Assessment of Transthyretin and Key Biomarkers for Lung Cancer Diagnosis

Assessment of Transthyretin and Key Biomarkers for Lung Cancer Diagnosis | 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 Assessment of Transthyretin and Key Biomarkers for Lung Cancer Diagnosis Talib Dabshee Al-Tmimee, Moones Rahmandoust, Fatin Fadhel Al-Kazazz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7339549/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Lung cancer (LC) is one of the leading causes of cancer-related mortality worldwide, with poor prognosis in advanced stages. Early detection through reliable biomarkers is essential for improving survival rates. Transthyretin (TTR), a transport protein for thyroid hormones, has been suggested as a potential diagnostic marker, but its clinical utility in LC remains underexplored. Purpose: This study aimed to evaluate the diagnostic potential of serum TTR in combination with thyroid hormones and other biochemical markers in LC patients, and to determine which parameters provide the highest sensitivity and specificity for early diagnosis. Methods: A case-control study was conducted on 60 participants (30 LC patients—19 with NSCLC and 11 with SCLC—and 30 healthy controls). Serum levels of TTR, T3, T4, TSH, Albumin, HDL, Triglycerides (TG), Cholesterol, and Total Protein were measured using ELISA and standard biochemical assays. Data were analyzed using ANOVA, Mann–Whitney U test, Spearman/Pearson correlation, and ROC curve analysis to assess diagnostic performance. Results: LC patients showed significantly lower TTR, T3, TSH, Albumin, and Total Protein levels, and higher T4, HDL, TG, and Cholesterol levels compared to controls (p < 0.05). TTR and T4 demonstrated the strongest diagnostic performance, with AUC values of 0.831 and 0.922, respectively; T4 achieved 100% specificity. TTR correlated positively with Albumin, HDL, BMI, and TSH, and inversely with T4 and age. Conclusions: TTR and T4 are promising biomarkers for distinguishing LC patients from healthy individuals. Incorporating these markers into diagnostic panels may enhance early detection and improve clinical outcomes. Further research is warranted to validate these findings in larger, diverse populations. Lung Neoplasms Transthyretin Thyroid Hormones Biomarkers Receiver Operating Characteristic Curve Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Lung cancer (LC) also known as Lung Neoplasms stands as a leading cause of cancer-related mortality worldwide. Annually, 1.8 million cases of LC are diagnosed, with projections indicating deaths could reach 3 million by 2035 ( 1 ). LC encompasses two primary subtypes: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) ( 2 ). NSCLC, the predominant histological type, carries a five-year life expectancy that may improve with early detection ( 3 – 4 ). Common symptoms include persistent coughing, chest pain, breathlessness, and hemoptysis, often leading to delayed diagnosis ( 5 ). Factors impacting LC survival include smoking history, age, weight loss, gender, and inflammatory markers ( 6 ). Additionally, body mass index (BMI) correlates with LC development in some cases ( 7 ). Treatment strategies vary by cancer stage but typically involve chemotherapy and surgery. Late-stage LC presents a low recovery probability, underscoring the critical role of early diagnosis ( 8 – 9 ). Co-occurrence of chronic diseases like chronic obstructive pulmonary disease further complicates LC outcomes, contributing to increased mortality ( 10 ). Diagnostic methods include low-dose CT screening, chest radiography, and biomarker detection, with chest X-ray (CXR) often utilized despite limitations in detection efficacy ( 11 – 12 ). Biomarker analysis remains pivotal in LC research, focusing on diagnostic and prognostic indicators such as Albumin (ALB), Cholesterol (Chol), Total Protein (TP), High-Density Lipoprotein (HDL), Thyroid-Stimulating Hormone (TSH), Triiodothyronine (T3), and Tetraiodothyronine (T4) ( 6 , 13 – 19 ). BMI and age are recognized as influencing factors in LC onset and progression ( 20 ). Transthyretin (TTR), known as prealbumin, emerges as a crucial biomarker with implications for LC outcomes due to its short half-life and role in cancer cell growth inhibition ( 21 – 22 ). This study aimed to evaluate TTR and other LC indicators in patients compared to controls, examining their diagnostic efficiency and correlation with inflammatory markers. The findings underscore TTR's potential as a biomarker for LC diagnosis and its association with inflammatory conditions in LC patients (Fig. 1 ). Figure 1 2. MATERIAL AND METHODS 2.1 Patients and sample collection This study was conducted at Imam Hussein Teaching Hospital in Karbala city from June 28, 2023, to September 24, 2023. The research involved 60 Iraqi individuals divided into two groups: a control group comprising 30 individuals and an LC group comprising another 30 individuals. Among the 30 patients in the LC group, 19 were diagnosed with NSCLC and 11 with SCLC (Fig 2). Samples were collected along with pertinent information obtained through oral questioning, including medical history, smoking habits, and other potential factors. Information was also sourced from pathology records, as all LC patients have a pathology report. Statistics indicate that the survival rate for most LC patients does not exceed 15%, according to data from the Statistics Department. Each group was stratified based on clinical and demographic characteristics such as age, BMI, gender, and smoking status. 2.2 Ethical Approval All human samples used in this research were obtained with informed consent from each subject or the subject’s guardian. Additionally, the experimental protocols were approved by the Mustansiriyah University-College of Science ethical committee under Registration No: 15. The ethical considerations for this paper were conducted in accordance with the Helsinki Declaration. 2.3 Sample collection Two milliliters of venous blood were collected from controls and patients. The blood was left to clot and subsequently centrifuged for 5 minutes at 8000 x g. The separated serum was then preserved at -80 °C. 2.4 Methodology Results of TTR, TSH, T3, T4, TP, ALB, Chol, and TG were initially compared among all groups, including healthy controls, to explore their relationships with various clinical characteristics and identify potential indicators of disease or its progression. All samples from the healthy control group were selected based on the absence of any chronic diseases, adhering to the exclusion criteria outlined in the study protocol. Due to the prolonged hospitalization and extended infection periods exceeding ten years among some LC patients, their results were not compared with those of other patients. This was due to decomposition of most blood components and the lack of access to effective serum in the respiratory system or other organs, as well as the absence of recent surgical operations. 2.5 Materials The TTR kit (Biotech Company, China), T3, T4, TSH kits (Pishtaz Teb Diagnostic company, Iran), HDL, Chol, TG, and TP kits (Biorexfars company, Iran) and Alb kit (Darman Kave company, Iran) were utilized for detecting biomarkers concentration in blood samples. 2.6 Biomarkers Detection For the comprehensive detection of T3, T4, TTR, Alb, Chol, HDL, Total Protein, and TSH in a single blood sample, blood was collected from the patient using a sterile syringe and placed into a vacutainer tube. The sample was centrifuged to separate serum or plasma from blood cells. Specific test kits for each analyte were prepared, and a defined volume of serum/plasma was dispensed into wells of microplates pre-coated with antibodies specific to each analyte. Biomarker detection methods, such as enzyme-linked immunosorbent assay (ELISA), utilize specific antibodies to capture target molecules like proteins or hormones in biological samples. These assays often involve enzyme reactions that produce measurable signals, correlating the signal intensity with the biomarker concentration, crucial for diagnostic and research purposes. The assay for detecting Human TTR utilized Biotin double antibody sandwich technology. Wells were pre-coated with monoclonal antibodies specific to TTR. TTR samples were added and allowed to bind, followed by incubation with biotin-labeled anti-TTR antibodies. Streptavidin-Horseradish Peroxidase (HRP) was then introduced to form an immune complex. After washing to remove unbound enzymes, chromogens were added, causing a color change from blue to yellow due to acidification. Absorbance at 450 nm was measured within 10 minutes of adding the stop solution, correlating with the TTR concentration. For T3 detection, the competitive ELISA technique was employed. Wells were coated with anti-T3 monoclonal antibodies. Patient serum and standards were added, followed by the T3-HRP conjugate. After incubation and washing to remove unbound substances, a chromogen-substrate solution was added, resulting in a blue color change. Absorbance was measured at 450 nm within 30 minutes. For T4 detection, wells were coated with anti-T4 monoclonal antibodies. Patient serum, standards, and T4-HRP conjugate were added and incubated. After washing, a chromogen-substrate solution induced a blue color change, stopped by the addition of a stop solution. Absorbance was measured at 450 nm. For TSH detection, the