Impact of DNA methylation on the recurrence risk of stage I non-small cell lung cancer with EGFR mutations

Impact of DNA methylation on the recurrence risk of stage I non-small cell lung cancer with EGFR mutations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of DNA methylation on the recurrence risk of stage I non-small cell lung cancer with EGFR mutations Yunyi Li, Zhihui Yang, Fang Wu, Qingchun Liang, James G. Herman, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5808504/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Clinical Epigenetics → Version 1 posted 8 You are reading this latest preprint version Abstract Background Recent studies have demonstrated that patients with stage IB-IIIA non-small cell lung cancer (NSCLC) harboring EGFR mutations (EGFRm) can significantly benefit from adjuvant therapy with EGFR-TKIs. Nevertheless, there is remains controversial in clinical practice about the use of EGFR-TKI adjuvant therapy for patients with stage IB EGFRm NSCLC. Methods This retrospective cohort study was conducted at the Second Xiangya Hospital of Central South University. From January 2011 to December 2020, completely resected stage IA-IB NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. FFPE tumor and lymph node specimens were collected and subjected to the 8-gene methylation panel using modified MOB-qMSP approach. We employed stepwise regression to select variables and logistic regression to establish the predictive model. Cross-validation and decision curve analysis were performed. Results A total of 242 patients with IA2-IB EGFRm NSCLC were included in the study. Among these patients, 86 constituted the recurrence (Rec) group, while 156 formed the non-recurrence (Non-Rec) group. Through stepwise logistic regression, seven crucial feature variables were identified, including five gene methylation variables (CDO1, TAC1, p16, CDH13, APC) and two clinical variables (tumor invasion and differentiation). The ROC analysis revealed an AUC of 0.873 for the model with these seven variables. Internal cross-validation (CV) demonstrated a model accuracy exceeding 77%. The nomogram and decision curve analysis (DCA) underscored the clinical utility of the model. We calculated the total score for each patient based on the nomogram and divided the patients into high-risk and low-risk groups. The cumulative risk curves for both groups evidenced that the recurrence risk in the high-risk group was significantly higher than in the low-risk group. We further divided the dataset into two cohorts—stage IA2-IA3 patients and stage IB patients. The model maintained a high AUC value (0.879) in stage IA2-A3 patients. Conclusions Our study demonstrates that the methylation of five genes—CDO1, TAC1, p16, CDH13, and APC—in N2 lymph nodes represents a strong biomarker panel for predicting recurrence in stage IB EGFRm NSCLC after curative resection. This approach also shows exceptional predictive accuracy for postoperative recurrence in stage IA2-IA3 EGFRm NSCLC. non-small cell lung cancer recurrence DNA methylation EGFR mutation risk factor Figures Figure 1 Figure 2 Figure 3 Introduction Recent studies have demonstrated that patients with stage IB-IIIA non-small cell lung cancer (NSCLC) harboring EGFR mutations (EGFRm) can significantly benefit from adjuvant therapy with EGFR-TKIs (Tyrosine Kinase Inhibitors) ( 1 – 3 ). Notably, the use of third-generation EGFR-TKIs has been associated with a marked reduction in post-surgical recurrence, alongside notable improvements in both Progression-Free Survival (PFS) and Overall Survival (OS) for this patient group ( 2 – 4 ). Consequently, many clinical guidelines now advocate for incorporating EGFR-TKIs in the adjuvant treatment regimen for NSCLC patients exhibiting EGFR mutations ( 5 , 6 ). Nevertheless, there remains controversy in clinical practice about using EGFR-TKI adjuvant therapy for patients with stage IB EGFRm NSCLC, as many with stage IB disease can achieve excellent prognosis with surgery alone ( 7 ). The potential for overtreatment with adjuvant targeted therapy in stage IB EGFRm NSCLC cases has garnered increasing clinical