Search

Diabetic foot osteomyelitis (DFO) is a severe, difficult to treat infection. Local antibiotic delivery has been studied as a potential therapeutic adjunct following surgery for DFO. This review aims to summarize the evidence on local antibiotic delivery systems in DFO. PubMed database was searched up to March 2020. Overall, 16 studies were identified and included: 3 randomized controlled trials (RCTs), 3 retrospective studies (RSs), and 10 case series. In the RCTs, gentamicin-impregnated collagen sponges significantly improved clinical healing rates and slightly improved duration of hospitalization. In the RSs, antibiotic-impregnated calcium sulfate beads non-significantly improved all healing parameters, but did not reduce post-operative amputation rates or time of healing. The majority of case series used calcium sulfate beads, achieving adequate rates of healing and eradication of infection. In conclusion, evidence for add-on local antibiotic delivery in DFO is still limited; more data are needed to assess this therapeutic measure.

The objective of our study was to compare a novel squared section, tapered design - with four conicity - short stem, the MINIMA® short stem with the cementless Profemur® TL standard femoral stem in primary total hip arthroplasty (THA) in terms of functional outcomes, radiologic evaluation and other peri-operative and post-operative data.This is a comparative study including 46 patients undergoing primary THA. In 23 patients, the MINIMA® short stem was used. These patients were matched with another 23 patients in whom a cementless Profemur® TL standard femoral stem was used. The levels of the pain were evaluated according to the Visual Analog Scale/Numerical Rating Scale (VAS/NRS). The functional and clinical evaluation of the patients was performed with Harris Hip Score (HHS), Charnley's Hip score, EuroQol (EQ-5D)-(EQ-100), Patient Health Questionnaire (PHQ-9), and neuropathic pain questionnaire (DN-4). The rest of the comparison data included demographic data, the American Society of Anesthesiologists score (ASA), Charlson Index score, the pre-operative diagnosis, radiographic evaluation, the days of hospitalization, the operating time, incision length, blood loss, and blood transfusion requirements and complication rates.The two cohorts had comparable results regarding all patients' peri-operative data. The radiographic assessment revealed considerable higher levels of femoral offset and femoral subsidence for the MINIMA group, but within acceptable limits for both cohorts. The majority of the functional and other scores did not give strong prominence to one specific femoral stem.Our comparative study underlined the efficacy of the MINIMA® short stem, due to the fact that it revealed comparable and, in some cases, relatively better short-term outcomes compared with the TL standard femoral stem. Yet, more well-designed long-term research is required in order to further establish its effectiveness.

BACKGROUND: Supercapsular Percutaneously Assisted total hip arthroplasty (SuperPATH approach) is a relatively new minimal invasive approach which has been associated with encouraging postoperative outcomes. The aim of this study is to compare the minimal invasive (MIS) SuperPATHapproach with the standard modified Hardinge approach at the base of muscle damage due to serum markers, functional results and other perioperative and postoperative data. MATERIAL AND METHODS: Forty eight (48) consecutive patients undergone primary total hip arthroplasty (THA) by the same surgeon (GD), were enrolled in our study. From this study population, the modified Hardinge approach was performed in 23 patients and the SuperPATH approach was performed in 25 patients. Soft tissue impairment was studied based on three representative markers, C-reactive-protein (CRP) and two enzymes, creatine kinase (CK) and lactate dehydrogenase (LDH). We measured these markers 10 min after surgery, on 1st and 2nd postoperative day. The levels of the perceived pain were evaluated according to the Visual Analog Scale/Numerical Rating Scale (VAS/NRS) score which was registered 6 h, 12 h, 1 day and 2 days postoperatively. The functional and clinical evaluation of the patients was achieved with Harris Hip Score (HHS), Charnley's Hip score, EuroQol (EQ-5D)-(EQ-100), Patient Health Questionnaire (PHQ-9) and neuropathic pain questionnaire (DN-4) 6 months and 1 year postoperatively. The rest of the collected data included patient's age, gender, body mass index (BMI), other comorbitities, the American Society of Anesthiologists score (ASA), Charlson Index score, the pre-operative diagnosis, implant positioning through radiographic evaluation, the type of anesthesia, the days of hospitalization, the operating time, incision length, blood loss and blood transfusion requirements and complication rates. RESULTS: SuperPATH approach was related with statistically considerable lower levels of CRP at 10 min (p = 0,001) and at 24 h (p = 0,047) postoperatively, as well as lower LDH levels in all time points postoperatively. It was also associated with shorter incision length (p

