Bioinformatics for protein biomarker panel classification: what is needed to bring biomarker panels into in vitro diagnostics?

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis

The prognostic value of copper homeostasis-related genes in breast cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using Cox regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.

Optimization of sensitivity and specificity of a biomarker-based blood test (LVOCheck-Opti): A protocol for a multicenter prospective observational study of patients suspected of having a stroke

Introduction

Acute ischemic stroke (AIS) is a time-critical medical emergency. For patients with large-vessel occlusions (LVO), mechanical thrombectomy (MT) is the gold-standard treatment. Mobile Stroke Units (MSUs) provide on-site diagnostic capabilities via computed tomography (CT) and have been shown to improve functional outcomes in stroke patients, but are cost-efficient only in urban areas. Blood biomarkers have recently emerged as possible alternative to cerebral imaging for LVO diagnosis. Prehospital LVO diagnosis offers the potential to transport patients directly to centers that have MT treatment available. In this study, we assess the accuracy of combining two biomarkers, HFABP and NT-proBNP, with clinical indicators to detect LVO using ultra-early prehospital blood samples. The study was registered in the German Clinical Trials Register (DRKS-ID: DRKS00030399).

Methods and analysis

We plan a multicenter prospective observational study with 800 patients with suspected stroke enrolled within 24 h of symptom onset. Study participants will be recruited at three sites (MSUs) in Berlin, Germany. Blood-samples will be taken pre-hospitally at the scene and tested for HFABP and NT-proBNP levels. Additional clinical data and information on final diagnosis will be collected and documented in an electronic case report form (eCRF). Sensitivity and specificity of the combination will be calculated through iterative permutation-response calculations.

Discussion

This study aims to evaluate the diagnostic capabilities of a combination of the biomarkers HFABP and NT-proBNP in LVO prediction. In contrast to most other biomarker studies to date, by employing MSUs as study centers, ultra-early levels of biomarkers can be analyzed. Point-of-care LVO detection in suspected stroke could lead to faster treatment in both urban and rural settings and thus improve functional outcomes on a broader scale.

Clinical trial registration

Deutsches Register klinischer Studien https://drks.de/search/de/trial/DRKS00030399, DRKS00030399.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BACKGROUND

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

OBJECTIVE

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

METHODS

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.

RESULTS

We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64).

CONCLUSION

Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Identification of potential microRNA diagnostic panels and uncovering regulatory mechanisms in breast cancer pathogenesis

Early diagnosis of breast cancer (BC), as the most common cancer among women, increases the survival rate and effectiveness of treatment. MicroRNAs (miRNAs) control various cell behaviors, and their dysregulation is widely involved in pathophysiological processes such as BC development and progress. In this study, we aimed to identify potential miRNA biomarkers for early diagnosis of BC. We also proposed a consensus-based strategy to analyze the miRNA expression data to gain a deeper insight into the regulatory roles of miRNAs in BC initiation. Two microarray datasets (GSE106817 and GSE113486) were analyzed to explore the differentially expressed miRNAs (DEMs) in serum of BC patients and healthy controls. Utilizing multiple bioinformatics tools, six serum-based miRNA biomarkers (miR-92a-3p, miR-23b-3p, miR-191-5p, miR-141-3p, miR-590-5p and miR-190a-5p) were identified for BC diagnosis. We applied our consensus and integration approach to construct a comprehensive BC-specific miRNA-TF co-regulatory network. Using different combination of these miRNA biomarkers, two novel diagnostic models, consisting of miR-92a-3p, miR-23b-3p, miR-191-5p (model 1) and miR-92a-3p, miR-23b-3p, miR-141-3p, and miR-590-5p (model 2), were obtained from bioinformatics analysis. Validation analysis was carried out for the considered models on two microarray datasets (GSE73002 and GSE41922). The model based on similar network topology features, comprising miR-92a-3p, miR-23b-3p and miR-191-5p was the most promising model in the diagnosis of BC patients from healthy controls with 0.89 sensitivity, 0.96 specificity and area under the curve (AUC) of 0.98. These findings elucidate the regulatory mechanisms underlying BC and represent novel biomarkers for early BC diagnosis.

