Potential Evaluation of SULT1A3 as an Early Diagnostic Marker for Nasopharyngeal Carcinoma: A Study Based on Plasma Proteomics Screening and ELISA Validation

Potential Evaluation of SULT1A3 as an Early Diagnostic Marker for Nasopharyngeal Carcinoma: A Study Based on Plasma Proteomics Screening and ELISA Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Potential Evaluation of SULT1A3 as an Early Diagnostic Marker for Nasopharyngeal Carcinoma: A Study Based on Plasma Proteomics Screening and ELISA Validation Zhifang Lin, Wei Song, Wenhai Liang, Jian Liu, Ruiwei Gan, Yueying Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8856378/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract (1) Background: Nasopharyngeal carcinoma (NPC) is a highly prevalent and aggressive malignancy in Southeast Asia, and early diagnosis is pivotal for reducing mortality; however, the lack of early specific biomarkers remains a major clinical bottleneck; (2) Methods: We conducted a plasma proteomic analysis using mass spectrometry on 15 untreated early stage NPC patients and 15 healthy individuals positive for VCA-IgA antibodies, followed by bioinformatics analysis to identify differentially expressed proteins (DEPs) and ELISA Validation; (3) Results: Proteomic analysis identified 1,428 serum proteins, with 1,410 quantifiable. The analysis identified 31 upregulated and 189 downregulated proteins, with the key upregulated proteins being LTA4H, SULT1A3, and FGL1. LTA4H with plasma cells (0.38305252), CD8 T cells (0.407959118), SULT1A3 with M1 macrophages (0.509905899), and FGL1 with M1 macrophages (0.430029014). These findings indicate specific relationships between the upregulated proteins LTA4H, SULT1A3, FGL1 and certain immune cell types/proportions verified by ELISA experiments.The ELISA validation results demonstrated: LTA4H AUC=0.631, SULT1A3 AUC=0.787, and FGL1 AUC=0.688 (n=80). SULT1A3 AUC=0.826 (cohort 1, n=196),SULT1A3 AUC=0.793 (cohort 2, n=112); (4) Conclusions: By integrating a machine learning-based method with deep proteomic analysis, we explored the pathological mechanisms and identified biomarkers for NPC. We validated the effectiveness of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma through ELISA, which contributes to the development of more effective diagnostic strategies and may pave the way for further research on targeted therapies. NPC Biomarker Plasma proteomics Machine Learning Early Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Nasopharyngeal Carcinoma (NPC) is the most prevalent malignant neoplasm in the head and neck region, exhibiting distinct epidemiological patterns characterized by significant geographical and ethnic variations as well as familial predisposition. In China, the highest incidence rates are concentrated in the Pearl River Delta and Xijiang River Basin, with Sihui District in Zhaoqing City, Guangdong Province, reporting the highest national incidence rates of 30.96 and 15.45 cases per 100,000 population for males and females, respectively. The disease demonstrates a pronounced male predominance, with a male-to-female ratio of approximately 3.4:1, and primarily affects individuals aged 30–60 years, particularly those between 40 and 59 years of age [1–2]. While early stage patients exhibit a favorable 5-year survival rate of up to 90%, this rate significantly declines to 60% in advanced-stage cases [3]. The insidious onset of early symptoms contributes to delayed diagnosis, with approximately 70% of patients presenting at advanced stages, thereby limiting substantial improvements in the overall 5-year survival rates in recent years. Current diagnostic and therapeutic approaches, including imaging examinations, biopsies, radiotherapy, and chemotherapy, remain limited by the accuracy and timeliness of early detection. Consequently, the identification of novel biomarkers is imperative to enhance the early diagnostic efficacy, facilitate timely intervention, and ultimately improve patient prognosis. NPC represents a malignant epithelial tumor arising from the nasopharyngeal mucosa, characterized by a multifactorial etiology involving genetic predisposition, environmental influences, and viral agents, with Epstein-Barr virus (EBV) infection constituting a pivotal pathogenic factor [4]. Epidemiological evidence demonstrates that EBV infection exhibits a near-universal association with NPC, establishing persistent latency predominantly within B lymphocytes. Serological detection of EBV-specific antibodies, particularly viral capsid antigen IgA (VCA-IgA) and early antigen IgA (EA-IgA), has been epidemiologically correlated with NPC risk stratification [5–6], rendering EBV serological testing the most widely implemented screening modality for NPC surveillance. Nevertheless, the limited positive predictive value (PPV) of this approach underscores its limited efficacy for early stage disease detection [7–9]. This study focused on the critical challenge of early NPC detection, underscored by the observation that only 15.8% of newly diagnosed cases were identified at an early stage in our institution between 2019 and 2020. Current screening methodologies, particularly those reliant on EBV antibody detection, demonstrate suboptimal sensitivity and a low positive predictive value. These limitations not only result in unnecessary psychological distress for healthy individuals, but also contribute to the inefficient allocation of healthcare resources. Consequently, improving the sensitivity and positive predictive value of early NPC screening is of utmost clinical significance. We propose an innovative methodological framework that integrates state-of-the-art mass spectrometry and proteomic technologies for the systematic identification of potential serum biomarkers in NPC [10–11]. Our approach incorporates the "Blood+" detection platform, which synergistically combines Field Asymmetric Ion Mobility Spectrometry (FAIMS) with advanced machine learning algorithms, thereby enhancing both the detection sensitivity and analytical reliability for large-scale sample processing [12–16]. The research methodology was structured into three sequential phases: (1) initial biomarker discovery utilizing high-throughput small-sample screening techniques, (2) preliminary validation through small-scale enzyme-linked immunosorbent assay (ELISA) analysis, and (3) comprehensive confirmation via large-scale ELISA validation. This systematic investigation is anticipated to yield robust data supporting NPC screening initiatives with the potential to improve early detection accuracy, reduce false-positive rates associated with EBV antibody testing, and consequently mitigate patient psychological distress while optimizing clinical outcomes. 2. Results 2.1. Proteomic Analysis of Nasopharyngeal Carcinoma Patients The comprehensive proteomic analysis workflow involved meticulous examination of serum samples obtained from patients with NPC and healthy controls. This process combines sample preparation techniques with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ion mobility separation. The entire workflow encompassed several critical stages, including raw data conversion, precise peptide and protein identification, label-free quantification, and subsequent bioinformatic analysis. A total of 1,428 serum proteins were successfully identified in all analyzed samples, demonstrating extensive coverage of the circulating proteome. Of these identified proteins, 1,410 were quantifiable, indicating the robustness and integrity of the data acquisition and processing workflow (Fig. 1 a). Strict quality control measures were implemented throughout proteomic analysis to ensure data reliability. The Pearson correlation coefficients (PCC) for quality control samples ranged consistently from 0.80 to 0.94, signifying excellent reproducibility and consistency of the LC-MS/MS measurements (Fig. 1 b). To investigate the distribution of the protein intensity values and differences among various samples, the protein intensity values for each sample were extracted. Sample means at the same level further indicated good sample quality and stable and reliable results (Fig. 1 c). 2.2. Identification of Nasopharyngeal Carcinoma Biomarkers Based on Plasma Proteomic Profiles To identify differentially expressed proteins, the ratio of the mean relative quantitative values of proteins between the NPC patient and control groups was calculated to determine fold change (FC). To ascertain the statistical significance of these differences, a t-test was performed on the relative quantitative values of the proteins in the two groups, and the corresponding p-values were calculated. This comprehensive analysis successfully identified 31 significantly upregulated and 189 significantly downregulated proteins (Fig. 2 a). Among the significantly up-regulated proteins, three key proteins were identified: LTA4H, SULT1A3, and FGL1. These three proteins have well-established roles in tumor immune evasion and metastasis, suggesting their potential as diagnostic markers, as well as promising therapeutic targets or prognostic indicators. Gene Set Enrichment Analysis (GSEA) was extensively used for both Gene Ontology (GO) and KEGG pathway enrichment analyses to elucidate the perturbed biological processes and molecular pathways that are characteristic of nasopharyngeal carcinoma. GO enrichment analysis revealed pathways involving processes such as positive regulation of exocytosis, zymogen activation, RNA processing, and regulation of the MAPK cascade (Fig. 2 b). KEGG pathway enrichment analysis revealed several essential biological processes, including platelet activation, neutrophil extracellular trap formation, tight junctions, and phagosome formation and function (Fig. 2 c) [17]. Furthermore, to gain a deeper understanding of the interaction relationships among the differentially expressed proteins in NPC, the STRING protein interaction network