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Related Experiment Video

Updated: Aug 26, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression.

Omri Nayshool^1,2, Nitzan Kol¹, Elisheva Javaski¹

¹Bioinformatics Unit, Sheba Cancer Research Center and Wohl Institute for Translational Medicine, Sheba Medical Center, Tel HaShomer, Israel.

Cancer Informatics

|October 13, 2022

Summary

We developed accurate cancer outcome prediction models using The Cancer Genome Atlas (TCGA) data. These models, validated externally, identify key genes for potential therapeutic targets in renal cancer.

Keywords:

Cancer survivors classification gene expression molecular targeted therapy supervised machine learning

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Last Updated: Aug 26, 2025

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Published on: July 22, 2020

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Predicting cancer outcomes is crucial for effective treatment planning.
Large-scale cancer datasets like The Cancer Genome Atlas (TCGA) offer valuable resources for developing predictive models.
External validation is essential to ensure the reliability of prediction models.

Purpose of the Study:

To develop and validate reliable cancer outcome prediction models using TCGA data.
To identify key genes and pathways associated with cancer prognosis.
To make these prediction models accessible for broader use.

Main Methods:

Optimized Random Forest models using parameter grid search and backward feature elimination on 16 TCGA cancer cohorts.
Validated models using Clinical Proteomic Tumor Analysis Consortium (CPTAC3) data.
Utilized Python SciKit-Learn package and developed a web interface (surviveAI).

Main Results:

Five prediction models achieved an Area Under the Curve (AUC-ROC) greater than 80%.
Specific models for the TCGA-KIRP cohort showed high AUC-ROC values (0.86 for 42 genes, 0.85 for 300 genes).
Identified five key genes (DMBT1, IL11, HOXB6, TRIB3, PIM1) common to top models, significant in renal cancer.

Conclusions:

The developed prediction models demonstrate high accuracy in predicting cancer outcomes.
The identified key genes hold potential as prognostic markers and therapeutic targets for renal cancer.
The surviveAI web interface provides accessible tools for cancer outcome prediction.