Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry - Bernardo, Ricardo Alves (2022)
Oral squamous cell carcinoma (OSCC) is the most common oral cavity cancer, responsible for 90% of all cancers in the head-neck region, except for non-melanoma skin cancer. A fast, accurate, and cheap diagnosis is required to detect the presence of OSCC in the preliminary stages providing better chances of success in cancer treatment. In general, the diagnosis is performed using proteomics and histological data. However, lipids play a key role in cellular metabolism. Therefore, understanding the lipid differentiation between healthy and cancer tissues could be important to predict biomarkers candidates and improve the diagnosis system. Furthermore, a new methodology for simultaneous RNA and lipid extraction using TRIzol® solution was developed, and the lipid profile in both sample groups was studied. The samples stored in TRIzol® solution were homogenized and submitted to liquid-liquid microextraction (LLME) using chloroform as an extractive solvent. Therefore, the organic phase was collected and submitted to the Bligh &Dyer extraction. The simultaneous RNA and lipid extraction was validated according to the parameters described by the Brazilian Nation Health Surveillance Agency. An analytical curve of tissue in methanol and another one of tissue in TRIzol® solution were performed for method evaluation. The sample solutions were spiked with different concentrations of PC 17:0/17:0 standard solution, and caffeine-(trimethyl-13C) was used as the internal standard and directly infused into the mass spectrometer on positive ion mode. Intra-day and inter-day precision, accuracy, absolute recovery, and matrix effect were evaluated in three concentration levels in replicate (n=5). Limits of detection and quantification were estimated in the order of ng mL-1 with good linearity (r² >0.99), precision and accuracy (<15%), and absolute recovery values ranging from 90 to 110%. The mass spectra were submitted to the Global Natural Product Social Network (GNPS) platform for peak annotation. The partial least-square discriminant analysis (PLS-DA) was performed in all samples clustering the healthy samples and separating them from the cancer ones. The PLS-DA revealed that 15 lipids were responsible for describing the healthy group in the positive ion mode, while 8 lipids described the cancer one. In the negative ion mode, 10 lipids described the healthy group, while 10 lipids described the cancer one. Furthermore, cryosections of gingiva tissue (healthy and cancer ones) with 10 µm thickness were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate which lipids pointed by the PLS-DA delimit the cancer region. The MALDI-MSI analyses showed that the lipids responsible for OSCC group classification are more abundant in the cancer tissue compared to the healthy one. The extraction methodology reported here, and the MALDI-MSI as confirmation technique are adequate for classification of OSCC samples regarding lipid content.