Discovery of Novel Serum Peptide Biomarkers for Cholangiocarcinoma Recurrence Through MALDI-TOF MS and LC-MS/MS Peptidome Analysis

Discovery of Novel Serum Peptide Biomarkers for Cholangiocarcinoma Recurrence Through MALDI-TOF MS and LC-MS/MS Peptidome Analysis | 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 Article Discovery of Novel Serum Peptide Biomarkers for Cholangiocarcinoma Recurrence Through MALDI-TOF MS and LC-MS/MS Peptidome Analysis Vasin Thanasukarn, Piya Prajumwongs, Nattha Muangritdech, Watcharin Loilome, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5399896/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Cholangiocarcinoma (CCA) is an aggressive cancer originating from bile duct epithelial cells, with a high rate of recurrence following surgical resection. Recurrence is categorized as early linked to aggressive tumor biology than late recurrence. This study aimed to identify novel peptide mass fingerprints (PMFs) and potential biomarker panels in the serum of CCA patients with early and late recurrence using mass spectrometry. Serum samples of 81 CCA patients were analyzed using MALDI-TOF MS and LC-MS/MS, with statistical analysis correlating peptide profiles with clinical outcomes like disease-free survival (DFS) and overall survival (OS). A 365-day DFS cutoff effectively distinguished early from late recurrence, with early recurrence linked to poorer survival outcomes. The PMFs from MALDI-TOF MS differentiated recurrence types based on specific mass signatures. LC-MS/MS analysis identified 95 peptides associated with cancer progression in early recurrence and 60 in late recurrence. Distinct protein associations were found: ATR, POLA1, BLM, SP100, and PPP1R15A for early recurrence, and SERPINA1, TGFB2, SERPING1, and CAD for late recurrence, with strong interactions with chemotherapeutic drugs. This study successfully demonstrated the use of PMFs for rapid discrimination between early and late recurrence in CCA and identified potential serum peptide biomarkers to improve accuracy in recurrence classification. Biological sciences/Cancer/Tumour biomarkers Health sciences/Biomarkers/Diagnostic markers Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers Health sciences/Medical research/Biomarkers Biological sciences/Biochemistry/Peptides Biological sciences/Biological techniques/Proteomic analysis Biological sciences/Biological techniques/High throughput screening Biological sciences/Molecular biology/Proteomics/Protein protein interaction networks Biological sciences/Biochemistry/Proteomics Biological sciences/Biochemistry/Proteomics/Protein protein interaction networks Biological sciences/Biological techniques/Mass spectrometry Cholangiocarcinoma recurrence peptidome peptide biomarker MALDI-TOF MS LC-MS/MS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cholangiocarcinoma (CCA) is an aggressive malignancy arising in the biliary tract, often diagnosed at advanced stages due to its asymptomatic early phases. Surgical resection followed by adjuvant chemotherapy is the primary curative treatment; however, outcomes of poor survival remain 1 . A significant challenge in managing CCA is disease recurrence after initial treatment, categorized as early or late based on the onset post-surgery. Early recurrence occurs within one year and affects 20 to 65 percent of patients. Early recurrence is often linked to aggressive tumor biology, poor differentiation, and lymphovascular invasion. Late recurrence, is associated with slow-growing tumor cells or the patient's immune response 2 – 6 . Therefore, accurately predicting early recurrence for each regimen in individual patients may guide the selection or modification of adjuvant treatment plans. Although carcinoembryonic Antigen (CEA) and Cancer Antigen 19 − 9 (CA 19 − 9) have been utilized in screening, diagnosis, treatment monitoring, recurrence detection, and disease progression for CCA, they also have several limitations, including specificity to cancer types, overlap with benign conditions, limited diagnostic values, inconsistent levels of biomarker, lack of established cut-off values and limited role in early detection 7 , 8 . Thus, multiple biomarkers or biomarker panels are discussed as potential tools for improving the diagnosis and management of CCA. These approaches aims to enhance specificity and sensitivity beyond what individual biomarkers like CEA and CA 19 − 9 can provide 9 , 10 . Peptide biomarkers, small proteins or peptides detectable in biological samples, play a pivotal role in diagnostics by providing insights into physiological and pathological conditions. They are crucial in diagnostics, as they can indicate the presence or progression of diseases, monitor therapeutic responses, or predict disease outcomes. In medical practice, peptide biomarkers are increasingly used to enhance early detection, improve diagnostic accuracy, and support personalized treatment approaches. Their specificity and capacity to reflect molecular-level changes in biological processes underpin their utility. Prior research has highlighted the critical role of peptide biomarkers in cancer studies 11 , 12 . Numerous studies have identified differentially expressed peptides across various cancers, contributing to the development of diagnostic tools and therapeutic strategies. For example, peptide biomarkers have significantly improved early detection, staging, and the monitoring of treatment responses and recurrence in cancers such as prostate, breast, and ovarian 13 – 15 . These findings demonstrate the potential of peptidome approaches to drive advancements in personalized medicine and cancer management, with peptide biomarkers offering substantial promise for enhancing detection, diagnosis, and disease monitoring. Based on information above, serum peptidome in CCA patients with early and late recurrence has yet to be fully explored. This study aims to identify novel peptide mass fingerprints (PMFs), peptide clusters, and potential biomarkers in the serum of CCA patients with early and late recurrence. We investigated disease-specific peptide profiles by matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry (MALDI-TOF MS) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, we examined the associations between these peptides and chemotherapy drugs. We anticipated that serum peptide biomarkers could potentially aid in prognosis and inform treatment strategies for CCA. Results Clinical characteristics and survival analysis of cholangiocarcinoma (CCA) patients The clinical characteristics in CCA patients were showed in Table 1 . We performed cut-off value for categorizing CCA patients with recurrent status (early and late recurrence) using 365 days according to previous publications. By CCA patients had DFS ≥ 365 days that were categorized early recurrence (33%), while CCA patients had DFS < 365 days that were categorized late recurrence (67%). Table 1 Univariate and multivariate analysis of the survival of CCA patients Univariate Multivariate Variable N = 81 (%) MST (month) (95%CI) HR (95%) p -value HR (95%) p -value Age (Median) <63 45 (55%) 19.70 (13.64–25.76) 1 - ≥63 36 (45%) 19.70 (14.11–25.29) 1.27 (0.77–2.09) 0.355 - - Gender Male 50 (62%) 26.80 (13.83–39.77) 1 - Female 31 (38%) 18.20 (15.23–21.17) 1.65 (0.963–2.81) 0.065 - - Tumor location iCCA 49 (60%) 19.70 (16.37–23.03) 1 - pCCA 28 (35%) 21.60 (10.13–33.07) 0.66 (0.38–1.16) 0.149 - - dCCA 4 (5%) 5.20 (0-44.60) 1.203 (0.426–3.39) 0.727 - - Surgical margin (R) R0 46 (57%) 22.90(11.24–34.56) 1 1 R1 35 (43%) 15.90 (12.47–19.33) 1.70 (1.02–2.82) 0.039* 1.42 (0.85–2.38) 0.185 Histological differentiation Well 65 (80%) 22.10 (7.200–27.00) 1 1 Moderately/Poorly 16 (20%) 16.60 (8.76–24.44) 1.89 (1.045-3.40) 0.031* 1.83 (1.01–3.34) 0.047 Lymph node status (N) N0 47 (58%) 22.9 (14.57–31.23) 1 - N1 34 (42%) 15.9 (13.59–18.22) 1.55 (0.93–2.57) 0.089 - - TNM stage Early (0-II) 21 (26%) 35.8 (2.70–74.30) 1 1 Late (III-IV) 60 (74%) 16.6 (13.17–20.03) 2.21 (1.19–4.11) 0.01* 1.61 (0.85–3.04) 0.142 Recurrent status Early 37 (33%) 9 (6.68–11.15) 7.06 (3.96–12.58) < 0.001* 6.36 (3.51–11.35) < 0.001* Late 54 (67%) 32.8 (19.77–45.80) 1 1 n, Number; CI, Confidence interval; 5-YSR, 5-year survival rate; MST, median survival time; HR, hazard ratio; dCCA, distal cholangiocarcinoma; iCCA, Intrahepatic cholangiocarcinoma; pCCA, perihilar cholangiocarcinoma; TNM, tumor node metastasis from 8th AJCC/UICC staging system. * Indicates a p -value < 0.05 (statically significant) Survival analysis using the Log rank test and multivariate analysis through Cox regression revealed that factors such as positive surgical margin, moderately and poorly differentiated differentiation, late staging, and early recurrence were significantly associated with shorted survival rates. Specifically, multivariate Cox regression analysis indicated that surgical margins, histological differentiation, cancer staging, and recurrent status were significant predictors of survival compared to their referent categories. Notably, early recurrence displayed a markedly high hazard ratio as 6.36 folds, p < 0.001 when compared with late recurrence, underscoring its critical impact on patient prognosis (Table 1 ). Consequently, this study prioritized recurrence status to enable further peptidome analysis using mass spectrometry. [Table 1 about here] Serum peptide barcode of CCA patients with early and late recurrence The criteria for categorizing patients with recurrent CCA were based on a time frame of 365 days post-surgery. Specifically, patients who experienced recurrence before 365 days were classified into the early recurrence group, comprising 34 individuals. In contrast, those with recurrence occurring at 365 days or later were assigned to the late recurrence group of 47 individuals. Following this categorization, serum samples from CCA patients underwent peptide profiling using MALDI-TOF MS. The results revealed that the peptide patterns in the serum of early recurrence patients (red spectrum) differed significantly, with three unique peptides observed at m/z 2697.984489, 3041.741068 and 4288.621730 when compared to those from late recurrence patients (green spectrum) (Fig. 1 A). Subsequently, the peptide profiles from two groups of patients were subjected to statistical analysis using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). OPLS-DA generated a clear separation between early and late recurrent patients indicated that potential peptide biomarkers associated with the differences in patient conditions. (Fig. 1 B). [Fig. 1 . about here] Upon obtaining PMFs of serum peptides to distinguish between early and late recurrence in CCA patients, MALDI-TOF MS provides rapid screening for stratification of recurrent status. Additionally, to improve the power and accuracy of detection between early and late recurrence in CCA patients, we investigated peptide-base biomarkers through LC-MS/MS analysis. We aimed to identify potential peptide biomarkers for integration with PMFs from MALDI-TOF MS, to improve the accuracy and precision in diagnosing recurrence in CCA patients. Identification of differentially expressed peptides in plasma of CCA patients with early and late recurrence Serum peptides were analyzed using LC-MS/MS, identifying 5,798 proteins. A Venn diagram illustrated the overlap and differences between early and late recurrence groups, with 1,747 shared peptides and 2,327 and 1,724 unique peptides exclusive to early and late recurrence, respectively. A Principal Component Analysis (PCA) revealed distinct patterns, with Component 1 effectively separating the two groups. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) further confirmed clear separation between the groups, identifying key discriminating peptides. VIP score analysis revealed 1,025 significant peptides, with the top 15 highlighted. Volcano plot analysis, using a p 2 threshold (Fig. 2 .), identified 155 peptides, 95 upregulated in early recurrence and 60 in late recurrence, listed in supplementary table 1 and 2. [Fig. 2 . about here] Network analysis of serum peptides in CCA patients with early and late recurrence A total of 155 peptides were filtered through a stringent screening process, with 95 peptides identified in early recurrence and 60 peptides in late recurrence. Subsequently, candidate peptides from both groups were analyzed for protein-chemical interactions using the STITCH database. Common chemotherapeutic drugs, including Gemcitabine, Cisplatin, Capecitabine, Oxaliplatin, and 5-Fluorouracil (5-Fu) widely used in the treatment of CCA, were incorporated into the interaction list to predict associations and computational interactions. This analysis aimed to present a comprehensive network of interactions between the candidate peptides and these chemotherapeutic drugs (Table 2 ). Table 2 Candidate peptides involved in biological process and molecular function in early and late recurrences Gene name Protein ID Protein name Peptide sequence Biological Process Molecular function ATR Q13535 Ataxia telangiectasia and Rad3-related protein ASHEPFPGHWA0 DNA damage response, DNA repair and cell proliferation serine/threonine kinase activity, DNA binding BLM P54132 RecQ-like DNA helicase BLM EFDDDDYDTDF- VPPS0 DNA damage response, DNA double-strand break processing, DNA repair 3'-5' DNA helicase activity, DNA helicase activity POLA1 P09884 DNA polymerase alpha catalytic subunit DIDGVFKSLLL- LKKKKYA0 DNA repair, Double-strand break repair via nonhomologous end joining, DNA synthesis involved in DNA repair Chromatin binding, DNA binding, DNA replication origin binding SP100 P23497 SP100 nuclear antigen SHDLQRMFTE- DQGVDDR DNA damage response, signal transduction by p53 class mediator DNA binding, DNA-binding transcription factor activity, RNA polymerase II-specific PPP1R15A O75807 Protein phosphatase 1 regulatory subunit 15A DSDSGSDEEEG- EAEASSS0 DNA damage response, Regulation of cell cycle, Apoptotic process protein kinase binding, protein phosphatase 1 binding, protein phosphatase activator activity SERPINA1 P01009 Alpha-1-antitrypsin EAIPMSIPPEV- KFNKPFVF0 Blood coagulation Protease binding, Serine-type endopeptidase inhibitor activity SERPING1 P05155 Plasma protease C1 inhibitor FVLWDQQHKF- PVF0 Blood circulation, Blood coagulation, Innate immune response Serine-type endopeptidase inhibitor activity TGFB2 P61812 Transforming growth factor beta-2 proprotein RLQNPKARVP- EQ Activation of protein kinase activity, Cell migration, Cell morphogenesis Cytokine activity, Growth factor activity, Protein homodimerization Activity, Signaling receptor binding CAD P27708 Multifunctional protein CAD IDRWFLHRMK0 'de novo' pyrimidine nucleobase biosynthetic process, Cellular response to epidermal growth factor stimulus Aspartate carbamoyltransferase activity, ATP binding, Carbamoyl-phosphate synthase (ammonia) activity [Table 2 . about here] In early recurrent patients, the network analysis performed using STRING revealed intricate interactions between proteins and chemotherapy drugs. The results showed the peptide interaction network of SP100 (Nuclear autoantigen Sp-100), ATR (Serine/threonine-protein kinase ATR), POLA1 (DNA polymerase alpha catalytic subunit) and PPP1R15A (Protein phosphatase 1 regulatory subunit 15A) showed a strong relationship with the chemotherapy drug, Cisplatin, while we also found less of BLM (RecQ-like DNA helicase BLM) with Cisplatin. In addition, ATR, BLM and CEP164 (Centrosomal protein of 164 kD) also with their predicted functional partner CHEK1 (Checkpoint kinase 1) which had strong associations with serval chemotherapeutic drugs such as Gemcitabine, Cisplatin and 5-Fu. Additional to peptide-chemotrophic drugs interactions, we also found peptide-peptide interaction which related with signaling pathways to promote cancer progression, such as cell proliferation, angiogenesis, tumor microenvironment and metastasis. Main nodes including SP100, BLM and ATR proteins have been reported to play a crucial role in numerous pathways of progression as shown in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway maps and publications. "In this study, SP100 showed a strong association with SUMO1 (Small Ubiquitin-Like Modifier 1), which is predicted to be a functional partner. SUMO1 is an upstream regulator of several proteins involved in signaling pathways identified in this analysis including MAP3K1 (Mitogen-activated protein kinase kinase kinase 1), ZFHX3 (Zinc finger homeobox protein 3)/EHBP1 (EH domain-binding protein 1-like protein 1) signaling, HNRNPH3 (Heterogeneous nuclear ribonucleoprotein H3; hnRNP H3), BLM and UBTF (Nucleolar transcription factor 1)/NCL (Nucleolin) signaling. For BLM it exhibited strong interactions with ATR/CEP164/CEP70 (Centrosomal protein of 70 kD) which play roles in cell cycle checkpoint, DNA damage response and DNA repair, thereby protecting against cell death and sustaining cancer cell survival. In addition, BLM also interacted with SETX (Probable helicase senataxin)/ predicted POLR2A (DNA-directed RNA polymerase II subunit RPB1) interaction /INTS5 (Integrator complex subunit 5) as well as SUPT5H (Transcription elongation factor SPT5) which plays roles in RNA processing, transcription regulation, and DNA damage response (Fig. 3 ). Additionally, non-interaction nodes were identified that have oncogenic roles in cancer progression and recurrence. A total of 95 peptides were reported to have oncogenic functions, as listed in supplementary table 1 . [Fig. 3 . about here] In late recurrent patients, we found one direct peptide-chemotherapeutic drug and one indirect peptide-chemotherapeutic drug. SERPINA1 (Alpha-1-antitrypsin) had strong association with Cisplatin, while CAD (Carbamoyl phosphate synthetase, aspartate transcarbamylase, and dihydroorotase) showed indirect relationship with Cisplatin, 5-Fu and Capecitabine through strong interaction with predicted partner DPYD (Dihydropyrimidine Dehydrogenase) and medium interaction predicted partner TYMS (Thymidylate Synthase). It had that strong association with Gemcitabine, Cisplatin, Capecitabine, and 5-Fluorouracil (5-Fu). In addition, we also found strong peptide-peptide interaction including SERPINA1, SERPING1 Plasma protease C1 inhibitor and TGFB2 (Transforming Growth Factor Beta 2) which play roles in tumor growth and metastasis. Moreover, we found that CAD was a central node or hub of strong interaction with SLC23A3 (Solute carrier family 23 member 3) which has an essential role in the transport of certain molecules across cell membranes. Predicted partners, including DPYSL3 and 4 (Dipeptidyl Peptidase-Like 3 and 4), DPYS (Dipeptidyl Peptidase I), DPYD (Dihydropyrimidine Dehydrogenase), CRMP1 (Collapsin Response Mediator Protein 1), CPS1 (Carbamoyl-Phosphate Synthetase 1), DHODH (Dihydroorotate Dehydrogenase) and TYMS have functions in cellular metabolism. In addition, we also found a strong relationship of CXXC1 (Receptor-transporting protein 5) and SETD1B (Histone-lysine N-methyltransferase) (Fig. 4 ). Our result also showed non-interaction nodes that have been reported in several publications in cancer progression as shown in a supplementary table 2. [Fig. 4 about here] Discussion Our results were based on cut-off values using median of DFS or about 365 days after surgical treatment. This consistent use of a 365-day threshold underscores its potential as a standard marker for assessing recurrence risk. Survival analysis and Cox regression further illustrate that early recurrence was associated with significantly shorter survival compared to late recurrence. In addition, we have identified that early recurrence was an independent factor contributing to poor survival outcomes. This cut-off value aligned with findings from several previous studies. They established that this time frame is crucial for understanding recurrence patterns across various cancer types, including bile duct 4 pancreatic 5 and colorectal cancers 6 . The consistency of these findings across multiple studies highlights the importance of early detection and intervention strategies for patients at risk of recurrence. Implementing routine surveillance protocols that focus on this critical time frame could lead to improved patient management and outcomes. The use of CEA and CA 19 − 9 as biomarkers for recurrence is currently being debated due to several issues, especially their limited specificity 7 , 8 . Thus, biomarker panels or multiple biomarkers are essential to improve accuracy and specificity predicting these outcomes, providing a valuable tool for identifying patients at higher risk for early recurrence 9 , 10 . Generally, cancer recurrence is considered a result of the cancer progression. Typically, this progression involves the production of peptides and proteins that promote cancer development. These molecules are secreted into the bloodstream to drive tumor growth, immune evasion, metastasis, and intercellular communication 16 . Many secreted peptides and proteins serve as biomarkers, indicating the presence or progression of cancer, making them valuable for peptide- and protein-based biomarker detection 17 . In practically, MALDI-TOF MS has been reported as a useful tool for diagnosis and prognosis in several abnormalities, as the peptide signature in serum or PMFs showed specific patterns for several disease, especially cancers 15 , 18 – 20 . To explore peptide patterns of recurrent cancer, our study provides novel evidence from serum peptidome to categorize early and late recurrent status using PMFs through MALDI-TOF MS. Our finding showed that the peptide pattern of early recurrence was markedly different from those patterns in late recurrence. Moreover, we also found peptide signatures that only appeared in early recurrence at m/z 2697.984489, 3041.741068 and 4288.621730. These results showed that PMFs MALDI-TOF MS could be useful to discriminate between early and late recurrence. Our study was consistent with previous reports on bile duct cancer, also known as CCA. Our study has revealed that PMFs via MALDI-TOF in the serum of 92 bile duct cancer patients at University College Hospital, UK, compared with healthy volunteers, had distinct differences in the peptide profiles of bile duct cancer patients. Analysis of peptide positions on the combined spectrum distinguished eight peptides with statistically significant differences in peak area under the curve, specifically at m/z values of 887.2, 1263.7, 1350.8, 2082.1, 2210.3, 2554.5, 2903.3, and 5805.0 18 . Additionally, in 2023, a study on PMFs in patients with cervical cancer at various stages found clear differences in peptide profiles among the groups, which included healthy volunteers, precancerous lesions, and cervical cancer stages I, II, and III. 15 . Based on previous reports, PMFs via MALDI-TOF could not only differentiate between healthy individuals and cancer patients but also stratify the aggressiveness of the disease by stage. This provides strong evidence supporting our findings in using PMFs to distinguish between early and late recurrent CCA patients. Therefore, due to its high efficiency and sensitivity in detecting PMFs, MALDI-TOF could be a primary choice for rapid screening of disease abnormalities, especially recurrent status of cancer. For clinical advantage, rapid diagnosis enables prompt treatment and ensures that improve the treatment plans for patients. However, in addition to the speed of diagnosis, the accuracy of the diagnosis is crucial factor to consider in clinical application. While MALDI-TOF facilitates rapid screening, it is important to recognize that in some patient cases, ambiguous peptide patterns might overlap. Consequently, the identification of biomarkers incorporates with PMFs using MALDI-TOF MS is essential for enhancing the accuracy of disease screening and diagnosis 21 . Therefore, to identify sequence of peptide biomarker in serum, LC-MS/MS was undertaken in our study. The identified candidate peptide-based biomarkers in both groups were separately analyzed for peptide-chemotherapeutic drug interaction network using the STITCH database. In early recurrence, we found that there were 16 peptides and 5 peptides having peptide-peptide and-drug interaction, respectively. This study proposed, ATR, POLA1, BLM, SP100 and PPP1R15A (GADD34) as major candidate peptides that had a significant impact in the interaction network, while the remaining peptides also served as co-biomarkers for this condition (Fig. 3 . and supplementary table 3). This study identified a set of proteins involved in DNA stress response, DNA repair, and the maintenance of genomic instability, which are key features of cancer progression and chemoresistance. ATR (Ataxia Telangiectasia and Rad3-related protein) is protein kinase family, phosphoinositide 3-kinase–related kinase (PIKKs), a key protein involved in the cellular response to DNA damage. It plays a critical role in the DNA damage response (DDR) by detecting DNA replication stress and activating repair pathways 22 . ATR also has non-canonical roles in cancer migration and invasion 23 , 24 . In addition, high levels of ATR were associated with poor survival of cancer patients 24 . Inhibition of ATR has been reported as a target cancer treatment 22 , 24 , 25 . ATR is also reported co-interaction with POLA1 in DNA replication, the cell cycle and involving DNA repair for maintaining genome stability 26 . A previous study has indicated a potential role