Serum MALR Improves Diagnosis and Malignancy Assessment of Esophageal Cancer Among Patients With Esophageal Symptoms

Serum MALR Improves Diagnosis and Malignancy Assessment of Esophageal Cancer Among Patients With Esophageal Symptoms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Serum MALR Improves Diagnosis and Malignancy Assessment of Esophageal Cancer Among Patients With Esophageal Symptoms Di Liu, Chunlin Li, Xiaojie Li, Yazhou Su, Huiling Shen, Yonglian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970016/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Esophageal cancer (EC) shares symptoms with non-malignant esophageal diseases (NM-ED), and endoscopy is invasive and not always well tolerated, creating a need for blood-based markers in symptomatic patients. We enrolled 290 patients undergoing endoscopy for esophageal symptoms (180 EC and 110 NM-ED) and measured serum levels of six cancer-related long non-coding RNAs by quantitative PCR. MALR, HOXA10-AS, and LINC00324 were independently associated with EC after adjustment for clinical risk factors, with MALR showing the strongest effect (adjusted odds ratio per standard deviation 2.401, 95% CI 1.738–3.316). MALR alone yielded an area under the curve of 0.716. Adding MALR to a clinical model increased the area under the curve to 0.782 (ΔAUC 0.079, 95% CI 0.033–0.124) and significantly improved net reclassification improvement (0.626, 95% CI 0.403–0.850) and integrated discrimination improvement (0.112, 95% CI 0.076–0.148), whereas HOXA10-AS and LINC00324 provided limited incremental value. Higher MALR levels correlated with advanced TNM stage (R = 0.426), poorer differentiation (R = 0.454), and higher lymph node stage (R = 0.493). Serum MALR is a promising biomarker for distinguishing EC from NM-ED and may also reflect tumor aggressiveness in symptomatic patients. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Gastroenterology Health sciences/Oncology esophageal cancer lncRNA MALR biomarker diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Esophageal cancer (EC) remains one of the most lethal malignancies worldwide 1 . According to recent global cancer statistics, approximately 600,000 new EC cases and over 540,000 EC-related deaths occurred in 2020, reflecting the aggressive nature and poor prognosis of this disease 2 , 3 . Despite advances in endoscopic techniques, surgery, chemotherapy, and chemoradiotherapy, the overall 5-year survival rate of EC, particularly esophageal squamous cell carcinoma (ESCC), remains unsatisfactory 4 , 5 . Early detection is crucial for improving outcomes, yet many patients are still diagnosed at advanced stages 6 , 7 . Patients with esophageal cancer and those with non-malignant esophageal diseases (NM-ED), such as benign esophageal tumors, gastroesophageal reflux disease (GERD), and achalasia, often present with similar symptoms, including dysphagia, heartburn, retrosternal discomfort, and regurgitation 4 , 6 . Upper gastrointestinal endoscopy with biopsy is the current gold standard for differentiating malignant from non-malignant esophageal disorders 4 , 8 , 9 . However, endoscopy has important limitations: it is invasive, may be painful or poorly tolerated, requires specialized equipment and expertise, and carries risks that limit its widespread use in primary and community settings 4 . Consequently, there is an urgent need for novel, minimally invasive blood-based biomarkers that can help identify patients with EC among those presenting with esophageal symptoms, thereby optimizing referral for endoscopy and facilitating earlier diagnosis 10 – 12 . Long non-coding RNAs (lncRNAs) are a class of non-protein-coding transcripts longer than 200 nucleotides that regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels 13 . Aberrant lncRNA expression has been implicated in key cancer-related processes including proliferation, invasion, metastasis, apoptosis, and treatment resistance 14 , 15 . In esophageal cancer, accumulating evidence indicates that specific lncRNAs function as oncogenes or tumor suppressors and may serve as promising diagnostic and prognostic biomarkers 16 . Circulating and exosomal lncRNAs in blood have attracted particular interest as stable, accessible indicators of tumor burden and behavior 17 , 18 . Recent experimental studies have identified several lncRNAs—MALR 19 , HOXA10-AS 20 , LINC00324 21 , LINC00942 22 , KTN1-AS1 23 , and XIST 24 —as oncogenic drivers in esophageal cancer, promoting tumor growth and progression through diverse mechanisms, including modulation of hypoxia signaling, HOX gene expression, MAPK pathways, RNA-binding proteins, and epithelial–mesenchymal transition–related axes. However, it remains unclear whether circulating levels of these lncRNAs in peripheral blood can discriminate EC from NM-ED in patients with esophageal symptoms, and whether they reflect tumor aggressiveness beyond traditional risk factors. In particular, the incremental diagnostic value of these lncRNAs when added to simple clinical variables has not been defined. Therefore, the primary aim of this study was to evaluate whether serum levels of six cancer-related lncRNAs (MALR, HOXA10-AS, LINC00324, LINC00942, KTN1-AS1, and XIST) can aid in the diagnosis of esophageal cancer among patients with dysphagia, reflux, and other esophageal complaints. We further investigated the associations between lncRNA expression and key pathological indicators of tumor malignancy, including pathological TNM stage, histological grade, and lymph node status, in a subset of surgically treated EC patients. Methods Human subjects and sample collection This single-center, observational study prospectively enrolled consecutive patients who underwent upper gastrointestinal endoscopy with biopsy for the evaluation of esophageal symptoms at The First Affiliated Hospital of Henan Medical University between March 2022 and March 2025. Eligible patients met the following inclusion criteria: (1) presence of esophageal symptoms such as dysphagia, retrosternal discomfort, heartburn, or regurgitation; (2) undergoing diagnostic upper gastrointestinal endoscopy with biopsy in our hospital for these symptoms; (3) no severe hepatic or renal dysfunction; and (4) absence of severe active infection. Based on histopathological results, 180 patients were diagnosed with esophageal cancer (EC group), and 110 patients were confirmed to have non-malignant esophageal diseases (NM-ED group), including benign esophageal tumors, gastroesophageal reflux disease, achalasia, and other non-malignant conditions. In total, 290 patients were included in the final analysis. The study was approved by the Ethics Committee of The First Affiliated Hospital of Henan Medical University, and all participants provided written informed consent prior to enrollment, in accordance with the Declaration of Helsinki. For each patient, peripheral venous blood was drawn at admission before any anti-tumor treatment. Blood samples were allowed to clot at room temperature and then centrifuged to obtain serum. Serum aliquots were immediately stored at − 80°C until RNA extraction. RNA isolation and quantitative real-time PCR Total RNA was extracted from 200µL of serum using TRIzol® reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. RNA concentration and purity were assessed spectrophotometrically by measuring absorbance at 260 nm and 280 nm using a NanoDrop ND-3000 (Thermo Fisher Scientific, Waltham, MA, USA). Samples with an A260/A280 ratio between 1.8 and 2.1 were considered acceptable. For each sample, a fixed amount of total RNA (300ng) was reverse-transcribed into complementary DNA (cDNA) using a commercially available cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s protocol. Quantitative real-time PCR (qPCR) was performed on a real-time PCR system (StepOnePlus Real-Time PCR System, Applied Biosystems, Foster City, CA, USA) using a SYBR Green–based master mix (PowerUp SYBR Green Master Mix, Applied Biosystems, Foster City, CA, USA). All reactions were run in duplicate in 96-well plates, including no-template controls to exclude contamination. The relative expression levels of MALR, HOXA10-AS, LINC00324, LINC00942, KTN1-AS1, and XIST were determined using gene-specific primers. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal reference. The thermal cycling conditions consisted of an initial denaturation step, followed by 40 cycles of denaturation, annealing, and extension, with a final melt-curve analysis to verify amplification specificity. The primer sequences used in this study were designed based on published lncRNA sequences and verified by BLAST to ensure specificity. Forward and reverse primer sequences for each lncRNA and GAPDH are summarized below: - MALR: Forward 5′-TTACATCAAGAACCAGCACTA-3′ Reverse 5′-GAATAAACTCATACCTTGAAAAC-3′ - HOXA10-AS: Forward 5′-CCCAGTAAGCCAAAGTCAAGCC-3′ Reverse 5′-CTGAGGTCAATGGTGCAAAGG-3′ - LINC00324: Forward 5′-TGTGGATGACAGTGTTCGGG-3′ Reverse 5′-ACGCTGACCAGAAACCGTAG-3′ - LINC00942: Forward 5′-GGTGTCTGCGGGAAACAGTAC-3′ Reverse 5′-GAACAAAGAGTCAGGTTGTGTGG-3′ - KTN1-AS1: Forward 5′-CAACTTCTGGGTCCAGGCTA-3′ Reverse 5′-CTCAGGGCCTCTCTACATGG-3′ - XIST: Forward 5′-AGCTCCTCGGACAGCTGTAA-3′ Reverse 5′-CTCCAGATAGCTGGCAACC-3′ - GAPDH: Forward 5′-GAAGGTGAAGGTCGGAGTC-3′ Reverse 5′-GAAGATGGTGATGGGATTTC-3′ Calculation of relative lncRNA expression For each sample, the cycle threshold (Ct) values of the target lncRNAs and GAPDH were obtained. The ΔCt value was calculated as: ΔCt=Ct(lncRNA) - Ct(GAPDH). The relative expression of each lncRNA was then derived using the 2^(-ΔCt) method. To facilitate between-group comparisons, lncRNA expression levels in the EC and NM-ED groups were expressed as fold changes relative to the average (or median) expression in the NM-ED group. Clinical and pathological data collection For all enrolled patients, demographic and clinical data were collected at baseline, including age, sex, smoking status, regular drinking, unhealthy dietary habits, and family history of upper gastrointestinal (UGI) diseases. Smoking status was categorized as current or former smoking versus never smoking. Regular drinking was defined as alcohol consumption at least once per week for ≥6 months, with an average intake of no less than one standard drink (≈10 g of ethanol) per occasion. Unhealthy dietary habits were defined as frequent intake of pickled, smoked, or high-salt foods and/or low consumption of fresh fruits and vegetables. Family history of UGI diseases was defined as having a first-degree relative diagnosed with esophageal, gastric, or other upper gastrointestinal diseases. Routine laboratory parameters, including hemoglobin (Hb), white blood cell count, neutrophil and lymphocyte counts, serum albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine, were also recorded. Among the 180 patients with esophageal cancer, 142 underwent curative-intent surgical resection. For these patients, key pathological parameters were collected from surgical pathology reports, including pathological TNM stage, histological grade, and lymph node status. Pathological TNM stage was categorized as stage 0–I, stage II, or stage III. Histological grade was classified as well differentiated (G1), moderately differentiated (G2), or poorly differentiated (G3). Lymph node status was grouped as N0, N1, N2, or N3 according to the number of metastatic lymph nodes. Statistical analysis Continuous variables are presented as mean ± standard deviation (SD) for approximately normally distributed data or median (interquartile range, IQR) for skewed distributions. Group comparisons between the EC and NM-ED groups were performed using independent-samples t-tests for normally distributed variables or Mann–Whitney U tests for non-normally distributed variables, as appropriate. Categorical variables are shown as counts and percentages, and between-group differences were assessed by χ² tests or Fisher’s exact tests. To evaluate the association between serum lncRNA levels and the diagnosis of EC, univariable and multivariable logistic regression analyses were performed. In the multivariable models, covariates included age, sex, smoking status, regular drinking, unhealthy dietary habits, and family history of UGI diseases. Each lncRNA was entered into the logistic regression models either as a continuous variable (per 1-SD increase after z-score standardization) or as a categorical variable tertiled (T1–T3) according to its distribution in the overall cohort. