Abstract
Background: Late-onset pre-eclampsia (LO-PE) remains difficult to
predict because placental angiogenic markers perform poorly once
maternal cardiometabolic factors dominate. Methods: We reanalyzed a
publicly available cell-free RNA (cfRNA) cohort (12 EO-PE, 12 LO-PE,
and 24 matched controls). After RNA-seq normalization, we derived LO-PE
candidate genes using (i) differential expression and (ii) elastic-net
feature selection. Predictive accuracy was assessed with nested
Monte-Carlo cross-validation (10 × 70/30 outer splits; 5-fold inner
grid-search for λ). Results: The best LO-PE elastic-net model achieved
a mean ± SD AUROC of 0.88 ± 0.08 and F1 of 0.73 ± 0.17—substantially
higher than an EO-derived baseline applied to the same samples (AUROC ≈
0.69). Enrichment analysis highlighted immune-tolerance and metabolic
pathways; three genes (HLA-G, IL17RB, and KLRC4) recurred across >50%
of cross-validation repeats. Conclusions: Plasma cfRNA signatures can
outperform existing EO-based screens for LO-PE and nominate
biologically plausible markers of immune and metabolic dysregulation.
Because the present dataset is small (n = 48) and underpowered for
single-gene claims, external validation in larger, multicenter cohorts
is essential before clinical translation.
Keywords: late-onset preeclampsia, cell-free RNA, biomarkers, maternal
immunity, machine learning, maternal factors
1. Introduction
Preeclampsia is a condition characterized by the new onset of
hypertension and proteinuria—or organ dysfunction such as liver or
kidney impairment—after 20 weeks of gestation. It is reported to occur
in approximately 2–5% of pregnancies worldwide [[28]1]. This disorder
significantly increases morbidity and mortality for both mothers and
fetuses and can lead to preterm delivery or severe complications (e.g.,
HELLP syndrome).
Traditionally, preeclampsia has been categorized into early-onset
(occurring before 34 weeks of gestation) and late-onset (occurring at
or after 34 weeks of gestation) forms. Early-onset preeclampsia is
typically associated with marked placental insufficiency and vascular
dysfunction and tends to present with more severe clinical outcomes. In
contrast, late-onset preeclampsia is thought to be more influenced by
maternal factors (obesity, hypertension, metabolic risks, etc.)
[[29]2,[30]3]. Although late-onset preeclampsia is often regarded as
relatively mild, it still raises the risk of maternal–fetal
complications and frequently necessitates cesarean delivery or other
medical interventions.
Currently, the only definitive cure for preeclampsia is delivery, and
effective pharmacological interventions remain limited—especially for
late-onset cases. For instance, low-dose aspirin has been shown to
significantly reduce the incidence of early-onset preeclampsia (before
34 weeks), but meta-analyses suggest that this prophylactic effect is
less pronounced in late-onset disease [[31]4]. Hence, early risk
stratification and management of late-onset preeclampsia remain
crucial.
Several screening approaches have been proposed to enable early risk
assessment of late-onset preeclampsia, combining maternal background
factors (e.g., chronic hypertension, obesity, and history of diabetes),
uterine artery Doppler measurements, and serum biomarkers (e.g., PlGF
and sFlt-1). However, changes in placental-derived factors are less
pronounced in late-onset cases than in early-onset cases, and
predictive models relying solely on placental angiogenic factors often
reach a sensitivity of around only 40% [[32]2,[33]5] and, in systematic
reviews, rarely exceed an AUROC of 0.70 for late-onset disease [[34]3].
Consequently, late-onset preeclampsia has proven more challenging to
predict with high accuracy. While it has been noted that maternal
factors (e.g., high BMI and advanced maternal age) play a major role in
late-onset cases [[35]3,[36]6], the specific molecular mechanisms
underlying this subtype have not yet been fully elucidated.
Recent literature points to two converging biological axes in
late-onset PE—failure of maternal–fetal immune tolerance and
exacerbated third-trimester metabolic stress. Placental and review data
highlight diminished HLA-G/KIR signaling and a Th17/IL-17-skewed
cytokine milieu in LO-PE [[37]7,[38]8], while large cohort and cfRNA
studies report the early enrichment of Allograft Rejection and
Estrogen-response pathways [[39]9]. Complementarily, metabolic-syndrome
traits—insulin resistance, dyslipidemia, and altered glycolytic
flux—have been proposed as principal maternal drivers of LO-PE
[[40]10]. Guided by this evidence, we focus our cfRNA feature-selection
and downstream interpretation on genes mapping to immune-tolerance and
metabolic pathways.
