Affiliations: [a] Department of Pharmaceutics, Medical College of Jiaxing University, Jiaxing, Zhejiang, China | [b] School of Pharmaceutical Science, Nanchang University, Nanchang, Jiangxi, China | [c] Department of Respiratory and Critical Care Medicine, Huadu District People’s Hospital of Guangzhou, Southern Medical University, Guangzhou, Guangdong, China | [d] Medical Oncology, People’s Hospital of Gansu Province, Lanzhou, Gansu, China | [e] Thoracic Surgery, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
Correspondence:
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Corresponding author: Baogang Xie, Department of Pharmaceutics, Medical College of Jiaxing University, Jiaxing 314001, Zhejiang, China. E-mail: [email protected].
Abstract: BACKGROUND: Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC. PATIENTS AND METHODS: A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification. RESULTS: The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters. CONCLUSION: Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.
Keywords: Subtypes of lung cancer classification, serum biomarkers, metabolomics, 1H-NMR, UPLC-MS/MS
