Candidate Biomarkers For Early Diagnosis Research Articles

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning

BackgroundOsteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. MethodsWe exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. ResultsIn total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. ConclusionWe identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

E2F-mediated POLA2 upregulation is correlated with macrophage infiltration and poor prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the major histological type of primary liver cancer with a relatively poor prognosis, because most HCC cases are usually diagnosed at an advanced stage. Thus, it is essential to explore novel candidate biomarkers for early diagnosis and prognosis predication of HCC. HCC transcription sequencing data were downloaded and analyzed. POLA2 expression pattern was characterized in tumor development process. POLA2 expression was evaluated by western blotting. Kaplan-Meier plot was utilized to evaluate POLA2's ability for prognosis predication. Correlation analysis and enrichment analysis were performed to explore the functional role of POLA2 in HCC development. Knockdown of E2F1 or E2F4 was used to evaluate their ability in regulating POLA2 expression. CYBERSORT (https://cibersortx.stanford.edu/) was used to estimate the infiltration levels of immune cells. POLA2 was overexpressed in HCC. Moreover, western blotting results showed that POLA2 was upregulated by transcription factors E2F1 rather than E2F4 in HCC. POLA2 facilitated cell cycle progression, DNA replication and DNA repair. High POLA2 expression was correlated with poor overall survival in HCC patients. POLA2 induced macrophage infiltration in the tumor microenvironment by upregulating the expression CSF1 and VEGFA expression. POLA2 is a novel diagnostic and prognostic biomarker of HCC with potential clinical value.

Metabolomics profiling for diagnosis of acute renal failure after cardiopulmonary bypass.

Acute renal failure (ARF) is one of the most serious complications of cardiopulmonary bypass (CPB) surgery. Serum creatinine level is a key compound examined to understand whether renal function is normal. However, its level may vary based on age, gender, race, muscle mass, nutrition, and drugs taken by an individual. In addition, it may not be detected without a 50% reduction in renal function and may lead to delays in treatment. New markers are needed for early diagnosis of ARF. They were determined for early diagnosis of ARF after CPB. Metabolic differences in plasma samples of individuals who developed and did not develop ARF after cardiopulmonary bypass were determined. This study was the first to perform an untargeted metabolomics analysis for early diagnosis of ARF after CPB surgery. Plasma samples were taken from 105 patients (9 ARF patients) at five time points to identify the time at which a more accurate ARF diagnosis can be made. A total of 687 samples, including quality control samples, were analyzed. Two hundred twenty-six metabolites were identified using retention index libraries. Based on the statistical evaluations, tryptophan, threonine, and methionine were found in lower concentrations in patients with ARF compared to the control group at all time points. Whereas gluconic acid, hypoxanthine, and lactic acid showed a decreasing trend over time, longitudinal analysis showed that cysteine, hippuric acid, and uric acid levels increased over time in the ARF group. These metabolites are candidate biomarkers for early diagnosis of ARF as well as biomarkers for tracking the recovery of ARF patients.

Plasma thrombomodulin as a candidate biomarker for the diagnosis and prognosis of HBV-related acute-on-chronic liver failure.

Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a complicated syndrome with high short-term mortality. Effective biomarkers are required for its early diagnosis and prognosis. This study aimed to determine the diagnostic and prognostic value of thrombomodulin (TM) in patients with HBV-ACLF. The expression of TM during disease progression was evaluated through transcriptomics analysis. The plasma TM concentrations of 393 subjects with HBV-ACLF (n=213), acute-on-chronic hepatic dysfunction (ACHD, n=50), liver cirrhosis (LC, n=50) or chronic hepatitis B (CHB, n=50), and normal controls (NC, n=30) from a prospective multicenter cohort, were measured to verify the diagnostic and prognostic significance of plasma TM for HBV-ACLF patients by enzyme-linked immunosorbent assay (ELISA). TM mRNA was highly expressed in the HBV-ACLF group compared with the ACHD group (AUROC=0.710). High expression of TM predicted poor prognosis for HBV-ACLF patients at 28/90 days (AUROCs=0.823/0.788). Functional analysis showed that TM was significantly associated with complement activation and the inflammatory signaling pathway. External validation confirmed its high diagnostic accuracy for HBV-ACLF patients (AUROC=0.796). Plasma TM concentrations were correlated with organ failure, including coagulation and kidney failure. Plasma TM concentrations showed a potential prognostic value for 28-day mortality rates (AUROC=0.702). Risk stratification specifically identified HBV-ACLF patients with a high risk of death as having a plasma TM concentration of ≥8.4 ng/mL. This study reveals that the plasma TM can be a candidate biomarker for early diagnosis and prognosis of HBV-ACLF, and might play a vital role in coagulation and inflammation.

