Abstract
Objective
We aimed to investigate the significance of autophagy proteins and
their association with clinical data on pregnancy loss in polycystic
ovary syndrome (PCOS), while also constructing predictive models.
Methods
This study was a secondary analysis. we collected endometrial samples
from 33 patients with polycystic ovary syndrome (PCOS) and 7 patients
with successful pregnancy control women at the Reproductive Center of
the Second Hospital of Lanzhou University between September 2019 and
September 2020. Liquid chromatography tandem mass spectrometry was
employed to identify expressed proteins in the endometrium of 40
patients. R was use to identify differential expression proteins(DEPs).
Subsequently, Metascape was utilized for Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.
Multivariate Cox analysis was performed to analyze autophagy proteins
associated with reproductive outcomes, while logistic regression was
used for analyzing clinical data. Linear correlation analysis was
conducted to examine the relationship between autophagy proteins and
clinical data. We established prognostic models and constructed the
nomograms based on proteome data and clinical data respectively. The
performance of the prognostic model was evaluated by the receiver
operating characteristic curve (ROC) and decision curve analysis (DCA).
Results
A total of 5331 proteins were identified, with 450 proteins exhibiting
significant differential expression between the PCOS and control
groups. A prognostic model for autophagy protein was developed based on
three autophagy proteins (ARSA, ITGB1, and GABARAPL2). Additionally,
another prognostic model for clinical data was established using
insulin, TSH, TPOAB, and VD3. Our findings revealed a significant
positive correlation between insulin and ARSA (R = 0.49), as well as
ITGB1 (R = 0.3). Conversely, TSH exhibited a negative correlation with
both ARSA (-0.33) and ITGB1 (R = -0.26).
Conclusion
Our research could effectively predict the occurrence of pregnancy loss
in PCOS patients and provide a basis for subsequent research.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12884-024-06273-w.
Keywords: Polycystic ovary syndrome, Prognostic model, Quantitative
proteomics, Autophagy, ARSA, ITGB1, GABARAPL2
Introduction
Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder
with an overall prevalence ranging from 6 to 10% according to the
diagnostic criteria used [[31]1, [32]2]. The impact of PCOS on patients
is not limited to oligo- or anovulation, as a large number of patients
experience poor reproductive outcomes [[33]3–[34]5]. The current
research on PCOS mainly focuses on improving ovulatory function, while
the mechanisms associated with adverse fertility are rarely mentioned
[[35]6, [36]7]. In other words, PCOS patients still face the risks and
challenges of adverse fertility outcomes.
A plethora of data showed that poor reproductive outcomes are
associated with endometrial dysfunction [[37]3]. Several studies have
found that some clinical and biochemical factors can exert a
deleterious effect on endometrium [[38]8–[39]10]. This indicates that
clinical and biochemical factors may affect reproductive outcomes.
Autophagy, the primary intracellular degradation system, plays a
pivotal role in cellular renovation and homeostasis by recycling waste
materials [[40]11]. Previous studies have elucidated the intricate
interplay between autophagy, apoptosis, and necrosis. For instance,
autophagy can trigger other forms of cell death through selective
degradation [[41]12]. Recently, some studies found that autophagic
degradation of ferritin leads to ferroptosis due to elevated levels of
labile iron and ROS [[42]13, [43]14]. Some studies have proved that
defects in autophagy can lead to follicular development disorders
[[44]15, [45]16]. Furthermore, emerging research has unveiled a link
between autophagy and pregnancy loss as it influences immune tolerance
at the maternal–fetal interface [[46]17].
This study conducted a secondary analysis of PCOS proteomic and
clinical data to investigate the association between autophagy and
endometrium, as well as their impact on reproductive outcomes. We
analyzed and screened autophagic proteins and biochemical indicators
that have a critical impact on the pregnancy outcomes of PCOS.