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) curves were generated to assess the diagnostic performance of individual lncRNAs and of multivariable prediction models. The optimal cut-off values were determined by maximizing Youden’s index (sensitivity + specificity − 1). Comparisons of areas under the ROC curves (AUCs) between models were performed using DeLong’s test implemented in the pROC package in R (version 4.2.0). To quantify the incremental predictive value of lncRNAs beyond traditional risk factors, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated using the PredictABEL package in R. In the surgically treated EC subgroup, Spearman’s rank correlation coefficients were used to examine the relationships between MALR expression and pathological TNM stage, histological grade, and lymph node status. A two-sided P-value <0.05 was considered statistically significant. All statistical analyses were performed using SPSS software (version 22.0; IBM Corp., Armonk, NY, USA) and R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria). Results Baseline characteristics of patients with EC and NM-ED A total of 290 patients with esophageal symptoms were included, comprising 180 patients with biopsy-proven esophageal cancer (EC group) and 110 patients with non-malignant esophageal diseases (NM-ED group). As shown in Table 1 , patients with EC were significantly older than those with NM-ED (64.4 ± 9.9 vs 58.7 ± 10.6 years, P < 0.001). The proportion of males was numerically higher in the EC group than in the NM-ED group (63.9% vs 55.5%), but this difference did not reach statistical significance (P = 0.193). Table 1 Baseline clinical characteristics, laboratory parameters, and serum lncRNA expression levels in patients with non-malignant esophageal diseases (NM-ED) and esophageal cancer (EC). Variables Overall (n = 290) NM-ED group (n = 110) EC group (n = 180) P-value Age, years 62.2 ± 10.5 58.7 ± 10.6 64.4 ± 9.9 < 0.001 Male, n (%) 176 (60.7) 61 (55.5) 115 (63.9) 0.193 Smoking, n (%) 129 (44.5) 39 (35.5) 90 (50.0) 0.022 Regular alcohol, n (%) 50 (17.2) 11 (10.0) 39 (21.7) 0.017 Unhealthy diet, n (%) 86 (29.7) 23 (20.9) 63 (35.0) 0.016 Family history, n (%) 62 (21.4) 18 (16.4) 44 (24.4) 0.139 Hb, g/l 139 ± 11 141 ± 10 138 ± 12 0.032 White blood count, 10 3 /ul 7.2 ± 3.7 6.8 ± 2.9 7.4 ± 4.2 0.153 Neutrophil count, 10 3 /ul 3.9 ± 2.6 3.6 ± 2.0 4.0 ± 2.9 0.138 Lymphocyte count, 10 3 /ul 2.2 ± 1.4 2.0 ± 1.1 2.3 ± 1.5 0.128 Albumin, g/l 42.3 ± 3.5 42.0 ± 3.5 42.4 ± 3.4 0.278 ALT, ul 23 (15–32) 23 (16–32) 23 (15–31) 0.831 AST, ul 22 (15–32) 22 (14–34) 22 (17–31) 0.785 Creatinine, umol/l 78 (68–90) 76 (66–86) 81 (68–91) 0.091 MALR 1.17 (0.93–1.40) 0.97 (0.78–1.21) 1.24 (1.03–1.50) < 0.001 HOXA10-AS 1.01 (0.81–1.30) 0.92 (0.71–1.22) 1.07 (0.85–1.33) 0.001 LINC00324 0.98 (0.82–1.27) 0.91 (0.74–1.18) 1.03 (0.86–1.30) 0.004 LINC00942 1.01 (0.79–1.28) 0.94 (0.74–1.25) 1.02 (0.82–1.34) 0.047 KTN1-AS1 0.97 (0.80–1.20) 0.96 (0.80–1.17) 0.99 (0.80–1.24) 0.432 XIST 0.99 (0.74–1.26) 0.94 (0.70–1.18) 1.06 (0.77–1.27) 0.052 Classical lifestyle risk factors were more prevalent in EC patients. Compared with the NM-ED group, the EC group had higher proportions of smokers (50.0% vs 35.5%, P = 0.022), regular alcohol drinkers (21.7% vs 10.0%, P = 0.017), and individuals with unhealthy dietary habits (35.0% vs 20.9%, P = 0.016). Family history of UGI diseases tended to be more common among EC patients (24.4% vs 16.4%), although this difference was not statistically significant (P = 0.139). Regarding laboratory parameters, EC patients had slightly lower hemoglobin levels than NM-ED patients (138 ± 12 vs 141 ± 10 g/L, P = 0.032). In contrast, white blood cell and neutrophil counts, lymphocyte count, serum albumin, liver enzymes (ALT and AST), and creatinine levels did not differ significantly between the two groups (all P > 0.05; Table 1 ). Serum expression of six esophageal cancer–related lncRNAs in EC and NM-ED The relative expression levels of the six selected esophageal cancer–related lncRNAs in serum samples are shown in Fig. 1 and Table 1 . Among the six lncRNAs, four showed significantly elevated expression in the EC group compared with the NM-ED group. MALR exhibited the most pronounced difference, with median expression of 1.24 (IQR: 1.03–1.50) in the EC group versus 0.97 (0.78–1.21) in the NM-ED group (P < 0.001, Fig. 1 A). HOXA10-AS expression was also significantly higher in EC patients [1.07 (0.85–1.33) vs. 0.92 (0.71–1.22), P = 0.001, Fig. 1 B], as was LINC00324 [1.03 (0.86–1.30) vs. 0.91 (0.74–1.18), P = 0.004, Fig. 1 C]. LINC00942 showed a modest but statistically significant elevation in the EC group [1.02 (0.82–1.34) vs. 0.94 (0.74–1.25), P = 0.047, Fig. 1 D]. In contrast, KTN1-AS1 [0.99 (0.80–1.24) vs. 0.96 (0.80–1.17), P = 0.432, Fig. 1 E] and XIST [1.06 (0.77–1.27) vs. 0.94 (0.70–1.18), P = 0.052, Fig. 1 F] did not show significant differences between the two groups. Logistic regression analysis of lncRNAs Multivariable logistic regression analyses adjusted for age, sex, smoking status, regular alcohol consumption, unhealthy dietary habits, and family history of UGI diseases were used to evaluate the independent predictive value of the six lncRNAs for EC. Results for lncRNAs analyzed as continuous and categorical (tertile) variables are summarized in Table 2 and Table 3 . Table 2 Associations between serum lncRNA levels (per 1-SD increase) and the presence of esophageal cancer: unadjusted and multivariable logistic regression models. Variables Continuous Unadjusted Model Adjusted Model 1 Adjusted Model 2 OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value MALR Per SD 2.472 (1.818–3.361) < 0.001 2.322 (1.698–3.175) < 0.001 2.401 (1.738–3.316) < 0.001 HOXA10-AS Per SD 1.483 (1.136–1.934) 0.004 1.519 (1.158–1.994) 0.003 1.495 (1.132–1.974) 0.005 LINC00324 Per SD 1.408 (1.077–1.840) 0.012 1.429 (1.079–1.893) 0.013 1.377 (1.033–1.836) 0.029 LINC00942 Per SD 1.332 (1.032–1.718) 0.027 1.310 (1.005–1.709) 0.046 1.308 (0.993–1.722) 0.056 KTN1-AS1 Per SD 1.157 (0.903–1.484) 0.249 1.160 (0.895–1.505) 0.262 1.155 (0.884–1.508) 0.290 XIST Per SD 1.293 (0.997–1.676) 0.053 1.254 (0.958–1.642) 0.099 1.269 (0.949–1.698) 0.109 Table 3 Associations between tertiles of serum lncRNA expression and the presence of esophageal cancer: unadjusted and multivariable logistic regression models. Variables Categorical Unadjusted Model Adjusted Model 1 Adjusted Model 2 OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value MALR T2 vs T1 3.105 (1.724–5.593) < 0.001 2.629 (1.431–4.829) 0.002 2.812 (1.495–5.288) 0.001 T3 vs T1 6.374 (3.340-12.164) < 0.001 5.582 (2.881–10.816) < 0.001 5.887 (2.971–11.664) < 0.001 HOXA10-AS T2 vs T1 1.671 (0.939–2.976) 0.081 1.519 (0.832–2.771) 0.173 1.539 (0.827–2.865) 0.174 T3 vs T1 1.984 (1.103–3.571) 0.022 2.059 (1.116–3.799) 0.021 2.047 (1.090–3.845) 0.026 LINC00324 T2 vs T1 2.140 (1.193–3.840) 0.011 2.121 (1.155–3.896) 0.015 1.944 (1.041–3.629) 0.037 T3 vs T1 2.280 (1.268–4.098) 0.006 2.353 (1.277–4.337) 0.006 2.164 (1.155–4.054) 0.016 LINC00942 T2 vs T1 1.660 (0.927–2.974) 0.088 1.613 (0.880–2.955) 0.122 1.643 (0.881–3.063) 0.118 T3 vs T1 1.538 (0.863–2.741) 0.144 1.559 (0.855–2.845) 0.148 1.569 (0.843–2.920) 0.156 KTN1-AS1 T2 vs T1 0.863 (0.485–1.537) 0.617 0.889 (0.488–1.618) 0.699 0.939 (0.507–1.741) 0.842 T3 vs T1 1.196 (0.665–2.151) 0.550 1.271 (0.690–2.343) 0.442 1.251 (0.665–2.354) 0.487 XIST T2 vs T1 0.941 (0.531–1.668) 0.836 0.849 (0.467–1.543) 0.592 0.924 (0.497–1.719) 0.803 T3 vs T1 1.645 (0.909–2.979) 0.100 1.474 (0.795–2.730) 0.218 1.619 (0.850–3.081) 0.142 When modeled as continuous variables (per SD increase), MALR showed the strongest association with EC in the fully adjusted model (OR = 2.401, 95% CI 1.738–3.316, P < 0.001). HOXA10-AS and LINC00324 also remained independently associated with EC (HOXA10-AS: OR = 1.495, 95% CI 1.132–1.974, P = 0.005; LINC00324: OR = 1.377, 95% CI 1.033–1.836, P = 0.029). LINC00942 showed only a borderline association and lost statistical significance after full adjustment (P = 0.056), whereas KTN1-AS1 and XIST were not significantly associated with EC in any model. When lncRNAs were categorized into tertiles, MALR again exhibited the most pronounced gradient, with both the middle and highest tertiles independently associated with EC in Model 2 (T2 vs. T1: OR = 2.812, 95% CI 1.495–5.288, P = 0.001; T3 vs. T1: OR = 5.887, 95% CI 2.971–11.664, P < 0.001). For HOXA10-AS, only the highest tertile was significantly associated with EC (T3 vs. T1: OR = 2.047, 95% CI 1.090–3.845, P = 0.026), whereas the middle tertile was not. LINC00324 showed significant associations for both the middle and highest tertiles (T2 vs. T1: OR = 1.944, 95% CI 1.041–3.629, P = 0.037; T3 vs. T1: OR = 2.164, 95% CI 1.155–4.054, P = 0.016). In contrast, LINC00942, KTN1-AS1, and XIST did not demonstrate independent associations with EC when analyzed as tertiles (all P > 0.05 for T3 vs. T1 in Model 2). Incremental diagnostic value of MALR, HOXA10-AS, and LINC00324 To assess the clinical utility of lncRNAs as diagnostic biomarkers, we first constructed a clinical prediction model using traditional risk factors (age, sex, smoking status, regular alcohol consumption, unhealthy dietary habits, and family history of UGI diseases), which yielded an AUC of 0.703 (95% CI: 0.642–0.765). We then evaluated the incremental diagnostic value of the three lncRNAs that showed independent predictive associations (MALR, HOXA10-AS, and LINC00324) in continuous variable analysis. MALR demonstrated the most robust diagnostic performance when analyzed individually. ROC analysis revealed that MALR alone exhibited good discriminative ability for EC, with an AUC of 0.716 (95% CI: 