for CEP164 in ATM/ATR-mediated DNA damage response (DDR) and UV-induced nucleotide excision repair pathways 27 . ATR function in regulated DNA damage response and repair processes has been reported to be mediated through UBTF, a multifunctional architectural protein. UBTF is a multifunctional architectural protein containing multiple HMG boxes or the nucleolar proteins. It has been found that the Pol I transcription factor UBTF plays a dual role in regulating both Pol I and Pol II-mediated transcription. UBTF has also been reported to participate in DNA damage and repair processes, acting through mediators of the ATR/ATM-regulated DNA damage response 28 . Additionally, UBTF is involved in the cellular response to growth factor stimulation and can regulate cancer progression through the MAPK/ERK signaling pathway 29 . In addition, UBTF also has been reported to interact with NCL to be nucleolar proteins not only in genotoxic stress sensing but wound healing also 30 . BLM is a DNA helicase essential for maintaining genomic stability. In cancer, various mutations can disrupt BLM function, leading to genomic instability and promoting cancer progression. Under these conditions, BLM aids cancer cell survival by supporting mechanisms that adapt to the genomic stress 31 . In addition, BLM has been reported to correlate with malignant progression in pancreatic adenocarcinoma 32 . Furthermore, BLM has been reported as downstream of ATR signaling via phosphorylation at Thr99 and Thr122 33 . Cohen S et al . have revealed that BLM and SETX are recruited to transcription-coupled DNA double-strand breaks (DSBs) during DSB repair 34 . SETX is involved in various processes related to genome integrity, transcription, RNA metabolism, and DNA damage repair 35 . SP100 is a nuclear autoantigen that plays a role in several cellular processes. Several studies have reported that SP100 has tumor suppressive functions, and some studies ahve suggested dual-functions in cancer suppression and cancer progression depended on cancer types. In pancreatic adenocarcinoma, SP100 has been reported to be associated with poorer survival and adverse clinical features 36 . A study on glioma demonstrated that elevated expression of the Speckled Protein (SP) family, including SP100 and SP140, were associated with poorer prognosis in glioma patients. Furthermore, the SP family contributes to glioma progression through the TRIM22/PI3K/AKT signaling pathway 37 . The functions of SP100 in CCA progression is not well understood. Our results for the first time have found that SP100 was overexpressed in early recurrent CCA patients. PPP1R15A (GADD34) is a regulatory subunit of protein phosphatase 1. For HCC, PPP1R15A could promote immunosuppressive in generating tumor micro environment and progression as well as high expression of PPP1R15A associated with poor clinical outcomes in HCC 38 . In early recurrent patients, a total of 5 of 16 peptides, including, ATR, BLM, POLA1, SP100 and PPP1R15A, were directly associated with chemotherapeutic drugs for CCA treatment, Cispatin, while CEP167 showed indirect interactions with three chemotherapeutic drugs, namely, Gemcitabine, Cisplatin and 5-Fu as demonstrated in protein-chemical interaction network (Fig. 3 ). In late recurrence, we found that there were 5 peptides and 2 peptides having peptide-peptide and-drug interaction, respectively. Our results showed that SERPINA1, TGFB2, SERPING1 and CAD were major candidate peptides that had a significant impact in the interaction network, while the remaining peptides also served as co-biomarkers for this condition (Fig. 4 . and supplementary table 4). SERPINA1, also known as alpha-1 antitrypsin (AAT), is a member of the serpin (serine protease inhibitor) superfamily. Its primary function is to inhibit serine proteases, particularly neutrophil elastase, which plays a significant role in inflammatory processes. Elevated levels of SERPINA1 have been reported in CCA and are associated with poorer survival outcomes and more advanced tumor staging. Furthermore, high SERPINA1 expression is linked to enriched pathways related to the complement system and extracellular matrix interactions, highlighting its potential role in the tumor microenvironment and cancer progression 39 . In STICTH database, SERPINA1 has a strong association with TGFB2 and SERPING1. TGFB2 is a member of the transforming growth factor-beta (TGF-β) which plays roles in immunosuppressive tumor microenvironment and promote epithelial-to-mesenchymal transition (EMT), contributing to cancer cell migration and invasion 40 . While, SERPING1 is a member of the serpin (serine protease inhibitor) superfamily like SERPINA1. It encodes a highly glycosylated plasma protein that has a critical role in regulating the complement cascade and the immune response. Synthesized in the liver, SERPING1 is important for managing several physiological processes, including complement activation, blood coagulation, fibrinolysis, and the generation of kinins. Diseases linked to SERPING1 include hereditary angioedema and partial deficiency of complement components 41 . Currently there are no studies relating to SERPINA1, TGFB2 and SERPING. CAD (Carbamoyl-phosphate synthetase 2, Aspartate transcarbamoylase, and Dihydroorotase) is a multifunctional enzyme involved in the first three rate-limiting steps of pyrimidine nucleotide synthesis. It plays a central role in the production of nucleic acids, active intermediates, and cell membranes. Dysregulation of CAD-related pathways or mutations in CAD are linked to cancer, neurological disorders, and inherited metabolic diseases. The de novo synthesis of pyrimidine nucleotides provides crucial precursors for various growth-related processes in higher eukaryotes. It is involved in the production of activated intermediates, such as pyrimidine sugars, and contributes to the synthesis of polysaccharides and phospholipids. One key product of CAD, UDP, serves as a precursor for UDP-sugar intermediates essential for UDP-dependent glycosylation and post-translational protein modifications. Disruptions in UDP-nucleotide sugar metabolism, including UDP-glucose pyrophosphorylase activity, have been implicated in cancer progression, particularly in pancreatic and breast cancers, by altering cancer cell glycosylation. CAD is regulated by post-translational phosphorylation by MAPK/cAMP-Dependent PKA/PKC and PI3K-AKT-mTORC1-S6K1 Pathways 42 . CAD was enriched in a set of cancer types (liver cancer, breast cancer, colon cancer, etc.) with poor clinical outcomes by using on The Cancer Genome Atlas and Gene Expression Omnibus datasets. In addition, late recurrent patients, a total of 2 of 5 peptides, including, SERPINA1 was directly associated with chemotherapeutic drugs for CCA treatment, Cisplatin, while CAD was showed as a hub of interactions to all chemotherapeutic drugs through DHODH, DPYD and TYMS (Fig. 4 ). This interaction should be validated in further investigations. According to PMFs using MALDI-TOF MS and peptides-base biomarkers of CCA patients with recurrence. these results hold potential for development into PMFs and biomarker panels, enabling the precise classification of disease severity. Moreover, our results provide a foundation for future research aimed at developing personalized treatment strategies based on recurrence timing and highlight the role of specific rapid screening by PMFs and peptide biomarker panel in guiding therapeutic decisions to improve patient outcomes (Fig. 5 ). [Fig. 5 about here] In conclusion, our findings showed that early recurrence was an independent prognostic factor for poor outcomes in CCA patients, emphasizing the necessity for early detection and intervention strategies. Serum analysis through peptidome, utilizing rapid screening of PMFs via MALDI-TOF MS, coupled with peptide-based biomarker panel identified by LC-MS/MS, enhanced diagnostic accuracy. This approach provides a strong foundation for developing precise personalized treatment strategies, with the potential to improve CCA patient management and predict clinical outcomes. Materials and Methods Ethics approval and consent to participate This study was conducted based on the principles of Good Clinical Practice, the Declaration of Helsinki, and national laws and regulations about clinical studies. In addition, informed consent was obtained from all patients. All processes of this study were accepted and approved by the Khon Kaen University Ethics Committee for Human Research under the reference number HE661318. Population and Sample Group This research was a single-center study conducted as a retrospective-prospective analytical observational study involving a sample of 81 patients diagnosed with cholangiocarcinoma. Data were collected retrospectively from the medical records at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, including clinical data of patients from January 1, 2017, to December 31, 2021. Serum samples were obtained from the biobank at the Cholangiocarcinoma Research Institute, Khon Kaen University. Prognostic factors were collected using a retrospective data collection form from the patient medical records, utilizing the ISAN Cohort database from the Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University. The data collected included age at diagnosis, gender, histological confirmation, tumor size, cancer grade, cancer staging, surgical margin, lymph node metastasis, lympho-vascular invasion, histological grade, and chemotherapy received. Clinical Outcome Follow-up The follow-up period for patients with cholangiocarcinoma extended from the date of surgery for at least 5 years, starting from January 1, 2017. All causes of death were monitored through a life status verification from the database of the Ministry of Interior, and additional data were collected from medical records documented by physicians. Typically, after treatment, patients were scheduled for follow-up at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, every 6 months for at least 5 years. The variables studied included recurrence, 5-year survival, overall survival (OS), and disease-free survival (DFS). Sample Collection and Serum Preservation For serum sample collection from patients with cholangiocarcinoma prior to surgical treatment, blood was drawn from a vein (venipuncture) with a volume of 5 milliliters into a clot blood tube. It was ensured that clot formation was complete before centrifugation. Serum was then separated from red blood cells using a centrifuge at 3,000 – 3,500 RPM at 4 °C for 10 minutes. The serum was aspirated and aliquoted into 1 microliter portions in Eppendorf tubes to avoid repeated thawing of samples, and then stored at -80 °C in the biobank of the Cholangiocarcinoma Research Institute, Khon Kaen University, until further analysis. protein quantification was performed using the Lowry assay. Peptide Barcode Analysis Using MALDI-TOF MS Serum samples were mixed with a matrix solution (MALDI solution: α-cyano-4-hydroxycinnamic acid (CHCA) in 50% acetonitrile (ACN) with 0.1% trifluoroacetic acid) at a sample-to-matrix ratio of 1:5. The resulting mixture was applied onto a MALDI target plate (MTP 384 ground steel, JEOL, Japan), with each sample spotted 30 times on the plate. The plate was allowed to dry at ambient temperature before analysis using the JMS-S3000 SpiralTOF (JEOL, Japan) in linear positive mode, focusing on the detection of peptide barcodes with molecular weights between 1,000 and 10,000 Da. Each sample was subjected to 1,500 laser shots. For this study, samples were categorized into a training set (collected from 2017 to 2023) and a calibration set (collected from 2024 to 2026). External calibration was conducted using peptides with known mass-to-charge ratios (m/z), including Angiotensin II (m/z = 1,046), P14R (m/z = 1,533), human ACTH fragment 18–39 (m/z = 2,465), bovine insulin oxidized B chain (m/z = 3,465), and bovine insulin (m/z = 5,731). Peptidomics Analysis in Serum Using LC-MS/MS Serum samples were purified using C18 ZipTip and analyzed for peptide content via LC-MS/MS on the Q-TOF Impact II™ system. Specifically, one microliter of peptide digests was enriched on a µ-Precolumn (300 µm i.d. × 5 mm, C18 Pepmap 100, 5 µm, 100 Å; Thermo Scientific, UK) and subsequently separated on a 75 µm i.d. × 15 cm column packed with Acclaim PepMap RSLC C18, 2 µm, 100 Å (nanoViper, Thermo Scientific, UK). The C18 column was maintained at 60 °C in a thermostatted oven. Solvents A and B, containing 0.1% formic acid in water and 0.1% formic acid in 80% acetonitrile, respectively, were used to elute peptides with a gradient of 5–55% solvent B over 30 minutes at a constant flow rate of 0.30 µL/min. Electrospray ionization