Plasma MERTK Is a Promising Biomarker for the Diagnosis and Prognosis of Hepatitis B Virus-Related Acute-on-Chronic Liver Failure.

Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) has a high short-term mortality. This study aimed to determine the diagnostic and prognostic role of MER tyrosine kinase (MERTK) in patients with HBV-ACLF. Transcriptomics analysis evaluated MERTK expression and function during disease progression. The diagnostic and prognostic significance of MERTK for patients with HBV-ACLF were verified by enzyme-linked immunosorbent assay, area under the receiver operating characteristic curve (AUROC) analysis, and immunohistochemistry (IHC) of liver tissues. MERTK mRNA was highly expressed in patients with HBV-ACLF compared to those with liver cirrhosis (LC), chronic hepatitis B (CHB), and normal controls (NC). Elevated MERTK mRNA predicted poor prognosis for HBV-ACLF at 28 and 90 days (AUROC = 0.814 and 0.731, respectively). Functional analysis showed MERTK was significantly associated with toll-like receptor and inflammatory signaling and several key biological processes. External validation with 285 plasma subjects confirmed the high diagnostic accuracy of plasma MERTK for HBV-ACLF (AUROC = 0.859) and potential prognostic value for 28- and 90-day mortality rates (AUROC = 0.673 and 0.644, respectively). Risk stratification analysis indicated higher mortality risk for patients with plasma MERTK level above the cutoff value. Moreover, IHC staining showed increasing MERTK expression from NC, CHB, and LC to HBV-ACLF. MERTK shows promise as a candidate biomarker for early diagnosis and prognosis of HBV-ACLF.

Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma

BackgroundEarly diagnosis of lung adenocarcinoma (LUAD), one of the most common types of lung cancer, is very important to improve the prognosis of patients. The current methods can’t meet the requirements of early diagnosis. There is a pressing need to identify novel diagnostic biomarkers. Secretory proteins are the richest source for biomarker research. This study aimed to identify candidate secretory protein biomarkers for early diagnosis of LUAD by integrated bioinformatics analysis and clinical validation.MethodsDifferentially expressed genes (DEGs) of GSE31210, gene expression data of early stage of LUAD, were analyzed by GEO2R. Upregulated DEGs predicted to encode secreted proteins were obtained by taking the intersection of the DEGs list with the list of genes encoding secreted proteins predicted by the majority decision-based method (MDSEC). The expressions of the identified secreted proteins in the lung tissues of early-stage LUAD patients were further compared with the healthy control group in mRNA and protein levels by using the UALCAN database (TCGA and CPTAC). The selected proteins expressed in plasma were further validated by using Luminex technology. The diagnostic value of the screened proteins was evaluated by receiver operating characteristic (ROC) analysis. Cell counting kit-8 assay was carried out to investigate the proliferative effects of these screened proteins.ResultsA total of 2183 DEGs, including 1240 downregulated genes and 943 upregulated genes, were identified in the GSE31210. Of the upregulated genes, 199 genes were predicted to encode secreted proteins. After analysis using the UALCAN database, 16 molecules were selected for further clinical validation. Plasma concentrations of three proteins, Midkine (MDK), WAP four-disulfide core domain 2 (WFDC2), and C-X-C motif chemokine ligand 14 (CXCL14), were significantly higher in LUAD patients than in healthy donors. The area under the curve values was 0.944, 0.881, and 0.809 for MDK, WFDC2, and CXCL14, 0.962 when combined them. Overexpression of the three proteins enhanced the proliferation activity of A549 cells.ConclusionsMDK, WFDC2, and CXCL14 were identified as candidate diagnostic biomarkers for early-stage LUAD and might also play vital roles in tumorigenesis.