Subsequently, prognostic models were constructed based on
characteristic proteins and clinical data, both of which demonstrated
robust predictive power. This research significantly contributes to the
existing knowledge regarding the relationship between autophagy and
pregnancy outcomes in PCOS.
Materials and methods
Samples collection
This study is a secondary analysis based on the proteome dataset of
endometrium samples obtained from PCOS patients and controls, aged from
21 to 40 years, collected from The Reproductive Center of the Second
Hospital of Lanzhou University during the period from September 2019 to
September 2020. This dataset included 33 PCOS patients and 7 normal
control subjects. The patients who were recruited had to satisfy the
Rotterdam criteria meet the following 2–3 items: (1) Oligo-and/or
anovulation; (2) Clinical and/or biochemical signs of hyperandrogenism;
(3) Polycystic ovaries. The exclusion criteria were as follows: (1)
Subjects suffer from hypothyroidism, hyperprolactinemia, adrenal
disease, hypertension, and diabetes; (2) hormone-medication and drugs
affecting glucose metabolism within the last 3 months. The control
group was non-PCOS with successful pregnancy and live birth. They had
regular menstrual cycles and normal ovarian morphology via routine
ultrasound scans. Informed consent was obtained from all participants
before collecting samples. The study was authorized by the Ethics
Committee of Lanzhou University Second Hospital (2017A-057).
The endometrial samples were the proliferative endometrium. The
endometrial samples were obtained using a pipelle endometrial aspirator
and stored at-80℃.
Clinical and prognosis data collection
Demographic characteristics, including age and BMI, were recorded from
outpatient medical records. Serum samples collected during the 2–5 days
of menstruation were utilized for the analysis of biochemical
indicators, coagulation index, and sex hormones. The analyzed
biochemical indicators encompassed serum lipid concentration, fasting
plasma glucose levels(FPG), insulin levels, thyroid hormone levels,
homocysteine levels, vitamin D3 levels, CA125 levels, and D-dimer. Sex
hormones include basal testosterone (T), basal luteinizing hormone
(LH), basal follicle-stimulating hormone (FSH), and the anti-mullerian
hormone (AMH). The insulin resistance index (IR) is calculated by the
HOMA-IR index, which was calculated as fasting plasma glucose (FPG)
(mmol/l) × fasting insulin (lU/ml)/22.5, and a value of > 2.6 was
considered IR [[47]18]. Endometrial thickness (ET) was examined by
ultrasound scanning.
Reproductive outcomes and gestational duration were used as prognostic
data, Reproductive outcomes include live birth and adverse fertility.
Gestational duration includes the gestational time of live birth and
adverse gestational time weeks. Gestational time was estimated in
weeks.
Sample preparation and fractionation, data-dependent acquisition (DDA)
mass spectrometry, mass spectrometry data analysis, and database search
have been described in detail in previous articles [[48]4].
Obtain the DEPs and the autophagy related proteins
The differential expression protein analysis was based on R package
(limma). The screening criteria were |Log[2]fold change
(Log[2]Fc)|> 0.585 and adjusted P < 0.05 [[49]4]. Autophagy-related
proteins (ARPs) derived from the Autophagy Database
([50]http://www.autophagy.lu/).
The functional enrichment analysis of DEPs
Import DEPs into [51]http://metascape.org/gp/index.html for metascape
analysis. Functional and pathway enrichment analysis by Gene Ontology
(GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway enrichment analysis. Min overlap = 3 and Min
Enrichment = 1.5 were the screening conditions. The P-value < 0.01 was
considered significant.
Identification of candidate autophagy proteins
We overlapped the ARPs and endometriosis-related proteins. Univariate
Cox regression analysis was used to identify the proteins related to
pregnancy outcomes. To further identify more reliable autophagy
proteins, we conducted LASSO regression algorithm. The “glmnet” package
was used to construct the LASSO model with penalty parameter tuning
conducted by ten-fold cross-validation. The expressions of candidate
autophagy proteins were used to establish a risk model.
