Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. It presents a very bad prognosis with a patients’ overall survival of 12–15 months; treatment failure is mainly ascribable to tumor recurrence. The development of new tools, that could help the precise detection of the tumor border, is thus an urgent need. During the last decades, different vibrational spectroscopy techniques have been developed to distinguish cancer tissue from heathy tissue; in the present work, we compared GBM cells deriving from four patients with healthy human astrocytes using Raman spectroscopy. We have shown that the region between 1000 and 1300 cm−1 is enough informative for this discrimination, indeed highlighting that peaks related to DNA/RNA and cytochrome c are increased in cancer cells. Finally, our model has been able to discriminate cancer cells from healthy cells with an average accuracy of 92.5%. We believe that this study might help to further understand which are the essential Raman peaks exploitable in the detection of cancer cells, with important perspectives under a diagnostic point of view.
Discrimination of glioma patient-derived cells from healthy astrocytes by exploiting Raman spectroscopy
Fiaschi P.;
2022-01-01
Abstract
Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. It presents a very bad prognosis with a patients’ overall survival of 12–15 months; treatment failure is mainly ascribable to tumor recurrence. The development of new tools, that could help the precise detection of the tumor border, is thus an urgent need. During the last decades, different vibrational spectroscopy techniques have been developed to distinguish cancer tissue from heathy tissue; in the present work, we compared GBM cells deriving from four patients with healthy human astrocytes using Raman spectroscopy. We have shown that the region between 1000 and 1300 cm−1 is enough informative for this discrimination, indeed highlighting that peaks related to DNA/RNA and cytochrome c are increased in cancer cells. Finally, our model has been able to discriminate cancer cells from healthy cells with an average accuracy of 92.5%. We believe that this study might help to further understand which are the essential Raman peaks exploitable in the detection of cancer cells, with important perspectives under a diagnostic point of view.
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