database was used to compare the database IDs or protein sequences of proteins filtered by a fold change greater than 1.5. Interaction relationships were extracted based on a confidence score of > 0.7. The R package "visNetwork" tool was used to visualize the resulting differential protein interaction network. To clarify the relationships between protein and protein interactions, the top 50 most tightly interacting proteins were selected to construct the protein interaction network (Fig. 2 d). These comprehensive analyses revealed a complex signalling network with relevant pathways encompassing key biological processes, including platelet activation, neutrophil extracellular trap formation, tight junctions, and phagosome formation and function. Notably, the platelet activation pathway has been found to have a critical impact on tumor development and progression. This pathway is characterized by a vicious cycle, whereby tumor cells stimulate platelet aggregation, thereby promoting platelet activation and inducing thrombosis. Concurrently, tumor cell secretion also promotes platelet production, and the increased platelet count can further stimulate tumor growth, thus forming a self-perpetuating cycle. These novel findings significantly enhance our understanding of the pathogenesis of NPC and provide new perspectives for the development of targeted therapeutic strategies. Intervening at key nodes within these identified signalling pathways has the potential to more effectively control tumor cell growth and proliferation, thereby improving the clinical prognosis of patients with NPC. 2.3. Characterization of Nasopharyngeal Carcinoma Immune Features Based on Plasma Proteomic Profiles The CIBERSORT method was applied to blood proteomic features to further characterize the immune profile associated with NPC. This analysis aimed to estimate the relative proportions of the 22 major immune cell types in each subject, including both NPC patients and controls (Fig. 3 a). Preliminary cluster analysis based on these estimated immune cell proportions did not reveal any significant overall differences in immune cell types between NPC patients and the control group (Fig. 3 b). This observation may be related to the use of serum samples in this project, as these may not fully capture the immune cell composition dynamics observed in the tissue. Based on the preliminary proteomic data, three key proteins, LTA4H, SULT1A3, and FGL1, which showed significantly upregulated expression in the blood of NPC patients, were selected for Spearman’s correlation analysis with the estimated immune cell proportions. The analysis revealed specific and statistically significant correlations; LTA4H was significantly correlated with plasma cells (0.38305252) and CD8 T cells (0.407959118). SULT1A3 expression significantly correlated with M1 macrophages (0.509905899). Similarly, FGL1 was significantly correlated with M1 macrophages (0.430029014) (Fig. 3 c). These findings suggest that there are specific and statistically significant relationships between the three key upregulated proteins (LTA4H, SULT1A3, and FGL1) and the types and proportions of certain immune cells. Therefore, although there may not have been significant broad changes to the overall immune cell composition in the peripheral blood, the activity or presence of certain immune cell types appears to be altered in the context of nasopharyngeal carcinoma. This suggests that the immune dysregulation associated with NPC is targeted and nuanced, rather than a generalized systemic immune shift. These correlations strongly suggest that LTA4H, SULT1A3, and FGL1 are involved in the complex interactions between tumor cells and the immune system. Given their association with specific immune cell profiles, they may serve as potential biomarkers for identifying patients with NPC. 2.4. ELISA Validation of Potential Nasopharyngeal Carcinoma Biomarkers Proteomic analysis of Nasopharyngeal Carcinoma patients identified three proteins,LTA4H, SULT1A3, and FGL1, as candidate biomarkers for Nasopharyngeal Carcinoma. We initially validated these three proteins using ELISA with a small sample size and evaluated their efficacy as predictors of nasopharyngeal carcinoma through ROC curve discriminant analysis. The discriminant efficacy of the individual proteins LTA4H, SULT1A3, and FGL1 in distinguishing nasopharyngeal carcinoma patients from VCA-IgA normal individuals was 0.631, 0.787, and 0.688, respectively (Fig. 4 ). We observed that compared to healthy controls, the SULT1A3 protein exhibited characteristics warranting further validation. Therefore, we collected an independent cohort for ELISA validation. This cohort included 100 nasopharyngeal carcinoma patients, 106 VCA-IgA+ normal individuals, and 22 healthy controls.The discriminative efficacy of SULT1A3 in distinguishing nasopharyngeal carcinoma patients from VCA-IgA+ Normal Individuals and healthy controls was 0.826 and 0.793, respectively (Fig. 5 ). An ELISA was performed to validate the expression levels of SULT1A3 in a large and diverse cohort. This provides robust validation of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma. The ELISA validation results were consistent with the initial proteomic findings, further supporting the association of SULT1A3 with tumor-related pathways and its potential clinical utility. These results indicate that the nasopharyngeal carcinoma biomarker SULT1A3, identified through proteomics screening, has potential as a detection marker. The three proteins LTA4H, SULT1A3, and FGL1 play significant roles in various diseases, including cancer, infectious diseases, and bone and joint disorders. LTA4H has regulatory functions in liver cancer, laryngeal squamous cell carcinoma, tuberculous meningitis, and osteoarthritis, whereas FGL1 shows potential application value in cancer immunotherapy [18]. 3. Discussion NPC is a common malignant tumor of the head and neck region in southern China and Southeast Asia. Zhaoqing City in Guangdong Province has a high incidence rate of approximately 15–30/100,000, which is significantly higher than the national average (3–5/100,000). The pathogenesis may be related to EB infection, environmental factors, and genetic factors; however, the specific mechanisms have not been fully elucidated. Since patients do not exhibit obvious symptoms in the early stages, 70% of cases are diagnosed at an advanced stage when they seek medical attention, leading to a lack of significant improvement in the 5-year survival rate of patients with NPC in recent years. Therefore, early detection and timely treatment are crucial to improve the survival rate of patients with NPC. Currently, the diagnosis of NPC relies mainly on EB serological markers (such as VCA-IgA and EA-IgA), imaging examinations (such as MRI and CT), and pathological biopsy. Although existing markers play an important role, they still have limitations. First, the positive predictive value of serological markers is not high, and some benign diseases (such as chronic rhinitis) can produce false positives, causing unnecessary psychological burden on healthy individuals and leading to a waste of medical resources due to repeated follow-up monitoring. Second, imaging has insufficient sensitivity for early small lesions, often resulting in missed diagnoses; pathological biopsy is an invasive procedure and is difficult to use for screening. Therefore, there is an urgent need to identify early diagnostic markers with high sensitivities and specificities. Third, there is a lack of specific markers for high-risk populations (such as the Zhaoqing region), which makes it impossible to reflect population differences. Blood is considered an ideal biological specimen because of its noninvasive or minimally invasive nature and ease of collection. Many studies on tumor markers have been based on strategies using blood as the research object, attempting to diagnose tumors by identifying serum/plasma markers. In recent years, with the advancement of mass spectrometry technology, the use of omics methods to screen new blood markers has become an increasingly important direction for research and translational applications. Proteomics can be used to observe the composition and changes of proteins within cells at a holistic level, providing a comprehensive and systematic understanding of protein expression, modification, and interactions in a given physiological state, thereby revealing protein functions and offering important reference significance for theoretical research in life sciences. Proteomics can be used to identify biomarkers for disease diagnosis, with comprehensive, systematic, efficient, and direct characteristics for the identification of serum proteins, and it has good reproducibility and sensitivity. In 2017, a review published in the renowned journal Nature Reviews Cancer strongly predicted that protein molecular markers would play an extremely important role in the precise diagnosis of tumors. The "Blood+" blood proteomics detection technology integrates FAIMS ion filtering technology and machine learning marker screening methods, significantly improving detection depth and reliability, and covering as many low-abundance proteins as possible. FAIMS is a gaseous ion-filtering device that can deflect certain ion signals through the action of an electric field, thereby reducing sample complexity and facilitating subsequent mass spectrometry identification of peptide signals. According to the test results, further filtering and screening of high-abundance interference signals through the FAIMS device can increase the detection depth of blood from over 600 to 1000, an improvement of more than 50%, while maintaining good quantitative parallelism. This detection method has high sensitivity and resolution, can detect low-abundance proteins, and is a powerful tool for NPC marker screening. This study is based on the Blood+/PRM proteomics