Itzel, Timo and Spang, Rainer and Maass, Thorsten and Munker, Stefan and Roessler, Stephanie and Ebert, Matthias P. and Schlitt, Hans J. and Herr, Wolfgang and Evert, Matthias and Teufel, Andreas (2019) Random gene sets in predicting survival of patients with hepatocellular carcinoma. JOURNAL OF MOLECULAR MEDICINE-JMM, 97 (6). pp. 879-888. ISSN 0946-2716, 1432-1440
Full text not available from this repository. (Request a copy)Abstract
Despite multiple publications, molecular signatures predicting the course of hepatocellular carcinoma (HCC) have not yet been integrated into clinical routine decision-making. Given the diversity of published signatures, optimal number, best combinations, and benefit of functional associations of genes in prognostic signatures remain to be defined. We investigated a vast number of randomly chosen gene sets (varying between 1 and 10,000 genes) to encompass the full range of prognostic gene sets on 242 transcriptomic profiles of patients with HCC. Depending on the selected size, 4.7 to 23.5% of all random gene sets exhibit prognostic potential by separating patient subgroups with significantly diverse survival. This was further substantiated by investigating gene sets and signaling pathways also resulting in a comparable high number of significantly prognostic gene sets. However, combining multiple random gene sets using swarm intelligence resulted in a significantly improved predictability for approximately 63% of all patients. In these patients, approx. 70% of all random 50-gene containing gene sets resulted in equal and stable prediction of survival. For all other patients, a reliable prediction seems highly unlikely for any selected gene set. Using a machine learning and independent validation approach, we demonstrated a high reliability of random gene sets and swarm intelligence in HCC prognosis. Ultimately, these findings were validated in two independent patient cohorts and independent technical platforms (microarray, RNASeq). In conclusion, we demonstrate that using swarm intelligence of multiple gene sets for prognosis prediction may not only be superior but also more robust for predictive purposes.Key messagesMolecular signatures predicting HCC have not yet been integrated into clinical routineDepending on the selected size, 4.7 to 23.5% of all random gene sets exhibit prognostic potential; independent of the technical platform (microarray, RNASeq)Using swarm intelligence resulted in a significantly improved predictabilityIn these patients, approx. 70% of all random 50-gene containing gene sets resulted in equal and stable prediction of survivalOverall, swarm intelligence is superior and more robust for predictive purposes in HCC
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CHEMOTHERAPY; HCC; HCC; Liver cancer; Prognostic; Signature; Gene set; Bioinformatics; Transcriptome; Profiling; Random; Swarm intelligence; Microarray; RNA Seq |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Chirurgie Medicine > Lehrstuhl für Innere Medizin III (Hämatologie und Internistische Onkologie) Medicine > Lehrstuhl für Pathologie Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 Apr 2020 05:12 |
| Last Modified: | 08 Apr 2020 05:12 |
| URI: | https://pred.uni-regensburg.de/id/eprint/26962 |
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