Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy

Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.

Automated summary

Human colorectal tissues from ten cancer patients were analyzed using multiple micro-Raman spectroscopic measurements. Different spectral profiles were observed for different tissue types, including the predominant 'typical' colorectal tissue, as well as those with high lipid, blood, or collagen content. Principal component analysis revealed specific Raman bands related to amino acids, proteins, and lipids, allowing efficient discrimination between normal and cancer tissues. Tree-based machine learning was applied to identify significant spectroscopic features for accurate cancer tissue identification and to match spectroscopic results with biochemical changes.