Establishment and evaluation of model
Based on the expressions of candidate autophagy proteins, multivariate
Cox regression analysis was used to establish AutoSig Risk Model,
Forward and backward method were empolyed for filtering models. The
risk score was evaluated by formula as follows:
[MATH: AutoSig(PCOS)=∑i<
/mi>=1nco
ef(Autoproi)∗expr(Autoproi) :MATH]
. AutoSig (PCOS) represents a prognostic risk score,
[MATH: coef(Autoproi) :MATH]
represents the risk coefficient of ith prognostic autophagy protein.
[MATH: expr(Autoproi) :MATH]
is the expression level of the ith prognostic autophagy protein for the
patient. The PCOS samples were separated into high-risk and low-risk by
the risk score cutoff value (median risk score). Kaplan–Meier method
was used to estimate the reproductive outcomes of different groups in R
package (survival and survminer). At the same time, Logistic regression
was performed for clinical data. The outcome variable was the presence
or absence of a live birth. Similarly, forward and backward methods
were used for filtering models. We obtained a CliSig Risk model formula
as follows:
[MATH: P=1/1+exp-β+β1∗x1+β2∗x2+β3∗<
msub>x3+β4∗x4<
/mfenced> :MATH]
The ROC curves were evaluated for the AutoSig Risk Model and CliSig
Risk Model. The decision curve analysis (DCA) curves was performed to
assess the net benefits with the Risk Model.
Statistical analysis
The StataSE 15.0 software was used to calculate clinical data. The
proteomic data were analyzed by R software. The results were shown as
mean ± standard deviation (SD) or median (interquartile range)
according to the normal disttribution assumption. The binary logistic
regression model was used to develop a CliSig Risk Model, the Cox
regression model was used to develop an AutoSig Risk Model. All
statistical tests were two-sided, and P values of < 0.05 were
considered significant.
Result
Participant clinical characteristics
Participant clinical characteristics showed significant differences
(P < 0.05) between PCOS patients and normal controls except for age
(P = 0.37) (Table [52]1). The BMI, AMH, FSH, LH, LH/FSH, T, FPG,
Insulin, and HOMA-IR of the PCOS group were significantly higher than
those of the control group. The ET of the PCOS is thinner than the
control. The pregnancy outcomes differences between PCOS and the
control group were significant(P = 0.043).
Table 1.
Participant clinical characteristics of patients with PCOS and controls
Variable (n) PCOS (n = 33) Control (n = 7) p-value
Age (year) 25.8 (3.1) 27.0 (2.9) 0.37
BMI (kg/m^2) 23.9 (21.1, 27.3) 20.8 (19.5, 22.0) 0.03
AMH (ng/mL) 9.0 (4.1) 1.8 (1.1) < 0.01
FSH (mIU/mL) 7.2 (5.9, 7.9) 5.3 (5.2, 6.3) 0.02
LH (mIU/mL) 11.8 (4.3) 5.3 (0.6) < 0.01
LH/FSH ratio 1.8 (0.7) 1.0 (0.1) < 0.01
T (ng/dL) 42.1 (17.6) 24.7 (11.4) 0.02
FPG (mmol/L) 5.2 (0.5) 4.5 (0.4) < 0.01
Insulin (mIU/mL) 16.2 (9.77, 25.84) 7.34 (6.49, 12) 0.015
HOMA-IR 3.89 (2.35, 6.29) 1.74 (1.29, 2.16) 0.003
ET (mm) 4.0 (1.4) 9.6 (0.8) < 0.01
Live birth (%) 20(60.6) 7(100) 0.043
Adverse gestation (%) 13(39.4) 0(0)
[53]Open in a new tab
BMI Body mass index, AMH Anti-mullerian hormone, FSH
Follicle-stimulating hormone, LH Luteinizing hormone, T Testosterone,
FPG Fasting plasma glucose, FINS Fasting insulin, HOMA-IR Homeostasis
model assessment of insulin resistance, ET Endometrial thickness
p < .05 was considered statistically significant
Endometrial proteomic analysis and differential expression protein analysis
A total of 5331 proteins were identified, with 4425 proteins overlapped
in PCOS and control group (Fig. [54]1A). A lot of 450 DEPs (121
up-regulated and 329 down-regulated) were identified. (Fig. [55]1B,
Supplementary file [56]1).