technology, following a three-step research strategy to address clinical issues: first, using small-sample proteomics technology (the "4D-Blood+" blood proteomics method combining FAIMS ion filtering technology and machine learning marker screening) to screen and discover serum protein molecular markers, and then using the mainstream targeted proteomics data acquisition method PRM to preliminarily validate the screened markers, reducing the risk of subsequent large-sample validation; and finally, using ELISA, which is suitable for large samples and clinical application, to revalidate and evaluate the candidate markers, while constructing an optimized combination of protein molecular markers for NPC screening. This project will provide population data and experimental evidence for the development of molecular markers for NPC screening and diagnosis, improve the sensitivity and positive predictive value of early NPC screening, and may increase the cure rate of NPC and alleviate the psychological pressure of most EBV antibody-positive individuals without NPC risk. This will provide a basis for truly high-risk individuals to guide further examinations and take early preventive measures, ultimately making NPC screening more acceptable and improving the early diagnosis rate of NPC. Our research found that NPC, a highly aggressive tumor prevalent in southern China, requires early diagnosis to improve prognosis. We discovered that the expression levels of three proteins, LTA4H, SULT1A3, and FGL1, in the serum of patients showed significantly different from those in the healthy control group. Through quantitative proteomics analysis, targeted PRM validation, and subsequent large-sample ELISA testing, it was confirmed that this combination of markers has high sensitivity and specificity in distinguishing NPC patients (especially early lesions) from healthy individuals, which not only effectively improves the detection rate of early NPC but also significantly increases the positive predictive value when used in combination, effectively reducing the risk of false-positive results, thereby alleviating the unnecessary psychological burden of EBV antibody-positive individuals without actual cancer risk and providing a more precise examination basis for truly high-risk individuals, demonstrating great potential as early diagnostic markers. SULT1A3, an important sulfotransferase, mainly functions in the metabolism of monoamine neurotransmitters (such as dopamine). It exhibits different expression patterns in different tissues and developmental stages and is associated with various diseases (such as neurodegenerative diseases and liver cancer). Research has shown that the substrate specificity and enzymatic activity of SULT1A3 are influenced by its structural flexibility and genetic variations. The expression of SULT1A3 in liver cancer tissues is significantly higher than that in normal tissues, and its expression level is related to the invasion and migration abilities of liver cancer cells [19]. LTA4H is a key enzyme in the arachidonic acid metabolic pathway that catalyzes the conversion of leukotriene A4 (LTA4) to leukotriene B4 (LTB4), the latter being a potent pro-inflammatory lipid mediator. In addition to its classic role in inflammation, LTA4H also has important functions in cancer, especially in chronic inflammation-related cancers. Studies have explored the potential role of Bestatin (an LTA4H inhibitor) in inhibiting acute skin inflammation and skin cancer occurrence, validating the key role of the LTA4H/BLT1 signaling pathway in skin inflammation and carcinogenesis. After acute simulated sunlight (SSL) exposure, the expression of BLT1 in human and mouse skin tissues is significantly increased [20]. LTA4H and BLT1 are also highly expressed in chronic skin inflammation and squamous cell carcinoma, indicating an important role of this pathway in skin pathological processes [21]. Additionally, LTA4H inhibitors have shown therapeutic potential in preclinical models of asthma, inflammatory bowel disease, and arthritis [22]. FGL1 (fibrinogen-like protein 1) is a member of the fibrinogen-related protein family, initially noted for its role in liver regeneration and metabolism as a liver-secreted protein related to proliferation and metabolism. FGL1 is abnormally expressed in various malignant tumors, forming an immune checkpoint pathway with the immune inhibitory receptor LAG3, which mediates tumor immune escape [23]. In recent years, its key role in tumor immune escape (particularly its interaction with LAG3) has attracted research attention [24]. The expression and function of FGL1 is highly heterogeneous in various cancers. 4. Materials and Methods 4.1 Study Population For the proteomics screening cohort, serum samples were collected from 15 untreated, pathologically confirmed, early stage (I/II) nasopharyngeal carcinoma patients. The control group consisted of 15 healthy individuals who tested positive for VCA-IgA antibodies. Forty NPC specimens were included in the preliminary validation cohort, 40 nasopharyngeal carcinoma specimens were included, with 40 VCA-IgA-positive healthy individuals serving as controls. Given the significant regional differences in nasopharyngeal carcinoma prevalence and the potential influence of environmental or genetic factors on disease development and protein profiles, efforts were made to minimize potential confounding factors by ensuring that the participants in the screening and preliminary validation cohorts were as similar as possible across key demographic and lifestyle factors, including age, sex, place of residence, native origin, and smoking history. A large-scale ELISA validation cohort was designed to comprehensively assess the specificity and generalizability of biomarkers. The cohort included 90 patients with clinically and pathologically confirmed nasopharyngeal carcinoma. Additionally, two distinct healthy control groups were included: 106 VCA-IgA-positive individuals and 22 VCA-IgA and EA-IgA double-negative individuals. Written informed consent was obtained from all the participants. All studies were approved by the Ethics Review Committee of the Zhaoqing First People's Hospital, thereby ensuring adherence to ethical research guidelines (B2024-07-03). 4.2 Protein Enrichment, Digestion, and Separation For protein extraction, the samples were stored at -80°C and centrifuged at 4°C at 12,000 × g for 10 min to remove cellular debris. The supernatant was then transferred to a new centrifuge tube. High-abundance proteins were depleted using the Pierce™ Top 14 Abundant Protein Depletion Spin Column Kit (Thermo Scientific), following the manufacturer's instructions. The total protein concentration was determined using a BCA assay kit. Equal amounts of protein from each sample were used for enzymatic digestion. The sample volume was adjusted for consistency using lysis buffer. Dithiothreitol (DTT) was added to a final concentration of 5 mM, and protein reduction was performed at 56°C for 30 min. Subsequently, iodoacetamide (IAA) was added at a final concentration of 11 mM, and the alkylation reaction proceeded at room temperature in the dark for 15 min. The alkylated samples were transferred to ultrafiltration tubes, centrifuged at 12,000 × g for 20 min at room temperature, and exchanged three times with 8 M urea, followed by three exchanges with replacement buffer. Trypsin was then added at a proteinase-to-protein ratio of 1:50 (m/m), and digestion was performed overnight. Peptides were recovered by centrifugation at 12,000 × g for 10 min at room temperature, followed by an additional recovery wash with ddH2O. The combined peptide solutions were stored for further use. 4.3 Mass Spectrometry Analysis Tryptic peptides were dissolved in solvent A (0.1% formic acid, 2% acetonitrile in water) and directly loaded onto a custom-made reversed-phase analytical column (25-cm length, 100 µm i.d.). The mobile phase consisted of solvents A and B (0.1% formic acid and 90% acetonitrile in water). Peptide separation was achieved using a sophisticated gradient on an EASY-nLC 1200 UPLC system (Thermo Fisher Scientific, Waltham, MA, USA) at a constant flow rate of 500 nl/min. The gradient program was as follows: 0–16 min, 7%-20%B; 16–24 min, 20%-32%B; 24–27 min, 32%-80%B; 27–30 min, 80%B. The separated peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer equipped with a nanoelectrospray ion source. An electrospray voltage of 2300 V was applied. High-Field Asymmetric-waveform Ion Mobility Spectrometry (FAIMS) compensation voltages (CV) were precisely set at -45V and − 70V. Both the precursors and fragments were analyzed using an Orbitrap detector. The full MS scan resolution was set to 30000 for the scan range of 390–810 m/z. MS/MS scanning was performed with a fixed first mass of 200 m/z at a resolution of 30000. Higher-energy Collisional Dissociation (HCD) fragmentation was performed at normalized collision energies (NCE) of 25%, 30%, and 35%. The automatic gain control (AGC) target was set at 3E6, with the maximum injection time set to Auto. 4.4 Database processing The Data-Independent Acquisition (DIA) data were meticulously processed using Spectronaut (v.17) software. Tandem mass spectra were searched against H. sapiens_9606_SP_20230103. fasta database contains 20389 entries. This database was concatenated with a reverse decoy database. Trypsin/P was specified as the cleavage enzyme, allowing for up to two missing cleavages. Carbamidomethylation of cysteine residues was specified as a fixed modification, while acetylation of protein N-terminals and oxidation of methionine residues were specified as variable modifications. A stringent false discovery rate (FDR) of less than 1% was applied at the protein, peptide, and Peptide Spectrum Match (PSM) levels. 