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

A Roadmap towards Precision Periodontics

Periodontitis is among the most common health conditions and represents a major public health issue related to increasing prevalence and seriously negative socioeconomic impacts. Periodontitis-associated low-grade systemic inflammation and its pathological interplay with systemic conditions additionally raises awareness on the necessity for highly performant strategies for the prevention and management of periodontitis. Periodontal diagnosis is the backbone of a successful periodontal strategy, since prevention and treatment plans depend on the accuracy and precision of the respective diagnostics. Periodontal diagnostics is still founded on clinical and radiological parameters that provide limited therapeutic guidance due to the multifactorial complexity of periodontal pathology, which is why biomarkers have been introduced for the first time in the new classification of periodontal and peri-implant conditions as a first step towards precision periodontics. Since the driving forces of precision medicine are represented by biomarkers and machine learning algorithms, with the lack of periodontal markers validated for diagnostic use, the implementation of a precision medicine approach in periodontology remains in the very initial stage. This narrative review elaborates the unmet diagnostic needs in periodontal diagnostics, the concept of precision periodontics, periodontal biomarkers, and a roadmap toward the implementation of a precision medicine approach in periodontal practice.

Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury

Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI).

Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI.

Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid isoforms 1–40 (Aβ40) and 1–42 (Aβ42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale—Extended 5–8, n = 49) and unfavorable (Glasgow Outcome Scale—Extended 1–4, n = 33) groups. The outcome was assessed 6–12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90–100%.

Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9–100) and specificity of 22.4% (95% CI: 10.2–32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1–4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were Aβ40, Aβ42, and neurofilament light. The optimal panel included IL-10, Aβ40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7–6.2) with a sensitivity of 90.9% (95% CI: 81.8–100) and specificity of 59.2% (95% CI: 40.8–69.4).

Conclusion: Admission plasma levels of IL-10 and Aβ40 significantly improve the prognostication ability of the HCTS after TBI.

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Background

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.

Methods

We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.

Results

We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.

Conclusions

We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Background

Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma.

Objective

The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma.

Methods

A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy.

Results

The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV₁), forced vital capacity (FVC), the ratio of FEV₁/FVC, and forced expiratory flow (FEF_25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV₁: 98.1%; FVC: 97.5%; FEV₁/FVC ratio: 97%; and FEF_25%-75%: 96.7%, respectively).

Conclusions

The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury

Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without clinical predictors. Methods: Blood samples from patients with acute TBI (all severities) were collected <24 h post trauma. The outcome was measured >6 months post injury using the Glasgow Outcome Scale Extended (GOSE) score, dichotomizing patients into: (i) those with favorable (GOSE≥5)/unfavorable outcome (GOSE ≤ 4) and complete (GOSE = 8)/incomplete (GOSE ≤ 7) recovery, and (ii) patients with mild TBI (mTBI) and patients with TBIs of all severities. Results: When sensitivity was set at 95-100%, the proteins' individual specificities remained low. H-FABP showed the best specificity (%) and sensitivity (100%) in predicting complete recovery in patients with mTBI. IL-10 had the best specificity (50%) and sensitivity (96%) in identifying patients with favorable outcome in patients with TBIs of all severities. When individual proteins were combined with clinical parameters, a model including H-FABP, NF-L, and ISS yielded a specificity of 56% and a sensitivity of 96% in predicting complete recovery in patients with mTBI. In predicting favorable outcome, a model consisting IL-10, age, and TBI severity reached a specificity of 80% and a sensitivity of 96% in patients with TBIs of all severities. Conclusion: Combining novel TBI biomarkers H-FABP and IL-10 with GFAP, NF-L and S100β and clinical parameters improves outcome prediction models in TBI.

Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

Plasma Lysyl-tRNA Synthetase 1 (KARS1) as a Novel Diagnostic and Monitoring Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is one of the leading causes of world cancer deaths. To improve the survival rate of CRC, diagnosis and post-operative monitoring is necessary. Currently, biomarkers are used for CRC diagnosis and prognosis. However, these biomarkers have limitations of specificity and sensitivity. Levels of plasma lysyl-tRNA synthetase (KARS1), which was reported to be secreted from colon cancer cells by stimuli, along with other secreted aminoacyl-tRNA synthetases (ARSs), were analyzed in CRC and compared with the currently used biomarkers. The KARS1 levels of CRC patients (n = 164) plasma were shown to be higher than those of healthy volunteers (n = 32). The diagnostic values of plasma KARS1 were also evaluated by receiving operating characteristic (ROC) curve. Compared with other biomarkers and ARSs, KARS1 showed the best diagnostic value for CRC. The cancer specificity and burden correlation of plasma KARS1 level were validated using azoxymethane (AOM)/dextran sodium sulfate (DSS) model, and paired pre- and post-surgery CRC patient plasma. In the AOM/DSS model, the plasma level of KARS1 showed high correlation with number of polyps, but not for inflammation. Using paired pre- and post-surgery CRC plasma samples (n = 60), the plasma level of KARS1 was significantly decreased in post-surgery samples. Based on these evidence, KARS1, a surrogate biomarker reflecting CRC burden, can be used as a novel diagnostic and post-operative monitoring biomarker for CRC.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

Is the profile of transcripts altered in the eutopic endometrium of infertile women with endometriosis during the implantation window?