Fig. 1.
[57]Fig. 1
[58]Open in a new tab
DEPs and Metascape analysis: A Venn plot of inter group samples in DIA
data; B Histogram of protein quantitative difference results; C
Enriched ontology clusters colored by p-value. the dark the color, the
more statistically significant the node is. D Enriched ontology
clusters across studies
The functional enrichment analysis of DEPs
Metascape revealed biological processes containing amide biosynthetic
process, ribonucleoprotein complex biogenesis, regulation of cellular
macromolecule biosynthetic process, and regulation of DNA metabolic
process. The significant biological pathways were the metabolism of
RNA, VEGFA-VEGFR2 signaling pathway, and Extracellular matrix
organization (Fig. [59]1C-D, Supplementary file [60]2). These results
indicated that autophagy was greatly involved in the pathogenesis and
prognosis of PCOS.
Identification of candidate autophagy proteins
Two hundred thirty two ARPs derived from the Autophagy Database
(Supplementary file [61]3). 83 overlapping proteins were obtained by
intersecting ARPs with endometriosis-related proteins. 17 autophagy
proteins were significantly correlated with fertility outcomes through
univariate Cox regression analysis. To clarify the regulatory
relationship between Autophagy proteins related to reproductive
outcomes in PCOS, we conducted a correlation analysis using R packets
(Fig. [62]2A-B). Lasso regression analysis was performed to ultimately
screen 8 prognostic related autophagy proteins (Fig. [63]2C). 8
prognostic related autophagy proteins were ARSA, EIF4G1, IKBKB, ITGB1,
HSPA8, ATIC, GABARAPL2, PRKCD. 8 candidate proteins selected as for
subsequent analysis and construction of risk model.
Fig. 2.
[64]Fig. 2
[65]Open in a new tab
Variable screening of autophagy proteins: A Heat map of autophagy
proteins related to PCOS pregnancy outcomes; B Correlation analysis of
pregnancy outcome related proteins; C Lasso regression analysis; D
Forest plots of four autophagy proteins identified by multivariate Cox
regression analysis; E Expression differences of three autophagy
proteins used for modeling in high-risk and low-risk groups
Establishment and evaluation of the risk prognostic model
Multivariate Cox regression analysis was performed to ultimately screen
3 prognostic related autophagic proteins (ARSA, ITGB1, GABARAPL2)
(Fig. [66]2D). The risk score calculation formula can be obtained:
[MATH: AutoSigPCOS=0.00490
8∗exprARSA+-0.00272
∗exprITGB1+<
mn>0.00628∗exprGABARAPL2.
:MATH]
we found that ARSA and GABARAPL2 had the positive coefficient,
suggesting they might be risk factors for a poor prognosis, while ITGB1
had a negative coefficient which indicated it could be a protective
factor for live birth. Then, we could use the median risk score to
divide the PCOS subjects into high and low-risk groups (Fig. [67]3A).
The differences between the three proteins in high and low-risk groups
are plotted in Fig. [68]2E. Survival analysis showed that the outcomes
of pregnancy of low-risk group were consistently better than high-risk
group (Fig. [69]3B). With the increase of risk score, the status of
pregnancy decreases significantly. The AutoSig Risk model that
incorporated the above independent predictors was developed and
presented as the nomogram (Fig. [70]3E).
Fig. 3.
