(2024) 22:310
Liu et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03531-8

BMC Medicine

RESEARCH ARTICLE

Open Access

Early detection of uterine corpus
endometrial carcinoma utilizing plasma cfDNA
fragmentomics
Jing Liu1†, Dan Hu2†, Yibin Lin1†, Xiaoxi Chen3, Ruowei Yang3, Li Li1, Yanyan Zhan3, Hua Bao3, LeLe Zang1,
Mingxuan Zhu1, Fei Zhu1, Junrong Yan3, Dongqin Zhu3, Huiqi Zhang1, Benhua Xu4* and Qin Xu1*   

Abstract
Background Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable
prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study
aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA)
fragmentomics.
Methods Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Lowcoverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.
Results The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training(n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort.
Physiological variables and preanalytical procedures had no significant impact on the classifier’s outcomes. In termsof clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.
Conclusion This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics
and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further
optimization and validation of this approach are warranted to establish its clinical utility.
Keywords Early detection, Endometrial carcinoma, cfDNA, Fragmentomics†

Jing Liu, Dan Hu, and Yibin Lin contributed equally to this work.

*Correspondence:
Benhua Xu
benhuaxu@163.com
Qin Xu1379423879@qq.com
1
Department of Gynecology, Clinical Oncology School of Fujian Medical
University, Fujian Cancer Hospital (Fujian Branch of Fudan University
Shanghai Cancer Center), Fuzhou, China
2
Department of Pathology, Clinical Oncology School of Fujian Medical
University, Fujian Cancer Hospital (Fujian Branch of Fudan University
Shanghai Cancer Center), Fuzhou, China

3
Geneseeq Research Institute, Nanjing Geneseeq Technology Inc.,
Nanjing, Jiangsu, China
4
Department of Radiation, Fujian Medical University Union Hospital,
Fuzhou, China© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Liu et al. BMC Medicine(2024) 22:310

Background
Uterine corpus endometrial carcinoma (UCEC) is aprevalent gynecologic malignancy and a major cause ofcancer-related morbidity in women [1]. It is generally
perceived that UCEC has a favorable prognosis during the early stage. The 5-year survival rate for patientsdiagnosed with stage I/II endometrial carcinoma is typically between 82 and 90%, whereas for those diagnosed
at stage III/IV, the rate significantly drops to between 34
and 42% [2]. Consequently, the importance of accurate
initial diagnosis and prompt treatment are key in the
management of endometrial carcinoma. Early detectionof UCEC allows for the utilization of minimally invasive surgical techniques and diminishes the necessity for
adjuvant treatments, thereby lowering healthcare costs,
morbidity, and mortality rates [3, 4]. However, there
is currently no sufficiently accurate and reliable early
detection test for UCEC that can be applied to high-risk
women with suspected UCEC. Transvaginal ultrasonography (TVU), the most commonly employed method,
exhibits high sensitivity in detecting UCEC in postmenopausal women with abnormal uterine bleeding. However,its relatively low specificity necessitates further tests todefinitively rule out endometrial malignancy [5]. Conversely, while hysteroscopy provides enhanced precision, its invasive character can lead to complications or
patient-related issues, thereby contributing to its potential failure [6]. Therefore, the development of a precise,
non-invasive diagnostic method for endometrial carcinoma is of great importance.
Peripheral blood collection, a minimally invasive procedure, has surfaced as a novel method for the early detection of a wide array of solid malignancies, particularly
by providing genetic information of circulating tumor
markers [7]. At present, several studies are analyzing circulating tumor DNA (ctDNA) and microRNAs for the
early detection of endometrial carcinoma [8, 9]. However,with regard to early diagnosis of endometrial carcinoma,
the technology to diagnose this condition solely throughgene sequencing of circulating tumor components in the
blood is yet to be fully developed and optimized.
Currently, plasma cfDNA fragmentomics has been
applied to numerous solid malignancies for enhanced
diagnostic precision [10–12]. A significant advancement was made with the development of DNA Evaluation of Fragments for Early Interception (DELFI) [13],which assesses fragment coverage, size, and other summary statistics within 5 Mb windows. It was observedthat the ratio of short to long cfDNA fragments withineach window varies between cancer patients and healthy
individuals, thereby providing potential discriminationof disease status. Studies in fragmentomics have also
leveraged other distinguishing patterns between cancer

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and non-cancer groups, such as preferred end coordinates and end motifs [14, 15]. Recently, there has been a
research focus on the nucleosome footprint of cfDNA,
with successful identification of patient-specific andtumor-specific patterns with substantial accuracy [16,17].
In the present study, we developed a robust machinelearning model that utilizes low-coverage whole-genome
sequencing and an extensive set of genome-wide features derived from cfDNA fragmentomics to differentiate between UCEC and healthy conditions. The primaryobjective of this research is to provide a highly sensitiveand cost-effective model for UCEC detection, which
could potentially yield substantial clinical advantages in
the management and control of UCEC.