Polymethylmethacrylate (PMMA) represents the current gold standard as an antibiotic delivery carrier in orthopaedic surgery. Despite the accepted use of local antibiotic carriers, there aren't any conclusive data comparing PMMA to Bone Graft Substitutes. The aim of this in vitro study was to compare the elution profile of gentamicin from various preparations of PMMA cements and Herafill beads. All cements had high initial elution during the first hour which then slowly decreased, Herafill beads on the other hand showed its higher elution around the eighth hour. Herafill, in general, presented the highest elution of gentamicin regardless of its input amount.

Background: The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM. Methods: ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models. Results: ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927). Conclusions: Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.

Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups. The methylation status of a panel of cancer-related genes chosen based on previous expression and epigenetic data (KLK10, SOX17, WNT5A, MSH2, GATA3) was assessed by quantitative methylation-specific PCR. All but the GATA3 gene was more frequently methylated in all the patient groups than in healthy individuals (all p

Circulating cell-free DNA (ccfDNA) is a biological entity of great interest due to its potential as liquid biopsy biomaterial carrying clinically valuable information. To better understand its nature, we studied ccfDNA in vitro in two human cancer cell lines MCF-7 and HeLa. Normalized indexes of ccfDNA per cell population decreased over time of culture but were significantly elevated after exposure to IC50 doses of the demethylating/apoptotic agent 5-azacytidine (5-AZA-CR). Fragment-size profiling was indicative of active release, whereas exposure to 5-AZA-CR induced the release of additional shorter fragments, indicative of apoptosis. Finally, the methylation profile of a panel of cancer-specific genes as assessed by quantitative methylation analysis in ccfDNA was identical to the corresponding genomic DNA and followed accurately changes caused by 5-AZA-CR. Overall, our in vitro findings support that ccfDNA can be a reliable biosource of clinically relevant information that can be further studied in these cell culture models.

Simple Summary Breast cancer (BrCa) is characterized by aberrant DNA methylation. We leveraged high-throughput methylation data from BrCa and normal breast tissues and identified 11,176 to 27,786 differentially methylated genes (DMGs) against clinically relevant end-points. Innovative automated machine learning was employed to construct three highly performing signatures for (1) the discrimination of BrCa patients from healthy individuals, (2) the identification of BrCa metastatic disease and (3) the early diagnosis of BrCa. Furthermore, functional analysis revealed that most genes selected in the signatures showed associations to BrCa, with regulation of transcription being the main biological process, the nucleus being the main cellular component and transcription factor activity and sequence-specific DNA binding being the main molecular functions. Overall, revisiting methylome datasets led to three high-performance signatures that are readily available for improving BrCa precision management and significant knowledge mining related to disease pathophysiology. Abstract DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723–0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865–0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899–0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.

