Differential pathway network analysis used to identify key pathways associated with pediatric pneumonia

Analysis of carcinogenic signaling networks in endometrial cancer identifies RAB17 as a potential target

Endometrial cancer is one of the most common malignancies in postmenopausal women. Several potential therapeutic targets have been investigated in current research, but few have been used clinically. Therefore, further investigating the potential pathogenesis of endometrial cancer and new effective therapeutic targets for endometrial malignancies is still necessary. Our study used a The Cancer Genome Atlas dataset and two Gene Expression Omnibus datasets for weighted gene coexpression network analysis to identify important genes associated with the histological grades of endometrial cancer. In addition, we performed gene set enrichment analysis on the three datasets and found that abnormally activated signaling pathways and metabolic pathways are the main biological behaviors of endometrial cancer. Moreover, we further used different algorithms and identified the RAB17 gene as a potential study object. To further illustrate the potential role of the genes we analyzed in clinical and cellular aspects, we performed a clinical correlation analysis. Finally, we demonstrated the important roles and mechanisms of the RAB17 gene in the cell cycle, proliferation, and metastasis of endometrial cancer. Using repeated database analysis and cell-level assays, we propose RAB17 as a potential target gene for endometrial cancer for further study.

PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

Background

Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.

Results

We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data.

Conclusions

The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users.