method followed the same immunoassay procedure, utilizing specific anti-TSH antibodies for accurate measurement. TP was detected using the Biuret reagent protein assay. Proteins reacted with copper salts in an alkaline medium, producing a violet-blue color. Absorbance at 540 nm was measured using an ELISA reader after incubation. Alb was detected using the Bromcresol Green method. Alb reacted with bromcresol green reagent in a moderately acidic environment, producing a complex with a color change. Absorbance at 628 nm indicated Alb concentration. Chol detection involved hydrolysis of cholesteryl esters by cholesterol esterase. Chol reacted with phenol and 4-aminophenazone to form a red quinone, resulting in a pink-red solution after incubation. Absorbance at 200 nm was measured. HDL measurement was carried out using similar biochemical reactions appropriate for lipoproteins. TG detection utilized lipoprotein lipase to release glycerol, which reacted with peroxidase to form a quinonimine compound. Absorbance at 450 nm was measured after incubation. Each assay followed the specific instructions provided with the biochemical kits concerning sample addition, incubation, and measurement. The obtained signals were compared to standard curves to determine the concentrations of T3, T4, TTR, Albumin, Cholesterol, HDL, Total Protein, and TSH. Quality control samples were included to ensure the accuracy and reliability of test results. Manufacturer instructions for each test kit were followed meticulously, and all instruments were properly calibrated. 2.7 Statistical analysis Data are presented as frequencies or percentages for categorical variables and as mean ± standard deviation for continuous variables, unless otherwise specified. Differences in categorical clinical variables between two groups were evaluated using the chi-square test or Fisher’s exact test. Mean value differences between groups were analyzed using the Mann-Whitney U test. One-way analysis of variance (ANOVA) was employed to compare variables among more than two groups, with Tukey's post hoc test used for multiple comparisons. Associations between variables were quantified using Spearman's rank correlation coefficient. This study assessed the diagnostic performance of a predictive model using values related to biomarker concentration and BMI in patients diagnosed with LC. The primary outcome was a binary variable indicating the presence or absence of LC, coded as 0 for negative and 1 for positive outcomes. The ROC curve analysis was conducted in SPSS to evaluate the model's ability to discriminate between positive and negative cases. The ROC analysis in SPSS provided a table detailing sensitivity (true positive rate) and specificity (true negative rate) at various cutoff points. The Area Under the Curve (AUC) was calculated to quantify the model's overall performance. A higher AUC suggests better performance, indicating the model's effectiveness in distinguishing between the outcome classes. To determine the optimal cutoff point, sensitivity and specificity values were reviewed across different thresholds. The cutoff point that maximized Youden’s Index (Sensitivity + Specificity - 1) was selected to balance sensitivity and specificity. This chosen cutoff point demonstrated a practical balance for clinical decision-making, highlighting the model’s capability to effectively classify positive and negative outcomes. These findings have significant implications for its application in clinical settings. Figure 2 3. RESULTS 3.1 Descriptive statistics Table 1 presents the means ± SD of the patient group compared to the control group, demonstrating a significant difference (p=0.001). 3.2 Smoking and weight impact on Cancer and control group BMI calculations in LC patients and the healthy control group yielded an insignificant result (p=0.246). Analysis by gender distribution revealed a higher prevalence of LC, among males compared to females. We gathered weight and height data for each participant, finding that excess weight is less prevalent among LC patients. In our study, smoking impact on LC incidence appeared to be minor with a p-value of 0.056. 3.3 Comparison of TTR level in two age group in cancer and patients Figure 3 indicates a significant decrease in TTR concentration among cancer patients aged 51 to 80 compared to those aged 20 to 50, with a p-value of 0.0003. Specifically, the mean TTR concentration in the older age group was significantly lower than in the younger age group. This trend was consistent across different cancer subtypes and was independent of variables such as gender, smoking status, and BMI. The duration of cancer diagnosis also did not significantly impact TTR levels, indicating the decrease is more strongly associated with age rather than disease duration. 3.4 Comparison between level of biomarkers in Male and Female group According to Table 2, the comparison by gender between all LC patients and the control group indicated significant differences in hormone and protein levels. Specifically, levels of T3, TTR, and TSH hormones were lower in females with LC compared to males (p<0.05). In contrast, T4 levels were higher in males compared to females and the control group (p<0.05). In terms of lipid profiles, Chol and HDL levels were significantly higher in female patients than in male patients (p<0.05), while TP and Alb levels were lower in females compared to male patients and the control group (p<0.05). These results indicate a distinct gender disparity in the biochemical response to LC. Overall, these findings highlight a significant gender difference in the biochemical markers of LC, with females exhibiting lower levels of certain hormones and proteins but higher lipid levels compared to males and controls. 3.5 The research parameters of all patients-based Lung cancer To gather a general comparison of each biomarker in patients and the control group, we measured the mean concentration of each component in the cancer and control groups. Table 3 shows that serum hormones, including TTR, T3, and TSH, significantly decreased in patients with LC (p=0.001), while T4 notably increased in patients (p=0.002). HDL, TG, and Chol levels were significantly higher in LC patients compared to the healthy group (p=0.005, 0.04, and 0.012, respectively). BMI was significantly lower in patients with LC (p=0.05). TP levels were notably decreased in cancer patients. Generally, Chol levels did not show a significant difference between the cancer and control groups, although there was a slight increase in the cancer group. Alb serum levels were moderately lower in the cancer group (p=0.003). HDL concentration was higher in LC patients (p=0.005). TP levels slightly decreased in LC patients compared to the control group (p=0.040). TG levels in LC patients were approximately twice as high as those in the control group (p=0.040). 3.6 Patients Correlation Coefficient Among TTR and other parameters The Pearson correlation test was applied to the statistical data to assess the correlation and p-values between TTR and other biomarkers and factors (TSH, T3, T4, TP, ALB, HDL, Chol, TG, BMI, and Age) in LC. Figure 4 illustrates the correlation coefficients among these parameters. Demographic and serum TTR levels showed significant correlations with serum Alb (r=0.763, p<0.001). TSH levels exhibited a significant correlation with serum TTR (r=0.393, p<0.032). Serum T4 levels showed a significant inverse correlation with TTR (r=-0.439, p<0.001). Additionally, serum TTR levels demonstrated a significant inverse correlation with age (r=-0.589, p<0.001). Serum T3 concentration showed a significant correlation with serum TTR levels in LC patients (p=0.012). HDL and TTR exhibited a significant and strong correlation (Fig. 4e). TP concentration showed a not significant positive correlation with TTR levels (Fig. 4g). In our study, higher BMI was correlated with higher TTR concentration (Fig. 4h). TG and TTR have showed a weak correlation (Fig. 4i). The Pearson correlation test revealed significant relationships between TTR and various biomarkers in LC patients, with notable inverse correlations between TTR and both age and T4 levels. TTR levels, which are influenced by nutritional status and inflammation, showed a positive correlation with BMI. Additionally, the study found weak correlations between TTR and both T3 and TG levels. 3.7 ROC analysis In this study, we performed ROC and AUC analysis to evaluate the effectiveness of various parameters (TTR, T4, Chol, HDL, T3, TSH, TP, TG, Alb, BMI, and Age) in diagnosing LC (Table 4). The ROC metrics for TTR demonstrated good differentiation, indicating a strong ability to distinguish between LC and non-LC cases. T4 exhibited excellent performance, showing high sensitivity and specificity. Cholesterol levels, however, showed poor performance in comparison, with lower sensitivity and specificity, making it a less reliable marker for LC. HDL levels demonstrated acceptable accuracy with moderate sensitivity and high specificity, suggesting its potential as a somewhat reliable indicator in diagnosis. T3 showed moderate discriminative power, comparable to HDL but not as robust as TTR or T4. TSH proved to be more effective, with impressive sensitivity and specificity, making it one of the more reliable parameters in this study. In contrast, TP was found to be less effective in distinguishing LC patients, suggesting its limited utility in such diagnostic contexts. TG showed moderate discriminative power, somewhat similar to T3, indicating moderate effectiveness. Alb, on the other hand, was a poor indicator for LC due to its low sensitivity. Both BMI and Age showed poor performance as diagnostic indicators in this context, highlighting their limitations in contributing to the diagnosis of LC. Overall, TTR, T4, and TSH emerged as the most reliable parameters, while Chol, Alb, BMI, and Age were less effective. 