The rapid outbreak of COVID-19 brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Despite the global scientific effort, there is lack of efficient predictive models for patient stratification and successful management of the disease. Here, we employed Automated Machine Learning (AutoML) to analyze 3 publicly available COVID-19 datasets, including serum proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset produced ten equivalent signatures of two features each, with AUC 0.840(CI 0.723 – 0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each with AUC 0.914(CI 0.865 - 0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. A second transcriptomic dataset led to two equivalent signatures of nine features each with AUC 0.967(CI 0.899 - 0.996) in identifying COVID-19 patients from virus-free individuals. Multiple new features emerged implicated in a wide range of pathways including viral mRNA translation pathways, interferon gamma signaling and Innate Immune System. In conclusion, by application of AutoML multiple biosignatures were built in a fast automated way, presenting reduced feature number and high predictive performance that remained high upon validation. These favorable characteristics are eminent for further development of cost-effective clinical assays to contribute to better disease management. Our results also highlight the importance of revisiting precious and well-built datasets for maximal conclusion extraction from a given experimental observation. Funding Statement: No funding was received for this research. Declaration of Interests: GP, MK, and NT are employees of Gnosis Data Analysis that offers the JADBio service commercially. IT and VL are co-founders of Gnosis Data Analysis that offers the JADBio service commercially and members of its scientific advisory board.

This retrospective study aimed to compare the outcomes and healing parameters of 3 groups of surgical treatment combined with and without local antibiotic administration in diabetic foot osteomyelitis (DFO). Overall, 25 patients with DFO who met the criteria were included in the study. Surgical debridement was used with systemic antibiotic administration alone (group A; n = 8) or combined with local application of antibiotic-loaded polymethylmethacrylate beads (group B; n = 9) or antibiotic-loaded hydroxyapatite and calcium sulfate beads (group C; n = 8). In total, 87.5% patients in group A, 100% in group B, and 87.5% in group C healed ( P = .543). Median time to healing was 17 weeks in group A, 18 weeks in group B, and 19 weeks in group C ( P = .094). One patient (12.5%) in group A was amputated. DFO recurrence rate was 12.5% in group A and 12.5% in group C ( P = .543). Median hospitalization was 9 days in group A, 8 days in group B, and 9 days in group C ( P = .081). In conclusion, adjunctive local antibiotic therapy was not shown to improve outcomes in surgically treated DFO.

Alzheimer&rsquo, s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age&ndash, sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

Autotaxin (ATX), encoded by the ctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene, is a key enzyme in lysophosphatidic acid (LPA) synthesis. We have recently described ENPP2 methylation profiles in health and multiple malignancies and demonstrated correlation to its aberrant expression. Here we focus on breast cancer (BrCa), analyzing in silico publicly available BrCa methylome datasets, to identify differentially methylated CpGs (DMCs) and correlate them with expression. Numerous DMCs were identified between BrCa and healthy breast tissues in the gene body and promoter-associated regions (PA). PA DMCs were upregulated in BrCa tissues in relation to normal, in metastatic BrCa in relation to primary, and in stage I BrCa in relation to normal, and this was correlated to decreased mRNA expression. The first exon DMC was also investigated in circulating cell free DNA (ccfDNA) isolated by BrCa patients; methylation was increased in BrCa in relation to ccfDNA from healthy individuals, confirming in silico results. It also differed between patient groups and was correlated to the presence of multiple metastatic sites. Our data indicate that promoter methylation of ENPP2 arrests its transcription in BrCa and introduce first exon methylation as a putative biomarker for diagnosis and monitoring which can be assessed in liquid biopsy.

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

Sulfonylureas are insulin secretagogues which act in pancreatic β cells by blocking the KATP channels encoded by KCNJ11 and ABCC8 genes. In the present study, a pharmacoepigenetic approach was applied for the first time, investigating the correlation of KCNJ11 and ABCC8 gene promoter methylation with sulfonylureas-induced mild hypoglycemic events as well as the KCNJ11 E23K genotype. Sodium bisulfite-treated genomic DNA of 171 sulfonylureas treated T2DM patients previously genotyped for KCNJ11 E23K, including 88 that had experienced drug-associated hypoglycemia and 83 that had never experienced hypoglycemia, were analyzed for DNA methylation of KCNJ11 and ABCC8 gene promoters via quantitative Methylation-Specific PCR. KCNJ11 methylation was detected in 19/88 (21.6%) of hypoglycemic and in 23/83 (27.7%) of non-hypoglycemic patients (p=0.353), while ABCC8 methylation in 6/83 (7.2%) of non-hypoglycemic and none (0/88) of the hypoglycemic patients (p=0.012). Methylation in at least one promoter (KCNJ11 or ABCC8) was significantly associated with non-hypoglycemic patients who are carriers of KCNJ11 EK allele (p=0.030). Our data suggest that ABCC8 but not KCNJ11 methylation is associated to hypoglycemic events in sulfonylureas-treated T2DM patients. Furthermore, it is demonstrated that the KCNJ11 E23K polymorphism in association to either of the two genes’ DNA methylation may have protective role against sulfonylurea-induced hypoglycemia.

