Identification of Potential Biomarkers of EGFR Mutation in Pleural Effusion of Non-Small Cell Lung Cancer Patients Based on Metabolomics.

BACKGROUND: Malignant pleural effusion (MPE) is a common complication of non-small cell lung cancer (NSCLC). Patients with NSCLC exhibit a high rate of epidermal growth factor receptor (EGFR) mutations. The detection of EGFR mutations is usually time-consuming and costly. This study aimed at identifying potential biomarkers of EGFR mutations in MPE of NSCLC patients by metabolomics. METHODS: In total, 58 MPE samples from 30 EGFR mutant and from 28 wild-type NSCLC patients were collected and analyzed by using hydrogen nuclear magnetic resonance (1H NMR) based metabolomics and UPLC-MS/MS based amino acid analysis. RESULTS: Our 1H NMR study showed a significant increase in the lysine levels but a significant decrease in the alanine levels in MPE of NSCLC patients with EGFR-mutant. Twelve amino acids in MPE were further determined by UPLC-MS/MS. It showed that alanine in MPE (6.34 ± 1.88 vs. 8.73 ± 3.68) were significantly decreased and leucine (3.13 ± 0.57 vs. 2.22 ± 0.13), lysine (2.19 ± 0.50 vs. 1.53 ± 0.40), and tyrosine (2.69 ± 0.71 vs. 1.89 ± 0.46) were increased in the EGFR mutation group; leucine (2.19 ± 0.50 vs. 1.53 ± 0.40), methionine (2.19 ± 0.50 vs. 1.53 ± 0.40), and threonine (2.19 ± 0.50 vs. 1.53 ± 0.40) in MPE were significantly lower in the EGRF 19 mutation compared with 21 mutation patients. The area under the receiver operating characteristic curve of 0.851 and 0.931 would be achieved by the logistic model for classification of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients. CONCLUSIONS: Amino acids in MPE are significantly altered and helpful in the diagnosis of EGFR-mutant patients from the wild-type controls or the exon 19 from exon 21 mutant patients with high accuracy, which is worthy of further study.

Differences in 1HNMR Based Metabolomic Patterns of Malignant and Benign Pleural Effusions.

OBJECTIVE: Malignant pleural effusion (MPE) is a common complication of lung cancer with poor prognosis. Benign pleural effusion (BPE), such as tuberculous and pneumonic pleural effusion, usually has a good prognosis. Differential diagnosis between MPE and BPE remains a clinical challenge. METHODS: 52 MPE, 93 BPE, and their corresponding serum samples were analyzed by hydrogen nuclear magnetic resonance (1HNMR) based metabolomics. RESULTS: The 1HNMR study showed that some amino acids and betaine in MPE are significantly altered in pleural effusion and serum compared to BPE patients. Levels of serum glucose and glutamine have strong positive correlation with those in pleural effusion (r>0.6) for MPE patients. The area under the receiver operating characteristic curve (AUROC) values of metabolites in pleural effusion or serum were less than 0.805 in differentiating MPE from BPE. Improved an AUROC value of 0.901 was observed using pleural effusion-serum ratios of glutamic acid in differentiating MPE from BPE, which was further validated by 15 double-blind samples. CONCLUSIONS: Compared with BPE patients, amino acids and betaine in MPE are significantly altered in pleural effusion and serum. Pleural effusion-serum ratio of glutamic acid may contribute to the rapid diagnosis of MPE from BPE by 1HNMR analysis.

Correlation between hs-CRP, VCAM-1, LEP levels and blood pressure variability in OSAHS patients.

This study was to explore the relationship between hs-CRP, VCAM-1, LEP and blood pressure variability in obstructive sleep apnea-hypopnea syndrome (OSAHS) patients. For this purpose, 92 OSAHS patients treated in our hospital from January 2019 to December 2022 were randomly divided into a mild group (MIG) (AHI > 5, < 20), moderate group (MOG) (AHI > 20 and not more than 40) and severe group (SEG) (AHI > 40). According to sleep AHI, they were divided into MIG (n = 28), MOG (n = 30) and SEG (n = 26). The age and sex of the patients in the study group were compared with those in the control group (COG). The serum levels of hs-CRP, VCAM-1, LEP and blood pressure variability were observed and compared. Pearson correlation test was used to analyze the correlation between hs-CRP, VCAM-1, LEP levels and blood pressure variability in patients with OSAHS. Results showed that the age, sex and BMI between each group have no difference(P>0.05), but the AHI in the study group was raised than that in the COG (P<0.05). Compared with the COG, the serum levels of hs-CRP, VCAM-1, LEP and dDBP, dSBP, nDBP and nSBPraised with the increase of the severity of the disease (P<0.05). The dDBP among MOG, MIG and SEG was no different (P>0.05). The blood pressure variability of patients with OSAHS was raised, especially at night. Pearson correlation test showed that nSBP was positively correlated with serum levels of hs-CRP, VCAM-1 and LEP, that is, blood pressure variability was positively correlated with serum levels of hs-CRP, VCAM-1 and LEP (P<0.05). In conclusion, the serum levels of hs-CRP, VCAM-1 and LEP in patients with OSAHS are raised than those in healthy subjects, and they are significantly correlated with blood pressure variability, which can be used as an important index to judge the severity of OSAHS.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma.

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.

Discovery and Validation of Potential Serum Biomarkers for Pediatric Patients with Congenital Heart Diseases by Metabolomics.

To identify and screen serum biomarkers to determine pediatric patients with congenital heart diseases (PCH) from healthy control children (NC), a total of 614 clinically diagnosed subjects from three hospitals, including 491 PCH and 234 NC, were enrolled for nontargeted proton nuclear magnetic resonance spectroscopy (1H NMR)-based and targeted ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS)-based metabolomics studies. Nineteen serum metabolites distinguishing PCH from NC were identified by 1H NMR-based metabolomic analysis. The amino acid and choline metabolic pathways were considered to be closely related to PCH. The serum levels of 13 metabolites in these two pathways were further determined by UPLC-MS/MS and observed to be altered significantly in PCH. Taurine, glutamine, and glutamate presented considerable diagnostic value for the diagnosis of PCH (AUROC > 0.80). Logistic regression analysis showed that a combination of four variables, namely, betaine, taurine, glutamine, and phenylalanine, yields a high diagnostic value (AUROC = 0.949) and prediction accuracy (89.1%) for differentiating PCH from the NC, and the sensitivity and specificity were 93.9 and 95.2%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 83.8% for 80 unknown samples. Our results showed that the serum amino acid and choline metabolite levels in PCH were changed considerably. The combination of four metabolites, namely, betaine, taurine, glutamine, and phenylalanine, can be used as potential serum biomarkers in PCH diagnosis, which contributes to the early PCH screening.