Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.     

LC-MS-based serum metabolic profiling for genitourinary cancer classification and cancer type-specific biomarker discovery

Bladder cancer (BC) and kidney cancer (KC) are the first two commonly occurring genitourinary cancers in China. In this study, a comprehensive LC-MS-based method, which utilizes both reversed phase liquid chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of BC, KC, and noncancer controls. An independent test set consisting of different patients has been used to objectively evaluate the predictive ability of the analysis platform. Excellent sensitivity and specificity have been achieved in detection of KC and BC. The results suggest that serum metabolic profiling could be used for different types of genitourinary cancer diagnosis. Furthermore, cancer type-specific biomarkers were found through a critical selection criterion. As a result, eicosatrienol, azaprostanoic acid, docosatrienol, retinol, and 14'-apo-beta-carotenal were found as specific biomarkers for BC; and PE(P-16:0e/0:0), glycerophosphorylcholine, ganglioside GM3 (d18:1/22:1), C17 sphinganine, and SM(d18:0/16:1(9Z)) were found as specific biomarkers for KC. Receiver operating characteristic (ROC) analysis was used for the preliminary evaluation of the biomarkers. These biomarkers have great potential to be used in the clinical diagnosis after further rigorous assessment.     

Novel urinary biomarkers for diagnosing bipolar disorder

Bipolar disorder (BD) is a debilitating mental disorder. However, there are no biomarkers available to support objective laboratory testing for this disorder. Here, a nuclear magnetic resonance spectroscopy-based metabonomic method was used to characterize the urinary metabolic profiling of BD subjects and healthy controls in order to identify and validate urinary metabolite biomarkers for BD. Four metabolites, α-hydroxybutyrate, choline, isobutyrate, and N-methylnicotinamide, were defined as biomarkers. A combined panel of these four urinary metabolites could effectively discriminate between BD subjects and healthy controls, achieving an area under the receiver operating characteristic curve (AUC) of 0.89 in a training set (n = 60 BD patients and n = 62 controls). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 26 BD patients and n = 34 controls) with an AUC of 0.86. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of BD.     

The role of D-dimers in the diagnosis of acute aortic dissection

Acute aortic dissection (AAD) is a life threatening cardiovascular medical emergency with a poor prognosis. To explore the utility of D-dimers (DD) in the diagnosis of AAD, we performed a prospective study and conducted a meta-analysis of previous studies. 368 suspected patients were enrolled, including AAD n = 89, PE n = 12, AMI n = 167, normal controls n = 100. All patients had a DD test immediately after admission. We then performed a comprehensive computer search to identify studies investigating using DD as a screening tool for AAD. Finally, we pooled these data to estimate sensitivity, specificity, positive and negative likelihood ratios (LRs) by using DerSimonian–Laird random-effects models. The DD concentrations in the AAD group were significantly higher than those in the AMI and normal control groups. However, the DD level of 500 ng/ml had a poor sensitivity of 51.7 % and specificity of 89.2 % in the diagnosis of AAD. Subgroup analyses found that DD only showed a well discriminative ability of distinguishing AAD patients from normal controls (specificity and positive LR was 97 % and 17.2, respectively). The pooled sensitivity, specificity, positive and negative LR in our meta-analysis was 89, 68 %, 2.71, 0.07, respectively. In conclusion, our results suggest that plasma DD levels cannot add to the certainty of AAD diagnosis and it is not a good biomarker for AAD. In the future, prospective research on patients from many parts of the world is warranted to validate our findings. In addition, different controls, methods of plasma DD assays and other factors should be considered.     