4. DISCUSSION In our study, we found that disease duration exceeding one year significantly reduces survival rates, aligning with previous research (23). Additionally, lower BMI increases the risk of LC, while higher BMI appears to decrease this risk (24). These observations were supported by our findings, which showed significant weight loss in 60% of cases, likely due to treatment effects (25). Despite smoking’s recognized impact on respiratory diseases globally, our study found it had a minor influence, suggesting it may not be a major factor in our patient cohort (26). Regarding age-related trends, our findings concur with Sanguinetti et al. (27), showing higher TTR levels in younger individuals compared to older age groups. This decline in older cancer patients reflects age-related decreases in TTR, potentially complicating diagnosis and management in this demographic. Gender-specific differences post-LC onset were also noted, with biomarkers showing higher levels in females, contrary to general incidence trends (28). Female patients exhibited elevated Chol and HDL levels but lower TP and Alb levels, emphasizing the importance of gender-specific considerations in LC assessment and treatment (29-31). Thyroid hormone analysis revealed significant decreases in TSH and T3 levels and increased T4 levels among LC patients, consistent with Ma Z et al. (32). These hormonal shifts play critical roles in cellular processes and may influence cancer cell mechanisms (33). Notably, TTR levels were significantly reduced in LC patients, particularly in advanced stages, underscoring its potential as a biomarker for disease progression (34). Our study demonstrated significant alterations in thyroid hormone profiles among LC patients, including marked decreases in TTR, T3, and TSH levels alongside elevated T4 levels (35). Analysis of biomarkers like HDL and TG showed significant elevations compared to controls, supporting previous findings on their roles in LC pathophysiology (18, 30, 36). Conversely, BMI was significantly lower in LC patients, consistent with literature highlighting body composition changes in this population (37). Lower Alb levels in cancer patients suggest implications for prognosis and survival, whereas slightly decreased TP levels could serve as minimally invasive markers (38-41). While Chol levels showed variability, they indicated limited reliability as an LC biomarker (42). The strong correlation between Alb and TTR underscores their interdependent roles in LC diagnostics. Additionally, the correlation between TSH and TTR (r=0.393, p<0.032) suggests TSH’s influence on thyroid hormone transport via TTR binding, affecting T4 levels inversely correlated with TTR and age-related declines (43-44). TTR’s role as a carrier protein for thyroid hormones was evident in its correlations with T3 and TG levels, highlighting its potential as a biomarker in LC diagnosis (22, 45-46). ROC and AUC analyses confirmed TTR and T4 as robust markers for LC, demonstrating high diagnostic efficacy. In contrast, Chol, TP, TG, Alb, BMI, and age showed lower sensitivity and specificity, limiting their utility as primary diagnostic markers. Our study expands upon existing literature by elucidating significant biomarker alterations in LC patients. These findings underscore the potential of multi-marker approaches, emphasizing the diagnostic efficacy of TTR and T4 while highlighting the complex interplay of thyroid hormones, gender-specific responses, and other biomarkers in LC management. Future research should validate these biomarkers across diverse populations to refine LC diagnostics and improve clinical outcomes. Figure 3 Figure 4 Table 1 Table 2 Table 3 Table 4 5. CONCLUSION In summary, when LC duration exceeds one year, healing becomes challenging, highlighting the need for early diagnosis. Recent advancements in detecting disease-specific biomarkers in fluids, such as Alb, Chol, HDL, TG, TP, T3, and T4, offer promising diagnostic avenues. Low TTR levels in LC patients emerged as strong diagnostic indicators. ROC and AUC analyses identified T4 and TTR as the most effective LC markers, with T4 exhibiting the highest specificity. These findings underscore the importance of selecting appropriate biomarkers for accurate LC prediction, aiming to improve diagnosis and treatment outcomes. Further research should validate these biomarkers across diverse populations and clinical settings. Declarations 7. FUNDING The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution Author Contributions Statement: Talib Dabshee Al-Tmimee: Conceptualization, study design, patient recruitment, data collection, laboratory analysis, and drafting of the manuscript.Moones Rahmandoust: Leadership, study supervision, methodology design, data interpretation, critical revision of the manuscript, and final approval for submission.Fatin Fadhel Al-Kazazz: Laboratory analysis, statistical analysis, data curation, and preparation of figures/tables.Leili Sharif Bakhtiar: Manuscript editing and final revision, graphical abstract design, and formatting for submission.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. 6. ACKNOWLEDGMENTS Gratitude and much appreciation to Faculty of Protein Research Center, Shahid Beheshti University and Mustansiriyah University. References Wadowska K, Bil-Lula I, Trembecki Ł, Śliwińska-Mossoń M. Title: Genetic Markers in Lung Cancer Diagnosis: A Review. 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BMC Cancer [Internet]. 2022 Jul 5 [cited 2023 Oct 12];22(1). https://doi.org/10.1186/s12885-022-09830-8 Kim Y, Koh JS, Woo S-D, Lee S-I, Kang DH, Park D, et al. The Tri-iodothyronine (T3) Level Is a Prognostic Factor for Patients With Advanced NSCLC: Receiving Immune Checkpoint Inhibitors and Is Associated With Liver Metastasis. Clinical Medicine Insights: Oncology. 2022 Jan;16. https://doi.org/10.1177/11795549221139522 Luo F, Zeng K, Cao J, Zhou T, Lin S, Ma W, et al. Predictive value of a reduction in the level of high-density lipoprotein-cholesterol in patients with non-small-cell lung cancer undergoing radical resection and adjuvant chemotherapy: a retrospective observational study. Lipids in Health and Disease. 2021 Sep 20;20(1). https://doi.org/10.1186/s12944-021-01538-1 Branco MG, Mateus C, Capelas ML, Pimenta N, Santos T, Mäkitie A, et al. Bioelectrical Impedance Analysis (BIA) for the Assessment of Body Composition in Oncology: A Scoping Review. 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Anticancer effects of epigallocatechin-3-gallate nanoemulsion on lung cancer cells through the activation of AMP-activated protein kinase signaling pathway. Scientific Reports [Internet]. 2020 Mar 20;10(1):5163. https://doi.org/10.1038/s41598-020-62136-2 Qi S, Wu Q, Chen Z, Zhang W, Zhou Y, Mao K, et al. High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Scientific Reports [Internet]. 2021 Jun 3;11(1):11805. https://doi.org/10.1038/s41598-021-91276-2 LI li, Zhong J, Zou J, Zhang T, Xu C. The prognostic role of tissue and serum transthyretin expression in patients with surgically resected non-small cell lung cancer. Research Square (Research Square). 2022 Nov 9; https://doi.org/10.21203/rs.3.rs-2242767/v1 Walsh JP. Thyroid Function across the Lifespan: Do Age-Related Changes Matter? Endocrinology and Metabolism. 2022 Apr 14; https://doi.org/10.3803/enm.2022.1463 Villeneuve D, Dries Knapen. Thyroperoxidase inhibition leading to increased mortality via reduced anterior swim bladder inflation. OECD series on adverse outcome pathways. 2022 Dec 15; https://doi.org/10.1787/447c5cba-en Khalid Akkour, Alanazi IO, Alfadda AA, Masood A, Hani Alhalal, Joy SS, et al. Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer. BMC Cancer. 