{"paper_id":"1eaae691-6f0a-4737-820b-d2e1c491e334","body_text":"Prognostic Value of Serum Lambda Free Light Chains in Nasopharyngeal Carcinoma | 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 Prognostic Value of Serum Lambda Free Light Chains in Nasopharyngeal Carcinoma Yingjun Liu, Ruixin Liu, Zhiting Sun, Dafeng Lin, Suchen Li, Jiamin Chen, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7917303/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Free light chain proteins (FLCs) are small protein fragments produced by plasma cells, which are crucial for antibody production. Recently, abnormal increases in FLCs have been observed in various solid tumors, but their role in nasopharyngeal carcinoma (NPC) remains underexplored. A total of 170 NPC patients treated at Sun Yat-sen University Cancer Center from October 2018 to June 2023 were retrospectively analyzed. The X-tile tool was used to determine the optimal cutoff values for FLC levels, and Cox regression and log-rank tests were performed to evaluate the associations between FLC levels and distant metastasis-free survival (DMFS), progression-free survival (PFS) and overall survival (OS). The optimal cutoff value for serum lambda free light chain proteins (λ FLCs) in the 5-year PFS analysis were 2.1 g/L. Patients with high λ FLC levels had significantly lower DMFS and PFS rates than those with low λ FLC levels (P = 0.0144 and P = 0.0106), while OS did not differ significantly (P = 0.3735). Multivariate analysis identified pretreatment the level of λ FLC as an independent risk factor for DMFS and PFS in NPC patients. The optimal cutoff for 5-year PFS of kappa light chain proteins (κ FLCs) were 3.8 g/L, and for the κ/λ ratio, it was 2.1. No significant differences were found in DMFS, PFS, or OS when stratified by κ FLC and κ/λ values. A high serum λ FLC level is an independent risk factor for DMFS and PFS in NPC patients, indicating it may serve as a prognostic biomarker. Figures Figure 1 Figure 2 Figure 3 Introduction Nasopharyngeal carcinoma (NPC) is one of the most common malignant head and neck tumors in East Asia and Southeast Asia. It is less common in Caucasians and more prevalent in males 1 , 2 . NPC has an insidious nature, with early symptoms being mild and atypical, resulting in a low early diagnosis rate. Advanced NPC frequently recurs or metastasizes within 1 to 2 years following treatment. Research has shown that the 5-year survival rate of patients with recurrent NPC in the middle and late stages is only 35%, indicating that the prognosis of advanced NPC patients is not ideal 3 – 5 . Therefore, this study aimed to identify a new biomarker to assist in the clinical monitoring and prognosis evaluation of NPC. Multiple detection methods are available for the early diagnosis and prognosis evaluation of NPC, and it is widely known that serological testing is an important detection method for NPC. EBV DNA assessment is currently the most widely used detection method for NPC screening and prognosis evaluation in clinical practice. However, the process of detecting EBV DNA lacks standardization, which may compromise the accuracy and reliability of the test results and make it difficult to compare results obtained from different centers 6 . Therefore, exploring a new diagnostic biomarker that is stable and economical and can be quickly assessed to assist in NPC diagnosis and prognosis evaluation is highly important. Free light chain proteins (FLCs), including kappa free light chain proteins (κ FLCs) and lambda free light chain proteins (λ FLCs), are inflammatory mediators that are derived from antibodies secreted by plasma cells. Under normal physiological conditions, FLCs maintain a harmonious equilibrium within the bloodstream. Each of these light chains contributes to the overall diversity and function of antibodies. These FLCs are highly versatile and can bind to antigens, which are foreign substances or molecules that trigger an immune response. The ability of FLCs to recognize and bind to antigens is a critical step in the immune response. It helps to safeguard the body from various infectious agents, such as bacteria, viruses, and fungi 7 , 8 . Furthermore, the balanced circulation of κ and λ light chains are crucial for the proper functioning of the immune system. Any disruption in the ratio of these two types of FLCs could indicate an underlying medical condition, such as multiple myeloma or other plasma cell disorders 9 – 12 . Recent studies have revealed that FLC levels are significantly elevated in the serum of patients with lung cancer, breast cancer, colon cancer or gastric cancer. FLC levels are significantly correlated with a poor prognosis 9 , 13 . Thus, abnormally high levels of FLCs may indicate the excessive growth of malignant tumors. However, the clinical significance of FLC levels in NPC has not yet been reported. This study aimed to analyze the prognostic value of serum FLC levels in patients with NPC evaluating their correlation with clinical factors with the ultimate goal of determining whether they can be used to assist in NPC diagnosis and treatment evaluation. Results Determining the optimal cutoff value for serum free light chain proteins For the analysis of the 5-year PFS of patients, we determined the optimal cutoff values for κ FLCs, λ FLCs, and κ/λ via the X-tile tool. The results revealed that among 170 patients, the optimal cutoff value for the 5-year PFS of κ FLCs was 3.8 g/L (maximum χ2 log-rank value of 2.5807) (Supplementary Fig. 1). The optimal cutoff value for the 5-year PFS of λ FLCs was 2.1 g/L (maximum χ2 log-rank value of 10.3560) (Fig. 1 ). For κ/λ, the optimal cutoff value for 5-year PFS was 2.1 (the maximum χ2 log-rank value was 2.3806) (Supplementary Fig. 1). Clinical characteristics Patients were divided into two groups on the basis of the optimal cutoff value for λ FLC levels (2.1 g/L): a low-level group with λ FLC concentration < 2.1 g/L (85 patients) and a high-level group with λ FLC concentration ≥ 2.1 g/L (85 patients). Owing to the lack of statistical significance of κ FLCs and κ/λ in subsequent survival analysis, we did not show data on the correlation of these factors with clinical characteristics. The clinical characteristics of the 170 NPC patients are presented in Table 1 . The mean age of patients in the low-level group was 45.9412 years (mean ± SD: 45.9412 ± 11.32985), and the mean age of patients in the high-level group was 44.8471 years (mean ± SD: 44.8471 ± 11.12323). The mean body mass index (BMI) was 23.:2330 (mean ± SD: 23.2330 ± 3.50845) in the low-level group and 24.:2758 (mean ± SD: 24.2758 ± 3.25735) in the high-level group. Patients in the high-level group had a greater BMI than those in the low-level group (P = 0.046, Table 1 ). Among these patients, the serum λ FLC levels of patients with a serum VCA-IgA ratio ≥ 1:160 was greater than that of patients with a serum VCA-IgA ratio < 1:160 (P = 0.0006). Patients with a serum EA-IgA ratio ≥ 1:40 presented higher serum λ FLCs levels than those with a serum EA-IgA ratio < 1:40 (P = 0.002), patients with serum albumin (ALB) concentration ≥ 45.5 g/L presented higher serum λ FLC levels than patients with serum ALB concentration < 45.5 g/L (P = 0.0057), patients with serum albumin/globulin (A/G) ratio ≥ 1.64 presented higher serum λ FLC levels than patients with serum A/G ratio < 1.64 (P < 0.0001), and patients with serum cholesterol (CHO) concentration ≥ 5.36 mmol/L presented higher serum λ FLC levels than patients with serum CHO concentration < 5.36 mmol/L (P = 0.0007) (Fig. 2 ). Chi-square tests further revealed that the serum λ FLC levels were correlated with sex, smoking history, VCA-IgA, EA-IgA, ALB, the A/G ratio, and CHO (P < 0.05, Table 1 ). Table 1 Clinical characteristics of NPC patients grouped based on λ FLC levels Variables Low λ FLCs(n = 85) High λ FLCs(n = 85) P Gender 0.009 Male 74 60 Female 11 25 Age, mean ± SD 45.9412 ± 11.32985 44.8471 ± 11.12323 0.526 BMI, mean ± SD 23.2330 ± 3.50845 24.2758 ± 3.25735 0.046 Smoking <0.001 Yes 42 18 No 43 67 Drinking 0.051 Yes 17 8 No 68 77 History of tumor 0.278 Yes 17 23 No 68 62 History of NPC 0.599 Yes 7 9 No 78 76 T stage 0.635 T1 1 0 T2 9 6 T3 49 52 T4 26 27 N stage 0.78 N0 6 4 N1 14 15 N2 39 35 N3 26 31 M stage 0.316 M0 84 85 M1 1 0 Clinical stage 0.556 II 1 1 III 42 35 IVA 42 49 Radiotherapy 0.155 Yes 83 85 No 2 0 Chemotherapy 0.155 Yes 83 85 No 2 0 EBVDNA(copies/mL) 0.607 <4000 63 60 ≥4000 22 25 VCA-IgA 0.02 <1:160 28 37 ≥1: 160 57 42 EA-IgA 0.014 <1: 40 36 46 ≥1: 40 49 33 ALB (g/L) 0.045 <45.5 32 45 ≥45.5 53 40 A/G <0.001 <1.64 52 72 ≥1.64 33 13 HDL-C(mmol/L) 0.726 <1.39 62 64 ≥1.39 23 21 LDL-C(mmol/L) 0.217 <3.61 60 67 ≥3.61 25 18 TG(mmol/L) 0.862 <1.97 62 63 ≥1.97 23 22 CHO(mmol/L) 0.002 <5.36 54 72 ≥5.36 31 13 ApoA1(g/L) 0.385 <1.49 60 65 ≥1.49 25 20 ApoB(g/L) 0.23 <1.09 58 65 ≥1.09 27 20 Correlation analysis of serum-free light chain proteins with DMFS, PFS, and OS We employed the Kaplan‒Meier method and found that NPC patients with higher serum concentrations of λ FLCs had shorter DMFS (median DMFS: 20.09 m vs. 20.20 m, P = 0.0144) and PFS (median PFS: 20.00 m vs. 20.07 m, P = 0.0106), but there was no significant difference in OS between the two groups (median OS: 20.75 m vs. 20.72 m, P = 0.3735) (Fig. 3 ). Additionally, there were no statistically significant differences in DMFS, PFS, or OS between the high and low groups for patients stratified according to κ FLC levels or κ/λ (Supplementary Fig. 2). Therefore, the survival analysis results suggested that serum level of λ FLC has the potential to become a prognostic biomarker for NPC. λ FLC level can serve as a prognostic indicator for in nasopharyngeal carcinoma patients To identify the independent risk factors for