[71]Fig. 3
[72]Open in a new tab
Establishment and evaluation of the AutoSig risk prognostic model: A
Risk and survival status of PCOS under different risk scores; B
Survival analysis between high and low-risk groups; C ROC of the
AutoSig risk model at weeks 6, 28, 37; D Decision curve analysis for
the AutoSig Risk model; E Nomogram to estimate the probability of
pregnancy outcome of PCOS use autophagy proteins
The AutoSig risk model was tested by time-dependent ROC curve analysis.
At 6 weeks, the mode had the lowest AUC (0.893), At 28 and 37 weeks,
the AUC was 0.915, 0.922 (Fig. [73]3C). The model’s AUC at 37 weeks was
significantly higher than the AUC of Insulin (0.665), TSH(0.657), TPOAB
(0.68), and VD3 (0.637) (Fig. [74]3C). This proved that AutoSig had
superior prognostic performance. The decision curve analysis for the
nomogram is presented in Fig. [75]3D. indicating that DCA shows a
greater net benefit for the AutoSig model over clinical indexes.
27 clinical data as variables (Table [76]2), Logistic regression was
used to ultimately obtain 4 prognostic related clinical data. The
clinical data model formula is as follows:
[MATH: P=1/1+exp--8.414+0.698∗TSH<
/mtext>+0.292∗VD3+2.682∗
TPOAB+0.056∗Insulin. :MATH]
CliSig Risk Model was developed and presented as the nomogram based on
the above independent predictors (Fig. [77]4A). This nomogram had
excellent discriminative power with AUC of 0.8615 (Fig. [78]4B). The
Calibration curve was plotted in Fig. [79]4C. The nomograms were well
calibrated, there were no significant differences between the predicted
and the observed probability. We did DCA on our prediction model to
assess the net benefit that patients could receive (Fig. [80]4D). The
nomogram model has an obvious net benefit for almost all threshold
probabilities.
Table 2.
Analysis of 27 clinical data in pregnancy loss and nonpregnancy loss
Variable (n) NON-pregnancy loss (n = 20) Pregnancy loss (n = 13)
p-value
Age(year) 1.00
< 30 18 (90%) 12 (92%)
> 30 2 (10%) 1 (8%)
BMI (kg/m^2) 23.3 (3.4) 25.6 (4.0) 0.09
AMH (ng/mL) 9.4 (4.4) 8.2 (3.5) 0.43
FSH (mIU/mL) 6.7 (1.4) 7.0 (2.0) 0.64
LH (mIU/mL) 12.3 (3.5) 11.2 (5.3) 0.47
LH/FSH ratio 1.9 (0.6) 1.6 (0.8) 0.33
T (ng/dL) 42.0 (11.8) 42.2 (24.5) 0.98
FPG (mmol/L) 5.2 (0.6) 5.2 (0.5) 0.81
Insulin (mIU/mL) 16.3 (9.0) 24.5 (16.1) 0.07
HOMA-IR 3.9 (2.3) 5.7 (4.0) 0.10
ET (mm) 4.3 (1.6) 3.5 (1.1) 0.17
T3(nmol/L) 1.8 (0.3) 1.9 (0.2) 0.45
T4(nmol/L) 109.3 (18.2) 119.8 (15.6) 0.10
FT3 (pmol/L) 5.4 (0.5) 5.5 (0.4) 0.71
FT4 (pmol/L) 15.8 (2.0) 15.3 (1.5) 0.47
TSH (uIU/ml) 2.7 (1.2) 3.2 (1.2) 0.28
thyroglobulin (ng/ml) 14.3 (8.5) 23.8 (21.2) 0.08
ATG-AB(U/ml) 1.00
< 35 19 (95%) 12 (92%)
> 35 1 (5%) 1 (8%)
TPO-AB(U/ml) 0.07
< 35 12 (60%) 3 (23%)
> 35 8 (40%) 10 (77%)
TC (mmol/L) 4.0 (0.8) 4.1 (0.6) 0.73
TG (mmol/L) 1.4 (0.9) 1.6 (0.9) 0.55
HDL(mmol/L) 1.3 (0.3) 1.3 (0.3) 0.82
LDL(mmol/L) 2.6 (0.7) 2.7 (0.7) 0.71
HCY(umol/L) 12.6 (3.5) 12.4 (6.8) 0.89
VD3(ng/L) 10.0 (4.0) 12.3 (5.1) 0.16
D2 polyme(mg/L) 0.6 (1.2) 0.5 (0.6) 0.79
CA125(U/ml) 15.7 (9.8) 16.5 (9.3) 0.83
[81]Open in a new tab
T3 Triiodothyronine, T4 Thyroxine, TSH Thyroid-stimulating hormone, FT3
Free triiodothyronine, FT4 Free thyroxine, ATG-AB Antithyroglobulin
antibody, TPO-AB Thyroid peroxidase antibody, TC Cholesterol, TG
Triglyceride, HDL High-density lipoprotein, LDL Low density
Lipoprotein, HCY Homocysteine
p < .05 was considered statistically significant
Fig. 4.
