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

Liu, Ke; Long, Tingting; Wang, Yi; Yin, Tai-Shuang; Li, Jiaoyuan; Shen, Ying; Cheng, Liming

doi:10.7717/peerj.13272

Cited by 10 publications

(11 citation statements)

References 53 publications

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“…This finding was validated in an analysis of the AA profile in our other case-control study. 24 Moreover, prior work reported a higher concentration of phenylalanine in lung cancer tissues than paired paracarcinomous tissues, 17 and a correlation of this AA was also found between plasma and cancer tissues of lung cancer patients. 25 The enrichment analysis revealed that the metabolism of phenylalanine, together with its derivates tyrosine and tryptophan were the most significantly enriched pathways.…”

Section: Discussionmentioning

confidence: 91%

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Serum untargeted metabolomics reveal metabolic alteration of non‐small cell lung cancer and refine disease detection

Liu

et al. 2022

Cancer Science

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This study was performed to characterize the metabolic alteration of non–small‐cell lung cancer (NSCLC) and discover blood‐based metabolic biomarkers relevant to lung cancer detection. An untargeted metabolomics‐based approach was applied in a case–control study with 193 NSCLC patients and 243 healthy controls. Serum metabolomics were determined by using an ultra high performance liquid chromatography–tandem mass spectrometry (UHPLC‐MS/MS) method. We screened differential metabolites based on univariate and multivariate analysis, followed by identification of the metabolites and related pathways. For NSCLC detection, machine learning was employed to develop and validate the model based on the altered serum metabolite features. The serum metabolic pattern of NSCLC was definitely different from the healthy condition. In total, 278 altered features were found in the serum of NSCLC patients comparing with healthy people. About one‐fifth of the abundant differential features were identified successfully. The altered metabolites were enriched in metabolic pathways such as phenylalanine metabolism, linoleic acid metabolism, and biosynthesis of bile acids. We demonstrated a panel of 10 metabolic biomarkers which representing excellent discriminating capability for NSCLC discrimination, with a combined area under the curve (AUC) in the validation set of 0.95 (95% CI: 0.91–0.98). Moreover, this model showed a desirable performance for the detection of NSCLC at an early stage (AUC = 0.95, 95% CI: 0.92–0.97). Our study offers a perspective on NSCLC metabolic alteration. The finding of the biomarkers might shed light on the clinical detection of lung cancer, especially for those cancers in an early stage in Chinese population.

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Section: Discussionmentioning

confidence: 91%

“…This finding was validated in an analysis of the AA profile in our other case–control study. 24 Moreover, prior work reported a higher concentration of phenylalanine in lung cancer tissues than paired paracarcinomous tissues, 17 and a correlation of this AA was also found between plasma and cancer tissues of lung cancer patients. 25…”

Section: Discussionmentioning

confidence: 92%

Serum untargeted metabolomics reveal metabolic alteration of non‐small cell lung cancer and refine disease detection

Liu

et al. 2022

Cancer Science

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“…At the same time, increased concentrations of ADMA and SDMA were found in endothelial dysfunction associated with different cancers and chronic diseases [34]. Other signi cantly changed metabolites -citrulline and ornithine -take part in the urea cycle serving for the conversion of ammonia into urea, as well as acting as transcriptome reprogramming in cancer metabolism [25]. Thus, we hypothesize that signi cant accumulation of ornithine and citrulline in the plasma of NSCLC patients is associated with dysregulation in the urea cycle.…”

Section: Discussionmentioning

confidence: 94%

Targeted metabolomic profiling as a tool for diagnostics of patients with non- small-cell lung cancer

Shestakova,

Moskaleva,

Boldin

et al. 2023

Preprint

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Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with poor diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics and treatment through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed significantly changed new ratios of the analyzed metabolites. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

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“…Dysregulated metabolism of amino acids, especially glutamine, serine and glycine, have been identified to function as important regulators in supporting cancer cell growth 29 . A recent case–control study in a Chinese population found that serum glutamic acid and Asp‐Glu were found to be elevated in lung cancer patients, especially those in late stage disease 30 . In general, our global nontargeted metabolomic efforts in pleural samples from MPE and BE individuals showed multiple differential metabolic signatures, which pend further validation studies.…”

Section: Discussionmentioning

confidence: 99%

Variations in pleural microbiota and metabolic phenotype associated with malignant pleural effusion in human lung adenocarcinoma

Huang

Zhuo

et al. 2023

Thoracic Cancer

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Background Lung cancer is the most common cancer‐related death worldwide. In 2022, the number of daily deaths of lung cancer was estimated to reach around 350 in the United States. Lung adenocarcinoma is the main subtype of lung cancer and patients with malignant pleural effusion (MPE) suffer from poor prognosis. Microbiota and its metabolites are associated with cancer progression. However, the effect of pleural microbiota on pleural metabolic profile of MPE in lung adenocarcinoma patients remains largely unknown. Methods Pleural effusion samples collected from lung adenocarcinoma patients with MPE (n = 14) and tuberculosis pleurisy patients with benign pleural effusion (BPE group, n = 10) were subjected to microbiome (16S rRNA gene sequencing) and metabolome (liquid chromatography tandem mass spectrometry [LC‐MS/MS]) analyses. The datasets were analyzed individually and integrated for combined analysis using various bioinformatic approaches. Results The metabolic profile of MPE in lung adenocarcinoma patients were clearly distinguished from BPE with 121 differential metabolites across six significantly enriched pathways identified. Glycerophospholipids, fatty and carboxylic acids, and derivatives were the most common differential metabolites. Sequencing of microbial data revealed nine significantly enriched genera (i.e., Staphylococcus, Streptococcus, Lactobacillus) and 26 enriched ASVs (i.e., species Lactobacillus_delbrueckii) in MPE. Integrated analysis correlated MPE‐associated microbes with metabolites, such as phosphatidylcholine and metabolites involved in the citrate cycle pathway. Conclusion Our results provide substantial evidence of a novel interplay between the pleural microbiota and metabolome, which was drastically perturbed in MPE in lung adenocarcinoma patients. Microbe‐associated metabolites can be used for further therapeutic explorations.

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

Cited by 10 publications

References 53 publications

Serum untargeted metabolomics reveal metabolic alteration of non‐small cell lung cancer and refine disease detection

Serum untargeted metabolomics reveal metabolic alteration of non‐small cell lung cancer and refine disease detection

Targeted metabolomic profiling as a tool for diagnostics of patients with non- small-cell lung cancer

Variations in pleural microbiota and metabolic phenotype associated with malignant pleural effusion in human lung adenocarcinoma

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