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前列腺间质状态在良性前列腺增生的发生发展中的转录组学分析

Identification of stroma-related key regulators in benign prostatic hyperplasia

    摘要
  • 摘要:
    目的 通过转录组分析良性前列腺增生（BPH）与正常前列腺组织之间的间质状态差异，及此种差异在BPH病程中的意义。
    方法 从Gene Expression Omnibus（GEO）数据库获得BPH和正常前列腺样本的转录组和临床表型数据。发现集为GSE119195、GSE7307、GSE101486，验证集为GSE132714。使用ESTIMATE和xCell计算BPH和正常前列腺组织的间质状态和细胞组分。采用加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）探索和BPH间质状态相关的基因模块，对相关模块联合差异基因进行富集分析。最后通过蛋白互作分析，筛选出12个关键基因，并通过套索（least absolute shrinkage and selection operator，LASSO）回归建模，构建出BPH的基因诊断模型。
    结果 BPH样品的间质评分高于正常前列腺样本（P=0.002 5）。通过xCell算法发现，BPH样品中的成纤维细胞、周细胞显著增加（P均 < 0.05）。相反，内皮细胞、肌细胞、成骨细胞和平滑肌细胞组织减少（P均 < 0.05）。通过WGCNA分析，得到4个和BPH间质状态相关的基因模块。和差异基因联合分析后进行蛋白互作网络分析，筛选出12个潜在的关键基因。最后，经LASSO回归构建了4个基因的基因诊断模型，并在验证集数据里得到验证。
    结论 BPH组织中间质成分显著高于正常前列腺组织。进一步分析发现，BPH组织存在较多的成纤维细胞和较少的平滑肌细胞，这可能和前列腺增生的发生发展相关。本研究也建立了4个基因的BPH诊断模型，对BPH的分子生物学机制研究及药物研究可能具有一定的参考价值。

     

    Abstract:
    Objective To investigate the differences of stromal status between benign prostatic hyperplasia (BPH) and normal prostate tissues by transcriptomic analysis, and the significance of this difference in the course of BPH.
    Methods Transcriptome data and clinical phenotype data of BPH and normal prostate samples were searched from Gene Expression Omnibus (GEO) database. The discovery set was composed of GSE119195, GSE7307, and GSE101486, and the verification set was GSE132714. ESTIMATE and xCell were used to calculate the stromal state and cellular components of BPH and normal prostate tissues. Weighted gene co-expression network analysis (WGCNA) was used to explore gene modules related to the stromal status of BPH samples and carry out related modules combined differential gene enrichment analysis. Finally, through protein-protein interaction analysis, 12 hub genes were found, and through least absolute shrinkage and selection operator (LASSO) regression, a gene diagnosis model of BPH was constructed.
    Results The stromal score of BPH samples was higher than that of normal prostate samples (P=0.002 5). Through the xCell algorithm, this research found that fibroblasts and pericytes in BPH samples increased significantly (both P < 0.05). In contrast, endothelial cells, myocytes, osteoblasts and smooth muscle cells reduced (all P < 0.05). Through WGCNA analysis, four gene modules related to the stromal state of BPH were obtained. Finally, we constructed a genetic diagnosis model of 4 genes (NEB, DMD, IGF1, KRAS) through LASSO regression, and verified it in the validation set data.
    Conclusions The stromal score of BPH tissue is significantly higher than that of normal prostate tissue. Further analysis shows that there are more fibroblasts and fewer smooth muscle cells in BPH tissue, which may be related to the occurrence and development of prostate hyperplasia. This paper also establishes a BPH diagnostic model of 4 genes. These results may lead the investigation of molecular biological mechanism of BPH and future drug research.

     

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