attention. Current practice leans towards reserving postoperative EGFR-TKI therapy for stage IB EGFRm NSCLC patients exhibiting specific “high-risk factors.” These mainly include visceral pleural invasion, vascular invasion, lymphatic invasion, the presence of lung neuroendocrine tumors, and micropapillary histological patterns ( 5 ). It merits emphasis that these high-risk factors currently in use originated from the conventional chemotherapy era, aimed at identifying patients at increased risk of postoperative recurrence in stage IB NSCLC. These factors are not specifically tailored to EGFR-mutant NSCLC. Consequently, the relevance and efficacy of applying these risk factors to direct EGFR-TKI adjuvant therapy in the modern therapeutic context remains an ongoing clinical debate. Contemporary investigations have focused on delineating specific risk factors associated with tumor recurrence in stage I EGFRm NSCLC. These studies highlight variables including advanced stage within the category, presence of micropapillary subtype, pleural invasion, high-grade histological subtypes, and lymphovascular invasion as critical predictors of recurrence ( 7 – 11 ). A novel study has investigated the potential of a 14-gene expression-based model to forecast postoperative recurrence in patients with stage I EGFRm NSCLC ( 12 ). Additionally, the role of minimal residual disease (MRD) as a predictive biomarker is gaining traction ( 13 ). Recent evidence suggests a stark contrast in recurrence rates between postoperative MRD-negative and MRD-positive patients, with the latter showing notable benefits from EGFR-TKI adjuvant therapy ( 14 ). Despite these promising developments, the quest for effective and precise molecular biomarkers for adjuvant therapy guidance in early-stage EGFRm NSCLC is still hindered by challenges such as tumor heterogeneity, the sensitivity of detection methodologies, and an evolving comprehension of the disease, particularly in stage I NSCLC. DNA methylation, a key epigenetic mechanism, regulates gene expression reversibly without altering the genetic code, thereby modulating cellular functions ( 15 – 17 ). Previous studies have identified that distinctive DNA methylation patterns in clinically negative lymph nodes and bronchial margins can predict postoperative recurrence in stage I NSCLC ( 18 , 19 ). The assessment of DNA methylation in circulating tumor DNA (ctDNA) from blood samples has shown efficacy in monitoring NSCLC post-surgery ( 20 ). Expanding upon these insights, our study zeroes in on stage I EGFRm NSCLC, employing modified MOB-qMSP method to examine promotor methylation of 8 genes (CDO1, TAC1, p16, SOX17, HOXA7, RASSF1A, CDH13, and APC) in both tumor tissues and pathologically negative lymph nodes. The goal is to evaluate the predictive value of DNA methylation markers in predicting post-surgical recurrence in stage IB EGFRm NSCLC and to investigate their potential applicability in stage IA cases, hoping to provide precise guidance for postoperative EGFR-TKI adjuvant therapy in stage I EGFRm NSCLC patients. Patients and Methods Study Population The patients enrolled in this retrospective cohort analysis were conducted at the Second Xiangya Hospital of Central South University from January 2011 to December 2020. Eligible patients were aged 18 years or older and had a histopathologically confirmed diagnosis of stage IA–IB NSCLC ( 21 ). All patients harbored sensitizing EGFR mutations (exon 19 deletion or L858R) and underwent curative surgical resection. Clinicopathological data were available for all participants. Patients without disease progression had a minimum postoperative follow-up of 5 years, whereas those who experienced disease progression were followed until the time of progression within the 5 years. The following patients were excluded: those who underwent incomplete surgical resection, had a previous malignancy within the past 5 years, or had received prior systemic antitumor therapies, including chemotherapy, radiotherapy, or targeted therapy. All patients were staged according to the revised eighth edition of TNM guidelines classification criteria ( 21 ), including the histologic status of the tumor and regional lymph nodes (N1LNs) and mediastinal lymph nodes (N2LNs) that were resected en bloc from levels II, IV, VII, VIII, and IX on the right side and levels IV, V, VI, VII, VIII, and IX on the left side. Tumor differentiation was evaluated by experienced pathologists in the Department of Pathology at the Second Xiangya Hospital of Central South University, based on hematoxylin and eosin (H&E) staining assessed under light microscopy. All patients had negative surgical margins, with no macroscopic or microscopic residual disease. Clinical characteristics—including demographics, smoking history, pulmonary function tests, and comorbidities—were collected from clinical records. Tumor size was obtained from pathological reports. Preparation of tumor and lymph-node specimens The distribution of tumors and lymph nodes and the number of samples per patient did not significantly differ between the recurrence and non-recurrence groups. Formalin-fixed, paraffin-embedded (FFPE) tumor and lymph node specimens were collected and assigned study-specific coded identifiers to maintain blinding of investigators throughout the study. All tumor specimens were histologically confirmed as stage I NSCLC and contained more than 50% tumor cells. All lymph node specimens were histologically negative for metastatic involvement. Gene mutation determination The DNA extraction was performed using the QIAamp DNA FFPE Tissue Kit (Qiagen, USA), following the manufacturer’s instructions. Detection of EGFR mutations within exons 18 to 21 (codons 688–875) was carried out using Sanger sequencing, as previously described ( 22 , 23 ). DNA methylation analysis using modified MOB-qMSP DNA extraction from FFPE samples was performed using the MOB method, which enables DNA extraction and bisulfite conversion in a single tube via silica supermagnetic beads, as previously described ( 24 , 25 ). This approach yields a 1.5- to 5-fold improvement in extraction efficiency compared to conventional techniques ( 26 ). We optimized the protocol for tissue samples based on the previously established plasma protocol ( 26 – 28 ), involving tissue incubation with proteinase K and Buffer AL at 55°C overnight. Subsequent steps followed the standard MOB procedure. Genomic sequences for the target genes and 1,000 bases upstream were obtained from the UCSC Genome Browser website ( 29 ). Primers and hybridization probes for methylation analysis were designed using Primer3 (v4.0.0) based on these sequences ( 30 , 31 ), with all primer and probe sequences listed in Supplementary Table S1 . Quantitative real-time methylation-specific PCR (qMSP) was performed and normalized to a β-actin (ACTB) control assay. Each reaction consisted of a 25 µL PCR mixture containing 2 µL of bisulfite-converted DNA, primers, probe, fluorescein reference dye, dNTPs, and Platinum Taq® DNA Polymerase, as previously described ( 27 ). Amplification was carried out using an ABI StepOnePlus Real-Time PCR system, with all samples analyzed in triplicate under the following thermocycling conditions: 95°C for 5 minutes; 40 cycles of 95°C for 15 seconds, 60°C for 1 minute, and 72°C for 30 minutes. The 2 −ΔCT was calculated for each methylation detection replicate relative to the mean Ct for β-Actin (ACTB). For undetected replicates (N.D.), a Ct value of 100 was assigned to yield a near-zero 2 −ΔCT value. The mean 2 −ΔCT value was calculated using the formula described in previous study ( 25 – 27 ). Statistical Analysis Demographic, methylation, and genotype variables were summarized by case-control status with percentages for categorical variables and means and standard deviations for continuous variables. Differences in demographic variables between cases and controls were assessed with Fisher’s exact test for categorical variables and the Wilcoxon rank sum test for continuous variables. The Pack-years of cigarette smoking were defined as the average number of packs smoked per day multiplied by the number of years of smoking. Former smokers were defined as those individuals who had quit smoking one year or more at the time of