Methods
Patients and sample collection

In this study, a total of 121 patients with UCEC and 119
healthy female donors were initially enrolled at the Fujian
Cancer Hospital (Fujian Branch of Fudan University
Shanghai Cancer Center) from August 2021 to July 2022
and from November 2022 to February 2023 (Additional
file 1: Fig. S1). Participants were subsequently excluded
if they were lost to follow-up, had post-treatment status,
withdrew their consent, or failed to meet quality control criteria. After the exclusions, the study proceededwith 111 UCEC patients and 111 healthy donors for further analysis. A minimum collection volume of 30 ml ofperipheral blood samples was collected from patients
before any treatment or healthy donors and proceeded
for low-depth whole-genome sequencing (WGS). One
participant was excluded for failed NGS quality control.
All healthy control participants underwent a thoroughphysical examination and were monitored for a period of
one year, with routine follow-up conducted every threemonths to promptly identify any onset of cancer. A training cohort and an independent validation cohort were
established separately based on time of enrollment. The
training cohort was dedicated to train a multi-dimensional machine learning model, whereas the independent validation cohort set out to assess the performanceof the model. An additional cohort of 47 patients withhysteromyoma was retrospectively collected to further
validate our findings and assess the generalizability of ourmodel across diverse patient populations. The genetic
tests were performed in a centralized clinical testing
center (Nanjing Geneseeq Technology Inc., China; Certified to CAP, CLIA, and ISO15189). The study was performed in accordance with the Declaration of Helsinki
and approved by the Ethics Committee of Fujian Cancer
Hospital. All patients provided oral and written informed
consent to participation and publication.

Liu et al. BMC Medicine(2024) 22:310

Next‑generation sequencing and data processing

Blood samples, drawn into EDTA tubes, were centrifuged at 16,000 × for 10 min within 4 h post-collection.
The QIAamp Circulating Nucleic Acid Kit (Qiagen) wasused to extract cfDNA from the plasma samples, without
the inclusion of carrier RNA, following the manufacturer’s instructions. To ensure sufficient cfDNA for further analysis, the concentration of cfDNA in the plasma
was determined using the Qubit dsDNA HS Assay Kit(Thermo Fisher Scientific) according to the manufacturer’s guidelines.
The extracted plasma cfDNA was then used for WGS
with the KAPA Hyper Prep Kit (KAPA Biosystems), following the manufacturer’s instructions. Briefly, 5–10 ng
of cfDNA per sample underwent a series of steps including end-repair, A-tailing, and ligation with adapters. The
resulting libraries were quantified using the KAPA SYBRFAST qPCR Master Mix (KAPA Biosystems), and thenloaded onto NovaSeq platforms (Illumina) for paired-end
sequencing, as recommended by the manufacturer.
To ensure data quality, the sequencing output was subjected to quality control measures. Trimmomatic [18]was used for read trimming, followed by PCR duplicate
removal using Picard tools (Broad Institute, MA, USA).
The trimmed reads were aligned to the human reference genome (GRCh37/UCSC hg19) using the BurrowsWheeler Aligner [19]. The median coverage depth acrossall samples was 8.17 × (Additional file 2). To standardize
the data and mitigate the effects of variable sequencing
depth, we applied a down-sampling procedure. Coverage depths that exceeded 5 × were reduced to a uniform
5 × , ensuring consistency across these samples. For samples that originally had lower coverage, no down-sampling was performed; they were analyzed at their initial
sequencing depths.
Genome‑wide cfDNA features

To construct a multi-facet machine learning model thatdifferentiates UCEC and healthy individuals, fragmentomics and CNV features were extracted from the processed WGS data using an in-house script. Copy number
variation (CNV), fragment size distribution (FSD), andnucleosome footprint (NF) profiling were used to construct the final model (Additional file 3).
The profiling of copy number variations was adaptedfrom the method as described by Wan et al. [20]. Eachsample’s genome was partitioned into bins of 1 Mb,
resulting in a total of 2475 bins. A Hidden Markov Modelwas employed to compare the depth of each bin with thesoftware baseline, generating a log2 ratio for each bin.
This analysis allowed for the identification of copy number variations across the genome.