Coronary artery disease (CAD) is a major global health burden whereby gene-by-environment-by-sex interactions play an important role. Coronary artery bypass graft (CABG) surgery involves patients with well-documented and severe CAD. Hence, the study of CAD in a context of the CABG surgery serves as an advantageous model for disease phenotype ascertainment and genetic association studies. We report here new observations from a case-control genetic association study on promoter methylation of the cystathionine γ-lyase (CTH) gene and its association with CAD. To the best of our knowledge, this is the first clinical study to show the DNA methylation status of the CTH promoter in relation to this clinical phenotype. CTH encodes for the hydrogen sulfide generating enzyme named CSE in the endothelium that is mechanistically highly relevant for CAD. In a sample of 334 subjects from Greece (178 cases with CAD and who underwent CABG, and 156 controls), CTH promoter methylation was analyzed using a SYBR Green-based quantitative methylation-specific polymerase chain reaction. We found increased methylation in CTH promoter in cases (19.1%) compared to controls (10.3%) (p = 0.024). Gene-by-sex analysis sustained the significant association in men (p = 0.032) but not in women (p = 0.884). By using multivariate analyses after controlling for potential confounders such as smoking, age, and gender, we found that increased CTH gene promoter methylation was associated with CAD in the total sample (odds ratio [OR] = 2.163, 95% confidence interval [CI] 1.038-4.506, p = 0.039) and in men (OR = 2.418, 95% CI 1.048-5.581, p = 0.039) but not in women (OR = 0.542, 95% CI 0.094-3.140, p = 0.495). These observations collectively warrant further precision medicine and biomarker research to examine the CTH methylation status as a putative epigenetic regulator of CAD risk in larger and independent samples.

Precision medicine (PM) is currently placed at the center of global attention following decades of research towards the improvement of medical practice. The subject of this study was to examine whether this trend had emerged earlier, in fact if the fundamentals of PM can be traced back to the ancient Greek era. For this reason, we studied the collection of all the Hippocratic texts, called the Corpus Hippocraticum, using original translations, and attempted an interpretation of the ancient authors in the context of the modern concept of PM. The most important points located in the ancient passages were: (1) medicine in not 'absolute', thus its directions cannot be generalized to everybody, (2) each human body/organism is different and responds differently to therapy; therefore, the same treatment cannot be suitable for everybody and (3) the physician should choose the appropriate treatment, depending on the patients' individual characteristics, such as different health status and life style (activities, diet, etc.). Although the ancient 'precision medicine' is different from its modern description, the latter derived from well-established experimental conclusions, it becomes apparent that there is a common conception, aiming to achieve more effective healing by focusing on the individual.

Corticotropin Releasing Factor (CRF) neuropeptides coordinate the stress response via two distinct membrane receptors (CRF-Rs). We have previously shown expression of both CRF-Rs in human breast cancer tissues. In the present study, we examined in vitro using the MCF-7 cell line model, the regulation of CRF-Rs expression and their signaling in hormone-dependent breast cancer growth. Our findings show that similarly to breast cancer biopsies, the predominant receptor type expressed in the cell line is CRF-R2α. The transcription of CRF-R1 and CRF-R2 is up and down-regulated respectively by exposure to estradiol (E2); however this effect seems not to be exerted at the level of promoter gene methylation, although in human breast cancer specimens, CRF-R1 methylation was found to be positively associated with the presence of steroid hormone receptors. Finally, we showed that specific activation of CRF-R2 increased the migration of MCF-7 cells and potentiated an estrogen-inducing effect. Our data support an involvement of CRF-R signaling in breast cancer pathophysiology via a regulatory steroid-hormone interplay.