In recent years, machine learning (ML) and artificial intelligence (AI)
approaches have gained attention for their potential to integrate these
complex risk factors multidimensionally and are now highlighted as
promising avenues for PE prediction in dedicated reviews [[41]11]. For
example, analyses of large-scale electronic health records (EHRs)
incorporating diverse maternal background and laboratory data have
demonstrated high-accuracy prediction with an AUC exceeding 0.9
[[42]6]. Nonetheless, most studies to date are retrospective and
confined to specific cohorts and thus lack external validation or
prospective evaluation. Although attempts to merge multi-omics data
(e.g., genetic risk scores, proteomics, and metabolomics) have been
reported [[43]12], the cost and clinical feasibility remain significant
barriers.
Parallel to the surge in AI-driven risk models, cell-free RNA
sequencing (cfRNA-seq) of maternal plasma has become a leading avenue
for non-invasive biomarker discovery. Recent high-throughput studies
using this technique repeatedly implicate two pathophysiologic axes in
late-onset PE—(i) the loss of maternal–fetal immune tolerance,
exemplified by downregulated placental HLA-G/KIR checkpoints and a
Th17-skewed cytokine milieu [[44]7,[45]8], and (ii) third-trimester
metabolic stress, marked by systemic insulin-resistance signatures and
enrichment of glycolysis- and estrogen-response gene sets
[[46]9,[47]10]. A 2024 systematic review synthesized these observations
into a dual-hit model in which rising maternal metabolic load amplifies
incipient immune dysfunction to trigger clinical LO-PE [[48]3].
Circulating cfRNA originates from both maternal blood cells and the
placenta; trophoblast-derived transcripts are detectable from the first
trimester and increase steadily with gestation. Unlike cfDNA, cfRNA
captures the moment-to-moment transcriptional state of maternal–fetal
tissues, providing time-resolved insight into immune, angiogenic, and
metabolic pathways [[49]9]. Longitudinal, deep-coverage cfRNA-seq has
shown that placental, endothelial, and leukocyte signatures begin to
diverge weeks before symptom onset. Because LO-PE is driven more by
maternal cardiometabolic stress and systemic inflammation than by early
placental maldevelopment, cfRNA offers a unique window into these
evolving maternal responses—signals that may be missed by placental
protein biomarkers alone.
* Early-Onset Preeclampsia (EO-PE): This form is predominantly driven
by placental abnormalities and immune dysregulation that begin
early in gestation; distinct differential expression of cfRNA has
been reported. For example, Moufarrej et al. demonstrated a
high-accuracy model (AUC ≈ 0.9) using cfRNA derived from maternal
plasma, suggesting an impairment of immune response and angiogenic
pathways [[50]9].
* Late-Onset Preeclampsia (LO-PE): Maternal comorbidities such as
obesity or chronic hypertension play a substantial role, often
diminishing the utility of purely placental biomarkers for
high-sensitivity prediction. Indeed, many studies investigating
cfRNA- or metabolite-based tests focus on overall PE risk and do
not provide separate metrics (e.g., AUC) for LO-PE alone. For
example, while Maric et al. [[51]13] report robust performance in
predicting PE, their models do not isolate late-onset cases. As a
result, the true accuracy for LO-PE remains unclear, and some data
even suggest that maternal factors may overshadow direct placental
signals, leading to potentially lower AUCs for late-onset compared
to early-onset PE. Moving forward, it will be crucial to refine
LO-PE-specific molecular signatures—possibly through multi-omics
approaches integrated with maternal clinical data—and validate such
signatures in large external cohorts. This line of research is
expected to clarify whether dedicated LO-PE models can outperform
current one-size-fits-all approaches and ultimately improve risk
stratification in this patient population.
This study focuses on LO-PE and aims to (1) identify cfRNA-based
biomarker candidates specific to LO-PE and (2) develop and evaluate
machine learning models using these markers. More specifically, our
objectives are:
1. To characterize cfRNA profiles in LO-PE and compare them with known
markers predominantly associated with EO-PE.