4.5 ELISA Analysis For the significantly differential indicators identified through the initial proteomics screening and analysis, an enzyme-linked immunosorbent assay (ELISA) kit (Human LTA4H ELISA KIT, Human SULT1A3 ELISA KIT, and Human FGL1 ELISA KIT) was used. The ELISA protocol strictly adhered to the manufacturer's instructions to ensure consistency and reproducibility. This included meticulous sample preparation, generation of a standard curve for accurate quantification, precise sample addition to the wells, appropriate incubation steps, addition of substrate for the colorimetric reaction, and subsequent measurement of absorbance at a specified wavelength using a microplate reader (Fig. 6 ). 5. Conclusions In this study, we present a new strategy of integrating a machine learning-based method with proteomic analysis for the discovery of biomarkers and elucidation of early nasopharyngeal carcinom. Proteomic analysis of Nasopharyngeal Carcinoma patients identified three proteins,LTA4H, SULT1A3, and FGL1, as candidate biomarkers for Nasopharyngeal Carcinoma. Specifically, the validation of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma through Elisa provides robust evidence.The characterization of immune features in early nasopharyngeal carcinoma provided insights into the underlying molecular mechanisms of the disease. These findings will contribute to the development of more effective diagnostic strategies and may pave the way for further research on targeted therapies. However, the limitations of this study, such as the relatively small sample size and need for external validation, should be considered. Future studies with larger cohorts and more comprehensive analyses are warranted to confirm the clinical utility of SULT1A3 and the immune-related proteins identified. Abbreviations The following abbreviations are used in this manuscript: NPC Nasopharyngeal carcinoma DEPs Differentially expressed proteins ELISA Enzyme-linked immunosorbent assay EBV Epstein-Barr virus VCA-IgA viral capsid antigen IgA EA-IgA early antigen IgA PPV positive predictive value FAIMS Field Asymmetric Ion Mobility Spectrometry LC-MS/MS liquid chromatography-tandem mass spectrometry PCC Pearson correlation coefficients FC fold change GSEA Gene Set Enrichment Analysis GO Gene Ontology DTT Dithiothreitol IAA iodoacetamide HCD Higher-energy Collisional Dissociation AGC automatic gain control DIA Data-Independent Acquisition FDR false discovery rate PSM Peptide Spectrum Match Declarations Author Contributions: Conceptualization, Z.L., W.L. and Y.W.; writing—original draft preparation, W.S. and Y.W.; writing—review and editing, Z.L. and Y.W.; visualization, W.L.,and Y.W.; supervision, W.S. and Y.W.; Validation, Z.L., W.L., J.L. and R.W.; Resources, Z.L., W.S. and Y.W.; Methodology, Z.L.,W.L., J.L., R.W. and Y.W.; Data curation, Z.L.,W.L. and Y.W.; Formal analysis, Z.L.,W.L. and W.S.; Project administration, Z.L. and Y.W. All authors have read and agreed to the published version of the manuscript. Funding: This work was financially supported by the Guangdong Provincial Clinical Research Center for Laboratory Medicine (2023B110008) and the Scientific Research Fund of the First People's Hospital of Zhaoqing (YJJ-2022-02-01). Institutional Review Board Statement: This study was approved by the Institute Research Ethics Committee of First People's Hospital of Zhaoqing, Zhaoqing, China (B2024-07-03). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Acknowledgments: We acknowledge the members of the Clinical Laboratory, particularly those involved in our research program. We would like to express our gratitude to Dr. Weimin Jia from the First People's Hospital of Zhaoqing for his valuable suggestions on the proteomics screening protocol used in this study. We thank all the patients for their participation in this study. Conflicts of Interest: The authors declare no conflicts of interest. Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Wei KR, Zheng RS, Zhang SW, et al. Nasopharyngeal carcinoma incidence and mortality in China, 2013. CHIN J CANCER . 2017, 36(1): 90. doi: 10.1186/s40880-017-0257-9. Zhang LF, Li YH, Xie SH et al. Incidence trend of nasopharyngeal carcinoma from 1987 to 2011 in Sihui County, Guangdong Province, South China: An age-period-cohort analysis. CHIN J CANCER . 2015, 34(8): 350-357. doi: 10.1186/s40880-015-0018-6. Ribassin-Majed L, Marguet S et al. What is the best treatment option for locally advanced Nasopharyngeal Carcinoma? Individual Patient Data Network Meta-Analysis. J Clin Oncol . 2017, 35(5): 498-505. doi: 10.1200/JCO.2016.67.4119. Epub 2016 Dec 5. Chen YP, Chan ATC, Le QT et al. Nasopharyngeal carcinoma. 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Histone serotonylation is a permissive modification that enhances TFIID binding to H3K4me3. NATURE . 2019, 567(7749): 535-539. doi: 10.1038/s41586-019-1024-7. Epub 2019 Mar 13. Huang H, Weng H, Zhou K et al. Histone H3 trimethylation at lysine 36 co-transcriptionally guides m6A RNA modification co-transcriptionally. NATURE . 2019, 567(7748): 414-419. doi: 10.1038/s41586-019-1016-7. Epub 2019 Mar 13. Borrebaeck CA. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. NAT REV CANCER . 2017, 17(3): 199-204. doi: 10.1038/nrc.2016.153. Epub 2017 Feb 3. Rauniyar N. Parallel Reaction Monitoring: A Targeted Experiment Performed Using High Resolution and High Mass Accuracy Mass Spectrometry. Int J Mol Sci . 2015, 16(12): 28566-28581. doi: 10.3390/ijms161226120. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res . 2000, 28(1):27-30. doi: 10.1093/nar/28.1.27. PMID: 10592173; PMCID: PMC102409. XiHua L, Lian Wen Q, et al. Implication of the hepatokine fibrinogen-like protein 1 in liver diseases, metabolic disorders, and cancer: The need to harness its full potential. Int J Biol Sci . 2022, 18 (1): 292-300. doi: 10.7150/ijbs.66834. eCollection 2022. Zou J, Li H, Huang Q, et al. Dopamine-induced SULT1A3/4 promotes EMT and cancer stemness in hepatocellular carcinoma. Tumour Biol . 2017, 39 (10): 1010428317719272. doi: 10.1177/1010428317719272. Zhao S, Yao K, Liu K et al. Bestatin cream impairs solar-simulated light-driven skin inflammation and skin carcinogenesis in mice. J Invest Dermatol . 2021 Nov;141(11):2699-2709.e2. doi: 10.1016/j.jid.2021.03.032. Naomi O, Hiroyuki Y, Alyssa L, et al. LTA4H regulates cell cycle and skin carcinogenesis. Carcinogenesis . 2017, 38 (7): 728-737. doi: 10.1093/carcin/bgx049. Raj K K, Pathak L, Muttineni R, et al. Analog-based pharmacophore strategy to identify novel leukotriene a4 hydrolase (LTA4H) inhibitors. International Journal of Drug Discovery . 2010, 2 (2): 20-50. Wenjing Q, Mingfang Z, Ruoyu W et al. Fibrinogen-like protein 1 (FGL1): the next immune checkpoint target. J Hematol Oncol . 2021, 14 (1): 147-147. doi: 10.1186/s13045-021-01161-8. XiYang T, Yan Lu X, AnPing S, et al. Downregulation of fibrinogen-like protein 1 inhibits the proliferation of lung adenocarcinoma by regulating MYC-target genes. Transl Lung Cancer Res . 2022, 11 (3): 404-419. doi: 10.21037/tlcr-22-151. Additional Declarations No competing interests reported. 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Zhaoqing","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Song","suffix":""},{"id":602388554,"identity":"882bd154-51d2-408a-94e8-b978c5ca2ec5","order_by":2,"name":"Wenhai Liang","email":"","orcid":"","institution":"The First People’s Hospital of Zhaoqing","correspondingAuthor":false,"prefix":"","firstName":"Wenhai","middleName":"","lastName":"Liang","suffix":""},{"id":602388555,"identity":"4cd53723-52d0-4397-8cc5-a5d123ca41d9","order_by":3,"name":"Jian Liu","email":"","orcid":"","institution":"The First People’s Hospital of Zhaoqing","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liu","suffix":""},{"id":602388557,"identity":"68703843-ba44-44d3-b38b-5029e25b030e","order_by":4,"name":"Ruiwei Gan","email":"","orcid":"","institution":"The First People’s Hospital of 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01:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8856378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8856378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104387316,"identity":"eceb0c55-e145-4362-995f-bdfd27327d89","added_by":"auto","created_at":"2026-03-11 08:57:31","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1403192,"visible":true,"origin":"","legend":"\u003cp\u003eProteomic Analysis of Nasopharyngeal Carcinoma Patients.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/a102bef30fec2fbcab0241c0.jpeg"},{"id":104387362,"identity":"7e35fd30-5e8b-4752-a2b6-2216c830b1d7","added_by":"auto","created_at":"2026-03-11 08:57:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1620758,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Nasopharyngeal Carcinoma biomarkers based on the plasma proteomic profiles.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/2c6e223d0bf75cf802bdf394.jpeg"},{"id":104387373,"identity":"a64f3a9b-4b7d-4663-b8a7-709635385aa0","added_by":"auto","created_at":"2026-03-11 08:57:48","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":935148,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of immune features of NPC based on plasma proteomic profiles.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/2867767bf04c46cae2a8b3ee.jpeg"},{"id":104387382,"identity":"dfb7cb46-f7ea-4714-ae5f-709147292219","added_by":"auto","created_at":"2026-03-11 08:57:50","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":267777,"visible":true,"origin":"","legend":"\u003cp\u003eELISA validation of proteins LTA4H, SULT1A3, and FGL1 in distinguishing Nasopharyngeal Carcinoma patients from healthy individuals positive for VCA-IgA+ antibodies.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/96bb1d928824edf514b8fef9.jpeg"},{"id":104387325,"identity":"8ae44998-6140-48df-bad8-09324081a168","added_by":"auto","created_at":"2026-03-11 08:57:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":299053,"visible":true,"origin":"","legend":"\u003cp\u003eELISA validation of Protein SULT1A3 in healthy individuals positive for VCA-IgA+ antibodies and in VCA-IgA normal individuals.