2024 Jun 20;24(1). https://doi.org/10.1186/s12885-024-12522-0 Servier Medical Art. (n.d.). Medical illustrations . Servier. https://smart.servier.com/ Licensed under Creative Commons Attribution 3.0 Unported License (CC BY 3.0). Tables Table 1 Mean values of Age, disease duration, smoking and BMI for cancer and control group Groups Control Cancer P-value No. 30 30 N/A Age (years) range Age Mean ± SD 27.77 ± 12.31 42.23 ± 4.65 0.001*** Duration with No. of years (%) Less than one year None 80.00% 0.002** More than one year 20.00% Smoking No. (%) Yes 56.70% 66.70% 0.056** No 43.30% 33.30% BMI Normal weight 33.3% 53.3% 0.233 Overweight 30.0% 26.7% obesity more 36.7.0% 20.0% Table 2 Concentration of biomarkers and BMI values in female and male patients. Variable Variable Level Patients Control P-Value Mean Std. Deviation Mean Std. Deviation T3 Male 1.12 1.2 8.45 1.03 0.001*** Female 0.95 1.39 9.01 0.9 0.001*** T4 Male 1.24 0.41 0.83 0.22 0.001*** Female 1.52 0.3 0.93 0.33 0.001*** TSH Male 2.96 2.52 3.51 3.29 0.459 Female 2.91 2.16 4.55 3.14 0.137 CHO Male 218.44 54.28 193.93 43.14 0.167 Female 247.67 49.78 205.87 50.95 0.018* TP Male 65.06 9.43 71.53 7.84 0.042* Female 64.5 10.9 70.4 8.02 0.118 ALB Male 43.44 6.6 48.87 9.01 0.095 Female 42 5.17 48.8 7.23 0.012* HDL Male 40.02 21.64 26.47 8.94 0.039* Female 43.58 22.69 30.82 10.18 0.040* TG Male 157.94 75.11 210.67 148.27 0.814 Female 157.08 73.66 199.47 104.05 0..245 TTR Male 29.92 22.67 85.67 24.22 0.001*** Female 28.69 17.39 98.57 26.09 0.001*** BMI Male 24.99 3.49 29.34 6.4 0.033* Female 27.38 6.39 28.22 5.89 0.726 Table 3 Comparison of the research parameters of all patients-based Lung Cancer compared with control group. Types Parameters Disease Status Mean Std. Deviation P-Value Hormone T3 Cancer 1.05 1.26 0.001*** Control 8.73 0.99 T4 Cancer 1.35 0.39 0.002** Control 0.88 0.28 TSH Cancer 2.94 2.35 0.107 Control 4.03 3.20 TTR Cancer 29.43 20.40 0.001*** Control 92.12 25.59 Proteins and lipid TP Cancer 64.83 9.86 0.040* Control 70.97 7.81 Alb Cancer 42.87 6.02 0.003** Control 48.83 8.03 HDL Cancer 41.44 21.75 0.005** Control 28.65 9.67 TG Cancer 205.07 125.98 0.04* Control 102.60 73.25 Chol Cancer 230.13 53.66 0.012* Control 199.90 46.78 Body mass index BMI Cancer 25.95 4.90 0.052 Control 28.78 6.07 Table 4 Sensitivity, specificity, cut off and AUC values for biomarkers. Parameters Sensitivity Specificity Cut off AUC TTR 0.767 0.867 1.114 0.831 T4 0.800 1.000 43.565 0.922 CHO 0.500 0.867 230.500 0.689 HDL 0.567 0.967 40.580 0.719 T3 0.600 0.833 229.000 0.716 TSH 0.733 1.000 42.215 0.779 TP 0.333 0.900 1.153 0.464 TG 0.500 0.700 101.040 0.573 Alb 0.230 0.970 127.970 0.562 BMI 0.000 1.000 2.573 0.378 Age 0.333 0.867 231.000 0.586 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers invited by journal 27 Sep, 2025 Editor assigned by journal 19 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 10 Aug, 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. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":80400,"visible":true,"origin":"","legend":"\u003cp\u003eThe graphical abstract presents a study on the role of Transthyretin (TTR) in lung cancer progression. The research involves 30 lung cancer patients and 30 healthy controls, with blood samples analyzed for thyroid-related hormones, lipids, and proteins. The study examines correlations between TTR levels and various parameters, including BMI, gender, smoking impact, and specific lung cancer biomarkers. Analytical methods such as concentration analysis, correlation assessment, and ROC analysis are used to evaluate TTR’s potential as a biomarker for lung cancer. Graphic elements were sourced from Servier Medical Art (Servier, n.d.; https://smart.servier.com/), used under Creative Commons Attribution 3.0 Unported License (CC BY 3.0). [47]. \u0026nbsp;\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339549/v1/84a0cfd87cbc1e279b4a20aa.jpg"},{"id":93246123,"identity":"a62b9288-b2de-4171-9e0a-85fff989d579","added_by":"auto","created_at":"2025-10-10 15:11:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44589,"visible":true,"origin":"","legend":"\u003cp\u003eAfter dividing the samples into four groups and placing the serums of these samples in Eppendorf tubes for the purpose of conducting tests on it for each of these parameters (TTR, TSH, T3, T4, TP, Alb, HDL, Chol and TG)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339549/v1/d5321644f9b3c2e6e06cfe49.jpg"},{"id":93246124,"identity":"1d51751e-aead-4910-8c9a-05484c342541","added_by":"auto","created_at":"2025-10-10 15:11:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58900,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of TTR level by percentage in patients with age range of (20-50) and patients with age range of (51-80).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339549/v1/175174e895379b9e186a4ff4.jpg"},{"id":93246122,"identity":"3637ee5f-6b4f-4de5-896f-0bbb11e94d08","added_by":"auto","created_at":"2025-10-10 15:11:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":108069,"visible":true,"origin":"","legend":"\u003cp\u003eThe\u003cstrong\u003e \u003c/strong\u003eCorrelation Coefficient between TTR and ALB (a), TSH (b), Age (c), T3 (d), HDL (e), T4 (f), TP (g), BMI (h) and TG (i).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7339549/v1/9864e6999e3fc68c176ec410.jpg"},{"id":93250509,"identity":"f800c00d-d74e-401d-8640-15b887aa543a","added_by":"auto","created_at":"2025-10-10 15:43:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1166151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7339549/v1/ead55079-2b5e-4489-b505-15fc2783d77e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssessment of Transthyretin and Key Biomarkers for Lung Cancer Diagnosis\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLung cancer (LC) also known as Lung Neoplasms stands as a leading cause of cancer-related mortality worldwide. Annually, 1.8\u0026nbsp;million cases of LC are diagnosed, with projections indicating deaths could reach 3\u0026nbsp;million by 2035 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). LC encompasses two primary subtypes: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). NSCLC, the predominant histological type, carries a five-year life expectancy that may improve with early detection (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Common symptoms include persistent coughing, chest pain, breathlessness, and hemoptysis, often leading to delayed diagnosis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Factors impacting LC survival include smoking history, age, weight loss, gender, and inflammatory markers (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, body mass index (BMI) correlates with LC development in some cases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Treatment strategies vary by cancer stage but typically involve chemotherapy and surgery. Late-stage LC presents a low recovery probability, underscoring the critical role of early diagnosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Co-occurrence of chronic diseases like chronic obstructive pulmonary disease further complicates LC outcomes, contributing to increased mortality (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Diagnostic methods include low-dose CT screening, chest radiography, and biomarker detection, with chest X-ray (CXR) often utilized despite limitations in detection efficacy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Biomarker analysis remains pivotal in LC research, focusing on diagnostic and prognostic indicators such as Albumin (ALB), Cholesterol (Chol), Total Protein (TP), High-Density Lipoprotein (HDL), Thyroid-Stimulating Hormone (TSH), Triiodothyronine (T3), and Tetraiodothyronine (T4) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). BMI and age are recognized as influencing factors in LC onset and progression (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Transthyretin (TTR), known as prealbumin, emerges as a crucial biomarker with implications for LC outcomes due to its short half-life and role in cancer cell growth inhibition (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This study aimed to evaluate TTR and other LC indicators in patients compared to controls, examining their diagnostic efficiency and correlation with inflammatory markers. The findings underscore TTR's potential as a biomarker for LC diagnosis and its association with inflammatory conditions in LC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Patients and sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at Imam Hussein Teaching Hospital in Karbala city from June 28, 2023, to September 24, 2023. The research involved 60 Iraqi individuals divided into two groups: a control group comprising 30 individuals and an LC group comprising another 30 individuals. Among the 30 patients in the LC group, 19 were diagnosed with NSCLC and 11 with SCLC (Fig 2). Samples were collected along with pertinent information obtained through oral questioning, including medical history, smoking habits, and other potential factors. Information was also sourced from pathology records, as all LC patients have a pathology report. Statistics indicate that the survival rate for most LC patients does not exceed 15%, according to data from the Statistics Department. Each group was stratified based on clinical and demographic characteristics such as age, BMI, gender, and smoking status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human samples used in this research were obtained with informed consent from each subject or the subject\u0026rsquo;s guardian. Additionally, the experimental protocols were approved by the Mustansiriyah University-College of Science ethical committee under Registration No: 15. The ethical considerations for this paper were conducted in accordance with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo milliliters of venous blood were collected from controls and patients. The blood was left to clot and subsequently centrifuged for 5 minutes at 8000 x g. The separated serum was then preserved at -80 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of TTR, TSH, T3, T4, TP, ALB, Chol, and TG were initially compared among all groups, including healthy controls, to explore their relationships with various clinical characteristics and identify potential indicators of disease or its progression. All samples from the healthy control group were selected based on the absence of any chronic diseases, adhering to the exclusion criteria outlined in the study protocol. Due to the prolonged hospitalization and extended infection periods exceeding ten years among some LC patients, their results were not compared with those of other patients. This was due to decomposition of most blood components and the lack of access to effective serum in the respiratory system or other organs, as well as the absence of recent surgical operations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe TTR kit (Biotech Company, China), T3, T4, TSH kits (Pishtaz Teb Diagnostic company, Iran), HDL, Chol, TG, and TP kits (Biorexfars company, Iran) and Alb kit (Darman Kave company, Iran) were utilized for detecting biomarkers concentration in blood samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Biomarkers Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the comprehensive detection of T3, T4, TTR, Alb, Chol, HDL, Total Protein, and TSH in a single blood sample, blood was collected from the patient using a sterile syringe and placed into a vacutainer tube. The sample was centrifuged to separate serum or plasma from blood cells. Specific test kits for each analyte were prepared, and a defined volume of serum/plasma was dispensed into wells of microplates pre-coated with antibodies specific to each analyte.