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The fragment size distribution (FSD) feature quantifies the coverage of cfDNA fragments ranging from 110
to 220 bps in 5-bp stepwise (e.g., 110–114 bps, 115–119
bps, …, 215–220 bps; 24 bins) at every chromosome
arm. This creates a detailed series of 24 bins across representing the distribution of cfDNA fragment size ofeach chromosome arm. A total of 39 chromosome arms
are resolved into 936 discrete FSD features, contributing to the granularity of our analysis. The short arms of
5 acrocentric chromosomes were not included as theyremained largely unsequenced to date. To ensure a focus
on intrinsic, biologically relevant patterns rather thanvariations introduced by whole genome sequencing, westandardized the raw FSD coverage values by convertingthem into z-scores. This normalization contrasts eachvalue against the overall mean within each sample. Thisapproach enabled the enhanced detection of high-resolution chromosome-level patterns, which could potentiallyreveal further distinctions between the cancer and noncancer groups.
For nucleosome footprint (NF) profiling, the framework developed by Doebley et al. [17] was utilized to analyze nucleosome occupancy in cfDNA while accounting
for GC bias. We quantified the GC-corrected coverage
profile using three observable characteristics: the central
coverage value 30 bp away from the location, the “averagecoverage” value within a 1000-bp distance from the location, and the amplitude determined through fast-Fourier
yransform. In this study, we analyzed a total of 854 transcription factor binding sites to optimize the ability todetect cancer from low-pass WGS data.
Model construction and cross‑validation analyses

We developed a two-tiered machine learning framework
to differentiate between cancerous and non-cancerous
samples. The initial tier of our framework comprised afeature-specific module that processed one of three distinct feature sets: copy number variations (CNV), feature selection dimensionality (FSD), or nuclear features(NF). This module systematically applied a suite of fivefoundational algorithms, namely Generalized Linear
Model (GLM), Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Deep Learning (DL), and
XGBoost. A grid search methodology was employed to
optimize the hyperparameters for each algorithm, drawing from predefined candidate values. We assessed theefficacy of these models using a fivefold cross-validation
scheme, ensuring consistency in fold assignment acrossall feature types for both training and validation.
In the second phase of our analysis, the ensemble
model of each feature type was constructed by averaging the outputs of the five leading models (determined bytheir cross-validation area under the curve (AUC) scores)

Liu et al. BMC Medicine(2024) 22:310

from each category. For incoming samples, the final classification score was computed as the average prediction
score from these three top-tier ensemble models, withthe score ranging between 0 and 1—where a higher scoreindicated an increased probability of the sample being
cancerous.
In the training cohort, our classifiers underwent a fivefold cross-validation, aiming for a specificity thresholdof 95% to set the model’s decision boundary. An independent validation cohort was subsequently employed
to assess the efficacy of our composite machine learningmodel.
To assess the stability and generalizability of theensemble model, we employed a method of repeated random partitioning of the cohort. The entire dataset was
randomly split into three distinct sets: a training set, avalidation set, and a test set. This random partitioning
was not a single event but was repeated multiple times to
produce a comprehensive range of data subsets, thereby
simulating a variety of potential training and testing scenarios. The training set was used to build the model, thevalidation set to tune the hyperparameters, and the test
set to evaluate the model’s performance. This approach
mitigated the risk of overfitting and provided a morerobust estimation of the model’s performance in unseen
data.
Tumor fraction calculation

To quantify the proportion of ctDNA within the cellfree DNA (cfDNA) and to evaluate copy number variations (CNVs) characteristic of tumor-derived DNA, weutilized the computational tool ichorCNA[20]., which
is designed to work with high-throughput sequencing
data. Each sample’s genome was partitioned into bins of
1 Mb, resulting in a total of 2475 bins. A Hidden Markov
Model was employed to compare the depth of each binwith the software baseline, generating a log2 ratio foreach bin. This analysis allowed for the identification of
copy number variations across the genome. The model
also integrates several data features, including totalread depth, B-allele frequency, and the distribution of
cfDNA fragment lengths. Through this integrated analysis, ichorCNA provides estimates of the tumor fraction,
defined as the proportion of cfDNA attributable to tumor
cells. The clinical limit of detection was further calculated following the approach described by Jamshidi et al.
[21], defining it as the tumor fraction corresponding to a
50% probability of detecting a cancer signal.
Nucleosome footprint differentiation analysis
and corresponding gene‑enriched pathways

To ascertain nucleosome footprint (NF) featuresthat exhibited unique signatures in uterine corpus

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endometrial carcinoma (UCEC) relative to healthy individuals, we engaged in a comparative study of NF profiles from both cohorts. Employing the multiple t-tests,
we pinpointed NF features with significant variancesin central coverage, average coverage, and amplitude.
Features with adjusted p-value < 0.01 (Benjamini–
Hochberg method) were retained for further analysis.
These selected features were presumed to be reflective of UCEC-specific chromatin organization. Subsequently, we mapped the genes associated with these
NF features to their respective biological pathways via
the Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We then rigorously evaluated the connections between these genes and their related biological
functions and pathways, adopting a significance cutoff(P < 0.05) to identify pathways potentially implicated in
the pathogenesis or progression of UCEC.
Pre‑analytical and physiological variables analysis