Application of gas chromatography mass spectrometry (GC–MS) in conjunction with multivariate classification for the diagnosis of gastrointestinal diseases

Gastrointestinal diseases such as irritable bowel syndrome, Crohn’s disease (CD) and ulcerative colitis are a growing concern in the developed world. Current techniques for diagnosis are often costly, time consuming, inefficient, of great discomfort to the patient, and offer poor sensitivities and specificities. This paper describes the development and evaluation of a new methodology for the non-invasive diagnosis of such diseases using a combination of gas chromatography mass spectrometry (GC–MS) and chemometrics. Several potential sample matrices were tested: blood, breath, faeces and urine. Faecal samples provided the only statistically significant results, providing discrimination between CD and healthy controls with an overall classification accuracy of 85 % (78 % specificity; 93 % sensitivity). Differentiating CD from other diseases proved more challenging, with overall classification accuracy dropping to 79 % (83 % specificity; 68 % sensitivity). This diagnostic performance compares well with the gold standard technique of colonoscopy, suggesting that GC–MS may have potential as a non-invasive screening tool.     

Plasma choline-containing phospholipids: potential biomarkers for colorectal cancer progression

Colorectal cancer (CRC) is believed to progress through the adenoma–carcinoma sequence. The adenoma–carcinoma transition is an important window for early detection and intervention of CRC. In the present study, plasma samples from patients with CRC (n = 120), patients with adenomatous polyps (AP) (n = 120), and healthy controls (n = 120) were collected. Plasma phospholipid levels were analyzed with liquid chromatography–tandem mass spectrometry. It was found that the plasma levels of major lysophosphatidylcholine (LPC) species were gradationally decreased from healthy controls, AP to CRC subjects. A formula including total saturated LPCs, 18:2 LPC and sphingosylphosphorylcholine (SPC) yielded a sensitivity and specificity of 88.3 and 80 % for separating CRC from healthy controls. An optimized model with total saturated LPCs, 20:4 LPC and sphingomyelins (SM) as markers yielded a sensitivity and specificity of 89 and 80 % for separating AP from the healthy controls. Moreover, with SM, SPC and saturated LPCs as markers, a model was made to separate CRC from AP with the sensitivity and specificity of 90 and 92.5 %, respectively. These data indicate that the plasma choline-containing phospholipid levels represent potential biomarkers to distinguish between healthy controls, AP and CRC cases, implying their clinical usage in CRC and/or AP-CRC progression detection.     

LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery

A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.     

Serum proteomic profiling for the early diagnosis of colorectal cancer

No ideal serum biomarker currently exists for the early diagnosis of colorectal cancer (CRC). Magnetic bead‐based fractionation coupled with MALDI‐TOF MS was used to screen serum samples from CRC patients, healthy controls, and other cancer patients. A diagnostic model with five proteomic features (m/z 1778.97, 1866.16, 1934.65, 2022.46, and 4588.53) was generated using Fisher algorithm with best performance. The Fisher‐based model could discriminate CRC patients from the controls with 100% (46/46) sensitivity and 100% (35/35) specificity in the training set, 95.6% (43/45) sensitivity and 83.3% (35/42) specificity in the test set. We further validated the model with 94.4% (254/269) sensitivity and 75.5% (83/110) specificity in the external independent group. In other cancers group, the Fisher‐based model classified 25 of 46 samples (54.3%) as positive and the other 21 as negative. With FT‐ICR‐MS, the proteomic features of m/z 1778.97, 1866.16, 1934.65, and 2022.46, of which intensities decreased significantly in CRC, were identified as fragments of complement C3f. Therefore, the Fisher‐based model containing five proteomic features was able to effectively differentiate CRC patients from healthy controls and other cancers with a high sensitivity and specificity, and may be CRC‐specific. Serum complement C3f, which was significantly decreased in CRC group, may be relevant to the incidence of CRC. J. Cell. Biochem. 114: 448–455, 2013. © 2012 Wiley Periodicals, Inc.     