\u003c/p\u003e\n\u003cp\u003eBiomarker detection methods, such as enzyme-linked immunosorbent assay (ELISA), utilize specific antibodies to capture target molecules like proteins or hormones in biological samples. These assays often involve enzyme reactions that produce measurable signals, correlating the signal intensity with the biomarker concentration, crucial for diagnostic and research purposes. The assay for detecting Human TTR utilized Biotin double antibody sandwich technology. Wells were pre-coated with monoclonal antibodies specific to TTR. TTR samples were added and allowed to bind, followed by incubation with biotin-labeled anti-TTR antibodies. Streptavidin-Horseradish Peroxidase (HRP) was then introduced to form an immune complex. After washing to remove unbound enzymes, chromogens were added, causing a color change from blue to yellow due to acidification. Absorbance at 450 nm was measured within 10 minutes of adding the stop solution, correlating with the TTR concentration. For T3 detection, the competitive ELISA technique was employed. Wells were coated with anti-T3 monoclonal antibodies. Patient serum and standards were added, followed by the T3-HRP conjugate. After incubation and washing to remove unbound substances, a chromogen-substrate solution was added, resulting in a blue color change. Absorbance was measured at 450 nm within 30 minutes. For T4 detection, wells were coated with anti-T4 monoclonal antibodies. Patient serum, standards, and T4-HRP conjugate were added and incubated. After washing, a chromogen-substrate solution induced a blue color change, stopped by the addition of a stop solution. Absorbance was measured at 450 nm. For TSH detection, the method followed the same immunoassay procedure, utilizing specific anti-TSH antibodies for accurate measurement. TP was detected using the Biuret reagent protein assay. Proteins reacted with copper salts in an alkaline medium, producing a violet-blue color. Absorbance at 540 nm was measured using an ELISA reader after incubation. Alb was detected using the Bromcresol Green method. Alb reacted with bromcresol green reagent in a moderately acidic environment, producing a complex with a color change. Absorbance at 628 nm indicated Alb concentration.\u003c/p\u003e\n\u003cp\u003eChol detection involved hydrolysis of cholesteryl esters by cholesterol esterase. Chol reacted with phenol and 4-aminophenazone to form a red quinone, resulting in a pink-red solution after incubation. Absorbance at 200 nm was measured. HDL measurement was carried out using similar biochemical reactions appropriate for lipoproteins. TG detection utilized lipoprotein lipase to release glycerol, which reacted with peroxidase to form a quinonimine compound. Absorbance at 450 nm was measured after incubation. Each assay followed the specific instructions provided with the biochemical kits concerning sample addition, incubation, and measurement. The obtained signals were compared to standard curves to determine the concentrations of T3, T4, TTR, Albumin, Cholesterol, HDL, Total Protein, and TSH. Quality control samples were included to ensure the accuracy and reliability of test results. Manufacturer instructions for each test kit were followed meticulously, and all instruments were properly calibrated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as frequencies or percentages for categorical variables and as mean \u0026plusmn; standard deviation for continuous variables, unless otherwise specified. Differences in categorical clinical variables between two groups were evaluated using the chi-square test or Fisher\u0026rsquo;s exact test. Mean value differences between groups were analyzed using the Mann-Whitney U test. One-way analysis of variance (ANOVA) was employed to compare variables among more than two groups, with Tukey\u0026apos;s post hoc test used for multiple comparisons. Associations between variables were quantified using Spearman\u0026apos;s rank correlation coefficient. This study assessed the diagnostic performance of a predictive model using values related to biomarker concentration and BMI in patients diagnosed with LC. The primary outcome was a binary variable indicating the presence or absence of LC, coded as 0 for negative and 1 for positive outcomes. The ROC curve analysis was conducted in SPSS to evaluate the model\u0026apos;s ability to discriminate between positive and negative cases.\u003c/p\u003e\n\u003cp\u003eThe ROC analysis in SPSS provided a table detailing sensitivity (true positive rate) and specificity (true negative rate) at various cutoff points. The Area Under the Curve (AUC) was calculated to quantify the model\u0026apos;s overall performance. A higher AUC suggests better performance, indicating the model\u0026apos;s effectiveness in distinguishing between the outcome classes.\u003c/p\u003e\n\u003cp\u003eTo determine the optimal cutoff point, sensitivity and specificity values were reviewed across different thresholds. The cutoff point that maximized Youden\u0026rsquo;s Index (Sensitivity + Specificity - 1) was selected to balance sensitivity and specificity. This chosen cutoff point demonstrated a practical balance for clinical decision-making, highlighting the model\u0026rsquo;s capability to effectively classify positive and negative outcomes. These findings have significant implications for its application in clinical settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003c/p\u003e"},{"header":"3. RESULTS ","content":"\u003cp\u003e\u003cstrong\u003e3.1 Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the means \u0026plusmn; SD of the patient group compared to the control group, demonstrating a significant difference (p=0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Smoking and weight impact on Cancer and control group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI calculations in LC patients and the healthy control group yielded an insignificant result (p=0.246). Analysis by gender distribution revealed a higher prevalence of LC, among males compared to females. We gathered weight and height data for each participant, finding that excess weight is less prevalent among LC patients. In our study, smoking impact on LC incidence appeared to be minor with a p-value of 0.056.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Comparison of TTR level in two age group in cancer and patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 indicates a significant decrease in TTR concentration among cancer patients aged 51 to 80 compared to those aged 20 to 50, with a p-value of 0.0003. Specifically, the mean TTR concentration in the older age group was significantly lower than in the younger age group. This trend was consistent across different cancer subtypes and was independent of variables such as gender, smoking status, and BMI. The duration of cancer diagnosis also did not significantly impact TTR levels, indicating the decrease is more strongly associated with age rather than disease duration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Comparison between level of biomarkers in Male and Female group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Table 2, the comparison by gender between all LC patients and the control group indicated significant differences in hormone and protein levels. Specifically, levels of T3, TTR, and TSH hormones were lower in females with LC compared to males (p\u0026lt;0.05). In contrast, T4 levels were higher in males compared to females and the control group (p\u0026lt;0.05). In terms of lipid profiles, Chol and HDL levels were significantly higher in female patients than in male patients (p\u0026lt;0.05), while TP and Alb levels were lower in females compared to male patients and the control group (p\u0026lt;0.05). These results indicate a distinct gender disparity in the biochemical response to LC. Overall, these findings highlight a significant gender difference in the biochemical markers of LC, with females exhibiting lower levels of certain hormones and proteins but higher lipid levels compared to males and controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 The research parameters of all patients-based Lung cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gather a general comparison of each biomarker in patients and the control group, we measured the mean concentration of each component in the cancer and control groups. Table 3 shows that serum hormones, including TTR, T3, and TSH, significantly decreased in patients with LC (p=0.001), while T4 notably increased in patients (p=0.002). HDL, TG, \u0026nbsp;and Chol levels were significantly higher in LC patients compared to the healthy group (p=0.005, 0.04, and 0.012, respectively). BMI was significantly lower in patients with LC (p=0.05). TP levels were notably decreased in cancer patients. Generally, Chol levels did not show a significant difference between the cancer and control groups, although there was a slight increase in the cancer group. Alb serum levels were moderately lower in the cancer group (p=0.003). HDL concentration was higher in LC patients (p=0.005). TP levels slightly decreased in LC patients compared to the control group (p=0.040). TG levels in LC patients were approximately twice as high as those in the control group (p=0.040).