Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling

PurposeEndometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient’s serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection.Material and methodsUsing targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n ​= ​15) and controls (n ​= ​21) were examined for 232 endogenous metabolites.ResultsTop performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5–94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.ConclusionsUsing mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.     

Homocysteine and inflammatory biomarkers plasma levels, and severity of acute coronary syndrome

Recently, homocysteine and new inflamatory biomarkers are demonstrated to be involved in cardiovascular diseases. These risk factors are not well studied in acute coronary syndrome. We investigated the distribution of homocysteine and inflammatory markers in patients with acute coronary syndrome and evaluated the association between these parameters and severity of the disease. One hundred and twenty-two patients with acute coronary syndrome were recruited in the cardiac intensive unit care of military hospital of Tunis. Classic risk factors, lipid parameters, total homocysteine, HsCRP, IL-6 and TNFα were determined for all participants. We investigated the distribution of these parameters according to the number of diseased vessels in patients with acute coronary syndrome. Patients with three affected vessels showed significant elevated homocysteine, HsCRP, IL-6, TNF-α, total cholesterol, LDL-cholesterol and Lp (a) compared to those with one and those with two affected vessels. Homocysteine (OR?=?1.14; 95%IC: 1.04-1.25; P?=?0.006), TNF-α (OR?=?1.27; 95%IC: 1.13-1.44; P?=?10(-3)), HsCRP (OR?=?1.09; 95%IC:1.03-1.16; P?=?0.005) and IL-6 (OR?=?1.15; 95%IC: 1.06-1.25; P?=?0.001) were significant predictors of severity of the disease. We conclude that homocysteine and inflammatory biomarkers appear to enhance the degree of affected arteries and so the severity of coronary artery disease.     

Identification of a novel panel of cerebrospinal fluid biomarkers for Alzheimer's disease

An early and accurate diagnosis of Alzheimer's disease (AD) is required to initiate symptomatic treatment with currently approved drugs and will be of even greater importance if disease modifying compounds in development display a clinical effect. Protein profiles of human cerebrospinal fluid samples from AD patients (n=95) and population-based healthy controls (n=72) were analyzed by SELDI-TOF-MS in order to discover and characterize novel candidate biomarker combinations that differentiate AD patients from normal aging in this explorative study. Thirty candidate biomarkers (ROC AUC>0.7) were discovered that could differentiate patients with AD from healthy controls. Protein sequence determination and positive identification of 15 biomarkers revealed potential associations between the identified markers and AD pathogenesis. A multi-marker combination of five peaks could distinguish AD from healthy control individuals with high sensitivity (97%) and specificity (98%). The panel of five markers was tested on a blinded independent data set of 30 AD samples and 28 controls giving 100% sensitivity and 97% specificity. This novel panel of biomarkers could potentially be used to improve the accuracy of diagnosis of AD.     

Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

The early diagnosis of phytopathogens is of a great importance; it could save large economical losses due to crops damaged by fungal diseases, and prevent unnecessary soil fumigation or the use of fungicides and bactericides and thus prevent considerable environmental pollution. In this study, 18 isolates of three different fungi genera were investigated; six isolates of Colletotrichum coccodes, six isolates of Verticillium dahliae and six isolates of Fusarium oxysporum. Our main goal was to differentiate these fungi samples on the level of isolates, based on their infrared absorption spectra obtained using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) sampling technique. Advanced statistical and mathematical methods: principal component analysis (PCA), linear discriminant analysis (LDA), and k-means were applied to the spectra after manipulation. Our results showed significant spectral differences between the various fungi genera examined. The use of k-means enabled classification between the genera with a 94.5% accuracy, whereas the use of PCA [3 principal components (PCs)] and LDA has achieved a 99.7% success rate. However, on the level of isolates, the best differentiation results were obtained using PCA (9 PCs) and LDA for the lower wavenumber region (800-1775 cm(-1)), with identification success rates of 87%, 85.5%, and 94.5% for Colletotrichum, Fusarium, and Verticillium strains, respectively.     