[82]Fig. 4
[83]Open in a new tab
Establishment and evaluation of the CliSig risk model. A Nomogram to
estimate the probability of pregnancy outcome of pregnancy outcome of
PCOS use clinical data; B ROC of the CliSig model; C The calibration
curve of CliSig risk model for predicting pregnancy outcome of PCOS. D
Decision curve analysis for the CliSig Risk model
Analysis of linear correlation between clinical data and protein expression
As both models have strong predictive value, we conducted a Pearson
correlation analysis between the variables of clinical model and the
autophagic protein model. In our study, we found a significant positive
correlation between insulin and ARSA (R = 0.49), and ITGB1 (R = 0.3).
TSH has a negative correlation with ARSA (-0.33), and ITGB1 (R = -0.26)
(Fig. [84]5).
Fig. 5.
[85]Fig. 5
[86]Open in a new tab
Correlation between clinical data and protein expression
Discussion
PCOS is a common gynecological disease characterized by reproductive
and metabolic disorders which are related to the occurrence and
progression of diseases [[87]19, [88]20]. The comorbidities of PCOS,
including (obesity, metabolic syndrome, hyperinsulinemia or
hyperandrogenism), may contribute to pregnancy loss [[89]21]. Obesity
and T2DM associated features such as dyslipidemia, oxidative stress,
hyperglycemia, hyperinsulinemia could interrupt and compromise
autophagy [[90]22]. While poor endometrial receptivity can lead to
adverse reproductive outcomes [[91]23]. We could hypothesize that
downregulation of autophagy in PCOS patients might lead to poor
endometrial receptivity, thereby increasing the incidence of
miscarriage. A study using an obese mouse model showed that autophagy
was more up-regulated in decidualizing cells of control mice compared
to high-fat/high-sugar diet mice [[92]24]. This study attempts to
screen the factors most closely related to pregnancy loss in PCOS based
on the analysis of PCOS proteomic data and clinical data. Providing a
reference for the mechanism research and clinical decision-making.
Molecular functions and pathways could explain the reasons for poor
endometrial receptivity in PCOS patients. In our study, the DEPs were
shown to be involved in metabolism of RNA pathway. Different types of
RNA and RNA-related complexes are recruited to and degraded by
autophagy pathway [[93]25]. Lots of studies have demonstrated that
inhibitors of autophagosome formation significantly block
starvation-induced RNA degradation [[94]26, [95]27]. The autophagy
pathway is damaged, while the metabolism of RNA pathway will be
inhibited. This has been positively validated in our research.
In our research, we established two prognostic models based on
proteomics and clinical data. After evaluating the models, we found
that both models had good predictive performance. The autophagic
protein model based on 3 proteins (ARSA, ITGB1, GABARAPL2).
