{"paper_id":"4ac30120-3917-4b5e-8a42-6bfb1b737c79","body_text":"Ultrasensitive Detection of Chromosomal Instability in Ascites as a Novel Diagnostic for Peritoneal Carcinomatosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ultrasensitive Detection of Chromosomal Instability in Ascites as a Novel Diagnostic for Peritoneal Carcinomatosis Hewei Zhang, Zhongwu Ma, Lixia Zhu, Renze Wu, Haibo Yu, Xiaoping Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8280082/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Background Diagnosing peritoneal carcinomatosis (PC) remains challenging. This study aims to evaluate the value of ascites chromosomal instability (CIN) analysis in the diagnosis of PC. Methods We performed low-coverage whole-genome sequencing on 50 human ascites samples. CIN was profiled using ultrasensitive chromosomal aneuploidy detection (UCAD), and its diagnostic performance was validated against pathological standards. Results CIN analysis detected peritoneal metastases with 90.0% sensitivity and 82.0% overall accuracy. In malignant ascites, 72.7% were CIN-positive, showing recurrent alterations (e.g., 3q gain, 14q loss) that formed co-occurring gain/loss modules. Unsupervised clustering identified three CIN subtypes with distinct genomic instability patterns. Conclusion Ascites CIN analysis shows promise as a highly sensitive, minimally invasive diagnostic tool for PC. The identified CIN patterns and subtypes provide insights into peritoneal metastasis biology and hold promise for clinical stratification. Chromosomal Instability Peritoneal Carcinomatosis Ascites Liquid Biopsy Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Peritoneal carcinomatosis (PC) is an aggressive malignancy with a poor prognosis, which can be primary or secondary to gastrointestinal or gynecological cancers. Due to its nonspecific early symptoms, most patients are diagnosed at an advanced stage, with a median survival generally less than one year[ 1 , 2 ]. Current clinical diagnosis primarily relies on imaging (e.g., CT, MRI, PET-CT), serum tumor marker tests, ascitic fluid cytology, and laparoscopic exploration. However, these methods have significant limitations: imaging examination exhibits low sensitivity for detecting micrometastases smaller than 5 mm[ 3 ];serum tumor markers lack specificity and can be influenced by benign conditions such as inflammation or cirrhosis[ 4 ]༛conventional ascitic fluid cytology has a sensitivity of only 30%–50%, affected by tumor cell heterogeneity and background benign cells[ 5 ]. Although laparoscopic exploration is considered the gold standard for diagnosis, its invasive nature and associated complications make it unsuitable for early screening. Therefore, there is an urgent need to develop highly sensitive, minimally invasive, and cost-effective methods for early detection. Chromosomal instability (CIN), one of the hallmarks of malignant tumors, drives tumor evolution, metastasis, and therapy resistance by promoting genomic heterogeneity[ 6 – 8 ]. CIN primarily involves changes in chromosome number and structure: numerical abnormalities include aneuploidy resulting from copy number variations, while structural abnormalities encompass polyploidy, chromosomal translocations, genomic chaos, and non-clonal aberrations, among others[ 9 ], generally exhibiting as somatic copy number variations (CNVs). In recent years, the Ultrasensitive Chromosomal Aneuploidy Detector (UCAD), based on low-coverage whole-genome sequencing (lcWGS) and quantitative bioinformatics algorithms such as Z-score, has been successfully applied for early liquid biopsy-based screening in urothelial carcinoma, liver cancer, biliary tract cancer, pancreatic cancer, and colorectal cancer[ 10 – 14 ]. Ascites, as a direct product of the peritoneal tumor microenvironment, is rich in tumor cells and circulating tumor DNA (ctDNA), providing an ideal sample for liquid biopsy-based molecular analysis. However, due to the large difference in the proportion of tumor DNA and high background interference in ascites samples [ 15 ], a reliable method that can stably identify CIN is urgently needed. Choi et al. further demonstrated that mutations detected in ascites from ovarian cancer patients covered 92.3–100% of somatic mutations in those cases, with phylogenetic analysis suggesting a polyclonal origin, highlighting the significant value of ascites in revealing tumor biological characteristics[ 16 ]. However, the systematic identification of CIN in ascites and the specific application of CIN in the diagnosis of peritoneal cancer still need to be explored by existing research. Based on this, our study aims to investigate the diagnostic value of CIN detection in ascites for PC. We employed low-coverage whole-genome sequencing to deeply analyze the CIN profile in ascites samples and evaluate its diagnostic performance, with the aim of providing new perspectives and methods for the early clinical diagnosis of PC. 2. Materials and methods 2.1. Study Population and Ascites Sample Collection Patients with suspected PC and cancer patients with ascites were consecutively enrolled from Wenzhou Central Hospital between June 2024 and June 2025. The inclusion criteria required meeting at least two of the following: (1) clinical symptoms suggestive of PC (e.g., abdominal pain, distension, palpable mass); (2) Imaging manifestations suggest peritoneal tumors; (3) elevated serum tumor markers (CEA, CA125, or CA19-9); (4) suspicious or positive malignant cells in ascites. The exclusion criteria included patients who did not meet the inclusion criteria and those who refused to participate in this study. The final diagnosis of peritoneal metastasis was confirmed by either positive ascites cytology or histopathological examination of peritoneal biopsies. A control group of benign ascites samples was collected from patients with liver cirrhosis or peritonitis. This study was approved by the Institutional Review Board of Wenzhou Central Hospital (KY2024001), and written informed consent was obtained from all participants. Ascites samples were obtained via standard percutaneous paracentesis. Subsequently, the samples were centrifuged at 3,000 rpm for 10 minutes. The cell pellets were stored at -80°C for subsequent DNA extraction. 2.2. DNA Extraction and Library Preparation Genomic DNA was extracted from ascites cell pellets using the Amp Genomic DNA Kit. DNA concentration and purity were measured using a Nanodrop spectrophotometer and Qubit fluorometer. Only samples with a DNA concentration ≥ 10 ng/µL, an A260/A280 ratio between 1.8–2.0, and intact genomic DNA (as confirmed by agarose gel electrophoresis) were proceeded. Sequencing libraries were constructed from 100 ng of DNA using the NEBnext Ultra II FS DNA Library Prep Kit, following the manufacturer's instructions. 2.3. High-Throughput Sequencing and Data Processing Paired-end sequencing (2×150 bp) was performed on the Illumina HiSeq Xten platform, with a target output of ≥ 4 Gb of data per sample. Raw sequencing reads were processed through the following bioinformatic pipeline: First, quality control was conducted using Trimmomatic to filter out low-quality reads (Phred score Q < 20) and adapter sequences. Subsequently, the resulting clean reads were aligned to the human reference genome (hg19) using BWA (Burrows-Wheeler Aligner). Coverage depth was then calculated for 200-kb genomic bins across the genome using SAM tools. For the detection of CNVs, Z-score normalization was applied to the coverage values within each bin. The Z-scor was calculated for each tumor sample bin using the formula Z= [ Vtumor​−averag (Vcontrols​)​]/ stdev (Vcontrols​), where V represents the bin's coverage value. The mean (mean (Vcontrols​)) and standard deviation (stdev (Vcontrols​)) for each bin were derived exclusively from the benign ascites control group samples. Genomic regions exhibiting |Z-score|>2 were defined as significant CNVs. 2.4. CNV and Mutation Analysis CNV detection was performed by identifying genomic breakpoints and significant CNV regions using the Circular Binary Segmentation (CBS) algorithm implemented in the R package \"DNACopy\". Analysis was prioritized for clinically relevant chromosomal arms (e.g., 8q, 10q, 17q). For mutation screening, single nucleotide variants (SNVs) and insertions/deletions (Indels) were called using GATK (v4.6.0.0). Germline variants were excluded based on a minor allele frequency threshold (MAF > 1% in population databases). 2.5. Definitions and data processing CIN-positive samples were defined as those harboring ≥ 1 chromosomal arm-level alteration. CIN burden was defined as the total number of distinct arm-level gains or losses per sample. All detected events were normalized and standardized into arm-level calls (e.g., “3q+” for gain, “8p−” for loss). Negative cases (neg) indicated no detectable CIN events. 2.6 Chromosomal Instability Analysis CIN was defined as the presence of arm-level gains or losses detected in malignant ascites samples, with CIN burden quantified as the number of altered chromosomal arms per sample; cases with ≥ 1 arm-level event was classified as CIN-positive, while those without alterations were considered CIN-negative. Differences in CIN burden between these groups were assessed using the Mann–Whitney U test (with tie correction), with results reported as median and interquartile range (IQR). For recurrent events (occurring in ≥ 8 cases), binarized alterations were evaluated pairwise using phi coefficients (φ) and Jaccard indices to assess co-occurrence or mutual exclusivity, with strong co-occurrence defined as φ ≥ 0.3 accompanied by ≥ 3 concurrent cases, and strong mutual exclusivity as φ ≤ −0.3. To delineate CIN subtypes, directional alteration matrices (coded as − 1 for loss, + 1 for gain, and 0 for no change) were generated for events with ≥ 5 cases and subjected to k-means clustering (k = 3, with multiple random initializations); resulting clusters were annotated based on CIN burden distributions and enrichment of recurrent arm-level events such as 3q+, 8q+, 20+, 14−, and 17p−. For all frequency-based comparisons across chromosomal arms, the Benjamini–Hochberg method was applied to control the false discovery rate at FDR < 0. 