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Recursive ensemble feature selection provides a robust mRNA expression signature for ME/CFS, 2021, Metselaar et al

Discussion in 'ME/CFS research' started by Sly Saint, Feb 25, 2021.

  1. Sly Saint

    Sly Saint Senior Member (Voting Rights)

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    Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome
    https://www.nature.com/articles/s41598-021-83660-9
     
    merylg, sebaaa, Ravn and 17 others like this.
  2. Kitty

    Kitty Senior Member (Voting Rights)

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    I know so little about genetics and stats that I only understand about one word in six! – but it's good to see work coming from a group in the Netherlands.
     
    merylg, sebaaa, Ravn and 8 others like this.
  3. Dolphin

    Dolphin Senior Member (Voting Rights)

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    This used the CAMDA data set which used the Reeves et al. (2005) very odd operationalisation of the Fukuda et al. criteria, the so-called empiric criteria. These gave a prevalence of 2.54% for the population. A lot probably have primary psychiatric problems rather than ME/CFS. I consider them probably worse than the Oxford criteria.
     
    Amw66, Robert 1973, sebaaa and 20 others like this.
  4. Snowdrop

    Snowdrop Senior Member (Voting Rights)

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    I'd guess that the selection of criteria came from the author in the dept of psychology Amsterdam as his contribution.
     
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  5. Dolphin

    Dolphin Senior Member (Voting Rights)

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    The whole CAMDA dataset used the Reeves et al. (2005) criteria. I'm not sure whether it was possible to select patients in any other way using this dataset. Every paper looks like it used the criteria.

    It was frustrating. The data was analysed in a special conference at the time with lots of numerate people analysing it, but I couldn't have any confidence in any of the results.
     
    Last edited: Feb 25, 2021
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  6. Snowdrop

    Snowdrop Senior Member (Voting Rights)

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    Yes, I misspoke when calling the selection a selection of criteria. My memory can't even king in mind what I've read past the moment I read things. I wonder if there were any other choice of data-sets available to them or was this the only choice? I confess I don't have any idea of what these things even are.
     
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  7. Grigor

    Grigor Senior Member (Voting Rights)

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    The good news is that Aletta Kraneveld was involved in the whole Dutch biomedical research agenda process. She went to the Invest in ME 2019 conference. I think it's important for us Dutch patients to remind them of better criteria to use. They did however mention this in the study.

     
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  8. DokaGirl

    DokaGirl Senior Member (Voting Rights)

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    Swings and roundabouts. Excited about possible biomarkers; astonished at the overly broad selection criteria used.
     
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  9. Forbin

    Forbin Senior Member (Voting Rights)

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    FWIW:

    In this 2011 article by Leonard Jason, et al., the relative ability of the empiric and CCC criteria to identify patients from controls did not seem wildly different (79% vs 87%), but it seems to beg the question of how they determined who was a "true" patient in the first place.

     
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  10. Andy

    Andy Committee Member

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    Full text available here, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3228898/
     
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  11. Hutan

    Hutan Moderator Staff Member

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    This protein expression stuff is exciting I think, and it looks like we have a number of teams interested in it. @DMissa

    I am tempted to leap into googling each gene where expression has been found here to be down regulated, to find out what it does. I've been looking at the Human Protein Atlas just now - the link I have given there is to CORO6, one of the genes found to have down-regulated expression.
    It feels a little bit like reading a newspaper astrology item though, where the information for a protein is vague enough that it's easy to find something that seems to fit with what we think is happening.

    One interesting thing from the Human Protein Atlas is that it underlines the variability of expression in different tissues. See for example the screenshot of a bit of the list of tissues where CORO6 is expressed. It's not expressed much in blood cells at all, and even there, it's more highly expressed in eosinophils and basophils (which weren't looked at in this study) than in monocytes (which were).

    Screen Shot 2021-02-26 at 10.21.50 PM.png

    So, if you thought the finding of down regulation of e.g. CORO6 in patients compared to controls was worth following up, it might be useful to look at different tissues, maybe muscle, or maybe the eosinophils. And of course, the protein might be doing different, and important, things in the different tissues, even at low concentrations . It feels a bit like a needle in a haystack sort of problem - but surely the needles are there in the haystack. People surely can't feel as bad as we do without there being some proteins expressed differently. And the sifting of the straw has begun.
     
