scholarly journals Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xianlan Zhu ◽  
Kun Wang ◽  
Gaoshuang Liu ◽  
Yuqing Wang ◽  
Jin Xu ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Early Stage ◽  
Principal Component ◽  
Roc Curves ◽  
Tca Cycle ◽  
Metabolic Reprogramming ◽  
Serum Samples ◽  
Diagnosis And Prognosis ◽  
Flight Mass Spectrometry ◽  
Amino Acid Turnover

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

GC/TOFMS analysis of metabolites in serum and urine reveals metabolic perturbation of TCA cycle in db/db mice involved in diabetic nephropathy

2013 ◽  
Vol 304 (11) ◽  
pp. F1317-F1324 ◽  
Author(s):  
Mengjie Li ◽  
Xufang Wang ◽  
Jiye Aa ◽  
Weisong Qin ◽  
Weibin Zha ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Tricarboxylic Acid Cycle ◽  
Principal Component ◽  
Tca Cycle ◽  
Underlying Mechanism ◽  
Sharp Drop ◽  
Urine Metabolites ◽  
Flight Mass Spectrometry ◽  
Amino Acid Turnover ◽  
Serum And Urine

Early diagnosis of diabetic nephropathy (DN) is difficult although it is of crucial importance to prevent its development. To probe potential markers and the underlying mechanism of DN, an animal model of DN, the db/db mice, was used and serum and urine metabolites were profiled using gas chromatography/time-of-flight mass spectrometry. Metabolic patterns were evaluated based on serum and urine data. Principal component analysis of the data revealed an obvious metabonomic difference between db/db mice and controls, and db/db mice showed distinctly different metabolic patterns during the progression from diabetes to early, medium, and later DN. The identified metabolites discriminating between db/db mice and controls suggested that db/db mice have perturbations in the tricarboxylic acid cycle (TCA, citrate, malate, succinate, and aconitate), lipid metabolism, glycolysis, and amino acid turnover. The db/db mice were characterized by acidic urine, high TCA intermediates in serum at week 6 and a sharp decline thereafter, and gradual elevation of free fatty acids in the serum. The sharp drop of serum TCA intermediates from week 6 to 8 indicated the downregulated glycolysis and insulin resistance. However, urinary TCA intermediates did not decrease in parallel with those in the serum from week 6 to 10, and an increased portion of TCA intermediates in the serum was excreted into the urine at 8, 10, and 12 wk than at 6 wk, indicating kidney dysfunction occurred. The relative abundances of TCA intermediates in urine relative to those in serum were suggested as an index of renal damage.

scholarly journals Improving the Detection of Hepatocellular Carcinoma using serum AFP expression in combination with GPC3 and micro-RNA miR-122 expression

2019 ◽  
Vol 14 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Jian Li ◽  
Sun Qiyu ◽  
Tiezheng Wang ◽  
Boxun Jin ◽  
Ning Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Early Stage ◽  
Roc Curves ◽  
Micro Rna ◽  
Serum Samples ◽  
Serum Alpha Fetoprotein ◽  
Laboratory Investigations ◽  
Chronic Hcv ◽  
Positive Controls

AbstractEarly diagnosis of hepatocellular carcinoma (HCC) greatly improves the survival and prognosisfor patients. In this study weevaluate the diagnostic promise of combining serum alpha-fetoprotein (AFP) expression with two potential biomarkers, serum glypican-3 (GPC3) and expression of the micro-RNA miR-122 for hepatitis C virus (HCV) related early-stage HCC. For this study serum samples from 47 patients with early-stage HCC, 54 chronic HCV (CH) carriers, 35 patients with liver cirrhosis (LC) and 54 health controls (HC) were collected. In addition to routine laboratory investigations, serum AFP, GPC3 and miR-122 were measured in all patients and healthy controls. Receiver operating characteristic (ROC) curves were used to present sensitivity and specificity for the biomarkers. The three markers were all significantly elevated in the serum samples from HCC patients. ROC curves showed the three markers had similar diagnostic capacities for distinguishing early-stage HCC from HCV-positive controls (LC + CH). In order to distinguish early-stage HCC from high-risk LC patients, the expression of miR-122 was superior to GPC3. Combination of the three markers as a panel showed a better diagnostic performance than any of the single markers (P <0.05). Overall, this study revealed that serum expression of GPC3 and miR-122 may be useful biomarkers to combine with serum AFP expression for the diagnosis of HCV related early-stage HCC.

