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1.
风险致病基因预测有助于揭示癌症等复杂疾病发生、发展机理,提高现有复杂疾病检测、预防及治疗水平,为药物设计提供靶标.全基因组关联分析(GWAS)和连锁分析等传统方法通常会产生数百种候选致病基因,采用生物实验方法进一步验证这些候选致病基因往往成本高、费时费力,而通过计算方法预测风险致病基因,并对其进行排序,可有效减少候选致病基因数量,帮助生物学家优化实验验证方案.鉴于目前随机游走算法在风险致病基因预测方面的卓越表现,本文从单元分子网络、多重分子网络和异构分子网络出发,对基于随机游走预测风险致病基因研究进展进行较全面的综述分析,讨论其所存在的计算问题,展望未来可能的研究方向.  相似文献   

2.
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。  相似文献   

3.
为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。  相似文献   

4.
吕炳建  崔晶  徐静  张昊  罗敏捷  朱益民  来茂德 《遗传》2006,28(4):385-392
为进一步分析腺瘤相对正常SSH文库(A-N)的差异表达候选基因的表达谱,结合通用的生物信息学软件,自行开发、搭建包括核酸自动分析平台及GetUNi软件包的生物信息学平台,实现了将A-N文库109个差异克隆序列与本地下载的非冗余核酸数据库、人UniGene数据库比对、聚类,至获取差异表达候选基因的自动化分析。对这些基因进行GOTM(GOTree Machine)初步生物信息学分析及RT-PCR验证。结果共发现62个候选基因,包括6个核糖体蛋白成员,6个免疫相关基因。Reg4和FAM46A两个基因出现频次最高,分别为13次和4次,半定量RT-PCR显示这两个基因分别在10/10和9/10例腺瘤相对正常黏膜表达上调。对于这些差异表达候选基因的进一步分析和研究将有助于揭示结肠腺瘤发生的分子机制。  相似文献   

5.
紫鸭跖草是一种高耐铜的超积累植物,本研究首次应用RNA-Seq技术对其转录组进行分析。通过全长转录组分析组装了紫鸭跖草耐铜相关候选基因,通过转录组分析,共获得82 471条N50长度为2 299 bp的高质量unigene,为紫鸭跖草的进一步研究提供了丰富的数据。对照组(CK)、300 μmol/L胁迫组(CT1)和1 000 μmol/L胁迫组(CT2)的测序数据已保存在NCBI的SRA数据库中,登录号为SAMN11265427。CT1相比于CK,共有5 028条unigene在根组织中有显著性差异表达,约占全部unigene的6.10%,其中富集上调和下调的基因分别为3 138和1 890条;CT2相比于CT1,根中共有6 813个unigene富集差异表达,占所有unigene的8.26%。其中富集上调和下调的基因分别为2 555和4 258。随机选取10个基因进行qRT-PCR荧光定量分析,结果与Illumina测序数据一致,验证了基因数据差异表达的有效性。上述实验从分子水平上为研究铜胁迫下铜耐受的分子机制提供了理论依据。  相似文献   

6.
表型趋同的分子证据探索一直是趋同演化研究的热点.高通量测序带来的多组学数据为分子趋同演化提供了大量研究材料和更加多样的研究角度.分子趋同的概念和研究方法也从编码基因的氨基酸替换趋同扩展到基因丢失趋同、表达调控趋同、表达模式趋同、肠道微生物趋同等多个不同分子层级.本文对近年来基因组时代下基于多组学水平的分子趋同演化研究的新进展进行综述,并对该领域未来研究方向进行了展望.  相似文献   

7.
Proline rich 11(PRR11)是本课题组鉴定的一个新的肿瘤相关基因。为研究PRR11介导肺癌发生发展相关的分子机制,本研究分析了PRR11表达被抑制后人肺癌细胞系H1299的全基因组基因表达谱的变化。首先,采用siRNA抑制H1299细胞中PRR11的表达,提取总RNA,采用基因芯片分析全基因组基因表达谱的变化。然后,对呈现差异表达的基因进行GO和Pathway富集分析,并对部分重要的候选基因进行定量RT-PCR验证。基因芯片结果表明,采用siRNA有效抑制H1299细胞中PRR11表达后,共有550个基因的mRNA水平出现明显变化,其中139个基因表达上调,411个基因表达下调。生物信息学分析结果表明,上述差异表达的基因显著富集于细胞周期和MAPK通路。定量RT-PCR验证分析结果表明,PRR11表达抑制后确实可导致多个与细胞周期和肿瘤发生发展密切相关的基因(包括DHRS2、EPB41L3、CCNA1、MAP4K4、RRM1、NFIB)呈现显著的表达变化。这些结果提示,PRR11可能通过上述通路和/或基因的表达变化参与肺癌的发生发展过程。  相似文献   

8.
《植物杂志》2009,(12):7-7
肺癌是危害人类的重大疾病之一,具有非常高的致死率。中国科学院上海生命科学研究院与华东师范大学合作开发了一个肺癌相关基因、蛋白以及小分子RNA的信息库平台,不仅有助于对某些特定的分子或生物学标记物进行深入的了解,也为系统性地研究肺癌发病相关的分子机理奠定了坚实的基础。  相似文献   