STUDY QUESTION

Compared to healthy women, is the profile of transcripts altered in the eutopic endometrium of infertile women with endometriosis during the implantation window (IW)?

SUMMARY ANSWER

The eutopic endometrium of infertile women with endometriosis seems to be transcriptionally similar to the endometrium of infertile and fertile controls (FC) during the IW.

WHAT IS KNOWN ALREADY

Endometriosis is a disease related to infertility; nevertheless, little is known regarding the ethiopathogenic mechanisms underlying this association. Some studies evaluating the eutopic endometrium of endometriosis patients suggest there is an endometrial factor involved in the disease-related infertility. However, no study to date has evaluated the endometrial transcriptome (mRNA and miRNA) by next generation sequencing (NGS), comparing patients with endometriosis as the exclusive infertility factor (END) to infertile controls (IC; male and/or tubal factor) and FC.

STUDY DESIGN, SIZE, DURATION

From November 2011 to November 2015 we performed a case-control study, where 17 endometrial samples (six END, six IC, five FC) were collected during the IW.

PARTICIPANTS/MATERIALS, SETTING, METHODS

All endometrial samples had the RNA extracted. Two libraries were prepared for each one (mRNA and miRNA), which were sequenced, respectively, at HISEQ 2500 (RNA-Seq) and MiSeq System (miRNA-Seq), Illumina. The normalization and differential expression were conducted in statistical R environment using DESeq2 package. qPCR was used for data validation, which were analyzed by Kruskal–Wallis test and Dunn posttest (P &lt; 0.05).

MAIN RESULTS AND THE ROLE OF CHANCE

RNA-Seq revealed no differentially expressed genes (DEG) among END, IC and FC groups. miRNA-Seq revealed three differentially expressed miRNAs (has-27a-5p, has-miR-150-5p, has-miR-504-5p) in END group compared to FC group. However, none of the miRNAs identified in the sequencing was validated by qPCR.

LIMITATIONS, REASONS FOR CAUTION

The main limitation of this study was the small sample size evaluated as a result of the restrictive eligibility criteria adopted, limiting the generalization of the results obtained here. On the other hand, strict eligibility criteria, which eliminated factors potentially related to impaired endometrial receptivity, were required to increase the study’s internal validity.

WIDER IMPLICATIONS OF THE FINDINGS

This study brings new perspectives on the mechanisms involved in endometriosis-related infertility. The present findings suggest the eutopic endometrium of infertile women with endometriosis, without considering the disease’s stage, is transcriptionally similar to controls during the IW, possibly not affecting receptivity. Further studies are needed to evaluate endometrial alterations related to endometriosis’ stages.

STUDY FUNDING/COMPETING INTEREST(S)

This study received financial support from the Sao Paulo Research Foundation (FAPESP—Fundação de Amparo à Pesquisa do Estado de São Paulo; fellowship 2011/17614–6, MGB) and from the National Council for Scientific and Technological Development (CNPq—Conselho Nacional de Desenvolvimento Científico e Tecnológico; INCT—National Institutes of Hormones and Woman’s Health, grant 471 943/2012-6, 309 397/2016-2, PAN; fellowship 140 137/2015-7, MGB). The authors have no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Cell-Free Circulating Nucleic Acids as Early Biomarkers for NAFLD and NAFLD-Associated Disorders

