MaxwellPlus+, 7 Diddams Lane, Fortitude Valley, Queensland 4006, Australia. sriganesh/
Mol Omics. 2018 Oct 8;14(5):320-329. doi: 10.1039/c8mo00170g.
The prostate exhibits a unique metabolism that changes during initial neoplasia to aggressive prostate cancer (PCa) and metastasis. The study of PCa metabolism thus represents a new avenue for diagnostics, particularly early diagnosis of aggressive PCa cases.
Here, by clustering tissue transcriptomics data from The Cancer Genome Atlas (498 PCa patients), we identified six metabolic subgroups (C1-C6) of PCa that show distinct disease-free survival (DFS) outcomes (p < 0.0001). In particular, we identified at least two subgroups (C5 & C3) that exhibit significant poor prognosis (∼70% and 30-40% relapse within the first 72 months; hazards ratios of 9.4 and 4.4, respectively, relative to the best prognosis cluster C4 that showed <20% relapse even by 120 months). We were able to reproduce the subgroups in several independent datasets including B. S. Taylor et al. (2010) data; 215 patients; DFS p = 0.00088) using a multinomial regression classifier. The subgroups displayed distinct metabolic profiles vis-à-vis normal tissues, measured as 'deregulation' observed for 20 metabolic pathways (using Pathifier; Y. Drier and E. Domany, 2013). In particular, C5 and C3 showed considerable deregulation for pathways involved in synthesis and catabolism of complex forms of lipids and carbohydrates, and these were exhibited in parallel or in the face of glycolysis, a common form of energy production in cancer cells. The subgroups were significantly over-enriched for different sets of genetic alterations [BRCA1, MSH2, FOXA1, TP53 (C5), RB1 and STK11(C3); and AR (C1); p ≤ 8.6 × 10-4], suggesting that distinct sets of alterations underpinning the PCa subgroups that 'push' the subgroups towards their unique metabolic profiles. Finally, applying the classifier to blood protein expression profiles from 42 active surveillance (AS) and 65 advanced castrate resistant PCa (ACRPC) patients (D. Olmos et al., 2012) assigned 70.77% ACPRC and interestingly reassigned 59.52% AS patients to at least one of the poor prognosis subgroups with 35.71% to the metabolically active poor-prognosis subgroup C3.
The identification of PCa subgroups displaying distinct clinical outcomes solely from metabolic expression profiles of PCa tumours reiterates the significant link between deregulated metabolism and PCa outcomes (E. Eidelman et al., 2017). On the other hand, the time to biochemical relapse (rise in PSA levels) was not indicative of early relapse seen for subgroups C3 and C5 (these show considerably late BCR compared to C4). Our study thus highlights specific processes (elevated lipid and carbohydrate metabolism pathways) that could be better indicators than PSA for early diagnosis of aggressive PCa.
https://maxwellplus.com/research/metabolic-deregulation-in-prostate-cancer/.
前列腺表现出独特的代谢方式，这种代谢方式在初期肿瘤向侵袭性前列腺癌（PCa）和转移过程中发生变化。因此，研究 PCa 的代谢为诊断，特别是侵袭性 PCa 病例的早期诊断提供了新的途径。
在这里，通过对癌症基因组图谱（498 名 PCa 患者）的组织转录组学数据进行聚类，我们确定了 6 个代谢亚组（C1-C6），这些亚组显示出不同的无病生存（DFS）结果（p < 0.0001）。特别是，我们确定了至少两个亚组（C5 和 C3）表现出显著的不良预后（∼70%和 30-40%在最初 72 个月内复发；风险比分别为 9.4 和 4.4，与显示出 <20%复发的最佳预后亚组 C4 相比，甚至在 120 个月时也是如此）。我们能够使用多项回归分类器在包括 B.S.Taylor 等人的几个独立数据集（2010 年；215 名患者；DFS p = 0.00088）中重现这些亚组。这些亚组与正常组织的代谢谱存在明显差异，表现为 20 种代谢途径的“失调”（使用 Pathifier；Y.Drier 和 E.Domany，2013）。特别是，C5 和 C3 显示出与复杂形式的脂质和碳水化合物合成和分解代谢途径有关的相当大的失调，这些失调是平行或面对糖酵解发生的，糖酵解是癌细胞中常见的能量产生形式。这些亚组显著富集了不同的基因突变集[BRCA1、MSH2、FOXA1、TP53（C5）、RB1 和 STK11（C3）；和 AR（C1）；p ≤ 8.6×10-4]，这表明不同的基因突变集为“推动”这些亚组向其独特的代谢谱发展的亚组提供了支持。最后，将分类器应用于 42 名主动监测（AS）和 65 名晚期去势抵抗性 PCa（ACRPC）患者的血液蛋白表达谱（D.Olmos 等人，2012 年），将 70.77%的 ACPRC 和有趣的是，59.52%的 AS 患者被分配到至少一个预后不良的亚组，其中 35.71%的患者被分配到代谢活跃的预后不良亚组 C3。
从 PCa 肿瘤的代谢表达谱中仅识别出显示出不同临床结果的 PCa 亚组，再次强调了失调代谢与 PCa 结果之间的显著联系（E.Eidelman 等人，2017）。另一方面，生化复发（PSA 水平升高）的时间并不能说明 C3 和 C5 亚组的早期复发（这些亚组与 C4 相比，BCR 明显延迟）。因此，我们的研究强调了特定的过程（升高的脂质和碳水化合物代谢途径）可能是比 PSA 更好的早期诊断侵袭性 PCa 的指标。
https://maxwellplus.com/research/metabolic-deregulation-in-prostate-cancer/。
