Cancer survival analysis using RNA sequencing and clinical data

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Survival outcomes were assessed in cancer patients from whom cancer tissue was analyzed with Illumina Hi-Seq ribonucleic acid (RNA) sequencing (RNA-Seq) (accessible on National Cancer Institute Genomic Data Commons (GDC) and The Cancer Genome Atlas (TCGA)). Cancerrelevant genes with the most significant correlations with the clinical outcome of overall survival were assessed in Kaplan Meier survival analysis plots. In addition, clinical variables as well as the interaction of clinical variables and cancer relevant genes were assessed in survival analysis. Results show that TP53, BRCA1, NBN, MADIL1, and EP300 were significant predictors of overall survival for prostate cancer patients. While these genes and clinical variables (Gleason Score group and biochemical recurrence) were significant predictors of overall survival when assessed separately, the combination of gene levels along with Gleason score groups provided the most predictive power for overall survival. In this study, cancer-relevant genes predicted survival outcomes, although various genes may interact with genes currently known to contribute to cancer. These findings indicate that multiple cancer types should be assessed together to determine which genes are relevant for cancer in general and for specific cancer types. Future studies will assess all RNA sequencing results available on the Genomic Data Commons, including those not yet associated with cancer. These findings have implications for assessing gene-gene interactions and gene-environment interactions prostate cancer as well as for other types of cancer.

Original languageEnglish (US)
Pages (from-to)80-87
Number of pages8
JournalProcedia Computer Science
StatePublished - 2020
Event2020 Complex Adaptive Systems Conference, CAS 2019 - Malvern, United States
Duration: Nov 13 2019Nov 15 2019

All Science Journal Classification (ASJC) codes

  • General Computer Science


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