scholarly journals Proteomic Fingerprints for Potential Application to Early Diagnosis of Severe Acute Respiratory Syndrome

2005 ◽  
Vol 51 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Xixiong Kang ◽  
Yang Xu ◽  
Xiaoyi Wu ◽  
Yong Liang ◽  
Chen Wang ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Early Diagnosis ◽  
Influenza A ◽  
Early Stage ◽  
Case Definition ◽  
Serum Samples ◽  
Ionization Time ◽  
Flight Mass Spectrometry ◽  
Decision Tree Classification ◽  
Surface Enhanced

Abstract Background: Definitive early-stage diagnosis of severe acute respiratory syndrome (SARS) is important despite the number of laboratory tests that have been developed to complement clinical features and epidemiologic data in case definition. Pathologic changes in response to viral infection might be reflected in proteomic patterns in sera of SARS patients. Methods: We developed a mass spectrometric decision tree classification algorithm using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Serum samples were grouped into acute SARS (n = 74; &lt;7 days after onset of fever) and non-SARS [n = 1067; fever and influenza A (n = 203), pneumonia (n = 176); lung cancer (n = 29); and healthy controls (n = 659)] cohorts. Diluted samples were applied to WCX-2 ProteinChip arrays (Ciphergen), and the bound proteins were assessed on a ProteinChip Reader (Model PBS II). Bioinformatic calculations were performed with Biomarker Wizard software 3.1.1 (Ciphergen). Results: The discriminatory classifier with a panel of four biomarkers determined in the training set could precisely detect 36 of 37 (sensitivity, 97.3%) acute SARS and 987 of 993 (specificity, 99.4%) non-SARS samples. More importantly, this classifier accurately distinguished acute SARS from fever and influenza with 100% specificity (187 of 187). Conclusions: This method is suitable for preliminary assessment of SARS and could potentially serve as a useful tool for early diagnosis.

scholarly journals Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer

Cancers ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 591 ◽  
Author(s):  
Masaru Hayashi ◽  
Koji Matsuo ◽  
Kazuhiro Tanabe ◽  
Masae Ikeda ◽  
Mariko Miyazawa ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Multivariate Analysis ◽  
Cancer Patients ◽  
Early Stage ◽  
Principal Component ◽  
Control Group ◽  
Serum Samples ◽  
Spectra Analysis ◽  
Heat Maps ◽  
Validation Parameters

Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC–MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps. Results: Examined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps. Conclusions: Our study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection.

scholarly journals Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2373
Author(s):  
Kazuhiro Tanabe ◽  
Masae Ikeda ◽  
Masaru Hayashi ◽  
Koji Matsuo ◽  
Miwa Yasaka ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Diagnostic Accuracy ◽  
Early Stage ◽  
Low Cost ◽  
Expression Patterns ◽  
Principal Component ◽  
Serum Levels ◽  
Serum Samples ◽  
Spectra Analysis

Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.

scholarly journals Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1497 ◽  
Author(s):  
Kim ◽  
Cho ◽  
Yu ◽  
Jang ◽  
Yoon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Hepatocellular Carcinoma ◽  
Early Stage ◽  
Receiver Operating Curve ◽  
Serum Samples ◽  
Cross Sectional ◽  
Flight Mass Spectrometry ◽  
Metabolomic Profiling ◽  
Early Hcc ◽  
Disease Stages

The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection.

scholarly journals Impulse oscillometry for detection of small airway dysfunction in subjects with chronic respiratory symptoms and preserved pulmonary function

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Liang-Yuan Li ◽  
Tian-Sheng Yan ◽  
Jing Yang ◽  
Yu-Qi Li ◽  
Lin-Xi Fu ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Respiratory Symptoms ◽  
Early Stage ◽  
Roc Curves ◽  
Diagnostic Value ◽  
Small Airway ◽  
Impulse Oscillometry ◽  
Additional Method ◽  
Cutoff Values ◽  
Higher Sensitivity

