Keiron O'Shea1, Simon J S Cameron2, Keir E Lewis3, Chuan Lu4, Luis A J Mur5. 1. Institute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales SY23 3DA, UK. 2. Institute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales SY23 3DA, UK; Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, W6 8RP, UK. 3. Department of Respiratory Medicine, Prince Philip Hospital, Llanelli, Wales SA14 8LY, UK. 4. Department of Computer Science, Aberystwyth University, Aberystwyth, Wales SY23 3DA, UK. 5. Institute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales SY23 3DA, UK. Electronic address: lum@aber.ac.uk.
Abstract
BACKGROUND: Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis. METHODS: In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers. RESULTS: The neural network model showed excellent performance in classification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able to discriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC. CONCLUSIONS: These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
BACKGROUND:Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis. METHODS: In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers. RESULTS: The neural network model showed excellent performance in classification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able to discriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC. CONCLUSIONS: These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
Authors: Lun Zhang; Jiamin Zheng; Rashid Ahmed; Guoyu Huang; Jennifer Reid; Rupasri Mandal; Andrew Maksymuik; Daniel S Sitar; Paramjit S Tappia; Bram Ramjiawan; Philippe Joubert; Alessandro Russo; Christian D Rolfo; David S Wishart Journal: Cancers (Basel) Date: 2020-03-07 Impact factor: 6.639