ArticleGenome medicine2026
Survival Genie 2: a next-generation web server for targeted and single-cell-based survival analyses.
Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the growing number of publicly available cancer genomics datasets, numerous web-based survival analysis tools have been developed to link gene expression data with patient outcomes, aiming to discover novel biomarkers and therapeutic targets. However, existing tools primarily support single-gene or gene-set analyses catered to targets derived from traditional sequencing approaches, limiting their applicability in the era of single-cell genomics.
resultsHere, we introduce Survival Genie 2, the first web-based platform uniquely designed to facilitate survival analyses integrating user-generated single-cell data. Through this novel approach, our tool enables researchers to identify outcome-associated genes from single-cell cluster markers and weighted gene co-expression network modules. Furthermore, Survival Genie 2 offers specialized capabilities for parsing user inputs into biologically relevant categories, including long noncoding RNAs (lncRNAs), transcription factors, and ligand-receptor signaling genes, enhancing the robustness and interpretability of survival associations. Importantly, Survival Genie 2 also hosts the most comprehensive database to date, comprising 132 datasets from 53 cancer genome projects across 38 distinct malignancies, including both adult and pediatric cancers. The tool further provides versatile partitioning methods, generating analysis results through univariate Cox proportional hazards modeling, which are presented in publication-ready Kaplan-Meier and integrated forest plots. Additionally, Survival Genie 2 uniquely bridges the gap between publicly available bulk sequencing datasets and cell-type-specific targets derived from the user's single-cell sequencing data by correlating user input gene expression with predicted immune cell enrichment, estimated through deconvolution analysis on the selected cancer dataset.
conclusionsBy integrating these innovative features, with specialized survival analyses on single-cell cluster markers, co-expression modules, regulatory networks, and ligand-receptor pairs, Survival Genie 2 represents a novel tool for expanding the translational impact of cancer research, enabling the precise identification of biomarkers and therapeutic targets. Survival Genie 2 is available at https://bhasinlab.bmi.emory.edu/SurvivalGenie2/home.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.