was performed at 1.6 kV using the CaptiveSpray, with nitrogen as the drying gas at a flow rate of approximately 50 L/h. Collision-induced dissociation (CID) was conducted with nitrogen as the collision gas, and both MS and MS/MS spectra were acquired in positive-ion mode at a frequency of 2 Hz over an m/z range of 150–2200, with collision energy adjusted to 10 eV according to m/z. Each sample was analyzed in triplicate, and MaxQuant version 2.5.0.0 (Tyanova et al., 2016) was used for peptide quantification and sequencing, employing the Andromeda search engine to match MS/MS spectra against the Uniprot Homo sapiens database. Label-free quantification was conducted using MaxQuant's standard parameters: a maximum of two missed cleavages, a mass tolerance of 0.6 Da for the main search, and trypsin as the digestion enzyme. Carbamidomethylation of cysteine was set as a fixed modification, while methionine oxidation and N-terminal acetylation were included as variable modifications. Protein identification required peptides with a minimum length of seven amino acids and at least one unique peptide. Proteins were considered identified if they contained at least two peptides, including at least one unique peptide. The protein false discovery rate (FDR) was controlled at 1%, estimated using reverse-sequence searches, with a maximum of five modifications allowed per peptide for data analysis. Bioinformatics Analysis of Peptidomics Data Visualization and statistical analysis of the LC-MS data, including Principal Component Analysis (PCA), differential analysis (one-way ANOVA, volcano plots, boxplots, and heatmaps), were performed using MetaboAnalyst version 6.0, applying a significance threshold of P < 0.05 (Pang et al., 2022). Functional analysis of proteins containing identified peptides was conducted with the Panther database (Mi et al., 2019) and ShinyGO (Ge et al., 2020). Additionally, associations between peptides, their source proteins, and related proteins or chemicals were investigated using the STITCH database (Szklarczyk et al., 2016). The overall survival (OS) was calculated using the Kaplan-Meier method, where disease-free survival was defined as the time from surgery to recurrence, and overall survival was defined as the time from surgery to death. Patients who survived beyond the study period had their median DFS, median survival time, and survival rates calculated with a 95% confidence interval. Comparisons between groups were analyzed using the Log-rank test, while univariate and multivariate analyses to identify prognostic factors were performed using Cox regression models. A p-value of < 0.05 was considered statistically significant. All analyses were conducted using IBM SPSS Statistics version 26. Declarations Competing interests The authors declare that they have no conflicts of interest. Funding This work was supported by the National Science Research and Innovation Fund (NSRF) through Khon Kaen University to WL. Author Contribution Conceptualization— W.L., and A.T; Methodology—P.P., N.M., W.L., N.N., P.K., A.W. S.C. and J.J.; Formal analysis— V.T. and P.P.; Investigation— P.P., S.C., J.J. and S.R.; Resources— W.L., S.R. and A.T.; Data curation— V.T. and P.P.; Visualization—V.T., P.P., W.L., S.R. and A.T.; Supervision— W.L., S.R. and A.T.; Project administration— W.L. and A.T.; Funding acquisition— W.L.; Original draft preparation— V.T. and P.P.; Draft review and editing—all authors Acknowledgement All authors are truly thankful for helpful discussions with the late Prof. Narong Khuntikeo at Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand, Cholangiocarcinoma Research Institute (CARI), Khon Kaen University, Khon Kaen, Thailand and Cholangiocarcinoma Screening and Care Program (KKU), Khon Kaen University, Khon Kaen, Thailand. We are also indebted to all members of CASCAP, particularly the cohort members, and researchers at CARI, Faculty of Medicine, Khon Kaen University for collecting and proofing of CCA patient data. We also acknowledge Professor Ross H. Andrews for editing the MS via the Publication Clinic KKU, Thailand. Data Availability The raw MS/MS spectra data are available in ProteomeXchange: JPST003447 and PXD057396 (preview URL for Reviewers: https://repository.jpostdb.org/preview/1049237987672425a2e1cca, Access key: 6186). References Banales, J. M. et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat. Reviews Gastroenterol. Hepatol. 17 , 557–588. 10.1038/s41575-020-0310-z (2020). Nassar, A. et al. Factors of Early Recurrence After Resection for Intrahepatic Cholangiocarcinoma. World J. Surg. 46 , 2459–2467. 10.1007/s00268-022-06655-1 (2022). YAMAUCHI, N. et al. Clinical Significance of Early Recurrence After Curative Resection of Colorectal Cancer. 42, 5553–5559, doi: (2022). 10.21873/anticanres.16061%J Anticancer Research. Nassar, A. et al. Factors of Early Recurrence After Resection for Intrahepatic Cholangiocarcinoma. World J. Surg. 46 , 2459–2467. 10.1007/s00268-022-06655-1 (2022). Groot, V. 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Supplementary Files SupplementaryTable14.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 03 Dec, 2024 Reviews received at journal 21 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Editor invited by journal 08 Nov, 2024 Submission checks completed at journal 06 Nov, 2024 First submitted to journal 06 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5399896","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377410685,"identity":"e4fd64f6-5495-46ab-b890-e1a172d78651","order_by":0,"name":"Vasin Thanasukarn","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Vasin","middleName":"","lastName":"Thanasukarn","suffix":""},{"id":377410688,"identity":"5b7762ec-7762-4123-b64f-b80001802dc3","order_by":1,"name":"Piya Prajumwongs","email":"","orcid":"","institution":"Khon Kaen 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06:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5399896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5399896/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87124-2","type":"published","date":"2025-01-20T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70409327,"identity":"c4596d7c-bbea-42ad-959f-43d1ec3eabf2","added_by":"auto","created_at":"2024-12-03 01:15:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePeptide mass fingerprint of serum peptides using MALI-TOF MS analysis from CCA patients. \u003c/strong\u003e(A)The average spectra of serum samples from early recurrent CCA (red line) and late redcurrant CCA (green line) in the range of 1000–6,000 m/z. (B) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). The arrow represented different pattern and asterisk identifies unique peptides at mass/change position.\u003c/p\u003e","description":"","filename":"Figure1..png","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/38fe05d60801acee4ac749e9.png"},{"id":70410162,"identity":"19f90edb-341a-4c4b-96d5-3e034e78b944","added_by":"auto","created_at":"2024-12-03 01:23:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe data analysis and candidate peptide pre-filtration\u003c/strong\u003e. (A)Venn diagram created using the program Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny), illustrating the total count of proteins that exhibited differential expression of each group. (B) Principal Component Analysis (PCA), the scores plot of PCA discriminate two groups. (C) Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) showing clear separation of two groups. (D) The Variable Importance in Projection (VIP) showed top 15 peptides. (E) The Volcano plot analysis was performed to filter significant peptides that met the established criteria.\u003c/p\u003e","description":"","filename":"Figure2..png","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/2599a8605922b7b47231ea5e.png"},{"id":70409330,"identity":"fecfe000-3fd7-4f19-b7a8-36f07dbe74ec","added_by":"auto","created_at":"2024-12-03 01:15:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":460352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein–chemical interaction using the STRITH software in early recurrence. \u003c/strong\u003eIn the network, proteins were represented as nodes, and the thickness of the connecting lines indicated the degree of association between the proteins or chemicals. Gray line was peptide-peptide interaction, green line was peptide-drug interaction, red line was drug-drug interaction. Circle represented network interaction of peptides that were found in early recurrence.\u003c/p\u003e","description":"","filename":"Figure3..png","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/5b5902fb62c3d4d9a2fdb5b1.png"},{"id":70410819,"identity":"d12d11e3-795d-4f20-80fe-71c73893e019","added_by":"auto","created_at":"2024-12-03 01:31:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein–chemical interaction using the STRITH software in late recurrence. \u003c/strong\u003eIn the network, proteins were represented as nodes, and the thickness of the connecting lines indicated the degree of association between the proteins or chemicals A peptide that was centrally located and interacts with multiple other peptides was referred to as a 'hub' peptide. Gray line was peptide-peptide interaction, green line was peptide-drug interaction, red line was drug-drug interaction. Circle represented network interaction of peptides that were found in late recurrence.\u003c/p\u003e","description":"","filename":"Figure4..png","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/078fdb138d53a7993196da65.png"},{"id":70409332,"identity":"d082748b-b2fc-4205-8bb9-a03b5aad2eb4","added_by":"auto","created_at":"2024-12-03 01:15:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3209776,"visible":true,"origin":"","legend":"\u003cp\u003eA figure presenting a visual summary that highlights the key findings and concepts discussed in the paper.\u003c/p\u003e","description":"","filename":"Figure5..png","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/b46f9c5bd956b669a87cba53.png"},{"id":74858354,"identity":"b246379e-01d8-4671-bd4b-6272d10cfd7c","added_by":"auto","created_at":"2025-01-27 16:08:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5159173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/1da248c5-1e1e-4516-b7dd-1af657d6b1db.pdf"},{"id":70410163,"identity":"267d521e-b220-4d62-be74-6f8d3096b02f","added_by":"auto","created_at":"2024-12-03 01:23:40","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":35710,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable14.docx","url":"https://assets-eu.researchsquare.com/files/rs-5399896/v1/30906bdcb25e75bf99af303a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of Novel Serum Peptide Biomarkers for Cholangiocarcinoma Recurrence Through MALDI-TOF MS and LC-MS/MS Peptidome Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCholangiocarcinoma (CCA) is an aggressive malignancy arising in the biliary tract, often diagnosed at advanced stages due to its asymptomatic early phases. Surgical resection followed by adjuvant chemotherapy is the primary curative treatment; however, outcomes of poor survival remain \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A significant challenge in managing CCA is disease recurrence after initial treatment, categorized as early or late based on the onset post-surgery. Early recurrence occurs within one year and affects 20 to 65 percent of patients. Early recurrence is often linked to aggressive tumor biology, poor differentiation, and lymphovascular invasion. Late recurrence, is associated with slow-growing tumor cells or the patient's immune response \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, accurately predicting early recurrence for each regimen in individual patients may guide the selection or modification of adjuvant treatment plans. Although carcinoembryonic Antigen (CEA) and Cancer Antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9) have been utilized in screening, diagnosis, treatment monitoring, recurrence detection, and disease progression for CCA, they also have several limitations, including specificity to cancer types, overlap with benign conditions, limited diagnostic values, inconsistent levels of biomarker, lack of established cut-off values and limited role in early detection \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thus, multiple biomarkers or biomarker panels are discussed as potential tools for improving the diagnosis and management of CCA. These approaches aims to enhance specificity and sensitivity beyond what individual biomarkers like CEA and CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 can provide \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePeptide biomarkers, small proteins or peptides detectable in biological samples, play a pivotal role in diagnostics by providing insights into physiological and pathological conditions. They are crucial in diagnostics, as they can indicate the presence or progression of diseases, monitor therapeutic responses, or predict disease outcomes. In medical practice, peptide biomarkers are increasingly used to enhance early detection, improve diagnostic accuracy, and support personalized treatment approaches. Their specificity and capacity to reflect molecular-level changes in biological processes underpin their utility. Prior research has highlighted the critical role of peptide biomarkers in cancer studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Numerous studies have identified differentially expressed peptides across various cancers, contributing to the development of diagnostic tools and therapeutic strategies. For example, peptide biomarkers have significantly improved early detection, staging, and the monitoring of treatment responses and recurrence in cancers such as prostate, breast, and ovarian \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These findings demonstrate the potential of peptidome approaches to drive advancements in personalized medicine and cancer management, with peptide biomarkers offering substantial promise for enhancing detection, diagnosis, and disease monitoring.