Circulating microRNAs,miR‐92a,miR‐100 and miR‐143, as non‐invasive biomarkers for bladder cancer diagnosis

The application of microRNAs (miRNAs) as potential biomarkers and therapy targets has been widely investigated in many kinds of cancers. Recent advantages of serum miRNAs open a new realm of possibilities for non‐invasive diagnosis and prognosis of bladder cancer (BC). The aim of our study was to identify plasma miR‐92a, miR‐100 and miR‐143 expression signatures in patients with BC to introduce new markers for establishing BC diagnosis and prognosis. Blood samples were collected from 70 BC patients and 62 controls. An expression of three target miRNAs (miR‐92a, miR‐100 and miR‐143) was measured using quantitative real‐time PCR method. Results were correlated with clinicopathological data and analysed. Plasma levels of miR‐92a, miR‐100 and miR‐143 were significantly lower in BC patients than in control group. Receiver operator characteristic analysis revealed that the sensitivity and specificity values of miR‐92a were 97·1% and 76·7%, respectively, with a cut‐off value of 0·573. The sensitivity and specificity values of miR‐100 were 90% and 66·7%, respectively, with a cut‐off value of 0·644. The sensitivity and specificity values of miR‐143 were 78·6% and 93·3%, respectively, with a cut‐off value of 0·164. This study explores the existence of specific plasma miRNAs as early diagnostic biomarkers for BC in Egyptian patients; and these findings suggest that plasma miR‐92a, miR‐100 and miR‐143 could be promising novel circulating biomarkers in clinical detection of BC. Copyright © 2016 John Wiley & Sons, Ltd.     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

Metabonomic models of human pancreatic cancer using 1D proton NMR spectra of lipids in plasma

In this study, we hypothesized that the altered insulin and glucose levels in male pancreatic cancer patients reported in a recent JAMA article would result in an altered lipid profile in the blood of pancreatic cancer patients when compared to controls (Stolzenberg-Solomon et al., 2005). Proton nuclear magnetic resonance (NMR) spectra of human lipophilic plasma extracts were used in order to build partial least squares discriminant function (PLS-DF) models that classified samples as belonging to the pancreatic control group or to the pancreatic cancer group. The sensitivity, specificity, and overall accuracy of the PLS-DF models based on 4 bins were 96%, 88%, and 92%, respectively. The sensitivity, specificity, and overall accuracy of the PLS-DF models based on 5 bins were 98%, 94%, and 96%, respectively. The sensitivity, specificity and overall accuracy of both the 4-bin and 5-bin PLS-DF models dropped only 1–2% during leave-25%-out cross-validation testing. Mass spectrometric profiling of phospholipids in plasma found three phosphatidylinositols that were significantly lower in pancreatic cancer patients than in healthy controls. The cancer models are based upon changes in lipid profiles that may provide a more sensitive and accurate diagnosis of pancreatic cancer than current methods that are based upon a single biomarker.     

A metabolic discrimination model for nasopharyngeal carcinoma and its potential role in the therapeutic evaluation of radiotherapy

Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in Southeast Asia and radiotherapy or radiotherapy, in combination with chemotherapy is the primary treatment strategy. In this study, we adopted a metabolomic method to investigate the metabolic disorders in NPC and evaluate the effect of radiotherapy on metabolic profile alterations in NPC patients. To generate the NPC metabolic profiles, 402 serum samples were collected from 100 newly-diagnosed NPC patients and 100 healthy volunteers. Based on gas chromatography–mass spectrometry (GC–MS) metabolomics coupled with partial least squares-discriminant analysis, a NPC discrimination model was constructed with a sensitivity of 88 % (88/100) and a specificity of 92 % (92/100). Seven metabolites, including glucose, linoleic acid, stearic acid, arachidonic acid, proline, β-hydroxy butyrate and glycerol 1-hexadecanoate, were identified as contributing mostly to the discrimination of NPC serum from healthy controls. To validate if the model can be applied for therapeutic evaluation, 202 serum samples were collected from 20 patients receiving standard radiotherapy for up to a 3-year follow-up period. The metabolic footprints of 20 NPC patients treated with standard radiotherapy are visually presented. Based on the footprint trends of the sera samples in irradiation-treated NPC patients who were gradually closer to healthy controls or not, patients were divided into positive and negative groups, respectively. The coincident rate of the trends of metabolic footprints to the actual clinical prognosis trend was approximately 80 %. This study demonstrates that a GC–MS-based metabolic profiling approach as a novel strategy may be capable to delineating the potential of metabolite alterations in discrimination and therapeutic evaluation of NPC patients.     