surgery. Using R Project (version 4.3.2) for data analysis and modeling, we employed stepwise regression to select variables and logistic regression to establish the predictive model. Stepwise regression was performed using the “MASS” package in the R Project. Brier score calculation, calibration curve, and nomogram were conducted using the “rms” package. ROC analysis was carried out using the “pROC” package. ROC curves were plotted using the “modEvA” package. Cross-validation was performed using the “caret” package. The Hosmer-Lemeshow test was conducted using the “ResourceSelection” package. Decision curve analysis (DCA) was performed using the “rmda” package. Results Patient characteristic A total of 242 patients with IA2-IB EGFRm NSCLC who underwent curative surgical resection were included in the study. Among these patients, 86 constituted the recurrence (Rec) group, while 156 formed the non-recurrence (Non-Rec) group. Baseline characteristics are detailed in Table 1 . There were no statistically significant differences between the two groups regarding gender, age, smoking history, tumor recurrence location, tumor size, TNM stage, or the presence of chronic obstructive pulmonary disease (COPD). In contrast, significant differences were observed in tumor size, invasion, and differentiation between the Rec and Non-Rec groups. Table 1 Comparison of recurrence and non-recurrence groups using univariate and multivariate analyses of clinical predictors. Non-Rec Rec Total p-value Univariable Multivariable (n = 156) (n = 86) (n = 242) OR (95% CI) p-value OR (95% CI) p-value Gender Female 98 (62.8%) 59 (68.6%) 157 (64.9%) 0.666 Male 58 (37.2%) 27 (31.4%) 85 (35.1%) 0.77 (0.44–1.35) 0.367 0.56 (0.26–1.21) 0.141 Age , Mean ± SD 59.0 ± 9.51 58.5 ± 10.0 58.8 ± 9.67 0.936 0.99 (0.97–1.02) 0.711 0.98 (0.95–1.01) 0.200 Smoking status Current 9 (5.8%) 10 (11.6%) 19 (7.9%) 0.584 Former 18 (11.5%) 11 (12.8%) 29 (12.0%) 0.55 (0.17–1.78) 0.317 0.52 (0.12–2.32) 0.394 Never 129 (82.7%) 65 (75.6%) 194 (80.2%) 0.45 (0.18–1.17) 0,102 0.37 (0.10–1.34) 0.131 Tumor location LLL 21 (13.5%) 12 (14.0%) 33 (13.6%) 0.741 LUL 37 (23.7%) 31 (36.0%) 68 (28.1%) 1.47 (0.62–3.45) 0.380 1.77 (0.62–5.05) 0.282 RLL 28 (17.9%) 15 (17.4%) 43 (17.8%) 0.94 (0.36–2.42) 0.894 0.91 (0.29–2.87) 0.879 RML 11 (7.1%) 5 (5.8%) 16 (6.6%) 0.80 (0.22–2.84) 0.725 1.33 (0.30–5.89) 0.710 RUL 59 (37.8%) 23 (26.7%) 82 (33.9%) 0.68 (0.29–1.61) 0.382 0.73 (0.25–2.11) 0.563 Tumor size , Mean ± SD 2.68 ± 0.804 2.52 ± 0.728 2.62 ± 0.780 0.270 0.76 (0.54–1.07) 0.117 1.41 (0.58–3.47) 0.448 Invasion Non 121 (77.6%) 45 (52.3%) 166 (68.6%) 0.023 Lymphovascular 6 (3.8%) 7 (8.1%) 13 (5.4%) 3.14 (1.00-9.84) 0.050 2.44 (0.68–8.80) 0.171 Pleura 29 (18.6%) 34 (39.5%) 63 (26.0%) 3.15 (1.73–5.76) < 0.001 7.38 (1.68–32.49) 0.008 TNM stage IA2 36 (23.1%) 18 (20.9%) 54 (22.3%) 0.966 IA3 39 (25.0%) 19 (22.1%) 58 (24.0%) 0.97 (0.44–2.14) 0.949 0.53 (0.14–1.94) 0.338 IB 81 (51.9%) 49 (57.0%) 130 (53.7%) 1.21 (0.62–2.36) 0.576 0.28 (0.04–2.07) 0.213 Differentiation High_differ 48 (30.8%) 10 (11.6%) 58 (24.0%) < 0.001 Mod_differ 102 (65.4%) 49 (57.0%) 151 (62.4%) 2.31 (1.08–4.94) 0.032 2.57 (1.13–5.84) 0.024 Poor_differ 6 (3.8%) 27 (31.4%) 33 (13.6%) 21.60 (7.07–65.97) < 0.001 29.50 (8.55-101.79) < 0.001 FPT Normal 138 (88.5%) 73 (84.9%) 211 (87.2%) 0.728 COPD 18 (11.5%) 13 (15.1%) 31 (12.8%) 1.37 (0.63–2.94) 0.427 1.04 (0.35–3.06) 0.942 Follow-up results The median follow-up duration was 56.6 months, with the last assessment conducted on December 31, 2022. Among the patients in the Rec group, the median time to recurrence was 22.5 months. The most common sites of tumor recurrence were the lungs and local regions (34 cases, 43.6%), followed by other distant or multiple metastases (25 cases, 32.1%), brain metastases (9 cases, 11.5%), bone metastases (8 cases, 10.3%), and liver metastases (2 cases, 2.6%). Risk of cancer recurrence according to clinical predictors To identify clinical factors associated with tumor recurrence, we conducted univariate and multivariate logistic regression analyses on nine clinical variables: age, gender, smoking status, tumor location, invasion