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Patients Correlation Coefficient Among TTR and other parameters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pearson correlation test was applied to the statistical data to assess the correlation and p-values between TTR and other biomarkers and factors (TSH, T3, T4, TP, ALB, HDL, Chol, TG, BMI, and Age) in LC. Figure 4 illustrates the correlation coefficients among these parameters. Demographic and serum TTR levels showed significant correlations with serum Alb (r=0.763, p\u0026lt;0.001). TSH levels exhibited a significant correlation with serum TTR (r=0.393, p\u0026lt;0.032). Serum T4 levels showed a significant inverse correlation with TTR (r=-0.439, p\u0026lt;0.001). Additionally, serum TTR levels demonstrated a significant inverse correlation with age (r=-0.589, p\u0026lt;0.001). Serum T3 concentration showed a significant correlation with serum TTR levels in LC patients (p=0.012). HDL and TTR exhibited a significant and strong correlation (Fig. 4e). TP concentration showed a not significant positive correlation with TTR levels (Fig. 4g). In our study, higher BMI was correlated with higher TTR concentration (Fig. 4h). TG and TTR have showed a weak correlation (Fig. 4i). The Pearson correlation test revealed significant relationships between TTR and various biomarkers in LC patients, with notable inverse correlations between TTR and both age and T4 levels. TTR levels, which are influenced by nutritional status and inflammation, showed a positive correlation with BMI. Additionally, the study found weak correlations between TTR and both T3 and TG levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 ROC analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we performed ROC and AUC analysis to evaluate the effectiveness of various parameters (TTR, T4, Chol, HDL, T3, TSH, TP, TG, Alb, BMI, and Age) in diagnosing LC (Table 4). The ROC metrics for TTR demonstrated good differentiation, indicating a strong ability to distinguish between LC and non-LC cases. T4 exhibited excellent performance, showing high sensitivity and specificity. Cholesterol levels, however, showed poor performance in comparison, with lower sensitivity and specificity, making it a less reliable marker for LC. HDL levels demonstrated acceptable accuracy with moderate sensitivity and high specificity, suggesting its potential as a somewhat reliable indicator in diagnosis. T3 showed moderate discriminative power, comparable to HDL but not as robust as TTR or T4. TSH proved to be more effective, with impressive sensitivity and specificity, making it one of the more reliable parameters in this study. In contrast, TP was found to be less effective in distinguishing LC patients, suggesting its limited utility in such diagnostic contexts. TG showed moderate discriminative power, somewhat similar to T3, indicating moderate effectiveness. Alb, on the other hand, was a poor indicator for LC due to its low sensitivity. Both BMI and Age showed poor performance as diagnostic indicators in this context, highlighting their limitations in contributing to the diagnosis of LC. Overall, TTR, T4, and TSH emerged as the most reliable parameters, while Chol, Alb, BMI, and Age were less effective.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn our study, we found that disease duration exceeding one year significantly reduces survival rates, aligning with previous research (23). Additionally, lower BMI increases the risk of LC, while higher BMI appears to decrease this risk (24). These observations were supported by our findings, which showed significant weight loss in 60% of cases, likely due to treatment effects (25). Despite smoking\u0026rsquo;s recognized impact on respiratory diseases globally, our study found it had a minor influence, suggesting it may not be a major factor in our patient cohort (26). Regarding age-related trends, our findings concur with Sanguinetti et al. (27), showing higher TTR levels in younger individuals compared to older age groups. This decline in older cancer patients reflects age-related decreases in TTR, potentially complicating diagnosis and management in this demographic. Gender-specific differences post-LC onset were also noted, with biomarkers showing higher levels in females, contrary to general incidence trends (28). Female patients exhibited elevated Chol and HDL levels but lower TP and Alb levels, emphasizing the importance of gender-specific considerations in LC assessment and treatment (29-31). Thyroid hormone analysis revealed significant decreases in TSH and T3 levels and increased T4 levels among LC patients, consistent with Ma Z et al. (32). These hormonal shifts play critical roles in cellular processes and may influence cancer cell mechanisms (33). Notably, TTR levels were significantly reduced in LC patients, particularly in advanced stages, underscoring its potential as a biomarker for disease progression (34). Our study demonstrated significant alterations in thyroid hormone profiles among LC patients, including marked decreases in TTR, T3, and TSH levels alongside elevated T4 levels (35). Analysis of biomarkers like HDL and TG showed significant elevations compared to controls, supporting previous findings on their roles in LC pathophysiology (18, 30, 36). Conversely, BMI was significantly lower in LC patients, consistent with literature highlighting body composition changes in this population (37). Lower Alb levels in cancer patients suggest implications for prognosis and survival, whereas slightly decreased TP levels could serve as minimally invasive markers (38-41). While Chol levels showed variability, they indicated limited reliability as an LC biomarker (42). The strong correlation between Alb and TTR underscores their interdependent roles in LC diagnostics. Additionally, the correlation between TSH and TTR (r=0.393, p\u0026lt;0.032) suggests TSH\u0026rsquo;s influence on thyroid hormone transport via TTR binding, affecting T4 levels inversely correlated with TTR and age-related declines (43-44). TTR\u0026rsquo;s role as a carrier protein for thyroid hormones was evident in its correlations with T3 and TG levels, highlighting its potential as a biomarker in LC diagnosis (22, 45-46). ROC and AUC analyses confirmed TTR and T4 as robust markers for LC, demonstrating high diagnostic efficacy. In contrast, Chol, TP, TG, Alb, BMI, and age showed lower sensitivity and specificity, limiting their utility as primary diagnostic markers.\u003c/p\u003e\n\u003cp\u003eOur study expands upon existing literature by elucidating significant biomarker alterations in LC patients. These findings underscore the potential of multi-marker approaches, emphasizing the diagnostic efficacy of TTR and T4 while highlighting the complex interplay of thyroid hormones, gender-specific responses, and other biomarkers in LC management. Future research should validate these biomarkers across diverse populations to refine LC diagnostics and improve clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn summary, when LC duration exceeds one year, healing becomes challenging, highlighting the need for early diagnosis. Recent advancements in detecting disease-specific biomarkers in fluids, such as Alb, Chol, HDL, TG, TP, T3, and T4, offer promising diagnostic avenues. Low TTR levels in LC patients emerged as strong diagnostic indicators. ROC and AUC analyses identified T4 and TTR as the most effective LC markers, with T4 exhibiting the highest specificity. These findings underscore the importance of selecting appropriate biomarkers for accurate LC prediction, aiming to improve diagnosis and treatment outcomes. Further research should validate these biomarkers across diverse populations and clinical settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e7. FUNDING\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions Statement: Talib Dabshee Al-Tmimee: Conceptualization, study design, patient recruitment, data collection, laboratory analysis, and drafting of the manuscript.Moones Rahmandoust: Leadership, study supervision, methodology design, data interpretation, critical revision of the manuscript, and final approval for submission.Fatin Fadhel Al-Kazazz: Laboratory analysis, statistical analysis, data curation, and preparation of figures/tables.Leili Sharif Bakhtiar: Manuscript editing and final revision, graphical abstract design, and formatting for submission.All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003e6. ACKNOWLEDGMENTS\u003c/h2\u003e\u003cp\u003eGratitude and much appreciation to Faculty of Protein Research Center, Shahid Beheshti University and Mustansiriyah University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWadowska K, Bil-Lula I, Trembecki Ł, Śliwińska-Mossoń M. Title: Genetic Markers in Lung Cancer Diagnosis: A Review. International Journal of Molecular Sciences [Internet]. 