To evaluate the potential effect of various pre-analyticalvariables and physiological variables on model robustness, a subset of true positive or true negative participants, specifically 21 healthy participants and 4 UCEC
patients in the validation cohort were further analyzed.
Blood samples were collected and tested multiple times
to assess consistency and reproducibility, with the Positive Percent Agreement (PPA) calculated for each set ofresults. The impact of different transportation (24, 48,
and 72 h post-collection) and storage conditions (roomtemperature and with an ice pack) were examined, withthe reference condition defined as the state of the sample within 2 h of collection. The effect of freezing duration of plasma (3 days, 7 days, 1 month, and 6 months)
on the test outcomes was also studied. Additionally, theinfluence of different physiological states, specifically
before and after meals and exercise, was also evaluated by repeated blood collection. For each set of conditions, the PPA was calculated, and a 95% confidence
interval was computed, using the stability of the PPA
under different conditions to gauge the robustness andreliability of the test outcomes.
Clinical benefit analysis

To evaluate the potential clinical advantages of ourmodel in practical settings, we employed a methodology proposed by Hubbell et al. [22], which involved
integrating their interception model with the predictions generated in our current study. This approach wasapplied to assess the impact on colorectal cancer incidence in a Chinese cohort.

Liu et al. BMC Medicine(2024) 22:310

Statistical analysis

Multivariate analysis was performed with clinical variables that were statistically significant in univariate analysis. All P values were based on two-sided testing unlessspecified, and differences were considered significant at
P < 0.05. For the calculation of 95% confidence intervals(CIs) for sensitivity and specificity, we applied Wilson’sscore interval formula. To estimate the 95% CI for the
AUC, we employed the bootstrap resampling technique.
Specifically, 1000 bootstrap samples were generated fromthe validation dataset, and the AUC was recalculated foreach sample to create an empirical distribution of AUC
values. The 95% CI was then determined by identifying
the 2.5th and 97.5th percentiles of this distribution. Positive Percent Agreement (PPA) is measured between theagreement between the results of optimal condition andtest condition. Statistical analysis was performed using R
software, version 4.2.3.

Results
Patient characteristics

A total of 111 UCEC and 111 healthy individuals were
included in the study. The demographic and clinical characteristics of all participants were provided in Additional
file 4. The training cohort dataset, comprising 66 UCEC
patients and 67 healthy individuals, was exclusively used

Fig. 1 Schematic diagram illustrating the study design

Page 5 of 12

for training the model. To validate the model, an independent cohort was utilized, which was distinct from thetraining dataset in terms of temporal recruitment. Thisvalidation cohort consisted of 89 participants, including 44 UCEC patients and 45 healthy individuals (Fig. 1;
Additional file 1: Fig. S1).
The mean age for the UCEC patients was 54.5 years
in the training cohort and 56.7 years in the validation
cohort, compared to the mean age of healthy participants
as 57.3 years in the training cohort and 51.5 years in thevalidation cohort (Table 1). The distribution of cancer
stages was similar across both cohorts, with the majority of participants being in stage I. In the training cohort,
77.3% (n = 51) of participants were in stage I, while in thevalidation cohort, 60.0% (n = 27) were in stage I. In thecontext of histological grade, a substantial proportionof patients in both cohorts were classified as low risk,
encompassing both Grade 1 and Grade 2. Specifically, inthe training cohort, 81.8% (n = 54) of the patients werecategorized as low risk, while the validation cohort had
71.1% (n = 32).
UCEC detection classifier performance

The constructed model demonstrated excellent prediction power, with an area under the curve (AUC) of 0.991(95% CI (0.9788, 0.999)) in the training cohort. This

Liu et al. BMC Medicine(2024) 22:310

Page 6 of 12

Table 1 Patient characteristic
All

Train

Valid

Healthy

UCEC

Healthy

UCEC

Healthy

UCEC(N=111)(N=111)(N=67)(N=66)(N=44)(N=45)

56.0 [44, 72]

55.4 [33, 76]

57.3 [45, 72]

54.5 [33, 69]

51.5 [44, 67]

56.7 [35, 76]

Age
Mean [Min, Max]
Stage
I (%)

-

78 (70.3%)-

51 (77.3%)-

27 (60.0%)II-

17 (15.3%)-

9 (13.6%)-

8 (17.8%)III-

10 (9.0%)-

4 (6.1%)-6 (13.3%)IV-

4 (3.6%)-2 (3.0%)-2 (4.4%)

Not available-

2 (1.8%)-0 (0%)-

2 (4.4%)

Grade
G1

-

35 (31.5%)-

26 (39.4%)-

9 (20.0%)