\u003c/p\u003e \u003cp\u003eBased on information above, serum peptidome in CCA patients with early and late recurrence has yet to be fully explored. This study aims to identify novel peptide mass fingerprints (PMFs), peptide clusters, and potential biomarkers in the serum of CCA patients with early and late recurrence. We investigated disease-specific peptide profiles by matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry (MALDI-TOF MS) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, we examined the associations between these peptides and chemotherapy drugs. We anticipated that serum peptide biomarkers could potentially aid in prognosis and inform treatment strategies for CCA.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics and survival analysis of cholangiocarcinoma (CCA) patients\u003c/h2\u003e \u003cp\u003eThe clinical characteristics in CCA patients were showed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We performed cut-off value for categorizing CCA patients with recurrent status (early and late recurrence) using 365 days according to previous publications. By CCA patients had DFS\u0026thinsp;\u0026ge;\u0026thinsp;365 days that were categorized early recurrence (33%), while CCA patients had DFS\u0026thinsp;\u0026lt;\u0026thinsp;365 days that were categorized late recurrence (67%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of the survival of CCA patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;81\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMST (month) (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.70 (13.64\u0026ndash;25.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.70 (14.11\u0026ndash;25.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.77\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.80 (13.83\u0026ndash;39.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.20 (15.23\u0026ndash;21.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 (0.963\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.70 (16.37\u0026ndash;23.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.60 (10.13\u0026ndash;33.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.38\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20 (0-44.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.203 (0.426\u0026ndash;3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical margin (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.90(11.24\u0026ndash;34.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.90 (12.47\u0026ndash;19.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70 (1.02\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.42 (0.85\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.10 (7.200\u0026ndash;27.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately/Poorly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.60 (8.76\u0026ndash;24.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89 (1.045-3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83 (1.01\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node status (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.9 (14.57\u0026ndash;31.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9 (13.59\u0026ndash;18.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55 (0.93\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly (0-II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.8 (2.70\u0026ndash;74.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate (III-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6 (13.17\u0026ndash;20.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21 (1.19\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61 (0.85\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrent status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.68\u0026ndash;11.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06 (3.96\u0026ndash;12.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.36 (3.51\u0026ndash;11.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8 (19.77\u0026ndash;45.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003en, Number; CI, Confidence interval; 5-YSR, 5-year survival rate; MST, median survival time; HR, hazard ratio; dCCA, distal cholangiocarcinoma; iCCA, Intrahepatic cholangiocarcinoma; pCCA, perihilar cholangiocarcinoma; TNM, tumor node metastasis from 8th AJCC/UICC staging system.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e*\u003c/sup\u003e Indicates a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (statically significant)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSurvival analysis using the Log rank test and multivariate analysis through Cox regression revealed that factors such as positive surgical margin, moderately and poorly differentiated differentiation, late staging, and early recurrence were significantly associated with shorted survival rates. Specifically, multivariate Cox regression analysis indicated that surgical margins, histological differentiation, cancer staging, and recurrent status were significant predictors of survival compared to their referent categories. Notably, early recurrence displayed a markedly high hazard ratio as 6.36 folds, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 when compared with late recurrence, underscoring its critical impact on patient prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, this study prioritized recurrence status to enable further peptidome analysis using mass spectrometry.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSerum peptide barcode of CCA patients with early and late recurrence\u003c/h3\u003e\n\u003cp\u003eThe criteria for categorizing patients with recurrent CCA were based on a time frame of 365 days post-surgery. Specifically, patients who experienced recurrence before 365 days were classified into the early recurrence group, comprising 34 individuals. In contrast, those with recurrence occurring at 365 days or later were assigned to the late recurrence group of 47 individuals. Following this categorization, serum samples from CCA patients underwent peptide profiling using MALDI-TOF MS. The results revealed that the peptide patterns in the serum of early recurrence patients (red spectrum) differed significantly, with three unique peptides observed at m/z 2697.984489, 3041.741068 and 4288.621730 when compared to those from late recurrence patients (green spectrum) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequently, the peptide profiles from two groups of patients were subjected to statistical analysis using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). OPLS-DA generated a clear separation between early and late recurrent patients indicated that potential peptide biomarkers associated with the differences in patient conditions. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. about here]\u003c/p\u003e \u003cp\u003eUpon obtaining PMFs of serum peptides to distinguish between early and late recurrence in CCA patients, MALDI-TOF MS provides rapid screening for stratification of recurrent status. Additionally, to improve the power and accuracy of detection between early and late recurrence in CCA patients, we investigated peptide-base biomarkers through LC-MS/MS analysis. We aimed to identify potential peptide biomarkers for integration with PMFs from MALDI-TOF MS, to improve the accuracy and precision in diagnosing recurrence in CCA patients.\u003c/p\u003e\n\u003ch3\u003eIdentification of differentially expressed peptides in plasma of CCA patients with early and late recurrence\u003c/h3\u003e\n\u003cp\u003eSerum peptides were analyzed using LC-MS/MS, identifying 5,798 proteins. A Venn diagram illustrated the overlap and differences between early and late recurrence groups, with 1,747 shared peptides and 2,327 and 1,724 unique peptides exclusive to early and late recurrence, respectively. A Principal Component Analysis (PCA) revealed distinct patterns, with Component 1 effectively separating the two groups. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) further confirmed clear separation between the groups, identifying key discriminating peptides. VIP score analysis revealed 1,025 significant peptides, with the top 15 highlighted. Volcano plot analysis, using a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.), identified 155 peptides, 95 upregulated in early recurrence and 60 in late recurrence, listed in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. about here]\u003c/p\u003e\n\u003ch3\u003eNetwork analysis of serum peptides in CCA patients with early and late recurrence\u003c/h3\u003e\n\u003cp\u003eA total of 155 peptides were filtered through a stringent screening process, with 95 peptides identified in early recurrence and 60 peptides in late recurrence. Subsequently, candidate peptides from both groups were analyzed for protein-chemical interactions using the STITCH database. Common chemotherapeutic drugs, including Gemcitabine, Cisplatin, Capecitabine, Oxaliplatin, and 5-Fluorouracil (5-Fu) widely used in the treatment of CCA, were incorporated into the interaction list to predict associations and computational interactions. This analysis aimed to present a comprehensive network of interactions between the candidate peptides and these chemotherapeutic drugs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate peptides involved in biological process and molecular function in early and late recurrences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeptide sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBiological Process\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMolecular function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtaxia telangiectasia and Rad3-related protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASHEPFPGHWA0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA damage response,\u003c/p\u003e \u003cp\u003eDNA repair and cell proliferation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eserine/threonine kinase activity, DNA binding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP54132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecQ-like DNA helicase BLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEFDDDDYDTDF-\u003c/p\u003e \u003cp\u003eVPPS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA damage response,\u003c/p\u003e \u003cp\u003eDNA double-strand break processing, DNA repair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3'-5' DNA helicase activity,\u003c/p\u003e \u003cp\u003eDNA helicase activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOLA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA polymerase alpha catalytic subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIDGVFKSLLL-\u003c/p\u003e \u003cp\u003eLKKKKYA0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA repair,\u003c/p\u003e \u003cp\u003eDouble-strand break repair via nonhomologous end joining,\u003c/p\u003e \u003cp\u003eDNA synthesis involved in DNA repair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChromatin binding, DNA binding, DNA replication origin binding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP23497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSP100 nuclear antigen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSHDLQRMFTE-\u003c/p\u003e \u003cp\u003eDQGVDDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA damage response, signal transduction by p53 class mediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDNA binding, DNA-binding transcription factor activity, RNA polymerase II-specific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPP1R15A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO75807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein phosphatase 1 regulatory subunit 15A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDSDSGSDEEEG-\u003c/p\u003e \u003cp\u003eEAEASSS0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA damage response,\u003c/p\u003e \u003cp\u003eRegulation of cell cycle,\u003c/p\u003e \u003cp\u003eApoptotic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eprotein kinase binding, protein phosphatase 1 binding,\u003c/p\u003e \u003cp\u003eprotein phosphatase activator activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSERPINA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP01009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlpha-1-antitrypsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEAIPMSIPPEV-\u003c/p\u003e \u003cp\u003eKFNKPFVF0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBlood coagulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtease binding, Serine-type endopeptidase inhibitor activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSERPING1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma protease C1 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFVLWDQQHKF-\u003c/p\u003e \u003cp\u003ePVF0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBlood circulation, Blood coagulation, Innate immune response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSerine-type endopeptidase inhibitor activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP61812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransforming growth factor beta-2 proprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRLQNPKARVP-\u003c/p\u003e \u003cp\u003eEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActivation of protein kinase activity, Cell migration,\u003c/p\u003e \u003cp\u003eCell morphogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCytokine activity, Growth factor activity, Protein homodimerization Activity, Signaling receptor binding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP27708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultifunctional protein CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIDRWFLHRMK0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e'de novo' pyrimidine nucleobase biosynthetic process, Cellular response to epidermal growth factor stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAspartate carbamoyltransferase activity,\u003c/p\u003e \u003cp\u003eATP binding, Carbamoyl-phosphate synthase (ammonia) activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. about here]\u003c/p\u003e \u003cp\u003eIn early recurrent patients, the network analysis performed using STRING revealed intricate interactions between proteins and chemotherapy drugs. The results showed the peptide interaction network of SP100 (Nuclear autoantigen Sp-100), ATR (Serine/threonine-protein kinase ATR), POLA1 (DNA polymerase alpha catalytic subunit) and PPP1R15A (Protein phosphatase 1 regulatory subunit 15A) showed a strong relationship with the chemotherapy drug, Cisplatin, while we also found less of BLM (RecQ-like DNA helicase BLM) with Cisplatin. In addition, ATR, BLM and CEP164 (Centrosomal protein of 164 kD) also with their predicted functional partner CHEK1 (Checkpoint kinase 1) which had strong associations with serval chemotherapeutic drugs such as Gemcitabine, Cisplatin and 5-Fu. Additional to peptide-chemotrophic drugs interactions, we also found peptide-peptide interaction which related with signaling pathways to promote cancer progression, such as cell proliferation, angiogenesis, tumor microenvironment and metastasis. Main nodes including SP100, BLM and ATR proteins have been reported to play a crucial role in numerous pathways of progression as shown in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway maps and publications. \"In this study, SP100 showed a strong association with SUMO1 (Small Ubiquitin-Like Modifier 1), which is predicted to be a functional partner. SUMO1 is an upstream regulator of several proteins involved in signaling pathways identified in this analysis including MAP3K1 (Mitogen-activated protein kinase kinase kinase 1), ZFHX3 (Zinc finger homeobox protein 3)/EHBP1 (EH domain-binding protein 1-like protein 1) signaling, HNRNPH3 (Heterogeneous nuclear ribonucleoprotein H3; hnRNP H3), BLM and UBTF (Nucleolar transcription factor 1)/NCL (Nucleolin) signaling. For BLM it exhibited strong interactions with ATR/CEP164/CEP70 (Centrosomal protein of 70 kD) which play roles in cell cycle checkpoint, DNA damage response and DNA repair, thereby protecting against cell death and sustaining cancer cell survival. In addition, BLM also interacted with SETX (Probable helicase senataxin)/ predicted POLR2A (DNA-directed RNA polymerase II subunit RPB1) interaction /INTS5 (Integrator complex subunit 5) as well as SUPT5H (Transcription elongation factor SPT5) which plays roles in RNA processing, transcription regulation, and DNA damage response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, non-interaction nodes were identified that have oncogenic roles in cancer progression and recurrence. A total of 95 peptides were reported to have oncogenic functions, as listed in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. about here]\u003c/p\u003e \u003cp\u003eIn late recurrent patients, we found one direct peptide-chemotherapeutic drug and one indirect peptide-chemotherapeutic drug. SERPINA1 (Alpha-1-antitrypsin) had strong association with Cisplatin, while CAD (Carbamoyl phosphate synthetase, aspartate transcarbamylase, and dihydroorotase) showed indirect relationship with Cisplatin, 5-Fu and Capecitabine through strong interaction with predicted partner DPYD (Dihydropyrimidine Dehydrogenase) and medium interaction predicted partner TYMS (Thymidylate Synthase). It had that strong association with Gemcitabine, Cisplatin, Capecitabine, and 5-Fluorouracil (5-Fu). In addition, we also found strong peptide-peptide interaction including SERPINA1, SERPING1 Plasma protease C1 inhibitor and TGFB2 (Transforming Growth Factor Beta 2) which play roles in tumor growth and metastasis. Moreover, we found that CAD was a central node or hub of strong interaction with SLC23A3 (Solute carrier family 23 member 3) which has an essential role in the transport of certain molecules across cell membranes. Predicted partners, including DPYSL3 and 4 (Dipeptidyl Peptidase-Like 3 and 4), DPYS (Dipeptidyl Peptidase I), DPYD (Dihydropyrimidine Dehydrogenase), CRMP1 (Collapsin Response Mediator Protein 1), CPS1 (Carbamoyl-Phosphate Synthetase 1), DHODH (Dihydroorotate Dehydrogenase) and TYMS have functions in cellular metabolism. In addition, we also found a strong relationship of CXXC1 (Receptor-transporting protein 5) and SETD1B (Histone-lysine N-methyltransferase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our result also showed non-interaction nodes that have been reported in several publications in cancer progression as shown in a supplementary table 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results were based on cut-off values using median of DFS or about 365 days after surgical treatment. This consistent use of a 365-day threshold underscores its potential as a standard marker for assessing recurrence risk. Survival analysis and Cox regression further illustrate that early recurrence was associated with significantly shorter survival compared to late recurrence. In addition, we have identified that early recurrence was an independent factor contributing to poor survival outcomes. This cut-off value aligned with findings from several previous studies. They established that this time frame is crucial for understanding recurrence patterns across various cancer types, including bile duct \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e pancreatic \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and colorectal cancers \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The consistency of these findings across multiple studies highlights the importance of early detection and intervention strategies for patients at risk of recurrence. Implementing routine surveillance protocols that focus on this critical time frame could lead to improved patient management and outcomes. The use of CEA and CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 as biomarkers for recurrence is currently being debated due to several issues, especially their limited specificity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thus, biomarker panels or multiple biomarkers are essential to improve accuracy and specificity predicting these outcomes, providing a valuable tool for identifying patients at higher risk for early recurrence \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenerally, cancer recurrence is considered a result of the cancer progression. Typically, this progression involves the production of peptides and proteins that promote cancer development. These molecules are secreted into the bloodstream to drive tumor growth, immune evasion, metastasis, and intercellular communication \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Many secreted peptides and proteins serve as biomarkers, indicating the presence or progression of cancer, making them valuable for peptide- and protein-based biomarker detection \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In practically, MALDI-TOF MS has been reported as a useful tool for diagnosis and prognosis in several abnormalities, as the peptide signature in serum or PMFs showed specific patterns for several disease, especially cancers \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To explore peptide patterns of recurrent cancer, our study provides novel evidence from serum peptidome to categorize early and late recurrent status using PMFs through MALDI-TOF MS. Our finding showed that the peptide pattern of early recurrence was markedly different from those patterns in late recurrence. Moreover, we also found peptide signatures that only appeared in early recurrence at m/z 2697.984489, 3041.741068 and 4288.621730. These results showed that PMFs MALDI-TOF MS could be useful to discriminate between early and late recurrence. Our study was consistent with previous reports on bile duct cancer, also known as CCA. Our study has revealed that PMFs via MALDI-TOF in the serum of 92 bile duct cancer patients at University College Hospital, UK, compared with healthy volunteers, had distinct differences in the peptide profiles of bile duct cancer patients. Analysis of peptide positions on the combined spectrum distinguished eight peptides with statistically significant differences in peak area under the curve, specifically at m/z values of 887.2, 1263.7, 1350.8, 2082.1, 2210.3, 2554.5, 2903.3, and 5805.0 \u003csup\u003e18\u003c/sup\u003e. Additionally, in 2023, a study on PMFs in patients with cervical cancer at various stages found clear differences in peptide profiles among the groups, which included healthy volunteers, precancerous lesions, and cervical cancer stages I, II, and III. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on previous reports, PMFs via MALDI-TOF could not only differentiate between healthy individuals and cancer patients but also stratify the aggressiveness of the disease by stage. This provides strong evidence supporting our findings in using PMFs to distinguish between early and late recurrent CCA patients. Therefore, due to its high efficiency and sensitivity in detecting PMFs, MALDI-TOF could be a primary choice for rapid screening of disease abnormalities, especially recurrent status of cancer. For clinical advantage, rapid diagnosis enables prompt treatment and ensures that improve the treatment plans for patients. However, in addition to the speed of diagnosis, the accuracy of the diagnosis is crucial factor to consider in clinical application. While MALDI-TOF facilitates rapid screening, it is important to recognize that in some patient cases, ambiguous peptide patterns might overlap. Consequently, the identification of biomarkers incorporates with PMFs using MALDI-TOF MS is essential for enhancing the accuracy of disease screening and diagnosis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, to identify sequence of peptide biomarker in serum, LC-MS/MS was undertaken in our study.