    Last edited: Feb 26, 2021
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  12. unicorn7

    unicorn7 Senior Member (Voting Rights)

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    Just a little bit more patience, the research agenda will be public within a few months.
     
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  13. Ravn

    Ravn Senior Member (Voting Rights)

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    Way above my head. However, this study highlights the possibilities inherent in publicly available datasets that other teams can dive into, in this case with machine learning algorithms.

    In this study they only used a few and quite small datasets. Yet for machine learning I would have thought the more (good) data the merrier. I wonder what the limitation was, lack of funding* to do more or no additional datasets publicly available?

    * They appear to have been quite creative to get whatever funding they had:
     
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  14. FMMM1

    FMMM1 Senior Member (Voting Rights)

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    Perhaps broad criteria are OK in a biomarker study; if you filtered too much then you could exclude people who provide insight into the disease mechanism.
     
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  15. FMMM1

    FMMM1 Senior Member (Voting Rights)

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    Way above my head too.

    I'm not sure if this post by @Jonathan Edwards is in any way related to this publication [they both suggest immune dysregulation I guess]:
    https://www.s4me.info/threads/me-cf...maintains-status-quo.12949/page-2#post-228586

    The other thing is that I don't think Jonathan's a big fan of studies which use peripheral blood mononuclear cells (PBMCs) - from memory his view is that PBMCs are not doing a lot.

    Interesting to see what Jonathan's take on this is.
     
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  16. Jonathan Edwards

    Jonathan Edwards Senior Member (Voting Rights)

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    Yes, afraid so. And if you have to torture someone else's data with statistics to get drops of data out I cannot raise enthusiasm.
     
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  17. FMMM1

    FMMM1 Senior Member (Voting Rights)

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    Thanks & for the laugh - as always!
     
  18. DMissa

    DMissa Established Member (Voting Rights)

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    The analysis here looks very nice!

    With regard to gene products the study here is looking at mRNA levels. In my opinion it's hard to infer downstream or functional consequences with mRNA levels without protein data alongside it (the authors are responsible about this in my opinion and focus on the diagnostic parts which I think is good. They don't overclaim). I found this out firsthand, eg in the paper we just published: if we only had transcriptome (mRNA) data it would've looked like the electron transport chain was downregulated in expression, when the reality is that it's downregulated at the transcript level but still upregulated at the protein level (likely due to specific dysregulation of involved signalling pathways).

    The best performing diagnostic parts here look nice. Looks like the mRNA measurements might generally achieve better specificity than sensitivity (the mRNA ROC curves ramp upwards from the Y axis). I've actually seen this trend when exploring transcriptome data, so that's very interesting. Sensitive tests lend themselves well to an initial screening step since you are less likely to miss any true positive patients. Specific tests lend themselves well to a subsequent confirmatory test after initial screening since you can better filter out false-positives.
     
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  19. ME/CFS Skeptic

    ME/CFS Skeptic Senior Member (Voting Rights)

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    The CAMDA dataset can be downloaded here: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/CFS/

    Do you happen to have a link or reference that explains that the CAMDA dataset was based on the CDC study that used the empirical criteria?
     
    Peter Trewhitt likes this.
  20. Dolphin

    Dolphin Senior Member (Voting Rights)

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    I'm afraid I'm not sure how easy I could do that at this stage.
    It relates to this cohort:
    Chronic Fatigue Syndrome – A clinically empirical approach to its definition and study
    https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-3-19
    At the time I used to look at the CAMDA papers that came out and they all used the empiric criteria as far as I could see so I eventually stop reading them.

    Lots of people missed it at the time because it was described as an operationalisation of the Reeves et al (2003) criteria, basically the Fukuda et al. (1994) criteria. However, the Reeves et al. (2005) criteria are a very weird operationalisation of the criteria.
     
    Last edited: Nov 21, 2021

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