2. To apply two feature selection strategies—(A) an approach based on
differential expression analysis and (B) an approach leveraging
prediction errors (via the elastic-net solution path)—and then
assess LO-PE prediction performance in terms of AUC, sensitivity,
and specificity.
3. To examine the performance trade-offs involved in simultaneously
predicting both EO- and LO-PE, and to investigate how immune
tolerance and metabolic pathways might be affected.
Ultimately, this study seeks to elucidate the mechanisms underlying
late-onset preeclampsia—particularly those related to immune modulation
and placental invasion—by leveraging cfRNA signatures, with the goal of
informing future clinical management of preeclampsia.
2. Materials and Methods
This study analyzed cfRNA sequencing data from a total of 48 samples,
comprising EO-PE, LO-PE, and corresponding control groups for each
subtype. Our goal was to identify potential biomarkers specifically
associated with LO-PE and then construct and evaluate a diagnostic
prediction model. The overall analytical workflow is illustrated in
[52]Figure 1.
Figure 1.
[53]Figure 1
[54]Open in a new tab
Schematic of the analytical workflow. Panel 1 (feature selection):
cfRNA features were prioritized by two complementary routes. 1a
Sparse-regression (elastic net) solution paths identified genes whose
coefficients remained non-zero across a 50-value λ grid, and the
resulting models were validated by AUC. 1b Differential expression
(DEG) analysis compared Early-PE vs. Control, Late-PE vs. Control, and
Early- vs. Late-PE; three cut-off schemes were explored—A p < 0.05, B p
< 0.05 and |log FC| > 1, and C p < 0.01—yielding signature candidates
shown in the Venn diagram. In the volcano plot, red dots indicate
significantly up-regulated genes, blue dots down-regulated genes, and
grey dots non-significant genes. Panel 2 (model training): for each
DEG-derived signature (A–C) and for the elastic-net signature, separate
elastic-net classifiers were trained on the Late-PE training data.
Panel 3 (model evaluation): nested Monte-Carlo cross-validation
supplied independent test sets; ROC curves illustrate that
Late-PE-specific signatures (green) outperform naïvely transferred
Early-PE signatures (pink), while the diagonal denotes chance.
2.1. Dataset
The dataset is based on a cfRNA cohort described in Reference [[55]14],
which includes 12 subjects with LO-PE, 12 subjects with EO-PE, and 12
controls for each group. All participants carried singleton pregnancies
without structural fetal anomalies and were recruited prospectively at
Stanford University Medical Center between 2014 and 2017 under IRB
protocol #28979. Maternal blood was drawn at the time of clinical
diagnosis (mean ± SD gestational age: EO-PE 29.2 ± 2.3 weeks; matched
EO-controls 29.3 ± 2.3 weeks; LO-PE 35.6 ± 1.3 weeks; matched
LO-controls 35.9 ± 0.8 weeks). The 24 normotensive controls were
selected from a single pool and randomly assigned 1:1 to the EO and LO
subgroups so that gestational age matching with their respective case
groups was preserved. Written informed consent for cfRNA sequencing and
data deposition in dbGaP (accession phs002017.v1) was obtained from
every participant in accordance with the Declaration of Helsinki. Blood
samples were collected at the time of PE diagnosis, and cfRNA
(cell-free RNA) was extracted from maternal plasma for Next-Generation
Sequencing (NGS). Because the resulting RNA reads may include
transcripts of both placental and maternal origin, it offers the
intriguing possibility of capturing both maternal and placental
factors. Given that our analysis involves a relatively small sample of
48 total specimens, special attention must be paid to sample-size
limitations, the risk of overfitting, and the need for further external
validation when constructing prediction models. Because the published
study supplies an already aligned gene-count matrix, our workflow
starts from this matrix; additional read-level QC or alignment was not
required. Participant demographics are summarized in [56]Table S1.