0.655–0.777, Fig. 2 A). The optimal cutoff value for MALR was 1.139, which achieved a sensitivity of 67.2%, specificity of 69.1%, positive predictive value (PPV) of 78.1%, and negative predictive value (NPV) of 56.3% (Fig. 2 B). More importantly, incorporating MALR into the clinical model significantly improved diagnostic accuracy, increasing the AUC to 0.782 (95% CI: 0.726–0.838), representing a significant increase of ΔAUC = 0.079 (95% CI: 0.033–0.124, P < 0.001 by DeLong test; Table 4 ). At the optimal cutoff probability of 0.618, the model incorporating MALR achieved enhanced performance with a sensitivity of 72.2%, specificity of 73.6%, PPV of 81.8%, and NPV of 61.8% (Fig. 2 C). Net reclassification improvement analysis confirmed that MALR significantly improved patient risk stratification (NRI = 0.626, 95% CI 0.403–0.850, P < 0.001), and integrated discrimination improvement demonstrated enhanced discriminative accuracy (IDI = 0.112, 95% CI 0.076–0.148, P < 0.001; Table 4 ). Table 4 Incremental diagnostic value of MALR, HOXA10-AS, and LINC00324 beyond traditional risk factors for esophageal cancer: changes in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Variables ΔAUC (95%CI) P-value NRI (95%CI) P-value IDI (95%CI) P-value MALR 0.079 (0.033 to 0.124) < 0.001 0.626 (0.403 to 0.850) < 0.001 0.112 (0.076 to 0.148) < 0.001 HOXA10-AS 0.020 (-0.011 to 0.050) 0.204 0.262 (0.030 to 0.493) 0.027 0.029 (0.010 to 0.048) 0.003 LINC00324 0.018 (-0.004 to 0.040) 0.111 0.217 (-0.014 to 0.449) 0.066 0.018 (0.004 to 0.032) 0.014 In contrast, HOXA10-AS and LINC00324 provided only limited incremental value beyond the clinical model. As standalone markers, HOXA10-AS (AUC = 0.615, 95% CI: 0.546–0.683) and LINC00324 (AUC = 0.602, 95% CI: 0.534–0.670) showed modest discriminative ability (Figs. 2 D, E, G, and H). Adding HOXA10-AS or LINC00324 to the clinical model resulted in only small, statistically non-significant increases in AUC (to 0.723 and 0.721, respectively; both ΔAUC 0.10; Table 4 and Figs. 2 F and 2 I). Although modest improvements were observed in NRI and/or IDI, these effects were much weaker than those observed for MALR, indicating that HOXA10-AS and LINC00324 contribute only marginally to risk discrimination when added to traditional clinical factors. Subgroup analysis of MALR To explore the robustness of the association between MALR and EC, subgroup analyses were performed using stratified logistic regression and presented as a forest plot (Fig. 3 ). The positive association between MALR and EC was consistently observed across subgroups defined by age ( 0.05 for these factors). Interestingly, the association between MALR and EC appeared stronger in patients without unhealthy dietary habits (OR 3.019, 95% CI 2.011–4.532) than in those with unhealthy diets (OR 1.414, 95% CI 0.804–2.487), with a statistically significant interaction (P for interaction = 0.033). A similar pattern was observed for family history: MALR showed a stronger association in patients without a family history of UGI diseases (OR 2.981, 95% CI 2.006–4.431) compared with those with such a history (OR 1.456, 95% CI 0.780–2.716; P for interaction = 0.025). Despite these differences in effect size, MALR tended to be positively associated with EC risk across all examined subgroups, suggesting robust predictive performance in diverse clinical contexts. Correlation between MALR and pathological indicators of tumor malignancy In the subset of 142 EC patients who underwent surgery, we further examined the relationship between serum MALR expression and pathological indicators of tumor aggressiveness. As shown in Fig. 4 , higher MALR levels were significantly correlated with more advanced pathological TNM stage, poorer histological differentiation, and more severe lymph node involvement. Spearman’s correlation analysis revealed that MALR expression was positively correlated with pathological TNM stage (R = 0.426, P < 0.001), histological grade (R = 0.454, P < 0.001), and lymph node stage (R = 0.493, P < 0.001). These findings suggest that serum MALR not only discriminates EC from NM-ED but also reflects the malignant potential of the tumor. Discussion In this single-center study of patients presenting with esophageal symptoms, we systematically evaluated the diagnostic value of six cancer-related lncRNAs measured in peripheral serum. We found that (1) serum MALR, HOXA10-AS, LINC00324, and LINC00942 levels were significantly higher in EC patients than in those with non-malignant esophageal diseases, while KTN1-AS1 and XIST showed no clear differences; (2) MALR, HOXA10-AS, and LINC00324 were independently associated with EC in multivariable logistic regression models, with MALR showing the largest effect size; (3) MALR provided substantial incremental diagnostic value beyond traditional clinical risk factors, as reflected by significant improvements in AUC, NRI, and IDI; and (4) higher MALR levels were positively correlated with advanced TNM stage, poor differentiation, and lymph node metastasis, indicating a relationship between circulating MALR and tumor aggressiveness. From a clinical perspective, these findings highlight MALR as a particularly promising blood-based biomarker for the diagnosis and risk stratification of esophageal cancer among patients with esophageal symptoms 4 . In real-world practice, patients with dysphagia, reflux, and other esophageal complaints commonly undergo endoscopy, but capacity constraints and patient intolerance limit its universal application, especially in primary care or resource-limited settings 4 . Our data suggest that incorporating serum MALR into a simple clinical model comprising age, sex, smoking, alcohol use, diet, and family history substantially improves discrimination between EC and NM-ED. The MALR-enhanced model achieved an AUC of 0.782, with significant gains in reclassification indices, while MALR alone already showed reasonable diagnostic performance (AUC 0.716). These results support the potential use of MALR as a minimally invasive screening or triage biomarker to prioritize high-risk patients for endoscopic evaluation, and to reassure those at lower risk. Biologically, our clinical findings are consistent with and extend previous mechanistic studies. The macrophage-associated lncRNA MALR has been shown to facilitate ILF3 liquid–liquid phase separation, amplifying HIF1α signaling and promoting a pro-tumorigenic microenvironment in esophageal cancer 19 . Our observation that higher circulating MALR levels are associated not only with the presence of EC but also with more advanced TNM stage, poorer differentiation, and more extensive lymph node involvement provides clinical support for MALR’s role in driving tumor progression and metastasis. Similarly, HOXA10-AS has been reported to promote esophageal carcinoma by upregulating HOXA10 and enhancing tumor cell proliferation, migration, and invasion 20 , and LINC00324 has been shown to promote ESCC cell proliferation and metastasis via a miR-493-5p/MAPK1 pathway 21 . The independent associations of HOXA10-AS and LINC00324 with EC risk in our serum-based analysis are consistent with their oncogenic functions at the tissue and cellular levels. On the other hand, although LINC00942, KTN1-AS1, and XIST have been implicated in esophageal or other malignancies 22 – 24 , they did not show clear independent diagnostic value for EC in our cohort. LINC00942 exhibited only borderline associations in some models, whereas KTN1-AS1 and XIST were not significantly associated with EC in either continuous or tertile-based analyses. Taken together, these non-significant or borderline findings suggest that the circulating levels of these lncRNAs may be more variable or less tightly coupled to tumor burden, and that oncogenic lncRNAs identified in tumor tissues do not necessarily translate into useful blood-based biomarkers. This underscores the need for systematic clinical validation of candidate lncRNAs before their implementation in practice. This study has several strengths. First, we focused on a clinically relevant population—patients with esophageal symptoms undergoing diagnostic endoscopy—rather than healthy controls, making our results highly applicable to real-world diagnostic decision-making. Second, we evaluated both continuous and categorical (tertile-based) lncRNA measures and used multiple complementary indices (AUC, NRI, IDI) to quantify incremental predictive value. Third, by correlating MALR levels with detailed pathological features in surgically treated patients, we provide evidence that circulating MALR reflects tumor malignancy, not merely its presence. Several limitations should also be acknowledged. First, this was a single-center study with a moderate sample size and conducted in a Chinese population; external validation in independent, multicenter cohorts with different ethnic and geographic backgrounds is required to confirm the generalizability of our findings. Second, the cross-sectional design precludes assessment of temporal changes in lncRNA levels and their prognostic value for survival or recurrence. Third, although we adjusted for major clinical risk factors, residual confounding by unmeasured variables (e.g., environmental exposures, comorbidities, other medications) cannot be excluded. Fourth, we focused on six pre-selected lncRNAs based on recent mechanistic studies; it is possible that other circulating lncRNAs or combinations thereof may further improve diagnostic performance. Finally, we measured serum lncRNAs using qPCR under controlled research conditions; standardization of pre-analytical handling, assay platforms, and cut-off definitions will be essential before MALR can be translated into routine clinical practice. In conclusion, our study demonstrates that serum MALR is a strong and independent biomarker for esophageal cancer among patients with esophageal symptoms and provides significant incremental diagnostic value beyond traditional risk factors. MALR also correlates with key pathological indicators of tumor aggressiveness. HOXA10-AS and LINC00324 are additionally associated with EC but confer only modest incremental value. These findings support further prospective, multicenter studies to validate MALR-based blood tests and to explore their integration with existing clinical and endoscopic pathways for the early detection and risk stratification of esophageal cancer. Declarations Author contributions Conceptualization, Di Liu; Investigation, Di Liu, Chunlin Li, Xiaojie Li, Yazhou Su, Huiling Shen; Writing, Di Liu; Review, Yonglian Wang. Data availability statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests Statement The authors declare that they have no competing interests. Funding The project was funded by the Henan Provincial Medical Science and Technology Research Program (grant LHGJ20230511). References Yang, H., Wang, F., Hallemeier, C. L., Lerut, T. & Fu, J. Oesophageal cancer. Lancet 404 , 1991–2005. 10.1016/S0140-6736(24)02226-8 (2024). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 , 209–249. 