\u003c/p\u003e \u003cp\u003eThe identified candidate peptide-based biomarkers in both groups were separately analyzed for peptide-chemotherapeutic drug interaction network using the STITCH database. In early recurrence, we found that there were 16 peptides and 5 peptides having peptide-peptide and-drug interaction, respectively. This study proposed, ATR, POLA1, BLM, SP100 and PPP1R15A (GADD34) as major candidate peptides that had a significant impact in the interaction network, while the remaining peptides also served as co-biomarkers for this condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. and supplementary table 3). This study identified a set of proteins involved in DNA stress response, DNA repair, and the maintenance of genomic instability, which are key features of cancer progression and chemoresistance.\u003c/p\u003e \u003cp\u003eATR (Ataxia Telangiectasia and Rad3-related protein) is protein kinase family, phosphoinositide 3-kinase\u0026ndash;related kinase (PIKKs), a key protein involved in the cellular response to DNA damage. It plays a critical role in the DNA damage response (DDR) by detecting DNA replication stress and activating repair pathways \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. ATR also has non-canonical roles in cancer migration and invasion \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In addition, high levels of ATR were associated with poor survival of cancer patients \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Inhibition of ATR has been reported as a target cancer treatment \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. ATR is also reported co-interaction with POLA1 in DNA replication, the cell cycle and involving DNA repair for maintaining genome stability\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A previous study has indicated a potential role for CEP164 in ATM/ATR-mediated DNA damage response (DDR) and UV-induced nucleotide excision repair pathways \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. ATR function in regulated DNA damage response and repair processes has been reported to be mediated through UBTF, a multifunctional architectural protein. UBTF is a multifunctional architectural protein containing multiple HMG boxes or the nucleolar proteins. It has been found that the Pol I transcription factor UBTF plays a dual role in regulating both Pol I and Pol II-mediated transcription. UBTF has also been reported to participate in DNA damage and repair processes, acting through mediators of the ATR/ATM-regulated DNA damage response \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Additionally, UBTF is involved in the cellular response to growth factor stimulation and can regulate cancer progression through the MAPK/ERK signaling pathway \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In addition, UBTF also has been reported to interact with NCL to be nucleolar proteins not only in genotoxic stress sensing but wound healing also \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBLM is a DNA helicase essential for maintaining genomic stability. In cancer, various mutations can disrupt BLM function, leading to genomic instability and promoting cancer progression. Under these conditions, BLM aids cancer cell survival by supporting mechanisms that adapt to the genomic stress \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In addition, BLM has been reported to correlate with malignant progression in pancreatic adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, BLM has been reported as downstream of ATR signaling via phosphorylation at Thr99 and Thr122 \u003csup\u003e33\u003c/sup\u003e. Cohen S \u003cem\u003eet al\u003c/em\u003e. have revealed that BLM and SETX are recruited to transcription-coupled DNA double-strand breaks (DSBs) during DSB repair \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. SETX is involved in various processes related to genome integrity, transcription, RNA metabolism, and DNA damage repair \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSP100 is a nuclear autoantigen that plays a role in several cellular processes. Several studies have reported that SP100 has tumor suppressive functions, and some studies ahve suggested dual-functions in cancer suppression and cancer progression depended on cancer types. In pancreatic adenocarcinoma, SP100 has been reported to be associated with poorer survival and adverse clinical features \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. A study on glioma demonstrated that elevated expression of the Speckled Protein (SP) family, including SP100 and SP140, were associated with poorer prognosis in glioma patients. Furthermore, the SP family contributes to glioma progression through the TRIM22/PI3K/AKT signaling pathway \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The functions of SP100 in CCA progression is not well understood. Our results for the first time have found that SP100 was overexpressed in early recurrent CCA patients.\u003c/p\u003e \u003cp\u003ePPP1R15A (GADD34) is a regulatory subunit of protein phosphatase 1. For HCC, PPP1R15A could promote immunosuppressive in generating tumor micro environment and progression as well as high expression of PPP1R15A associated with poor clinical outcomes in HCC \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn early recurrent patients, a total of 5 of 16 peptides, including, ATR, BLM, POLA1, SP100 and PPP1R15A, were directly associated with chemotherapeutic drugs for CCA treatment, Cispatin, while CEP167 showed indirect interactions with three chemotherapeutic drugs, namely, Gemcitabine, Cisplatin and 5-Fu as demonstrated in protein-chemical interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn late recurrence, we found that there were 5 peptides and 2 peptides having peptide-peptide and-drug interaction, respectively. Our results showed that SERPINA1, TGFB2, SERPING1 and CAD were major candidate peptides that had a significant impact in the interaction network, while the remaining peptides also served as co-biomarkers for this condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. and supplementary table 4).\u003c/p\u003e \u003cp\u003eSERPINA1, also known as alpha-1 antitrypsin (AAT), is a member of the serpin (serine protease inhibitor) superfamily. Its primary function is to inhibit serine proteases, particularly neutrophil elastase, which plays a significant role in inflammatory processes. Elevated levels of SERPINA1 have been reported in CCA and are associated with poorer survival outcomes and more advanced tumor staging. Furthermore, high SERPINA1 expression is linked to enriched pathways related to the complement system and extracellular matrix interactions, highlighting its potential role in the tumor microenvironment and cancer progression \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In STICTH database, SERPINA1 has a strong association with TGFB2 and SERPING1. TGFB2 is a member of the transforming growth factor-beta (TGF-β) which plays roles in immunosuppressive tumor microenvironment and promote epithelial-to-mesenchymal transition (EMT), contributing to cancer cell migration and invasion \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. While, SERPING1 is a member of the serpin (serine protease inhibitor) superfamily like SERPINA1. It encodes a highly glycosylated plasma protein that has a critical role in regulating the complement cascade and the immune response. Synthesized in the liver, SERPING1 is important for managing several physiological processes, including complement activation, blood coagulation, fibrinolysis, and the generation of kinins. Diseases linked to SERPING1 include hereditary angioedema and partial deficiency of complement components \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Currently there are no studies relating to SERPINA1, TGFB2 and SERPING.\u003c/p\u003e \u003cp\u003eCAD (Carbamoyl-phosphate synthetase 2, Aspartate transcarbamoylase, and Dihydroorotase) is a multifunctional enzyme involved in the first three rate-limiting steps of pyrimidine nucleotide synthesis. It plays a central role in the production of nucleic acids, active intermediates, and cell membranes. Dysregulation of CAD-related pathways or mutations in CAD are linked to cancer, neurological disorders, and inherited metabolic diseases. The de novo synthesis of pyrimidine nucleotides provides crucial precursors for various growth-related processes in higher eukaryotes. It is involved in the production of activated intermediates, such as pyrimidine sugars, and contributes to the synthesis of polysaccharides and phospholipids. One key product of CAD, UDP, serves as a precursor for UDP-sugar intermediates essential for UDP-dependent glycosylation and post-translational protein modifications. Disruptions in UDP-nucleotide sugar metabolism, including UDP-glucose pyrophosphorylase activity, have been implicated in cancer progression, particularly in pancreatic and breast cancers, by altering cancer cell glycosylation. CAD is regulated by post-translational phosphorylation by MAPK/cAMP-Dependent PKA/PKC and PI3K-AKT-mTORC1-S6K1 Pathways \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. CAD was enriched in a set of cancer types (liver cancer, breast cancer, colon cancer, etc.) with poor clinical outcomes by using on The Cancer Genome Atlas and Gene Expression Omnibus datasets. In addition, late recurrent patients, a total of 2 of 5 peptides, including, SERPINA1 was directly associated with chemotherapeutic drugs for CCA treatment, Cisplatin, while CAD was showed as a hub of interactions to all chemotherapeutic drugs through DHODH, DPYD and TYMS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This interaction should be validated in further investigations.\u003c/p\u003e \u003cp\u003eAccording to PMFs using MALDI-TOF MS and peptides-base biomarkers of CCA patients with recurrence. these results hold potential for development into PMFs and biomarker panels, enabling the precise classification of disease severity. Moreover, our results provide a foundation for future research aimed at developing personalized treatment strategies based on recurrence timing and highlight the role of specific rapid screening by PMFs and peptide biomarker panel in guiding therapeutic decisions to improve patient outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eIn conclusion, our findings showed that early recurrence was an independent prognostic factor for poor outcomes in CCA patients, emphasizing the necessity for early detection and intervention strategies. Serum analysis through peptidome, utilizing rapid screening of PMFs via MALDI-TOF MS, coupled with peptide-based biomarker panel identified by LC-MS/MS, enhanced diagnostic accuracy. This approach provides a strong foundation for developing precise personalized treatment strategies, with the potential to improve CCA patient management and predict clinical outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted based on the principles of Good Clinical Practice, the Declaration of Helsinki, and national laws and regulations about clinical studies. In addition, informed consent was obtained from all patients. All processes of this study were accepted and approved by the Khon Kaen University Ethics Committee for Human Research under the reference number HE661318.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation and Sample Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was a single-center study conducted as a retrospective-prospective analytical observational study involving a sample of 81 patients diagnosed with cholangiocarcinoma. Data were collected retrospectively from the medical records at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, including clinical data of patients from January 1, 2017, to December 31, 2021. Serum samples were obtained from the biobank at the Cholangiocarcinoma Research Institute, Khon Kaen University. Prognostic factors were collected using a retrospective data collection form from the patient medical records, utilizing the ISAN Cohort database from the Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University. The data collected included age at diagnosis, gender, histological confirmation, tumor size, cancer grade, cancer staging, surgical margin, lymph node metastasis, lympho-vascular invasion, histological grade, and chemotherapy received.