Trace Elements Homeostasis in Biological Samples as New Candidate Biomarkers for Early Diagnosis and Prognostic of Female Breast Cancer and Therapeutic Response: Systematic Review

Background: Female breast cancer (BC) remains the most common cause of total cancer deaths around the world. Several studies have investigated BC biomarkers, but vital circulating biomarkers for early diagnosis of malignancy are still scarce. Thus, finding sensitive, selective and accurate biomarkers is required to get better BC outcome and to prolong patients’ survival. Therefore, this review investigated the feasibility of using circulating trace elements (TEs) as the new promising biological biomarkers for BC diagnosis and prevention. Methods: We systematically searched EMBASE, Medline, Google Scholar, PubMed, SciELO, Scopus databases or Web of Science for orginal studies presenting the significant changes in the concentrations of circulating TEs in terms of serum, plasma or blood from female breast cancer patients. Results: The search yielded 2697 articles, of which 39 were considered for this review. The study showed that four essential TEs (Se, Cu, Zn and Mn) significantly decreased when only one essential trace elemnt (Fe) increased consistently, while five toxics circulating TEs (Cd, Cr, Pb, Co, Mo) increased significantly with a significant difference compared to healthy groups. The essential TEs, Se and Cu were reported to decrease the most in fifteen and twenty-one studies, respectively. However, regarding the toxic circulating TEs, Cd and Pb were found to increase most significantly in seven studies. Among the essential TEs, Se and Zn were reported to have the most potential, with Cd and Pb having the most potential for use as new promising biomarkers to diagnose or prevent BC. Conclusion: The findings provide an insight into the TEs circulating biomarkers for early BC diagnosis and prevention. Due to its high heterogeneity, meta-analysis was not assessed; hence, futher investigation may be required on their clinical outcomes in BC with high sensitivity and specificity for accurate therapeutic response.

Dual detection of spinal cord injury biomarkers in rat model using gold nanorod array substrate based on surface-enhanced Raman scattering

Tumor necrosis factor alpha (TNF­α) and glial fibrillary acidic protein (GFAP) are candidate biomarkers for early diagnosis and evaluation of spinal cord injury (SCI). In fact, their concentrations in serum, plasma, or cerebrospinal fluid are highly correlated with the SCI severity and neurological prognosis. Therefore, development of a rapid, ultrasensitive, and accurate technique for dual-detection of these important biomarkers is critical for SCI diagnosis, monitoring, and recovery assessment. We synthesized gold nanorods (AuNRs) with stable aspect ratio and fabricated an AuNR array parallelly aligned on a substrate using evaporation-induced self-assembly. The uniform morphology and neat arrangement of the AuNRs produce many hotspots that increase the sensitivity and reproducibility of the array. For dual detection of TNF-α and GFAP, the self-assembled AuNR array substrate was modified with anti-TNF­α and anti-GFAP. Then, special Raman probe molecules, specific antibodies, and AuNRs were combined to fabricate surface-enhanced Raman scattering (SERS) tags of AuNR@4-MPBA and AuNR@NBA modified with anti-TNF­α and anti-GFAP, respectively. Sandwich immunoassay using standard solutions of TNF­α and GFAP provided limits of detection of 0.023 and 0.018 pg/mL, respectively. The detector was evaluated on concentrations of TNF­α and GFAP in plasma of SCI rat models. Cross-validation against ELISA (enzyme-linked immunosorbent assay) indicated that the SERS sandwich immunoassay detector based on AuNR array substrate has a high dual-detection accuracy. Overall, the proposed detector provides high sensitivity, reproducibility, stability, and accuracy, and it correctly performs dual detection. The fabricated detector platform seems promising for detection of multiple molecules to achieve early and accurate disease diagnosis in clinical practice.

Potential of immune-related genes as promising biomarkers for premature coronary heart disease through high throughput sequencing and integrated bioinformatics analysis.

