Title: Metabolism-associated marker gene-based predictive model for prognosis, targeted
therapy, and immune landscape in ovarian cancer: An integrative analysis of single-cell and bulk
RNA sequencing with spatial transcriptomics
Version: 4 Date: 15 Dec 2024
Reviewer's report:
The manuscript ‘Multi-Omics Profiling Uncovers Arginine Biosynthetic Imbalance in
Endometriosis and Spotlights Itaconate as an Innovative Anti-Inflammatory Metabolite for
Therapy’ by Zhang et al tackles a clinically important question, which is to achieve a better
understanding of the metabolic changes occurring in endometriosis in order to identify potential
therapeutic targets.  Strengths of the manuscript include a very comprehensive metabolic
profiling including both positive and negative modes, and the high technical quality of the
metabolomic, proteomic, and transcriptomic measurements. An unrealized strength of the
manuscript is the existence of PAIRED ENDOMETRIAL SAMPLES including both ectopic
and eutopic samples from 6 of the 9 patients with endometriosis.  Unfortunately, the authors
failed to understand the basic disease aspects of endometriosis and utilize these paired samples
in an effective manner; this failure is most clear in the PCA analysis in Figure 1A.  As expected,
endometrial samples from ‘normal control’ patients (red) are completely separate from ectopic
endometrial samples (green) but eutopic samples from patients with endometriosis overlap the
NC and EC groups. Firstly, this indicates that endometrial tissues growing in ectopic locations
are metabolically distinct from both normal endometrium and endometrial tissues in the normal
location in a patient with endometriosis.  A very important question in understanding the disease
endometriosis is understanding the metabolic and other changes in the uterine endometrium
that enable and drive ectopic growth.  Had the authors chosen to compare the metabolome,
transcriptome, and proteome of ectopic versus eutopic samples from the same patient, in a
direct pairwise fashion, they might have been able to provide some direct insights into our
understanding of the disease. They could at the very least have identified the patient contributing
each of the paired EC and EU samples in the PCA analysis.
There is a similar missed opportunity in the in vivo analysis of the mouse model.  As I read the
methods section, the mouse model involves taking NORMAL endometrium from control donor
females, and placing it in an ectopic location in the recipient mouse.  This merely shows that
normal endometrium is capable of ectopic growth; it does not in any way address the changes
in uterine endometrium that lead to endometrial cells detaching from the uterus and growing
ectopically.  If we are to prevent endometriosis from developing, that is a truly essential question.
The claims regarding itaconate as a therapeutic agent are seriously overstated.  Firstly, there
is no rationale provided for the focus on itaconate, when similar decreases were observed
in numerus other metabolites including L-carnitine, L-pyroglutamic acid, and 4-hydroxy-l-
isoleucine. Itaconate appears to have been pre-selected for in vivo analysis based on their own
prior work. Given the convincing evidence of significant cytotoxicity of high dose itaconate
both in vitro and in vivo, as well as in vivo evidence of clinically unacceptable adhesions, it
would seem that the results of this global, untargeted metabolic analysis could have been more
usefully directed at the identification of other potential targets.
The above comments reflect major conceptual flaws in the manuscript.  In addition, there are
a number of moderate to minor concerns, which could probably be addressed adequately in a
revision.
1. The ‘n’ values are very low, especially since 6 of the 15 endometrial samples represent paired
eutopic samples from the same patient. While this weakness is acknowledged in the Discussion
section, the literature is rife with ‘candidate biomarkers and therapeutic targets’ based on studies
of small ‘n’ that completely failed to pan out in larger studies. At this point in the development
of biomarker science, one would expect verification of results in at least a small number of
completely independent samples as a metric for robust, reproducible science.
2. The abbreviations EC and EU are not explained until the Methods section (lines 566-568).
The number of samples in each group is also reserved for lines 566-568. This is extremely
inconvenient to the reader of the manuscript. There are multiple instances of key experimental
variables in this manuscript that are not addressed until the Methods section; certainly, the
authors can spare a few words in the main narrative to tell us that EC and EU samples were
paired, or to tell us the actual ‘n’ in each group.
3. To undertake a clustering analysis of 9 patients that results in 8 clusters is absolutely
ridiculous. Essentially, each patient is her own ‘cluster’.  Clustering algorithms are rife with
arbitrary assumptions, and in this case unhelpful, beyond the EC group and both the NC and EU
groups (which look highly similar)
4. Table 2 is useless without statistics
5. The choice to focus the multi-omics component on pairwise comparisons of RNA vs protein
vs metabolites is bewildering, as it is well known that these often change in different directions,
and that enzyme activity is more important than enzyme abundance.  The pathway analyses in
Figures 3B and 3C are the real point of the multi-omics
6. In general, all of the figure legends would benefit from more detail. Particularly egregious
are the lack of definitions for EC and EU in Figure 1A and the lack of any indication in Figure
5B as to what exactly the arrows are pointing at. Figure 5B is particularly frustrating as it is
unclear whether the highlighted tissues are endometriosis tissue, or necrotic tissue remaining
after itaconate treatment.
7. Since the Introduction devotes considerable text to the importance of developing a non-
invasive diagnostic test for endometriosis, it is exceedingly frustrating that the authors
did not analyze any ‘non-invasive’ samples from their patients. Although this weakness is
acknowledged in the Discussion, it is still serious. Although obtaining clinical samples might
have been difficult, the mouse model presented the opportunity to sample peritoneal fluid, urine,
and blood to determine which tissue level changes were reflected in accessible biofluids.