前列腺细胞拉曼光谱的无监督自组织映射显示疾病状态亚聚类
Unsupervised self-organising map of prostate cell Raman spectra shows disease-state subclustering
AI总结：
本研究利用无监督自组织映射分析前列腺细胞拉曼光谱，成功区分正常与癌细胞，并发现癌细胞亚聚类及脂质差异表达，为前列腺癌生物分子亚分类提供新方法。
摘要：
前列腺癌是一种提出有趣临床问题的疾病：是否应该治疗？一小部分前列腺癌具有侵袭性，需要切除和治疗以防止转移扩散。然而，传统诊断在对此类患者进行风险分层方面仍面临挑战，因此，需要新的生物分子亚分类方法。在这里，我们使用无监督自组织映射方法分析从前列腺细胞系获得的活细胞拉曼光谱数据；我们的目标是测试该方法在单细胞水平上使用高维数据集且最少预处理的情况下区分癌细胞与正常细胞的可行性。结果不仅成功分离了正常前列腺细胞和癌细胞，还发现了前列腺癌细胞系新的亚聚类，将其分为两组。对每个癌症亚聚类光谱的初步分析显示脂质的差异表达，与正常对照相比，这可能与疾病相关的细胞信号传导变化有关。
英文摘要：
Prostate cancer is a disease which poses an interesting clinical question: should it be treated? A small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients, hence, new methods of approach to biomolecularly subclassify the disease are needed. Here we use an unsupervised, self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to test the feasibility of this method to differentiate, at the single-cell-level, cancer from normal using high-dimensional datasets with minimal preprocessing. The results demonstrate not only successful separation of normal prostate and cancer cells, but also a new subclustering of the prostate cancer cell-line into two groups. Initial analysis of the spectra from each of the cancer subclusters demonstrates a differential expression of lipids, which, against the normal control, may be linked to disease-related changes in cellular signalling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
