PeerJ (Apr 2022)

Changes in serum amino acid levels in non-small cell lung cancer: a case-control study in Chinese population

  • Ke Liu,
  • Jiaoyuan Li,
  • Tingting Long,
  • Yi Wang,
  • Tongxin Yin,
  • Jieyi Long,
  • Ying Shen,
  • Liming Cheng

DOI
https://doi.org/10.7717/peerj.13272
Journal volume & issue
Vol. 10
p. e13272

Abstract

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Background Previous studies have shown the alteration of amino acid (AA) profile in patients with non-small cell lung cancer (NSCLC). However, there is little data regarding AA profile in NSCLC in Chinese population. The aim of this study was to evaluate AA profile in Chinese NSCLC patients, explore its utility in sample classification and further discuss its related metabolic pathways. Methods The concentrations of 22 AAs in serum samples from 200 patients with NSCLC and 202 healthy controls were determined by liquid chromatography-tandem mass spectrometer (LC-MS/MS). AA levels in different tumor stages and histological types were also discussed. The performance of AA panel in classifying the cases and controls was evaluated in the training data set and validation data set based on the receiver operating characteristic (ROC) curve, and the important metabolic pathways were identified. Results The concentrations of tryptophan (Trp), phenylalanine (Phe), isoleucine (Ile), glycine (Gly), serine (Ser), aspartic acid (Asp), asparagine (Asn), cystein (Cys), glutamic acid (Glu), ornithine (Orn) and citrulline (Cit) were significantly altered in NSCLC patients compared with controls (all P-FDR < 0.05). Among these, four AAs including Asp, Cys, Glu and Orn were substantially up-regulated in NSCLC patients (FC ≥ 1.2). AA levels were significantly altered in patients with late-stage NSCLC, but not in those with early-stage when comparing with healthy controls. In terms of histological type, these AAs were altered in both adenocarcinoma and squamous cell carcinoma. For discrimination of NSCLC from controls, the area under the ROC curve (AUC) was 0.80 (95% CI [0.74–0.85]) in the training data set and 0.79 (95%CI [0.71–0.87]) in the validation data set. The AUCs for early-stage and late-stage NSCLC were 0.75 (95% CI [0.68–0.81]) and 0.86 (95% CI [0.82–0.91]), respectively. Moreover, the model showed a better performance in the classification of squamous cell carcinoma (AUC = 0.90, 95% CI [0.85–0.95]) than adenocarcinoma (AUC = 0.77, 95% CI [0.71–0.82]) from controls. Three important metabolic pathways were involved in the alteration of AA profile, including Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism; and Arg biosynthesis. Conclusions The levels of several AAs in serum were altered in Chinese NSCLC patients. These altered AAs may be utilized to classify the cases from the controls. Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism and Arg biosynthesis pathways may play roles in metabolism of the NSCLC patient.

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