Non-alcoholic fatty liver disease (NAFLD) is the worldwide most common cause of chronic liver pathology, which prevalence strongly correlates with the increasing incidence of diabetes, obesity and metabolic syndrome in the general population. Simple steatosis, the earliest NAFLD stage, usually remains asymptomatic, and appropriate changes in the lifestyle, as well as the diet, can reverse the affected liver into the healthy state. The potential of simple steatosis to progress into severe fibrotic stages and to facilitate carcinogenesis necessitates timely NAFLD detection and risk stratification in community-based healthcare settings. Since their initial discovery a decade ago, extracellular circulating miRNAs have been found in all human biological fluids including blood and shown to hold great promises as non-invasive biomarkers. Normally, intracellular miRNAs participate in the regulation of gene expression, but once released by dying/dead cells they remain highly stable in the extracellular environment for prolonged periods. Therefore, circulating miRNA profiles can reflect the ongoing pathogenic processes in body's tissues and organs, and enable highly sensitive non-invasive diagnosis of multiple disorders. A non-urgent character of the NAFLD-related decision-making justifies the use of chronic liver diseases as an excellent test case for examining the practical utility of circulating miRNAs as biomarkers for longitudinal monitoring of human health. In this review, we summarize the state-of-the-art in the field of early diagnosis of NAFLD using circulating blood miRNAs, and stress the necessity of additional experimental validation of their diagnostic potential. We further emphasize on the potential diagnostics promises of other cell-free RNA species found in human biological fluids.

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually-H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10-for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins-H-FABP, IL-10, S100B and GFAP-showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23-40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36-55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43-61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients.

Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma

AIM

In our previous study, we have built a nine-gene (GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1B, CXCR4, PFN1, and CALR) expression detection system based on the GeXP system. Based on peripheral blood and GeXP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma (HCC) patients and healthy people.

METHODS

Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fifty-two patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.

RESULTS

Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.

CONCLUSION

Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.

A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency

Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Emerging Role of MicroRNAs as Liquid Biopsy Biomarkers in Gastrointestinal Cancers

Cancer has emerged as a leading cause of mortality worldwide, claiming more than 8 million lives annually. Gastrointestinal cancers account for about 35% of these mortalities. Recent advances in diagnostic and treatment strategies have reduced mortality among patients with gastrointestinal cancer, yet a significant number of patients still develop late-stage cancer, where treatment options are inadequate. Emerging interests in "liquid biopsies" have encouraged investigators to identify and develop clinically relevant noninvasive genomic and epigenomic signatures that can be exploited as biomarkers capable of detecting premalignant and early-stage cancers. In this context, microRNAs (miRNA), which are small, noncoding RNAs that are frequently dysregulated in cancers, have emerged as promising entities for such diagnostic purposes. Even though the future looks promising, current approaches for detecting miRNAs in blood and other biofluids remain inadequate. This review summarizes existing efforts to exploit circulating miRNAs as cancer biomarkers and evaluates their potential and challenges as liquid biopsy-based biomarkers for gastrointestinal cancers. Clin Cancer Res; 23(10); 2391-9. ©2017 AACR.

Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC-MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC-MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.

Post-acute brain injury urinary signature: a new resource for molecular diagnostics

Heterogeneity within brain injury presents a challenge to the development of informative molecular diagnostics. Recent studies show progress, particularly in cerebrospinal fluid, with biomarker assays targeting one or a few structural proteins. Protein-based assays in peripheral fluids, however, have been more challenging to develop, in part because of restricted and intermittent barrier access. Further, a greater number of molecular variables may be required to inform on patient status given the multi-factorial nature of brain injury. Presented is an alternative approach profiling peripheral fluid for a class of small metabolic by-products rendered by ongoing brain pathobiology. Urine specimens were collected for head trauma subjects upon admission to acute brain injury rehabilitation and non-traumatized matched controls. An innovative data-independent mass spectrometry approach was employed for reproducible molecular quantification across osmolarity-normalized samples. The postacute human traumatic brain injury urinary signature encompassed 2476 discriminant variables reproducibly measured in specimens for subject classification. Multiple subprofiles were then discerned in correlation with injury severity per the Glasgow Comma Scale and behavioral and neurocognitive function per the Patient Competency Rating Scale and Frontal Systems Behavioral Scale. Identified peptide constituents were enriched for outgrowth and guidance, extracellular matrix, and post-synaptic density proteins, which were reflective of ongoing post-acute neuroplastic processes demonstrating pathobiological relevance. Taken together, these findings support further development of diagnostics based on brain injury urinary signatures using either combinatorial quantitative models or pattern-recognition methods. Particularly, these findings espouse assay development to address unmet diagnostic and theragnostic needs in brain injury rehabilitative medicine.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

Assessment of endothelial dysfunction: the role of symmetrical dimethylarginine and proinflammatory markers in chronic kidney disease and renal transplant recipients

OBJECTIVES

The study was designed to evaluate associations between symmetric dimethylarginine (SDMA), inflammation, and superoxide anion (O2∙-) with endothelial function and to determine their potential for screening of endothelial dysfunction in patients with chronic kidney disease (CKD) and renal transplant (RT) recipients.