05. 2.7. Statistical Analysis CNV positivity (defined as |Z-score| > 2) and the presence of driver mutations were integrated as combined predictive markers. Diagnostic performance metrics, including sensitivity (true positive rate), specificity (true negative rate), positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were calculated. Statistical comparisons were performed using Chi-square or Fisher’s exact tests in SPSS software (version 26.0), with a p-value < 0.05 considered statistically significant. All experimental procedures were conducted following CLIA-certified laboratory standards, implementing strict contamination prevention measures throughout the study. 3. Results 3.1. Patient characteristics A total of 50 ascites samples were collected for this study. The mean age of cancer patients was 64.90 (±14.39) years (range: 37–92 years), while that of non-cancer patients was 66.00 (±7.77) years (range: 62–83 years). Males accounted for 52% of the cohort and females 48%. The cohort consisted of 44 samples from patients with suspected malignant ascites and 6 samples from patients with ascites of non-malignant origin (e.g., liver cirrhosis or infectious peritonitis), which served as the control group. A total of 44 tumor ascites samples were collected from patients with the following cancer types: gastric cancer (n=10), colorectal cancer (n=9), liver cancer (n=8), ovarian cancer (n=6), pancreatic cancer (n=5), cholangiocarcinoma (n=3), lung cancer (n=2), and synovial sarcoma (n=1). The final diagnosis of peritoneal cancer is based on the gold standard of peritoneal histopathological examination. Pathological confirmation identified peritoneal metastases in 30 of the 44 (68.2%) suspected malignant ascites samples. The remaining 14 samples from the suspected group were confirmed to have no metastatic involvement. The baseline characteristics of the study cohort are summarized in Table 1. Table 1 ． Baseline Characteristics of the Study Cohort Number (n=50) Percentage (%) Total Samples 50 100 Suspected Malignant Ascites 44 88.0 With Peritoneal Metastases 30 68.2* Without Peritoneal Metastases 14 31.8* Non-Malignant Ascites (Control) 6 12.0 *Percentage within the suspected malignant ascites group. 3.2. Diagnostic Performance of CIN Analysis The performance of CIN analysis in detecting peritoneal metastases was evaluated against the pathological diagnosis. Among the 30 patients with confirmed peritoneal metastases, CIN analysis yielded positive results in 27 cases, indicating a high sensitivity of 90.0%(95% CI: 0.74–0.98) (Table 2). The specificity of the CIN assay was assessed across the 20 samples without metastases (including both the 14 non-metastatic cancer patients and the 6 non-malignant controls). CIN analysis correctly identified 14 of these 20 cases as negative, resulting in a specificity of 70.0% (95% CI: 0.46–0.88). Further analysis of the false-positive cases (n=6) revealed that 5 originated from tumor patients without peritoneal metastases and 1 originated from the non-malignant control group. The positive predictive value (PPV) of the CIN assay, which indicates the probability that a positive test result truly reflects peritoneal metastasis, was 81.8%(95% CI: 0.65–0.92). The negative predictive value (NPV), representing the probability that a negative test result accurately rules out metastasis, was 82.4%(95% CI: 0.57–0.95). The overall accuracy of CIN analysis for the detection of peritoneal metastasis was 82.0%(95% CI: 0.69–0.91). Table 2: Diagnostic Performance of CIN Analysis in Detecting Peritoneal Metastasis (N=50) Pathology: Positive (n=30) Pathology: Negative (n=20) Total CIN Analysis Positive 27 (True Positive) 6 (False Positive) 33 CIN Analysis Negative 3 (False Negative) 14 (True Negative) 17 Total 30 20 50 3.3. Analysis of Discordant Results A detailed review of the 6 discordant (false-positive) cases was conducted. In one case, CIN positivity was detected in a patient with ascites due to liver cirrhosis, suggesting that severe inflammatory or regenerative processes may rarely lead to chromosomal instability. The other 5 false-positive cases occurred in patients with confirmed malignancies but without evidence of peritoneal metastasis. This suggests that the presence of malignant cells shed from the primary tumor, prior to the establishment of a metastatic niche, can be detected by CIN analysis, leading to a classification discordant with the pathological assessment of metastasis. 3.4. Landscape of Chromosomal Instability in Malignant Ascites CIN Positivity and Burden Chromosomal instability was prevalent in the cohort of malignant ascites samples. Specifically, 32 of the 44 samples (72.7%, 95% CI: 57.2%–84.4%) were classified as CIN-positive, based on the presence of at least one arm-level alteration. The CIN burden, reflected by the number of altered chromosomal arms per sample, varied widely across the cohort, with a median of 11 arms (IQR: 5–16). A subset of cases exhibited a high CIN burden (>20 altered arms), suggesting a state of profound genomic instability. Recurrent Arm-Level Alterations Analysis of recurrent copy number alterations revealed several frequently gained (Figure 1A) and lost (Figure 1B) chromosomal arms. The most common gains were identified at 3q (43.2%, FDR adjusted P value<0.05), 8q (29.5%), and 20q (29.5%). Conversely, the most frequent losses occurred at 14q (43.2%, FDR adjusted P value<0.05) and 17p (31.8%). Notably, the high frequency of 8q gain (harboring the MYC oncogene) and 17p loss (harboring the TP53 tumor suppressor) aligns with their well-established roles in cancer pathogenesis. Other recurrent alterations included gains of chromosome 7 (31.8%) and 13 (29.5%), as well as losses of 4q (31.8%), 21q (29.5%), and 22q (29.5%). 3.5. Chromosome-Level CIN Patterns Building upon the arm-level analysis, we further consolidated the alterations to identify patterns at the whole-chromosome level. This revealed that CIN events were predominantly clustered on chromosomes 3, 7, 8, 13, 14, 17, and 20. A distinct asymmetry was observed between copy-number gains and losses: recurrent gains were primarily located on chromosomes 3, 7, 8, 13, and 20, whereas recurrent losses were predominantly found on chromosomes 4, 14, 17, 21, and 22 (Figure 2). This non-random pattern of genomic imbalances underscores a specific chromosome instability signature characteristic of malignant ascites cells. 3.6. Visualization of the CIN Landscape The Oncoprint plot comprehensively delineates the landscape of arm-level CIN across all 44 malignant ascites samples (Figure 3). This visualization corroborates the recurrent alterations identified in our frequency analysis, prominently featuring hotspots such as 3q gain and 14q loss. Furthermore, it vividly illustrates the substantial inter-patient heterogeneity in both the spectrum and burden of chromosomal alterations, with some cases exhibiting a few focal changes while others display genome-wide aneuploidy. 3.7. Patterns of CIN Burden, Co-alteration, and Molecular Subtypes CIN Burden Distribution Consistent with the definition, CIN-positive cases (n=32) exhibited a median CIN burden of 11 altered arms per sample (IQR: 5–16), which was significantly higher than that of CIN-negative cases (n=12), all of which had a burden of 0 (Mann-Whitney U test, U = 0, z=−5.72，p <0.001) (Figure 4). This stark contrast underscores a fundamental divergence in the degree of genomic instability within the cohort. Co-occurrence and Mutual Exclusivity Analysis of recurrent arm-level events (frequency ≥8 cases) revealed non-random patterns of co-alteration. Specifically, a co-occurring \"gain module\" was identified, involving concurrent gains of 3q, 8q, and 20q (φ ≥ 0.45). Conversely, a \"loss module\" was characterized by co-deletions of 14q, 17p, and 22q (φ ≥ 0.40). Notably, gains in 3q and losses in 14q demonstrated a trend of mutual exclusivity (φ ≤ -0.35), suggesting potentially distinct underlying oncogenic mechanisms in different patient subsets (Figure 5). Identification of CIN Subtypes Unsupervised clustering (k=3) of the arm-level alteration matrix robustly classified the malignant ascites into three distinct CIN subtypes (Figure 6): Low-burden Subtype: Characterized by minimal chromosomal alterations (median burden: 2 arms; IQR: 1–4), phenotypically resembling the CIN-negative group. Focal Subtype: Exhibited a moderate CIN burden (median: 9 arms; IQR: 7–12) with significant enrichment for the specific gain (3q/8q/20q) and loss (14q/17p) modules described above. Genome-unstable Subtype: Displayed widespread, high-level aneuploidy (median burden: 18 arms; IQR: 15–22) with broad-spectrum gains and losses across the genome. This classification result reveals significant heterogeneity in genomic instability patterns among patients with malignant ascites. Discussion Peritoneal carcinomatosis, which can be classified into primary peritoneal tumors and peritoneal metastases, is a major cause of mortality in patients with various advanced malignancies, including gastric, ovarian, colorectal, and pancreatic cancers[ 17 ].The prognosis for these patients is generally poor, with a median survival often less than one year [ 18 , 19 ]. Their quality of life is significantly compromised due to refractory ascites, adhesive intestinal obstruction, and other symptoms that severely affect survival and may lead to rapid deterioration[ 20 ]. Currently, the diagnosis of PC primarily relies on imaging studies, serum tumor marker tests, and ascitic fluid cytological analysis. However, the detection capability of CT for PC is significantly influenced by factors such as lesion size, location, and the presence of ascites. Archer et al. reported that the sensitivity of CT for peritoneal lesions smaller than 0.5 cm is only 25%; Coakley et al. indicated that the sensitivity of spiral CT for peritoneal tumors smaller than 1 cm ranges from 25% to 50%; similarly, Koh et al. demonstrated that CT detected only 11% of colorectal cancer peritoneal metastatic nodules smaller than 0.5 cm[ 21 , 22 ]。