NPC, patients were subjected to univariate and multivariate analyses. The results of the univariate analysis indicated that age, serum HDL-C levels and serum λ FLC levels were associated with DMFS and PFS(Table 2 ). Multivariate analysis revealed that serum λ FLC levels were independent risk factors for DMFS and PFS (DMFS: HR 0.422,95% CI 95% CI 0.186–0.956, P = 0.039; PFS: HR 0.442,95% CI 95% CI 0.196–0.995, P = 0.049;Table 3 ). Multivariate analysis also revealed that age and the serum HDL-C levels were independent risk factors for DMFS and PFS. Table 2 Univariate Cox proportional hazards regression analysis Variables DMFS PFS HR 95%CI P HR 95%CI P Gender(male/female) 0.58 0.174–1.930 0.374 0.545 0.164–1.814 0.323 Age(years) 1.044 1.007–1.083 0.02 1.04 1.004–1.079 0.031 BMI 0.932 0.837–1.039 0.205 0.93 0.835–1.036 0.187 Smoking(yes/no) 1.679 0.797–3.538 0.173 1.604 0.762–3.377 0.214 Drinking(yes/no) 1.802 0.764–4.251 0.179 1.794 0.730–4.232 0.182 History of tumor(yes/no) 0.306 0.092–1.017 0.053 0.303 0.091–1.007 0.051 History of NPC(yes/no) 0.507 0.120–2.152 0.357 0.523 0.123–2.219 0.379 T stage(T1/T2/T3/T4) 0.987 0.543–1.792 0.965 1.007 0.554–1.831 0.981 N stage(N0/N1/N2/N3) 0.987 0.603–1.615 0.957 1.048 0.638–1.720 0.853 M stage(M0/M1) 7.163 0.949–54.050 0.056 6.56 0.875–49.192 0.067 Clinical stage(II/III/IVA) 0.983 0.448–2.155 0.966 1.098 0.503–2.397 0.814 Radiotherapy(yes/no) 0.183 0.024–1.381 0.099 0.197 0.026–1.482 0.115 Chemotherapy(yes/no) 0.151 0.020–1.140 0.067 0.164 0.022–1.230 0.079 EBVDNA(copies/mL)༈<4000/≥4000༉ 0.94 0.417–2.117 0.881 1.003 0.445–2.262 0.994 VCA-IgA(<1:160/≥1:160) 1.278 0.589–2.775 0.535 1.286 0.592–2.793 0.525 EA-IgA(<1: 40/≥1:40) 1.472 0.694–3.122 0.314 1.477 0.696–3.135 0.309 ALB (g/L)(<45.5/≥45.5) 1.184 0.562–2.496 0.657 1.19 0.565–2.508 0.647 A/G(<1.64/≥1.64) 1.211 0.507–2.890 0.667 1.113 0.468–2.649 0.808 HDL-C(mmol/L) 2.641 1.246-5.600 0.011 2.571 1.214–5.449 0.014 (<1.39/≥1.39) LDL-C(mmol/L) 0.997 0.399–2.493 0.995 0.916 0.367–2.285 0.851 (<3.61/≥3.61) TG(mmol/L) 0.975 0.414-2.300 0.954 0.931 0.395–2.194 0.87 (<1.97/≥1.97) λFLCs(g/L)(<2.1/≥2.1) 0.4 0.180–0.889 0.024 0.417 0.188–0.925 0.031 Table 3 Multivariate Cox proportional hazards regression analysis Variables DMFS PFS HR 95% CI P HR 95% CI P Age (years) 1.04 1.004–1.078 0.03 1.037 1.001–1.074 0.044 HDL-C (mmol/L) (< 1.39/ ≥ 1.39) 2.833 1.322–6.073 0.007 2.67 1.251–5.698 0.011 λFLCs (g/L) (< 2.1/ ≥ 2.1) 0.422 0.186–0.956 0.039 0.442 0.196–0.995 0.049 Discussion With the advancement of diagnostic and treatment technologies, there are constantly emerging approaches for the diagnosis and treatment of NPC. Currently, synchronous chemoradiotherapy has emerged as the primary treatment for NPC, achieving a high cure rate in clinical settings. However, most NPC patients who present with significant symptoms are already in the advanced stage of the disease because of its insidious nature and rarity, and the existing treatment methods are not ideal for improving the prognosis of advanced NPC, resulting in a relatively high mortality rate at this stage 1 , 14 . Therefore, early diagnosis plays a crucial role in the treatment of NPC. Our research revealed that the λ FLC level was significantly elevated in the serum of NPC patients and that an elevated serum λ FLC level may indicate NPC progression and deterioration and is associated with a poor prognosis for NPC for the first time. Chronic inflammation is one of the key components in the development of malignant tumors, indicating that abnormally elevated inflammatory factors may serve as biomarkers for tumor progression. After activation of the immune system, mature B lymphocytes differentiate into plasma cells and select either κ or λ light chain genes to synthesize antibodies, leading to the production of FLCs 15 , 16 . FLCs are currently mainly used in the diagnosis of hematological and immune-related diseases and play a significant role in the diagnosis of multiple myeloma. Common indicators include κ FLCs, λ FLCs, and the κ/λ ratio. Studies have shown that cancer cells can produce structurally abnormal immunoglobulins, including κ FLCs and λ FLCs 15 , 17 . Subsequent research confirmed the overexpression of FLCs in malignant tumors such as lung cancer, breast cancer, colorectal cancer, and gastric cancer 9 , 13 , 18 – 21 . Agoulnik et al. 22 demonstrated that in NPC cell lines, the EBV-encoded oncoprotein LMP1 promotes the expression of Ig κ genes through the ERK/Ets-1 signaling pathway, thereby regulating the expression of κ FLCs. However, FLCs have not yet been evaluated in NPC patients. Therefore, this study focused on assessing the expression of FLCs in the serum of NPC patients to identify new biomarkers for this type of cancer. In this study, we measured the concentration of FLCs in the serum of NPC patients and determined the optimal cutoff value for survival analysis via the X-tile tool. There was no significant difference in survival among patients categorized by κ FLC level and κ/λ, but a significant difference was observed among patients classified based on the concentration of serum λ FLCs (DMFS: P = 0.0144; PFS: P = 0.0106). Thus, we selected λ FLCs in the serum of NPC patients for further analysis. We employed the X-tile tool with the minimum p value method of log-rank χ2 statistics to determine the optimal cutoff point for λ FLCs (2.1 g/L) 23 . On the basis of its cutoff value, NPC patients were divided into low-level and high-level groups. The multivariate results revealed that the pretreatment serum level of λ FLCs is an important independent risk factor for predicting distant metastasis and disease progression. Recent researches have shown that lipid metabolism plays a significant role in the development of malignant tumors, including breast cancer, colorectal cancer, gastric cancer, lung cancer, and NPC 24 – 28 . Luo et al. 28 reported that high levels of HDL-C can transform tumor-associated macrophages into M1-type macrophages, increasing sensitivity to immunotherapy in NPC patients. Additionally, Li et al. 29 reported that high levels of triglycerides (TGs) are associated with a poor NPC prognosis and that metabolic processes are closely related to the infiltration of B cells and T cells in the tumor microenvironment. Serum cholesterol has also been found to be associated with a poor prognosis for malignant tumors 29 . Consistent with the above research results, we identified serum cholesterol levels as a key factor influencing serum λ FLC levels. Further analysis via a t test revealed that the serum cholesterol levels in the high-level group were significantly higher than those in the low-level group. These findings suggest that cholesterol metabolism may impact the expression of λ FLCs in the development of NPC, although the exact mechanism requires further exploration. In addition, multivariate analysis revealed that the serum level of HDL-C is an important independent risk factor affecting NPC distant metastasis and progression, further indicating that cholesterol metabolism may affect the development of NPC. However, the study failed to reveal a significant correlation between the serum levels of λ FLCs and OS, which may be related to inadequate follow-up time or sample size limitations. The study also revealed that there was no significant correlation between DMFS or PFS and serum λ FLC levels within approximately 20 months after treatment, which is consistent with previous research findings. Previous studies have shown that the peak period of local recurrence in advanced-stage NPC patients mostly occurs between 18 and 24 months after treatment 5 . Therefore, we believe that the serum λ FLC level may be an indicator of recurrence in advanced-stage NPC patients. By monitoring changes in serum λ FLC levels, we can detect the recurrence of advanced-stage NPC early and take corresponding treatment measures. These findings are highly important for improving patient survival rates and disease prognosis. A study revealed that serum free FLC levels in COVID-19 patients are positively correlated with specific anti-SARS-CoV-2 antibodies and IL-6. This finding suggests a potential link between the presence of FLCs and the body's immune response to the SARS-CoV-2 virus 7 . Wang et al. 8 reported that the nonstructural protein 3 (NS3) protease of dengue virus can degrade antibodies, leading to a significant increase in serum λ FLC levels in patients, which suggests that viral infection is also a crucial factor that influences the expression of serum FLCs. Our study also revealed that the expression of serum VCA-IgA and EA-IgA are important factors that influence the expression of serum λ FLCs, suggesting that there might be a potential link between EBV infection and these expression levels. This finding suggests that the presence of the EBV virus in the body may affect the production of VCA-IgA and EA-IgA, which affects the expression of serum λ FLCs in turn. However, further research is needed to explore the exact mechanisms and implications of this association. Malnutrition is a common complication of malignant tumors, and the increased risk of malnutrition seriously affects the prognosis of cancer patients, thereby affecting their survival rate 30 . We found that the values for nutritional indicators such as BMI, ALB, and A/G were greater in the high-level group than in the low-level group, indicating that changes in patients' nutritional status can impact the expression of FLCs, indirectly demonstrating the impact of patients' nutritional status on NPC prognosis. There are still some limitations to this study. First, this was a single-center retrospective study, which inevitably leads to selection bias and limits the sample size. Second, the follow-up time was insufficient