The diagnosis of hypovolemia using advanced statistical methods

Aim

Diagnosing hypovolemia is not a trivial task. Hypovolemia itself has several physical signs, but their specificity and sensitivity is limited, even using sophisticated monitoring techniques. However, diagnosing hypovolemia is crucial in critically ill patients to avoid worse outcomes. The aim of this paper is to provide methods for better estimation of the degree of hypovolemia in ill patients.

Methods

The so-called hypovolemic index (HVI) is introduced which classifies the degree of hypovolemia with a number in the interval [0, 1]. Four new methods are presented for the more precise diagnosis of hypovolemia. All methods rely on fuzzy logic. In the first method, clinical thresholds are used in the fuzzy rule system. The second method uses an iterative ROC analysis to determine the thresholds. The third one determines the thresholds using one single ROC analysis (“One step” method). The fourth method uses a genetic algorithm (GA) for the determination of the thresholds. The HVI is calculated using the data of patients from a previous investigation. Each method (except the first one) is tuned on a so called training database. Afterwards, they are carried out on a test database in order to determine the potential of the method.

Results

All four methods are capable of differentiating between hypovolemic and normovolemic patients. However, using the first and the second methods, several patients get a HVI of around 0.5, therefore, their degree of hypovolemia is ambiguous. The third and fourth methods deliver a better classification, hypovolemic and normovolemic patients are clearly separated from each other.

Conclusion

All four novel methods deliver powerful tools for the diagnosis of hypovolemic patients. The degree of the hypovolemic state of each patient can be estimated with a hitherto unattained degree of reliability. Using ROC analysis and GA the estimation can be improved further.     

Identification of immunohistochemical biomarkers for papillary thyroid carcinoma using gene expression profiling

We report the successful validation of a combined gene expression profiling and tissue microarray approach to papillary thyroid carcinoma (PTC) biomarker identification. Our ultimate goal is the identification of protein biomarkers that can be effectively used in immunocytochemical assays applied to thyroid fine needle aspiration biopsy (FNAB) samples. To that end, we designed our approach to prioritize molecules that were minimally expressed in normal thyroid and highly expressed in PTC. We first generated gene expression profiles from 11 normal thyroid tissue samples and 9 samples of classic PTC. The results were segregated to rank most highly those molecules not expressed in normal thyroid and up-regulated at least 6-fold in PTC. From this list, we chose 2 molecules (P-cadherin and Bax) for immunohistochemical analysis for which commercial antibodies were available. These were compared with 2 other molecules that have been previously studied in thyroid cancer (cytokeratin-19 and galectin-3). For immunohistochemistry, a tissue microarray was generated that contained the following tissues: classic PTC (n = 20), follicular variant of PTC (n = 9), normal thyroid (n = 19), Hashimoto thyroiditis (n = 11), follicular adenoma (n = 15), and follicular carcinoma (n = 14). Immunohistochemical staining was scored and compared with the gene expression profiling. As anticipated, cytokeratin-19 and galectin-3 were highly expressed in PTC and less expressed in other tissues. Bax and P-cadherin were also expressed in PTC, but to a lower level than cytokeratin-19 and galectin-3; however, Bax and P-cadherin demonstrated virtually no staining of normal thyroid, unlike cytokeratin-19 and galectin-3. These results validate our approach for PTC biomarker discovery and identify several candidate biomarkers for further development.     

Identification of biomarkers for disease severity in nasopharyngeal secretions of infants with upper or lower respiratory tract viral infections

Lower respiratory tract infections (LRTIs) produced by viruses are the most frequent cause of morbidity and mortality in children younger than 5 years of age. The immune response triggered by viral infection can induce a strong inflammation in the airways and cytokines could be considered as biomarkers for disease severity as these molecules modulate the inflammatory response that defines the outcome of patients. Aiming to predict the severity of disease during respiratory tract infections, we conducted a 1-year follow-up observational study in infants who presented upper or lower respiratory tract infections caused by seasonal respiratory viruses. At the time of enrollment, nasopharyngeal swabs (NPS) were obtained from infants to measure mRNA expression and protein levels of IL-3, IL-8, IL-33, and thymic stromal lymphopoietin. While all cytokines significantly increased their protein levels in infants with upper and lower respiratory tract infections as compared to control infants, IL-33 and IL-8 showed a significant increase in respiratory syncytial virus (RSV)-infected patients with LRTI as compared to patients with upper respiratory tract infection. We also found higher viral loads of RSV-positive samples with a greater IL-8 response at the beginning of the symptoms. Data obtained in this study suggest that both IL-8 and IL-33 could be used as biomarkers for clinical severity for infants suffering from LRTIs caused by the RSV.     