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/9795d76eebe39311960cddc3.jpeg"},{"id":104387306,"identity":"9317d2d6-ec66-4b4f-9b46-12b20684c37f","added_by":"auto","created_at":"2026-03-11 08:57:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":317106,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of Characterization of Immune Features and Discovery of Potential Biomarkers in Early-Stage Nasopharyngeal Carcinoma Using Plasma Proteomics.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/ec07544886980079e784c3fe.png"},{"id":104387431,"identity":"e5fb190d-baf7-4d3e-81f1-378c11439f11","added_by":"auto","created_at":"2026-03-11 08:58:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5466429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8856378/v1/563e8af2-2746-47e4-80d0-a89ca023d5d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential Evaluation of SULT1A3 as an Early Diagnostic Marker for Nasopharyngeal Carcinoma: A Study Based on Plasma Proteomics Screening and ELISA Validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNasopharyngeal Carcinoma (NPC) is the most prevalent malignant neoplasm in the head and neck region, exhibiting distinct epidemiological patterns characterized by significant geographical and ethnic variations as well as familial predisposition. In China, the highest incidence rates are concentrated in the Pearl River Delta and Xijiang River Basin, with Sihui District in Zhaoqing City, Guangdong Province, reporting the highest national incidence rates of 30.96 and 15.45 cases per 100,000 population for males and females, respectively. The disease demonstrates a pronounced male predominance, with a male-to-female ratio of approximately 3.4:1, and primarily affects individuals aged 30\u0026ndash;60 years, particularly those between 40 and 59 years of age [1\u0026ndash;2]. While early stage patients exhibit a favorable 5-year survival rate of up to 90%, this rate significantly declines to 60% in advanced-stage cases [3]. The insidious onset of early symptoms contributes to delayed diagnosis, with approximately 70% of patients presenting at advanced stages, thereby limiting substantial improvements in the overall 5-year survival rates in recent years. Current diagnostic and therapeutic approaches, including imaging examinations, biopsies, radiotherapy, and chemotherapy, remain limited by the accuracy and timeliness of early detection. Consequently, the identification of novel biomarkers is imperative to enhance the early diagnostic efficacy, facilitate timely intervention, and ultimately improve patient prognosis.\u003c/p\u003e \u003cp\u003eNPC represents a malignant epithelial tumor arising from the nasopharyngeal mucosa, characterized by a multifactorial etiology involving genetic predisposition, environmental influences, and viral agents, with Epstein-Barr virus (EBV) infection constituting a pivotal pathogenic factor [4]. Epidemiological evidence demonstrates that EBV infection exhibits a near-universal association with NPC, establishing persistent latency predominantly within B lymphocytes. Serological detection of EBV-specific antibodies, particularly viral capsid antigen IgA (VCA-IgA) and early antigen IgA (EA-IgA), has been epidemiologically correlated with NPC risk stratification [5\u0026ndash;6], rendering EBV serological testing the most widely implemented screening modality for NPC surveillance. Nevertheless, the limited positive predictive value (PPV) of this approach underscores its limited efficacy for early stage disease detection [7\u0026ndash;9].\u003c/p\u003e \u003cp\u003eThis study focused on the critical challenge of early NPC detection, underscored by the observation that only 15.8% of newly diagnosed cases were identified at an early stage in our institution between 2019 and 2020. Current screening methodologies, particularly those reliant on EBV antibody detection, demonstrate suboptimal sensitivity and a low positive predictive value. These limitations not only result in unnecessary psychological distress for healthy individuals, but also contribute to the inefficient allocation of healthcare resources. Consequently, improving the sensitivity and positive predictive value of early NPC screening is of utmost clinical significance. We propose an innovative methodological framework that integrates state-of-the-art mass spectrometry and proteomic technologies for the systematic identification of potential serum biomarkers in NPC [10\u0026ndash;11]. Our approach incorporates the \"Blood+\" detection platform, which synergistically combines Field Asymmetric Ion Mobility Spectrometry (FAIMS) with advanced machine learning algorithms, thereby enhancing both the detection sensitivity and analytical reliability for large-scale sample processing [12\u0026ndash;16]. The research methodology was structured into three sequential phases: (1) initial biomarker discovery utilizing high-throughput small-sample screening techniques, (2) preliminary validation through small-scale enzyme-linked immunosorbent assay (ELISA) analysis, and (3) comprehensive confirmation via large-scale ELISA validation. This systematic investigation is anticipated to yield robust data supporting NPC screening initiatives with the potential to improve early detection accuracy, reduce false-positive rates associated with EBV antibody testing, and consequently mitigate patient psychological distress while optimizing clinical outcomes.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Proteomic Analysis of Nasopharyngeal Carcinoma Patients\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe comprehensive proteomic analysis workflow involved meticulous examination of serum samples obtained from patients with NPC and healthy controls. This process combines sample preparation techniques with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ion mobility separation. The entire workflow encompassed several critical stages, including raw data conversion, precise peptide and protein identification, label-free quantification, and subsequent bioinformatic analysis.\u003c/p\u003e \u003cp\u003eA total of 1,428 serum proteins were successfully identified in all analyzed samples, demonstrating extensive coverage of the circulating proteome. Of these identified proteins, 1,410 were quantifiable, indicating the robustness and integrity of the data acquisition and processing workflow (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Strict quality control measures were implemented throughout proteomic analysis to ensure data reliability. The Pearson correlation coefficients (PCC) for quality control samples ranged consistently from 0.80 to 0.94, signifying excellent reproducibility and consistency of the LC-MS/MS measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To investigate the distribution of the protein intensity values and differences among various samples, the protein intensity values for each sample were extracted. Sample means at the same level further indicated good sample quality and stable and reliable results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of Nasopharyngeal Carcinoma Biomarkers Based on Plasma Proteomic Profiles\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed proteins, the ratio of the mean relative quantitative values of proteins between the NPC patient and control groups was calculated to determine fold change (FC). To ascertain the statistical significance of these differences, a t-test was performed on the relative quantitative values of the proteins in the two groups, and the corresponding p-values were calculated. This comprehensive analysis successfully identified 31 significantly upregulated and 189 significantly downregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among the significantly up-regulated proteins, three key proteins were identified: LTA4H, SULT1A3, and FGL1. These three proteins have well-established roles in tumor immune evasion and metastasis, suggesting their potential as diagnostic markers, as well as promising therapeutic targets or prognostic indicators.\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was extensively used for both Gene Ontology (GO) and KEGG pathway enrichment analyses to elucidate the perturbed biological processes and molecular pathways that are characteristic of nasopharyngeal carcinoma. GO enrichment analysis revealed pathways involving processes such as positive regulation of exocytosis, zymogen activation, RNA processing, and regulation of the MAPK cascade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). KEGG pathway enrichment analysis revealed several essential biological processes, including platelet activation, neutrophil extracellular trap formation, tight junctions, and phagosome formation and function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) [17]. Furthermore, to gain a deeper understanding of the interaction relationships among the differentially expressed proteins in NPC, the STRING protein interaction network database was used to compare the database IDs or protein sequences of proteins filtered by a fold change greater than 1.5. Interaction relationships were extracted based on a confidence score of \u0026gt;\u0026thinsp;0.7. The R package \"visNetwork\" tool was used to visualize the resulting differential protein interaction network. To clarify the relationships between protein and protein interactions, the top 50 most tightly interacting proteins were selected to construct the protein interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThese comprehensive analyses revealed a complex signalling network with relevant pathways encompassing key biological processes, including platelet activation, neutrophil extracellular trap formation, tight junctions, and phagosome formation and function. Notably, the platelet activation pathway has been found to have