2.2. Strategy for Selecting Signature Genes
To identify biomarkers specific to LO-PE, we employed two approaches:
differential expression gene (DEG) and an elastic-net-based machine
learning method leveraging prediction error. Differential expression
(DE) analysis tests whether the mean read count for each gene differs
between conditions. First, we used RNA-seq count data to conduct three
intergroup comparisons: “early-onset vs. control”, “late-onset vs.
control”, and “early-onset vs. late-onset”. We then used edgeR
[[57]15,[58]16] and limma [[59]17,[60]18] to extract genes showing
statistically significant differential expression. This process
involved adjusting the p-values via the Benjamini–Hochberg method
[[61]19] to control the false discovery rate (FDR) and using log2 fold
change (logFC) values as an additional criterion for candidate gene
selection. By excluding genes that were differentially expressed in
both early- and late-onset groups, we obtained a set of candidate genes
more specific to LO-PE.
Next, we applied an elastic-net regression model using the glmnet
[[62]20] package, which implements coordinate-descent optimization
[[63]20], to tackle two classification tasks—“control vs. early-onset”
and “control vs. late-onset”. We optimized the model’s hyperparameter,
λ, through cross-validation to maximize prediction performance (AUC)
and simultaneously minimize the number of genes used. By examining the
solution path, we extracted genes that contributed most significantly
to predicting LO-PE and designated them as late-onset-specific
signatures for subsequent functional analysis. Since the elastic net
combines both
[MATH:
L1
:MATH]
(Lasso) and
[MATH:
L2
:MATH]
(Ridge) regularization, it effectively prevents overfitting and
performs variable selection automatically. This makes it particularly
useful for scenarios, such as ours, where one must narrow down
important features from a large pool of genes.
2.3. Building and Evaluating the Predictive Model
Using the selected signatures, we constructed models to predict LO-PE
and evaluated their classification accuracy. Model performance was
assessed with a nested Monte-Carlo cross-validation (MC-CV) procedure.
A stratified 70/30 split was repeated 10 times (outer loop); within
each outer training set, a 5-fold inner CV tuned the elastic-net λ
across a 50-value grid. We trained an elastic-net model separately on
the “early-onset signature” and the “late-onset signature” and then
computed the AUC to assess performance for the classifications “control
vs. late-onset” and “control vs. early-onset”. In addition, we
evaluated its performance when combining the early-onset and late-onset
signatures to investigate whether handling both simultaneously would
induce any performance trade-off. The AUC (Area Under the ROC Curve)
serves as a comprehensive measure of a model’s ability to discriminate
between true positives and false positives, with 1.0 indicating perfect
accuracy and 0.5 indicating performance equivalent to random guessing.
Where necessary, we also considered sensitivity and specificity to gain
insight into the balance between false positives and false negatives.
2.4. Searching for Biomarker Candidates
We further investigated the late-onset signature genes extracted via
prediction-error analysis by conducting gene set and pathway analyses
to clarify their functional characteristics. Specifically, we
cross-referenced the gene lists with databases such as Gene Ontology
and KEGG [[64]21] to statistically evaluate the enrichment of pathways
related to metabolism, immunity, and other processes, using Fisher’s
exact test. Of particular interest were genes involved in immune
tolerance or placental invasion, such as HLA-G and IL17RB; their
inclusion in the signature could suggest associations with maternal
immune dysregulation or trophoblast (EVT) dysfunction in LO-PE. We
compared such findings against previous studies to explore their
biological significance. Ultimately, this functional validation of
late-onset-specific gene groups helps lay the groundwork for
determining their potential clinical utility as diagnostic biomarkers
in future research.
2.5. Performance Metrics
Classifier performance was evaluated with the following standard
measures:
[MATH: AUROC=∫01TPRα dα :MATH]
(1)
[MATH:
Sensitivity (Rec
all)=TPTP+FN,
Specificity=TNTN+FP
:MATH]
(2)
[MATH: Precision=TPTP+FP,
F1=2 Precision
×SensitivityPrecision+
Sensitivity :MATH]
(3)
where TP, TN, FP, and FN denote true positives, true negatives, false
positives, and false negatives, respectively. AUROC provides an overall
measure of discrimination; the
[MATH:
F1
:MATH]
-score complements AUROC by balancing Precision and Sensitivity, which
is useful when class sizes are imbalanced.
2.6. Model Evaluation
Finally, to benchmark the proposed elastic net against alternative
learners, we repeated the entire nested Monte-Carlo pipeline with
Random Forest, linear SVM, and XGBoost (hyper-parameters tuned in the
inner loop). Mean ± SD AUROC and F1 are reported in [65]Supplementary
Tables S3 and S4, and pooled ROC curves are shown in [66]Supplementary
Figures S2 and S3.