Interestingly, our new autophagy proteins model achieved an AUC of
0.922 with only 3 feature proteins, surpassing our previous model which
used 5 feature proteins and had an AUC of 0.884. This demonstrates the
superiority of our current model. ARSA, ITGB1, GABARAPL2 were rarely
studied in PCOS in previous studies. ITGB1 is integrin, which can
affect tumor process by regulating angiogenesis, apoptosis, and
metastasis [[96]28, [97]29]. It is widely recognized that ITGB1
involved and promotes the adhesion ability of NCAM1^birgh NK cells at
the maternal–fetal interface [[98]30–[99]32]. This indicates that there
is significant research value in exploring the relationship between
ITGB1 and PCOS, and it can also serve as a predictor of pregnancy
outcomes in individuals with PCOS. RASA is a lysosomal enzyme that
catalyze degradation of sulfatides into galactosylceramides (GalC)
[[100]33, [101]34]. Research has found that the lack or complete
absence of ARSA presents metachromatic leukodystrophy which is
characterized by the degradation of intellectual function and motor
skills and often fatal in early childhood [[102]35–[103]37]. This
indicates ARSA may be an important factor in pregnancy loss in PCOS.
GABARAPL2 (also called GATE-16) belongs to the GABARAP subfamily of
Atg8 proteins [[104]38]. The Atg8 proteins play a key role in the
sealing of the isolation membrane which is a vital role in autophagy
[[105]39]. This further demonstrates the important significance of
autophagy in PCOS.
The clinical data model is based on 4 variables (TSH, VD3, TPOAB,
Insulin). The results show that the insulin level is a reliable
predictor of pregnancy loss in PCOS. Hyperinsulinemia affects the
immune response of the endometrium by decreasing the expression of
glycodelin and IGF-binding protein-1 [[106]8], a large number of
studies have found that TSH is associated with adverse pregnancy
outcomes [[107]40, [108]41]. Multiple studies have demonstrated the
impact of vitamin D on PCOS phenotype and pregnancy loss
[[109]42–[110]44]. Research has found a significant correlation between
TPO-AB and infertility in patients with PCOS [[111]45].
Interestingly, we conducted a linear analysis of the variables in both
models. ITGB1 plays an important role in beta cell development and
function, while some studies have found a positive effect of EIF4G1 on
insulin secretion [[112]46, [113]47]. Recent studies have shown that
inactivation impairs insulin function [[114]48], which supports the
reliability of our results. This may be the mechanism behind pregnancy
loss in PCOS. In the present study, we observed a significant
correlation between ARSA Insulin and TSH expression, however, there is
limited research on the relationship between ARSA and insulin which
deserves further study.
Our study still has some limitations that require further study.
Firstly, it was a retrospective study, the sample size was relatively
small and the public database PCOS proteomics data was few. These
findings need to be verified in future intervention studies. Secondly,
although we obtained only three proteins with good predictive
performance, the use of machine learning algorithms may miss some
useful predictive factors that can’t be ignored. Thirdly, Numerous
experiments are needed to verify how these proteins and pathways affect
the receptive mechanism of the endometrium in PCOS patients.
Conclusions
We conducted proteomic analysis of samples, screened DEPs and analyzed
pathways related to PCOS and PCOS pregnancy loss. We further screened
autophagy proteins and constructed a robust model. The model based on
the ARSA, ITGB1 and GABARAPL2, demonstrated high predictive accuracy
for identifying pregnancy loss in PCOS patients, thus providing a solid
theoretical basis for future investigations.
Supplementary Information
[115]12884_2024_6273_MOESM1_ESM.xlsx^ (296.2KB, xlsx)
Additional file 1. Results of the Differentially Expressed Protein
(DEP) analysis.
[116]12884_2024_6273_MOESM2_ESM.xlsx^ (51.6KB, xlsx)
Additional file 2. The functional Enrichment Analysis of DEPs.
[117]Additional file 3. ^ (1.7KB, txt)
Acknowledgements