Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold.

The pathogenesis of carcinoma is believed to come from the combined effect of polygenic variation, and the initiation and progression of malignant tumors are closely related to the dysregulation of biological pathways. Quantifying the alteration in pathway activation and identifying coordinated patterns of pathway dysfunction are the imperative part of understanding the malignancy process and distinguishing different tumor stages or clinical outcomes of individual patients. In this study, we have conducted in silico pathway activation analysis using Riemannian manifold (RiePath) toward pan-cancer personalized characterization, which is the first attempt to apply the Riemannian manifold theory to measure the extent of pathway dysregulation in individual patient on the tangent space of the Riemannian manifold. RiePath effectively integrates pathway and gene expression information, not only generating a relatively low-dimensional and biologically relevant representation, but also identifying a robust panel of biologically meaningful pathway signatures as biomarkers. The pan-cancer analysis across 16 cancer types reveals the capability of RiePath to evaluate pathway activation accurately and identify clinical outcome-related pathways. We believe that RiePath has the potential to provide new prospects in understanding the molecular mechanisms of complex diseases and may find broader applications in predicting biomarkers for other intricate diseases.

SEPA: signaling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data.

MOTIVATION: Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments. RESULTS: In this study, we propose a novel method to evaluate personalized pathway activation based on signaling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high- and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability. AVAILABILITY AND IMPLEMENTATION: The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metagenomic Profiling of Ocular Surface Microbiome Changes in Meibomian Gland Dysfunction.

Purpose: Ocular surface microbiome changes can affect meibomian gland dysfunction (MGD) development. This study aimed to delineate differences among the microbiome of eyelid skin, conjunctiva, and meibum in healthy controls (HCs) and patients afflicted with MGD. Methods: Shotgun metagenomic analysis was used to determine if there are differences between the microbial communities in ocular sites surrounding the meibomian gland in healthy individuals and patients afflicted with MGD. Results: The meibum bacterial content of these microbiomes was dissimilar in these two different types of individuals. Almost all of the most significant taxonomic changes in the meibum microbiome of individuals with MGD were also present in their eyelid skin, but not in the conjunctiva. Such site-specific microbe pattern changes accompany increases in the gene expression levels controlling carbohydrate and lipid metabolism. Most of the microbiomes in patients with MGD possess a microbe population capable of metabolizing benzoate. Pathogens known to underlie ocular infection were evident in these individuals. MGD meibum contained an abundance of Campylobacter coli, Campylobacter jejuni, and Enterococcus faecium pathogens, which were almost absent from HCs. Functional annotation indicated that in the microbiomes of MGD meibum their capability to undergo chemotaxis, display immune evasive virulence, and mediate type IV secretion was different than that in the microbiomes of meibum isolated from HCs. Conclusions: MGD meibum contains distinct microbiota whose immune evasive virulence is much stronger than that in the HCs. Profiling differences between the meibum microbiome makeup in HCs and patients with MGD characterizes changes of microbial communities associated with the disease status.