a critical impact on tumor development and progression. This pathway is characterized by a vicious cycle, whereby tumor cells stimulate platelet aggregation, thereby promoting platelet activation and inducing thrombosis. Concurrently, tumor cell secretion also promotes platelet production, and the increased platelet count can further stimulate tumor growth, thus forming a self-perpetuating cycle. These novel findings significantly enhance our understanding of the pathogenesis of NPC and provide new perspectives for the development of targeted therapeutic strategies. Intervening at key nodes within these identified signalling pathways has the potential to more effectively control tumor cell growth and proliferation, thereby improving the clinical prognosis of patients with NPC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Characterization of Nasopharyngeal Carcinoma Immune Features Based on Plasma Proteomic Profiles\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe CIBERSORT method was applied to blood proteomic features to further characterize the immune profile associated with NPC. This analysis aimed to estimate the relative proportions of the 22 major immune cell types in each subject, including both NPC patients and controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Preliminary cluster analysis based on these estimated immune cell proportions did not reveal any significant overall differences in immune cell types between NPC patients and the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This observation may be related to the use of serum samples in this project, as these may not fully capture the immune cell composition dynamics observed in the tissue. Based on the preliminary proteomic data, three key proteins, LTA4H, SULT1A3, and FGL1, which showed significantly upregulated expression in the blood of NPC patients, were selected for Spearman\u0026rsquo;s correlation analysis with the estimated immune cell proportions. The analysis revealed specific and statistically significant correlations; LTA4H was significantly correlated with plasma cells (0.38305252) and CD8 T cells (0.407959118). SULT1A3 expression significantly correlated with M1 macrophages (0.509905899). Similarly, FGL1 was significantly correlated with M1 macrophages (0.430029014) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThese findings suggest that there are specific and statistically significant relationships between the three key upregulated proteins (LTA4H, SULT1A3, and FGL1) and the types and proportions of certain immune cells. Therefore, although there may not have been significant broad changes to the overall immune cell composition in the peripheral blood, the activity or presence of certain immune cell types appears to be altered in the context of nasopharyngeal carcinoma. This suggests that the immune dysregulation associated with NPC is targeted and nuanced, rather than a generalized systemic immune shift. These correlations strongly suggest that LTA4H, SULT1A3, and FGL1 are involved in the complex interactions between tumor cells and the immune system. Given their association with specific immune cell profiles, they may serve as potential biomarkers for identifying patients with NPC.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. ELISA Validation of Potential Nasopharyngeal Carcinoma Biomarkers\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eProteomic analysis of Nasopharyngeal Carcinoma patients identified three proteins,LTA4H, SULT1A3, and FGL1, as candidate biomarkers for Nasopharyngeal Carcinoma. We initially validated these three proteins using ELISA with a small sample size and evaluated their efficacy as predictors of nasopharyngeal carcinoma through ROC curve discriminant analysis. The discriminant efficacy of the individual proteins LTA4H, SULT1A3, and FGL1 in distinguishing nasopharyngeal carcinoma patients from VCA-IgA normal individuals was 0.631, 0.787, and 0.688, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We observed that compared to healthy controls, the SULT1A3 protein exhibited characteristics warranting further validation. Therefore, we collected an independent cohort for ELISA validation. This cohort included 100 nasopharyngeal carcinoma patients, 106 VCA-IgA+ normal individuals, and 22 healthy controls.The discriminative efficacy of SULT1A3 in distinguishing nasopharyngeal carcinoma patients from VCA-IgA+ Normal Individuals and healthy controls was 0.826 and 0.793, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). An ELISA was performed to validate the expression levels of SULT1A3 in a large and diverse cohort. This provides robust validation of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma. The ELISA validation results were consistent with the initial proteomic findings, further supporting the association of SULT1A3 with tumor-related pathways and its potential clinical utility.\u003c/p\u003e \u003cp\u003eThese results indicate that the nasopharyngeal carcinoma biomarker SULT1A3, identified through proteomics screening, has potential as a detection marker. The three proteins LTA4H, SULT1A3, and FGL1 play significant roles in various diseases, including cancer, infectious diseases, and bone and joint disorders. LTA4H has regulatory functions in liver cancer, laryngeal squamous cell carcinoma, tuberculous meningitis, and osteoarthritis, whereas FGL1 shows potential application value in cancer immunotherapy [18].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNPC is a common malignant tumor of the head and neck region in southern China and Southeast Asia. Zhaoqing City in Guangdong Province has a high incidence rate of approximately 15\u0026ndash;30/100,000, which is significantly higher than the national average (3\u0026ndash;5/100,000). The pathogenesis may be related to EB infection, environmental factors, and genetic factors; however, the specific mechanisms have not been fully elucidated. Since patients do not exhibit obvious symptoms in the early stages, 70% of cases are diagnosed at an advanced stage when they seek medical attention, leading to a lack of significant improvement in the 5-year survival rate of patients with NPC in recent years. Therefore, early detection and timely treatment are crucial to improve the survival rate of patients with NPC.\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of NPC relies mainly on EB serological markers (such as VCA-IgA and EA-IgA), imaging examinations (such as MRI and CT), and pathological biopsy. Although existing markers play an important role, they still have limitations. First, the positive predictive value of serological markers is not high, and some benign diseases (such as chronic rhinitis) can produce false positives, causing unnecessary psychological burden on healthy individuals and leading to a waste of medical resources due to repeated follow-up monitoring. Second, imaging has insufficient sensitivity for early small lesions, often resulting in missed diagnoses; pathological biopsy is an invasive procedure and is difficult to use for screening. Therefore, there is an urgent need to identify early diagnostic markers with high sensitivities and specificities. Third, there is a lack of specific markers for high-risk populations (such as the Zhaoqing region), which makes it impossible to reflect population differences.\u003c/p\u003e \u003cp\u003eBlood is considered an ideal biological specimen because of its noninvasive or minimally invasive nature and ease of collection. Many studies on tumor markers have been based on strategies using blood as the research object, attempting to diagnose tumors by identifying serum/plasma markers. In recent years, with the advancement of mass spectrometry technology, the use of omics methods to screen new blood markers has become an increasingly important direction for research and translational applications. Proteomics can be used to observe the composition and changes of proteins within cells at a holistic level, providing a comprehensive and systematic understanding of protein expression, modification, and interactions in a given physiological state, thereby revealing protein functions and offering important reference significance for theoretical research in life sciences. Proteomics can be used to identify biomarkers for disease diagnosis, with comprehensive, systematic, efficient, and direct characteristics for the identification of serum proteins, and it has good reproducibility and sensitivity. In 2017, a review published in the renowned journal Nature Reviews Cancer strongly predicted that protein molecular markers would play an extremely important role in the precise diagnosis of tumors. The \"Blood+\" blood proteomics detection technology integrates FAIMS ion filtering technology and machine learning marker screening methods, significantly improving detection depth and reliability, and covering as many low-abundance proteins as possible. FAIMS is a gaseous ion-filtering device that can deflect certain ion signals through the action of an electric field, thereby reducing sample complexity and facilitating subsequent mass spectrometry identification of peptide signals. According to the test results, further filtering and screening of high-abundance interference signals through the FAIMS device can increase the detection depth of blood from over 600 to 1000, an improvement of more than 50%, while maintaining good quantitative parallelism. This detection method has high sensitivity and resolution, can detect low-abundance proteins, and is a powerful tool for NPC marker screening.