G2-

51 (45.9%)-

28 (42.4%)-

23 (51.1%)

G3-

16 (14.4%)-

8 (12.1%)-

8 (17.8%)

Not available-

9 (8.1%)-4 (6.1%)-5 (11.1%)

MSI status
dMMR/MSI-H-

24 (21.6%)-

41 (62.1%)-

26 (57.8%)

MSS-

67 (60.4%)-

12 (18.2%)-

12 (26.7%)

Not available

20 (18.0%)

model was then validated in the independent cohort,
where it achieved an AUC of 0.994 (95% CI (0.9810,
1.000)) (Fig. 2A). To highlight the superiority of theensemble model, the base model using individual featuresgenerated AUCs ranging from 0.881 to 0.986 (Additional
file 1: Fig. S2). Moreover, the stacked model demonstrated
enhanced robustness, maintaining its performance withlower sequencing coverage depth (Additional file 1: Fig
S3). This demonstrates that the ensemble model outperformed the base model in terms of predictive power. As acancer classifier, a specific threshold of 0.423 was applied
to the cancer scores to target a specificity of 95.0% in the
training dataset. The established threshold maintained
specificities of 95.5% (95% CI (87.5%, 98.8%)) within the
training cohort and 95.5% (95% CI (84.4%, 99.2%)) within
the validation cohort (Fig. 2B). The corresponding sensitivities were 98.5% (95% CI (91.9%, 99.9%)) in the training
cohort and 97.8% (95% CI (88.2%, 99.9%)) in the validation cohort. The stacked model exhibited robust performance with a mean sensitivity of 0.97 (95% CI: 0.96, 0.98)
and a mean specificity of 0.96 (95% CI: 0.94, 0.97) acrossmultiple repeated random cohort splits (Additional
file 1: Fig. S4), indicating stable and reliable diagnostic
accuracy.
Furthermore, cancer patients had significantly higher
predicted cancer scores compared to healthy individuals (p < 0.0001; Fig. 2C), further supporting the superior prediction power of the ensemble model, which

13 (19.7%)

7 (15.5%)

incorporated multiple cfDNA fragmentomics features.
Additionally, a retrospective analysis performed on a
separate cohort of 47 patients diagnosed with hysteromyoma revealed that the model retained a commendable accuracy of 91.5% (95% CI: 80.1–96.6%; Additional
file 1: Fig. S5).
We conducted further analysis in the validation
cohort to assess the model’s performance across various
UCEC subgroups. Notably, the classifier demonstrated
high sensitivity, accurately identifying cancer patientswith a sensitivity of 96.4% (95% CI (82.5%, 99.8%)) at
the early stage (stage I) and maintained a sensitivityof 100% in later stage (Fig. 2D). Moreover, the cancer
scores exhibited an upward trend in correlation withadvancing stages (p = 0.06; Jonckheere Terpstra test;
Fig. 2E). This trend is likely attributable to the higher
tumor fractions typically present in later-stage disease(Additional file 1: Fig. S6A). In line with this, there wasa significantly positive correlation between the cancer scores and the tumor fraction (Additional file 1:
Fig. S6B). Samples with a tumor fraction exceeding 1%
yielded significantly higher cancer scores compared tothose with a lower tumor fraction (Fig. 2F). This observation highlights the biological relevance reflected by
the cancer scores, suggesting that they may indeed capture the extent of tumor-derived cfDNA present in theplasma. Nevertheless, there were no significant differences observed in cancer scores among samples with

Liu et al. BMC Medicine(2024) 22:310

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Fig. 2 A ROC curve of ensemble stacked model of training and independent validation datasets. B Validation ROC curve of base models
constructed upon individual features (CNV, FSD, and NF) and their ensemble stacked model. C Violin plots illustrating ensemble stacked model
predicted cancer score distribution in the UCEC and healthy groups in the training and validation cohorts. D Sensitivity of the classifier in identifying
cancer patients at different stages. Error bar represented 95% confidence intervals. E Boxplot showing the correlation between cancer scores
and advancing stages. F Boxplot comparing cancer scores in samples with different tumor fraction. Tumor fraction was measured utilizing ichorCNA

varying histological grades or MSI status (Additional
file 1: Fig. S7).
Biological significance of Fragmentomics pattern in UCEC
cancerFurthermore, we investigate the biological implications of fragmentomics patterns in UCEC cancer. We
observed genome-wide variation in Copy Number Variation (CNV) features (Fig. 3A; Additional file 1: Fig.
S8A). A noteworthy proportion of these CNV features
exhibited statistically significant differences (adjusted
P-value < 0.05; Additional file 1: Fig. S9A). Our fragment size distribution (FSD) analysis revealed fragment
length patterns with remarkable resolution (Additional
file 1: Fig. S8B), even at the level of chromosomal arms.
The FSD features pointed to a significant differential
distribution across chromosome arm (Additional file 1:
Fig. S10), with notable alterations in chromosomes 1,
10, 6, 11, and 17 (Fig. 3B). These chromosomes intriguingly align with five of the top ten chromosomes withthe highest mutation frequencies in the TCGA-UCEC

