Anup Paul1,2, Shatakshi Srivastava1,3, Raja Roy4, Akshay Anand5, Kushagra Gaurav5, Nuzhat Husain6, Sudha Jain2, Abhinav A Sonkar7,8. 1. Centre of Biomedical Research, Formerly Centre of Biomedical Magnetic Resonance (CBMR), Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Rae Bareli Road, Lucknow, 226014, India. 2. Department of Chemistry, University of Lucknow, University Road, Lucknow, 226007, India. 3. Apeejay Stya University, Sohna, Gurugram, 122103, Haryana, India. 4. Centre of Biomedical Research, Formerly Centre of Biomedical Magnetic Resonance (CBMR), Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Rae Bareli Road, Lucknow, 226014, India. roy@cbmr.res.in. 5. Department of General Surgery, Kings George's Medical (KGMU), Lucknow, 226003, India. 6. Department of Pathology, Dr. Ram Manohar Lohia Institute of Medical Science, Lucknow, 226010, India. 7. Department of General Surgery, Kings George's Medical (KGMU), Lucknow, 226003, India. abhinavarunsonkar@gmail.com. 8. Department of General Surgery, King Georges Medical College (KGMU), Lucknow, 226001, India. abhinavarunsonkar@gmail.com.
Abstract
INTRODUCTION: Oral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis. OBJECTIVES: To profile and generate precise 1H HRMAS NMR spectral and quantitative statistical models of oral squamous cell carcinoma (OSCC) in tissue specimens including tumor, bed, margin and facial muscles. To apply the model in blinded prediction of malignancy among oral and neck tissues in an unknown set of patients suffering from OSCC along with neck invasion. METHODS: Statistical models of 1H HRMAS NMR spectral data on 180 tissues comprising tumor, margin and bed from 43 OSCC patients were performed. The combined metabolites, lipids spectral intensity and concentration-based malignancy prediction models were proposed. Further, 64 tissue specimens from twelve patients, including neck invasions, were tested for malignancy in a blinded manner. RESULTS: Forty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCC patients with an accuracy of above 90%. CONCLUSION: The efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.
INTRODUCTION:Oral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis. OBJECTIVES: To profile and generate precise 1H HRMAS NMR spectral and quantitative statistical models of oral squamous cell carcinoma (OSCC) in tissue specimens including tumor, bed, margin and facial muscles. To apply the model in blinded prediction of malignancy among oral and neck tissues in an unknown set of patients suffering from OSCC along with neck invasion. METHODS: Statistical models of 1H HRMAS NMR spectral data on 180 tissues comprising tumor, margin and bed from 43 OSCCpatients were performed. The combined metabolites, lipids spectral intensity and concentration-based malignancy prediction models were proposed. Further, 64 tissue specimens from twelve patients, including neck invasions, were tested for malignancy in a blinded manner. RESULTS: Forty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCCpatients with an accuracy of above 90%. CONCLUSION: The efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.
Entities:
Keywords:
1H HRMAS NMR spectroscopy; Blinded set of tissues; Glucose; Lipid and metabolite quantitation; Malignant tumor tissue; Oral squamous cell carcinoma; Predictive modelling
Authors: Eleni Alexandri; Raheel Ahmed; Hina Siddiqui; Muhammad I Choudhary; Constantinos G Tsiafoulis; Ioannis P Gerothanassis Journal: Molecules Date: 2017-10-05 Impact factor: 4.411
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