miRNA-9在不同肿瘤中调控作用的研究进展

随着对肿瘤发生机制研究的深入,基因对肿瘤调控的分子机制的不断明了,miRNA-9作为肿瘤发生、发展的重要调控基因之一,其研究已有了新的突破。本文主要对miRNA-9的生物学特性、与肿瘤关系及调控机制做一综述,企能在肿瘤发生机制、诊断和治疗方面提供新的线索,以降低病死率。     

癌基因Ets在肿瘤转移中的转录调控研究进展

肿瘤发生发展是多阶段性、多基因参与、多种转录因子调控的过程,涉及一系列病理生理反应,包括细胞异常的增殖、粘附、运动,细胞外基质降解,血管生成等.癌基因Ets家族在肿瘤发生发展中起着重要作用,特别是它作为转录因子在肿瘤转移中的调控作用.     

WIF-1研究进展

肿瘤发生、发展是一个极其复杂的多基因调控和多步骤发展过程.它涉及到机体、肿瘤细胞和靶组织的相互影响和作用,还涉及到一系列肿瘤相关基因的结构和功能的异常. 　　Wnt抑制因子-1(Wnt inhibitory factor-1,WIF-1)是近年来发现的肿瘤抑制因子,它通过结合Wnt蛋白抑制Wnt通路的活性,并因此被命名.……     

DNA甲基化基因DNMT1的研究进展

DNA甲基转移酶1(DNMT1)是哺乳动物基因组表观遗传修饰中DNA甲基化的关键基因,其编码的蛋白是一种分子量大且功能复杂的酶,具有多种调控功能,参与机体发育过程中干细胞生长、细胞增殖、器官发育、衰老和肿瘤发生等多个生物学过程。本文将对DNMT1基因的结构与分子作用机制进行介绍,并总结其表达调控、生物学功能及其与肿瘤等人类疾病相关的研究进展。     

肿瘤微环境及免疫调节系列研究指导乳腺癌精确治疗的临床实践北大核心CSCD

0引言尽管辅助治疗很大程度上改善了乳腺癌患者的预后,然而,来自循证医学的群体证据证明它不能使所有患者受益,因此治疗的个体化、精确化是目前乳腺癌治疗的重要方向。肿瘤发生发展是一个多因素、多个过程参与的复杂过程,具有精确而又复杂的调控机制。肿瘤微环境对肿瘤生长转移的重要调控作用已被众多学者关注并公认。     

乳腺癌转移相关基因的研究进展

摘 要　乳腺癌的转移是一个较为复杂、由多基因参与及多步骤完成的过程,转移相关基因对转移的调控是肿瘤发生转移的分子基础。所谓转移相关基因是一类功能上能够促进或阻断肿瘤转移潜能而不影响肿瘤细胞生长增殖的基因,其总体可分为两大类：转移促进基因和转移抑制基因,它们涉及癌基因、抑癌基因、信号转导基因以及黏附分子基因、细胞因子基因、基质金属蛋白酶基因等。乳腺癌转移相关基因的作用机制及下游信号转导途径的阐明,将为转移性乳腺癌的分子诊断和个体化治疗奠定基础。     

Dicer在肿瘤中的研究进展

Dicer是miRNA剪切成熟的关键酶,在肿瘤中扮演单倍体不足抑癌基因的角色.Dicer基因突变携带者易发生家族性肺胸膜母细胞瘤;Dicer在各种肿瘤的发生发展过程中发挥重要作用,基因低表达肿瘤更常表现出恶性程度高和预后差的特点;同时也能介导对药物治疗的敏感性;另外Dicer与miRNA之间复杂的调控体系是其参与肿瘤发生发展的根本所在,可为肿瘤的靶向治疗提供研究基础.     

肿瘤发生发展中Mina53的作用机制

c-Myc的下游靶基因Myc-induced nuclear antigen with a molecular mass of 53 kDa（Mina53）在多种肿瘤组织中异常表达,且与肿瘤临床分期、淋巴结转移及临床预后之间关系密切。Mina53可通过调控细胞分化、增殖、凋亡等参与肿瘤发生发展,但其相关作用机制还不十分清楚。为此,本文拟通过综述Mina53在肿瘤发生发展中的机制研究进展,探讨其在肿瘤治疗中的意义和价值。     

鼻咽癌中细胞凋亡与p16 p130 nm23-H1表达的关系

鼻咽癌（nasopharyngeal carciaoma，NPC）是我国南方五省及东南亚常见的上皮性恶性肿瘤。分子生物学的研究结果表明肿瘤是一种基因异常的疾患，它的发生是一个多基因、多步骤的逐步演变过程，这些基因在肿瘤中的作用及各基因之间相关性成为国内外研究的热点之一。近年来随着对机制研究的深入，发现多种肿瘤与许多癌基因、抑癌基因有关，凋亡调控基因已被看作是一类新的肿瘤发生相关基因。     