The purpose of this study was the development of new quantitative methodologies for the general (total) COL11A1 gene and the C transcript (RT-qPCR methods for A and E transcripts have already been developed by our group previously), the quantification of all COL11A1 transcripts and the investigation for the first time of their potential association with histopathological prognostic factors in lung cancer.Real-time RT-qPCR methodologies with dual hybridization probes were developed on the Light Cycler 1.5 platform (Roche,Germany). All COL11A1 transcripts were measured in 27 cDNA lung tissue specimens in a blinded fashion (8 control and 19 non-small cell lung cancer (NSCLC) tissues with known histopathological data). Statistical analysis was performed with the IBM SPSS program.The novel real-time RT-qPCR methodologies were appropriately validated. All 19 NSCLC samples were positive for the general COL11A1 transcript (range 11.2-1198.0 copies/μg total RNA, while 5 out of 8 control samples were negative: mean values were also statistically significantly different (p0.001). In 4 tumor samples (21%), no specific COL11A1 transcript was detected. Transcript C was detected in only 3 tumor samples. Regarding transcripts A and E, 13 out of 19 tumor samples were positive for either one (68%) and 11 for both (58%).No other statistically significant association of the specific transcripts with histopathological data was observed, most probably due to the limited number of samples. As the number of general COL11A1 transcripts/µg exceeds the sum of A+E+C transcripts in all samples, there is opportunity for discovery and identification of other transcripts as well.

The corticotropin-releasing factor (CRF) system has been strongly associated with gastrointestinal pathophysiology, including colorectal cancer (CRC). We previously showed that altered expression of CRF receptors (CRFRs) in the colon critically affects CRC progression and aggressiveness through regulation of colonic inflammation. Here, we aimed to assess the potential of CRFR methylation levels as putative biomarkers in CRC. In silico methylation analysis of CRF receptor 1 (CRFR1) and CRF receptor 2 (CRFR2) was performed using methylome data derived by CRC and Crohn’s disease (CD) tissues and CRC-derived circulating cell-free DNAs (ccfDNAs). In total, 32 and 33 differentially methylated sites of CpGs (DMCs) emerged in CRFR1 and CRFR2, respectively, between healthy and diseased tissues. The methylation patterns were verified in patient-derived ccfDNA samples by qMSP and associated with clinicopathological characteristics. An automated machine learning (AutoML) technology was applied to ccfDNA samples for classification analysis. In silico analysis revealed increased methylation of both CRFRs in CRC tissue and ccfDNA-derived datasets. CRFR1 hypermethylation was also noticed in gene body DMCs of CD patients. CRFR1 hypermethylation was further validated in CRC adjuvant-derived ccfDNA samples, whereas CRFR1 hypomethylation, observed in metastasis-derived ccfDNAs, was correlated to disease aggressiveness and adverse prognostic characteristics. AutoML analysis based on CRFRs methylation status revealed a three-feature high-performing biosignature for CRC diagnosis with an estimated AUC of 0.929. Monitoring of CRFRs methylation-based signature in CRC tissues and ccfDNAs may be of high diagnostic and prognostic significance in CRC.

