Identifying epigenetically dysregulated pathways from pathway-pathway interaction networks

Combination of bulk RNA sequencing and scRNA sequencing uncover the molecular characteristics of MAPK signaling in kidney renal clear cell carcinoma

The MAPK signaling pathway significantly impacts cancer progression and resistance; however, its functions remain incompletely assessed across various cancers, particularly in kidney renal clear cell carcinoma (KIRC). Therefore, there is an urgent need for comprehensive pan-cancer investigations of MAPK signaling, particularly within the context of KIRC. In this research, we obtained TCGA pan-cancer multi-omics data and conducted a comprehensive analysis of the genomic and transcriptomic characteristics of the MAPK signaling pathway. For in-depth investigation in KIRC, status of MAPK pathway was quantitatively estimated by ssGSEA and Ward algorithm was utilized for cluster analysis. Molecular characteristics and clinical prognoses of KIRC patients with distinct MAPK activities were comprehensively explored using a series of bioinformatics algorithms. Subsequently, a combination of LASSO and COX regression analyses were utilized sequentially to construct a MAPK-related signature to help identify the risk level of each sample. Patients in the C1 subtype exhibited relatively higher levels of MAPK signaling activity, which were associated with abundant immune cell infiltration and favorable clinical outcomes. Single-cell RNA sequencing (scRNA-seq) analysis of KIRC samples identified seven distinct cell types, and endothelial cells in tumor tissues had obviously higher MAPK scores than normal tissues. The immunohistochemistry results indicated the reduced expression levels of PAPSS1, MAP3K11, and SPRED1 in KIRC samples. In conclusion, our study represents the first integration of bulk RNA sequencing and single-cell RNA sequencing to elucidate the molecular characteristics of MAPK signaling in KIRC, providing a solid foundation for precision oncology.

Gene Saturation: An Approach to Assess Exploration Stage of Gene Interaction Networks

The gene interaction network is one of the most important biological networks and has been studied by many researchers. The gene interaction network provides information about whether the genes in the network can cause or heal diseases. As gene-gene interaction relations are constantly explored, gene interaction networks are evolving. To describe how much a gene has been studied, an approach based on a logistic model for each gene called gene saturation has been proposed, which in most cases, satisfies non-decreasing, correlation and robustness principles. The average saturation of a group of genes can be used to assess the network constructed by these genes. Saturation reflects the distance between known gene interaction networks and the real gene interaction network in a cell. Furthermore, the saturation values of 546 disease gene networks that belong to 15 categories of diseases have been calculated. The disease gene networks’ saturation for cancer is significantly higher than that of all other diseases, which means that the disease gene networks’ structure for cancer has been more deeply studied than other disease. Gene saturation provides guidance for selecting an experimental subject gene, which may have a large number of unknown interactions.

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.

Identifying dynamic pathway interactions based on clinical information

In this paper, we introduce approaches for inferring dynamic pathway interactions by converting static datasets into dynamic datasets using patients' clinical information. One approach uses survival time-based dynamic datasets, and the other uses grade- and stage-based dynamic datasets. Based on cancer grades and stages, we generated six dynamic levels and obtained two pairs of significant pathways out of twelve enriched pathways. One pair of the pathways included CELL ADHESION MOLECULES CAMS and SYSTEMIC LUPUS ERYTHEMATOSUS (correlation coefficient=1.00), in which CD28, CD86, HLA-DOA, and HLA-DOB were identified as common genes in the pathways. The other pair of the pathways included SPLICEOSOME and PRIMARY IMMUNODEFICIENCY (correlation coefficient=0.94) with no common genes identified.