BackgroundCoronary heart disease (CHD) is the most common progressive disease that is difficult to diagnose and predict in the young asymptomatic period. Our study explored a mechanistic understanding of the genetic effects of premature CHD (PCHD) and provided potential biomarkers and treatment targets for further research through high throughput sequencing and integrated bioinformatics analysis.MethodsHigh throughput sequencing was performed among recruited patients with PCHD and young healthy individuals, and CHD-related microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by using R software. Enrichment analysis and CIBERSORT were performed to explore the enriched pathways of DEGs and the characteristics of infiltrating immune cells. Hub genes identified by protein–protein interaction (PPI) networks were used to construct the competitive endogenous RNA (ceRNA) networks. Potential drugs were predicted by using the Drug Gene Interaction Database (DGIdb).ResultsA total of 35 DEGs were identified from the sequencing dataset and GEO database by the Venn Diagram. Enrichment analysis indicated that DEGs are mostly enriched in excessive immune activation pathways and signal transduction. CIBERSORT exhibited that resting memory CD4 T cells and neutrophils were more abundant, and M2 macrophages, CD8 T cells, and naïve CD4 T cells were relatively scarce in patients with PCHD. After the identification of 10 hub gens, three ceRNA networks of CD83, CXCL8, and NR4A2 were constructed by data retrieval and validation. In addition, CXCL8 might interact most with multiple chemical compounds mainly consisting of anti-inflammatory drugs.ConclusionsThe immune dysfunction mainly contributes to the pathogenesis of PCHD, and three ceRNA networks of CD83, CXCL8, and NR4A2 may be potential candidate biomarkers for early diagnosis and treatment targets of PCHD.

A Fucosylated Glycopeptide as a Candidate Biomarker for Early Diagnosis of NASH Hepatocellular Carcinoma Using a Stepped HCD Method and PRM Evaluation.

Aberrant specific N-glycosylation, especially the increase in fucosylation on specific peptide sites of serum proteins have been investigated as potential markers for diagnosis of nonalcoholic steatohepatitis (NASH)-related HCC. We have combined a workflow involving broad scale marker discovery in serum followed by targeted marker evaluation of these fucosylated glycopeptides. This workflow involved an LC-Stepped HCD-DDA-MS/MS method coupled with offline peptide fractionation for large-scale identification of N-glycopeptides directly from pooled serum samples (each n=10) as well as differential determination of N-glycosylation changes between disease states. We then evaluated the fucosylation level of the glycoprotein ceruloplasmin among 62 patient samples (35 cirrhosis, 27 early-stage NASH HCC) by LC-Stepped HCD-PRM-MS/MS to quantitatively analyze 18 targeted glycopeptides. Of these targets, we found the ratio of fucosylation of a tri-antennary glycopeptide from site N762, involving N762_ HexNAc(5)Hex(6)Fuc(2)NeuAc(3) (P=0.0486), increased significantly from cirrhosis to early HCC. This fucosylation ratio of a tri-antennary glycopeptide in CERU could be a potential biomarker for further validation in a larger sample set and could be a promising candidate for early detection of NASH HCC.

Creatine kinase-(MB) and hepcidin as candidate biomarkers for early diagnosis of pulmonary tuberculosis: a proof-of-concept study in Lambaréné, Gabon

BackgroundThe present study aimed to evaluate the diagnostic utility of creatine kinase-MB (CK-MB), hepcidin (HEPC), phospholipase A2 group IIA (PLa2G2A), and myosin-binding protein C (MYBPC1) for tuberculosis (TB). These four biomarkers are differentially regulated between quiescent Mycobacterium tuberculosis (Mtb) infected individuals (non-progressors to TB disease) and Mtb-infected TB disease progressors 6 months before the onset of symptoms.MethodsWe enrolled samples from patients experiencing moderate-to-severe pulmonary infections diseases including 23 TB cases confirmed by smear microscopy and culture, and 34 TB-negative cases. For each participant, the serum levels of the four biomarkers were measured using ELISA.ResultsThe levels of CK-MB and HEPC were significantly reduced in patients with active TB disease. CK-MB median level was 2045 pg/ml (1455–4000 pg/ml) in active TB cases and 3245 pg/ml (1645–4000 pg/ml) in non-TB pulmonary diseases. Using the receiver operating characteristic curve (ROC) analysis, HEPC and CK-MB had the Area Under the Curve (AUC) of 79% (95% CI 67–91%) and 81% (95% CI 69–93%), respectively. Both markers correlated with TB diagnosis as a single marker. PLa2G2A and MYBPC1 with AUCs of 48% (95% CI 36–65%) and 62% (95% CI 48–76%) did not performed well as single biomarkers. The three markers’model (CK-MB-HEPC-PLa2G2A) had the highest diagnostic accuracy at 82% (95% CI 56–82%) after cross-validation.ConclusionCK-MB and HEPC levels were statistically different between confirmed TB cases and non-TB cases. This study yields promising results for the rapid diagnosis of TB disease using a single marker or three biomarkers model.