10.3322/caac.21660 (2021). Morgan, E. et al. The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020. Gastroenterology 163 (e642), 649–658. 10.1053/j.gastro.2022.05.054 (2022). Deboever, N., Jones, C. M., Yamashita, K., Ajani, J. A. & Hofstetter, W. L. Advances in diagnosis and management of cancer of the esophagus. BMJ 385 , e074962. 10.1136/bmj-2023-074962 (2024). Zhu, H. et al. Esophageal cancer in China: Practice and research in the new era. Int. J. Cancer . 152 , 1741–1751. 10.1002/ijc.34301 (2023). Zhao, Y. X. et al. Latest insights into the global epidemiological features, screening, early diagnosis and prognosis prediction of esophageal squamous cell carcinoma. World J. Gastroenterol. 30 , 2638–2656. 10.3748/wjg.v30.i20.2638 (2024). Sheikh, M., Roshandel, G., McCormack, V. & Malekzadeh, R. Current Status and Future Prospects for Esophageal Cancer. Cancers (Basel) . 15 10.3390/cancers15030765 (2023). Li, S. W. et al. Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study. Sci. Transl Med. 16 , eadk5395. 10.1126/scitranslmed.adk5395 (2024). Yuan, X. L. et al. Effect of an artificial intelligence-assisted system on endoscopic diagnosis of superficial oesophageal squamous cell carcinoma and precancerous lesions: a multicentre, tandem, double-blind, randomised controlled trial. Lancet Gastroenterol. Hepatol. 9 , 34–44. 10.1016/S2468-1253(23)00276-5 (2024). Yuan, Z. et al. Liquid biopsy for esophageal cancer: Is detection of circulating cell-free DNA as a biomarker feasible? Cancer Commun. (Lond) . 41 , 3–15. 10.1002/cac2.12118 (2021). Li, K. et al. A signature of saliva-derived exosomal small RNAs as predicting biomarker for esophageal carcinoma: a multicenter prospective study. Mol. Cancer . 21 10.1186/s12943-022-01499-8 (2022). Wang, Y. et al. Highly sensitive detection platform-based diagnosis of oesophageal squamous cell carcinoma in China: a multicentre, case-control, diagnostic study. Lancet Digit. Health . 6 , e705–e717. 10.1016/S2589-7500(24)00153-5 (2024). Statello, L., Guo, C. J., Chen, L. L. & Huarte, M. Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell. Biol. 22 , 96–118. 10.1038/s41580-020-00315-9 (2021). Goodall, G. J. & Wickramasinghe, V. O. RNA in cancer. Nat. Rev. Cancer . 21 , 22–36. 10.1038/s41568-020-00306-0 (2021). Liu, S. J., Dang, H. X., Lim, D. A., Feng, F. Y. & Maher, C. A. Long noncoding RNAs in cancer metastasis. Nat. Rev. Cancer . 21 , 446–460. 10.1038/s41568-021-00353-1 (2021). Zhou, M. et al. The transcriptional landscape and diagnostic potential of long non-coding RNAs in esophageal squamous cell carcinoma. Nat. Commun. 14 , 3799. 10.1038/s41467-023-39530-1 (2023). Vosough, P. et al. Exosomal lncRNAs in gastrointestinal cancer. Clin. Chim. Acta . 540 , 117216. 10.1016/j.cca.2022.117216 (2023). Xie, K. et al. A RASSF8-AS1 based exosomal lncRNAs panel used for diagnostic and prognostic biomarkers for esophageal squamous cell carcinoma. Thorac. Cancer . 13 , 3341–3352. 10.1111/1759-7714.14690 (2022). Liu, J. et al. The Macrophage-Associated LncRNA MALR Facilitates ILF3 Liquid-Liquid Phase Separation to Promote HIF1alpha Signaling in Esophageal Cancer. Cancer Res. 83 , 1476–1489. 10.1158/0008-5472.CAN-22-1922 (2023). Kuai, J., Wu, K., Han, T., Zhai, W. & Sun, R. LncRNA HOXA10-AS promotes the progression of esophageal carcinoma by regulating the expression of HOXA10. Cell. Cycle . 22 , 276–290. 10.1080/15384101.2022.2108633 (2023). Sharma, U., Kaur Rana, M., Singh, K. & Jain, A. LINC00324 promotes cell proliferation and metastasis of esophageal squamous cell carcinoma through sponging miR-493-5p via MAPK signaling pathway. Biochem. Pharmacol. 207 , 115372. 10.1016/j.bcp.2022.115372 (2023). Wang, Z., Li, K., Zhang, X., Jiang, F. & Xu, L. LINC00942 Accelerates Esophageal Cancer Progression by Raising PRKDC Through Interaction With PTBP1. J. Biochem. Mol. Toxicol. 39 , e70220. 10.1002/jbt.70220 (2025). Chen, L. et al. LncRNA KTN1-AS1 facilitates esophageal squamous cell carcinoma progression via miR-885-5p/STRN3 axis. Genes Genomics . 46 , 241–252. 10.1007/s13258-023-01451-0 (2024). Ma, Y., Qian, L., Wang, D. & Chen, C. LncRNA XIST Promotes Proliferation, Migration and Invasion of Esophageal Squamous Cell Carcinoma Cells via Regulation of miR-186-5p/ZEB1. Anticancer Res. 45 , 897–908. 10.21873/anticanres.17477 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 05 Apr, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 26 Feb, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 25 Feb, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8970016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617956112,"identity":"d5f5822e-40ec-4894-a59b-77ed40533607","order_by":0,"name":"Di Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Liu","suffix":""},{"id":617956113,"identity":"55e3096b-cea4-4ab1-b817-879e0bc55293","order_by":1,"name":"Chunlin Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Li","suffix":""},{"id":617956114,"identity":"44a0ad94-d968-4b38-8b94-34273a81a681","order_by":2,"name":"Xiaojie Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Li","suffix":""},{"id":617956115,"identity":"c3cbb012-45bd-45b9-9ab2-4ade4f1e99bd","order_by":3,"name":"Yazhou Su","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yazhou","middleName":"","lastName":"Su","suffix":""},{"id":617956116,"identity":"d5a5b0ed-d664-4a4d-9809-d8bc9fa825f7","order_by":4,"name":"Huiling Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiling","middleName":"","lastName":"Shen","suffix":""},{"id":617956117,"identity":"aa410afa-1fa4-4ee3-83a1-a3e4417f2842","order_by":5,"name":"Yonglian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDADPmYgkVBhI8fPzHz4AVFa2EBaHpxJM5ZsZ0szIE4LEDM+bDucaHCeR0ECn0qD42cPv+apuGPXxs787EHCmcMJxod5GAwYamyicWo5k5dmzXPmWXIbM5u5QUJFep7ZYd4DDxiOpeU24NBidiDHzJi37XAy0C9mEglnrIvNDvMlGDA2HMat5fwbmBb2bxKJbcyJm5t5DCTwarmRY/wYqMWOjZnHDKjFOXEDMwEt9jfemDHOAfoaqKUM6LA0Y4nDwEBOwOMXyf4c4w9vKg7b8/Mf3yb5AxSV/YcPP/hQY4NTCxCwSfEwMCSiKkjArRwEmD/+ADoQv5pRMApGwSgY0QAAdQhbSjSRj8wAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Henan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yonglian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-25 16:54:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8970016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8970016/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725116,"identity":"7fa86b34-9115-4ee9-82ce-de2abaca2406","added_by":"auto","created_at":"2026-04-12 18:31:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum expression levels of six lncRNAs in patients with non-malignant esophageal diseases (NM-ED) and esophageal cancer (EC).\u003c/strong\u003e Boxplots depict relative serum expression of (A) MALR, (B) HOXA10-AS, (C) LINC00324, (D) LINC00942, (E) KTN1-AS1, and (F) XIST in the NM-ED group (n = 110) and the EC group (n = 180). The horizontal line within each box represents the median; the box and whiskers indicate the interquartile range (IQR) and 1.5× IQR, respectively. P-values are derived from Mann–Whitney U tests. MALR, HOXA10-AS, LINC00324, and LINC00942 show significantly higher expression in EC compared with NM-ED, whereas KTN1-AS1 and XIST do not differ significantly between groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8970016/v1/b01459150bcf473fbcdeefa8.png"},{"id":106725922,"identity":"43106ac2-49fa-407d-bd9d-7c388964a37f","added_by":"auto","created_at":"2026-04-12 18:34:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance of MALR, HOXA10-AS, and LINC00324 for esophageal cancer and their incremental value beyond clinical risk factors.\u003c/strong\u003e (A) Receiver operating characteristic (ROC) curves comparing MALR alone, the clinicalmodel (age, sex, smoking status, regular drinking, unhealthy dietary habits, and family history of upper gastrointestinal diseases), and the model combining MALR with the clinicalvariables. (B) ROC curve and performance metrics of MALR expression alone, with the optimal cut-off value and corresponding sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). (C) ROC curve and performance metrics of the MALR-enhanced prediction model, with the optimal predicted probability cut-off. Panels (D–F) and (G–I) show analogous ROC curves and cut-off-based performance metrics for HOXA10-AS and LINC00324, respectively. Area under the curve (AUC) values and 95% confidence intervals (CIs) are indicated in each panel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8970016/v1/f15b4c14e4cb0abdd707d13c.png"},{"id":106546583,"identity":"b25da8db-65db-4c77-9a30-320d3bfb2df4","added_by":"auto","created_at":"2026-04-09 17:05:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of the association between MALR and esophageal cancer.\u003c/strong\u003e Forest plot of odds ratios (ORs) and 95% confidence intervals for the association between a 1- standard deviation (SD) increase in serum MALR expression and the presence of esophageal cancer in the overall cohort and in clinically relevant subgroups. Subgroups include age (\u0026lt;60 vs ≥60 years), sex (male vs female), smoking status (yes vs no), regular alcohol consumption (yes vs no), unhealthy dietary habits (unhealthy vs healthy), and family history of upper gastrointestinal diseases (yes vs no). P-values for interaction are shown for each subgroup comparison. The positive association between MALR and EC is broadly consistent across subgroups, with stronger effects observed in patients without unhealthy dietary habits or family history of UGI diseases.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8970016/v1/d89f347aa1d3a785819062f3.png"},{"id":106725095,"identity":"12a2d276-8314-4df9-b3bb-dc2eded50f1a","added_by":"auto","created_at":"2026-04-12 18:31:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between serum MALR expression and pathological indicators of esophageal cancer malignancy.\u003c/strong\u003e Scatter plots showing the relationships between serum MALR expression and (A) pathological tumor–node–metastasis (TNM) stage (0–I, II, III), (B) histological grade (G1, G2, G3), and (C) lymph node stage (N0, N1, N2, N3) in surgically treated EC patients. Each dot represents an individual patient. MALR expression increases with advancing TNM stage, poorer histological differentiation, and more extensive lymph node metastasis. Spearman’s correlation coefficients (R) and corresponding p-values are displayed in each panel.