Objective Several studies have implicated PAX1 epigenetic regulation in cervical neoplasia. The aim of this meta-analysis was to assess PAX1 gene methylation as a potential biomarker in cervical cancer screening. Methods A systematical search of all major databases was performed, in order to include all relevant publications in English until December 31 st 2014. Studies with insufficient data, conducted in experimental models or associated with other comorbidities were excluded from the meta-analysis. Summary receiver operating characteristics (SROC) for Cervical Intraepithelial Neoplasia grade 2 or worse (CIN2 + ) versus normal, and CIN grade 3 or worse (CIN3 + ) versus normal, were estimated using the bivariate model. Results Out of the 20 initially included studies, finally 7 (comprising of 1385 subjects with various stages of CIN and normal cervical pathology) met the inclusion criteria. The sensitivity of CIN2 + versus normal was estimated to be 0.66 (CI 95%, 0.46-0.81) and the specificity 0.92 (CI 95%, 0.88-0.95). On the other hand, the sensitivity of CIN3 + versus normal was 0.77 (CI 95%, 0.58-0.89) and the specificity 0.92 (CI 95%, 0.88-0.94). Moreover, the area under the curve (AUC) in the former case was 0.923, and in the latter 0.931. Conclusion The results of this meta-analysis support the utility of PAX1 methylation as an auxiliary biomarker in cervical cancer screening. PAX1 could be used effectively to increase the specificity of HPV DNA by detecting women with more advanced cervical abnormalities.

Background Collagen XI is a key structural component of the extracellular matrix and consists of three alpha chains. One of these chains, the α1 (XI), is encoded by the COL11A1 gene and is transcribed to four different variants at least (A, B, C and E) that differ in the propensity to N-terminal domain proteolysis and potentially in the way the extracellular matrix is arranged. This could affect the ability of tumor cells to invade the remodeled stroma and metastasize. No study in the literature has so far investigated the expression of these four variants in breast cancer nor does a method for their accurate quantitative detection exist. Methods We developed a conventional PCR for the general detection of the general COL11A1 transcript and real-time qPCR methodologies with dual hybridization probes in the LightCycler platform for the quantitative determination of the variants. Data from 90 breast cancer tissues with known histopathological features were collected. Results The general COL11A1 transcript was detected in all samples. The developed methodologies for each variant were rapid as well as reproducible, sensitive and specific. Variant A was detected in 30 samples (33 %) and variant E in 62 samples (69 %). Variants B and C were not detected at all. A statistically significant correlation was observed between the presence of variant E and lymph nodes involvement (p = 0.037) and metastasis (p = 0.041). Conclusions With the newly developed tools, the possibility of inclusion of COL11A1 variants as prognostic biomarkers in emerging multiparameter technologies examining tissue RNA expression should be further explored. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1725-8) contains supplementary material, which is available to authorized users.

Cervical cancer is strongly related to certain high-risk types of human papilloma virus infection. Breast cancer metastasis suppressor 1 (BRMS1) is a tumor suppressor gene, its expression being regulated by DNA promoter methylation in several types of cancers. This study aims to evaluate the methylation status of BRMS1 promoter in relation to high-risk types of human papilloma virus infection and the development of pre-cancerous lesions and describe the pattern of BRMS1 protein expression in normal, high-risk types of human papilloma virus–infected pre-cancerous and malignant cervical epithelium. We compared the methylation status of BRMS1 in cervical smears of 64 women with no infection by high-risk types of human papilloma virus to 70 women with proven high-risk types of human papilloma virus infection, using real-time methylation-specific polymerase chain reaction. The expression of BRMS1 protein was described by immunohistochemistry in biopsies from cervical cancer, pre-cancerous lesions, and normal cervices. Methylation of BRMS1 promoter was detected in 37.5% of women with no high-risk types of human papilloma virus infection and was less frequent in smears with high-risk types of human papilloma virus (11.4%) and in women with pathological histology (cervical intraepithelial neoplasia) (11.9%). Methylation was detected also in HeLa cervical cancer cells. Immunohistochemistry revealed nuclear BRMS1 protein staining in normal high-risk types of human papilloma virus–free cervix, in cervical intraepithelial neoplasias, and in malignant tissues, where staining was occasionally also cytoplasmic. In cancer, expression was stronger in the more differentiated cancer blasts. In conclusion, BRMS1 promoter methylation and aberrant protein expression seem to be related to high-risk types of human papilloma virus–induced carcinogenesis in uterine cervix and is worthy of further investigation.