2020 Jun 27 [cited 2021 Apr 15];21(13). https://doi.org/10.3390/ijms21134569 \u003c/li\u003e\n\u003cli\u003eHowlader N, Forjaz G, Mooradian MJ, Meza R, Kong CY, Cronin KA, et al. The Effect of Advances in Lung-Cancer Treatment on Population Mortality. New England Journal of Medicine [Internet]. 2020 Aug 13;383(7):640\u0026ndash;9. https://doi.org/10.1056/nejmoa1916623 \u003c/li\u003e\n\u003cli\u003eCancer.org. 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Current Problems in Diagnostic Radiology. 2021 Nov;50(6):807\u0026ndash;10. https://doi.org/10.1067/j.cpradiol.2020.11.009 \u003c/li\u003e\n\u003cli\u003eZhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. International Journal of Cancer. 2020 Sep 23;148(5). https://doi.org/10.1002/ijc.33292 \u003c/li\u003e\n\u003cli\u003eSaponaro F, Kim JH, Grazia Chiellini. Transthyretin Stabilization: An Emerging Strategy for the Treatment of Alzheimer\u0026rsquo;s Disease? International Journal of Molecular Sciences [Internet]. 2020 Nov 17 [cited 2025 Mar 4];21(22):8672\u0026ndash;2. https://doi.org/10.3390/ijms21228672 \u003c/li\u003e\n\u003cli\u003eArora S, Behl T, Singh S, Sharma N, Gupta S. Metastatic Diseases. CRC Press; 2021. \u003c/li\u003e\n\u003cli\u003eZhang J, IJzerman MJ, Oberoi J, Karnchanachari N, Bergin RJ, Franchini F, et al. Time to diagnosis and treatment of lung cancer: A systematic overview of risk factors, interventions and impact on patient outcomes. Lung Cancer. 2022 Apr;166:27\u0026ndash;39. https://doi.org/10.1016/j.lungcan.2022.01.015 \u003c/li\u003e\n\u003cli\u003eHellyer JA, Aredo JV, Das M, Ramchandran K, Padda SK, Neal JW, et al. Role of Consolidation Durvalumab in Patients With EGFR- and HER2-Mutant Unresectable Stage III NSCLC. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer [Internet]. 2021 May 1 [cited 2024 May 27];16(5):868\u0026ndash;72. https://doi.org/10.1016/j.jtho.2020.12.020 \u003c/li\u003e\n\u003cli\u003eLigibel JA, Bohlke K, May AM, Clinton SK, Demark-Wahnefried W, Gilchrist SC, et al. Exercise, Diet, and Weight Management During Cancer Treatment: ASCO Guideline. Journal of Clinical Oncology. 2022 Aug 1;40(22):2491\u0026ndash;507. https://doi.org/10.1200/jco.22.00687 \u003c/li\u003e\n\u003cli\u003eLeiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nature Reviews Clinical Oncology [Internet]. 2023 Jul 21;20(20):1\u0026ndash;16. https://doi.org/10.1038/s41571-023-00798-3 \u003c/li\u003e\n\u003cli\u003eSanguinetti C, Minniti M, Susini V, Caponi L, Panichella G, Castiglione V, et al. The Journey of Human Transthyretin: Synthesis, Structure Stability, and Catabolism. Biomedicines [Internet]. 2022 Aug 1;10(8):1906. https://doi.org/10.3390/biomedicines10081906 \u003c/li\u003e\n\u003cli\u003eRuano-Ravi\u0026ntilde;a A, Provencio M, Juan, Enric Carcereny, Estival A, Delvys Rodr\u0026iacute;guez-Abreu, et al. Are there differences by sex in lung cancer characteristics at diagnosis? \u0026mdash;a nationwide study. Translational lung cancer research. 2021 Oct 1;10(10):3902\u0026ndash;11. https://doi.org/10.21037/tlcr-21-559 \u003c/li\u003e\n\u003cli\u003eLiu T, Zhou T, Luo F, Yang Y, Zhao S, Huang Y, et al. Clinical Significance of Kinetics of Low-Density Lipoprotein Cholesterol and Its Prognostic Value in Limited Stage Small Cell Lung Cancer Patients. Cancer Control. 2021 Jan 1;28. https://doi.org/10.1177/10732748211028257 \u003c/li\u003e\n\u003cli\u003eKong L, Zhao Q, Han Z, Xue W, Hu Z, Niu Z, et al. Prognostic significance of TG/HDL-C and non-HDL-C/HDL-C ratios in patients with non-small cell lung cancer: a retrospective study. Journal of International Medical Research. 2022 Aug;50(8). https://doi.org/10.1177/03000605221117211 \u003c/li\u003e\n\u003cli\u003eLu P, Ma Y, Wei S, Liang X. A Low Albumin-to-Globulin Ratio Predicts a Poor Prognosis in Patients With Metastatic Non-small-cell Lung Cancer. Frontiers in Medicine. 2021 Mar 1;8. https://doi.org/10.3389/fmed.2021.621592 \u003c/li\u003e\n\u003cli\u003eMa Z, Song P, Ji D, Zheng M, Qiu G, Liu Z, et al. Thyroid hormones as biomarkers of lung cancer: a retrospective study. Annals of Medicine. 2023 Apr 4;55(1). https://doi.org/10.1080/07853890.2023.2196088 \u003c/li\u003e\n\u003cli\u003eSmok-Kalwat J, Mertowska P, Mertowski S, Smolak K, Kozińska A, Koszałka F, et al. The Importance of the Immune System and Molecular Cell Signaling Pathways in the Pathogenesis and Progression of Lung Cancer. International Journal of Molecular Sciences [Internet]. 2023 Jan 1;24(2):1506. https://doi.org/10.3390/ijms24021506 \u003c/li\u003e\n\u003cli\u003eProvencio M, Cobo M, Rodriguez-Abreu D, Calvo V, Carcereny E, Cantero A, et al. Determination of essential biomarkers in lung cancer: a real-world data study in Spain with demographic, clinical, epidemiological and pathological characteristics. BMC Cancer [Internet]. 2022 Jul 5 [cited 2023 Oct 12];22(1). https://doi.org/10.1186/s12885-022-09830-8 \u003c/li\u003e\n\u003cli\u003eKim Y, Koh JS, Woo S-D, Lee S-I, Kang DH, Park D, et al. The Tri-iodothyronine (T3) Level Is a Prognostic Factor for Patients With Advanced NSCLC: Receiving Immune Checkpoint Inhibitors and Is Associated With Liver Metastasis. Clinical Medicine Insights: Oncology. 2022 Jan;16. https://doi.org/10.1177/11795549221139522 \u003c/li\u003e\n\u003cli\u003eLuo F, Zeng K, Cao J, Zhou T, Lin S, Ma W, et al. Predictive value of a reduction in the level of high-density lipoprotein-cholesterol in patients with non-small-cell lung cancer undergoing radical resection and adjuvant chemotherapy: a retrospective observational study. Lipids in Health and Disease. 2021 Sep 20;20(1). https://doi.org/10.1186/s12944-021-01538-1 \u003c/li\u003e\n\u003cli\u003eBranco MG, Mateus C, Capelas ML, Pimenta N, Santos T, M\u0026auml;kitie A, et al. Bioelectrical Impedance Analysis (BIA) for the Assessment of Body Composition in Oncology: A Scoping Review. Nutrients [Internet]. 2023 Jan 1;15(22):4792. https://doi.org/10.3390/nu15224792 \u003c/li\u003e\n\u003cli\u003eZhang C-L, Gao M-Q, Jiang X-C, Pan X, Zhang X-Y, Li Y, et al. Research progress and value of albumin-related inflammatory markers in the prognosis of non-small cell lung cancer: a review of clinical evidence. Annals of Medicine [Internet]. 2023 Dec 1 [cited 2024 Mar 26];55(1):1294\u0026ndash;307. https://doi.org/10.1080/07853890.2023.2192047 \u003c/li\u003e\n\u003cli\u003eMa S, Wang L. Fibrinogen-to-albumin ratio (FAR) is the best biomarker for the overall survival of patients with non-small-cell lung cancer. Frontiers in Oncology. 2024 Jun 24;14. https://doi.org/10.3389/fonc.2024.1396843 \u003c/li\u003e\n\u003cli\u003eAbolfathi H, Sheikhpour M, Shahraeini SS, Khatami S, Nojoumi SA. Studies in lung cancer cytokine proteomics: a review. Expert Review of Proteomics. 2021 Jan 2;18(1):49\u0026ndash;64. https://doi.org/10.1080/14789450.2021.1892491 \u003c/li\u003e\n\u003cli\u003eChen B-H, Hsieh C-H, Tsai S-Y, Wang C-Y, Wang C-C. Anticancer effects of epigallocatechin-3-gallate nanoemulsion on lung cancer cells through the activation of AMP-activated protein kinase signaling pathway. Scientific Reports [Internet]. 2020 Mar 20;10(1):5163. https://doi.org/10.1038/s41598-020-62136-2 \u003c/li\u003e\n\u003cli\u003eQi S, Wu Q, Chen Z, Zhang W, Zhou Y, Mao K, et al. High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Scientific Reports [Internet]. 2021 Jun 3;11(1):11805. https://doi.org/10.1038/s41598-021-91276-2 \u003c/li\u003e\n\u003cli\u003eLI li, Zhong J, Zou J, Zhang T, Xu C. The prognostic role of tissue and serum transthyretin expression in patients with surgically resected non-small cell lung cancer. Research Square (Research Square). 2022 Nov 9; https://doi.org/10.21203/rs.3.rs-2242767/v1 \u003c/li\u003e\n\u003cli\u003eWalsh JP. Thyroid Function across the Lifespan: Do Age-Related Changes Matter? Endocrinology and Metabolism. 2022 Apr 14; https://doi.org/10.3803/enm.2022.1463 \u003c/li\u003e\n\u003cli\u003eVilleneuve D, Dries Knapen. Thyroperoxidase inhibition leading to increased mortality via reduced anterior swim bladder inflation. OECD series on adverse outcome pathways. 2022 Dec 15; https://doi.org/10.1787/447c5cba-en \u003c/li\u003e\n\u003cli\u003eKhalid Akkour, Alanazi IO, Alfadda AA, Masood A, Hani Alhalal, Joy SS, et al. Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer. BMC Cancer. 2024 Jun 20;24(1). https://doi.org/10.1186/s12885-024-12522-0 \u003c/li\u003e\n\u003cli\u003eServier Medical Art. (n.d.). \u003cem\u003eMedical illustrations\u003c/em\u003e. Servier. https://smart.servier.com/ Licensed under Creative Commons Attribution 3.0 Unported License (CC BY 3.0).