Survival Genie 2: a next-generation web server for targeted and single-cell-based survival analyses.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Numerous cancer genomics datasets are publicly available, driving the development of web-based survival analysis tools.
• Existing tools often limit analysis to single genes or gene sets, hindering the integration of single-cell genomics data.
• There is a need for advanced tools capable of handling the complexity of single-cell data for biomarker discovery.

Purpose of the Study:

• To introduce Survival Genie 2, a novel web-based platform for survival analysis integrating user-generated single-cell data.
• To enable identification of outcome-associated genes from single-cell cluster markers and gene co-expression network modules.
• To provide specialized parsing for biologically relevant categories like long noncoding RNAs (lncRNAs), transcription factors, and ligand-receptor signaling genes.

Main Methods:

• Utilizes univariate Cox proportional hazards modeling for survival analysis.
• Incorporates analysis of single-cell cluster markers and weighted gene co-expression network modules.
• Performs deconvolution analysis to correlate gene expression with predicted immune cell enrichment.

Main Results:

• Survival Genie 2 facilitates survival analyses integrating user-generated single-cell data.
• The platform hosts a comprehensive database of 132 datasets from 53 cancer genome projects across 38 malignancies.
• It generates publication-ready Kaplan-Meier and integrated forest plots for visualized results.

Conclusions:

• Survival Genie 2 enhances the translational impact of cancer research by enabling precise biomarker and therapeutic target identification.
• The tool supports specialized survival analyses on single-cell data, including regulatory networks and ligand-receptor pairs.
• Survival Genie 2 is accessible at https://bhasinlab.bmi.emory.edu/SurvivalGenie2/home.