In contrast, MRI exhibits higher sensitivity and specificity, at 92% and 85%, respectively. PET/CT shows sensitivity and specificity of 87% and 92%, respectively, though its application is limited by high economic costs[ 23 ]. Furthermore, the detection efficacy of PET/CT depends on the uptake of 18F-fluorodeoxyglucose (FDG) by cancer cells, a process closely associated with the expression of glucose transporter 1 (GLUT1). It is noteworthy that GLUT1 expression is often low in common histological types of peritoneal metastases, such as signet ring cell carcinoma, mucinous adenocarcinoma, and poorly differentiated adenocarcinoma[ 24 ], which partly limits the diagnostic performance of PET/CT. Due to the low detection rate of conventional imaging for early peritoneal metastases, surgical exploration remains the most reliable method for assessing peritoneal tumor burden, offering high sensitivity and specificity. However, owing to its invasive nature, this approach is not suitable for early screening or repeated examinations. From a pathophysiological perspective, peritoneal fluid can carry tumor cells during intra-cavitary flow and distribute them randomly throughout the peritoneal cavity, which also provides a theoretical basis for ascitic fluid cytology[ 25 ]. As a minimally invasive technique, ascitic fluid cytology has a positivity rate of approximately 46%. Its results are influenced by various factors, including the primary tumor site, number of samples submitted, detection methods, and diagnostic experience. Repeated submissions are often required to improve accuracy. In this study, a prospective study included 44 cancer patients and 6 non-cancer patients. UCAD was used to detect chromosomal instability in ascites tumor cells and showed a relatively high prevalence of CIN (72.7%). It provides evidence for the first time that CIN signaling is very abundant in the ascites of PC. The sensitivity and specificity of CIN signaling based on ascites for diagnosing PC are 90% and 70%, respectively. CIN-positive cases exhibited markedly higher arm-level alteration burden than CIN-negative cases, highlighting widespread genomic instability in malignant ascites. CIN has long been recognized as a hallmark of tumorigenesis and disease progression [ 26 ]. Our results extend these findings to the context of PC, supporting the notion that CIN is a driving force of clonal evolution in metastatic settings[ 27 , 28 ]. With respect to recurrent events, we identified 3q gain and 14q loss as the most frequent alterations, often co-occurring with additional hotspots such as 8q+, 20q+ (for gains) and 17p−, 22q− (for losses). These modules suggest non-random patterns of CIN. Previous large-scale genomic studies, such as The Cancer Genome Atlas (TCGA), have reported recurrent 3q amplification in ovarian, cervical, and lung cancers, frequently harboring PIK3CA, SOX2, and TERC oncogenes[ 29 – 31 ]. Similarly, loss of 14q has been associated with deletion of NDRG2 and other tumor suppressors, contributing to aggressive phenotypes in gastric and colorectal cancers [ 32 , 33 ]. The frequent loss of 17p observed in our study is consistent with the canonical inactivation of TP53 , a pivotal event in tumor progression and therapeutic resistance[ 34 ]. Thus, our co-occurrence modules likely reflect cooperative oncogenic events shaping malignant ascites biology. It is important to note that although copy number variation driven by CIN represents a central mechanism in tumorigenesis and cancer progression, the underlying gene regulatory networks require further experimental validation[ 35 ]. Interestingly, we also observed mutual exclusivity between certain alterations, such as 3q + and 14q−. Such patterns have been described in other tumor types, where mutually exclusive alterations indicate functional redundancy or alternative evolutionary trajectories[ 36 ]. This suggests that CIN in ascites may follow distinct, yet convergent, genomic evolution pathways. Unsupervised clustering identified three CIN subtypes: Low-burden , Hotspot-focused , and High-burden . Similar stratifications have been reported in ovarian carcinoma, where CIN-low tumors exhibit relative genomic stability, while CIN-high tumors demonstrate widespread arm-level alterations and poor prognosis [ 37 , 38 ]. Our Hotspot-focused subtype, enriched for 3q/8q/20q gains and 14q/17p losses, resembles previously described \"CIN-intermediate\" clusters in ovarian and gastric cancer, characterized by selective yet recurrent chromosomal alterations[ 39 ]. These findings emphasize the heterogeneity of CIN in malignant ascites and suggest that distinct CIN subtypes may underlie differential clinical behaviors. CIN has been linked to poor prognosis, chemotherapy resistance, and immune evasion[ 40 – 42 ]. In particular, CIN-induced micronuclei formation and cytosolic DNA sensing via the cGAS–STING pathway have been implicated in shaping the tumor immune microenvironment[ 43 ]. Therefore, the identification of CIN modules and subtypes in malignant ascites not only deepens our understanding of tumor biology but also highlights potential avenues for prognostic stratification and therapeutic targeting. For instance, CIN-high patients may benefit from therapeutic approaches targeting replication stress or mitotic checkpoint vulnerabilities [ 44 ], whereas CIN-intermediate patients may be more suitable for therapies exploiting specific driver alterations within 3q or 17p regions. The present study has several limitations. First, the sample size was modest (n = 50) and lacked clinical annotations such as cancer subtype or treatment history, limiting translational interpretation. Second, our analysis was restricted to arm-level alterations without gene-level resolution. Finally, single-center sampling may limit generalizability. Nonetheless, our study provides novel insights into CIN architecture in malignant ascites, warranting validation in larger, multi-center cohorts and integrative analyses with clinical outcomes. Conclusion In summary, detecting chromosomal instability in ascites using low-coverage whole-genome sequencing provides a highly sensitive (90.0%) method for diagnosing PC. We characterized the CIN landscape in malignant ascites, identifying a 72.7% CIN-positive rate, recurrent arm-level alterations (e.g., 3q+, 14q-), and distinct CIN subtypes. These findings establish ascites as a valid substrate for genomic liquid biopsy and provide a molecular framework for understanding peritoneal metastases. Abbreviations PC peritoneal carcinomatosis CIN chromosomal instability UCAD ultrasensitive chromosomal aneuploidy detection LcWGS low-coverage whole-genome sequencing CtDNA circulating tumor DNA CNVs copy number variations CBS Circular Binary Segmentation SNVs single nucleotide variants IQR interquartile range PPV positive predictive value NPV negative predictive value CI Confidence Interval FDR False Discovery Rate 18-FDG 18F-fluorodeoxyglucose TCGA The Cancer Genome Atlas Declarations Author Contributions HWZ collected and analyzed the data and wrote the manuscript. ZWM analyzed the data. LXZ and RZW secured the ethical approval and created the figures. XPC and HBY participated in the design of the study and revised the manuscript. All authors read and approved the final manuscript. Funding This study was supported by the Zhejiang Clinovation Pride (Grant Number: CXTD202502006) and the Wenzhou \"Gazelle\" Clinical Innovation Team Fund. Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The datasets generated during the current study are available in the GSA-Human repository, No.HRA015680. Ethical approval and consent to participate Written informed consent has been obtained before the collection of ascites samples from patients, which can be used to publish this research report. This study was approved by the ethics committee of Wenzhou Central Hospital. Procedures were performed in accordance with the declaration of Helsinki. All methods were carried out according to the relevant guidelines and provisions in the declaration. Competing interests The authors declare no conflicts of interest. Consent for publication statement Not applicable. Acknowledgments None References Blair SL, Chu DZ, Schwarz RE. 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Gastric Cancer. 2018; 21: 55-67.http://doi.org/10.1007/s10120-017-0726-5. Coakley FV, Choi PH, Gougoutas CA, Pothuri B, Venkatraman E, Chi D, et al. Peritoneal metastases: detection with spiral CT in patients with ovarian cancer. Radiology. 2002; 223: 495-9.http://doi.org/10.1148/radiol.2232011081. Koh JL, Yan TD, Glenn D, Morris DL. Evaluation of preoperative computed tomography in estimating peritoneal cancer index in colorectal peritoneal carcinomatosis. Ann Surg Oncol. 2009; 16: 327-33.http://doi.org/10.1245/s10434-008-0234-2. van 't Sant I, Engbersen MP, Bhairosing PA, Lambregts DMJ, Beets-Tan RGH, van Driel WJ, et al. Diagnostic performance of imaging for the detection of peritoneal metastases: a meta-analysis. Eur Radiol. 2020; 30: 3101-12.http://doi.org/10.1007/s00330-019-06524-x. Shimada H, Okazumi S, Koyama M, Murakami K. Japanese Gastric Cancer Association Task Force for Research Promotion: clinical utility of ¹⁸F-fluoro-2-deoxyglucose positron emission tomography in gastric cancer. A systematic review of the literature. Gastric Cancer. 2011; 14: 13-21.http://doi.org/10.1007/s10120-011-0017-5. Halkia E, Spiliotis J, Sugarbaker P. Diagnosis and management of peritoneal metastases from ovarian cancer. Gastroenterol Res Pract. 2012; 2012: 541842.http://doi.org/10.1155/2012/541842. Lengauer C, Kinzler KW, Vogelstein B. Genetic instability in colorectal cancers. Nature. 