Exploration of molecular signatures associated with different clinical features of Takayasu arteritis based on a prospective cohort study.

Takayasu arteritis (TAK) is complicated disorder without reliable biomarkers. Here, we aimed to explore TAK-associated factor panels and their changes after biologic treatment. Five factor panels were identified: 1. systemic inflammation: C3, ESR, CRP, PLT, IL-6, C4, and IgG; 2. vascular inflammation: YKL40, IL-16, PTX3, and CCL2; 3. immune regulation panel: IL-10, IFN-γ, CCL5, and MMP1; 4. angiogenesis and fibrosis: FGF, PDGFAB, and VEGF; and 5. vascular remodeling: CD19+ B cell ratio, MMP3, and leptin. Panel 1 parameters were closely related to disease activity, while Panel 5 parameters, particularly CD19+ B cell ratio and leptin, were significantly higher in ischemic patients. After treatment, tocilizumab had a stronger inhibitory effect on Panel 1 parameters, PTX3, and YKL-40, while adalimumab led to an increase in IL-16, CCL2, and leptin levels. Altogether, these data expanded our knowledge regarding molecular background in TAK development and shed light on precise treatment in future studies.

Recognition of a Novel Gene Signature for Human Glioblastoma.

Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overexpressed genes COL6A2, ABCC3, COL8A1, FAM20A, ADM, CTHRC1, PDPN, IBSP, MIR210HG, GPX8, MYL9 and PDLIM4, as well as the underexpressed genes CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C, SHANK2 and VIPR2. Protein functional analysis by CELLO2GO implied that these signature genes might be involved in regulating various aspects of biological function, including anatomical structure development, cell proliferation and adhesion, signaling transduction and many of the genes were annotated in response to stress. Of these 33 signature genes, 23 have previously been reported to be functionally correlated with GBM; the roles of the remaining 10 genes in glioma development remain unknown. Our results were the first to reveal that GBM exhibited the overexpressed GPX8 gene and underexpressed signature genes including CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C and SHANK2, which might play crucial roles in the tumorigenesis of different gliomas.

Diagnostic potential of circulating cell-free microRNAs for community-acquired pneumonia and pneumonia-related sepsis.

Cell-free microRNAs (miRNAs) are transferred in disease state including inflammatory lung diseases and are often packed into extracellular vesicles (EVs). To assess their suitability as biomarkers for community-acquired pneumonia (CAP) and severe secondary complications such as sepsis, we studied patients with CAP (n = 30), sepsis (n = 65) and healthy volunteers (n = 47) subdivided into a training (n = 67) and a validation (n = 75) cohort. After precipitating crude EVs from sera, associated small RNA was profiled by next-generation sequencing (NGS) and evaluated in multivariate analyses. A subset of the thereby identified biomarker candidates was validated both technically and additionally by reverse transcription quantitative real-time PCR (RT-qPCR). Differential gene expression (DGE) analysis revealed 29 differentially expressed miRNAs in CAP patients when compared to volunteers, and 25 miRNAs in patients with CAP, compared to those with sepsis. Sparse partial-least discriminant analysis separated groups based on 12 miRNAs. Three miRNAs proved as a significant biomarker signature. While expression levels of miR-1246 showed significant changes with an increase in overall disease severity from volunteers to CAP and to sepsis, miR-193a-5p and miR-542-3p differentiated patients with an infectious disease (CAP or sepsis) from volunteers. Cell-free miRNAs are potentially novel biomarkers for CAP and may help to identify patients at risk for progress to sepsis, facilitating early intervention and treatment.

Efficient ℓ0 -norm feature selection based on augmented and penalized minimization.