3. Results
3.1. Identification of Signature Genes and Feature Selection
[67]Table S1 summarizes maternal age, body mass index (BMI), and
gestational age at sampling for each study group. Groups did not differ
in age or gestational week (all p > 0.5), whereas BMI was significantly
higher in the LO-PE group compared with its matched controls (p =
0.036) ([68]Supplementary Table S2). First, we performed differential
expression analyses (DEG) for three comparisons—(1) early-onset PE vs.
control, (2) late-onset PE vs. control, and (3) early-onset PE vs.
late-onset PE—and generated lists of signature candidates by
systematically varying the thresholds for p-values and log fold change
(logFC) ([69]Figure 1, panel 1a-1). We tested three cutoff conditions:
(A) p < 0.05, (B) p < 0.05 and |logFC| > 1, and (C) p < 0.01 (1a-1 in
[70]Figure 1). As shown in [71]Figure 2A, the late-onset-specific
signatures comprised 64 genes under condition A, 1 gene under condition
B, and 7 genes under condition C. In contrast, the early-onset-specific
signatures included 1334 genes (A), 11 genes (B), and 295 genes (C).
Figure 2.
[72]Figure 2
[73]Open in a new tab
Multi-stage feature selection and signature derivation for early-
(EO-PE) and late-onset pre-eclampsia (LO-PE). (A) Differential
expression (DEG) stage. Venn diagrams show EO- and LO-specific gene
sets obtained under three progressively stringent cut-offs: (a) p <
0.05; (b) p < 0.05 and |log[2]FC| > 1; (c) p < 0.01. Numbers indicate
genes unique to, or shared between, the three pairwise contrasts (Early
vs. Ctrl, Late vs. Ctrl, Late vs. Early). (B) Elastic-net solution
paths. For each subtype, the coefficients of all candidate genes are
traced across the log[10](λ) grid; colored curves enter the model as λ
decreases, illustrating automatic sparsification. (C) Model-selection
curve. Mean outer-loop AUROC vs. log[10](λ) for EO- (blue) and LO-PE
(red). Plateaus mark the λ values giving maximal discrimination with
minimal complexity. (D) Final signatures. Intersecting the optimal EO
and LO coefficient sets yields five shared genes (center), with 82
genes retained exclusively in each subtype-specific signature.
When we used these results to train an elastic-net model, the model
that relied solely on the single gene KLRC4 (from condition B) attained
an extremely high predictive performance for LO-PE (AUC = 1.0). A
logistic model based on the single gene KLRC4 yielded an apparent AUROC
= 0.875. A 1000-iteration label-permutation test produced a null AUROC
distribution centered at 0.874 ± 0.004; the observed AUROC of 0.875 lay
well within this range (one-sided p = 0.997). The full distribution is
visualized in [74]Supplementary Figure S1, underscoring that the
single-gene signal is indistinguishable from chance.
Benchmarking against three conventional classifiers confirmed the
superiority of the elastic net: for EO-PE the elastic net reached AUROC
0.91 vs. 0.87 (RF), 0.83 (SVM), and 0.74 (XGBoost); for LO-PE, the
corresponding figures were 0.83, 0.71, 0.73, and 0.61, respectively
([75]Supplementary Tables S3 and S4; Supplementary Figures S2 and S3).
3.2. Candidate Selection via Prediction Error (Elastic-Net Solution Path)
Next, we performed cross-validation on the elastic-net model while
varying the hyperparameter λ in 50 increments, adopting the parameter
setting that maximized predictive performance (AUC) while minimizing
the number of selected genes (1b in [76]Figure 1). This approach
extracted 52 genes as late-onset-specific signatures and 5 genes as
early-onset-specific signatures ([77]Figure 2B–D). These sets exhibited
very little overlap with the signatures identified via DEG analysis. As
shown in the Venn diagram in [78]Figure 3, most genes from the solution
path (SP)-based signatures and those from the DEG-based approach did
not overlap for both early- and late-onset PE, indicating that the two
methods complement each other.
Figure 3.