for OS analysis. Third, the participants are mainly consisted of patients with advanced-stage disease, affecting the reliability of research results. Conclusions In conclusion, we demonstrated that the serum λ FLCs levels are elevated in NPC patients and there is a strong correlation between the expression of serum λ FLCs and DMFS as well as PFS in patients. These results suggest that λ FLCs can serve as important indicators for NPC prognosis. By monitoring the concentrations of λ FLCs in the bloodstream, we can more accurately predict the survival time and disease progression in NPC patients, providing a new way to develop personalized treatment plans and evaluate the effectiveness of treatment. Materials and methods Patient selection Between October 2018 and June 2023, a total of 170 NPC patients treated at the Cancer Prevention and Treatment Center of Sun Yat-sen University were enrolled in this study. All participants were Chinese individuals. This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (date of approval: [2020/08/25]) and is affiliated with the parent project “Mechanism of complement C1q regulating glucose metabolism to promote metastasis of nasopharyngeal carcinoma” (No. 2021A155011139). Although the title of the parent project relates to complement C1q, the current study is strictly limited to FLCs analysis, and this part of the research protocol has been explicitly authorized by the ethics committee. Written informed consent was obtained from all participants. The methods employed in this study adhered strictly to the relevant ethical guidelines and regulations. The inclusion criteria were as follows: (1) Histopathological diagnosis of undifferentiated nonkeratinizing squamous cell carcinoma of the nasopharynx. (2) Good function of major organs. The exclusion criteria were as follows: (1) No pathological diagnosis or presence of mixed pathological types. (2) History of other malignancies or severe complications. (3) Previous treatment with chemotherapy, radiotherapy, immunotherapy, or targeted therapy. (4) Severe liver or kidney dysfunction. (5) Recent infections. (6) Concurrent autoimmune diseases such as systemic lupus erythematosus or sarcoidosis. (7) Loss to follow-up. This retrospective study was approved by the Clinical Research Ethics Committee of the Cancer Center at Sun Yat-sen University. All clinical data and information collection were carried out with informed consent from the patients. Treatment methods and evaluation All patients were required to undergo a series of examinations before treatment to determine the tumor stage and assess the pretreatment condition; the examinations included nasopharyngoscopy, MRI from the suprasellar cistern to the clavicle, chest X-ray, abdominal ultrasound, whole-body bone scan, or whole-body FDG PET/CT scan. All tumors were staged according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging manual. Clinical data, including sex, age, height, weight, tumor family history, NPC family history, smoking history and drinking history, were collected through the medical record system. A stratified comprehensive treatment plan was used to treat the patients, and stage II patients were treated with a combination of radiotherapy and cisplatin chemotherapy. Advanced-stage patients (stage III and IV) underwent concurrent chemoradiotherapy, with or without induction chemotherapy. The induction regimen consisted of the TPF regimen (docetaxel, cisplatin, 5-FU) administered every 3 weeks, typically for 2–3 cycles. Concurrent chemotherapy included weekly cisplatin administration. Study endpoints Progression-free survival (PFS) was the primary endpoint of the study. The secondary endpoints included distant metastasis-free survival (DMFS) and overall survival (OS). PFS refers to the time from the first diagnosis of NPC to the first recurrence at any site, any cause of death, or the last follow-up date. DMFS refers to the time from the first diagnosis of NPC to the occurrence of distant recurrence or the patient examination date at the last follow-up. OS refers to the time from the first treatment to death caused by any reason or the last follow-up. During the surveillance period, examinations were conducted to assess whether the tumor had recurred after treatment. These examinations were performed every 3 months of follow-up were performed for the first 3 years and then every 6 months thereafter. Determination of serum free light chain protein (FLC) levels A 3 mL fasting blood sample was collected from each NPC patient before treatment. The blood sample was placed in a blood collection tube and stored at 4°C or centrifuged within 30 minutes at 3000–4000 rpm/min for 10 minutes to separate the serum. Approximately 200 µL of the supernatant was transferred to EP tubes and stored at -20°C or analyzed immediately. Serum FLCs were measured via an immunoturbidimetric assay (Jingyuan Medical Devices Co., Ltd., Shanghai, China) with a Beckman Coulter AU5821 (Beckman Coulter Inc., California, USA) fully automated biochemistry analyzer at the Department of Clinical Laboratory of the First Affiliated Hospital of Jinan University. Statistical analysis SPSS 27.0 (Chicago, IL, USA) and GraphPad Prism 9.5.1 (GraphPad Prism software, USA) were used for data analysis and plotting. The clinical characteristics of the patients are expressed as the mean ± standard deviation (SD) (Due to the histograms and Q-Q plots showing that continuous variables approximate a normal distribution) and were compared via Student's t test. For categorical variables, the chi-square test was used for comparisons. The optimal cutoff values of κ FLCs, λ FLCs and κ/λ were determined via the X-tile tool 23 . Survival analysis was performed via the Kaplan‒Meier method to evaluate DMFS, PFS and OS. Variables with P < 0.05 from the univariate Cox analysis were included in the multivariate Cox analysis to identify independent prognostic factors. Data availability All data generated or analyzed during this study are included in this article and its supplementary information files. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University, approval number: No. 2021A155011139, and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians for this study. Competing interests The authors have no conflicts of interest to declare. Funding National Natural Science Foundation of China, 82072993 Natural Science Foundation of Guangzhou Province,China, 2021A1515011139 Scientific Research Project of Guangzhou, China, 202102020501 Science and Technology Project of Guangzhou ,China, 2025A03J4331 Author Contribution Dafeng Lin, Suchen Li, Shiqing Nie, and Yanzhang provided research proposals. YingJun Liu,Ruixin Liu, Jiamin Chen, Haoxiang Long, Liyuan Liu conducted data collection and analysis. YingJun Liu, Zhiting Sun and Wenhui Chen wrote the main manuscript text. and YingJun Liu and Ruixin Liu prepared figures. Linquan Tang, Wenhui Chen, and Jianfu Zhao supervised the experiment. All authors reviewed the manuscript. Acknowledgement We would like to express our heartfelt gratitude to all the individuals who contributed to this study. Data Availability All data generated or analyzed during this study are included in this article and its supplementary informationfiles. References Chen, Y. P. et al. 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Supplementary Files Supplementaryinformation.docx SupplementaryFig.1.tif SupplementaryFig.2.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 28 Oct, 2025 Editor invited by journal 27 Oct, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 21 Oct, 2025 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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The optimal cutoff value for serum λ FLC levels for 5-year PFS according to the X-tile tool was 2.1 g/L (\\u003cstrong\\u003ea-b\\u003c/strong\\u003e)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/98317072dfdbba5af85cce10.png\"},{\"id\":95528305,\"identity\":\"54e5cee8-a8ab-4edb-9200-797f5056092d\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:15:52\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":384206,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSerum λ FLC levels in different subgroups according to VCA-IgA, EA-IgA, and CHO levels in patients with NPC. (\\u003cstrong\\u003ea\\u003c/strong\\u003e) Serum VCA-IgA ≥1:160 vs. VCA-IgA \\u0026lt;1:160 (P=0.0006). (\\u003cstrong\\u003eb\\u003c/strong\\u003e) Serum EA-IgA ≥1:40 vs. serum EA-IgA \\u0026lt;1:40 (P=0.002). (\\u003cstrong\\u003ec\\u003c/strong\\u003e) Serum ALB ≥45.5 g/L vs. serum ALB \\u0026lt;45.5 g/L (P=0.0057). (\\u003cstrong\\u003ed\\u003c/strong\\u003e) Serum A/G ≥1.64 vs. serum A/G \\u0026lt;1.64 (P\\u0026lt;0.0001). (\\u003cstrong\\u003ee\\u003c/strong\\u003e) Serum CHO ≥5.36 mmol/L vs. serum CHO \\u0026lt;5.36 mmol/L (P=0.0007).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/6b4d670b7520eadc0d78bd49.png\"},{\"id\":95501064,\"identity\":\"e6eac195-05be-417b-9d56-ea547f38fc9c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:24:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":217332,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan‒Meier curves (n = 170) of distant metastasis-free survival (\\u003cstrong\\u003ea\\u003c/strong\\u003e), progression-free survival (\\u003cstrong\\u003eb\\u003c/strong\\u003e), and overall survival (\\u003cstrong\\u003ec\\u003c/strong\\u003e) in NPC patients grouped according to pretreatment λ FLC levels.