Advances in high-throughput technologies in genomics and imaging yield unprecedentedly large numbers of prognostic biomarkers. To accommodate the scale of biomarkers and study their association with disease outcomes, penalized regression is often used to identify important biomarkers. The ideal variable selection procedure would search for the best subset of predictors, which is equivalent to imposing an ℓ0 -penalty on the regression coefficients. Since this optimization is a nondeterministic polynomial-time hard (NP-hard) problem that does not scale with number of biomarkers, alternative methods mostly place smooth penalties on the regression parameters, which lead to computationally feasible optimization problems. However, empirical studies and theoretical analyses show that convex approximation of ℓ0 -norm (eg, ℓ1 ) does not outperform their ℓ0 counterpart. The progress for ℓ0 -norm feature selection is relatively slower, where the main methods are greedy algorithms such as stepwise regression or orthogonal matching pursuit. Penalized regression based on regularizing ℓ0 -norm remains much less explored in the literature. In this work, inspired by the recently popular augmenting and data splitting algorithms including alternating direction method of multipliers, we propose a 2-stage procedure for ℓ0 -penalty variable selection, referred to as augmented penalized minimization-L0 (APM-L0 ). The APM-L0 targets ℓ0 -norm as closely as possible while keeping computation tractable, efficient, and simple, which is achieved by iterating between a convex regularized regression and a simple hard-thresholding estimation. The procedure can be viewed as arising from regularized optimization with truncated ℓ1 norm. Thus, we propose to treat regularization parameter and thresholding parameter as tuning parameters and select based on cross-validation. A 1-step coordinate descent algorithm is used in the first stage to significantly improve computational efficiency. Through extensive simulation studies and real data application, we demonstrate superior performance of the proposed method in terms of selection accuracy and computational speed as compared to existing methods. The proposed APM-L0 procedure is implemented in the R-package APML0.

EBV and vitamin D status in relapsing-remitting multiple sclerosis patients with a unique cytokine signature.

Multiple sclerosis, a debilitating autoimmune and inflammatory disease of the central nervous system, is associated with both infectious and non-infectious factors. We investigated the role of EBV infection, vitamin D level, and cytokine signature in MS patients. Molecular and serological assays were used to investigate immune biomarkers, vitamin D level, and EBV status in 83 patients with relapsing-remitting multiple sclerosis and 62 healthy controls. In total, 98.8 % of MS patients showed a history of EBV exposure compared to 88.6 % in the healthy group (p = 0.005). EBV DNA load was significantly higher in MS patients than healthy subjects (p < 0.0001). Using a panel of biomarkers, we found a distinct transcriptional signature in MS patients compared to the healthy group with mRNA levels of CD73, IL-6, IL-23, IFN-γ, TNF-α, IL-15, IL-28, and IL-17 significantly elevated in MS patients (p < 0.0001). In contrast, the mRNA levels for TGF-ß, IDO, S1PR1, IL-10, and CCL-3 were significantly lower in MS patients compared to healthy controls (p < 0.0001). No significant differences were found with the mRNA levels of IL-13, CCL-5, and FOXP3. Interestingly, in MS patients we found an inverse correlation between vitamin D concentration and EBV load, but not EBNA-1 IgG antibody levels. Our data highlight biomarker correlates in MS patients together with a complex interplay between EBV replication and vitamin D levels.

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014.

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS: We searched for ADNI publications using established methods. RESULTS: ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION: ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

Identification of a novel gene signature related to prognosis and metastasis in gastric cancer.

BACKGROUND: Gastric Cancer (GC) presents poor outcome, which is consequence of the high incidence of recurrence and metastasis at early stages. GC patients presenting recurrent or metastatic disease display a median life expectancy of only 8 months. The mechanisms underlying GC progression remain poorly understood. METHODS: We took advantage of public available GC datasets from TCGA using GEPIA, and identified the matched genes among the 100 genes most significantly associated with overall survival (OS) and disease free survival (DFS). Results were confirmed in ACRG cohort and in over 2000 GC cases obtained from several cohorts integrated using our own analysis pipeline. The Kaplan-Meier method and multivariate Cox regression analyses were used for prognostic significance and linear modelling and correlation analyses for association with clinic-pathological parameters and biological hallmarks. In vitro and in vivo functional studies were performed in GC cells with candidate genes and the related molecular pathways were studied by RNA sequencing. RESULTS: High expression of ANKRD6, ITIH3, SORCS3, NPY1R and CCDC178 individually and as a signature was associated with poor prognosis and recurrent disease in GC. Moreover, the expression of ANKRD6 and ITIH3 was significantly higher in metastasis and their levels associated to Epithelial to Mesenchymal Transition (EMT) and stemness markers. In line with this, RNAseq analysis revealed genes involved in EMT differentially expressed in ANKRD6 silencing cells. Finally, ANKRD6 silencing in GC metastatic cells showed impairment in GC tumorigenic and metastatic traits in vitro and in vivo. CONCLUSIONS: Our study identified a novel signature involved in GC malignancy and prognosis, and revealed a novel pro-metastatic role of ANKRD6 in GC.

Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins.

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

The Ratio of Plasma Amyloid-ß 1-42 over Serum Albumin Can Be a Novel Biomarker Signature for Diagnosing End-Stage Renal Disease-Associated Cognitive Impairment.

BACKGROUND: Cognitive impairment (CI) is one of the major complications in chronic kidney disease patients, especially those with end-stage renal disease (ESRD). Limited biomarkers have been found that can significantly predict ESRD-associated cognitive decline. OBJECTIVE: This cohort study aimed to investigate de novo biomarkers for diagnosis of the ESRD-associated CI. METHODS: In this cohort study, qualified samples were divided into control (with an estimated glomerular filtration rate (eGFR) of≥60 mL/min and a Mini-Mental State Examination (MMSE) score of > 27), ESRD without CI (eGFR < 15 and MMSE > 27), and ESRD with CI (eGFR < 15 and MMSE < 27) groups. Levels of plasma amyloid-ß (Aß)1 - 42, serum indoxyl sulfate, and hematologic and biochemical parameters were measured. RESULTS: Compared to the control group, levels of blood urea nitrogen, creatinine, and indoxyl sulfate were elevated in ESRD patients both without and with CI. Interestingly, ESRD patients with CI had the lowest levels of serum albumin. In contrast, levels of plasma Aß1 - 42 were significantly higher in the ESRD with CI group than in the control and ESRD without CI groups. In addition, the ratio of plasma Aß1 - 42 over serum albumin was significantly higher in the ESRD with CI group than in the control or ESRD without CI groups. Importantly, the area under the receiver operating characteristic curve (AUROC) for CI in the total population by the ratio of Aß1 - 42 over albumin was 0.785 and significant (p < 0.05). CONCLUSIONS: This cohort study has shown that the ratio of plasma Aß1 - 42 over serum albumin can be a de novo biomarker for the diagnosis and prognosis of ESRD-associated cognitive decline.

Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of malignant lymphoma. Although the first-line treatment, R-CHOP treatment, shows efficacy in approximately 80% of patients with DLBCL, some patients have refractory disease or relapse after the initial response to therapy, resulting in a significantly poorer prognosis. In this study, we developed a microRNA (miRNA) signature-based companion diagnostic model to predict the response of patients with DLBCL to R-CHOP treatment by integrating two clinical study datasets. To select the optimum miRNA combination as a panel, we examined three feature selection methods (p-value-based ranking, stepwise method, and Boruta), together with 11 types of classifiers systematically. Boruta selection enabled a higher area under the curve (AUC) with a lower number of miRNAs compared with other feature selection methods, leading to an AUC of 0.751 via the random forest classifier using 36 miRNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that Boruta avoided multiple selection of miRNAs with similar functions, thereby preventing the decrease in diagnostic ability via collinearity. The AUC value first increased with an increasing number of miRNAs and then became almost constant at approximately 30 miRNAs, suggesting the existence of the optimum number of miRNAs as a panel for future clinical translation of multiple miRNA-based diagnostics.

Simultaneous Quantification of Serum Lipids and Their Association with Type 2 Diabetes Mellitus-Positive Hepatocellular Cancer.

