Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues

Cífková, Eva; Brumarová, Radana; Ovčačíková, Magdaléna; Dobešová, Dana; Mičová, Kateřina; Kvasnička, Aleš; Vaňková, Zuzana; Šiller, Jiří; Sákra, L; Friedecký, David; Holčapek, Michal

doi:10.1016/j.bbalip.2021.159082

Cited by 12 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, we found lactic acid was elevated in serum of NSCLC patients comparing with healthy subjects. This result is consistent with a previous study reporting serum NSCLC metabolomics based on nuclear magnetic resonance (NMR) fingerprints, 16 and supported by the finding of increased lactate level in lung tumors compared with surrounding normal tissues 17,18 . Lactate is a tricarboxylic acid (TCA) cycle carbon source for NSCLC and serves as a fuel for tumor cells in vivo 19,20 .…”

Section: Discussionsupporting

confidence: 92%

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

Liu

et al. 2022

Cancer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 92%

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

Liu

et al. 2022

Cancer Science

View full text Add to dashboard Cite

show abstract

“…With methanol extraction applied in our metabolomics study, we identified both metabolites and polar lipids in discriminant compound panels. Lipid metabolism is closely linked to physiological and pathological processes occurring in the lungs, e.g., phosphatidylcholine is a major phospholipid of lung surfactants and sphingolipid plays a critical role in inflammation and fetal pulmonary maturation. , Sphingosine was involved in panels 1, 2, and 4, for AC, SCC, and all NSCLC detections, respectively, with a significantly decreased level in tumor tissues, compared to paired ANT and DNT samples in AC and NSCLC cases (Table and Figure E,F). Sphingosine is converted to sphingosine 1-phosphate (S1P) by sphingosine kinase.…”

Section: Resultsmentioning

confidence: 94%

“…Lipid metabolism is closely linked to physiological and pathological processes occurring in the lungs, e.g., phosphatidylcholine is a major phospholipid of lung surfactants and sphingolipid plays a critical role in inflammation and fetal pulmonary maturation. 39,40 samples in AC and NSCLC cases (Table 2 and Figure 4E,F). Sphingosine is converted to sphingosine 1-phosphate (S1P) by sphingosine kinase.…”

Section: Discriminant Metabolite Identification and Their Biological ...mentioning

confidence: 98%

Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS-Based Tissue Metabolomics

et al. 2022

View full text Add to dashboard Cite

Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). Orthogonal partial least squares-discriminant analysis (oPLS-DA) analyses showed that AC, SCC, and NSCLC tumors were discriminated from adjacent noncancerous tissue (ANT) and distant noncancerous tissue (DNT) samples with good accuracies (91.3, 100, and 88.3%), sensitivities (85.7, 100, and 83.9%), and specificities (94.3, 100, and 90.7%), using 12, 4, and 7 discriminant metabolites, respectively. The discriminant panel for AC detection included valine, sphingosine, glutamic acid γ-methyl ester, and lysophosphatidylcholine (LPC) (16:0), levels of which in tumor tissues were significantly altered. Valine, sphingosine, LPC (18:1), and leucine derivatives were used for SCC detection. The discrimination between AC and SCC had 96.8% accuracy, 98.2% sensitivity, and 85.7% specificity using a five-metabolite panel, of which valine and creatine had significant differences. The classification models were further verified with external validation sets, showing a promising prospect for NSCLC tissue detection and subtyping.

show abstract

“…Considering the critical roles of the phospholipase D activity in efficient phagocytosis (Tanguy et al, 2019), here, the Fc gamma R-mediated phagocytosis and phospholipase D signaling pathway dysregulations might lead to the disorders of the two kinds of phagocytes in NIHL. As a hallmarker of cancerous diseases (Cífková et al, 2022)…”

Section: Discussionmentioning

confidence: 99%

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Zhang

Cui

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p < 0.05 and the variable importance of projection (VIP) value >1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

show abstract

Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues

Cited by 12 publications

References 38 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

Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS-Based Tissue Metabolomics

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Contact Info

Product

Resources

About