database (Additional file 1: Fig. S9B). Such a correlation hints at a potential interplay between disrupted
nucleosome organization and mutation-prone genomic
regions in UCEC.
Moreover, a total of 104 NF features that displayed
distinct characteristics in UCEC patients versus healthy
controls were selected (Additional file 5). The genes corresponding to these features were subjected to KEGG
pathway enrichment analysis (Fig. 3C). A number ofpathways related to cancer and immune response, suchas transcription misregulation in cancer, viral or chemical carcinogenesis, and Th17 cell differentiation, wereidentified in the UCEC patients in comparison to healthy
participants. Intriguingly, the enrichment of pathways
related to cancer and immune responses was consistent across various stages of UCEC (Additional file 1: Fig.
S9C), indicating that certain key pathogenic processes are
persistently disrupted irrespective of disease progression.
These findings underscore the biological relevance of the
NF features used in our study, suggesting that the fragmentomics patterns identified are not merely numericalLiu et al. BMC Medicine(2024) 22:310

Page 8 of 12

Fig. 3 A The ratio of CNV in 1 Mb bins across the genome in UCEC patients and healthy controls. B Violin plots showing the difference of FSD
distribution of each chromosome arm in UCEC and healthy participants. C KEGG pathway enrichment analysis of genes corresponding to NF
features that displayed distinct characteristics in UCEC patients versus healthy controls

abstractions but are deeply rooted in the complex biology
underlying cancer development and progression.
Evaluation of preanalytical and physiological variables
on cancer detection

The influence of physiological variables during bloodcollection and preanalytical procedures on the outcomes of the classifier was also assessed. Repeatedblood collection from both the healthy and canceraffected participants consistently yielded reproducible
results. The PPA was remarkably stable, registering at
100.0% with a 95% confidence interval statistically ranging between 67.6% and 100.0% (Fig. 4A). In the case of
the healthy participants, the transportation conditionsof the samples—whether they were transported within
24 h, 48 h, or 72 h, and regardless of whether they werestored at room temperature or with an ice pack—did
not affect the test outcomes. All samples consistently aligned with the reference condition, defined as
the state within 2 h of collection (Fig. 4B). This wasreflected in a PPA of 100.0%, with a 95% confidence
interval ranging from 51.0 to 100.0%. Furthermore,most samples that were frozen for various durations(3 days, 7 days, 1 month) exhibited the same level ofagreement with the reference condition as non-frozen
samples, with a PPA of 100.0% and a 95% confidence
interval ranging from 56.6 to 100.0%. However, samples
frozen for 6 months deviated from this pattern, showing a higher risk score and thereby becoming a positive signal. The PPA for these samples was 60.0%, with
a 95% confidence interval ranging from 23.1 to 88.2%(Fig. 4C). Physiological conditions, including states
before and after meals as well as before and after exercise, did not influence the test outcomes. All samples
under these conditions remained in agreement with thereference condition, with a PPA of 100.0% and a 95%
confidence interval ranging from 51 to 100% (Fig. 4D).
This suggests a high level of robustness and reliability
in the test outcomes, irrespective of various physiological and preanalytical conditions.

Liu et al. BMC Medicine(2024) 22:310

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Fig. 4 A The repeated test score of UCEC and healthy participants. B The impact of various transportation conditions on the test outcomesfor healthy participants. C The impact of frozen durations on the test outcomes for healthy participants. D The impact of physiological conditions(states before and after meals as well as before and after exercise) on the test outcomes

Discussion
In this study, we first developed and evaluated a classifier
for the detection of UCEC using cfDNA fragmentomics features. The constructed ensemble model demonstrated excellent predictive power, as evidenced by high
AUC values in both the training and independent validation cohorts. The AUCs achieved by the ensemble model
were significantly higher than those of the individual
base models, indicating the superiority of the ensemble
approach in terms of predictive performance. The established cut-off value for the cancer scores yielded highspecificities in both the training and validation cohorts,
while maintaining high sensitivities. This suggests thatthe classifier utilizing cfDNA fragmentomics features
can efficiently differentiate between cancer patientsand healthy subjects with a minimal false-positive rate,accomplishing a significantly higher specificity thanconventional transvaginal ultrasonography [5], and asuperior sensitivity than circulation tumor DNA methodologies [23, 24]. The significantly higher cancer scores
observed in UCEC patients compared to healthy individuals further support the robustness and accuracy ofthe classifier. Importantly, the classifier demonstrated
remarkable sensitivity in detecting UCEC at early stages,
with a sensitivity of 96.4% in stage I patients. Additionally, the prediction of the stage shift in UCEC diagnosis
revealed that our model could identify approximately 20%
of UCEC patients, who would otherwise be diagnosed ata more advanced stage (stage III/IV) under conventionalcare, at an earlier stage. This could result in enhanced
5-year survival rates, underscoring the classifier’s clinicalutility in expediting diagnosis and intervention.