肿瘤转移抑制基因KAI1／CD82的研究进展

肿瘤侵袭、转移是一个极其复杂的多基因调控和多步骤发展过程。KAIl／CD82是近年来发现的一种新的肿瘤转移抑制基因。本文主要综述KAIl／CD82的结构、功能、作用机制及其与多种恶性肿瘤的关系。     

The role of nutraceutical proteins and peptides in apoptosis,angiogenesis, and metastasis of cancer cells

The process of carcinogenesis is complex and not easy to eliminate. It includes the initial occurrence of genetic alterations which can lead to the inactivation of tumor-suppressor genes and further accumulation of genetic alterations during tumor progression. Looking for food and food components with biological properties, collectively called nutraceuticals, that can hinder such alterations and prevent the inactivation of tumor-suppressor genes is a very promising area for cancer prevention. Proteins and peptides are one group of nutraceuticals that show potential results in preventing the different stages of cancer including initiation, promotion, and progression. In this review, we summarized current knowledge on the use of nutraceutical proteins and peptides in cancer prevention and treatment. We focused on the role of plant protease inhibitors, lactoferrin and lactoferricin, shark cartilage, plant lectins, and lunasin in the apoptosis, angiogenesis, and metastasis of cancer cells. Also included are studies on bioavailability and clinical trials conducted on these promising proteins and peptides.     

Genomics of prostate cancer: Is there anything to „translate”?

This review provides an up-dated collection of data concerning the genetic and epigenetic changes during development, growth and progression of prostate cancer. Hereditary and susceptibility factors have a long list, similarly to the expression of single genes connected to various cell functions. It was a hope that covering a large set of genes, array technologies would clarify very rapidly the role of genetics in malignant diseases, offering targets for molecular diagnostics and therapy. The power of high-throughput techniques for the detection and global analysis of gene expression is unquestionable, interesting, astonishing as well as puzzling data have already been obtained. However, the standardization of the procedures is still missing and the reproducibility is rather low in many instances. Moreover, the different array methods can select different gene expression profiles, which makes the decision rather difficult. Another important question is, coming again from the array technologies, how far the genotype (the gene profiles or fingerprints) can reflect the actual phenotype in a highly complex and readily changing disease as cancer. Proteomics will provide a closer look to this seemingly unanswerable problem. We are at the beginning of the exploration of the behavior of cancer cells in order to apply a more effective therapy based on a more reliable set of diagnostic and prognostic informations.     

From chromosomal alterations to target genes for therapy: integrating cytogenetic and functional genomic views of the breast cancer genome

A vast number of recurrent chromosomal alterations have been implicated in cancer development and progression. However, most of the genes involved in recurrent chromosomal alterations in solid tumors remain unknown, despite the recent substantial progress in genomic research and availability of high-throughput technologies. For example, it is now possible to quickly identify large numbers of differentially expressed genes in cancer specimens using cDNA microarrays. Integration of this "functional genomic view" of the cancer genome with the "cytogenetic view" could lead to the identification of genes playing a critical role in cancer development and progression. In this review, we illustrate how the combination of three different microarray technologies, cDNA, CGH, and tissue microarrays, makes it possible to directly identify genes involved in chromosomal rearrangements in cell line model systems and then rapidly explore their significance as potential diagnostic and therapeutic targets in human primary breast cancer progression.     

The importance of replication in gene-gene interaction studies: multifactor dimensionality reduction applied to a two-stage breast cancer case-control study

A polygenic model has been proposed to explain the bulk of the genetic component of breast cancer aetiology and this is probably to include both main effects and interactions between multiple loci. However, the power to detect the interactions using traditional analytical methods is very limited for most studies. Multifactor dimensionality reduction (MDR) has been suggested to have increased power to detect interactions and is increasing being used in published studies. We applied MDR to a two-stage case-control breast cancer study conducted in Spain and Finland. In the stage 1 Spanish study of 864 cases and 845 controls, we evaluated interaction between 474 single-nucleotide polymorphisms in 120 cancer-related genes, subdivided into 34 genetic pathways and found evidence of a four-way interaction between genes in the FatiGO-defined B-cell receptor-signalling pathway (P < 0.006). However, this result was not replicated in the stage 2 Finnish study of 580 cases and 920 controls (P = 0.99). A number of technical issues in applying MDR to case-control data were identified and discussed. One of these is that the estimated sign test P-value can vary substantially at random, which raises doubts about its reliability. More generally, the present study serves as an important caution in the interpretation of results from single studies of gene-gene or gene-environment interaction in complex diseases. Just as for genetic main effects, the replication of positive findings in additional independent series is essential.     

