Mitochondrial Fraction of Circulating Cell-Free DNA as an Indicator of Human Pathology.

Circulating cell-free DNA (ccfDNA) of mitochondrial origin (ccf-mtDNA) consists of a minor fraction of total ccfDNA in blood or in other biological fluids. Aberrant levels of ccf-mtDNA have been observed in many pathologies. Here, we introduce a simple and effective standardized Taqman probe-based dual-qPCR assay for the simultaneous detection and relative quantification of nuclear and mitochondrial fragments of ccfDNA. Three pathologies of major burden, one malignancy (Breast Cancer, BrCa), one inflammatory (Osteoarthritis, OA) and one metabolic (Type 2 Diabetes, T2D), were studied. Higher levels of ccf-mtDNA were detected both in BrCa and T2D in relation to health, but not in OA. In BrCa, hormonal receptor status was associated with ccf-mtDNA levels. Machine learning analysis of ccf-mtDNA datasets was used to build biosignatures of clinical relevance. (A) a three-feature biosignature discriminating between health and BrCa (AUC: 0.887) and a five-feature biosignature for predicting the overall survival of BrCa patients (Concordance Index: 0.756). (B) a five-feature biosignature stratifying among T2D, prediabetes and health (AUC: 0.772); a five-feature biosignature discriminating between T2D and health (AUC: 0.797); and a four-feature biosignature identifying prediabetes from health (AUC: 0.795). (C) a biosignature including total plasma ccfDNA with very high performance in discriminating OA from health (AUC: 0.934). Aberrant ccf-mtDNA levels could have diagnostic/prognostic potential in BrCa and Diabetes, while the developed multiparameter biosignatures can add value to their clinical management.

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy.

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.

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 ß-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.

ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker.

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.

Circulating cell-free DNA release in vitro: kinetics, size profiling, and cancer-related gene methylation.

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.

Development of novel real-time RT-qPCR methodologies for quantification of the COL11A1 mRNA general and C transcripts and evaluation in non-small cell lung cancer specimens.

PURPOSE: 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. METHODS: 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. RESULTS: 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 (p<0.001). 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%). CONCLUSIONS: 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.

Gene promoter methylation and protein expression of BRMS1 in uterine cervix in relation to high-risk human papilloma virus infection and cancer.

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.

Development of novel real-time PCR methodology for quantification of COL11A1 mRNA variants and evaluation in breast cancer tissue specimens.

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.

Intratumoral Microbiome: Foe or Friend in Reshaping the Tumor Microenvironment Landscape?

The role of the microbiome in cancer and its crosstalk with the tumor microenvironment (TME) has been extensively studied and characterized. An emerging field in the cancer microbiome research is the concept of the intratumoral microbiome, which refers to the microbiome residing within the tumor. This microbiome primarily originates from the local microbiome of the tumor-bearing tissue or from translocating microbiome from distant sites, such as the gut. Despite the increasing number of studies on intratumoral microbiome, it remains unclear whether it is a driver or a bystander of oncogenesis and tumor progression. This review aims to elucidate the intricate role of the intratumoral microbiome in tumor development by exploring its effects on reshaping the multileveled ecosystem in which tumors thrive, the TME. To dissect the complexity and the multitude of layers within the TME, we distinguish six specialized tumor microenvironments, namely, the immune, metabolic, hypoxic, acidic, mechanical and innervated microenvironments. Accordingly, we attempt to decipher the effects of the intratumoral microbiome on each specialized microenvironment and ultimately decode its tumor-promoting or tumor-suppressive impact. Additionally, we portray the intratumoral microbiome as an orchestrator in the tumor milieu, fine-tuning the responses in distinct, specialized microenvironments and remodeling the TME in a multileveled and multifaceted manner.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery.

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

MINIMA Short Stem Versus Standard Profemur (TL) Stem in Primary Total Hip Replacement: A Comparative Study.

BACKGROUND: 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. MATERIAL AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Local Antibiotic Delivery Systems in the Surgical Treatment of Diabetic Foot Osteomyelitis: Again, No Benefit?

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.

Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning.

BACKGROUND: The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of ß-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 ß-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.

Local Antibiotic Delivery Systems in Diabetic Foot Osteomyelitis: A Brief Review.

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.

Efficacy of Bioelectrical Impedance Analysis for the Evaluation of Physical Impairment in Chronic Low Back Pain. Results from a Cohort Study.

INTRODUCTION: Determining the effect of body composition on chronic low back pain seems to have the potential to improve our understanding of its mechanism and to develop novel preventive and therapeutic approaches. Aim: The purpose of the present study was to assess by electrical impedance the composition of lower extremities of individuals with chronic low back pain. MATERIALS AND METHODS: One hundred and twenty-one adult participants with diagnosed chronic low back pain were recruited in this study. The study activities were divided into three phases: phase 1 - self-administered questionnaires, phase 2 - biomedical examination (including anthropometric measurements and physical function performance tests), and phase 3 - bioimpedance analysis. RESULTS: Our results showed that chronic low back pain differentiates the circumference of thigh and calf of the symptomatic leg. Besides, patients experience pain also in hip, thigh, and calf, which act as a barrier to patient's personal, professional, social, and recreational activities. Furthermore, patients appear with 'unstable' walking, reduced balance, and reduced general physical condition that affect all of the neuromuscular structures of the locomotor system. Interestingly, patients seem to be characterized by a tendency to deposit fat and to decrease muscle mass in the symptomatic limb regardless of the gender. CONCLUSIONS: In the present study, we determine the profile of a patient with chronic low back pain through a variety of measurements. Chronic low back pain causes several structural changes to the symptomatic leg of the patients leading to 'unstable' walking, reduced balance, and reduced general physical condition. It is clear that further studies using bioimpedance analysis are needed to address the concerns raised by investigating a multifactorial condition such as chronic low back pain.

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.

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.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning.

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.

Methylation Status of Corticotropin-Releasing Factor (CRF) Receptor Genes in Colorectal Cancer.

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.

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.

Alzheimer'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-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.