Metabolomics-Based Biomarkers for Frailty in Chinese Older Adults

Background: Frailty is a clinical state characterized by decline in physiological function, and increased vulnerability to adverse outcomes. The biological mechanisms underlying frailty have been extensively studied in recent years. Advances in the multi-omics platforms have provided new information on the molecular mechanisms of frailty. Thus, identifying omics-based biomarkers is helpful for both exploring the physiological mechanisms of frailty and evaluating the risk of frailty development and progression. Objective: To identify metabolomics biomarkers and possible pathogenic mechanisms for frailty with untargeted-metabolomics profiling. Methods: LC-MS-based untargeted metabolomics analysis was performed on serum samples of 25 frail older inpatients and 49 non-frail older controls. The metabolomics profiling was compared between the two groups. Results: We identified 349 metabolites belonging to 46 classes, in which 2 were increased and 3 were decreased in frail older adults. Citrate cycle (with up-regulated cis-Aconitic acid, Fumaric acid, L-Malic acid, and Isocitric acid), fatty acid metabolism (with up-regulated Palmitic acid and L-Palmitoylcarnitine) and tryptophan metabolism (with up-regulated 5-Hydroxy-L-tryptophan, L-Kynurenine, Kynurenic acid, and 5-Hydroxyindoleacetic acid) were significantly associated with frailty phenotype. Conclusions: Our results revealed characteristics of metabolites of frailty in Chinese older adults. The citrate cycle related metabolites (Isocitrate, (s)-Malate, Fumarate and cis-Aconitate), saturated fat (Palmitic acid), unsaturated fatty acid (Arachidonate and Linoleic acid), and some essential amino acid (Tryptophan) might be candidate biomarkers for early diagnosis of frailty. Disorders of energy metabolism, lipotoxicity of saturated fatty acids, disturbances of unsaturated fatty acid metabolism, and increased degradation of tryptophan were potential mechanisms and therapeutic targets of frailty.

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples.

Recent advances in mass spectrometry have resulted in deep proteomicanalysis along with the generation ofrobust andreproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions. Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.

Identification of Blood-Based Glycolysis Gene Associated with Alzheimer's Disease by Integrated Bioinformatics Analysis.

Alzheimer's disease (AD) is one of many common neurodegenerative diseases without ideal treatment, but early detection and intervention can prevent the disease progression. This study aimed to identify AD-related glycolysis gene for AD diagnosis and further investigation by integrated bioinformatics analysis. 122 subjects were recruited from the affiliated hospitals of Ningbo University between 1 October 2015 and 31 December 2016. Their clinical information and methylation levels of 8 glycolysis genes were assessed. Machine learning algorithms were used to establish an AD prediction model. Receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the model. An AD risk factor model was developed by SHapley Additive exPlanations (SHAP) to extract features that had important impacts on AD. Finally, gene expression of AD-related glycolysis genes were validated by AlzData. An AD prediction model was developed using random forest algorithm with the best average ROC_AUC (0.969544). The threshold probability of the model was positive in the range of 0∼0.9875 by DCA. Eight glycolysis genes (GAPDHS, PKLR, PFKFB3, LDHC, DLD, ALDOC, LDHB, HK3) were identified by SHAP. Five of these genes (PFKFB3, DLD, ALDOC, LDHB, LDHC) have significant differences in gene expression between AD and control groups by Alzdata, while three of the genes (HK3, ALDOC, PKLR) are related to the pathogenesis of AD. GAPDHS is involved in the regulatory network of AD risk genes. We identified 8 AD-related glycolysis genes (GAPDHS, PFKFB3, LDHC, HK3, ALDOC, LDHB, PKLR, DLD) as promising candidate biomarkers for early diagnosis of AD by integrated bioinformatics analysis. Machine learning has the advantage in identifying genes.

A Panel of Glycopeptides as Candidate Biomarkers for Early Diagnosis of NASH Hepatocellular Carcinoma Using a Stepped HCD Method and PRM Evaluation.