Liu et al. BMC Medicine(2024) 22:310

The fragmentomics patterns employed as features are
not arbitrary mathematical constructs, but rather are
embedded in the intricate biology of cancer [25–27].
cfDNA fragmentation patterns are not uniformly distributed across the genome. These patterns are postulated to be influenced by chromatin organization,offering valuable insights into nucleosome positioning
and gene expression [26]. The observed differential distribution of fragment length patterns in several chromosomes, particularly in chromosomes known to be
frequently mutated in UCEC, suggests that the FSD
fragmentomics features used in the classifier may reflect
the degree of openness of chromosomal DNA. The pathway enrichment analysis further revealed the involvement of cancer-related pathways and immune response
in UCEC patients, providing insights into the molecularmechanisms associated with the disease. The pathway
enrichment analysis of differential NF features further
revealed the involvement of cancer-related pathways
and immune response in UCEC patients. The pathways,
such as transcriptional misregulation in cancer, highlightthe aberrant control of gene expression central to tumor
development. Viral carcinogenesis underscores the roleof viral infections, particularly HPV, in genomic instability, while chemical carcinogenesis points to the impact ofenvironmental factors on DNA integrity. Lastly, Th17 celldifferentiation emphasizes the influence of the immune
system in the tumor microenvironment. Collectively,
these pathways provide a comprehensive view of themolecular mechanisms driving UCEC, suggesting potential therapeutic targets and offering insights into thepersistent nature of these pathogenic processes acrossall stages of the disease. The correlation between cancer
scores and advancing stages of UCEC and tumor fraction
further supports the clinical relevance of the classifier in
capturing disease progression.
The analysis of preanalytical and physiological variables revealed the robustness and reliability of the classifier under various conditions. The reproducibility of
results in repeated blood collections and the consistencyof outcomes regardless of transportation conditions or
physiological states highlight the stability of the classifier’s performance. These findings are important for thepractical implementation of the classifier in clinical settings (Fig. 4).
In this study, the test was designed to target for an
average sequencing depth of 5X with the considerationof cost-effectiveness and data uniformity in practicalapplications. Inevitably, technical variations during thesequencing process can lead to some samples achieving
coverage that exceeds our target of 5 × . To ensure uniformity across all samples and to ensure that the model
predicts based on genetic data rather than on an artifact

Page 10 of 12of the sequencing process (i.e. variation in sequencing
coverage)., we chose to down-sample any sample with acoverage higher than 5 × to a fixed depth of 5 × . Notably, based on our analysis (Fig. S3C), models trainedwith data at varying raw sequencing depths did not show
improved performance at higher depths. It is importantto highlight that our established threshold for downsampling was 5 × , yet our analyses suggest the potential
for obtaining reliable results even at sequencing depths
below this threshold. This underlines the robustness of
our findings against variations in sequencing depth, a factor that is of critical importance for the practical application of these methods in clinical settings. Specifically,
while individual models such as FSD and NF show variability and diminished performance at reduced sequencing depths, our ensemble model exhibits a remarkable
resilience, consistently maintaining reliable detection.
The resilience of the ensemble model likely stems fromits ability to compensate for the reduced informational
content that comes with lower sequencing depths, thus
ensuring stable performance.
We employed a previously established test [22] to estimate the potential clinical advantage our model could
provide. According to these estimates, our approach
could increase the detection of early-stage UCEC, potentially improving the 5-year survival rate from 84 to 95%
when compared to the detection rates of advanced-stage
disease under standard care (Additional file 1: Fig. S11).
However, these findings are speculative and based onmathematical modeling rather than clinical outcomes.
The study acknowledges specific limitations that must
be considered. The sample size, while adequate for initial exploration, is relatively modest and might not fully
represent the efficacy of our model. Consequently, theimpact of our model on patient outcomes requires thorough validation in a larger and more diverse clinical setting. To address potential batch effects stemming fromvariations in sample processing and sequencing, weestablished an independent validation set based on thetiming of patient enrollment and sample collection. Thismeasure was critical to reduce confounding influences
and to ensure that our model is trained and validated on
temporally distinct data sets, which more accurately mirrors the variability present in clinical environments. A
multi-site study design would be ideal for future research
to provide a more varied and comprehensive assessmentof our model’s performance.
Therefore, our findings should primarily be interpreted as a proof of concept, highlighting the importance of further research to validate and potentiallyexpand the utility of our model across different clinical contexts. Prospective clinical trials are needed toascertain if the early detection strengths of our modelLiu et al. BMC Medicine(2024) 22:310

Page 11 of 12

correlate with a significant improvement in patient survival. These trials would also offer valuable insights into
the cost-effectiveness, practicality, and overall enhancement of patient care that our model could bring about.