MATERIALS AND METHODS

We included 64 CKD and 52 RT patients. Patients were stratified according to brachial artery flow-mediated dilation (FMD).

RESULTS

Logistic regression analysis showed that high SDMA and high sensitive C-reactive protein (hs-CRP) were associated with impaired FMD in CKD and RT patients, after adjustment for glomerular filtration rate. The ability of inflammation, SDMA, and O2∙- to detect impaired FMD was investigated by receiving operative characteristic analysis. Hs-CRP (area under the curves (AUC) = 0.754, P < 0.001), IL-6 (AUC = 0.699, P = 0.002), and SDMA (AUC = 0.689, P = 0.007) had the highest ability to detect impaired FMD. SDMA in combination with inflammatory parameters and/or O2∙- had better screening performance than SDMA alone.

CONCLUSIONS

Our results indicate a strong predictable association between hs-CRP, SDMA, and endothelial dysfunction in CKD patients and RT recipients. The individual marker that showed the strongest discriminative ability for endothelial dysfunction is hs-CRP, but its usefulness as a discriminatory marker for efficient diagnosis of endothelial dysfunction should be examined in prospective studies.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

New biomarkers for stage determination in Trypanosoma brucei rhodesiense sleeping sickness patients

Accurate stage determination is crucial in the choice of treatment for patients suffering from sleeping sickness, also known as human African trypanosomiasis (HAT). Current staging methods, based on the counting of white blood cells (WBC) and the detection of parasites in the cerebrospinal fluid (CSF) have limited accuracy. We hypothesized that immune mediators reliable for staging T. b. gambiense HAT could also be used to stratify T. b. rhodesiense patients, the less common form of HAT.

A population comprising 85 T. b. rhodesiense patients, 14 stage 1 (S1) and 71 stage 2 (S2) enrolled in Malawi and Uganda, was investigated. The CSF levels of IgM, MMP-9, CXCL13, CXCL10, ICAM-1, VCAM-1, neopterin and B2MG were measured and their staging performances evaluated using receiver operating characteristic (ROC) analyses.

IgM, MMP-9 and CXCL13 were the most accurate markers for stage determination (partial AUC 88%, 86% and 85%, respectively). The combination in panels of three molecules comprising CXCL13-CXCL10-MMP-9 or CXCL13-CXCL10-IgM significantly increased their staging ability to partial AUC 94% (p value < 0.01).

The present study highlighted new potential markers for stage determination of T. b. rhodesiense patients. Further investigations are needed to better evaluate these molecules, alone or in panels, as alternatives to WBC to make reliable choice of treatment.

Chaperone probes and bead-based enhancement to improve the direct detection of mRNA using silicon photonic sensor arrays

Herein, we describe the utility of chaperone probes and a bead-based signal enhancement strategy for the analysis of full length mRNA transcripts using arrays of silicon photonic microring resonators. Changes in the local refractive index near microring sensors associated with biomolecular binding events are transduced as a shift in the resonant wavelength supported by the cavity, enabling the sensitive analysis of numerous analytes of interest. We employ the sensing platform for both the direct and bead-enhanced detection of three different mRNA transcripts, achieving a dynamic range spanning over 4 orders of magnitude and demonstrating expression profiling capabilities in total RNA extracts from the HL-60 cell line. Small, dual-use DNA chaperone molecules were developed and found to both enhance the binding kinetics of mRNA transcripts by disrupting complex secondary structure and serve as sequence-specific linkers for subsequent bead amplification. Importantly, this approach does not require amplification of the mRNA transcript, thereby allowing for simplified analyses that do not require expensive enzymatic reagents or temperature ramping capabilities associated with RT-PCR-based methods.

Serum amino acid profiles and their alterations in colorectal cancer

Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815-0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.

Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis

BACKGROUND

An early semi-invasive diagnosis of endometriosis has the potential to allow early treatment and minimize disease progression but no such test is available at present. Our aim was to perform a combined mRNA microarray and proteomic analysis on the same eutopic endometrium sample obtained from patients with and without endometriosis.

