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Random gene sets in predicting survival of patients with hepatocellular carcinoma

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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 messages

  • Molecular signatures predicting HCC have not yet been integrated into clinical routine

  • Depending 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 predictability

  • In these patients, approx. 70% of all random 50-gene containing gene sets resulted in equal and stable prediction of survival

  • Overall, “swarm intelligence” is superior and more robust for predictive purposes in HCC

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Acknowledgements

The authors thank Dr. Snorri Thorgeirsson, NIH/NCI, Bethesda, MD for his generous support and providing clinical parameters to the GSE4024 and GSE1898 data sets. S.R. was supported by the German Research Foundation (DFG) CRC SFB/TR 209 Liver Cancer project B01.

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Authors and Affiliations

  1. Division of Hepatology & Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany

    Timo Itzel & Andreas Teufel

  2. Statistical Bioinformatics, Department of Functional Genomics, University Medical Center, Regensburg, Germany

    Rainer Spang

  3. Hepacult GmbH, Regensburg, Germany

    Thorsten Maass

  4. Department of Medicine II, Großhadern University Medical Center, Ludwig Maximilians University, Munich, Germany

    Stefan Munker

  5. Institute of Pathology, Heidelberg University, Heidelberg, Germany

    Stephanie Roessler

  6. Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany

    Matthias P. Ebert

  7. Department of Surgery, University Medical Center, Regensburg, Germany

    Hans J. Schlitt

  8. Department of Medicine III, University Medical Center, Regensburg, Germany

    Wolfgang Herr

  9. Department of Pathology, University of Regensburg, Regensburg, Germany

    Matthias Evert

Authors
  1. Timo Itzel
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  2. Rainer Spang
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  3. Thorsten Maass
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  4. Stefan Munker
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  5. Stephanie Roessler
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  6. Matthias P. Ebert
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  7. Hans J. Schlitt
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  8. Wolfgang Herr
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  9. Matthias Evert
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  10. Andreas Teufel
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Contributions

Study concept and design: TI, RS, TM, SST, and AT; acquisition of data—public expression data, analysis, and interpretation of data: TI, RS, TM, SM, SR, MPE, ME, and AT; drafting of the manuscript: TI, RS, TM, SM, HJS, WH, ME, and AT; critical revision of the manuscript for important intellectual content: TI, RS, TM, SM, SR, SST, MPE, HJS, WH, ME, and AT; statistical analysis: RS; obtained funding: WH, ME, HJS, and AT.

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Correspondence to Andreas Teufel.

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The authors declare that they have no conflict of interest.

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Supplemental figure 1

Differences in clinical characteristics between patients with good and poor prognosis. (PNG 69 kb)

Supplemental figure 2

Unsupervised clustering based on 50 gene containing gene sets separating prognostic subgroups with high significance (p < 0.0001). (PNG 1126 kb)

Supplemental table 1

Randomly chosen gene expression gene sets ranging from 1 to 10,000 genes (compare Fig. 1) were investigated for prognostic capability in patients with HCC. Fivefold re-iteration demonstrated stable results. (PDF 56 kb)

Supplemental table 2

Summary of theoretical possible number of gene sets and percent of evaluated gene sets performing 500,000 (5 × 100,000) re-iterations. (PDF 41 kb)

Supplemental table 3

Analysis of gene sets and signaling pathways obtained from KEGG, Biocarta, Reactome, and PID as well as GO terms (Biological Product (BP), Cellular Component (CC), and Molecular Function (MF)) for prognostic capability in patients with HCC. (PDF 5756 kb)

Supplemental table 4

397 randomly chosen 50 genes containing gene sets evaluated for survival prediction with a significance level of p = 0.0001. (PDF 299 kb)

Supplemental table 5

Reduced data set containing only data from patients whose samples were assigned to either the good or poor prognosis group. Randomly chosen gene expression gene sets ranging from 1 to 10,000 genes (compare Fig. 1) were investigated for prognostic capability in patients with HCC. Fivefold re-iteration demonstrated stable results. (PDF 94 kb)

Supplemental table 6

Average clinical characteristics of patient groups with good, poor, or undetermined prognosis. (PDF 12 kb)

Supplemental table 7

Exemplary listing of 100 re-iterations of machine learning approach and results for validation of our swarm intelligence approach. Left: Heatmap of learning approach for the randomly chosen gene set. Middle: Survival analysis (Kaplan–Meier) of learning samples. Right: Survival analysis (Kaplan–Meier) of test samples. Full procedure contained 5 independent runs including 1000 re-iterations each. (PDF 24297 kb)

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Itzel, T., Spang, R., Maass, T. et al. Random gene sets in predicting survival of patients with hepatocellular carcinoma. J Mol Med 97, 879–888 (2019). https://doi.org/10.1007/s00109-019-01764-2

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  • DOI: https://doi.org/10.1007/s00109-019-01764-2

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