[79]Figure 3
[80]Open in a new tab
Overlap of gene signatures obtained with four selection strategies for
(left) early-onset and (right) late-onset pre-eclampsia. Each Venn
diagram contrasts the differential expression cut-offs A (p < 0.05), B
(p < 0.05 and |log[2]FC| > 1), and C (p < 0.01) with the
sparsity-optimized elastic-net solution path (SP). Numbers denote the
absolute gene count and, in parentheses, the percentage of the total
signature size for that subtype. The minimal intersection—five shared
genes in the early set and three in the late set—underscores that the
early- and late-onset signatures are largely distinct, supporting
divergent molecular mechanisms between the two PE subtypes.
3.3. Comparative Performance of Prediction Models
[81]Table 1 and [82]Figure 4 (ROC curves) present the results of
elastic-net regression models constructed and validated using each
signature set (DEG-based: A, B, C; elastic-net-based: SP). As a
baseline, we also examined models without any signature genes:
* Training on early-onset samples yielded an AUC of 0.9375 for
predicting early-onset PE but only 0.6875 for predicting late-onset
PE.
* Training on late-onset samples resulted in an AUC of 0.6875 for
predicting late-onset PE.
Table 1.
Discriminatory performance of gene-signature models for early- (EO-PE)
and late-onset pre-eclampsia (LO-PE). Models were trained on either the
EO or LO subset (columns 2–3) and evaluated on both subtypes (columns
4–5). “Prediction without signature” uses all expressed genes; the
three DEG signatures correspond to cut-offs (A) p < 0.05, (B) p < 0.05
and |log[2]FC| > 1, and (C) p < 0.01; “Elastic-net signatures” are the
sparse sets selected by nested Monte-Carlo cross-validation.
Prediction Without Signature Training Data Early Late Early Late
Early prediction AUC 0.9375 0.6875
Late prediction AUC 0.6875 0.6875
Signature Early Late Early and Late
DEG Signature
(a) p value < 0.05
Number of Early Signature 1334 Early prediction AUC 0.9944 0.6826
0.9938 0.6493
Number of Late Signature 64 Late prediction AUC 0.6625 0.9563 0.65
0.6979
(b) p value < 0.05 and logFC > 1
Number of Early Signature 11 Early prediction AUC 0.915 0.653 0.905
0.738
Number of Late Signature 1 Late prediction AUC 0.687 0.736 0.68 0.786
(c) p value < 0.01
Number of Early Signature 295 Early prediction AUC 0.997 0.66 0.995
0.606
Number of Late Signature 7 Late prediction AUC 0.67 0.755 0.656 0.651
Elastic Net–Based Signatures
Number of Early Signature 87 Early prediction AUC 0.988 0.828 0.869
0.856
Number of Late Signature 87 Late prediction AUC 0.71 0.988 0.687 0.869
[83]Open in a new tab
Figure 4.
[84]Figure 4
[85]Open in a new tab
ROC curves from 10 repeats of Monte-Carlo cross-validation comparing
three late-onset PE gene-signature models. The green, red, and violet
lines correspond to signatures derived from cut-off B (p < 0.05 and
|log[2]FC| > 1), C (p < 0.01; identical to A and therefore shown only
once), and the sparsity-optimized elastic-net solution path (SP),
respectively. Curves are obtained by aggregating predictions from the
outer 70/30 test folds (n = 10). The elastic-net model (violet)
maintains the highest sensitivity across the entire false-positive-rate
range, confirming its superior AUROC in [86]Table 1. Abbreviations:
FPR, false-positive rate; TPR, true-positive rate.
These findings indicate that the baseline approach is insufficient for
predicting late-onset PE with high accuracy. In contrast, introducing
late-onset-specific signatures (e.g., the 64 genes from condition A or
the single gene from condition B) increased the AUC for late-onset PE
prediction to around 0.88–1.0, highlighting the importance of markers
specific to the late-onset subtype. Notably, the model using only KLRC4
(condition B) achieved an AUC of 1.0, but its generalizability remains
uncertain. Additionally, when early-onset and late-onset signatures
were used together, late-onset AUC sometimes decreased, and a drop in
early-onset performance was also observed—indicative of a trade-off.
As shown in [87]Table 1, the major differences in pathophysiology and
molecular mechanisms between early- and late-onset PE make it
challenging for a single signature to achieve high accuracy for both
subtypes. Indeed, while adding late-onset-specific signatures improved
the AUC for late-onset PE, it sometimes slightly reduced performance
for early-onset predictions. These findings underscore previously
reported observations that without tailored markers for late-onset PE,
it is difficult to achieve high prediction accuracy.
