.     

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging

The grade of a cancer is a measure of the cancer''s malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.     

Identification of Common Prognostic Gene Expression Signatures with Biological Meanings from Microarray Gene Expression Datasets

Numerous prognostic gene expression signatures for breast cancer were generated previously with few overlap and limited insight into the biology of the disease. Here we introduce a novel algorithm named SCoR (Survival analysis using Cox proportional hazard regression and Random resampling) to apply random resampling and clustering methods in identifying gene features correlated with time to event data. This is shown to reduce overfitting noises involved in microarray data analysis and discover functional gene sets linked to patient survival. SCoR independently identified a common poor prognostic signature composed of cell proliferation genes from six out of eight breast cancer datasets. Furthermore, a sequential SCoR analysis on highly proliferative breast cancers repeatedly identified T/B cell markers as favorable prognosis factors. In glioblastoma, SCoR identified a common good prognostic signature of chromosome 10 genes from two gene expression datasets (TCGA and REMBRANDT), recapitulating the fact that loss of one copy of chromosome 10 (which harbors the tumor suppressor PTEN) is linked to poor survival in glioblastoma patients. SCoR also identified prognostic genes on sex chromosomes in lung adenocarcinomas, suggesting patient gender might be used to predict outcome in this disease. These results demonstrate the power of SCoR to identify common and biologically meaningful prognostic gene expression signatures.     

Expression analysis of selected miR-206 targets from the transforming growth factor-β signaling pathway in breast cancer

Breast cancer as a molecularly heterogeneous malignancy is associated with dysregulation of several signaling pathways, including transforming growth factor-β (TGF-β) signaling. On the other hand, several recent studies have demonstrated the role of microRNAs (miRNAs) in breast cancer pathogenesis. In the current study, we performed a computerized search to find miR-206 target genes that are functionally linked to the TGF-β signaling pathway. We selected LEF1, Smad2, and Snail2 genes to assess their expression in 65 breast cancer samples and their adjacent noncancerous tissues (ANCTs) in correlation with expression levels of miR-206 as well as clinicopathological characteristics of patients. miR-206 was significantly downregulated in (Estrogen receptor) ER-positive breast cancer samples compared with their corresponding ANCTs. Association analysis between expression levels of genes and demographic features of patients showed significant association between expressions of SMAD2 and LEF1 genes and body mass index ( P values of 0.03 and 0.02, respectively). miR-206 low-expression levels were associated with TNM stage, mitotic rate, and lymph node involvement ( P values of 0.02, 0.01, and 0.01 respectively). In addition, SMAD2 high-expression levels were associated with HER2 status ( P = 0.02). Consequently, our data highlight the role of TGF-β signaling dysregulation in the pathogenesis of breast cancer and warrant further evaluation of miRNAs and messenger RNA coding genes in this pathway to facilitate detection of cancer biomarkers and therapeutic targets.     

Inflammation-associated DNA methylation patterns in epithelium of ulcerative colitis

Aberrant DNA methylation patterns have been reported in inflamed tissues and may play a role in disease. We studied DNA methylation and gene expression profiles of purified intestinal epithelial cells from ulcerative colitis patients, comparing inflamed and non-inflamed areas of the colon. We identified 577 differentially methylated sites (false discovery rate <0.2) mapping to 210 genes. From gene expression data from the same epithelial cells, we identified 62 differentially expressed genes with increased expression in the presence of inflammation at prostate cancer susceptibility genes PRAC1 and PRAC2. Four genes showed inverse correlation between methylation and gene expression; ROR1, GXYLT2, FOXA2, and, notably, RARB, a gene previously identified as a tumor suppressor in colorectal adenocarcinoma as well as breast, lung and prostate cancer. We highlight targeted and specific patterns of DNA methylation and gene expression in epithelial cells from inflamed colon, while challenging the importance of epithelial cells in the pathogenesis of chronic inflammation.