| Title: | Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples |
| Author(s): | Rai SN; Das S; Pan J; Mishra DC; Fu XA; |
| Address: | "Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY, United States of America. School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, United States of America. Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY, United States of America. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, United States of America. Biostatistics and Informatics Facility, Center for Integrative Environmental Research Sciences, University of Louisville, Louisville, KY, United States of America. Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY, United States of America. ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar, Odisha, India. International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar, Odisha, India. ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, India. Department of Chemical Engineering, University of Louisville, Louisville, KY, United States of America" |
| DOI: | 10.1371/journal.pone.0277431 |
| ISSN/ISBN: | 1932-6203 (Electronic) 1932-6203 (Linking) |
| Abstract: | "Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection" |
| Keywords: | Humans *Volatile Organic Compounds *covid-19 *Lung Neoplasms/diagnosis *Multiple Pulmonary Nodules *Body Fluids; |
| Notes: | "MedlineRai, Shesh N Das, Samarendra Pan, Jianmin Mishra, Dwijesh C Fu, Xiao-An eng P30 ES030283/ES/NIEHS NIH HHS/ P20 GM113226/GM/NIGMS NIH HHS/ P20 GM125504/GM/NIGMS NIH HHS/ R01 ES029846/ES/NIEHS NIH HHS/ P42 ES023716/ES/NIEHS NIH HHS/ U54 HL120163/HL/NHLBI NIH HHS/ R35 ES028373/ES/NIEHS NIH HHS/ P30 GM127607/GM/NIGMS NIH HHS/ R21 CA229057/CA/NCI NIH HHS/ R01 ES027778/ES/NIEHS NIH HHS/ P20 GM135004/GM/NIGMS NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't 2022/12/01 PLoS One. 2022 Nov 30; 17(11):e0277431. doi: 10.1371/journal.pone.0277431. eCollection 2022" |