1997; 386: 623-7.http://doi.org/10.1038/386623a0. Sansregret L, Swanton C. The Role of Aneuploidy in Cancer Evolution. Cold Spring Harb Perspect Med. 2017; 7.http://doi.org/10.1101/cshperspect.a028373. Li J, Hubisz MJ, Earlie EM, Duran MA, Hong C, Varela AA, et al. Non-cell-autonomous cancer progression from chromosomal instability. Nature. 2023; 620: 1080-8.http://doi.org/10.1038/s41586-023-06464-z. Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010; 463: 899-905.http://doi.org/10.1038/nature08822. Integrated genomic analyses of ovarian carcinoma. Nature. 2011; 474: 609-15.http://doi.org/10.1038/nature10166. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012; 489: 519-25.http://doi.org/10.1038/nature11404. Chang X, Li Z, Ma J, Deng P, Zhang S, Zhi Y, et al. DNA methylation of NDRG2 in gastric cancer and its clinical significance. Dig Dis Sci. 2013; 58: 715-23.http://doi.org/10.1007/s10620-012-2393-z. Thorstensen L, Qvist H, Nesland JM, Giercksky KE, Lothe RA. Identification of two potential suppressor gene regions on chromosome arm 14q that are commonly lost in advanced colorectal carcinomas. Scand J Gastroenterol. 2001; 36: 1327-31.http://doi.org/10.1080/003655201317097209. Olivier M, Hollstein M, Hainaut P. TP53 mutations in human cancers: origins, consequences, and clinical use. Cold Spring Harb Perspect Biol. 2010; 2: a001008.http://doi.org/10.1101/cshperspect.a001008. Gu W, Choi H, Ghosh D. Global associations between copy number and transcript mRNA microarray data: an empirical study. Cancer Inform. 2008; 6: 17-23.http://doi.org/10.4137/cin.s342. Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 2012; 22: 398-406.http://doi.org/10.1101/gr.125567.111. Bakhoum SF, Ngo B, Laughney AM, Cavallo JA, Murphy CJ, Ly P, et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature. 2018; 553: 467-72.http://doi.org/10.1038/nature25432. Etemadmoghadam D, deFazio A, Beroukhim R, Mermel C, George J, Getz G, et al. Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas. Clin Cancer Res. 2009; 15: 1417-27.http://doi.org/10.1158/1078-0432.Ccr-08-1564. Zang ZJ, Ong CK, Cutcutache I, Yu W, Zhang SL, Huang D, et al. Genetic and structural variation in the gastric cancer kinome revealed through targeted deep sequencing. Cancer Res. 2011; 71: 29-39.http://doi.org/10.1158/0008-5472.Can-10-1749. Sansregret L, Vanhaesebroeck B, Swanton C. Determinants and clinical implications of chromosomal instability in cancer. Nat Rev Clin Oncol. 2018; 15: 139-50.http://doi.org/10.1038/nrclinonc.2017.198. Bakhoum SF, Cantley LC. The Multifaceted Role of Chromosomal Instability in Cancer and Its Microenvironment. Cell. 2018; 174: 1347-60.http://doi.org/10.1016/j.cell.2018.08.027. Watkins TBK, Lim EL, Petkovic M, Elizalde S, Birkbak NJ, Wilson GA, et al. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature. 2020; 587: 126-32.http://doi.org/10.1038/s41586-020-2698-6. Thompson SL, Bakhoum SF, Compton DA. Mechanisms of chromosomal instability. Curr Biol. 2010; 20: R285-95.http://doi.org/10.1016/j.cub.2010.01.034. O'Connor MJ. Targeting the DNA Damage Response in Cancer. Mol Cell. 2015; 60: 547-60.http://doi.org/10.1016/j.molcel.2015.10.040. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8280082\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":589929399,\"identity\":\"af1adbc3-db59-4afa-a5fb-a93c5c876a12\",\"order_by\":0,\"name\":\"Hewei Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wenzhou Central Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hewei\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":589929400,\"identity\":\"6b907a00-262d-47ff-9718-f06043f4ad42\",\"order_by\":1,\"name\":\"Zhongwu Ma\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wenzhou Central Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhongwu\",\"middleName\":\"\",\"lastName\":\"Ma\",\"suffix\":\"\"},{\"id\":589929401,\"identity\":\"43db9af7-90e0-446a-844d-961407d684be\",\"order_by\":2,\"name\":\"Lixia Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang Chinese Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lixia\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":589929402,\"identity\":\"cb822f57-b5a5-4e0c-a844-5e62605c66d0\",\"order_by\":3,\"name\":\"Renze Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Zhejiang Chinese Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Renze\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":589929403,\"identity\":\"7deade46-1a6f-4370-8b47-6fdfbbc04688\",\"order_by\":4,\"name\":\"Haibo Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wenzhou Central Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haibo\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":589929408,\"identity\":\"cc10e957-4698-4d7b-a4ac-1675e4695e75\",\"order_by\":5,\"name\":\"Xiaoping Cai\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2Pv0vDQBTHXzi4LkezXqkY/4QrXfPHvEPoLWkRXDI4tASui9S1LvpfOF8IdIrgKOhwRahrJ8kUPCm6Jc1Y8D7D+wHfD48H4PGcJMEc0DXqJotpfU57memsELEvzbjPNtj5Hh3cayMfeHLRGguXheb2Jp71h8uXIaPvSkMCUKVPjQovZSZwM7mmZ+XVmLHdVMOzCW7Lt0ZFgFxYpIXUPMFLxslUByskgW5Rwu3cYH1QCiaIosTVVoW7K1L/KCpfrLFASo8o/HWbCblyv/CEwN5MRppRzNt+Ce/UblB9xbNorT4rWcdR9PiR2yptVn5BACb+NnM0f1B6tkvQ4/F4/iHf3BZbv5Q5d+0AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Wenzhou Central Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xiaoping\",\"middleName\":\"\",\"lastName\":\"Cai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-12-04 13:53:32\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8280082/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8280082/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102788832,\"identity\":\"89fab232-d882-4028-bcdb-ef5c1f1ac55e\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:03\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":343774,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFrequency of recurrent chromosomal gains and losses. \\u003c/strong\\u003eBar plots summarizing the frequency of recurrent arm-level gains (A) and losses (B) across 44 malignant ascites samples. The most frequent gains included 3q+, 7+, 8q+, 13+, and 20+, while the most frequent losses included 14−, 4−, 17p−, 22−, and 21q−.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/b40af84769bbe2fa722df897.png\"},{\"id\":102788837,\"identity\":\"525089b8-3151-40a9-8542-a9b6b0ae65b7\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:09\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":133023,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of CIN burden across malignant ascites samples. \\u003c/strong\\u003eHistogram illustrating the distribution of CIN burden (number of altered chromosomal arms per sample) in 44 malignant ascites cases. The distribution demonstrates marked inter-patient heterogeneity, with a subset of samples exhibiting high CIN burden (\\u0026gt;20 altered arms).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/c43a78f920728eb2caba5b1c.png\"},{\"id\":102788834,\"identity\":\"135ac980-13f1-49ef-9965-e5c8329382eb\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:07\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":501983,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eOncoprint of arm-level chromosomal alterations in malignant ascites. \\u003c/strong\\u003eOncoprint visualization of 44 malignant ascites samples showing arm-level chromosomal instability events. Each column represents a chromosomal arm, and each row corresponds to an individual sample. Color intensity reflects alteration type as indicated by the color scale bar (−1 = loss, 0 = none, +1 = gain). The plot confirms the high prevalence of specific events like 3q gain and 14q loss, while also highlighting the extensive heterogeneity in CIN patterns among patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/48c8f0d711db6b49d847759a.png\"},{\"id\":102788836,\"identity\":\"f0164b8e-a805-4833-bedf-e95b365743ec\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:08\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":126487,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCIN burden distribution stratified by CIN status.\\u003c/strong\\u003e Boxplot comparing the number of altered chromosomal arms (CIN burden) between CIN-positive (n=32) and CIN-negative (n=12) malignant ascites samples. CIN-positive cases exhibited significantly higher CIN burden (median 11, IQR: 5–16） than CIN-negative cases (median 0, IQR: 0–0), Mann–Whitney U test, p\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/cb399527b8795fcf5df29f33.png\"},{\"id\":102788838,\"identity\":\"bc30326b-baf8-4866-8046-6d40553a5c3b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:10\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":354293,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCo-occurrence and mutual exclusivity patterns of recurrent chromosomal alterations. \\u003c/strong\\u003eHeatmap displaying pairwise φ coefficients among recurrent arm-level events （≥8 cases）. Positive φ values (yellow–green) indicate co-occurrence, while negative values (dark blue–purple) indicate mutual exclusivity. Recurrent clusters are highlighted, including a gain module (3q+, 8q+, 20+) and a loss module (14−, 17p−, 22−).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/02b1da0030429286b59971c2.png\"},{\"id\":102788839,\"identity\":\"807b431d-9caa-413c-8335-1271dc87757a\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:41:10\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":443498,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCIN-based molecular subtypes identified by unsupervised clustering. \\u003c/strong\\u003eHeatmap of directional chromosomal arm-level alterations (−1 for loss, +1 for gain, 0 for none) across malignant ascites samples, ordered by subtype classification (Low-burden, Hotspot-focused, High-burden). The Low-burden subtype displayed minimal alterations, the Hotspot-focused subtype was enriched for 3q/8q/20q gains and 14q/17p losses, while the High-burden subtype exhibited broad-spectrum chromosomal instability.