IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006026.html
   My bibliography  Save this article

Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification

Author

Listed:
  • Yishai Shimoni

Abstract

One of the goals of cancer research is to identify a set of genes that cause or control disease progression. However, although multiple such gene sets were published, these are usually in very poor agreement with each other, and very few of the genes proved to be functional therapeutic targets. Furthermore, recent findings from a breast cancer gene-expression cohort showed that sets of genes selected randomly can be used to predict survival with a much higher probability than expected. These results imply that many of the genes identified in breast cancer gene expression analysis may not be causal of cancer progression, even though they can still be highly predictive of prognosis. We performed a similar analysis on all the cancer types available in the cancer genome atlas (TCGA), namely, estimating the predictive power of random gene sets for survival. Our work shows that most cancer types exhibit the property that random selections of genes are more predictive of survival than expected. In contrast to previous work, this property is not removed by using a proliferation signature, which implies that proliferation may not always be the confounder that drives this property. We suggest one possible solution in the form of data-driven sub-classification to reduce this property significantly. Our results suggest that the predictive power of random gene sets may be used to identify the existence of sub-classes in the data, and thus may allow better understanding of patient stratification. Furthermore, by reducing the observed bias this may allow more direct identification of biologically relevant, and potentially causal, genes.Author summary: Multiple gene sets have been published as predictive of cancer progression and metastasis in several cancer types. Although many of these sets proved to be highly predictive of survival, even gene sets for the same cancer (but from different data-sets or different analyses) exhibit very little overlap and to date did not provide functional therapeutic targets. Recent studies found that in breast cancer, even random gene sets can predict survival much better than would be expected, and on average are better than many published gene sets. Together, these results undermine the causal role of the published gene sets and their potential clinical implications. We show that random gene sets predict survival in many cancer types, and that this property no longer exists after splitting the data into subclasses based on data-driven clusters. This suggests that such sub-classification could increase the likelihood to identify causal genes that are potential therapeutic targets, and that this property can be used as an indication that there may be subclasses within the dataset.

Suggested Citation

  • Yishai Shimoni, 2018. "Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-15, February.
  • Handle: RePEc:plo:pcbi00:1006026
    DOI: 10.1371/journal.pcbi.1006026
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006026
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006026&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1006026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jie Li & Anne E.G. Lenferink & Yinghai Deng & Catherine Collins & Qinghua Cui & Enrico O. Purisima & Maureen D. O'Connor-McCourt & Edwin Wang, 2010. "Identification of high-quality cancer prognostic markers and metastasis network modules," Nature Communications, Nature, vol. 1(1), pages 1-9, December.
    2. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leandro C. Hermida & E. Michael Gertz & Eytan Ruppin, 2022. "Predicting cancer prognosis and drug response from the tumor microbiome," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    3. María Elena Martínez & Jonathan T Unkart & Li Tao & Candyce H Kroenke & Richard Schwab & Ian Komenaka & Scarlett Lin Gomez, 2017. "Prognostic significance of marital status in breast cancer survival: A population-based study," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    5. Radhakrishnan Nagarajan & Marco Scutari, 2013. "Impact of Noise on Molecular Network Inference," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    6. R Joseph Bender & Feilim Mac Gabhann, 2013. "Expression of VEGF and Semaphorin Genes Define Subgroups of Triple Negative Breast Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
    7. Deepak Poduval & Zuzana Sichmanova & Anne Hege Straume & Per Eystein Lønning & Stian Knappskog, 2020. "The novel microRNAs hsa-miR-nov7 and hsa-miR-nov3 are over-expressed in locally advanced breast cancer," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
    8. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
    9. Markus Ringnér & Erik Fredlund & Jari Häkkinen & Åke Borg & Johan Staaf, 2011. "GOBO: Gene Expression-Based Outcome for Breast Cancer Online," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    10. Casey S Greene & Olga G Troyanskaya, 2012. "Chapter 2: Data-Driven View of Disease Biology," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-8, December.
    11. Mark Reimers, 2010. "Making Informed Choices about Microarray Data Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(5), pages 1-7, May.
    12. Alan A. Arslan & Yian Zhang & Nedim Durmus & Sultan Pehlivan & Adrienne Addessi & Freya Schnabel & Yongzhao Shao & Joan Reibman, 2021. "Breast Cancer Characteristics in the Population of Survivors Participating in the World Trade Center Environmental Health Center Program 2002–2019," IJERPH, MDPI, vol. 18(14), pages 1-11, July.
    13. Sandra M. Rocha & Sílvia Socorro & Luís A. Passarinha & Cláudio J. Maia, 2022. "Comprehensive Landscape of STEAP Family Members Expression in Human Cancers: Unraveling the Potential Usefulness in Clinical Practice Using Integrated Bioinformatics Analysis," Data, MDPI, vol. 7(5), pages 1-48, May.
    14. Martin H van Vliet & Christiaan N Klijn & Lodewyk F A Wessels & Marcel J T Reinders, 2007. "Module-Based Outcome Prediction Using Breast Cancer Compendia," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-10, October.
    15. Sung Gwe Ahn & Minkyung Lee & Tae Joo Jeon & Kyunghwa Han & Hak Min Lee & Seung Ah Lee & Young Hoon Ryu & Eun Ju Son & Joon Jeong, 2014. "[18F]-Fluorodeoxyglucose Positron Emission Tomography Can Contribute to Discriminate Patients with Poor Prognosis in Hormone Receptor-Positive Breast Cancer," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-7, August.
    16. Erhan Bilal & Janusz Dutkowski & Justin Guinney & In Sock Jang & Benjamin A Logsdon & Gaurav Pandey & Benjamin A Sauerwine & Yishai Shimoni & Hans Kristian Moen Vollan & Brigham H Mecham & Oscar M Rue, 2013. "Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-16, May.
    17. Maurizio Callari & Antonio Lembo & Giampaolo Bianchini & Valeria Musella & Vera Cappelletti & Luca Gianni & Maria Grazia Daidone & Paolo Provero, 2014. "Accurate Data Processing Improves the Reliability of Affymetrix Gene Expression Profiles from FFPE Samples," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    18. Silje Kjølle & Kenneth Finne & Even Birkeland & Vandana Ardawatia & Ingeborg Winge & Sura Aziz & Gøril Knutsvik & Elisabeth Wik & Joao A. Paulo & Heidrun Vethe & Dimitrios Kleftogiannis & Lars A. Aksl, 2023. "Hypoxia induced responses are reflected in the stromal proteome of breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    19. Zheqi Li & Olivia McGinn & Yang Wu & Amir Bahreini & Nolan M. Priedigkeit & Kai Ding & Sayali Onkar & Caleb Lampenfeld & Carol A. Sartorius & Lori Miller & Margaret Rosenzweig & Ofir Cohen & Nikhil Wa, 2022. "ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    20. Leann A. Lovejoy & Craig D. Shriver & Svasti Haricharan & Rachel E. Ellsworth, 2023. "Survival Disparities in US Black Compared to White Women with Hormone Receptor Positive-HER2 Negative Breast Cancer," IJERPH, MDPI, vol. 20(4), pages 1-15, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1006026. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.