3.4. Candidate Biomarkers and Functional Analysis
Based on an over-representation analysis of the 87 late-onset-specific
genes ([88]Figure 5), the most significantly enriched themes were (i)
pro-inflammatory and innate-immune signaling—notably interferon-γ/α and
TNF-α → NF-κB cascades, together with adaptive-immune terms such as
allograft-rejection/graft-vs.-host pathways—and (ii)
extracellular-matrix remodeling and cell-metabolic processes, including
elastic-fiber assembly and heme metabolism.
Figure 5.
[89]Figure 5
[90]Open in a new tab
Results of pathway enrichment analysis for the 87 late-onset signature
genes identified by the elastic-net model. The horizontal axis
represents −log10(p-value), while bubble size corresponds to fold
enrichment. Estrogen response, rejection-response pathways, and
glycolysis/ketone-body metabolism pathways appear among the top
enriched categories.
Notably, HLA-G, IL17RB, and KLRC4—genes previously implicated in immune
tolerance and trophoblast invasion—showed marked expression differences
in the LO-PE group compared with controls. This observation suggests a
potential role for impaired maternal–placental interactions.
4. Discussion
This study is constrained by its modest cohort size (12 LO-PE, 12
EO-PE, and 24 controls). An a priori power calculation shows that for
12 vs. 12 samples and a Cohen’s d = 0.8, a two-sided α = 0.05 yields
power ≈ 0.47 (β ≈ 0.53), confirming that the dataset is under-powered
for stable single-gene inference. Consistent with this, a
1000-iteration permutation test indicated that the KLRC4 single-gene
model does not outperform chance (p = 0.997; [91]Supplementary Figure
S1), underscoring the risk of over-fitting. No public cfRNA dataset
with late-onset PE labels is currently available, so external
replication remains an essential future step.
When the early-onset cfRNA signature was naïvely applied to the 12
LO-PE samples, discrimination was modest (AUROC ≈ 0.69); this serves as
our internal baseline. Incorporating a late-onset-specific signature
raised performance to AUROC = 0.88–1.00 under nested MC-CV,
demonstrating a clear gain over the EO-baseline despite the small
cohort.
For clinical context, protein/Doppler screens reach lower or comparable
accuracy: in a first-trimester cohort, Tan et al. [[92]5] reported
AUROC = 0.744 (0.776 with MAP), while a third-trimester screen by
Andrietti et al. [[93]22] achieved AUROC = 0.881 (0.902 with MAP).
These figures derive from different cohorts, time-points, and analytes
(PlGF ± UtA-PI ± MAP) and therefore do not constitute a head-to-head
comparison, but they indicate the present clinical performance ceiling.
Genomics-assisted ML models that blend polygenic risk scores with
routine clinical factors reach a similar range (AUROC ≈ 0.83) [[94]12].
Taken together, these benchmarks suggest that cfRNA signatures—once
validated in larger cohorts—could provide a non-invasive alternative
with competitive, and potentially earlier, discrimination for
late-onset PE.
Among the immune- and hormone/metabolic pathways, HLA-G and IL17RB
appear particularly relevant in LO-PE. Wedenoja et al. [[95]8] showed
that HLA-G is significantly downregulated in preeclamptic placentas,
indicating impaired fetal immune tolerance and reduced EVT
infiltration—both hallmarks of shallow placental invasion. Likewise,
IL17RB (the IL-25 receptor) fosters trophoblast proliferation; Liu et
al. [[96]23] reported that diminished IL-17RB expression in PE
placentas correlates with suboptimal placental development. Our
findings reinforce that a late-onset immune “collapse” may be tied to
early disruptions in maternal–fetal tolerance. Moreover, Ma et al.
[[97]24] identified maternal KIR2DL4–fetal HLA-G genotype combinations
that modulate preeclampsia risk, underscoring the genetic dimension of
immune tolerance. Altogether, these data underscore how dysregulated
HLA-G, IL17RB, and related genes (e.g., KLRC4) may drive LO-PE
pathophysiology.