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Outcome Follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe follow-up period for patients with cholangiocarcinoma extended from the date of surgery for at least 5 years, starting from January 1, 2017. All causes of death were monitored through a life status verification from the database of the Ministry of Interior, and additional data were collected from medical records documented by physicians. Typically, after treatment, patients were scheduled for follow-up at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, every 6 months for at least 5 years. The variables studied included recurrence, 5-year survival, overall survival (OS), and disease-free survival (DFS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Collection and Serum Preservation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor serum sample collection from patients with cholangiocarcinoma prior to surgical treatment, blood was drawn from a vein (venipuncture) with a volume of 5 milliliters into a clot blood tube. It was ensured that clot formation was complete before centrifugation. Serum was then separated from red blood cells using a centrifuge at 3,000 \u0026ndash; 3,500 RPM at 4 \u0026deg;C for 10 minutes. The serum was aspirated and aliquoted into 1 microliter portions in Eppendorf tubes to avoid repeated thawing of samples, and then stored at -80 \u0026deg;C in the biobank of the Cholangiocarcinoma Research Institute, Khon Kaen University, until further analysis. protein quantification was performed using the Lowry assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeptide Barcode Analysis Using MALDI-TOF MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum samples were mixed with a matrix solution (MALDI solution: \u0026alpha;-cyano-4-hydroxycinnamic acid (CHCA) in 50% acetonitrile (ACN) with 0.1% trifluoroacetic acid) at a sample-to-matrix ratio of 1:5. The resulting mixture was applied onto a MALDI target plate (MTP 384 ground steel, JEOL, Japan), with each sample spotted 30 times on the plate. The plate was allowed to dry at ambient temperature before analysis using the JMS-S3000 SpiralTOF (JEOL, Japan) in linear positive mode, focusing on the detection of peptide barcodes with molecular weights between 1,000 and 10,000 Da. Each sample was subjected to 1,500 laser shots. For this study, samples were categorized into a training set (collected from 2017 to 2023) and a calibration set (collected from 2024 to 2026). External calibration was conducted using peptides with known mass-to-charge ratios (m/z), including Angiotensin II (m/z = 1,046), P14R (m/z = 1,533), human ACTH fragment 18\u0026ndash;39 (m/z = 2,465), bovine insulin oxidized B chain (m/z = 3,465), and bovine insulin (m/z = 5,731).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeptidomics Analysis in Serum Using LC-MS/MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum samples were purified using C18 ZipTip and analyzed for peptide content via LC-MS/MS on the Q-TOF Impact II\u0026trade; system. Specifically, one microliter of peptide digests was enriched on a \u0026micro;-Precolumn (300 \u0026micro;m i.d. \u0026times; 5 mm, C18 Pepmap 100, 5 \u0026micro;m, 100 \u0026Aring;; Thermo Scientific, UK) and subsequently separated on a 75 \u0026micro;m i.d. \u0026times; 15 cm column packed with Acclaim PepMap RSLC C18, 2 \u0026micro;m, 100 \u0026Aring; (nanoViper, Thermo Scientific, UK). The C18 column was maintained at 60 \u0026deg;C in a thermostatted oven. Solvents A and B, containing 0.1% formic acid in water and 0.1% formic acid in 80% acetonitrile, respectively, were used to elute peptides with a gradient of 5\u0026ndash;55% solvent B over 30 minutes at a constant flow rate of 0.30 \u0026micro;L/min. Electrospray ionization was performed at 1.6 kV using the CaptiveSpray, with nitrogen as the drying gas at a flow rate of approximately 50 L/h. Collision-induced dissociation (CID) was conducted with nitrogen as the collision gas, and both MS and MS/MS spectra were acquired in positive-ion mode at a frequency of 2 Hz over an m/z range of 150\u0026ndash;2200, with collision energy adjusted to 10 eV according to m/z. Each sample was analyzed in triplicate, and MaxQuant version 2.5.0.0 (Tyanova et al., 2016) was used for peptide quantification and sequencing, employing the Andromeda search engine to match MS/MS spectra against the Uniprot \u003cem\u003eHomo sapiens\u003c/em\u003e database. Label-free quantification was conducted using MaxQuant\u0026apos;s standard parameters: a maximum of two missed cleavages, a mass tolerance of 0.6 Da for the main search, and trypsin as the digestion enzyme. Carbamidomethylation of cysteine was set as a fixed modification, while methionine oxidation and N-terminal acetylation were included as variable modifications. Protein identification required peptides with a minimum length of seven amino acids and at least one unique peptide. Proteins were considered identified if they contained at least two peptides, including at least one unique peptide. The protein false discovery rate (FDR) was controlled at 1%, estimated using reverse-sequence searches, with a maximum of five modifications allowed per peptide for data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics Analysis of Peptidomics Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualization and statistical analysis of the LC-MS data, including Principal Component Analysis (PCA), differential analysis (one-way ANOVA, volcano plots, boxplots, and heatmaps), were performed using MetaboAnalyst version 6.0, applying a significance threshold of P \u0026lt; 0.05 (Pang et al., 2022). Functional analysis of proteins containing identified peptides was conducted with the Panther database (Mi et al., 2019) and ShinyGO (Ge et al., 2020). Additionally, associations between peptides, their source proteins, and related proteins or chemicals were investigated using the STITCH database (Szklarczyk et al., 2016).\u003c/p\u003e\n\u003cp\u003eThe overall survival (OS) was calculated using the Kaplan-Meier method, where disease-free survival was defined as the time from surgery to recurrence, and overall survival was defined as the time from surgery to death. Patients who survived beyond the study period had their median DFS, median survival time, and survival rates calculated with a 95% confidence interval. Comparisons between groups were analyzed using the Log-rank test, while univariate and multivariate analyses to identify prognostic factors were performed using Cox regression models. A p-value of \u0026lt; 0.05 was considered statistically significant. All analyses were conducted using IBM SPSS Statistics version 26.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Science Research and Innovation Fund (NSRF) through Khon Kaen University to WL.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization\u0026mdash; W.L., and A.T; Methodology\u0026mdash;P.P., N.M., W.L., N.N., P.K., A.W. S.C. and J.J.; Formal analysis\u0026mdash; V.T. and P.P.; Investigation\u0026mdash; P.P., S.C., J.J. and S.R.; Resources\u0026mdash; W.L., S.R. and A.T.; Data curation\u0026mdash; V.T. and P.P.; Visualization\u0026mdash;V.T., P.P., W.L., S.R. and A.T.; Supervision\u0026mdash; W.L., S.R. and A.T.; Project administration\u0026mdash; W.L. and A.T.; Funding acquisition\u0026mdash; W.L.; Original draft preparation\u0026mdash; V.T. and P.P.; Draft review and editing\u0026mdash;all authors\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e All authors are truly thankful for helpful discussions with the late Prof. Narong Khuntikeo at Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand, Cholangiocarcinoma Research Institute (CARI), Khon Kaen University, Khon Kaen, Thailand and Cholangiocarcinoma Screening and Care Program (KKU), Khon Kaen University, Khon Kaen, Thailand. We are also indebted to all members of CASCAP, particularly the cohort members, and researchers at CARI, Faculty of Medicine, Khon Kaen University for collecting and proofing of CCA patient data. We also acknowledge Professor Ross H. Andrews for editing the MS via the Publication Clinic KKU, Thailand.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw MS/MS spectra data are available in ProteomeXchange: JPST003447 and PXD057396 (preview URL for Reviewers: https://repository.jpostdb.org/preview/1049237987672425a2e1cca, Access key: 6186).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBanales, J. M. et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. \u003cem\u003eNat. Reviews Gastroenterol. 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Its Function, Regulation, and Diagnostic Potential. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms221910253\u003c/span\u003e\u003cspan address=\"10.3390/ijms221910253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cholangiocarcinoma, recurrence, peptidome, peptide biomarker, MALDI-TOF MS, LC-MS/MS","lastPublishedDoi":"10.21203/rs.3.rs-5399896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5399896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCholangiocarcinoma (CCA) is an aggressive cancer originating from bile duct epithelial cells, with a high rate of recurrence following surgical resection. Recurrence is categorized as early linked to aggressive tumor biology than late recurrence. This study aimed to identify novel peptide mass fingerprints (PMFs) and potential biomarker panels in the serum of CCA patients with early and late recurrence using mass spectrometry. Serum samples of 81 CCA patients were analyzed using MALDI-TOF MS and LC-MS/MS, with statistical analysis correlating peptide profiles with clinical outcomes like disease-free survival (DFS) and overall survival (OS). A 365-day DFS cutoff effectively distinguished early from late recurrence, with early recurrence linked to poorer survival outcomes. The PMFs from MALDI-TOF MS differentiated recurrence types based on specific mass signatures. LC-MS/MS analysis identified 95 peptides associated with cancer progression in early recurrence and 60 in late recurrence. Distinct protein associations were found: ATR, POLA1, BLM, SP100, and PPP1R15A for early recurrence, and SERPINA1, TGFB2, SERPING1, and CAD for late recurrence, with strong interactions with chemotherapeutic drugs. This study successfully demonstrated the use of PMFs for rapid discrimination between early and late recurrence in CCA and identified potential serum peptide biomarkers to improve accuracy in recurrence classification.\u003c/p\u003e","manuscriptTitle":"Discovery of Novel Serum Peptide Biomarkers for Cholangiocarcinoma Recurrence Through MALDI-TOF MS and LC-MS/MS Peptidome Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 01:15:35","doi":"10.21203/rs.3.rs-5399896/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-03T13:47:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-03T13:05:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-21T12:52:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130364482123503335168303275125526459941","date":"2024-11-13T01:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263272009444941836794618051166369786678","date":"2024-11-12T09:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T07:51:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-12T07:48:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-08T13:44:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-06T07:09:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-06T06:02:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ddc09c5-4d23-4afc-85e8-47485584ebdf","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40180428,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":40180429,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":40180430,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":40180431,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":40180432,"name":"Health sciences/Medical research/Biomarkers"},{"id":40180433,"name":"Biological sciences/Biochemistry/Peptides"},{"id":40180434,"name":"Biological sciences/Biological techniques/Proteomic analysis"},{"id":40180435,"name":"Biological sciences/Biological techniques/High throughput screening"},{"id":40180436,"name":"Biological sciences/Molecular biology/Proteomics/Protein protein interaction networks"},{"id":40180437,"name":"Biological sciences/Biochemistry/Proteomics"},{"id":40180438,"name":"Biological sciences/Biochemistry/Proteomics/Protein protein interaction networks"},{"id":40180439,"name":"Biological sciences/Biological techniques/Mass spectrometry"}],"tags":[],"updatedAt":"2025-01-27T16:00:44+00:00","versionOfRecord":{"articleIdentity":"rs-5399896","link":"https://doi.org/10.1038/s41598-025-87124-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-20 15:57:22","publishedOnDateReadable":"January 20th, 2025"},"versionCreatedAt":"2024-12-03 01:15:35","video":"","vorDoi":"10.1038/s41598-025-87124-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87124-2","workflowStages":[]},"version":"v1","identity":"rs-5399896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5399896","identity":"rs-5399896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}