Serum microRNA biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73-0.91), sensitivity of 100% (95% CI; 0.93-1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results.     

Plasma-free amino acid profiling of cervical cancer and cervical intraepithelial neoplasia patients and its application for early detection

In this study, plasma-free amino acid profiles were used to investigate pre-cancerous cervical intraepithelial neoplasia (CIN) and cervical squamous cell carcinoma (CSCC) metabolic signatures in plasma. Additionally, the diagnostic potential of these profiles was assessed, as well as their ability to provide novel insight into CSCC metabolism and systemic effects. Plasma samples from CIN patients (n = 26), CSCC patients (n = 22), and a control healthy group (n = 35) were analyzed by high-performance liquid chromatography, and their spectral profiles were subjected to the t test for statistical significance. Potential metabolic biomarkers were identified using database comparisons that examine the significance of metabolites. Compared with healthy controls, patients with CIN and CSCC demonstrated lower levels of plasma amino acids; plasma levels of arginine and threonine were increased in CIN patients but were decreased in cervical cancer patients. Additionally, the levels of a larger group of amino acids (aspartate, glutamate, asparagine, serine, glycine, histidine, taurine, tyrosine, valine, methionine, lysine, isoleucine, leucine, and phenylalanine) were gradually reduced from CIN to invasive cancer. These findings suggest that plasma-free amino acid profiling has great potential for improving cancer screening and diagnosis and for understanding disease pathogenesis. Plasma-free amino acid profiles may have the potential be used to determine cancer diagnoses in the early stage from a single blood sample and may enhance our understanding of its mechanisms.     

Serum levels of angiogenic and anti-angiogenic factors: their prognostic relevance in locally advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prototype tumor wherein angiogenesis plays a vital role in its progression. The role of VEGF, a major angiogenic factor in HCC is known; however, the role of anti-angiogenic factors simultaneously with the angiogenic factors has not been studied before. Hence, in this study, the serum levels of major angiogenic [Vascular Endothelial Growth Factor (VEGF), angiopoietin-2 (Ang-2)] and anti-angiogenic (endostatin, angiostatin) factors were analyzed and correlated with clinico-radiological features and with outcome. A total of 150 patients (50 HCC, 50 cirrhosis and 50 chronic hepatitis) and 50 healthy controls were enrolled in this study. Serum levels of VEGF, Ang-2, endostatin, and angiostatin were estimated by enzyme-linked immunosorbent assay. HCC shows significantly elevated serum levels of angiogenic factors VEGF and Ang-2 and of anti-angiogenic factors endostatin and angiostatin. ROC curve analysis for serum VEGF yielded an optimal cut-off value of 225.14 pg/ml, with a sensitivity of 78 % and specificity of 84.7 % for a diagnosis of HCC and its distinction from other group. Using this value, the univariate and multivariate analysis revealed significantly poor outcome in patients with higher levels of serum VEGF (p = 0.009). Combinatorial analysis revealed that patients with higher levels of both angiogenic and anti-angiogenic factors showed poor outcome. Serum VEGF correlates with poor survival of HCC patients and, therefore, serves as a non-invasive biomarker of poor prognosis. Moreover, elevated levels of anti-angiogenic factors occur endogenously in HCC patients.     

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance (¹H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n = 25) and healthy controls (n = 25) was performed by using both ¹H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.     

Serum microRNA expression profile as a biomarker for the diagnosis of pertussis

Pertussis is a highly contagious, respiratory disease associated with substantial morbidity and mortality. A rapid and reliable diagnostic method is essential for appropriate treatment and prevention. Expression profiles of circulating microRNAs (miRNAs) have been proven as new non-invasive biomarkers for infectious diseases. We aimed to investigated the serum miRNA profile in pertussis patients and explored its potential as a novel diagnostic biomarker for pertussis. Among 664 different miRNAs analyzed using a miRNA array, 50 were overexpressed and 81 were underexpressed in the serum of pertussis patients. Expression levels of seven candidate miRNAs were further evaluated by real-time qRT-PCR. A panel of five miRNAs (miR-202, miR-342-5p, miR-206, miR-487b, miR-576-5p) was confirmed overexpressed in pertussis patients (p < 0.05). Risk score and receiver-operating characteristic (ROC) analysis showed that the area under the curve of the five-member miRNA profile was 0.980. At an optimal cutoff value (0.707), this panel of miRNAs yielded a sensitivity of 97.4 % and a specificity of 94.3 %. These data suggest that the five-member serum miRNA profile may serve as a new biomarker for pertussis diagnosis with high specificity and sensitivity.     

