原发性前列腺癌数据集的多指标分析揭示了新的生物标记物

IF 2.1 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Melis Tuncer, Muhammed Erkan Karabekmez, Filiz Kisaayak Collak
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引用次数: 0

摘要

前列腺癌(PCa)在男性癌症相关死亡中排名第二。目前用于诊断的筛查在早期阶段还不够充分,因此需要更多的诊断生物标志物研究。我们从 GEO 数据集中搜索了 PCa 组织和良性前列腺增生(BPH)或与 PCa 相邻的正常组织的现有微阵列,对 miRNA、mRNA 和甲基化在 PCa 早期阶段的生物标志物潜力进行了荟萃分析。利用 miRWalk 和 miRDB 数据集确定了 miRNA 的靶基因。在人类前列腺上皮细胞系(RWPE-1)和人类前列腺癌上皮细胞系(22RV1)中对潜在的 miRNA 和基因进行了 qPCR 定量。我们对诊断 PCa 的潜在生物标志物进行了荟萃分析,发现了几种候选标志物。结果表明,miR-7-5p 表达过高。CAMKK2和TMEM97表达上调,CLIP1表达下调,这些基因被证明是miR-7-5p的靶标。研究发现,CAMKK2、TMEM97 和 CLIP1 基因发生了高甲基化。虽然我们研究中使用的两种细胞系中 miR-7-5p、CAMKK2、TMEM97 和 CLIP1 表达水平的变化与荟萃分析中观察到的显著表达差异不一致,但我们的荟萃分析结果对人类前列腺组织或不同的人类肿瘤细胞系研究是有希望的。鉴于我们的大规模数据荟萃分析结果很难通过特定细胞系进行实验验证,这就凸显了我们的荟萃分析结果在前列腺癌生物标志物研究中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multi-Omics Analysis of Primary Prostate Cancer Datasets Reveals Novel Biomarkers.

Multi-Omics Analysis of Primary Prostate Cancer Datasets Reveals Novel Biomarkers.

Prostate cancer (PCa) ranks second in cancer-related deaths in men. Current screenings used in the diagnosis are not sufficient enough in the early stages therefore, more diagnostic biomarker studies are needed. We performed a meta-analysis on the biomarker potential of miRNAs, mRNAs, and methylation for the early stages of PCa by searching available microarrays from the GEO dataset for PCa tissue and benign prostatic hyperplasia (BPH) or normal adjacent to PCa. Target genes of miRNAs were determined using the miRWalk and miRDB datasets. The results were visualized using network analysis. qPCR quantification of potential miRNA and genes was performed in human prostate epithelial cell line (RWPE-1) and human prostate carcinoma epithelial cell line (22RV1). Our meta-analysis of potential biomarkers for the diagnosis of PCa identified several candidates. It was shown that miR-7-5p is overexpressed. CAMKK2, TMEM97 expression were upregulated and CLIP1 expression was downregulated and these genes were shown to be targets of miR-7-5p. CAMKK2, TMEM97, and CLIP1 genes were found to be hypermethylated. Although the changes in the expression levels of miR-7-5p and CAMKK2, TMEM97, and CLIP1 in the two cell lines used in our study were not consistent with the significant expression differences observed in the meta-analysis, our meta-analysis results would be promising in human prostate tissue or different human tumor cell line studies. This highlights the importance of our meta-analysis results in prostate cancer biomarker research, given the difficulty of experimental validation of our large-scale data meta-analysis results using a specific cell line.

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来源期刊
Biochemical Genetics
Biochemical Genetics 生物-生化与分子生物学
CiteScore
3.90
自引率
0.00%
发文量
133
审稿时长
4.8 months
期刊介绍: Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.
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