# SurvDB: Systematic Identification of Potential Prognostic Biomarkers in 33 Cancer Types

## TL;DR

SurvDB is a new database that identifies over 4 million potential cancer prognostic biomarkers across 33 cancer types using multi-omics data from TCGA.

## Contribution

The study introduces SurvDB, a comprehensive database integrating nine molecular data types for multi-omics cancer prognosis analysis.

## Key findings

- Over 4.4 million molecular biomarkers were identified as significantly associated with cancer prognosis.
- The database integrates nine molecular data types across 33 cancer types and over 10,000 samples.
- SurvDB supports interactive data retrieval, visualization, and download for precision oncology research.

## Abstract

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research.