\u003c/p\u003e \u003cp\u003eThis study is based on the Blood+/PRM proteomics technology, following a three-step research strategy to address clinical issues: first, using small-sample proteomics technology (the \"4D-Blood+\" blood proteomics method combining FAIMS ion filtering technology and machine learning marker screening) to screen and discover serum protein molecular markers, and then using the mainstream targeted proteomics data acquisition method PRM to preliminarily validate the screened markers, reducing the risk of subsequent large-sample validation; and finally, using ELISA, which is suitable for large samples and clinical application, to revalidate and evaluate the candidate markers, while constructing an optimized combination of protein molecular markers for NPC screening. This project will provide population data and experimental evidence for the development of molecular markers for NPC screening and diagnosis, improve the sensitivity and positive predictive value of early NPC screening, and may increase the cure rate of NPC and alleviate the psychological pressure of most EBV antibody-positive individuals without NPC risk. This will provide a basis for truly high-risk individuals to guide further examinations and take early preventive measures, ultimately making NPC screening more acceptable and improving the early diagnosis rate of NPC.\u003c/p\u003e \u003cp\u003eOur research found that NPC, a highly aggressive tumor prevalent in southern China, requires early diagnosis to improve prognosis. We discovered that the expression levels of three proteins, LTA4H, SULT1A3, and FGL1, in the serum of patients showed significantly different from those in the healthy control group. Through quantitative proteomics analysis, targeted PRM validation, and subsequent large-sample ELISA testing, it was confirmed that this combination of markers has high sensitivity and specificity in distinguishing NPC patients (especially early lesions) from healthy individuals, which not only effectively improves the detection rate of early NPC but also significantly increases the positive predictive value when used in combination, effectively reducing the risk of false-positive results, thereby alleviating the unnecessary psychological burden of EBV antibody-positive individuals without actual cancer risk and providing a more precise examination basis for truly high-risk individuals, demonstrating great potential as early diagnostic markers.\u003c/p\u003e \u003cp\u003eSULT1A3, an important sulfotransferase, mainly functions in the metabolism of monoamine neurotransmitters (such as dopamine). It exhibits different expression patterns in different tissues and developmental stages and is associated with various diseases (such as neurodegenerative diseases and liver cancer). Research has shown that the substrate specificity and enzymatic activity of SULT1A3 are influenced by its structural flexibility and genetic variations. The expression of SULT1A3 in liver cancer tissues is significantly higher than that in normal tissues, and its expression level is related to the invasion and migration abilities of liver cancer cells [19].\u003c/p\u003e \u003cp\u003eLTA4H is a key enzyme in the arachidonic acid metabolic pathway that catalyzes the conversion of leukotriene A4 (LTA4) to leukotriene B4 (LTB4), the latter being a potent pro-inflammatory lipid mediator. In addition to its classic role in inflammation, LTA4H also has important functions in cancer, especially in chronic inflammation-related cancers. Studies have explored the potential role of Bestatin (an LTA4H inhibitor) in inhibiting acute skin inflammation and skin cancer occurrence, validating the key role of the LTA4H/BLT1 signaling pathway in skin inflammation and carcinogenesis. After acute simulated sunlight (SSL) exposure, the expression of BLT1 in human and mouse skin tissues is significantly increased [20]. LTA4H and BLT1 are also highly expressed in chronic skin inflammation and squamous cell carcinoma, indicating an important role of this pathway in skin pathological processes [21]. Additionally, LTA4H inhibitors have shown therapeutic potential in preclinical models of asthma, inflammatory bowel disease, and arthritis [22].\u003c/p\u003e \u003cp\u003eFGL1 (fibrinogen-like protein 1) is a member of the fibrinogen-related protein family, initially noted for its role in liver regeneration and metabolism as a liver-secreted protein related to proliferation and metabolism. FGL1 is abnormally expressed in various malignant tumors, forming an immune checkpoint pathway with the immune inhibitory receptor LAG3, which mediates tumor immune escape [23]. In recent years, its key role in tumor immune escape (particularly its interaction with LAG3) has attracted research attention [24]. The expression and function of FGL1 is highly heterogeneous in various cancers.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Study Population\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFor the proteomics screening cohort, serum samples were collected from 15 untreated, pathologically confirmed, early stage (I/II) nasopharyngeal carcinoma patients. The control group consisted of 15 healthy individuals who tested positive for VCA-IgA antibodies. Forty NPC specimens were included in the preliminary validation cohort, 40 nasopharyngeal carcinoma specimens were included, with 40 VCA-IgA-positive healthy individuals serving as controls. Given the significant regional differences in nasopharyngeal carcinoma prevalence and the potential influence of environmental or genetic factors on disease development and protein profiles, efforts were made to minimize potential confounding factors by ensuring that the participants in the screening and preliminary validation cohorts were as similar as possible across key demographic and lifestyle factors, including age, sex, place of residence, native origin, and smoking history.\u003c/p\u003e\u003cp\u003eA large-scale ELISA validation cohort was designed to comprehensively assess the specificity and generalizability of biomarkers. The cohort included 90 patients with clinically and pathologically confirmed nasopharyngeal carcinoma. Additionally, two distinct healthy control groups were included: 106 VCA-IgA-positive individuals and 22 VCA-IgA and EA-IgA double-negative individuals.\u003c/p\u003e\u003cp\u003e Written informed consent was obtained from all the participants. All studies were approved by the Ethics Review Committee of the Zhaoqing First People's Hospital, thereby ensuring adherence to ethical research guidelines (B2024-07-03).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Protein Enrichment, Digestion, and Separation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor protein extraction, the samples were stored at -80\u0026deg;C and centrifuged at 4\u0026deg;C at 12,000 \u0026times; g for 10 min to remove cellular debris. The supernatant was then transferred to a new centrifuge tube. High-abundance proteins were depleted using the Pierce\u0026trade; Top 14 Abundant Protein Depletion Spin Column Kit (Thermo Scientific), following the manufacturer's instructions. The total protein concentration was determined using a BCA assay kit.\u003c/p\u003e \u003cp\u003eEqual amounts of protein from each sample were used for enzymatic digestion. The sample volume was adjusted for consistency using lysis buffer. Dithiothreitol (DTT) was added to a final concentration of 5 mM, and protein reduction was performed at 56\u0026deg;C for 30 min. Subsequently, iodoacetamide (IAA) was added at a final concentration of 11 mM, and the alkylation reaction proceeded at room temperature in the dark for 15 min. The alkylated samples were transferred to ultrafiltration tubes, centrifuged at 12,000 \u0026times; g for 20 min at room temperature, and exchanged three times with 8 M urea, followed by three exchanges with replacement buffer. Trypsin was then added at a proteinase-to-protein ratio of 1:50 (m/m), and digestion was performed overnight. Peptides were recovered by centrifugation at 12,000 \u0026times; g for 10 min at room temperature, followed by an additional recovery wash with ddH2O. The combined peptide solutions were stored for further use.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Mass Spectrometry Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTryptic peptides were dissolved in solvent A (0.1% formic acid, 2% acetonitrile in water) and directly loaded onto a custom-made reversed-phase analytical column (25-cm length, 100 \u0026micro;m i.d.). The mobile phase consisted of solvents A and B (0.1% formic acid and 90% acetonitrile in water). Peptide separation was achieved using a sophisticated gradient on an EASY-nLC 1200 UPLC system (Thermo Fisher Scientific, Waltham, MA, USA) at a constant flow rate of 500 nl/min. The gradient program was as follows: 0\u0026ndash;16 min, 7%-20%B; 16\u0026ndash;24 min, 20%-32%B; 24\u0026ndash;27 min, 32%-80%B; 27\u0026ndash;30 min, 80%B.\u003c/p\u003e \u003cp\u003eThe separated peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer equipped with a nanoelectrospray ion source. An electrospray voltage of 2300 V was applied. High-Field Asymmetric-waveform Ion Mobility Spectrometry (FAIMS) compensation voltages (CV) were precisely set at -45V and \u0026minus;\u0026thinsp;70V.\u003c/p\u003e \u003cp\u003eBoth the precursors and fragments were analyzed using an Orbitrap detector. The full MS scan resolution was set to 30000 for the scan range of 390\u0026ndash;810 m/z. MS/MS scanning was performed with a fixed first mass of 200 m/z at a resolution of 30000. Higher-energy Collisional Dissociation (HCD) fragmentation was performed at normalized collision energies (NCE) of 25%, 30%, and 35%. The automatic gain control (AGC) target was set at 3E6, with the maximum injection time set to Auto.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Database processing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Data-Independent Acquisition (DIA) data were meticulously processed using Spectronaut (v.17) software. Tandem mass spectra were searched against H. sapiens_9606_SP_20230103. fasta database contains 20389 entries. This database was concatenated with a reverse decoy database. Trypsin/P was specified as the cleavage enzyme, allowing for up to two missing cleavages. Carbamidomethylation of cysteine residues was specified as a fixed modification, while acetylation of protein N-terminals and oxidation of methionine residues were specified as variable modifications. A stringent false discovery rate (FDR) of less than 1% was applied at the protein, peptide, and Peptide Spectrum Match (PSM) levels.