Type 2 diabetes mellitus (T2DM) has been recognized as one of the most important and independent risk factors for hepatocellular cancer (HCC). However, there is still a lack of ideal tumor markers for HCC detection in the T2DM population. Serum lipids have been revealed as potential tumor markers for HCC. In this study, our objective was to develop a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to detect several lipids including 8,15-dihydroxy-5,9,11,13-eicosatetraenoic acid (8,15-DiHETE), hexadecanedioic acid (HDA), 15-keto-13,14-dihydroprostaglandin A2 (DHK-PGA2), ricinoleic acid (RCL), octadecanedioic acid (OA) and 16-hydroxy hexadecanoic acid (16OHHA) in serum and explore their diagnostic potential for T2DM-positive [T2DM(+)] HCC. A robust LC-MS/MS method was established for the measurement of 8,15-DiHETE, HDA, DHK-PGA2, RCL, OA, and 16OHHA. The methodology validation was conducted, and the results suggested the reliability of this LC-MS/MS method for targeted lipids. Several serum lipids, including 8,15-DiHETE, HDA, DHK-PGA2, and OA were increased in T2DM(+) HCC patients. A biomarker signature that incorporated HDA, DHK-PGA2, and AFP was established and showed good diagnostic potential for T2DM(+) HCC, and the area under the ROC curve (AUC) was 0.87 for diagnosing T2DM(+) HCC from T2DM individuals. Additionally, the biomarker signature diagnosed small-size (AUC = 0.88) and early-stage (AUC = 0.79) tumors with high efficacy. Moreover, the biomarker signature could differentiate T2DM(+) HCC from other T2DM(+) tumors, including pancreatic, gastric and colorectal cancer (AUC = 0.88) as well. In conclusion, our study develops a novel tool for early diagnosis of T2DM(+) HCC in T2DM patients.

Development and validation of circulating protein signatures as diagnostic biomarkers for biliary tract cancer.

Background & Aims: Biliary tract cancer (BTC) is associated with a dismal prognosis, partly because it is typically diagnosed late, highlighting the need for diagnostic biomarkers. The purpose of this project was to identify and validate multiprotein signatures that could differentiate patients with BTC from non-cancer controls. Methods: In this study, we included treatment-naïve patients with BTC, healthy controls, and patients with benign conditions including benign biliary tract disease. Participants were divided into three non-overlapping cohorts: a case-control-based discovery cohort (BTC = 186, controls = 249); a case-control-based validation cohort (validation cohort 1: BTC = 113, controls = 241); and a cohort study-based validation cohort including participants (BTC = 8, controls = 132) referred for diagnostic work-up for suspected cancer (validation cohort 2). Immuno-Oncology (I-O)-related proteins were measured in serum and plasma using a proximity extension assay (Olink Proteomics). Lasso and Ridge regressions were used to generate protein signatures of I-O-related proteins and carbohydrate antigen 19-9 (CA19-9) in the discovery cohort. Results: Sixteen protein signatures, including 2 to 82 proteins, were generated. All signatures included CA19-9 and chemokine C-C motif ligand 20. Signatures discriminated between patients with BTC vs. controls, with AUCs ranging from 0.95 to 0.99 in the discovery cohort and 0.94 to 0.97 in validation cohort 1. In validation cohort 2, AUCs ranged from 0.84 to 0.94. Nine signatures achieved a specificity of 82% to 84% while keeping a sensitivity of 100% in validation cohort 2. All signatures performed better than CA19-9, and signatures including >15 proteins showed the best performance. Conclusion: The study demonstrated that it is possible to generate protein signatures that can successfully differentiate patients with BTC from non-cancer controls. Impact and implications: We attempted to find blood sample-based protein profiles that could differentiate patients with biliary tract cancer from those without cancer. Several profiles were found and tested in different groups of patients. The profiles were successful at identifying most patients with biliary tract cancer, pointing towards the utility of multiprotein signatures in this context.

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics-MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery.

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of "key steps" in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

Two-Stage Adaptive Design for Prognostic Biomarker Signatures with a Survival Endpoint.