Abstract Background Subjects with chronic respiratory symptoms and preserved pulmonary function (PPF) may have small airway dysfunction (SAD). As the most common means to detect SAD, spirometry needs good cooperation and its reliability is controversial. Impulse oscillometry (IOS) may complete the deficiency of spirometry and have higher sensitivity. We aimed to explore the diagnostic value of IOS to detect SAD in symptomatic subjects with PPF. Methods The evaluation of symptoms, spirometry and IOS results in 209 subjects with chronic respiratory symptoms and PPF were assessed. ROC curves of IOS to detect SAD were analyzed. Results 209 subjects with chronic respiratory symptoms and PPF were included. Subjects who reported sputum had higher R5–R20 and Fres than those who didn’t. Subjects with dyspnea had higher R5, R5–R20 and AX than those without. CAT and mMRC scores correlated better with IOS parameters than with spirometry. R5, R5–R20, AX and Fres in subjects with SAD (n = 42) significantly increased compared to those without. Cutoff values for IOS parameters to detect SAD were 0.30 kPa/L s for R5, 0.015 kPa/L s for R5–R20, 0.30 kPa/L for AX and 11.23 Hz for Fres. Fres has the largest AUC (0.665, P = 0.001) among these parameters. Compared with spirometry, prevalence of SAD was higher when measured with IOS. R5 could detect the most SAD subjects with a prevalence of 60.77% and a sensitivity of 81% (AUC = 0.659, P = 0.002). Conclusion IOS is more sensitive to detect SAD than spirometry in subjects with chronic respiratory symptoms and PPF, and it correlates better with symptoms. IOS could be an additional method for SAD detection in the early stage of diseases.

scholarly journals Different Blood Metabolomics Profiles in Infants Consuming a Meat- or Dairy-Based Complementary Diet

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 388
Author(s):  
Minghua Tang ◽  
Nicholas E. Weaver ◽  
Lillian M. Berman ◽  
Laura D. Brown ◽  
Audrey E. Hendricks ◽  
...  
Keyword(s):  
Amino Acids ◽  
Dietary Intervention ◽  
Principal Component ◽  
Essential Amino Acids ◽  
Infant Growth ◽  
Serum Samples ◽  
Z Scores ◽  
Protein Rich ◽  
Over Time ◽  
Complementary Diet

Background: Research is limited in evaluating the mechanisms responsible for infant growth in response to different protein-rich foods; Methods: Targeted and untargeted metabolomics analysis were conducted on serum samples collected from an infant controlled-feeding trial that participants consumed a meat- vs. dairy-based complementary diet from 5 to 12 months of age, and followed up at 24 months. Results: Isoleucine, valine, phenylalanine increased and threonine decreased over time among all participants; Although none of the individual essential amino acids had a significant impact on changes in growth Z scores from 5 to 12 months, principal component heavily weighted by BCAAs (leucine, isoleucine, valine) and phenylalanine had a positive association with changes in length-for-age Z score from 5 to 12 months. Concentrations of acylcarnitine-C4, acylcarnitine-C5 and acylcarnitine-C5:1 significantly increased over time with the dietary intervention, but none of the acylcarnitines were associated with infant growth Z scores. Quantitative trimethylamine N-oxide increased in the meat group from 5 to 12 months; Conclusions: Our findings suggest that increasing total protein intake by providing protein-rich complementary foods was associated with increased concentrations of certain essential amino acids and short-chain acyl-carnitines. The sources of protein-rich foods (e.g., meat vs. dairy) did not appear to differentially impact serum metabolites, and comprehensive mechanistic investigations are needed to identify other contributors or mediators of the diet-induced infant growth trajectories.

scholarly journals The APAC Score: A Novel and Highly Performant Serological Tool for Early Diagnosis of Hepatocellular Carcinoma in Patients with Liver Cirrhosis