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/cb235070474b464507876bde.png\"},{\"id\":95531667,\"identity\":\"f5c02b14-adca-40d0-a59d-4fda87041d43\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 10:23:45\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2100154,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/7618c103-503e-4d44-b59b-0c6f0caed733.pdf\"},{\"id\":95501061,\"identity\":\"83909ca1-4d9f-4122-956b-67c6cca61e2f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:24:16\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":565592,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryinformation.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/61e8f18e3c350e66795b1572.docx\"},{\"id\":95501062,\"identity\":\"4fdb7138-9dec-4a14-8736-18caa6888cbf\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:24:16\",\"extension\":\"tif\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3541572,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFig.1.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/92bdcf9e82161c7dae853af2.tif\"},{\"id\":95501066,\"identity\":\"dce4e216-a26c-4004-afb1-4484a2931f36\",\"added_by\":\"auto\",\"created_at\":\"2025-11-10 05:24:16\",\"extension\":\"tif\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":5071328,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFig.2.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7917303/v1/2e4be9e7c9425ec28b13257e.tif\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Prognostic Value of Serum Lambda Free Light Chains in Nasopharyngeal Carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eNasopharyngeal carcinoma (NPC) is one of the most common malignant head and neck tumors in East Asia and Southeast Asia. It is less common in Caucasians and more prevalent in males \\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. NPC has an insidious nature, with early symptoms being mild and atypical, resulting in a low early diagnosis rate. Advanced NPC frequently recurs or metastasizes within 1 to 2 years following treatment. Research has shown that the 5-year survival rate of patients with recurrent NPC in the middle and late stages is only 35%, indicating that the prognosis of advanced NPC patients is not ideal \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, this study aimed to identify a new biomarker to assist in the clinical monitoring and prognosis evaluation of NPC.\\u003c/p\\u003e\\u003cp\\u003eMultiple detection methods are available for the early diagnosis and prognosis evaluation of NPC, and it is widely known that serological testing is an important detection method for NPC. EBV DNA assessment is currently the most widely used detection method for NPC screening and prognosis evaluation in clinical practice. However, the process of detecting EBV DNA lacks standardization, which may compromise the accuracy and reliability of the test results and make it difficult to compare results obtained from different centers \\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, exploring a new diagnostic biomarker that is stable and economical and can be quickly assessed to assist in NPC diagnosis and prognosis evaluation is highly important.\\u003c/p\\u003e\\u003cp\\u003eFree light chain proteins (FLCs), including kappa free light chain proteins (κ FLCs) and lambda free light chain proteins (λ FLCs), are inflammatory mediators that are derived from antibodies secreted by plasma cells. Under normal physiological conditions, FLCs maintain a harmonious equilibrium within the bloodstream. Each of these light chains contributes to the overall diversity and function of antibodies. These FLCs are highly versatile and can bind to antigens, which are foreign substances or molecules that trigger an immune response. The ability of FLCs to recognize and bind to antigens is a critical step in the immune response. It helps to safeguard the body from various infectious agents, such as bacteria, viruses, and fungi \\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Furthermore, the balanced circulation of κ and λ light chains are crucial for the proper functioning of the immune system. Any disruption in the ratio of these two types of FLCs could indicate an underlying medical condition, such as multiple myeloma or other plasma cell disorders \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR10 CR11\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Recent studies have revealed that FLC levels are significantly elevated in the serum of patients with lung cancer, breast cancer, colon cancer or gastric cancer. FLC levels are significantly correlated with a poor prognosis \\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, abnormally high levels of FLCs may indicate the excessive growth of malignant tumors. However, the clinical significance of FLC levels in NPC has not yet been reported. This study aimed to analyze the prognostic value of serum FLC levels in patients with NPC evaluating their correlation with clinical factors with the ultimate goal of determining whether they can be used to assist in NPC diagnosis and treatment evaluation.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDetermining the optimal cutoff value for serum free light chain proteins\\u003c/h2\\u003e\\u003cp\\u003eFor the analysis of the 5-year PFS of patients, we determined the optimal cutoff values for κ FLCs, λ FLCs, and κ/λ via the X-tile tool. The results revealed that among 170 patients, the optimal cutoff value for the 5-year PFS of κ FLCs was 3.8 g/L (maximum χ2 log-rank value of 2.5807) (Supplementary Fig.\\u0026nbsp;1). The optimal cutoff value for the 5-year PFS of λ FLCs was 2.1 g/L (maximum χ2 log-rank value of 10.3560) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). For κ/λ, the optimal cutoff value for 5-year PFS was 2.1 (the maximum χ2 log-rank value was 2.3806) (Supplementary Fig.\\u0026nbsp;1).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eClinical characteristics\\u003c/h3\\u003e\\n\\u003cp\\u003ePatients were divided into two groups on the basis of the optimal cutoff value for λ FLC levels (2.1 g/L): a low-level group with λ FLC concentration\\u0026thinsp;\\u0026lt;\\u0026thinsp;2.1 g/L (85 patients) and a high-level group with λ FLC concentration\\u0026thinsp;\\u0026ge;\\u0026thinsp;2.1 g/L (85 patients). Owing to the lack of statistical significance of κ FLCs and κ/λ in subsequent survival analysis, we did not show data on the correlation of these factors with clinical characteristics.\\u003c/p\\u003e\\u003cp\\u003eThe clinical characteristics of the 170 NPC patients are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The mean age of patients in the low-level group was 45.9412 years (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 45.9412\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.32985), and the mean age of patients in the high-level group was 44.8471 years (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 44.8471\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.12323). The mean body mass index (BMI) was 23.:2330 (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 23.2330\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.50845) in the low-level group and 24.:2758 (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 24.2758\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.25735) in the high-level group. Patients in the high-level group had a greater BMI than those in the low-level group (P\\u0026thinsp;=\\u0026thinsp;0.046, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Among these patients, the serum λ FLC levels of patients with a serum VCA-IgA ratio\\u0026thinsp;\\u0026ge;\\u0026thinsp;1:160 was greater than that of patients with a serum VCA-IgA ratio\\u0026thinsp;\\u0026lt;\\u0026thinsp;1:160 (P\\u0026thinsp;=\\u0026thinsp;0.0006). Patients with a serum EA-IgA ratio\\u0026thinsp;\\u0026ge;\\u0026thinsp;1:40 presented higher serum λ FLCs levels than those with a serum EA-IgA ratio\\u0026thinsp;\\u0026lt;\\u0026thinsp;1:40 (P\\u0026thinsp;=\\u0026thinsp;0.002), patients with serum albumin (ALB) concentration\\u0026thinsp;\\u0026ge;\\u0026thinsp;45.5 g/L presented higher serum λ FLC levels than patients with serum ALB concentration\\u0026thinsp;\\u0026lt;\\u0026thinsp;45.5 g/L (P\\u0026thinsp;=\\u0026thinsp;0.0057), patients with serum albumin/globulin (A/G) ratio\\u0026thinsp;\\u0026ge;\\u0026thinsp;1.64 presented higher serum λ FLC levels than patients with serum A/G ratio\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.64 (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001), and patients with serum cholesterol (CHO) concentration\\u0026thinsp;\\u0026ge;\\u0026thinsp;5.36 mmol/L presented higher serum λ FLC levels than patients with serum CHO concentration\\u0026thinsp;\\u0026lt;\\u0026thinsp;5.36 mmol/L (P\\u0026thinsp;=\\u0026thinsp;0.0007) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Chi-square tests further revealed that the serum λ FLC levels were correlated with sex, smoking history, VCA-IgA, EA-IgA, ALB, the A/G ratio, and CHO (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\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\\u003eClinical characteristics of NPC patients grouped based on λ FLC levels\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLow λ FLCs(n\\u0026thinsp;=\\u0026thinsp;85)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigh λ FLCs(n\\u0026thinsp;=\\u0026thinsp;85)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eP\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGender\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.009\\u003c/b\\u003e\\u003c/p\\u003e\\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\\u003e74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\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\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e45.9412\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.32985\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e44.8471\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.12323\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.526\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBMI, mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e23.2330\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.50845\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e24.2758\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.25735\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.046\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSmoking\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e\\u0026lt;0.001\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDrinking\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.051\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistory of tumor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.316\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClinical 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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.556\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eII\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRadiotherapy\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.155\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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colname=\\\"c3\\\"\\u003e\\u003cp\\u003e85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEBVDNA(copies/mL)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.607\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;4000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;4000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eVCA-IgA\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.02\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1:160\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1: 160\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEA-IgA\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.014\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1: 40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1: 40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eALB (g/L)\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.045\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;45.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;45.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e53\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eA/G\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e\\u0026lt;0.001\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHDL-C(mmol/L)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.726\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDL-C(mmol/L)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.217\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;3.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;3.61\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTG(mmol/L)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.862\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCHO(mmol/L)\\u003c/b\\u003e\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;5.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;5.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eApoA1(g/L)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.385\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eApoB(g/L)\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026ge;1.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eCorrelation analysis of serum-free light chain proteins with DMFS, PFS, and OS\\u003c/h3\\u003e\\n\\u003cp\\u003eWe employed the Kaplan‒Meier method and found that NPC patients with higher serum concentrations of λ FLCs had shorter DMFS (median DMFS: 20.09 m vs. 20.20 m, P\\u0026thinsp;=\\u0026thinsp;0.0144) and PFS (median PFS: 20.00 m vs. 20.07 m, P\\u0026thinsp;=\\u0026thinsp;0.0106), but there was no significant difference in OS between the two groups (median OS: 20.75 m vs. 20.72 m, P\\u0026thinsp;=\\u0026thinsp;0.3735) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Additionally, there were no statistically significant differences in DMFS, PFS, or OS between the high and low groups for patients stratified according to κ FLC levels or κ/λ (Supplementary Fig.\\u0026nbsp;2). Therefore, the survival analysis results suggested that serum level of λ FLC has the potential to become a prognostic biomarker for NPC.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eλ FLC level can serve as a prognostic indicator for in nasopharyngeal carcinoma patients\\u003c/h3\\u003e\\n\\u003cp\\u003eTo identify the independent risk factors for NPC, patients were subjected to univariate and multivariate analyses. The results of the univariate analysis indicated that age, serum HDL-C levels and serum λ FLC levels were associated with DMFS and PFS(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Multivariate analysis revealed that serum λ FLC levels were independent risk factors for DMFS and PFS (DMFS: HR 0.422,95% CI 95% CI 0.186\\u0026ndash;0.956, P\\u0026thinsp;=\\u0026thinsp;0.039; PFS: HR 0.442,95% CI 95% CI 0.196\\u0026ndash;0.995, P\\u0026thinsp;=\\u0026thinsp;0.049;Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Multivariate analysis also revealed that age and the serum HDL-C levels were independent risk factors for DMFS and PFS.\\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\\u003eUnivariate Cox proportional hazards regression analysis\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eDMFS\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003ePFS\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eHR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e95%CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eP\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender(male/female)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.174\\u0026ndash;1.930\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.374\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.545\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.164\\u0026ndash;1.814\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.323\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge(years)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.044\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.007\\u0026ndash;1.083\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.02\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.04\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.004\\u0026ndash;1.079\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.031\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.932\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.837\\u0026ndash;1.039\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.205\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.835\\u0026ndash;1.036\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.187\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.679\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.797\\u0026ndash;3.538\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.173\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.604\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.762\\u0026ndash;3.377\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.214\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDrinking(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.802\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.764\\u0026ndash;4.251\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.179\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.794\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.730\\u0026ndash;4.232\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.182\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistory of tumor(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.306\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.092\\u0026ndash;1.017\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.053\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.303\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.091\\u0026ndash;1.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.051\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistory of NPC(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.507\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.120\\u0026ndash;2.152\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.357\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.523\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.123\\u0026ndash;2.219\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.379\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT stage(T1/T2/T3/T4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.987\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.543\\u0026ndash;1.792\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.965\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.554\\u0026ndash;1.831\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.981\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN stage(N0/N1/N2/N3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.987\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.603\\u0026ndash;1.615\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.957\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.638\\u0026ndash;1.720\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.853\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM stage(M0/M1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7.163\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.949\\u0026ndash;54.050\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.056\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e6.56\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.875\\u0026ndash;49.192\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.067\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClinical stage(II/III/IVA)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.983\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.448\\u0026ndash;2.155\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.966\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.098\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.503\\u0026ndash;2.397\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.814\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRadiotherapy(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.183\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.024\\u0026ndash;1.381\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.099\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.197\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.026\\u0026ndash;1.482\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.115\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChemotherapy(yes/no)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.151\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.020\\u0026ndash;1.140\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.067\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.164\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.022\\u0026ndash;1.230\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.079\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEBVDNA(copies/mL)༈\\u0026lt;4000/\\u0026ge;4000༉\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.417\\u0026ndash;2.117\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.881\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.445\\u0026ndash;2.262\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.994\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVCA-IgA(\\u0026lt;1:160/\\u0026ge;1:160)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.278\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.589\\u0026ndash;2.775\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.535\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.286\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.592\\u0026ndash;2.793\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.525\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEA-IgA(\\u0026lt;1: 40/\\u0026ge;1:40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.472\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.694\\u0026ndash;3.122\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.314\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.477\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.696\\u0026ndash;3.135\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.309\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eALB 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colname=\\\"c1\\\"\\u003e\\u003cp\\u003eA/G(\\u0026lt;1.64/\\u0026ge;1.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.211\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.507\\u0026ndash;2.890\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.667\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.113\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.468\\u0026ndash;2.649\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.808\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHDL-C(mmol/L)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2.641\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.246-5.600\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.011\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2.571\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.214\\u0026ndash;5.449\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.014\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e(\\u0026lt;1.39/\\u0026ge;1.39)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDL-C(mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.997\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.399\\u0026ndash;2.493\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.995\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.916\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.367\\u0026ndash;2.285\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.851\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e(\\u0026lt;3.61/\\u0026ge;3.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTG(mmol/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.975\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.414-2.300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.954\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.931\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.395\\u0026ndash;2.194\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e(\\u0026lt;1.97/\\u0026ge;1.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eλFLCs(g/L)(\\u0026lt;2.1/\\u0026ge;2.1)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.180\\u0026ndash;0.889\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.024\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.417\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.188\\u0026ndash;0.925\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.031\\u003c/b\\u003e\\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\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eMultivariate Cox proportional hazards regression analysis\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eVariables\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eDMFS\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003ePFS\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e95% CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eP\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eHR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e95% CI\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eP\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge (years)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.04\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.004\\u0026ndash;1.078\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.03\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.037\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.001\\u0026ndash;1.074\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.044\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHDL-C (mmol/L) (\\u0026lt;\\u0026thinsp;1.39/\\u003c/b\\u003e\\u0026ge;\\u003cb\\u003e1.39)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2.833\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.322\\u0026ndash;6.073\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2.67\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.251\\u0026ndash;5.698\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.011\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eλFLCs (g/L) (\\u0026lt;\\u0026thinsp;2.1/\\u003c/b\\u003e\\u0026ge;\\u003cb\\u003e2.1)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.422\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.186\\u0026ndash;0.956\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.039\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.442\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.196\\u0026ndash;0.995\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.049\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWith the advancement of diagnostic and treatment technologies, there are constantly emerging approaches for the diagnosis and treatment of NPC. Currently, synchronous chemoradiotherapy has emerged as the primary treatment for NPC, achieving a high cure rate in clinical settings. However, most NPC patients who present with significant symptoms are already in the advanced stage of the disease because of its insidious nature and rarity, and the existing treatment methods are not ideal for improving the prognosis of advanced NPC, resulting in a relatively high mortality rate at this stage \\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, early diagnosis plays a crucial role in the treatment of NPC. Our research revealed that the λ FLC level was significantly elevated in the serum of NPC patients and that an elevated serum λ FLC level may indicate NPC progression and deterioration and is associated with a poor prognosis for NPC for the first time.\\u003c/p\\u003e\\u003cp\\u003eChronic inflammation is one of the key components in the development of malignant tumors, indicating that abnormally elevated inflammatory factors may serve as biomarkers for tumor progression. After activation of the immune system, mature B lymphocytes differentiate into plasma cells and select either κ or λ light chain genes to synthesize antibodies, leading to the production of FLCs \\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. FLCs are currently mainly used in the diagnosis of hematological and immune-related diseases and play a significant role in the diagnosis of multiple myeloma. Common indicators include κ FLCs, λ FLCs, and the κ/λ ratio. Studies have shown that cancer cells can produce structurally abnormal immunoglobulins, including κ FLCs and λ FLCs \\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. Subsequent research confirmed the overexpression of FLCs in malignant tumors such as lung cancer, breast cancer, colorectal cancer, and gastric cancer \\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR19 CR20\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Agoulnik et al. \\u003csup\\u003e22\\u003c/sup\\u003e demonstrated that in NPC cell lines, the EBV-encoded oncoprotein LMP1 promotes the expression of Ig κ genes through the ERK/Ets-1 signaling pathway, thereby regulating the expression of κ FLCs. However, FLCs have not yet been evaluated in NPC patients. Therefore, this study focused on assessing the expression of FLCs in the serum of NPC patients to identify new biomarkers for this type of cancer.