Changes in N-glycosylation on specific peptide sites of serum proteins have been investigated as potential markers for diagnosis of nonalcoholic steatohepatitis (NASH)-related HCC. To accomplish this work, a novel workflow involving broad-scale marker discovery in serum followed by targeted marker evaluation of these glycopeptides were combined. The workflow involved an LC-Stepped HCD-DDA-MS/MS method coupled with offline peptide fractionation for large-scale identification of N-glycopeptides directly from pooled serum samples (each n = 10) as well as differential determination of N-glycosylation changes between disease states. We then evaluated several potentially diagnostic N-glycopeptides among 78 individual patient samples (40 cirrhosis, 28 early stage NASH HCC, and 10 late-stage NASH HCC) by LC-Stepped HCD-PRM-MS/MS to quantitatively analyze 65 targeted glycopeptides from 7 glycoproteins. Of these targets, we found site-specific N-glycopeptides n169GSLFAFR_HexNAc(4)Hex(5)NeuAc(2) and n242ISDGFDGIPDNVDAALALPAHSYSGR_HexNAc(5)Hex(6)Fuc(1)NeuAc(3) from VTNC were significantly increased comparing samples from patients with NASH cirrhosis and NASH HCC (p < 0.05). When combining results of these 2 glycopeptides with AFP, the ROC curve analysis demonstrated the AUC value increased to 0.834 (95% CI, 0.748-0.921) and 0.847 (95% CI, 0.766-0.932), respectively, as compared to that of AFP alone (AUC = 0.791, 95% CI, 0.690-0.892). These 2 glycopeptides may serve as potential biomarkers for early HCC diagnosis in patients with NASH related cirrhosis.

Microbiome Markers of Pancreatic Cancer Based on Bacteria-Derived Extracellular Vesicles Acquired from Blood Samples: A Retrospective Propensity Score Matching Analysis.

Simple SummaryAlthough tremendous advances in diagnosis and treatment, pancreatic cancer still remains one of the lethal diseases with an overall survival rate of 10~15%. Early detection and diagnosis of pancreatic cancer is very important in improving the prognosis of patients. The aim of our study was to find new biomarkers, using microbiomes based on bacteria-derived extracellular vesicles, extracted from blood serum. With 38 patients with pancreatic cancer and 52 healthy controls with no history of pancreatic disease, we identified several compositional differences of microbiome between them. Using various combinations of the metagenomic markers which made the compositional differences, we also built a pancreatic cancer prediction model with high area under the receiver operating characteristic curve (0.966 at the phylum level and 1.000 at the genus level). These microbiome markers, based on bacteria-derived extracellular vesicles acquired from blood, show demonstrate the potential of candidate biomarkers for early diagnosis of pancreatic cancer.Novel biomarkers for early diagnosis of pancreatic cancer (PC) are necessary to improve prognosis. We aimed to discover candidate biomarkers by identifying compositional differences of microbiome between patients with PC (n = 38) and healthy controls (n = 52), using microbial extracellular vesicles (EVs) acquired from blood samples. Composition analysis was performed using 16S rRNA gene analysis and bacteria-derived EVs. Statistically significant differences in microbial compositions were used to construct PC prediction models after propensity score matching analysis to reduce other possible biases. Between-group differences in microbial compositions were identified at the phylum and genus levels. At the phylum level, three species (Verrucomicrobia, Deferribacteres, and Bacteroidetes) were more abundant and one species (Actinobacteria) was less abundant in PC patients. At the genus level, four species (Stenotrophomonas, Sphingomonas, Propionibacterium, and Corynebacterium) were less abundant and six species (Ruminococcaceae UCG-014, Lachnospiraceae NK4A136 group, Akkermansia, Turicibacter, Ruminiclostridium, and Lachnospiraceae UCG-001) were more abundant in PC patients. Using the best combination of these microbiome markers, we constructed a PC prediction model that yielded a high area under the receiver operating characteristic curve (0.966 and 1.000, at the phylum and genus level, respectively). These microbiome markers, which altered microbial compositions, are therefore candidate biomarkers for early diagnosis of PC.

Four potential microRNAs affect the progression of pancreatic ductal adenocarcinoma by targeting MET via the PI3K/AKT signaling pathway.

