转录组分析鉴定肿瘤免疫微环境信号网络支持转移性去势抵抗性前列腺癌

Onco Pub Date : 2023-06-01 Epub Date: 2023-04-10 DOI:10.3390/onco3020007
Lawrence P McKinney, Rajesh Singh, I King Jordan, Sooryanarayana Varambally, Eric B Dammer, James W Lillard
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摘要

前列腺癌(PCa)是美国男性癌症死亡的第二大常见原因。转移性去势抵抗性前列腺癌(mCRPC)是最致命的前列腺癌形式,并通过尚不完全了解的分子机制优先转移到骨骼。在此,我们处理了来自mCRPC患者(n = 60)的RNA测序数据,并鉴定出14个与mCRPC骨转移高度相关的基因簇(模块)。我们使用加权基因共表达网络分析(WGCNA)和上游调控因子以及临床注释转录组的基因本体论分析的新组合来识别基因。青色模块(M14)与mCRPC骨转移的正相关最强(0.81,p = 4 × 10−15)。通过KEGG富集分析发现它与两条重要的生物学途径(甲状旁腺激素的合成、分泌和作用以及蛋白质的消化和吸收)有关。特别是,我们使用Cytoscape的cytoHubba鉴定了10个枢纽基因(ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1和COL13A1)。通过基因本体(GO)富集分析,我们还发现了胶原形成、降解、吸收、细胞信号肽和骨调节过程的高基因表达。
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Transcriptome Analysis Identifies Tumor Immune Microenvironment Signaling Networks Supporting Metastatic Castration-Resistant Prostate Cancer.

Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10-15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis.

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