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5 ELISA Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the significantly differential indicators identified through the initial proteomics screening and analysis, an enzyme-linked immunosorbent assay (ELISA) kit (Human LTA4H ELISA KIT, Human SULT1A3 ELISA KIT, and Human FGL1 ELISA KIT) was used. The ELISA protocol strictly adhered to the manufacturer's instructions to ensure consistency and reproducibility. This included meticulous sample preparation, generation of a standard curve for accurate quantification, precise sample addition to the wells, appropriate incubation steps, addition of substrate for the colorimetric reaction, and subsequent measurement of absorbance at a specified wavelength using a microplate reader (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we present a new strategy of integrating a machine learning-based method with proteomic analysis for the discovery of biomarkers and elucidation of early nasopharyngeal carcinom. Proteomic analysis of Nasopharyngeal Carcinoma patients identified three proteins,LTA4H, SULT1A3, and FGL1, as candidate biomarkers for Nasopharyngeal Carcinoma. Specifically, the validation of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma through Elisa provides robust evidence.The characterization of immune features in early nasopharyngeal carcinoma provided insights into the underlying molecular mechanisms of the disease. These findings will contribute to the development of more effective diagnostic strategies and may pave the way for further research on targeted therapies. However, the limitations of this study, such as the relatively small sample size and need for external validation, should be considered. Future studies with larger cohorts and more comprehensive analyses are warranted to confirm the clinical utility of SULT1A3 and the immune-related proteins identified.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eNPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eNasopharyngeal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDEPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eDifferentially expressed proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEnzyme-linked immunosorbent assay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eEBV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEpstein-Barr virus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eVCA-IgA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eviral capsid antigen IgA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eEA-IgA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eearly antigen IgA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003epositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFAIMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eField Asymmetric Ion Mobility Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eLC-MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eliquid chromatography-tandem mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003ePearson correlation coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003efold change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eDithiothreitol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eIAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eiodoacetamide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eHCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eHigher-energy Collisional Dissociation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eautomatic gain control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eData-Independent Acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003efalse discovery rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003ePeptide Spectrum Match\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003eConceptualization, Z.L., W.L. and Y.W.; writing—original draft preparation, W.S. and Y.W.; writing—review and editing, Z.L. and Y.W.; visualization, W.L.,and Y.W.; supervision, W.S. and Y.W.; Validation,\u0026nbsp;Z.L., W.L., J.L. and R.W.; Resources,\u0026nbsp;Z.L., W.S. and Y.W.;\u0026nbsp;Methodology, Z.L.,W.L.,\u0026nbsp;J.L.,\u0026nbsp;R.W. and Y.W.;\u0026nbsp;Data curation, Z.L.,W.L.\u0026nbsp;and Y.W.;\u0026nbsp;Formal analysis, Z.L.,W.L.\u0026nbsp;and W.S.;\u0026nbsp;Project administration, Z.L. and Y.W.\u0026nbsp;All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was financially supported by the Guangdong Provincial Clinical Research Center for Laboratory Medicine (2023B110008) and the Scientific Research Fund of the First People's Hospital of Zhaoqing (YJJ-2022-02-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Institute Research Ethics Committee of First People's Hospital of Zhaoqing, Zhaoqing, China (B2024-07-03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We acknowledge the members of the Clinical Laboratory, particularly those involved in our research program. We would like to express our gratitude to Dr. Weimin Jia from the First People's Hospital of Zhaoqing for his valuable suggestions on the proteomics screening protocol used in this study. We thank all the patients for their participation in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWei KR, Zheng RS, Zhang SW, et al. Nasopharyngeal carcinoma incidence and mortality in China, 2013. \u003cem\u003eCHIN J CANCER\u003c/em\u003e. 2017, 36(1): 90. doi: 10.1186/s40880-017-0257-9.\u003c/li\u003e\n\u003cli\u003eZhang LF, Li YH, Xie SH et al. 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Downregulation of fibrinogen-like protein 1 inhibits the proliferation of lung adenocarcinoma by regulating MYC-target genes. \u003cem\u003eTransl Lung Cancer Res\u003c/em\u003e. 2022, 11 (3): 404-419. doi: 10.21037/tlcr-22-151.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NPC, Biomarker, Plasma proteomics, Machine Learning, Early Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8856378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8856378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e(1) Background: Nasopharyngeal carcinoma (NPC) is a highly prevalent and aggressive malignancy in Southeast Asia, and early diagnosis is pivotal for reducing mortality; however, the lack of early specific biomarkers remains a major clinical bottleneck;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) Methods: We conducted a plasma proteomic analysis using mass spectrometry on 15 untreated early stage NPC patients and 15 healthy individuals positive for VCA-IgA antibodies, followed by bioinformatics analysis to identify differentially expressed proteins (DEPs) and ELISA Validation;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) Results: Proteomic analysis identified 1,428 serum proteins, with 1,410 quantifiable. The analysis identified 31 upregulated and 189 downregulated proteins, with the key upregulated proteins being LTA4H, SULT1A3, and FGL1. LTA4H with plasma cells (0.38305252), CD8 T cells (0.407959118), SULT1A3 with M1 macrophages (0.509905899), and FGL1 with M1 macrophages (0.430029014). These findings indicate specific relationships between the upregulated proteins LTA4H, SULT1A3, FGL1 and certain immune cell types/proportions verified by ELISA experiments.The ELISA validation results demonstrated: LTA4H AUC=0.631, SULT1A3 AUC=0.787, and FGL1 AUC=0.688 (n=80). SULT1A3 AUC=0.826 (cohort 1, n=196),SULT1A3 AUC=0.793 (cohort 2, n=112);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(4) Conclusions: By integrating a machine learning-based method with deep proteomic analysis, we explored the pathological mechanisms and identified biomarkers for NPC. We validated the effectiveness of SULT1A3 as a potential biomarker for nasopharyngeal carcinoma through ELISA, which contributes to the development of more effective diagnostic strategies and may pave the way for further research on targeted therapies.\u003c/p\u003e","manuscriptTitle":"Potential Evaluation of SULT1A3 as an Early Diagnostic Marker for Nasopharyngeal Carcinoma: A Study Based on Plasma Proteomics Screening and ELISA Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 08:55:58","doi":"10.21203/rs.3.rs-8856378/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T10:00:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T16:23:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T11:48:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T07:41:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219553322893903937738188529879691461187","date":"2026-03-15T13:33:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87863698223773456441517309645877612312","date":"2026-03-14T11:31:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29622071169642063655047971705385043062","date":"2026-03-06T00:03:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243213899683360340582033363447182039921","date":"2026-03-05T23:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T15:57:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-13T09:48:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T04:33:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T04:31:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-02-12T01:09:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7221df5a-002b-4b55-a327-92a18e17b9c5","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:11:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 08:55:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8856378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8856378","identity":"rs-8856378","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}