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/bda3cd3b9bf7523ed14d84e0.png\"},{\"id\":103056355,\"identity\":\"6d4df42e-45ac-4305-ba22-9010d4909fef\",\"added_by\":\"auto\",\"created_at\":\"2026-02-20 09:07:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3090676,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8280082/v1/ecf0b8b6-b745-430a-88a3-23058700452e.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Ultrasensitive Detection of Chromosomal Instability in Ascites as a Novel Diagnostic for Peritoneal Carcinomatosis\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003ePeritoneal carcinomatosis (PC) is an aggressive malignancy with a poor prognosis, which can be primary or secondary to gastrointestinal or gynecological cancers. Due to its nonspecific early symptoms, most patients are diagnosed at an advanced stage, with a median survival generally less than one year[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Current clinical diagnosis primarily relies on imaging (e.g., CT, MRI, PET-CT), serum tumor marker tests, ascitic fluid cytology, and laparoscopic exploration. However, these methods have significant limitations: imaging examination exhibits low sensitivity for detecting micrometastases smaller than 5 mm[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e];serum tumor markers lack specificity and can be influenced by benign conditions such as inflammation or cirrhosis[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]༛conventional ascitic fluid cytology has a sensitivity of only 30%\\u0026ndash;50%, affected by tumor cell heterogeneity and background benign cells[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Although laparoscopic exploration is considered the gold standard for diagnosis, its invasive nature and associated complications make it unsuitable for early screening. Therefore, there is an urgent need to develop highly sensitive, minimally invasive, and cost-effective methods for early detection.\\u003c/p\\u003e \\u003cp\\u003eChromosomal instability (CIN), one of the hallmarks of malignant tumors, drives tumor evolution, metastasis, and therapy resistance by promoting genomic heterogeneity[\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. CIN primarily involves changes in chromosome number and structure: numerical abnormalities include aneuploidy resulting from copy number variations, while structural abnormalities encompass polyploidy, chromosomal translocations, genomic chaos, and non-clonal aberrations, among others[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], generally exhibiting as somatic copy number variations (CNVs). In recent years, the Ultrasensitive Chromosomal Aneuploidy Detector (UCAD), based on low-coverage whole-genome sequencing (lcWGS) and quantitative bioinformatics algorithms such as Z-score, has been successfully applied for early liquid biopsy-based screening in urothelial carcinoma, liver cancer, biliary tract cancer, pancreatic cancer, and colorectal cancer[\\u003cspan additionalcitationids=\\\"CR11 CR12 CR13\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Ascites, as a direct product of the peritoneal tumor microenvironment, is rich in tumor cells and circulating tumor DNA (ctDNA), providing an ideal sample for liquid biopsy-based molecular analysis. However, due to the large difference in the proportion of tumor DNA and high background interference in ascites samples [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], a reliable method that can stably identify CIN is urgently needed. Choi et al. further demonstrated that mutations detected in ascites from ovarian cancer patients covered 92.3\\u0026ndash;100% of somatic mutations in those cases, with phylogenetic analysis suggesting a polyclonal origin, highlighting the significant value of ascites in revealing tumor biological characteristics[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. However, the systematic identification of CIN in ascites and the specific application of CIN in the diagnosis of peritoneal cancer still need to be explored by existing research.\\u003c/p\\u003e \\u003cp\\u003eBased on this, our study aims to investigate the diagnostic value of CIN detection in ascites for PC. We employed low-coverage whole-genome sequencing to deeply analyze the CIN profile in ascites samples and evaluate its diagnostic performance, with the aim of providing new perspectives and methods for the early clinical diagnosis of PC.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Study Population and Ascites Sample Collection\\u003c/h2\\u003e \\u003cp\\u003ePatients with suspected PC and cancer patients with ascites were consecutively enrolled from Wenzhou Central Hospital between June 2024 and June 2025. The inclusion criteria required meeting at least two of the following: (1) clinical symptoms suggestive of PC (e.g., abdominal pain, distension, palpable mass); (2) Imaging manifestations suggest peritoneal tumors; (3) elevated serum tumor markers (CEA, CA125, or CA19-9); (4) suspicious or positive malignant cells in ascites. The exclusion criteria included patients who did not meet the inclusion criteria and those who refused to participate in this study. The final diagnosis of peritoneal metastasis was confirmed by either positive ascites cytology or histopathological examination of peritoneal biopsies. A control group of benign ascites samples was collected from patients with liver cirrhosis or peritonitis. This study was approved by the Institutional Review Board of Wenzhou Central Hospital (KY2024001), and written informed consent was obtained from all participants.\\u003c/p\\u003e \\u003cp\\u003eAscites samples were obtained via standard percutaneous paracentesis. Subsequently, the samples were centrifuged at 3,000 rpm for 10 minutes. The cell pellets were stored at -80\\u0026deg;C for subsequent DNA extraction.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. DNA Extraction and Library Preparation\\u003c/h2\\u003e \\u003cp\\u003eGenomic DNA was extracted from ascites cell pellets using the Amp Genomic DNA Kit. DNA concentration and purity were measured using a Nanodrop spectrophotometer and Qubit fluorometer. Only samples with a DNA concentration\\u0026thinsp;\\u0026ge;\\u0026thinsp;10 ng/\\u0026micro;L, an A260/A280 ratio between 1.8\\u0026ndash;2.0, and intact genomic DNA (as confirmed by agarose gel electrophoresis) were proceeded. Sequencing libraries were constructed from 100 ng of DNA using the NEBnext Ultra II FS DNA Library Prep Kit, following the manufacturer's instructions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. High-Throughput Sequencing and Data Processing\\u003c/h2\\u003e \\u003cp\\u003ePaired-end sequencing (2\\u0026times;150 bp) was performed on the Illumina HiSeq Xten platform, with a target output of \\u0026ge;\\u0026thinsp;4 Gb of data per sample. Raw sequencing reads were processed through the following bioinformatic pipeline: First, quality control was conducted using Trimmomatic to filter out low-quality reads (Phred score Q\\u0026thinsp;\\u0026lt;\\u0026thinsp;20) and adapter sequences. Subsequently, the resulting clean reads were aligned to the human reference genome (hg19) using BWA (Burrows-Wheeler Aligner). Coverage depth was then calculated for 200-kb genomic bins across the genome using SAM tools. For the detection of CNVs, Z-score normalization was applied to the coverage values within each bin. The Z-scor was calculated for each tumor sample bin using the formula Z= [ Vtumor​\\u0026minus;averag (Vcontrols​)​]/ stdev (Vcontrols​), where V represents the bin's coverage value. The mean (mean (Vcontrols​)) and standard deviation (stdev (Vcontrols​)) for each bin were derived exclusively from the benign ascites control group samples. Genomic regions exhibiting |Z-score|\\u0026gt;2 were defined as significant CNVs.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. CNV and Mutation Analysis\\u003c/h2\\u003e \\u003cp\\u003eCNV detection was performed by identifying genomic breakpoints and significant CNV regions using the Circular Binary Segmentation (CBS) algorithm implemented in the R package \\\"DNACopy\\\". Analysis was prioritized for clinically relevant chromosomal arms (e.g., 8q, 10q, 17q). For mutation screening, single nucleotide variants (SNVs) and insertions/deletions (Indels) were called using GATK (v4.6.0.0). Germline variants were excluded based on a minor allele frequency threshold (MAF\\u0026thinsp;\\u0026gt;\\u0026thinsp;1% in population databases).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5. Definitions and data processing\\u003c/h2\\u003e \\u003cp\\u003eCIN-positive samples were defined as those harboring\\u0026thinsp;\\u0026ge;\\u0026thinsp;1 chromosomal arm-level alteration. CIN burden was defined as the total number of distinct arm-level gains or losses per sample. All detected events were normalized and standardized into arm-level calls (e.g., \\u0026ldquo;3q+\\u0026rdquo; for gain, \\u0026ldquo;8p\\u0026minus;\\u0026rdquo; for loss). Negative cases (neg) indicated no detectable CIN events.