Previous placental-tissue studies have linked reduced HLA-G expression
to inadequate EVT invasion in early-onset PE, but evidence in
late-onset disease is scant and restricted to small
immunohistochemistry series [[98]8]. Our plasma-cfRNA data extend these
observations by showing a systemic downregulation of HLA-G transcripts
in LO-PE, detectable at the time of diagnosis. Similarly, IL17RB has
been reported as a trophoblast-proliferation receptor in experimental
work [[99]23], yet has not, to our knowledge, been quantified in
circulating RNA from LO-PE pregnancies. Finally, KLRC4 (NKG2F) appears
only once in the PE literature (as a placental mRNA outlier in EO-PE);
its repetition across five of the ten Monte-Carlo signature extractions
suggests it may serve as a novel peripheral marker of late-stage
maternal immune activation. Together, these results support the
recently proposed ‘dual-hit’ model—immune tolerance collapse compounded
by metabolic stress—in LO-PE [[100]10], and they illustrate how cfRNA
can surface candidate genes that traditional biomarker panels overlook.
Although various immune and metabolic pathways have been proposed in
late-onset preeclampsia (LO-PE), direct evidence for specific
processes—such as Allograft Rejection, Estrogen Response, or
Glycolysis—and for genes like HLA-G, IL17RB, and KLRC4 remains limited.
Recent cfRNA-based studies nonetheless suggest that maternal–placental
signaling abnormalities can be detected earlier than clinical onset,
potentially offering a broader diagnostic window for LO-PE risk
[[101]9,[102]13]. However, the current data primarily indicate general
immune dysregulation rather than a uniquely “late-onset-specific”
mechanism. Further validation—for example, in multi-ethnic cohorts and
via single-cell or multiomics approaches—will be crucial to pinpoint
precisely which genes or pathways diverge in LO-PE.
This study observed a trade-off wherein including both early- and
late-onset subtypes in a single model caused decreased accuracy for at
least one subtype. That outcome likely stems from the distinct
etiological underpinnings of EO-PE vs. LO-PE [[103]25]. As noted by
Moufarrej et al. [[104]9], while EO-PE exhibits prominent signals from
the placental formation stage, LO-PE often entails maternal metabolic
and immune dysfunction that becomes clinically apparent later in
gestation. Accordingly, future research should focus on (1) dynamic
risk models incorporating longitudinal data by gestational week or (2)
algorithms that screen early- and late-onset cases separately and then
generate an integrated risk score.
The present study is subject to several limitations and suggests
directions for future research. Consequently, prospective cohorts will
be required to verify whether circulating HLA-G, IL17RB, and KLRC4
decline before symptom onset, thereby establishing them as
early-warning markers rather than late epiphenomenal markers. First,
each group comprised only 12 samples, emphasizing the need for
validation in larger cohorts and multi-center collaborations to ensure
the generalizability of these findings. Second, the predictive
signatures identified in this study are sensitive to the choice of
thresholds in differential expression analyses (p-values) and the
setting of hyperparameters (λ in the elastic-net model). Comparative
evaluations under multiple conditions are therefore necessary to
confirm the robustness of the proposed signatures.
Finally, there are challenges to clinical implementation, as measuring
cfRNA and conducting NGS analyses remain expensive and require
specialized equipment. Standardizing sample processing and streamlining
analytic protocols will be critical for broader clinical adoption.
Moreover, integrating other omics data (e.g., metabolomics and
epigenomics) to provide a more comprehensive assessment of both
maternal and placental status is an important next step.
5. Conclusions
This study has three principal limitations: (i) the cohort is small (n
= 48), limiting statistical power; (ii) model selection on a modest
dataset raises a non-trivial risk of over-fitting; and (iii) no
external cfRNA dataset with late-onset PE labels is yet available for
validation. These caveats temper generalizability but also define clear
priorities for future work. This study identified late-onset-specific
cfRNA signatures and demonstrated that incorporating them into an
elastic-net model substantially boosts predictive performance.
Abnormalities in immune tolerance and metabolic systems—beyond what
conventional early-onset markers can detect—may underlie the pathology
of LO-PE. At the same time, challenges remain regarding sample size and
model generalizability, pointing to the need for large-scale
longitudinal studies and multi-omics integration. Ultimately,
leveraging cfRNA-seq-based composite maternal–placental biomarkers in
tandem with AI could significantly advance the early diagnosis and
management of LO-PE.
Acknowledgments