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eMean values of Age, disease duration, smoking and BMI for cancer and control group\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eAge (years) range Age Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e27.77 \u0026plusmn; 12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e42.23 \u0026plusmn; 4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDuration with No. of years (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eLess than one year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e80.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMore than one year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSmoking No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e56.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e66.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.056**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e43.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e33.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e53.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e30.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eobesity more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e36.7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eConcentration of biomarkers and BMI values in female and male patients.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVariable Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eCHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e218.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e54.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e193.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e43.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e247.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e205.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e50.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e65.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e71.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e43.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e48.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e40.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e21.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e26.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e43.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e30.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e157.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e75.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e210.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e148.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e157.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e73.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e199.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e104.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0..245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e85.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e24.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e28.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e98.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e26.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e24.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e29.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e27.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e28.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eComparison of the research parameters of all patients-based Lung Cancer compared with control group.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDisease Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHormone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eTSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eTTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e20.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e92.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eProteins and lipid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e64.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e9.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e70.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAlb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e42.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e41.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e21.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e205.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e125.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e102.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e73.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eChol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e230.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e53.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e199.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e46.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eSensitivity, specificity, cut off and AUC values for\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ebiomarkers.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n 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\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e231.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"comparative-clinical-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Comparative Clinical Pathology](https://link.springer.com/journal/580)","snPcode":"580","submissionUrl":"https://submission.springernature.com/new-submission/580/3","title":"Comparative Clinical Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lung Neoplasms, Transthyretin, Thyroid Hormones, Biomarkers, Receiver Operating Characteristic Curve","lastPublishedDoi":"10.21203/rs.3.rs-7339549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7339549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eLung cancer (LC) is one of the leading causes of cancer-related mortality worldwide, with poor prognosis in advanced stages. Early detection through reliable biomarkers is essential for improving survival rates. Transthyretin (TTR), a transport protein for thyroid hormones, has been suggested as a potential diagnostic marker, but its clinical utility in LC remains underexplored.\u003c/p\u003e\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eThis study aimed to evaluate the diagnostic potential of serum TTR in combination with thyroid hormones and other biochemical markers in LC patients, and to determine which parameters provide the highest sensitivity and specificity for early diagnosis.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA case-control study was conducted on 60 participants (30 LC patients\u0026mdash;19 with NSCLC and 11 with SCLC\u0026mdash;and 30 healthy controls). Serum levels of TTR, T3, T4, TSH, Albumin, HDL, Triglycerides (TG), Cholesterol, and Total Protein were measured using ELISA and standard biochemical assays. Data were analyzed using ANOVA, Mann\u0026ndash;Whitney U test, Spearman/Pearson correlation, and ROC curve analysis to assess diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eLC patients showed significantly lower TTR, T3, TSH, Albumin, and Total Protein levels, and higher T4, HDL, TG, and Cholesterol levels compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TTR and T4 demonstrated the strongest diagnostic performance, with AUC values of 0.831 and 0.922, respectively; T4 achieved 100% specificity. TTR correlated positively with Albumin, HDL, BMI, and TSH, and inversely with T4 and age.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eTTR and T4 are promising biomarkers for distinguishing LC patients from healthy individuals. Incorporating these markers into diagnostic panels may enhance early detection and improve clinical outcomes. Further research is warranted to validate these findings in larger, diverse populations.\u003c/p\u003e","manuscriptTitle":"Assessment of Transthyretin and Key Biomarkers for Lung Cancer Diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:11:41","doi":"10.21203/rs.3.rs-7339549/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-21T15:01:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T22:20:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-04T09:17:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144510455535361107525455613815569264414","date":"2025-10-01T06:21:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T05:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260005639762321520573851699009757113507","date":"2025-09-28T12:17:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205259251959671343412417835680702476408","date":"2025-09-27T18:14:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-27T17:48:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T02:17:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T02:16:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Comparative Clinical Pathology","date":"2025-08-10T14:39:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"comparative-clinical-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Comparative Clinical Pathology](https://link.springer.com/journal/580)","snPcode":"580","submissionUrl":"https://submission.springernature.com/new-submission/580/3","title":"Comparative Clinical Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"09916eb2-0581-456c-b3b5-d2ef9fbb0a36","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T10:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:11:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7339549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7339549","identity":"rs-7339549","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}