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Chromosomal Instability Analysis\\u003c/h2\\u003e \\u003cp\\u003eCIN was defined as the presence of arm-level gains or losses detected in malignant ascites samples, with CIN burden quantified as the number of altered chromosomal arms per sample; cases with \\u0026ge;\\u0026thinsp;1 arm-level event was classified as CIN-positive, while those without alterations were considered CIN-negative. Differences in CIN burden between these groups were assessed using the Mann\\u0026ndash;Whitney U test (with tie correction), with results reported as median and interquartile range (IQR). For recurrent events (occurring in \\u0026ge;\\u0026thinsp;8 cases), binarized alterations were evaluated pairwise using phi coefficients (φ) and Jaccard indices to assess co-occurrence or mutual exclusivity, with strong co-occurrence defined as φ\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.3 accompanied by \\u0026ge;\\u0026thinsp;3 concurrent cases, and strong mutual exclusivity as φ \\u0026le; \\u0026minus;0.3. To delineate CIN subtypes, directional alteration matrices (coded as \\u0026minus;\\u0026thinsp;1 for loss, +\\u0026thinsp;1 for gain, and 0 for no change) were generated for events with \\u0026ge;\\u0026thinsp;5 cases and subjected to k-means clustering (k\\u0026thinsp;=\\u0026thinsp;3, with multiple random initializations); resulting clusters were annotated based on CIN burden distributions and enrichment of recurrent arm-level events such as 3q+, 8q+, 20+, 14\\u0026minus;, and 17p\\u0026minus;. For all frequency-based comparisons across chromosomal arms, the Benjamini\\u0026ndash;Hochberg method was applied to control the false discovery rate at FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0. 05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7. Statistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eCNV positivity (defined as |Z-score| \\u0026gt; 2) and the presence of driver mutations were integrated as combined predictive markers. Diagnostic performance metrics, including sensitivity (true positive rate), specificity (true negative rate), positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were calculated. Statistical comparisons were performed using Chi-square or Fisher\\u0026rsquo;s exact tests in SPSS software (version 26.0), with a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered statistically significant. All experimental procedures were conducted following CLIA-certified laboratory standards, implementing strict contamination prevention measures throughout the study.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e3.1. Patient characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 50 ascites samples were collected for this study. The mean age of cancer patients was 64.90 (\\u0026plusmn;14.39) years (range: 37\\u0026ndash;92 years), while that of non-cancer patients was 66.00 (\\u0026plusmn;7.77) years (range: 62\\u0026ndash;83 years). Males accounted for 52% of the cohort and females 48%. The cohort consisted of 44 samples from patients with suspected malignant ascites and 6 samples from patients with ascites of non-malignant origin (e.g., liver cirrhosis or infectious peritonitis), which served as the control group.\\u0026nbsp;A total of 44 tumor ascites samples\\u0026nbsp;were collected from patients with the following cancer types: gastric cancer (n=10), colorectal cancer (n=9), liver cancer (n=8), ovarian cancer (n=6), pancreatic cancer (n=5), cholangiocarcinoma (n=3), lung cancer (n=2), and synovial sarcoma (n=1). The final diagnosis of peritoneal cancer is based on the gold standard of peritoneal histopathological examination.\\u0026nbsp;Pathological confirmation identified peritoneal metastases in 30 of the 44 (68.2%) suspected malignant ascites samples. The remaining 14 samples from the suspected group were confirmed to have no metastatic involvement. The baseline characteristics of the study cohort are summarized in\\u0026nbsp;Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e\\u003cstrong\\u003e．\\u003c/strong\\u003e\\u003cstrong\\u003eBaseline Characteristics of the Study Cohort\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber (n=50)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePercentage (%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal Samples\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSuspected Malignant Ascites\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e88.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026emsp;\\u0026emsp; With Peritoneal Metastases\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e68.2*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026emsp;\\u0026emsp; Without Peritoneal Metastases\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e31.8*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNon-Malignant Ascites (Control)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e12.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e*Percentage within the suspected malignant ascites group.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.2. Diagnostic Performance of CIN Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe performance of CIN analysis in detecting peritoneal metastases was evaluated against the pathological diagnosis. Among the 30 patients with confirmed peritoneal metastases,\\u0026nbsp;CIN analysis yielded positive results in 27 cases, indicating a high sensitivity of 90.0%(95% CI: 0.74\\u0026ndash;0.98) (Table 2).\\u003c/p\\u003e\\n\\u003cp\\u003eThe specificity of the CIN assay was assessed across the 20 samples without metastases (including both the 14 non-metastatic cancer patients and the 6 non-malignant controls). CIN analysis correctly identified 14 of these 20 cases as negative, resulting in a specificity of 70.0%\\u0026nbsp;(95% CI: 0.46\\u0026ndash;0.88). Further analysis of the false-positive cases (n=6) revealed that 5 originated from tumor patients without peritoneal metastases and 1 originated from the non-malignant control group.\\u003c/p\\u003e\\n\\u003cp\\u003eThe positive predictive value (PPV) of the CIN assay, which indicates the probability that a positive test result truly reflects peritoneal metastasis, was 81.8%(95% CI: 0.65\\u0026ndash;0.92). The negative predictive value (NPV), representing the probability that a negative test result accurately rules out metastasis, was 82.4%(95% CI: 0.57\\u0026ndash;0.95). The overall accuracy of CIN analysis for the detection of peritoneal metastasis was 82.0%(95% CI: 0.69\\u0026ndash;0.91).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2: Diagnostic Performance of CIN Analysis in Detecting Peritoneal Metastasis (N=50)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePathology: Positive (n=30)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePathology: Negative (n=20)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCIN Analysis Positive\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e27 (True Positive)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6 (False Positive)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCIN Analysis Negative\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3 (False Negative)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e14 (True Negative)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3. Analysis of Discordant Results\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA detailed review of the 6 discordant (false-positive) cases was conducted. In one case, CIN positivity was detected in a patient with ascites due to liver cirrhosis, suggesting that severe inflammatory or regenerative processes may rarely lead to chromosomal instability. The other 5 false-positive cases occurred in patients with confirmed malignancies but without evidence of peritoneal metastasis. This suggests that the presence of malignant cells shed from the primary tumor, prior to the establishment of a metastatic niche, can be detected by CIN analysis, leading to a classification discordant with the pathological assessment of metastasis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4. Landscape of Chromosomal Instability in Malignant Ascites\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCIN Positivity and Burden\\u003c/p\\u003e\\n\\u003cp\\u003eChromosomal instability was prevalent in the cohort of malignant ascites samples. Specifically, 32 of the 44 samples (72.7%, 95% CI: 57.2%\\u0026ndash;84.4%) were classified as CIN-positive, based on the presence of at least one arm-level alteration. The CIN burden, reflected by the number of altered chromosomal arms per sample, varied widely across the cohort, with a median of 11 arms (IQR: 5\\u0026ndash;16). A subset of cases exhibited a high CIN burden (\\u0026gt;20 altered arms), suggesting a state of profound genomic instability.\\u003c/p\\u003e\\n\\u003cp\\u003eRecurrent Arm-Level Alterations\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis of recurrent copy number alterations revealed several frequently gained (Figure 1A) and lost (Figure 1B) chromosomal arms. The most common gains were identified at 3q (43.2%,\\u0026nbsp;FDR\\u0026nbsp;adjusted\\u0026nbsp;P\\u0026nbsp;value\\u0026lt;0.05), 8q (29.5%), and 20q (29.5%). Conversely, the most frequent losses occurred at 14q (43.2%, FDR adjusted P value\\u0026lt;0.05) and 17p (31.8%). Notably, the high frequency of 8q gain (harboring the MYC oncogene) and 17p loss (harboring the TP53 tumor suppressor) aligns with their well-established roles in cancer pathogenesis. Other recurrent alterations included gains of chromosome 7 (31.8%) and 13 (29.5%), as well as losses of 4q (31.8%), 21q (29.5%), and 22q (29.5%).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.5. Chromosome-Level CIN Patterns\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBuilding upon the arm-level analysis, we further consolidated the alterations to identify patterns at the whole-chromosome level. This revealed that CIN events were predominantly clustered on chromosomes 3, 7, 8, 13, 14, 17, and 20. A distinct asymmetry was observed between copy-number gains and losses: recurrent gains were primarily located on chromosomes 3, 7, 8, 13, and 20, whereas recurrent losses were predominantly found on chromosomes 4, 14, 17, 21, and 22 (Figure 2). This non-random pattern of genomic imbalances underscores a specific chromosome instability signature characteristic of malignant ascites cells.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.6. Visualization of the CIN Landscape\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Oncoprint plot comprehensively delineates the landscape of arm-level CIN across all 44 malignant ascites samples (Figure 3). This visualization corroborates the recurrent alterations identified in our frequency analysis, prominently featuring hotspots such as 3q gain and 14q loss. Furthermore, it vividly illustrates the substantial inter-patient heterogeneity in both the spectrum and burden of chromosomal alterations, with some cases exhibiting a few focal changes while others display genome-wide aneuploidy.