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8970016/v1/d0621641aae6742809df93e5.png"},{"id":107704641,"identity":"dc277397-d031-4039-867b-129247ca2bf1","added_by":"auto","created_at":"2026-04-24 08:53:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8970016/v1/d8284916-5054-45c7-9a7e-89326376c67b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum MALR Improves Diagnosis and Malignancy Assessment of Esophageal Cancer Among Patients With Esophageal Symptoms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer (EC) remains one of the most lethal malignancies worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to recent global cancer statistics, approximately 600,000 new EC cases and over 540,000 EC-related deaths occurred in 2020, reflecting the aggressive nature and poor prognosis of this disease \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite advances in endoscopic techniques, surgery, chemotherapy, and chemoradiotherapy, the overall 5-year survival rate of EC, particularly esophageal squamous cell carcinoma (ESCC), remains unsatisfactory \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Early detection is crucial for improving outcomes, yet many patients are still diagnosed at advanced stages \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePatients with esophageal cancer and those with non-malignant esophageal diseases (NM-ED), such as benign esophageal tumors, gastroesophageal reflux disease (GERD), and achalasia, often present with similar symptoms, including dysphagia, heartburn, retrosternal discomfort, and regurgitation \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Upper gastrointestinal endoscopy with biopsy is the current gold standard for differentiating malignant from non-malignant esophageal disorders \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, endoscopy has important limitations: it is invasive, may be painful or poorly tolerated, requires specialized equipment and expertise, and carries risks that limit its widespread use in primary and community settings \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, there is an urgent need for novel, minimally invasive blood-based biomarkers that can help identify patients with EC among those presenting with esophageal symptoms, thereby optimizing referral for endoscopy and facilitating earlier diagnosis \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are a class of non-protein-coding transcripts longer than 200 nucleotides that regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Aberrant lncRNA expression has been implicated in key cancer-related processes including proliferation, invasion, metastasis, apoptosis, and treatment resistance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In esophageal cancer, accumulating evidence indicates that specific lncRNAs function as oncogenes or tumor suppressors and may serve as promising diagnostic and prognostic biomarkers \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Circulating and exosomal lncRNAs in blood have attracted particular interest as stable, accessible indicators of tumor burden and behavior \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Recent experimental studies have identified several lncRNAs\u0026mdash;MALR \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, HOXA10-AS \u003csup\u003e20\u003c/sup\u003e, LINC00324 \u003csup\u003e21\u003c/sup\u003e, LINC00942 \u003csup\u003e22\u003c/sup\u003e, KTN1-AS1 \u003csup\u003e23\u003c/sup\u003e, and XIST \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u0026mdash;as oncogenic drivers in esophageal cancer, promoting tumor growth and progression through diverse mechanisms, including modulation of hypoxia signaling, HOX gene expression, MAPK pathways, RNA-binding proteins, and epithelial\u0026ndash;mesenchymal transition\u0026ndash;related axes.\u003c/p\u003e \u003cp\u003eHowever, it remains unclear whether circulating levels of these lncRNAs in peripheral blood can discriminate EC from NM-ED in patients with esophageal symptoms, and whether they reflect tumor aggressiveness beyond traditional risk factors. In particular, the incremental diagnostic value of these lncRNAs when added to simple clinical variables has not been defined. Therefore, the primary aim of this study was to evaluate whether serum levels of six cancer-related lncRNAs (MALR, HOXA10-AS, LINC00324, LINC00942, KTN1-AS1, and XIST) can aid in the diagnosis of esophageal cancer among patients with dysphagia, reflux, and other esophageal complaints. We further investigated the associations between lncRNA expression and key pathological indicators of tumor malignancy, including pathological TNM stage, histological grade, and lymph node status, in a subset of surgically treated EC patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman subjects and sample collection\u003c/h2\u003e \u003cp\u003eThis single-center, observational study prospectively enrolled consecutive patients who underwent upper gastrointestinal endoscopy with biopsy for the evaluation of esophageal symptoms at The First Affiliated Hospital of Henan Medical University between March 2022 and March 2025. Eligible patients met the following inclusion criteria: (1) presence of esophageal symptoms such as dysphagia, retrosternal discomfort, heartburn, or regurgitation; (2) undergoing diagnostic upper gastrointestinal endoscopy with biopsy in our hospital for these symptoms; (3) no severe hepatic or renal dysfunction; and (4) absence of severe active infection.\u003c/p\u003e \u003cp\u003eBased on histopathological results, 180 patients were diagnosed with esophageal cancer (EC group), and 110 patients were confirmed to have non-malignant esophageal diseases (NM-ED group), including benign esophageal tumors, gastroesophageal reflux disease, achalasia, and other non-malignant conditions. In total, 290 patients were included in the final analysis. The study was approved by the Ethics Committee of The First Affiliated Hospital of Henan Medical University, and all participants provided written informed consent prior to enrollment, in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eFor each patient, peripheral venous blood was drawn at admission before any anti-tumor treatment. Blood samples were allowed to clot at room temperature and then centrifuged to obtain serum. Serum aliquots were immediately stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA isolation and quantitative real-time PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from 200\u0026micro;L of serum using TRIzol\u0026reg; reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer\u0026rsquo;s instructions. RNA concentration and purity were assessed spectrophotometrically by measuring absorbance at 260 nm and 280 nm using a NanoDrop ND-3000 (Thermo Fisher Scientific, Waltham, MA, USA). Samples with an A260/A280 ratio between 1.8 and 2.1 were considered acceptable.\u003c/p\u003e \u003cp\u003eFor each sample, a fixed amount of total RNA (300ng) was reverse-transcribed into complementary DNA (cDNA) using a commercially available cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer\u0026rsquo;s protocol. Quantitative real-time PCR (qPCR) was performed on a real-time PCR system (StepOnePlus Real-Time PCR System, Applied Biosystems, Foster City, CA, USA) using a SYBR Green\u0026ndash;based master mix (PowerUp SYBR Green Master Mix, Applied Biosystems, Foster City, CA, USA). All reactions were run in duplicate in 96-well plates, including no-template controls to exclude contamination.\u003c/p\u003e \u003cp\u003eThe relative expression levels of MALR, HOXA10-AS, LINC00324, LINC00942, KTN1-AS1, and XIST were determined using gene-specific primers. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal reference. The thermal cycling conditions consisted of an initial denaturation step, followed by 40 cycles of denaturation, annealing, and extension, with a final melt-curve analysis to verify amplification specificity.\u003c/p\u003e \u003cp\u003eThe primer sequences used in this study were designed based on published lncRNA sequences and verified by BLAST to ensure specificity. Forward and reverse primer sequences for each lncRNA and GAPDH are summarized below:\u003c/p\u003e\u003cp\u003e- MALR:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-TTACATCAAGAACCAGCACTA-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-GAATAAACTCATACCTTGAAAAC-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- HOXA10-AS:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-CCCAGTAAGCCAAAGTCAAGCC-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-CTGAGGTCAATGGTGCAAAGG-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- LINC00324:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-TGTGGATGACAGTGTTCGGG-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-ACGCTGACCAGAAACCGTAG-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- LINC00942:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-GGTGTCTGCGGGAAACAGTAC-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-GAACAAAGAGTCAGGTTGTGTGG-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- KTN1-AS1:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-CAACTTCTGGGTCCAGGCTA-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-CTCAGGGCCTCTCTACATGG-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- XIST:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-AGCTCCTCGGACAGCTGTAA-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-CTCCAGATAGCTGGCAACC-3\u0026prime;\u003c/p\u003e\n\u003cp\u003e- GAPDH:\u003c/p\u003e\n\u003cp\u003eForward 5\u0026prime;-GAAGGTGAAGGTCGGAGTC-3\u0026prime;\u003c/p\u003e\n\u003cp\u003eReverse 5\u0026prime;-GAAGATGGTGATGGGATTTC-3\u0026prime;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCalculation of relative lncRNA expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each sample, the cycle threshold (Ct) values of the target lncRNAs and GAPDH were obtained. The ΔCt value was calculated as: ΔCt=Ct(lncRNA) - Ct(GAPDH). The relative expression of each lncRNA was then derived using the 2^(-ΔCt) method. To facilitate between-group comparisons, lncRNA expression levels in the EC and NM-ED groups were expressed as fold changes relative to the average (or median) expression in the NM-ED group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and pathological data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all enrolled patients, demographic and clinical data were collected at baseline, including age, sex, smoking status, regular drinking, unhealthy dietary habits, and family history of upper gastrointestinal (UGI) diseases. Smoking status was categorized as current or former smoking versus never smoking. Regular drinking was defined as alcohol consumption at least once per week for ≥6 months, with an average intake of no less than one standard drink (≈10 g of ethanol) per occasion. Unhealthy dietary habits were defined as frequent intake of pickled, smoked, or high-salt foods and/or low consumption of fresh fruits and vegetables. Family history of UGI diseases was defined as having a first-degree relative diagnosed with esophageal, gastric, or other upper gastrointestinal diseases.\u003c/p\u003e\n\u003cp\u003eRoutine laboratory parameters, including hemoglobin (Hb), white blood cell count, neutrophil and lymphocyte counts, serum albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine, were also recorded.\u003c/p\u003e\n\u003cp\u003eAmong the 180 patients with esophageal cancer, 142 underwent curative-intent surgical resection. For these patients, key pathological parameters were collected from surgical pathology reports, including pathological TNM stage, histological grade, and lymph node status. Pathological TNM stage was categorized as stage 0–I, stage II, or stage III. Histological grade was classified as well differentiated (G1), moderately differentiated (G2), or poorly differentiated (G3). Lymph node status was grouped as N0, N1, N2, or N3 according to the number of metastatic lymph nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables are presented as mean ± standard deviation (SD) for approximately normally distributed data or median (interquartile range, IQR) for skewed distributions. Group comparisons between the EC and NM-ED groups were performed using independent-samples t-tests for normally distributed variables or Mann–Whitney U tests for non-normally distributed variables, as appropriate. Categorical variables are shown as counts and percentages, and between-group differences were assessed by χ² tests or Fisher’s exact tests.\u003c/p\u003e\n\u003cp\u003eTo evaluate the association between serum lncRNA levels and the diagnosis of EC, univariable and multivariable logistic regression analyses were performed. In the multivariable models, covariates included age, sex, smoking status, regular drinking, unhealthy dietary habits, and family history of UGI diseases. Each lncRNA was entered into the logistic regression models either as a continuous variable (per 1-SD increase after z-score standardization) or as a categorical variable tertiled (T1–T3) according to its distribution in the overall cohort. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs).\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves were generated to assess the diagnostic performance of individual lncRNAs and of multivariable prediction models. The optimal cut-off values were determined by maximizing Youden’s index (sensitivity + specificity − 1). Comparisons of areas under the ROC curves (AUCs) between models were performed using DeLong’s test implemented in the pROC package in R (version 4.2.0). To quantify the incremental predictive value of lncRNAs beyond traditional risk factors, continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated using the PredictABEL package in R.\u003c/p\u003e\n\u003cp\u003eIn the surgically treated EC subgroup, Spearman’s rank correlation coefficients were used to examine the relationships between MALR expression and pathological TNM stage, histological grade, and lymph node status.\u003c/p\u003e\n\u003cp\u003eA two-sided P-value \u0026lt;0.05 was considered statistically significant. All statistical analyses were performed using SPSS software (version 22.0; IBM Corp., Armonk, NY, USA) and R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of patients with EC and NM-ED\u003c/h2\u003e \u003cp\u003eA total of 290 patients with esophageal symptoms were included, comprising 180 patients with biopsy-proven esophageal cancer (EC group) and 110 patients with non-malignant esophageal diseases (NM-ED group). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients with EC were significantly older than those with NM-ED (64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 vs 58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of males was numerically higher in the EC group than in the NM-ED group (63.9% vs 55.5%), but this difference did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.193).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinical characteristics, laboratory parameters, and serum lncRNA expression levels in patients with non-malignant esophageal diseases (NM-ED) and esophageal cancer (EC).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;290)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNM-ED group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEC group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular alcohol, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy diet, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood count, 10\u003csup\u003e3\u003c/sup\u003e/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count, 10\u003csup\u003e3\u003c/sup\u003e/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count, 10\u003csup\u003e3\u003c/sup\u003e/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (15\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (16\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (15\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (14\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (17\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, umol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (68\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (66\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (68\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.93\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.78\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.03\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXA10-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.81\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.71\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.85\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.82\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.74\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.86\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.79\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.74\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.82\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKTN1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.80\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.80\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.80\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.74\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.70\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.77\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.052\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eClassical lifestyle risk factors were more prevalent in EC patients. Compared with the NM-ED group, the EC group had higher proportions of smokers (50.0% vs 35.5%, P\u0026thinsp;=\u0026thinsp;0.022), regular alcohol drinkers (21.7% vs 10.0%, P\u0026thinsp;=\u0026thinsp;0.017), and individuals with unhealthy dietary habits (35.0% vs 20.9%, P\u0026thinsp;=\u0026thinsp;0.016). Family history of UGI diseases tended to be more common among EC patients (24.4% vs 16.4%), although this difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.139).\u003c/p\u003e \u003cp\u003eRegarding laboratory parameters, EC patients had slightly lower hemoglobin levels than NM-ED patients (138\u0026thinsp;\u0026plusmn;\u0026thinsp;12 vs 141\u0026thinsp;\u0026plusmn;\u0026thinsp;10 g/L, P\u0026thinsp;=\u0026thinsp;0.032). In contrast, white blood cell and neutrophil counts, lymphocyte count, serum albumin, liver enzymes (ALT and AST), and creatinine levels did not differ significantly between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSerum expression of six esophageal cancer\u0026ndash;related lncRNAs in EC and NM-ED\u003c/h2\u003e \u003cp\u003eThe relative expression levels of the six selected esophageal cancer\u0026ndash;related lncRNAs in serum samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the six lncRNAs, four showed significantly elevated expression in the EC group compared with the NM-ED group. MALR exhibited the most pronounced difference, with median expression of 1.24 (IQR: 1.03\u0026ndash;1.50) in the EC group versus 0.97 (0.78\u0026ndash;1.21) in the NM-ED group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). HOXA10-AS expression was also significantly higher in EC patients [1.07 (0.85\u0026ndash;1.33) vs. 0.92 (0.71\u0026ndash;1.22), P\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB], as was LINC00324 [1.03 (0.86\u0026ndash;1.30) vs. 0.91 (0.74\u0026ndash;1.18), P\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC]. LINC00942 showed a modest but statistically significant elevation in the EC group [1.02 (0.82\u0026ndash;1.34) vs. 0.94 (0.74\u0026ndash;1.25), P\u0026thinsp;=\u0026thinsp;0.047, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD]. In contrast, KTN1-AS1 [0.99 (0.80\u0026ndash;1.24) vs. 0.96 (0.80\u0026ndash;1.17), P\u0026thinsp;=\u0026thinsp;0.432, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE] and XIST [1.06 (0.77\u0026ndash;1.27) vs. 0.94 (0.70\u0026ndash;1.18), P\u0026thinsp;=\u0026thinsp;0.052, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF] did not show significant differences between the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression analysis of lncRNAs\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression analyses adjusted for age, sex, smoking status, regular alcohol consumption, unhealthy dietary habits, and family history of UGI diseases were used to evaluate the independent predictive value of the six lncRNAs for EC. Results for lncRNAs analyzed as continuous and categorical (tertile) variables are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between serum lncRNA levels (per 1-SD increase) and the presence of esophageal cancer: unadjusted and multivariable logistic regression models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnadjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAdjusted Model 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAdjusted Model 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.472 (1.818\u0026ndash;3.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.322 (1.698\u0026ndash;3.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.401 (1.738\u0026ndash;3.316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXA10-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.483 (1.136\u0026ndash;1.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.519 (1.158\u0026ndash;1.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.495 (1.132\u0026ndash;1.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.408 (1.077\u0026ndash;1.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.429 (1.079\u0026ndash;1.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.377 (1.033\u0026ndash;1.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.332 (1.032\u0026ndash;1.718)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.310 (1.005\u0026ndash;1.709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.308 (0.993\u0026ndash;1.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKTN1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.157 (0.903\u0026ndash;1.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.160 (0.895\u0026ndash;1.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.155 (0.884\u0026ndash;1.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.293 (0.997\u0026ndash;1.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.254 (0.958\u0026ndash;1.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.269 (0.949\u0026ndash;1.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between tertiles of serum lncRNA expression and the presence of esophageal cancer: unadjusted and multivariable logistic regression models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnadjusted Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAdjusted Model 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAdjusted Model 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.105 (1.724\u0026ndash;5.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.629 (1.431\u0026ndash;4.