Cancer biomarker discoveries typically involve utilizing patient specimens. In practice, there is often strong desire to preserve high quality biospecimens for studies that are most likely to yield useful information. Previously, we proposed a two-stage adaptive design for binary endpoints which terminates the biomarker study in a futility interim if the model performance is unsatisfactory. In this work, we extend the two-stage design framework to accommodate time-to-event endpoints. The first stage of the procedure involves testing whether the measure of discrimination for survival models (C-index) exceeds a pre-specified threshold. We describe the computation of cross-validated C-index and evaluation of the statistical significance using re-sampling techniques. The second stage involves an independent model validation. Our simulation studies show that under the null hypothesis, the proposed design maintains type I error at the nominal level and has high probabilities of terminating the study early. Under the alternative hypothesis, power of the design is a function of the true event proportion, the sample size, and the targeted improvement in the discriminant measure. We apply the method to design of a prognostic biomarker study in patients with triple-negative breast cancer. Some practical aspects of the proposed method are discussed.

Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins.

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes.

BACKGROUND: Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant ß-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics. METHODS: Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls. RESULTS: The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-ß, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects. CONCLUSIONS: Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances.

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

Biomarker Signatures Predicting 10-Year All-Cause and Disease-Specific Mortality.

Novel wide array blood biomarkers of multisystem dysregulation, compared to conventional clinical and blood biomarkers, are potentially able to provide more accurate prognostic information of long-term mortality risks. We identified biomarker signatures of all-cause and disease-specific mortality from a comprehensive range of analytes related to six major physiological functions: cytokine, chemokine, and growth factors; glucose metabolism regulators and adipokines; adhesion molecules; acute phase response; pathogen-specific antibodies; and bone remodeling. A total of 144 elderly were prospectively followed up on mortality for median 136 months. Plasma levels of 93 biomarkers measured by enzyme-linked immunosorbent assay, Luminex, FlowCytomix, DNA quantification, and recombinant DNA technology at baseline were compared among deceased and surviving elderly and in a referent group of 72 healthy young adults. The elderly, and especially deceased elderly, exhibited differential profiles of the composite index of each physiological function from young adults. In Cox regression, we identified and validated in an independent cohort of 357 elderly the specific and common biomarkers predicting all-cause, cardiovascular disease-related, neoplasm-related, and respiratory disease-related mortalities after controlling age, sex, and major comorbidities. These biomarkers had 74.3% correct classification for deceased elderly from surviving elderly. We reported for the first time, stem cell growth factor-ß and gastric inhibitory polypeptide as specific biomarkers of mortality risk.

Multi-biomarker panel signature as the key to diagnosis of ovarian cancer.

Early detection of ovarian cancer has been a challenge to manage the high mortality rate caused by this deadly disease. The trends in mortality have been reduced by the scientific contributions from the corners across the globe, however accounting for the fifth leading cause of gynecological mortality. The complexities in the clinical presentation, origin of tumor, and gene expression profiles had added to much difficulty in understanding and diagnosis of the disease. Stage 1 diagnosis of ovarian cancer improves the 5-year survival rate to around 92%. Cancer antigen-125 (CA-125) is the gold standard tumor marker found at abnormally high levels in the blood of many women in ovarian cancer. However, many non-cancerous conditions exhibit high levels of CA-125 and several women have normal CA-125 level in the early stage of ovarian cancer, suggesting CA-125 biomarker is not specific enough for the screening of early stage ovarian cancer. In addition, several other biomarkers, including HE4 have been added in the diagnostic field for higher sensitivity and specificity in the diagnosis and progression of ovarian cancer. HE4 is a prospective single serum biomarker which has been approved by the FDA to monitor the disease progression in epithelial ovarian cancer. However, owing to low sensitivity and specificity, combination of a panel of biomarkers has been proposed in the diagnosis of the disease. Based on extensive biomarkers research findings, here we discuss current trends in diagnostic approaches and updated potential several panels of cancer biomarkers for early detection of ovarian cancer. It has been recently reported that CA125 in combinations with two or more biomarkers have outperformed single biomarker assays for early detection of the disease. Moreover, CA-125 with CA 19-9, EGFR, G-CSF, Eotaxin, IL-2R, cVCAM, MIF improved the sensitivity with 98.2 % and specificity of 98.7% in early stage detection of ovarian cancer. Overall, this review demonstrates a panel of biomarkers signature as the potential tool for prototype development in future and other advanced approaches for early diagnosis of ovarian cancer to avoid false-diagnosis and excessive cost.

A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning.

The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.