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cstrong\\u003e3.7. Patterns of CIN Burden, Co-alteration, and Molecular Subtypes\\u003c/strong\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCIN Burden Distribution\\u003c/strong\\u003e\\u003cbr\\u003e\\u0026nbsp;Consistent with the definition, CIN-positive cases (n=32) exhibited a median CIN burden of 11 altered arms per sample (IQR: 5\\u0026ndash;16), which was significantly higher than that of CIN-negative cases (n=12), all of which had a burden of 0 (Mann-Whitney U test,\\u0026nbsp;\\u003cem\\u003eU\\u003c/em\\u003e = 0, z=\\u0026minus;5.72，p\\u0026nbsp;\\u0026lt;0.001) (Figure 4). This stark contrast underscores a fundamental divergence in the degree of genomic instability within the cohort.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCo-occurrence and Mutual Exclusivity\\u003c/strong\\u003e\\u003cbr\\u003eAnalysis of recurrent arm-level events (frequency \\u0026ge;8 cases) revealed non-random patterns of co-alteration. Specifically, a co-occurring \\u0026quot;gain module\\u0026quot; was identified, involving concurrent gains of \\u003cstrong\\u003e3q, 8q, and 20q\\u003c/strong\\u003e (\\u0026phi; \\u0026ge; 0.45). Conversely, a \\u0026quot;loss module\\u0026quot; was characterized by co-deletions of \\u003cstrong\\u003e14q, 17p, and 22q\\u003c/strong\\u003e (\\u0026phi; \\u0026ge; 0.40). Notably, gains in 3q and losses in 14q demonstrated a trend of mutual exclusivity (\\u0026phi; \\u0026le; -0.35), suggesting potentially distinct underlying oncogenic mechanisms in different patient subsets (Figure 5).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIdentification of CIN Subtypes\\u003c/strong\\u003e\\u003cbr\\u003e\\u0026nbsp;Unsupervised clustering (k=3) of the arm-level alteration matrix robustly classified the malignant ascites into three distinct CIN subtypes (Figure 6):\\u003c/p\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eLow-burden Subtype:\\u003c/strong\\u003e Characterized by minimal chromosomal alterations (median burden: 2 arms; IQR: 1\\u0026ndash;4), phenotypically resembling the CIN-negative group.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eFocal Subtype:\\u003c/strong\\u003e Exhibited a moderate CIN burden (median: 9 arms; IQR: 7\\u0026ndash;12) with significant enrichment for the specific gain (3q/8q/20q) and loss (14q/17p) modules described above.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eGenome-unstable Subtype:\\u003c/strong\\u003e Displayed widespread, high-level aneuploidy (median burden: 18 arms; IQR: 15\\u0026ndash;22) with broad-spectrum gains and losses across the genome.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003eThis classification result reveals significant heterogeneity in genomic instability patterns among patients with malignant ascites.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003ePeritoneal carcinomatosis, which can be classified into primary peritoneal tumors and peritoneal metastases, is a major cause of mortality in patients with various advanced malignancies, including gastric, ovarian, colorectal, and pancreatic cancers[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].The prognosis for these patients is generally poor, with a median survival often less than one year [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Their quality of life is significantly compromised due to refractory ascites, adhesive intestinal obstruction, and other symptoms that severely affect survival and may lead to rapid deterioration[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCurrently, the diagnosis of PC primarily relies on imaging studies, serum tumor marker tests, and ascitic fluid cytological analysis. However, the detection capability of CT for PC is significantly influenced by factors such as lesion size, location, and the presence of ascites. Archer et al. reported that the sensitivity of CT for peritoneal lesions smaller than 0.5 cm is only 25%; Coakley et al. indicated that the sensitivity of spiral CT for peritoneal tumors smaller than 1 cm ranges from 25% to 50%; similarly, Koh et al. demonstrated that CT detected only 11% of colorectal cancer peritoneal metastatic nodules smaller than 0.5 cm[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]。In contrast, MRI exhibits higher sensitivity and specificity, at 92% and 85%, respectively. PET/CT shows sensitivity and specificity of 87% and 92%, respectively, though its application is limited by high economic costs[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Furthermore, the detection efficacy of PET/CT depends on the uptake of 18F-fluorodeoxyglucose (FDG) by cancer cells, a process closely associated with the expression of glucose transporter 1 (GLUT1). It is noteworthy that GLUT1 expression is often low in common histological types of peritoneal metastases, such as signet ring cell carcinoma, mucinous adenocarcinoma, and poorly differentiated adenocarcinoma[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], which partly limits the diagnostic performance of PET/CT.\\u003c/p\\u003e \\u003cp\\u003eDue to the low detection rate of conventional imaging for early peritoneal metastases, surgical exploration remains the most reliable method for assessing peritoneal tumor burden, offering high sensitivity and specificity. However, owing to its invasive nature, this approach is not suitable for early screening or repeated examinations. From a pathophysiological perspective, peritoneal fluid can carry tumor cells during intra-cavitary flow and distribute them randomly throughout the peritoneal cavity, which also provides a theoretical basis for ascitic fluid cytology[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. As a minimally invasive technique, ascitic fluid cytology has a positivity rate of approximately 46%. Its results are influenced by various factors, including the primary tumor site, number of samples submitted, detection methods, and diagnostic experience. Repeated submissions are often required to improve accuracy.\\u003c/p\\u003e \\u003cp\\u003eIn this study, a prospective study included 44 cancer patients and 6 non-cancer patients. UCAD was used to detect chromosomal instability in ascites tumor cells and showed a relatively high prevalence of CIN (72.7%). It provides evidence for the first time that CIN signaling is very abundant in the ascites of PC. The sensitivity and specificity of CIN signaling based on ascites for diagnosing PC are 90% and 70%, respectively. CIN-positive cases exhibited markedly higher arm-level alteration burden than CIN-negative cases, highlighting widespread genomic instability in malignant ascites. CIN has long been recognized as a hallmark of tumorigenesis and disease progression [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Our results extend these findings to the context of PC, supporting the notion that CIN is a driving force of clonal evolution in metastatic settings[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWith respect to recurrent events, we identified \\u003cb\\u003e3q gain\\u003c/b\\u003e and \\u003cb\\u003e14q loss\\u003c/b\\u003e as the most frequent alterations, often co-occurring with additional hotspots such as 8q+, 20q+ (for gains) and 17p\\u0026minus;, 22q\\u0026minus; (for losses). These modules suggest non-random patterns of CIN. Previous large-scale genomic studies, such as The Cancer Genome Atlas (TCGA), have reported recurrent 3q amplification in ovarian, cervical, and lung cancers, frequently harboring \\u003cb\\u003ePIK3CA, SOX2, and TERC\\u003c/b\\u003e oncogenes[\\u003cspan additionalcitationids=\\\"CR30\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Similarly, loss of 14q has been associated with deletion of \\u003cb\\u003eNDRG2\\u003c/b\\u003e and other tumor suppressors, contributing to aggressive phenotypes in gastric and colorectal cancers [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. The frequent loss of 17p observed in our study is consistent with the canonical inactivation of \\u003cb\\u003eTP53\\u003c/b\\u003e, a pivotal event in tumor progression and therapeutic resistance[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Thus, our co-occurrence modules likely reflect cooperative oncogenic events shaping malignant ascites biology. It is important to note that although copy number variation driven by CIN represents a central mechanism in tumorigenesis and cancer progression, the underlying gene regulatory networks require further experimental validation[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eInterestingly, we also observed mutual exclusivity between certain alterations, such as 3q\\u0026thinsp;+\\u0026thinsp;and 14q\\u0026minus;. Such patterns have been described in other tumor types, where mutually exclusive alterations indicate functional redundancy or alternative evolutionary trajectories[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. This suggests that CIN in ascites may follow distinct, yet convergent, genomic evolution pathways.\\u003c/p\\u003e \\u003cp\\u003eUnsupervised clustering identified three CIN subtypes: \\u003cb\\u003eLow-burden\\u003c/b\\u003e, \\u003cb\\u003eHotspot-focused\\u003c/b\\u003e, and \\u003cb\\u003eHigh-burden\\u003c/b\\u003e. Similar stratifications have been reported in ovarian carcinoma, where CIN-low tumors exhibit relative genomic stability, while CIN-high tumors demonstrate widespread arm-level alterations and poor prognosis [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Our Hotspot-focused subtype, enriched for 3q/8q/20q gains and 14q/17p losses, resembles previously described \\\"CIN-intermediate\\\" clusters in ovarian and gastric cancer, characterized by selective yet recurrent chromosomal alterations[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. These findings emphasize the heterogeneity of CIN in malignant ascites and suggest that distinct CIN subtypes may underlie differential clinical behaviors.