Whole-exome sequencing reveals recurrent somatic mutation networks in cancer

The second-generation sequencing technologies have been extensively used to reveal the mechanism of tumorigenesis and find critical genes in cancer progression that can be potential targets of clinic treatment. Exome is a part of genome formed by exons which are the protein-coding portions of genes. The whole-exome sequencing information can reflect the mutations of the protein-coding region in the genome and depict the causal relationship between the mutations and phenotypes. Now, many network-based methods have been developed to identify cancer driver modules or pathways, which not only provide new insights into molecular mechanism of disease progression at network level but also can avoid low coverage or lowly recurrent on disease samples in contrast to individual driver genes. In this review, we focus on the recent advances on network-based methods for identifying cancer driver modules or pathways, including methods of whole-exome sequencing, somatic mutation detection, driver mutation identification, and mutation network reconstruction.     

Gene expression profiling for prediction of response to chemotherapy

Numerous genes whose expression is controlled by complex regulatory networks are involved in the development and progression of each cancer,and those genes will be the key factors for determining each characteristic of the tumor. The recent development of DNA microarray and related technologies provides an opportunity to perform more detailed characterization( profiling) of individual tumor cells. Indeed, the gene expression profile of a tumor provides a unique molecular portrait or signature that can be correlated with clinical behavior and drug responsiveness. The development of personalized( or customized) medicine and molecularly targeted drugs is enthusiastically awaited based on the results of research examining genomic diversity such as SNPs( single nucleotide polymorphisms), gene expression profiling with DNA microarrays, and analysis of protein expression and interactions.     

Expression profiling for bladder cancer: strategies to uncover prognostic factors

Despite being a common cancer worldwide, management of transitional cell carcinoma of the bladder currently relies primarily on clinical staging and histopathologic parameters. Assaying alterations in molecular pathways can contribute valuable information that can accurately predict outcome and chemotherapeutic response in individual patients with bladder cancer. Medium- to high-throughput gene-expression profiling technologies are now allowing multiplexed assessment of alterations responsible for the genesis and progression of bladder tumors. These investigations employ global or pathway-based approaches to define molecular signatures that can predict prognosis independent of traditional clinical performance metrics. Prognostic panels generated using these strategies can also elucidate the biology of tumor progression and identify potential therapeutic targets.     

改进MSP法对肿瘤患者血清DNA的探测

目的 寻找一种切实可行的检测方法及肿瘤的生物标志物。方法 用改良的Herman新方法对111例不同癌症患者的血清及部分组织进行了p16异常甲基化检测,同时对55例患者检测突变K-ras基因作为比较。结果 各期住院患者血清及癌组织p16异常甲基化均在50％左右,与正常人及非恶性肿瘤对照组比较差异有显著性(P<0.05)。早期癌血清与组织无明显差异。晚期癌症患者阳性率明显上升(P<0.05);术后20 d基本消失。突变K-ras基因的阳性率仅为30％,较前者明显为低,如二者联合应用可提高阳性率13％。结论 改进MSP法检测血清中异常甲基化是一种行之有效的方法,与突变K-ras基因检测联合应用,则效果更好。     

Expression profiling for bladder cancer: strategies to uncover prognostic factors

Despite being a common cancer worldwide, management of transitional cell carcinoma of the bladder currently relies primarily on clinical staging and histopathologic parameters. Assaying alterations in molecular pathways can contribute valuable information that can accurately predict outcome and chemotherapeutic response in individual patients with bladder cancer. Medium- to high-throughput gene-expression profiling technologies are now allowing multiplexed assessment of alterations responsible for the genesis and progression of bladder tumors. These investigations employ global or pathway-based approaches to define molecular signatures that can predict prognosis independent of traditional clinical performance metrics. Prognostic panels generated using these strategies can also elucidate the biology of tumor progression and identify potential therapeutic targets.     

Gene-environment interaction and aetiology of cancer: what does it mean and how can we measure it?

One form of defence against cancer development involves a series of genes whose role is to metabolize and excrete potentially toxic compounds and to repair subtle mistakes in DNA. Much laboratory and epidemiological research over the past decade has concentrated on the identification of these genes and an assessment of their role in cancer aetiology. Of particular interest has been whether the risk of cancer associated with a particular environmental exposure differs with respect to functionally different polymorphisms of these genes, i.e. gene-environment interaction. A large number of studies have been conducted for numerous genes and also for all common cancer sites, although results have been very inconsistent and therefore inconclusive. This is partially due to the inadequate sample size of most studies to detect modest effects and the over-reporting of positive associations identified in subgroups of the dataset. There is also much confusion about the meaning of "gene-environment interaction", what type of studies should be conducted to study it and also how it should be measured. Furthermore, the very purpose of these studies is not clear; are they attempting to identify high-risk individuals, or are they simply trying to further understand the cancer process? This review will explore these questions and provide some recommendations to help ensure that the next phase of gene-environment interaction studies, which are likely to be much larger and based on many more genes, also provide some clearer answers.