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Hepatocellular carcinoma (HCC) is a noteworthy health problem with a poor diagnosis due to limited detection techniques. Transcriptome studies can be used to classify cancer further away from anatomical location and histopathology. Recent studies demonstrated the novelty of numerous types of specific RNA biomarkers that differentially expressed both the normal liver and the HCC tissues, but those specific types overlapped with the detection of other types of cancers. In this study, total RNA was used to ensure the existence of differences between different cancer types. A multispectral light source (340-1000 nm) interacted with the sample. Multi-wavelengths images were captured using a hyperspectral camera (wavelength 380-1000 nm). The optimum wavelength to discriminate between the normal and HCC samples was selected by calculating the optical properties (transmission, absorption and scattered light). Results showed specific spectral signatures for total RNA within the red-band (633-700 nm) that discriminate HCC from control. The amount of light scattering, transmission and absorption relatively changed due to the variations of size, shape, and concentration of total RNA. The spectral RNA signature that is dependent on the shape and size of total RNA may be utilized as the gold standard for HCC detection.

Transcriptome Spectral Analysis using Hyperspectral Imaging for Hepatocellular Carcinoma Detection