\\u003c/p\\u003e \\u003cp\\u003eCIN has been linked to poor prognosis, chemotherapy resistance, and immune evasion[\\u003cspan additionalcitationids=\\\"CR41\\\" citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. In particular, CIN-induced micronuclei formation and cytosolic DNA sensing via the cGAS\\u0026ndash;STING pathway have been implicated in shaping the tumor immune microenvironment[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Therefore, the identification of CIN modules and subtypes in malignant ascites not only deepens our understanding of tumor biology but also highlights potential avenues for prognostic stratification and therapeutic targeting. For instance, CIN-high patients may benefit from therapeutic approaches targeting replication stress or mitotic checkpoint vulnerabilities [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e], whereas CIN-intermediate patients may be more suitable for therapies exploiting specific driver alterations within 3q or 17p regions.\\u003c/p\\u003e \\u003cp\\u003eThe present study has several limitations. First, the sample size was modest (n\\u0026thinsp;=\\u0026thinsp;50) and lacked clinical annotations such as cancer subtype or treatment history, limiting translational interpretation. Second, our analysis was restricted to arm-level alterations without gene-level resolution. Finally, single-center sampling may limit generalizability. Nonetheless, our study provides novel insights into CIN architecture in malignant ascites, warranting validation in larger, multi-center cohorts and integrative analyses with clinical outcomes.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn summary, detecting chromosomal instability in ascites using low-coverage whole-genome sequencing provides a highly sensitive (90.0%) method for diagnosing PC. We characterized the CIN landscape in malignant ascites, identifying a 72.7% CIN-positive rate, recurrent arm-level alterations (e.g., 3q+, 14q-), and distinct CIN subtypes. These findings establish ascites as a valid substrate for genomic liquid biopsy and provide a molecular framework for understanding peritoneal metastases.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003ePC\\u0026nbsp;\\u003c/strong\\u003eperitoneal carcinomatosis\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCIN\\u0026nbsp;\\u003c/strong\\u003echromosomal instability\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eUCAD\\u0026nbsp;\\u003c/strong\\u003eultrasensitive chromosomal aneuploidy detection\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLcWGS\\u0026nbsp;\\u003c/strong\\u003elow-coverage whole-genome sequencing\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCtDNA\\u0026nbsp;\\u003c/strong\\u003ecirculating tumor DNA\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCNVs\\u0026nbsp;\\u003c/strong\\u003ecopy number variations\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCBS\\u0026nbsp;\\u003c/strong\\u003eCircular Binary Segmentation\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSNVs\\u003c/strong\\u003e single nucleotide variants\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIQR\\u0026nbsp;\\u003c/strong\\u003einterquartile range\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePPV\\u0026nbsp;\\u003c/strong\\u003epositive predictive value\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNPV\\u0026nbsp;\\u003c/strong\\u003enegative predictive value\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCI\\u003c/strong\\u003e Confidence Interval\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFDR\\u003c/strong\\u003e False Discovery Rate\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e18-FDG\\u0026nbsp;\\u003c/strong\\u003e18F-fluorodeoxyglucose\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTCGA\\u0026nbsp;\\u003c/strong\\u003eThe Cancer Genome Atlas\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHWZ collected and analyzed the data and wrote the manuscript. ZWM analyzed the data. LXZ and RZW secured the ethical approval and created the figures. XPC and HBY participated in the design of the study and revised the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by the Zhejiang Clinovation Pride (Grant Number: CXTD202502006) and the Wenzhou \\u0026quot;Gazelle\\u0026quot; Clinical Innovation Team Fund.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The datasets generated during the current study are available in the GSA-Human repository, No.HRA015680.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval and consent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWritten informed consent has been obtained before the collection of ascites samples from patients, which can be used to publish this research report. This study was approved by the ethics committee of Wenzhou Central Hospital. Procedures were performed in accordance with the declaration of Helsinki. All methods were carried out according to the relevant guidelines and provisions in the declaration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNone\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBlair SL, Chu DZ, Schwarz RE. Outcome of palliative operations for malignant bowel obstruction in patients with peritoneal carcinomatosis from nongynecological cancer. 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Mol Cell. 2015; 60: 547-60.http://doi.org/10.1016/j.molcel.2015.10.040.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cancer\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcan\",\"sideBox\":\"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcan/default.aspx\",\"title\":\"BMC Cancer\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Chromosomal Instability, Peritoneal Carcinomatosis, Ascites, Liquid Biopsy, Diagnosis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8280082/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8280082/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eDiagnosing peritoneal carcinomatosis (PC) remains challenging. This study aims to evaluate the value of ascites chromosomal instability (CIN) analysis in the diagnosis of PC.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe performed low-coverage whole-genome sequencing on 50 human ascites samples. CIN was profiled using ultrasensitive chromosomal aneuploidy detection (UCAD), and its diagnostic performance was validated against pathological standards.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eCIN analysis detected peritoneal metastases with 90.0% sensitivity and 82.0% overall accuracy. In malignant ascites, 72.7% were CIN-positive, showing recurrent alterations (e.g., 3q gain, 14q loss) that formed co-occurring gain/loss modules. Unsupervised clustering identified three CIN subtypes with distinct genomic instability patterns.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eAscites CIN analysis shows promise as a highly sensitive, minimally invasive diagnostic tool for PC. The identified CIN patterns and subtypes provide insights into peritoneal metastasis biology and hold promise for clinical stratification.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Ultrasensitive Detection of Chromosomal Instability in Ascites as a Novel Diagnostic for Peritoneal Carcinomatosis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-16 16:40:53\",\"doi\":\"10.21203/rs.3.rs-8280082/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-02T12:12:53+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-26T01:50:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-24T12:13:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"76476323785186575644693859665820558484\",\"date\":\"2026-02-19T22:21:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-19T06:42:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-18T22:09:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-18T14:36:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"267572422741794340612628823581314562357\",\"date\":\"2026-02-18T13:36:51+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"99844042538252295832427076775272075820\",\"date\":\"2026-02-16T03:16:22+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"10914733468157005646025965193097554263\",\"date\":\"2026-02-12T01:27:05+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"46238791169816792875500216236134654350\",\"date\":\"2026-02-11T08:37:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"293915769375824758549888751001847099006\",\"date\":\"2026-02-11T01:04:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"190092266203336282542376442766018036280\",\"date\":\"2026-02-10T20:08:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"30884223207440925201613917409796051087\",\"date\":\"2026-02-10T18:13:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-10T18:08:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-14T06:33:22+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-01-12T03:20:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-01-07T15:08:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Cancer\",\"date\":\"2026-01-07T15:03:48+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-cancer\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcan\",\"sideBox\":\"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bcan/default.aspx\",\"title\":\"BMC Cancer\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"35604f22-7701-41df-9ca8-75069a9d1618\",\"owner\":[],\"postedDate\":\"February 16th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-16T16:40:53+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-16 16:40:53\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8280082\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8280082\",\"identity\":\"rs-8280082\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}