Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers.

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways.

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.

Ameliorative Effects of Phytomedicines on Alzheimer's Patients.

INTRODUCTION: Alzheimer's disease (AD) is a progressive, neurodegenerative disease that severely affects individuals' cognitive abilities, memory, and quality of life. It affects the elderly population, and there is no permanent prevention or cures available to date, treatments mainly aiming to alleviate the symptoms as and when they appear. Alternate therapeutic approaches are being researched constantly, and there is a growing focus on phytomedicine, herbal medicine, organic compounds, and ayurvedic compounds for the treatment of AD. METHODS: The current study aims to provide an extensive review of these plants against AD from the currently existing literature. Most relevant keywords like Alzheimer's Disease, phytomedicines, ethnic medicines, the role of phytomedicine in neuroprotection, common phytomedicines against AD, etc., were used to select the plants and their metabolites effective in treating AD. The study focuses on six plants: Panax ginseng, Ginkgo biloba, Bacopa monnieri, Withania somnifera, Curcuma longa, and Lavandula angustifolia. Their active components have been studied along with neuroprotective properties, and evidence of in-vitro, pre-clinical, and clinical studies conducted to prove their therapeutic potential against the disease have been presented. RESULTS: All plants envisaged in the study show potential for fighting against AD to varying degrees. Their compounds have shown therapeutic effects by reversing the neurological changes such as clearing Aß plaque and neurofibrillary tangle formation, and ameliorative effects against neurodegeneration through processes including improving concentration, memory, cognition and learning, higher working and cue memory, improved spatial memory, inhibition of NF-κB expression, inhibiting the release of pro-inflammatory cytokines, inhibition of AChE and lipid peroxidase enzymes, and reduction of interleukin levels and tumor necrosis factor-alpha. CONCLUSION: The present review is a comprehensive and up-to-date analysis supported by the evidentiary proofs from pre-clinical studies, meta-analyses, and review papers related to natural phytochemicals' impact on neurodegenerative disorders like AD.

Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability.

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94-1 for the experimental CRCs and 0.94-1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91-0.97, whereas the third signature showed a significantly lower AUC of 0.69-0.88. Software for calculating these MSI signatures using RNAseq data is included.

FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier.

Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don't have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.

An analysis of reasons for failed back surgery syndrome and partial results after different types of surgical lumbar nerve root decompression.

BACKGROUND: Despite the evident progress in treating vertebral column degenerative diseases, the rate of a so-called "failed back surgery syndrome" associated with pain and disability remains relatively high. However, this term has an imprecise definition and includes several different morbid conditions following spinal surgery, not all of which directly illustrate the efficacy of the applied technology; furthermore, some of them could even be irrelevant. OBJECTIVE: To evaluate and systematize the reasons for persistent pain syndromes following surgical nerve root decompression. STUDY DESIGN: Prospective, nonrandomized, cohort study of 138 consecutive patients with radicular pain syndromes, associated with nerve root compression caused by lumbar disc herniation, and resistant to conservative therapy for at least one month. The minimal period of follow-up was 18 months. SETTING: Hospital outpatient department, Russian Federation METHODS: Pre-operatively, patients were examined clinically, applying the visual analog scale (VAS), Oswestry Disability Index (ODI), magnetic resonance imaging (MRI), discography and computed tomography (CT). According to the disc herniation morphology and applied type of surgery, all participants were divided into the following groups: for those with disc extrusion or sequester, microdiscectomy was applied (n = 65); for those with disc protrusion, nucleoplasty was applied (n = 46); for those with disc extrusion, nucleoplasty was applied (n = 27). After surgery, participants were examined clinically and the VAS and ODI were applied. All those with permanent or temporary pain syndromes were examined applying MRI imaging, functional roentgenograms, and, to validate the cause of pain syndromes, different types of blocks were applied (facet joint blocks, paravertebral muscular blocks, transforaminal and caudal epidural blocks). RESULTS: Group 1 showed a considerable rate of pain syndromes related to tissue damage during the intervention; the rates of radicular pain caused by epidural scar and myofascial pain were 12.3% and 26.1% respectively. Facet joint pain was found in 23.1% of the cases. Group 2 showed a significant rate of facet joint pain (16.9%) despite the minimally invasive intervention. The specificity of Group 3 was the very high rate of unresolved or recurred nerve root compression (63.0%); in other words, in the majority of cases, the aim of the intervention was not achieved. The results of the applied intervention were considered clinically significant if 50% pain relief on the VAS and a 40% decrease in the ODI were achieved. LIMITATIONS: This study is limited because of the loss of participants to follow-up and because it is nonrandomized; also it could be criticized because the dynamics of numeric scores were not provided. CONCLUSION: The results of our study show that an analysis of the reasons for failures and partial effects of applied interventions for nerve root decompression may help to understand better the efficacy of the interventions and could be helpful in improving surgical strategies, otherwise the validity of the conclusion could be limited because not all sources of residual pain illustrate the applied technology efficacy. In the majority of cases, the cause of the residual or recurrent pain can be identified, and this may open new possibilities to improve the condition of patients presenting with failed back surgery syndrome.