Authors’ contributions
Conceptualisation: Q. Xu, B. Xu. Data collection: J. Liu, D. Hu, Y. Lin. Data analysis: J. Liu, D. Hu, Y. Lin, X. Chen, R. Yang, Y. Zhan, J. Yan. All authors contributed
to data interpretation. Writing original draft: J. Liu, D. Hu, Y. Lin, X. Chen, H. Bao.

Conclusions
Summary, the results of our study underscore thepromising performance and clinical potential of ourclassifier for the detection of UCEC. The high predictive accuracy, sensitivity in detecting early-stage disease, robustness against preanalytical and physiologicalvariations, and the biological relevance of the fragmentomics patterns collectively affirm the classifier’s potential as a non-invasive tool for UCEC screening anddiagnosis. Our results may initiate further research andpotentially contribute to the advancements in the early
diagnosis and improved prognosis of UCEC patients.

Funding
This study was funded by grants from the Joint Funds for the National Clinical
Key Specialty Construction Program (2021); Innovative Medicine Subject of
Fujian Provincial Health Commission (2020CX0101); Natural Science Foundation of Fujian Province (2020J011126, 2023J011273); Fujian Provincial ClinicalResearch Center for Cancer Radiotherapy and Immunotherapy (2020Y2012);
Startup Fund for scientific research, Fujian Medical University (Grant number:
2020QH1233); Medical Innovation Program of Fujian Province (No. 2017-CX-9).

Abbreviations
AUC​	Area under the curve
cfDNA	Cell-free DNA
CNV	Copy number variation
ctDNA	Circulating tumor DNA
DELFI	Fragments for Early InterceptionDL	Deep Learning
DRF	Distributed Random Forest
FSD	Fragment size distribution
GBM	Gradient Boosting Machine
GLM	Generalized Linear Model
NF	Nucleosome footprintPP	A Positive Percent Agreement
TVU	Transvaginal ultrasonography
UCEC	Uterine corpus endometrial carcinoma
WGS	Whole genome sequencing

Authors’ Twitter handles
Twitter handles: @GeneseeqInc.

Availability of data and materials
All raw sequencing data have been deposited in the National Genomics Data
Center (NGDC) under the accession code: HRA006632. The raw sequencing data contain information unique to individuals and are available undercontrolled access. Access to the data can be requested by completing theapplication form via GSA-Human System and is granted by the corresponding
Data Access Committee. Additional guidance can be found at the GSAHuman System website (https://​ngdc.​cncb.​ac.​cn/​gsa-​human/​docum​ent/​GSA-​
Human_​Reque​st_​Guide_​for_​Users_​us.​pdf ).

Declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee of Fujian Cancer Hospital(K2021-085–01). All patients provided oral and written informed consent toparticipate.
Consent of publication
All patients provided oral and written informed consent to publication.
Competing interests
X. Chen, R. Yang, Y. Zhan, H. Bao, J. Yan, and D. Zhu are employees of Nanjing
Geneseeq Technology Inc. All remaining authors have declared no conflictsof interest.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03531-8.
Additional file 1: Fig S1- Patient enrollment flow chart; Fig S2 - Training
ROC curve of base models constructed upon individual features; Fig S3 –Model performance on validation cohort with lower sequencing coverage
depth; Fig S4 - Model Performance Evaluation through Repeated Random
Partitioning of the Cohort; Fig S5 - Cancer scores of 47 hysteromyoma
patients; Fig S6 - Tumor fraction with stage and score; Fig S7 - Boxplot
comparing cancer scores in samples with different grade and MSI status;
Fig S8 - Heatmap of features; Fig S9 – Biological significance of features;
Fig S10 - Fragment length distribution; Fig S11-Stage shift analysis.
Additional file 2. Sample sequencing quality metric.
Additional file 3. Features using for model.
Additional file 4. Detailed patient characteristic.
Additional file 5. 104 NF features related genes that displayed distinct
characteristics in UCEC patients versus healthy controls.
Acknowledgements
We would like to express our gratitude to the participants who volunteered
their time, the staff and colleagues for assisting the research process, theinvestigators for their guidance, and families and friends for their support andunderstanding during the course of this study.

Received: 16 October 2023 Accepted: 15 July 2024

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