METHODS

mRNA and protein fractions were extracted from 49 endometrial biopsies obtained from women with laparoscopically proven presence (n= 31) or absence (n= 18) of endometriosis during the early luteal (n= 27) or menstrual phase (n= 22) and analyzed using microarray and proteomic surface enhanced laser desorption ionization-time of flight mass spectrometry, respectively. Proteomic data were analyzed using a least squares-support vector machines (LS-SVM) model built on 70% (training set) and 30% of the samples (test set).

RESULTS

mRNA analysis of eutopic endometrium did not show any differentially expressed genes in women with endometriosis when compared with controls, regardless of endometriosis stage or cycle phase. mRNA was differentially expressed (P< 0.05) in women with (925 genes) and without endometriosis (1087 genes) during the menstrual phase when compared with the early luteal phase. Proteomic analysis based on five peptide peaks [2072 mass/charge (m/z); 2973 m/z; 3623 m/z; 3680 m/z and 21133 m/z] using an LS-SVM model applied on the luteal phase endometrium training set allowed the diagnosis of endometriosis (sensitivity, 91; 95% confidence interval (CI): 74-98; specificity, 80; 95% CI: 66-97 and positive predictive value, 87.9%; negative predictive value, 84.8%) in the test set.

CONCLUSION

mRNA expression of eutopic endometrium was comparable in women with and without endometriosis but different in menstrual endometrium when compared with luteal endometrium in women with endometriosis. Proteomic analysis of luteal phase endometrium allowed the diagnosis of endometriosis with high sensitivity and specificity in training and test sets. A potential limitation of our study is the fact that our control group included women with a normal pelvis as well as women with concurrent pelvic disease (e.g. fibroids, benign ovarian cysts, hydrosalpinges), which may have contributed to the comparable mRNA expression profile in the eutopic endometrium of women with endometriosis and controls.

Serum proteomic profiling reveals potential biomarkers for cutaneous malignant melanoma

Cutaneous malignant melanoma (CMM) is the most serious type of skin cancer because of its tendency to metastasize. The prognosis and therapeutic management of patients are primarily based on clinical criteria (number of cancerous lymph nodes and/or the presence of distant metastases) and histopathological criteria (tumor depth, presence of ulceration and mitotic index). Although these factors are informative in advanced stages of the disease, they are less important in the early stages. In recent years, a number of attempts have been made to identify new serological prognostic biomarkers, especially for early forms of CMM. The recent development of proteomic techniques may offer new perspectives in this field. This article details the considerations of each of the proteomic techniques used today and describes the results of the most recent clinical studies conducted to identify new potential prognostic serum biomarkers for CMM. However, independent and large validation studies are needed before such markers can be used in everyday clinical practice.

Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ∼ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.

Multimarker panels in sepsis

Biomarkers differentiate between 2 or more biologic states. The complexity of diseases like sepsis makes it unlikely that any single marker will allow for precise disease specification. Combining several biomarkers into a single classification rule should help to improve their accuracy and, therefore, their usefulness. This article reviews several studies using multimarker panels, and highlights the potential of more sophisticated diagnostic and prognostic techniques in future multimarker panels. More complex algorithms should accelerate the adoption of multimarker panels into the routine management of patients with sepsis, provided that clinicians understand the multimarker approach.

Using bacterial biomarkers to identify early indicators of cystic fibrosis pulmonary exacerbation onset

Acute periods of pulmonary exacerbation are the single most important cause of morbidity in cystic fibrosis patients, and may be associated with a loss of lung function. Intervening prior to the onset of a substantially increased inflammatory response may limit the associated damage to the airways. While a number of biomarker assays based on inflammatory markers have been developed, providing useful and important measures of disease during these periods, such factors are typically only elevated once the process of exacerbation has been initiated. Identifying biomarkers that can predict the onset of pulmonary exacerbation at an early stage would provide an opportunity to intervene before the establishment of a substantial immune response, with major implications for the advancement of cystic fibrosis care. The precise triggers of pulmonary exacerbation remain to be determined; however, the majority of models relate to the activity of microbes present in the patient's lower airways of cystic fibrosis. Advances in diagnostic microbiology now allow for the examination of these complex systems at a level likely to identify factors on which biomarker assays can be based. In this article, we discuss key considerations in the design and testing of assays that could predict pulmonary exacerbations.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

BACKGROUND

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.

RESULTS

With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.

CONCLUSIONS

pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

BACKGROUND

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.

RESULTS

With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.

CONCLUSIONS

pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.