Severe malaria in an unstable setting: clinical and laboratory correlates of cerebral malaria and severe malarial anemia and a paradigm for a simplified severity scoring

An interpretation of historical, clinical, and laboratory data was made to identify the correlates of and the diversity between cerebral malaria (CM) and severe malarial anemia (SMA) in a setting of low, seasonal, and unstable malaria transmission in eastern Sudan. Hemoglobin (Hb), random blood glucose (RBG), and anti-MSP antibodies were measured. Results showed that SMA and CM were significantly different with regard to age, malaria history, fever duration, convulsions, and hepatosplenomegaly. The MSP Ab response was inversely correlated with the number of previous malaria episodes but not with fever duration in the current attack. The spleen size was significantly inversely correlated with Hb level while hepatomegaly was significantly associated with low RBG. Furthermore, two malaria patients presented with neuropsychiatric upset. Finally, the correlates of SMA and CM fit perfectly with an adopted severity numeric scoring. Hayder A. Giha and Gehad Elghazali contributed equally to this paper.     

Identification of potential biomarkers for giant cell tumor of bone using comparative proteomics analysis

Giant cell tumor of bone can be locally aggressive and occasionally can metastasize in the lungs. To identify new markers predictive of aggressive behavior, we analyzed five patients who developed lung metastasis and five who remained disease free for a minimum of 5 years. Using two-dimensional electrophoresis, we detected 28 differentially expressed spots. Fourteen spots were identified using mass spectrometry, including seven up-regulated and seven down-regulated in metastatic samples and classified according to functional categories. We then selected five proteins involved in cell cycle or apoptosis. Thioredoxin peroxidase, allograft inflammatory factor 1, and ubiquitin E2N had more than threefold up-regulation; glutathione peroxidase 1 had 1.9-fold up-regulation; and heat shock protein 27 showed down-regulation in metastatic samples with a very low P value. After validation and analysis of protein levels, evaluation of clinical impact was assessed in a much wider cohort of primary archival specimens. Immunodetection showed a higher frequency of thioredoxin peroxidase, allograft inflammatory factor 1, ubiquitin E2N, and glutathione peroxidase 1 overexpression in primary tumors that developed into lung metastases or that locally relapsed than in the disease-free group, with variable stain intensity and distribution. Kaplan-Meier analysis showed that high expression of glutathione peroxidase 1 was strongly related to local recurrence and metastasis, suggesting that its up-regulation may identify a subset of high-risk patients with giant cell tumor prone to receive diverse clinical management.     

Identification of potential prognostic biomarkers for breast cancer using WGCNA and PPI integrated techniques

In this study, we aimed to detect promising prognostic factors of breast cancer and interpreted the relevant mechanisms using an integrated bioinformatics analysis. RNA sequencing profile of breast cancer was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, which were combined as a group (TCGA_GTEx). GSE70947 dataset was from Gene Expression Omnibus. Blue and turquoise modules, respectively identified in TCGA_GTEx database and GSE70947 dataset using weighted co-expression network analysis (WGCNA), were both notably associated with breast cancer. By comparing genes in the two significant modules with differentially expressed genes (DEGs), we obtained a set of 40 shared genes, which were mainly enriched in chromosome segregation and mismatch repair pathway. After protein-protein interaction (PPI) network and overall survival analysis, two hub genes EXO1 and KIF4A were extracted from the set of 40 shared genes, which were up-regulated and associated with the dismal outcome of breast cancer patients. There was a notable negative correlation between EXO1 and KIF4A expression and age of breast cancer patients, whereas a positive relationship with two another clinical traits stage and tumor category was detected. Univariate and multivariate Cox regression analysis revealed that the two hub genes could be independent prognostic factors of breast cancer. Mechanistically, gene correlation analysis suggested that EXO1 and KIF4A exerted their oncogenic role via promoting breast cancer cell proliferation. Overall, our findings identify two promising individual prognostic predictors of breast cancer and pave the new way for diagnosis and therapy strategy of breast cancer.     

