【作者】 钟惟德；

【导师】 张阳德；

【作者基本信息】 中南大学 ， 外科学， 2010， 博士

【摘要】 前列腺癌是一个以老年人为主的疾病。其发病率随着年龄的增长而增加,有75%新诊断的前列腺癌病例为65以上的男性。50岁以后,发病率和死亡率均呈指数增长。当前临床诊断中使用血清前列腺特异性抗原(prostate specific antigen, PSA)作为前列腺癌的和前列腺增生的主要鉴别诊断手段。然而由于PSA对PCa并不是特异的,血清PSA浓度升高并非总是提示PCa。当PSA水平轻度升高,在4-10ng/ml之间时,还难以判断是BPH还是早期PCa。并且PSA无法提供肿瘤内在本质信息,也即不能指导临床肿瘤分期,提供治疗和预后的决策信息。前列腺癌的发生是一个多基因渐变失衡的复杂过程,而且肿瘤本身是各种不同性的细胞失衡生长的总称,关联不同信号系统,凭单一指标的检测无法准确判断。近期,人们发现多个基因表达的变化与前列腺癌病程相关,如能结合临床病理学研究,未来可作为前列腺癌病程的生物标记,对前列腺癌进行早期诊断和分型。本研究拟从多基因入手,研究临床肿瘤基因表达谱特征,寻找比PSA更可靠的临床诊断标记物组合。在国内外关于前列腺癌基因表达谱的研究结果基础上,采用数据挖掘等方法筛选出研究基因,设计目的基因符合荧光定量PCR要求的引物和序列,克隆靶序列到质粒中,以备后期制作标准曲线。同时对收集的临床样本提取RNA,用荧光定量PCR方法分别研究了基因表达变化的表达谱特征和基因组表达比值变化的表达谱特征。第一批选择预选了9个基因(KLK3/PSA、KLK2、KLK11、pim-1、hepsin、PSMA、AR、p27、IGF-1)作为研究对象,通过荧光定量PCR检测临床样本中上述基因的表达变化,结果表明这9个基因组合能明显区分PCa和BPH,其中pim-1、KLK2、PSMA、AR可作为检测基因的核心组合。第二批选择预选了12个基因(trmp-2、RPS16、TMPRSS2、KLK11、Ki-67、PCNA、HER2/ERBB2、MYC、NFKBIA、SREBF2、PSMA/FOLH1、GSTP1),并组合为6组表达比值,通过荧光定量PCR技术,对前列腺癌细胞株(LNCaP和PC-3)、前列腺癌和前列腺增生临床样本的12个基因的6组表达比值进行检测。结果表明TMPRSS2/KLK11、Ki-67/PCNA、NFKBIA/SREBF2比值可作为检测基因的核心组合；而HER2/MYC基因组合虽然没有前列腺癌的特异性,但是HER2/MYC的高表达可能有助于前列腺癌的早期诊断。更多还原

【Abstract】 Prostate cancer (PCa) is frequently diagnosed in elder men, and its risk is associated with increase of age.75% newly diagnosed PCa are patients with an age of more than 65 years old. The incidence and mortality rates all present an index number growth when men beyond 50 years old. In clinical, the prostate-specific antigen (PSA) has been widely used in screening for prostate cancer. Since PSA elevation is also associated with prostatitis and benign prostatic hyperplasia (BPH), elevated PSA levels do not always indicate the presence of Pca because of its insufficent specificity. In particularly, it is dificult to distinguish PCa between BPH when an individual gets PSA between 4.0 ng/ml and 10.0 ng/ml. And the PSA test can’t give the inherent nature of PCa, so not to guide the clinical tumor classification and not to provide the decision-making information of treatment and prognosis. PCa is a multi-gene imbalance in the complex process of gradual change, and diffent clinical PCa may have different gene imbalance and associated with different signal systems, so virtually it is impossible to diagnosis PCa accurately by using a single biomarker. Recent years, a number of genes are reveaed that which expression changes with PCa progression. Forward study these result on clinical pathology; there are promice to develop some bettor biomarkers than PSA for early diagnosis and type of PCa.Thus, the objective of our research is the identification of more specific and predictive biomarkers for improving clinical management by studing multi-gene expression profile of clinical PCa samples. Harnessing the current research results on gene expression of PCa, target genes are selected by data mining methods, and then the genes’primer and sequence are designed according to FQ-PCR’s requirement. FQ-PCR with SYBR Green I are carried out to detect the mRNA expression levels of these genes in clinical samples.First, to study gene expression, we pre-choose 9 genes (KLK3/PSA、KLK2、KLK11、pim-1、hepsin、PSMA、AR、p27、IGF-1) as a biomarker combination to advance clinical trial on PCa tests. The results showed the PCa and BPH samples can be classified by the 9 genes expression profile, and among the 9 genes, pim-1, KLK2, AR and PSMA have the core contribution to classification. Second, to study gene expression ratio,12 genes (trmp-2、RPS16、TMPRSS2、KLK11、Ki-67、PCNA、HER2、MYC、NFKBIA、SREBF2、PSMA、GSTP1) are selected. The expression ratio result showed the 12 genes expression profile can provide useful imformation for PCa diagonosis, and among the 12 genes, TMPRSS2、KLK11、Ki-67、PCNA、NFKBIA、SREBF2 would be the most potential biomarker combinations. Howerver, HER2/MYC is the non-special gene combination in PCa, its overexpression may contribute to the early detection of PCa.更多还原