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.

前列腺癌的新的生物分子亚分类诊断方法是通过分析活细胞拉曼光谱数据，成功地将正常前列腺和癌细胞分离，并将前列腺癌细胞线分为两个亚群；进一步分析显示，脂质的差异表达可能与疾病相关的细胞信号变化有关。