Concurrent sickle-cell anemia and alpha-thalassemia: effect on severity of anemia

We studied 47 patients with sickle-cell anemia to determine the effect of alpha-thalassemia on the severity of their hemolytic anemia. We diagnosed alpha-thalassemia objectively by using alpha-globin-gene mapping to detect alpha-globin-gene deletions, studying 25 subjects with the normal four alpha-globin-genes, 18 with three, and four with two. The mean hemoglobin, hematocrit, and absolute reticulocyte levels (+/- S.D.) were 7.9 +/- 0.9 g per deciliter (4.9 +/- 0.6 mmol per liter), 22.9 +/- 2.9 per cent, and 501,000 +/- 126,000 per cubic millimeter, respectively, in the non-thalassemic group; 9.8 +/- 1.6 g per deciliter (6.1 +/- 1.0 mmol per liter), 29.0 +/- 5.0 per cent, and 361,000 +/- 51,000 per cubic millimeter in the group with three alpha-globin genes; and 9.2 +/- 1.0 g per deciliter (5.7 +/- 0.6 mmol per liter), 27.5 +/- 3.0 per cent, and 100,000 +/- 15,000 per cubic millimeter in the group with two alpha-globin genes. Deletion of alpha-globin genes was also accompanied by a decreased mean corpuscular hemoglobin concentration (MCHC) in post-reticulocyte erythrocytes and by increased hemoglobin F levels. The decreased intraerythrocytic hemoglobin S concentration and elevated hemoglobin F levels associated with alpha-thalassemia appear to diminish the degree of hemolytic anemia found in sickle-cell disease.     

Diagnostics of pancreatic insufficiency using multivariate statistical and pattern recognition methods

The complex evaluation of Lundh-test data was investigated in patients with pancreatic insufficiency and in controls using multivariate statistical methods. It has been shown that isolated parameters do not give satisfactory separation. The lower limits of the normal values were calculated in advance from the data of 77 control cases with the help of distribution analysis and of 2.5 percentile value for all parameters. On the basis of these normal values, and the results of other examinations, two groups were formed from the data of 137 more patients: (1) certainly pathologic patients, and (2) control cases. The application of multivariate statistical and pattern recognition methods considering the parameters simultaneously led to the correct diagnosis in 88-80-93% of the cases using the centroid, nearest neighbour and linear discriminant analysis, respectively. Linear discriminant analysis successfully separated controls from the patients with pancreatic insufficiency also in mild cases.     

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer(CRC)is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients.In this study,mRNA microarray datasets GSE113513,GSE21510,GSE44076,and GSE32323 were obtained from the Gene Expression Omnibus(GEO)and analyzed with bioinformatics to identify hub genes in CRC development.Differentially expressed genes(DEGs)were analyzed using the GEO2 R tool.Gene ontology(GO)and KEGG analyses were performed through the DAVID database.STRING database and Cytoscape software were used to construct a protein-protein interaction(PPI)network and identify key modules and hub genes.Survival analyses of the DEGs were performed on GEPIA database.The Connectivity Map database was used to screen potential drugs.A total of 865 DEGs were identified,including 374 upregulated and 491 downregulated genes.These DEGs were mainly associated with metabolic pathways,pathways in cancer,cell cycle and so on.The PPI network was identified with 863 nodes and 5817 edges.Survival analysis revealed that HMMR,PAICS,ETFDH,and SCG2 were significantly associated with overall survival of CRC patients.And blebbistatin and sulconazole were identified as candidate drugs.In conclusion,our study found four hub genes involved in CRC,which may provide novel potential biomarkers for CRC prognosis,and two potential candidate drugs for CRC.