\\u003c/p\\u003e\\u003cp\\u003eIn this study, we measured the concentration of FLCs in the serum of NPC patients and determined the optimal cutoff value for survival analysis via the X-tile tool. There was no significant difference in survival among patients categorized by κ FLC level and κ/λ, but a significant difference was observed among patients classified based on the concentration of serum λ FLCs (DMFS: P\\u0026thinsp;=\\u0026thinsp;0.0144; PFS: P\\u0026thinsp;=\\u0026thinsp;0.0106). Thus, we selected λ FLCs in the serum of NPC patients for further analysis.\\u003c/p\\u003e\\u003cp\\u003eWe employed the X-tile tool with the minimum p value method of log-rank χ2 statistics to determine the optimal cutoff point for λ FLCs (2.1 g/L) \\u003csup\\u003e23\\u003c/sup\\u003e. On the basis of its cutoff value, NPC patients were divided into low-level and high-level groups. The multivariate results revealed that the pretreatment serum level of λ FLCs is an important independent risk factor for predicting distant metastasis and disease progression. Recent researches have shown that lipid metabolism plays a significant role in the development of malignant tumors, including breast cancer, colorectal cancer, gastric cancer, lung cancer, and NPC \\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR25 CR26 CR27\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Luo et al. \\u003csup\\u003e28\\u003c/sup\\u003e reported that high levels of HDL-C can transform tumor-associated macrophages into M1-type macrophages, increasing sensitivity to immunotherapy in NPC patients. Additionally, Li et al. \\u003csup\\u003e29\\u003c/sup\\u003e reported that high levels of triglycerides (TGs) are associated with a poor NPC prognosis and that metabolic processes are closely related to the infiltration of B cells and T cells in the tumor microenvironment. Serum cholesterol has also been found to be associated with a poor prognosis for malignant tumors \\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Consistent with the above research results, we identified serum cholesterol levels as a key factor influencing serum λ FLC levels. Further analysis via a t test revealed that the serum cholesterol levels in the high-level group were significantly higher than those in the low-level group. These findings suggest that cholesterol metabolism may impact the expression of λ FLCs in the development of NPC, although the exact mechanism requires further exploration. In addition, multivariate analysis revealed that the serum level of HDL-C is an important independent risk factor affecting NPC distant metastasis and progression, further indicating that cholesterol metabolism may affect the development of NPC. However, the study failed to reveal a significant correlation between the serum levels of λ FLCs and OS, which may be related to inadequate follow-up time or sample size limitations.\\u003c/p\\u003e\\u003cp\\u003eThe study also revealed that there was no significant correlation between DMFS or PFS and serum λ FLC levels within approximately 20 months after treatment, which is consistent with previous research findings. Previous studies have shown that the peak period of local recurrence in advanced-stage NPC patients mostly occurs between 18 and 24 months after treatment \\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, we believe that the serum λ FLC level may be an indicator of recurrence in advanced-stage NPC patients. By monitoring changes in serum λ FLC levels, we can detect the recurrence of advanced-stage NPC early and take corresponding treatment measures. These findings are highly important for improving patient survival rates and disease prognosis.\\u003c/p\\u003e\\u003cp\\u003eA study revealed that serum free FLC levels in COVID-19 patients are positively correlated with specific anti-SARS-CoV-2 antibodies and IL-6. This finding suggests a potential link between the presence of FLCs and the body's immune response to the SARS-CoV-2 virus \\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. Wang et al. \\u003csup\\u003e8\\u003c/sup\\u003e reported that the nonstructural protein 3 (NS3) protease of dengue virus can degrade antibodies, leading to a significant increase in serum λ FLC levels in patients, which suggests that viral infection is also a crucial factor that influences the expression of serum FLCs. Our study also revealed that the expression of serum VCA-IgA and EA-IgA are important factors that influence the expression of serum λ FLCs, suggesting that there might be a potential link between EBV infection and these expression levels. This finding suggests that the presence of the EBV virus in the body may affect the production of VCA-IgA and EA-IgA, which affects the expression of serum λ FLCs in turn. However, further research is needed to explore the exact mechanisms and implications of this association.\\u003c/p\\u003e\\u003cp\\u003eMalnutrition is a common complication of malignant tumors, and the increased risk of malnutrition seriously affects the prognosis of cancer patients, thereby affecting their survival rate \\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. We found that the values for nutritional indicators such as BMI, ALB, and A/G were greater in the high-level group than in the low-level group, indicating that changes in patients' nutritional status can impact the expression of FLCs, indirectly demonstrating the impact of patients' nutritional status on NPC prognosis.\\u003c/p\\u003e\\u003cp\\u003eThere are still some limitations to this study. First, this was a single-center retrospective study, which inevitably leads to selection bias and limits the sample size. Second, the follow-up time was insufficient for OS analysis. Third, the participants are mainly consisted of patients with advanced-stage disease, affecting the reliability of research results.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, we demonstrated that the serum λ FLCs levels are elevated in NPC patients and there is a strong correlation between the expression of serum λ FLCs and DMFS as well as PFS in patients. These results suggest that λ FLCs can serve as important indicators for NPC prognosis. By monitoring the concentrations of λ FLCs in the bloodstream, we can more accurately predict the survival time and disease progression in NPC patients, providing a new way to develop personalized treatment plans and evaluate the effectiveness of treatment.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePatient selection\\u003c/h2\\u003e\\u003cp\\u003eBetween October 2018 and June 2023, a total of 170 NPC patients treated at the Cancer Prevention and Treatment Center of Sun Yat-sen University were enrolled in this study. All participants were Chinese individuals. This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (date of approval: [2020/08/25]) and is affiliated with the parent project \\u0026ldquo;Mechanism of complement C1q regulating glucose metabolism to promote metastasis of nasopharyngeal carcinoma\\u0026rdquo; (No. 2021A155011139). Although the title of the parent project relates to complement C1q, the current study is strictly limited to FLCs analysis, and this part of the research protocol has been explicitly authorized by the ethics committee. Written informed consent was obtained from all participants. The methods employed in this study adhered strictly to the relevant ethical guidelines and regulations.\\u003c/p\\u003e\\u003cp\\u003eThe inclusion criteria were as follows: (1) Histopathological diagnosis of undifferentiated nonkeratinizing squamous cell carcinoma of the nasopharynx. (2) Good function of major organs. The exclusion criteria were as follows: (1) No pathological diagnosis or presence of mixed pathological types. (2) History of other malignancies or severe complications. (3) Previous treatment with chemotherapy, radiotherapy, immunotherapy, or targeted therapy. (4) Severe liver or kidney dysfunction. (5) Recent infections. (6) Concurrent autoimmune diseases such as systemic lupus erythematosus or sarcoidosis. (7) Loss to follow-up. This retrospective study was approved by the Clinical Research Ethics Committee of the Cancer Center at Sun Yat-sen University. All clinical data and information collection were carried out with informed consent from the patients.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eTreatment methods and evaluation\\u003c/h2\\u003e\\u003cp\\u003eAll patients were required to undergo a series of examinations before treatment to determine the tumor stage and assess the pretreatment condition; the examinations included nasopharyngoscopy, MRI from the suprasellar cistern to the clavicle, chest X-ray, abdominal ultrasound, whole-body bone scan, or whole-body FDG PET/CT scan. All tumors were staged according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging manual. Clinical data, including sex, age, height, weight, tumor family history, NPC family history, smoking history and drinking history, were collected through the medical record system. A stratified comprehensive treatment plan was used to treat the patients, and stage II patients were treated with a combination of radiotherapy and cisplatin chemotherapy. Advanced-stage patients (stage III and IV) underwent concurrent chemoradiotherapy, with or without induction chemotherapy. The induction regimen consisted of the TPF regimen (docetaxel, cisplatin, 5-FU) administered every 3 weeks, typically for 2\\u0026ndash;3 cycles. Concurrent chemotherapy included weekly cisplatin administration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy endpoints\\u003c/h2\\u003e\\u003cp\\u003eProgression-free survival (PFS) was the primary endpoint of the study. The secondary endpoints included distant metastasis-free survival (DMFS) and overall survival (OS). PFS refers to the time from the first diagnosis of NPC to the first recurrence at any site, any cause of death, or the last follow-up date. DMFS refers to the time from the first diagnosis of NPC to the occurrence of distant recurrence or the patient examination date at the last follow-up. OS refers to the time from the first treatment to death caused by any reason or the last follow-up. During the surveillance period, examinations were conducted to assess whether the tumor had recurred after treatment. These examinations were performed every 3 months of follow-up were performed for the first 3 years and then every 6 months thereafter.