Pancreatic ductal adenocarcinoma (PDAC) is the most common tumor subtype of pancreatic cancer, which exhibits poor patient prognosis due to the lack of effective biomarkers in the diagnosis and treatment. The present study aimed to identify the potential biomarkers of PDAC carcinogenesis and progression using three microarray datasets, GSE15471, GSE16515 and GSE28735, which were downloaded from the Gene Expression Omnibus database. The datasets were analyzed to screen out differentially expressed genes (DEGs) in PDAC tissues and adjacent normal tissues. A total of 143 DEGs were identified, including 132 upregulated genes and 11 downregulated genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional and signaling pathway enrichment analyses were performed on the DEGs, and the Search Tool for the Retrieval of Interacting Genes/Proteins database was used to construct a protein-protein interaction network. The main functions of DEGs include extracellular matrix degradation, and regulation of matrix metalloproteinase activity and the PI3K-Akt signaling pathway. The five hub genes were subsequently screened using Cytoscape software, and survival analysis demonstrated that abnormal expression levels of the hub genes was associated with poor disease-free survival and overall survival. Biological experiments were performed to confirm whether mesenchymal-to-epithelial transition (MET) factors promote the proliferation, migration and invasion of PDAC cells via the PI3K/AKT signaling pathway. In addition, six MET-targeted microRNAs (miRNAs) were identified, four of which had conserved binding sites with MET. Based on the signaling pathway enrichment analysis of these miRNAs, it is suggested that they can affect the progression of PDAC by targeting MET via the PI3K/AKT signaling pathway. In conclusion, the hub genes and miRNAs that were identified in the present study contribute to the molecular mechanisms of PDAC carcinogenesis and progression. They also provide candidate biomarkers for early diagnosis and treatment of patients with PDAC.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression.

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Proteomic Profiling Change and Its Implies in the Early Mycosis Fungoides (MF) Using Isobaric Tags for Relative and Absolute Quantification (iTRAQ).

Purpose Mycosis fungoides (MF) is the most common T-cell lymphoma, with indolent biologic behavior in the early stage and features of invasive in the tumor stage. The diagnosis of MF is still ambiguous and difficult. We focused on the proteomic profiling change in the pathogenesis of early MF and identified candidate biomarkers for early diagnosis. Methods We collected peripheral blood samples of MF patients and healthy individuals (HI) performed proteomic profiling analysis using isobaric tags for relative and absolute quantification (iTRAQ) platform. Differently expressed proteins (DEPs) were filtered, and involved biological functions were analyzed through Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software. Results We identified 78 DEPs including fifty proteins were upregulated and 28 proteins were downregulated in the MF group with HI as a control. Total DEPs were analyzed according to the biological regulation and metabolic process through GO analysis. The pathways of LXR/RXR activation and FXR/RXR activation were significantly activated, in which APOH, CLU, and ITIH4 were involved. The top annotated disease and function network was (Cancer, Organismal Injury and Abnormalities, Reproductive System Disease), with a key node CLU. These DEPs were involved in cancer, including thyroid carcinoma, head and neck carcinoma, and cancer of secretory structure, in which CLU, GNAS, and PKM played an indirect role in the occurrence and development of cancer. Relevant causal network was IL12 (family), which is related to GNAS, PKM, and other DEPs. Conclusion Proteomic profiling of early-stage MF provided candidate protein biomarkers such as CLU, GNAS, and PKM, which benefit the early diagnosis and understanding of the mechanism of MF development. Besides, lipid metabolism may be one of the pathogenesis of MF, and IL12 was a potential marker for the diagnosis and treatment of early MF.

Diagnostic and prognostic significance of aberrant miR-652-3p levels in patients with acute decompensated heart failure and acute kidney injury.

ObjectiveThis study aimed to examine a novel microRNA (miR-652-3p) biomarker to improve early diagnosis of acute kidney injury (AKI) in patients with acute decompensated heart failure (ADHF) and to evaluate the survival predictive value of miR-652-3p.MethodsWe retrospectively analyzed the data of 196 patients with ADHF, including 65 who developed AKI during hospitalization. Neutrophil gelatinase-associated lipocalin (NGAL) levels were measured in serum and urine samples. Real-time quantitative PCR was applied to evaluate miR-652-3p mRNA expression. The diagnostic performance of miR-652-3p was examined using receiver operating characteristic curve analysis. The prognostic value of miR-652-3p was also analyzed.ResultsSerum and urinary NGAL and miR-652-3p levels were elevated in patients with ADHF and AKI. Serum and urinary miR-652-3p expression had diagnostic value in predicting AKI onset in patients with ADHF, and it had improved diagnostic performance when used with NGAL. Patients with AKI and high miR-652-3p levels had a high failure rate of renal recovery and poor 180-day survival.ConclusionSerum and urinary miR-652-3p may be a candidate biomarker for early diagnosis of AKI in patients with ADHF and for predicting the prognosis of AKI. The combination of NGAL and miR-652-3p may accurately predict AKI onset in ADHF.