Identification of miRNA-103 in the cellular fraction of human peripheral blood as a potential biomarker for malignant mesothelioma--a pilot study

Background

To date, no biomarkers with reasonable sensitivity and specificity for the early detection of malignant mesothelioma have been described. The use of microRNAs (miRNAs) as minimally-invasive biomarkers has opened new opportunities for the diagnosis of cancer, primarily because they exhibit tumor-specific expression profiles and have been commonly observed in blood of both cancer patients and healthy controls. The aim of this pilot study was to identify miRNAs in the cellular fraction of human peripheral blood as potential novel biomarkers for the detection of malignant mesothelioma.

Methodology/Principal Findings

Using oligonucleotide microarrays for biomarker identification the miRNA levels in the cellular fraction of human peripheral blood of mesothelioma patients and asbestos-exposed controls were analyzed. Using a threefold expression change in combination with a significance level of p<0.05, miR-103 was identified as a potential biomarker for malignant mesothelioma. Quantitative real-time PCR (qRT-PCR) was used for validation of miR-103 in 23 malignant mesothelioma patients, 17 asbestos-exposed controls, and 25 controls from the general population. For discrimination of mesothelioma patients from asbestos-exposed controls a sensitivity of 83% and a specificity of 71% were calculated, and for discrimination of mesothelioma patients from the general population a sensitivity of 78% and a specificity of 76%.

Conclusions/Significance

The results of this pilot study show that miR-103 is characterized by a promising sensitivity and specificity and might be a potential minimally-invasive biomarker for the diagnosis of mesothelioma. In addition, our results support the concept of using the cellular fraction of human blood for biomarker discovery. However, for early detection of malignant mesothelioma the feasibility of miR-103 alone or in combination with other biomarkers needs to be analyzed in a prospective study.     

Serum DSG2 as a potential biomarker for diagnosis of esophageal squamous cell carcinoma and esophagogastric junction adenocarcinoma

Background: Exploration of serum biomarkers for early detection of upper gastrointestinal cancer is required. Here, we aimed to evaluate the diagnostic potential of serum desmoglein-2 (DSG2) in patients with esophageal squamous cell carcinoma (ESCC) and esophagogastric junction adenocarcinoma (EJA).Methods: Serum DSG2 levels were measured by enzyme-linked immunosorbent assay (ELISA) in 459 participants including 151 patients with ESCC, 96 with EJA, and 212 healthy controls. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic accuracy.Results: Levels of serum DSG2 were significantly higher in patients with ESCC and EJA than those in healthy controls (P<0.001). Detection of serum DSG2 demonstrated an area under the ROC curve (AUC) value of 0.724, sensitivity of 38.1%, and specificity of 84.8% for the diagnosis of ESCC in the training cohort, and AUC 0.736, sensitivity 58.2%, and specificity 84.7% in the validation cohort. For diagnosis of EJA, measurement of DSG2 provided a sensitivity of 29.2%, a specificity of 90.2%, and AUC of 0.698. Similar results were observed for the diagnosis of early-stage ESCC (AUC 0.715 and 0.722, sensitivity 36.3 and 50%, and specificity 84.8 and 84.7%, for training and validation cohorts, respectively) and early-stage EJA (AUC 0.704, sensitivity 44.4%, and specificity 86.9%). Analysis of clinical data indicated that DSG2 levels were significantly associated with patient age and histological grade in ESCC (P<0.05).Conclusion: Serum DSG2 may be a diagnostic biomarker for ESCC and EJA.