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDetermination of serum free light chain protein (FLC) levels\\u003c/h2\\u003e\\u003cp\\u003eA 3 mL fasting blood sample was collected from each NPC patient before treatment. The blood sample was placed in a blood collection tube and stored at 4\\u0026deg;C or centrifuged within 30 minutes at 3000\\u0026ndash;4000 rpm/min for 10 minutes to separate the serum. Approximately 200 \\u0026micro;L of the supernatant was transferred to EP tubes and stored at -20\\u0026deg;C or analyzed immediately. Serum FLCs were measured via an immunoturbidimetric assay (Jingyuan Medical Devices Co., Ltd., Shanghai, China) with a Beckman Coulter AU5821 (Beckman Coulter Inc., California, USA) fully automated biochemistry analyzer at the Department of Clinical Laboratory of the First Affiliated Hospital of Jinan University.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003eSPSS 27.0 (Chicago, IL, USA) and GraphPad Prism 9.5.1 (GraphPad Prism software, USA) were used for data analysis and plotting. The clinical characteristics of the patients are expressed as the mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) (Due to the histograms and Q-Q plots showing that continuous variables approximate a normal distribution) and were compared via Student's t test. For categorical variables, the chi-square test was used for comparisons. The optimal cutoff values of κ FLCs, λ FLCs and κ/λ were determined via the X-tile tool \\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e. Survival analysis was performed via the Kaplan‒Meier method to evaluate DMFS, PFS and OS. Variables with P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 from the univariate Cox analysis were included in the multivariate Cox analysis to identify independent prognostic factors.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\u003cp\\u003eAll data generated or analyzed during this study are included in this article and its supplementary information\\u003c/p\\u003e\\u003cp\\u003efiles.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eEthics approval and consent to participate\\u003c/h2\\u003e\\u003cp\\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University, approval number: No. 2021A155011139, and was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians for this study.\\u003c/p\\u003e\\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e\\u003cp\\u003eThe authors have no conflicts of interest to declare.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eNational Natural Science Foundation of China, 82072993\\u003c/p\\u003e\\u003cp\\u003eNatural Science Foundation of Guangzhou Province,China, 2021A1515011139\\u003c/p\\u003e\\u003cp\\u003eScientific Research Project of Guangzhou, China, 202102020501\\u003c/p\\u003e\\u003cp\\u003eScience and Technology Project of Guangzhou ,China, 2025A03J4331\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eDafeng Lin, Suchen Li, Shiqing Nie, and Yanzhang provided research proposals. YingJun Liu,Ruixin Liu, Jiamin Chen, Haoxiang Long, Liyuan Liu conducted data collection and analysis. YingJun Liu, Zhiting Sun and Wenhui Chen wrote the main manuscript text. and YingJun Liu and Ruixin Liu prepared figures. Linquan Tang, Wenhui Chen, and Jianfu Zhao supervised the experiment. All authors reviewed the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eWe would like to express our heartfelt gratitude to all the individuals who contributed to this study.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll data generated or analyzed during this study are included in this article and its supplementary informationfiles.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eChen, Y. P.\\u003cem\\u003e et al.\\u003c/em\\u003e Nasopharyngeal carcinoma. \\u003cem\\u003eLancet\\u003c/em\\u003e \\u003cstrong\\u003e394\\u003c/strong\\u003e, 64-80 https://doi.org/10.1016/s0140-6736(19)30956-0 (2019).\\u003c/li\\u003e\\n\\u003cli\\u003eWong, Y., Meehan, M. T., Burrows, S. 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Cancer\\u003c/em\\u003e \\u003cstrong\\u003e12\\u003c/strong\\u003e, e008146 https://doi.org/10.1136/jitc-2023-008146 (2024).\\u003c/li\\u003e\\n\\u003cli\\u003eLi, J.\\u003cem\\u003e et al.\\u003c/em\\u003e Triglyceride-inflammation score established on account of random survival forest for predicting survival in patients with nasopharyngeal carcinoma: a retrospective study. \\u003cem\\u003eFront. Immunol.\\u003c/em\\u003e \\u003cstrong\\u003e15\\u003c/strong\\u003e, 1375931 https://doi.org/10.3389/fimmu.2024.1375931 (2024).\\u003c/li\\u003e\\n\\u003cli\\u003eBeichmann, B., Henriksen, C., Paur, I. \\u0026amp; Paulsen, M. M. Barriers and facilitators of improved nutritional support for patients newly diagnosed with cancer: a pre-implementation study. \\u003cem\\u003eBMC Health Serv. Res.\\u003c/em\\u003e \\u003cstrong\\u003e24\\u003c/strong\\u003e, 815 https://doi.org/10.1186/s12913-024-11288-2 (2024).\\u003c/li\\u003e\\n\\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\":\"info@researchsquare.com\",\"identity\":\"discover-oncology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"dion\",\"sideBox\":\"Learn more about [Discover Oncology](https://www.springer.com/12672)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Discover Oncology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7917303/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7917303/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFree light chain proteins (FLCs) are small protein fragments produced by plasma cells, which are crucial for antibody production. Recently, abnormal increases in FLCs have been observed in various solid tumors, but their role in nasopharyngeal carcinoma (NPC) remains underexplored. A total of 170 NPC patients treated at Sun Yat-sen University Cancer Center from October 2018 to June 2023 were retrospectively analyzed. The X-tile tool was used to determine the optimal cutoff values for FLC levels, and Cox regression and log-rank tests were performed to evaluate the associations between FLC levels and distant metastasis-free survival (DMFS), progression-free survival (PFS) and overall survival (OS). The optimal cutoff value for serum lambda free light chain proteins (λ FLCs) in the 5-year PFS analysis were 2.1 g/L. Patients with high λ FLC levels had significantly lower DMFS and PFS rates than those with low λ FLC levels (P\\u0026thinsp;=\\u0026thinsp;0.0144 and P\\u0026thinsp;=\\u0026thinsp;0.0106), while OS did not differ significantly (P\\u0026thinsp;=\\u0026thinsp;0.3735). Multivariate analysis identified pretreatment the level of λ FLC as an independent risk factor for DMFS and PFS in NPC patients. The optimal cutoff for 5-year PFS of kappa light chain proteins (κ FLCs) were 3.8 g/L, and for the κ/λ ratio, it was 2.1. No significant differences were found in DMFS, PFS, or OS when stratified by κ FLC and κ/λ values. A high serum λ FLC level is an independent risk factor for DMFS and PFS in NPC patients, indicating it may serve as a prognostic biomarker.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Prognostic Value of Serum Lambda Free Light Chains in Nasopharyngeal Carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-10 05:24:11\",\"doi\":\"10.21203/rs.3.rs-7917303/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-11-18T09:53:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-14T13:49:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-14T13:40:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"266837044904883474767100537867759126415\",\"date\":\"2025-11-14T09:03:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-14T08:23:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"75318096163690992282159868517343180542\",\"date\":\"2025-11-12T06:01:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"136695289611866554389484285592361522143\",\"date\":\"2025-10-30T13:02:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-28T09:57:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-10-27T16:53:59+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-25T02:09:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-25T02:07:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Discover Oncology\",\"date\":\"2025-10-21T12:53:02+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"discover-oncology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"dion\",\"sideBox\":\"Learn more about [Discover Oncology](https://www.springer.com/12672)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Discover Oncology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"40bcc728-61de-4b12-bb68-93a32c716161\",\"owner\":[],\"postedDate\":\"November 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-23T14:42:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-10 05:24:11\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7917303\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7917303\",\"identity\":\"rs-7917303\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}