9.
PIWI和piRNA的表达水平与肿瘤类型密切相关。Piwil2 mRNA在肝癌中的表达量比肿瘤周围的肝脏组织的表达量高。PIWI/piRNA通路基因Piwil4、Mael和Ddx4为候选癌基因,在多种癌组织中表达,而在肝癌组织中的研究尚无报道。为探讨Piwil4、Mael和Ddx4基因在肝癌组织中表达水平变化的影响,建立了大鼠肝癌模型,并提取血清用ELISA方法检测肿瘤标记物,采用实时荧光定量PCR、蛋白质免疫印迹方法和免疫组织化学方法检测正常肝组织与模型组肝组织中Piwil4、Mael和Ddx4基因mRNA转录和蛋白表达水平。结果表明,大鼠肝癌动物模型血清中肿瘤标记物含量明显升高。Piwil4、Mael和Ddx4基因mRNA及蛋白在肝癌模型组织中高表达。研究表明,肝癌模型组织中Piwil4、Mael和Ddx4基因表达水平有望作为肝癌检测的一种分子标志物。  相似文献   

10.
高通量组学技术的快速发展使生命科学进入大数据时代。科学家们从基因组、转录组、蛋白质组和代谢组等多组学数据中剥茧抽丝, 逐步揭示生物体内复杂而巧妙的调控网络。近日, 华中农业大学李林课题组联合杨芳课题组和严建兵课题组构建了玉米(Zea mays)首个多组学整合网络。该网络包括3万个玉米基因在三维基因组水平、转录水平、翻译水平和蛋白质互作水平的调控关系, 由280万个网络连接组成, 构成1 412个调控模块。利用该整合网络, 研究团队预测并证实了5个调控玉米分蘖、侧生器官发育和籽粒皱缩的新基因。进一步结合机器学习方法, 他们预测出2 651个影响玉米开花期的候选基因, 鉴定到8条可能参与玉米开花期的调控通路, 并利用基因编辑技术和EMS突变体证实了20个候选基因的生物学功能。此外, 通过对整合调控网络的进化分析, 他们发现玉米两套亚基因组在转录组、翻译组和蛋白互作组水平上存在渐进式的功能分化。这套集合多组学数据构建的整合网络图谱是玉米功能基因组学研究的重大进展, 为玉米重要性状新基因克隆、分子调控通路解析和玉米基因组进化分析提供了新工具, 是解锁玉米功能基因组学的一把新钥匙。  相似文献   

11.
The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or 'potential' biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.  相似文献   

12.
The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.  相似文献   

13.
Lung cancer is often asymptomatic or causes only nonspecific symptoms in its early stages. Early detection represents one of the most promising approaches to reduce the growing lung cancer burden. Human saliva is an attractive diagnostic fluid because its collection is less invasive than that of tissue or blood. Profiling of proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of oral or systematic diseases, which may be used extensively in future medical diagnostics. There were 72 subjects enrolled in this study for saliva sample collection according to the approved protocol. Two-dimensional difference gel electrophoresis combined with MS was the platform for salivary proteome separation, quantification, and identification from two pooled samples. Candidate proteomic biomarkers were verified and prevalidated by using immunoassay methods. There were 16 candidate protein biomarkers discovered by two-dimensional difference gel electrophoresis and MS. Three proteins were further verified in the discovery sample set, prevalidation sample set, and lung cancer cell lines. The discriminatory power of these candidate biomarkers in lung cancer patients and healthy control subjects can reach 88.5% sensitivity and 92.3% specificity with AUC = 0.90. This preliminary data report demonstrates that proteomic biomarkers are present in human saliva when people develop lung cancer. The discriminatory power of these candidate biomarkers indicate that a simple saliva test might be established for lung cancer clinical screening and detection.  相似文献   

14.
The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.  相似文献   

15.
Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.  相似文献   

16.
Zhang F  Chen JY 《BMC genomics》2010,11(Z2):S12

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.
  相似文献   

17.
18.
Malignant pleural effusion (MPE) obtained from lung adenocarcinoma may contain potentially useful biomarkers for detection of lung cancer. In this study, we used a removal system for high-abundance proteins followed by one-dimensional SDS-PAGE combined with nano-LC-MS/MS to generate a comprehensive MPE proteome data set with 482 nonredundant proteins. Next, we integrated the MPE proteome and secretome data sets from three adenocarcinoma cell lines, with a view to identifying potential PE biomarkers originating from malignant cells. Four potential candidates, alpha-2-HS-glycoprotein (AHSG), angiogenin, cystatin-C, and insulin-like growth factor-binding protein 2, (IGFBP2), were isolated for preclinical validation using ELISA. Both AHSG and IGFBP2 levels were increased in lung patients with MPE (n = 68), compared to those with nonmalignant pleural effusion (n = 119). Notably, the IGFBP2 level was higher in MPE, compared with that in benign diseases (bacteria pneumonia and tuberculosis pleuritis), and significantly associated with malignancy, regardless of the cancer type. Our data additionally support an extracellular function of IGFBP2 in migration in lung cancer cells. These findings collectively suggest that the adenocarcinoma MPE proteome provides a useful data set for malignancy biomarker research.  相似文献   

19.
Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.  相似文献   

20.

Background

Colorectal cancer is the second most common cause of cancer related death in the developed world. To date, no blood or stool biomarkers with both high sensitivity and specificity for potentially curable early stage disease have been validated for clinical use. SELDI and MALDI profiling are being used increasingly to search for biomarkers in both blood and urine. Both techniques provide information predominantly on the low molecular weight proteome (<15 kDa). There have been several reports that colorectal cancer is associated with changes in the serum proteome that are detectable by SELDI and we hypothesised that proteomic changes would also be detectable in urine.

Results

We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (β2-microglobulin).

Conclusion

Changes in the urine proteome may aid in the early detection of colorectal cancer.  相似文献   

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