2021 ◽  
Vol 10 (15) ◽  
pp. 3392
Author(s):  
Joeri Lambrecht ◽  
Mustafa Porsch-Özçürümez ◽  
Jan Best ◽  
Fabian Jost-Brinkmann ◽  
Christoph Roderburg ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
At Risk ◽  
Liver Cirrhosis ◽  
Early Stage ◽  
Treatment Options ◽  
Growth Factor Receptor ◽  
Serum Samples ◽  
Disease Etiology ◽  
Risk Patients ◽  
Scoring Tool

(1) Background: Surveillance of at-risk patients for hepatocellular carcinoma (HCC) is highly necessary, as curative treatment options are only feasible in early disease stages. However, to date, screening of patients with liver cirrhosis for HCC mostly relies on suboptimal ultrasound-mediated evaluation and α-fetoprotein (AFP) measurement. Therefore, we sought to develop a novel and blood-based scoring tool for the identification of early-stage HCC. (2) Methods: Serum samples from 267 patients with liver cirrhosis, including 122 patients with HCC and 145 without, were collected. Expression levels of soluble platelet-derived growth factor receptor beta (sPDGFRβ) and routine clinical parameters were evaluated, and then utilized in logistic regression analysis. (3) Results: We developed a novel serological scoring tool, the APAC score, consisting of the parameters age, sPDGFRβ, AFP, and creatinine, which identified patients with HCC in a cirrhotic population with an AUC of 0.9503, which was significantly better than the GALAD score (AUC: 0.9000, p = 0.0031). Moreover, the diagnostic accuracy of the APAC score was independent of disease etiology, including alcohol (AUC: 0.9317), viral infection (AUC: 0.9561), and NAFLD (AUC: 0.9545). For the detection of patients with (very) early (BCLC 0/A) HCC stage or within Milan criteria, the APAC score achieved an AUC of 0.9317 (sensitivity: 85.2%, specificity: 89.2%) and 0.9488 (sensitivity: 91.1%, specificity 85.3%), respectively. (4) Conclusions: The APAC score is a novel and highly accurate serological tool for the identification of HCC, especially for early stages. It is superior to the currently proposed blood-based algorithms, and has the potential to improve surveillance of the at-risk population.

scholarly journals Identification of BHLHE40 expression in peripheral blood mononuclear cells as a novel biomarker for diagnosis and prognosis of hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pattapon Kunadirek ◽  
Chaiyaboot Ariyachet ◽  
Supachaya Sriphoosanaphan ◽  
Nutcha Pinjaroen ◽  
Pongserath Sirichindakul ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Peripheral Blood Mononuclear Cells ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Early Stage ◽  
High Sensitivity ◽  
Peripheral Blood Mononuclear ◽  
Diagnosis And Prognosis ◽  
Transcription Profiles ◽  
Blood Mononuclear Cells

AbstractNovel and sensitive biomarkers is highly required for early detection and predicting prognosis of hepatocellular carcinoma (HCC). Here, we investigated transcription profiles from peripheral blood mononuclear cells (PBMCs) of 8 patients with HCC and PBMCs from co-culture model with HCC using RNA-Sequencing. These transcription profiles were cross compared with published microarray datasets of PBMCs in HCC to identify differentially expressed genes (DEGs). A total of commonly identified of 24 DEGs among these data were proposed as cancer-induced genes in PBMCs, including 18 upregulated and 6 downregulated DEGs. The KEGG pathway showed that these enriched genes were mainly associated with immune responses. Five up-regulated candidate genes including BHLHE40, AREG, SOCS1, CCL5, and DDIT4 were selected and further validated in PBMCs of 100 patients with HBV-related HCC, 100 patients with chronic HBV infection and 100 healthy controls. Based on ROC analysis, BHLHE40 and DDIT4 displayed better diagnostic performance than alpha-fetoprotein (AFP) in discriminating HCC from controls. Additionally, BHLHE40 and DDIT4 had high sensitivity for detecting AFP-negative and early-stage HCC. BHLHE40 was also emerged as an independent prognostic factor of overall survival of HCC. Together, our study indicated that BHLHE40 in PBMCs could be a promising diagnostic and prognostic biomarker for HBV-related HCC.

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