Cancer prognosis involves predicting outcomes like survival time and treatment response. Researchers use bioinformatics tools and databases for screening prognostic biomarkers. Some tools include:

1. LOGpc: Enables survival analysis based on gene expression data from 20 cancer types in TCGA. Users input genes and cancer type for Kaplan-Meier curves.
2. KM plotter: Integrates gene expression and clinical data for survival analysis across 54 cancer types using various inputs like genes, microRNAs, or lncRNAs.
3. GEPIA: Provides gene expression profiling and interactive analysis for 33 cancer types using TCGA and GTEx data. Allows differential expression and survival analysis.
4. TCPA: Analyzes TCGA proteomic data, offering protein expression, correlation, survival, and pathway analysis for 33 cancer types.
5. OncoLnc: Explores survival correlations and co-expression patterns between lncRNAs and protein-coding genes in TCGA data.
6. PrognoScan: Utilizes public cancer microarray datasets to assess gene prognostic value. Users input gene and cancer type for survival analysis.
7. MethSurv: Performs survival analysis based on DNA methylation data from TCGA and GEO. Users input gene symbol or CpG site ID.
8. SurvExpress: Evaluates the prognostic value of gene expression signatures or biomarkers in cancer. Allows custom or predefined gene sets for survival analysis.
9. UALCAN: Provides in-depth analysis of TCGA gene expression data, including differential expression, survival, subgroup, and correlation analysis across 31 cancer types.

These tools aid cancer prognosis research, offering diverse analyses based on various omics data. For further details and references, you can explore the respective sources provided.