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.812 (1.495\u0026ndash;5.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.374 (3.340-12.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.582 (2.881\u0026ndash;10.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.887 (2.971\u0026ndash;11.664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHOXA10-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.671 (0.939\u0026ndash;2.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.519 (0.832\u0026ndash;2.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.539 (0.827\u0026ndash;2.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.984 (1.103\u0026ndash;3.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.059 (1.116\u0026ndash;3.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.047 (1.090\u0026ndash;3.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLINC00324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.140 (1.193\u0026ndash;3.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.121 (1.155\u0026ndash;3.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.944 (1.041\u0026ndash;3.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.280 (1.268\u0026ndash;4.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.353 (1.277\u0026ndash;4.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.164 (1.155\u0026ndash;4.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLINC00942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.660 (0.927\u0026ndash;2.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.613 (0.880\u0026ndash;2.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.643 (0.881\u0026ndash;3.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.538 (0.863\u0026ndash;2.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.559 (0.855\u0026ndash;2.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.569 (0.843\u0026ndash;2.920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKTN1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863 (0.485\u0026ndash;1.537)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.889 (0.488\u0026ndash;1.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.939 (0.507\u0026ndash;1.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.196 (0.665\u0026ndash;2.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.271 (0.690\u0026ndash;2.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.251 (0.665\u0026ndash;2.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eXIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.941 (0.531\u0026ndash;1.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849 (0.467\u0026ndash;1.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.924 (0.497\u0026ndash;1.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3 vs T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.645 (0.909\u0026ndash;2.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.474 (0.795\u0026ndash;2.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.619 (0.850\u0026ndash;3.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen modeled as continuous variables (per SD increase), MALR showed the strongest association with EC in the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;2.401, 95% CI 1.738\u0026ndash;3.316, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HOXA10-AS and LINC00324 also remained independently associated with EC (HOXA10-AS: OR\u0026thinsp;=\u0026thinsp;1.495, 95% CI 1.132\u0026ndash;1.974, P\u0026thinsp;=\u0026thinsp;0.005; LINC00324: OR\u0026thinsp;=\u0026thinsp;1.377, 95% CI 1.033\u0026ndash;1.836, P\u0026thinsp;=\u0026thinsp;0.029). LINC00942 showed only a borderline association and lost statistical significance after full adjustment (P\u0026thinsp;=\u0026thinsp;0.056), whereas KTN1-AS1 and XIST were not significantly associated with EC in any model.\u003c/p\u003e \u003cp\u003eWhen lncRNAs were categorized into tertiles, MALR again exhibited the most pronounced gradient, with both the middle and highest tertiles independently associated with EC in Model 2 (T2 vs. T1: OR\u0026thinsp;=\u0026thinsp;2.812, 95% CI 1.495\u0026ndash;5.288, P\u0026thinsp;=\u0026thinsp;0.001; T3 vs. T1: OR\u0026thinsp;=\u0026thinsp;5.887, 95% CI 2.971\u0026ndash;11.664, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For HOXA10-AS, only the highest tertile was significantly associated with EC (T3 vs. T1: OR\u0026thinsp;=\u0026thinsp;2.047, 95% CI 1.090\u0026ndash;3.845, P\u0026thinsp;=\u0026thinsp;0.026), whereas the middle tertile was not. LINC00324 showed significant associations for both the middle and highest tertiles (T2 vs. T1: OR\u0026thinsp;=\u0026thinsp;1.944, 95% CI 1.041\u0026ndash;3.629, P\u0026thinsp;=\u0026thinsp;0.037; T3 vs. T1: OR\u0026thinsp;=\u0026thinsp;2.164, 95% CI 1.155\u0026ndash;4.054, P\u0026thinsp;=\u0026thinsp;0.016). In contrast, LINC00942, KTN1-AS1, and XIST did not demonstrate independent associations with EC when analyzed as tertiles (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for T3 vs. T1 in Model 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIncremental diagnostic value of MALR, HOXA10-AS, and LINC00324\u003c/h2\u003e \u003cp\u003eTo assess the clinical utility of lncRNAs as diagnostic biomarkers, we first constructed a clinical prediction model using traditional risk factors (age, sex, smoking status, regular alcohol consumption, unhealthy dietary habits, and family history of UGI diseases), which yielded an AUC of 0.703 (95% CI: 0.642\u0026ndash;0.765). We then evaluated the incremental diagnostic value of the three lncRNAs that showed independent predictive associations (MALR, HOXA10-AS, and LINC00324) in continuous variable analysis.\u003c/p\u003e \u003cp\u003eMALR demonstrated the most robust diagnostic performance when analyzed individually. ROC analysis revealed that MALR alone exhibited good discriminative ability for EC, with an AUC of 0.716 (95% CI: 0.655\u0026ndash;0.777, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The optimal cutoff value for MALR was 1.139, which achieved a sensitivity of 67.2%, specificity of 69.1%, positive predictive value (PPV) of 78.1%, and negative predictive value (NPV) of 56.3% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). More importantly, incorporating MALR into the clinical model significantly improved diagnostic accuracy, increasing the AUC to 0.782 (95% CI: 0.726\u0026ndash;0.838), representing a significant increase of ΔAUC\u0026thinsp;=\u0026thinsp;0.079 (95% CI: 0.033\u0026ndash;0.124, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 by DeLong test; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the optimal cutoff probability of 0.618, the model incorporating MALR achieved enhanced performance with a sensitivity of 72.2%, specificity of 73.6%, PPV of 81.8%, and NPV of 61.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Net reclassification improvement analysis confirmed that MALR significantly improved patient risk stratification (NRI\u0026thinsp;=\u0026thinsp;0.626, 95% CI 0.403\u0026ndash;0.850, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and integrated discrimination improvement demonstrated enhanced discriminative accuracy (IDI\u0026thinsp;=\u0026thinsp;0.112, 95% CI 0.076\u0026ndash;0.148, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental diagnostic value of MALR, HOXA10-AS, and LINC00324 beyond traditional risk factors for esophageal cancer: changes in AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIDI (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.079 (0.033 to 0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626 (0.403 to 0.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.112 (0.076 to 0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXA10-AS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020 (-0.011 to 0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.262 (0.030 to 0.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029 (0.010 to 0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018 (-0.004 to 0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217 (-0.014 to 0.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018 (0.004 to 0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, HOXA10-AS and LINC00324 provided only limited incremental value beyond the clinical model. As standalone markers, HOXA10-AS (AUC\u0026thinsp;=\u0026thinsp;0.615, 95% CI: 0.546\u0026ndash;0.683) and LINC00324 (AUC\u0026thinsp;=\u0026thinsp;0.602, 95% CI: 0.534\u0026ndash;0.670) showed modest discriminative ability (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E, G, and H). Adding HOXA10-AS or LINC00324 to the clinical model resulted in only small, statistically non-significant increases in AUC (to 0.723 and 0.721, respectively; both ΔAUC\u0026thinsp;\u0026lt;\u0026thinsp;0.03, P\u0026thinsp;\u0026gt;\u0026thinsp;0.10; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Although modest improvements were observed in NRI and/or IDI, these effects were much weaker than those observed for MALR, indicating that HOXA10-AS and LINC00324 contribute only marginally to risk discrimination when added to traditional clinical factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of MALR\u003c/h2\u003e \u003cp\u003eTo explore the robustness of the association between MALR and EC, subgroup analyses were performed using stratified logistic regression and presented as a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe positive association between MALR and EC was consistently observed across subgroups defined by age (\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 years), sex, and smoking status, with no significant interactions (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for these factors). Interestingly, the association between MALR and EC appeared stronger in patients without unhealthy dietary habits (OR 3.019, 95% CI 2.011\u0026ndash;4.532) than in those with unhealthy diets (OR 1.414, 95% CI 0.804\u0026ndash;2.487), with a statistically significant interaction (P for interaction\u0026thinsp;=\u0026thinsp;0.033). A similar pattern was observed for family history: MALR showed a stronger association in patients without a family history of UGI diseases (OR 2.981, 95% CI 2.006\u0026ndash;4.431) compared with those with such a history (OR 1.456, 95% CI 0.780\u0026ndash;2.716; P for interaction\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e \u003cp\u003eDespite these differences in effect size, MALR tended to be positively associated with EC risk across all examined subgroups, suggesting robust predictive performance in diverse clinical contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between MALR and pathological indicators of tumor malignancy\u003c/h2\u003e \u003cp\u003eIn the subset of 142 EC patients who underwent surgery, we further examined the relationship between serum MALR expression and pathological indicators of tumor aggressiveness. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, higher MALR levels were significantly correlated with more advanced pathological TNM stage, poorer histological differentiation, and more severe lymph node involvement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed that MALR expression was positively correlated with pathological TNM stage (R\u0026thinsp;=\u0026thinsp;0.426, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), histological grade (R\u0026thinsp;=\u0026thinsp;0.454, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lymph node stage (R\u0026thinsp;=\u0026thinsp;0.493, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that serum MALR not only discriminates EC from NM-ED but also reflects the malignant potential of the tumor.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center study of patients presenting with esophageal symptoms, we systematically evaluated the diagnostic value of six cancer-related lncRNAs measured in peripheral serum. We found that (1) serum MALR, HOXA10-AS, LINC00324, and LINC00942 levels were significantly higher in EC patients than in those with non-malignant esophageal diseases, while KTN1-AS1 and XIST showed no clear differences; (2) MALR, HOXA10-AS, and LINC00324 were independently associated with EC in multivariable logistic regression models, with MALR showing the largest effect size; (3) MALR provided substantial incremental diagnostic value beyond traditional clinical risk factors, as reflected by significant improvements in AUC, NRI, and IDI; and (4) higher MALR levels were positively correlated with advanced TNM stage, poor differentiation, and lymph node metastasis, indicating a relationship between circulating MALR and tumor aggressiveness.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, these findings highlight MALR as a particularly promising blood-based biomarker for the diagnosis and risk stratification of esophageal cancer among patients with esophageal symptoms \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In real-world practice, patients with dysphagia, reflux, and other esophageal complaints commonly undergo endoscopy, but capacity constraints and patient intolerance limit its universal application, especially in primary care or resource-limited settings \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Our data suggest that incorporating serum MALR into a simple clinical model comprising age, sex, smoking, alcohol use, diet, and family history substantially improves discrimination between EC and NM-ED. The MALR-enhanced model achieved an AUC of 0.782, with significant gains in reclassification indices, while MALR alone already showed reasonable diagnostic performance (AUC 0.716). These results support the potential use of MALR as a minimally invasive screening or triage biomarker to prioritize high-risk patients for endoscopic evaluation, and to reassure those at lower risk.\u003c/p\u003e \u003cp\u003eBiologically, our clinical findings are consistent with and extend previous mechanistic studies. The macrophage-associated lncRNA MALR has been shown to facilitate ILF3 liquid\u0026ndash;liquid phase separation, amplifying HIF1α signaling and promoting a pro-tumorigenic microenvironment in esophageal cancer \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Our observation that higher circulating MALR levels are associated not only with the presence of EC but also with more advanced TNM stage, poorer differentiation, and more extensive lymph node involvement provides clinical support for MALR\u0026rsquo;s role in driving tumor progression and metastasis. Similarly, HOXA10-AS has been reported to promote esophageal carcinoma by upregulating HOXA10 and enhancing tumor cell proliferation, migration, and invasion \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and LINC00324 has been shown to promote ESCC cell proliferation and metastasis via a miR-493-5p/MAPK1 pathway \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The independent associations of HOXA10-AS and LINC00324 with EC risk in our serum-based analysis are consistent with their oncogenic functions at the tissue and cellular levels.\u003c/p\u003e \u003cp\u003eOn the other hand, although LINC00942, KTN1-AS1, and XIST have been implicated in esophageal or other malignancies \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, they did not show clear independent diagnostic value for EC in our cohort. LINC00942 exhibited only borderline associations in some models, whereas KTN1-AS1 and XIST were not significantly associated with EC in either continuous or tertile-based analyses. Taken together, these non-significant or borderline findings suggest that the circulating levels of these lncRNAs may be more variable or less tightly coupled to tumor burden, and that oncogenic lncRNAs identified in tumor tissues do not necessarily translate into useful blood-based biomarkers. This underscores the need for systematic clinical validation of candidate lncRNAs before their implementation in practice.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, we focused on a clinically relevant population\u0026mdash;patients with esophageal symptoms undergoing diagnostic endoscopy\u0026mdash;rather than healthy controls, making our results highly applicable to real-world diagnostic decision-making. Second, we evaluated both continuous and categorical (tertile-based) lncRNA measures and used multiple complementary indices (AUC, NRI, IDI) to quantify incremental predictive value. Third, by correlating MALR levels with detailed pathological features in surgically treated patients, we provide evidence that circulating MALR reflects tumor malignancy, not merely its presence.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. First, this was a single-center study with a moderate sample size and conducted in a Chinese population; external validation in independent, multicenter cohorts with different ethnic and geographic backgrounds is required to confirm the generalizability of our findings. Second, the cross-sectional design precludes assessment of temporal changes in lncRNA levels and their prognostic value for survival or recurrence. Third, although we adjusted for major clinical risk factors, residual confounding by unmeasured variables (e.g., environmental exposures, comorbidities, other medications) cannot be excluded. Fourth, we focused on six pre-selected lncRNAs based on recent mechanistic studies; it is possible that other circulating lncRNAs or combinations thereof may further improve diagnostic performance. Finally, we measured serum lncRNAs using qPCR under controlled research conditions; standardization of pre-analytical handling, assay platforms, and cut-off definitions will be essential before MALR can be translated into routine clinical practice.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that serum MALR is a strong and independent biomarker for esophageal cancer among patients with esophageal symptoms and provides significant incremental diagnostic value beyond traditional risk factors. MALR also correlates with key pathological indicators of tumor aggressiveness. HOXA10-AS and LINC00324 are additionally associated with EC but confer only modest incremental value. These findings support further prospective, multicenter studies to validate MALR-based blood tests and to explore their integration with existing clinical and endoscopic pathways for the early detection and risk stratification of esophageal cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Di Liu; Investigation, Di Liu, Chunlin Li, Xiaojie Li, Yazhou Su, Huiling Shen; Writing, Di Liu; Review, Yonglian Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was funded by the Henan Provincial Medical Science and Technology Research Program (grant LHGJ20230511).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang, H., Wang, F., Hallemeier, C. 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LncRNA XIST Promotes Proliferation, Migration and Invasion of Esophageal Squamous Cell Carcinoma Cells via Regulation of miR-186-5p/ZEB1. \u003cem\u003eAnticancer Res.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 897\u0026ndash;908. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21873/anticanres.17477\u003c/span\u003e\u003cspan address=\"10.21873/anticanres.17477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"esophageal cancer, lncRNA, MALR, biomarker, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8970016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8970016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEsophageal cancer (EC) shares symptoms with non-malignant esophageal diseases (NM-ED), and endoscopy is invasive and not always well tolerated, creating a need for blood-based markers in symptomatic patients. We enrolled 290 patients undergoing endoscopy for esophageal symptoms (180 EC and 110 NM-ED) and measured serum levels of six cancer-related long non-coding RNAs by quantitative PCR. MALR, HOXA10-AS, and LINC00324 were independently associated with EC after adjustment for clinical risk factors, with MALR showing the strongest effect (adjusted odds ratio per standard deviation 2.401, 95% CI 1.738\u0026ndash;3.316). MALR alone yielded an area under the curve of 0.716. Adding MALR to a clinical model increased the area under the curve to 0.782 (ΔAUC 0.079, 95% CI 0.033\u0026ndash;0.124) and significantly improved net reclassification improvement (0.626, 95% CI 0.403\u0026ndash;0.850) and integrated discrimination improvement (0.112, 95% CI 0.076\u0026ndash;0.148), whereas HOXA10-AS and LINC00324 provided limited incremental value. Higher MALR levels correlated with advanced TNM stage (R\u0026thinsp;=\u0026thinsp;0.426), poorer differentiation (R\u0026thinsp;=\u0026thinsp;0.454), and higher lymph node stage (R\u0026thinsp;=\u0026thinsp;0.493). Serum MALR is a promising biomarker for distinguishing EC from NM-ED and may also reflect tumor aggressiveness in symptomatic patients.\u003c/p\u003e","manuscriptTitle":"Serum MALR Improves Diagnosis and Malignancy Assessment of Esophageal Cancer Among Patients With Esophageal Symptoms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 17:04:56","doi":"10.21203/rs.3.rs-8970016/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-17T04:41:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196122566073541560396852670476922888447","date":"2026-04-06T02:33:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-05T09:28:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T11:24:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T12:13:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T12:09:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-25T16:48:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cad2cb3a-27b2-42a3-bac8-ad211cb3b9e1","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65758927,"name":"Health sciences/Biomarkers"},{"id":65758928,"name":"Biological sciences/Cancer"},{"id":65758929,"name":"Health sciences/Gastroenterology"},{"id":65758930,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-09T17:04:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 17:04:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8970016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8970016","identity":"rs-8970016","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}