Meta-analysis integrated with multi-omics data analysis to elucidate pathogenic mechanisms of age-related knee osteoarthritis in mice

Increased mechanistic insight into the pathogenesis of knee osteoarthritis (KOA) is needed to develop efficacious disease-modifying treatments. Though age-related pathogenic mechanisms are most relevant to the majority of clinically-presenting KOA, the bulk of our mechanistic understanding of KOA has been derived using surgically induced post-traumatic OA (PTOA) models. Here, we took an integrated approach of meta-analysis and multi-omics data analysis to elucidate pathogenic mechanisms of age-related KOA in mice. Protein-level data were integrated with transcriptomic profiling to reveal inflammation, autophagy, and cellular senescence as primary hallmarks of age-related KOA. Importantly, the molecular profiles of cartilage aging were unique from those observed following PTOA, with less than 3% overlap between the two models. At the nexus of the three aging hallmarks, Advanced Glycation End-Product (AGE)/Receptor for AGE emerged as the most statistically robust pathway associated with age-related KOA. This pathway was further supported by analysis of mass spectrometry data. Notably, the change in AGE-RAGE signaling over time was exclusively observed in male mice, suggesting sexual dimorphism in the pathogenesis of age-induced KOA in murine models. Collectively, these findings implicate dysregulation of AGE-RAGE signaling as a sex-dependent driver of age-related KOA.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

Motivation The metabolome and microbiome disorders are highly associated with human health and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world data set was used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Availability 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. Supplementary information Supplementary data are available at Bioinformatics online.

Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multi-Omics Data Analysis

Rationale: Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision making and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multi-omics data analysis. Methods: This study was based on the multi-omics data (including mRNA, lncRNA, miRNA, methylation and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multi-omics clustering, and conducted pseudo-timing analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multi-omics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results: A prognosis predicting model of ccRCC was established by dividing patients into high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups (p<0.01). The area under the OS time dependent ROC curve for 1, 3, 5 and 10 years in the training set was 0.75, 0.72, 0.71 and 0.68 respectively. Conclusion: The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

Novel feature selection via kernel tensor decomposition for improved multi-omics data analysis

Motivation: Feature selection of multi-omics data analysis remains challenging since omics data include 102-105 features. How to weight an individual omics dataset is unclear and greatly affects feature selection consequences. In this study, a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) was extended to integrate multi-omics datasets measured over common samples in a weight-free manner. Results: KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples and was applied to synthetic, as well as real, datasets. The proposed advanced KTD-based unsupervised FE performed comparatively with the previously proposed KTD, as well as TD-based unsupervised FE, with reduced memory and central processing unit time. This advanced KTD method, specifically designed for multi-omics analysis, attributes P-values to features, which other multi-omics-oriented methods rarely do. Availability: Sample R code is available in https://github.com/tagtag/MultiR/

An R package for divergence analysis of omics data

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.