Abstract
The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.
Highlights
The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments
Survival Genie provides a range of input options to allow flexible and comprehensive survival analysis based on the user’s needs (Fig. 2, steps 1–2). (a) Gene-based input option requires HGNC approved gene symbols as input, the most popular and widely used option for identifying prognostic gene markers
An aggregated enrichment score is computed for a gene set using the single-sample gene set enrichment method (ssGSEA19) to estimate optimal cut-off in predicting survival outcomes
Summary
The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. The T ARGET3 program has generated comprehensive genetics, epigenetics, and transcriptome profiles of 6000 samples from nine pediatrics tumor types to understand molecular mechanisms and develop novel therapies These ambitious projects provided access to omics, clinical, demographic, and outcome information through the Genomic Data Commons (GDC) g ateway[4] to understand the impact of various associated factors on cancer outcomes besides collecting molecular information. The KMplotter[9,10] allows studying the clinical impact of individual genes in cancers from TCGA, GEO, and EGA using different splitting methods (e.g., median, percentile, quartiles) In this tool, users can upload multiple genes, ratios of two genes, and mean or median expression of genes, yet analysis will be performed at the individual gene level. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile
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