Cancer Stemness Online

Cancer Stemness Online integrates eight predictive algorithms and 27 signature gene sets to score cancer stemness potential at both bulk and single-cell levels and explore associations with immune response, cancer hallmarks, cellular states, and patient survival.

Key Features:

• Multi-algorithm stemness scoring: Integrates eight robust predictive algorithms for calculating cancer stemness scores.
• Signature gene set library: Incorporates 27 signature gene sets associated with cancer stemness for prediction.
• Signature gene identification: Identifies signature genes of cancer stemness from analytical results.
• Cancer hallmark and cellular state association: Explores associations between cancer stemness and cancer hallmarks and cellular states.
• Immune response and cell communication analysis: Explores associations with immune response and communications with immune cells.
• Survival analysis: Investigates the contributions of cancer stemness to patient survival.
• Robustness analysis: Performs robustness analysis of cancer stemness among different methods.
• Pre-calculated cancer stemness atlas: Provides pre-calculated cancer stemness data for more than 40 cancer types.
• Bulk and single-cell analysis: Supports stemness scoring at both bulk and single-cell levels.

Scientific Applications:

• Cancer stemness quantification: Scores cancer stemness potential using integrated algorithms and gene sets.
• Immunotherapy resistance research: Investigates cancer stemness as a potential culprit of immunotherapy resistance through immune response associations.
• Cancer progression studies: Analyzes the gradual loss of differentiated phenotype and acquisition of progenitor and stem cell-like features.
• Functional interpretation of cancer stemness: Expands downstream functional interpretation including immune response and cancer hallmarks.
• Pan-cancer stemness profiling: Accesses pre-calculated stemness data across more than 40 cancer types.

Methodology:

The resource integrates eight predictive algorithms and 27 signature gene sets to predict stemness scores, with downstream analyses performed from five distinct aspects: signature gene identification, cancer hallmark and cellular state association, immune response and immune cell communication analysis, survival analysis, and robustness analysis across methods.