status, tumor size, TNM stage, tumor differentiation, and pulmonary function tests (PFT). In the univariate analysis, ‘invasion’ and ‘differentiation’ emerged as significant predictors of tumor recurrence. These associations remained robust in the multivariate analysis, confirming that both ‘invasion’ and ‘differentiation’ are independent risk factors for tumor recurrence ( p < 0.001). Differential gene methylation patterns across sample types We investigated the promoter methylation status of eight genes across various sample types to determine their association with tumor recurrence. Significant differences in methylation rates were observed between the Rec and Non-Rec groups. In tumor tissues, p16, CDH13, and APC methylation rates showed statistically significant in the Rec group compared to the Non-Rec group. In N1LNs, CDO1 , TAC1 , and p16 showed statistically significant methylation rates in the Rec group compared to the Non-Rec group. Similarly, in N2LNs, significant differences were found in the methylation of CDO1 , p16 , and CDH13 between the two groups (Table 2 ). Table 2 Differential gene methylation patterns across sample types. Tumor N1LNs N2LNs Non-Rec Rec p-value Non-Rec Rec p-value Non-Rec Rec p-value CDO1 No 33 (21.2%) 28 (32.6%) 0.148 50 (32.1%) 47 (54.7%) 0.003 41 (26.3%) 44 (51.2%) < 0.001 Yes 123 (78.8%) 58 (67.4%) 106 (67.9%) 39 (45.3%) 115 (73.7%) 42 (48.8%) TAC1 No 27 (17.3%) 16 (18.6%) 0.969 34 (21.8%) 36 (41.9%) 0.004 32 (20.5%) 30 (34.9%) 0.050 Yes 129 (82.7%) 70 (81.4%) 122 (78.2%) 50 (58.1%) 124 (79.5%) 56 (65.1%) SOX17 No 68 (43.6%) 34 (39.5%) 0.830 71 (45.5%) 42 (48.8%) 0.884 70 (44.9%) 37 (43.0%) 0.962 Yes 88 (56.4%) 52 (60.5%) 85 (54.5%) 44 (51.2%) 86 (55.1%) 49 (57.0%) p16 No 115 (73.7%) 47 (54.7%) 0.011 149 (95.5%) 69 (80.2%) < 0.001 150 (96.2%) 55 (64.0%) < 0.001 Yes 41 (26.3%) 39 (45.3%) 7 (4.5%) 17 (19.8%) 6 (3.8%) 31 (36.0%) RASSF1A No 80 (51.3%) 53 (61.6%) 0.302 90 (57.7%) 55 (64.0%) 0.636 85 (54.5%) 53 (61.6%) 0.562 Yes 76 (48.7%) 33 (38.4%) 66 (42.3%) 31 (36.0%) 71 (45.5%) 33 (38.4%) CDH13 No 86 (55.1%) 33 (38.4%) 0.044 109 (69.9%) 54 (62.8%) 0.531 111 (71.2%) 36 (41.9%) < 0.001 Yes 70 (44.9%) 53 (61.6%) 47 (30.1%) 32 (37.2%) 45 (28.8%) 50 (58.1%) APC No 55 (35.3%) 52 (60.5%) < 0.001 86 (55.1%) 53 (61.6%) 0.619 85 (54.5%) 35 (40.7%) 0.121 Yes 101 (64.7%) 34 (39.5%) 70 (44.9%) 33 (38.4%) 71 (45.5%) 51 (59.3%) HOXA7 No 143 (91.7%) 76 (88.4%) 0.705 145 (92.9%) 79 (91.9%) 0.953 144 (92.3%) 78 (90.7%) 0.910 Yes 13 (8.3%) 10 (11.6%) 11 (7.1%) 7 (8.1%) 12 (7.7%) 8 (9.3%) When next analyzing genes that were methylation-positive in both tumor tissues and N1LNs, CDO1 , TAC1 , and p16 exhibited significantly higher methylation rates in the Rec group than in the Non-Rec group. For genes methylation-positive in both tumor tissues and N2LNs, significant differences were observed in CDO1 , p16 , and CDH13 . Notably, among genes that were methylation-positive across tumor tissues, N1LNs and N2LNs, CDO1 , TAC1 , and p16 consistently demonstrated statistically significant differences (Supplemental Table S2 ). These results suggest that the methylation status of specific genes—particularly CDO1, p16, TAC1 , and CDH13 —in tumor tissues and lymph nodes may be valuable biomarkers for predicting tumor recurrence in patients with EGFRm NSCLC. Exploration of the detection combination of gene methylation for predicting cancer recurrence We further explored the predictive performance of different gene methylation combinations for tumor recurrence and sought to determine the optimal combination. Univariate logistic regression analyses were conducted on all investigated indicators to identify statistically significant differences, calculating odds ratios (OR) and areas under the receiver operating characteristic curve (AUC). The results indicated that single-gene methylation detection was ineffective in predicting tumor recurrence in tumor tissues, N1LNs, N2LNs, and various combinations of these samples. This was evidenced by AUC values below 0.67, even for indicators with statistically significant differences (Table 3 ). Further multivariate regression analysis using stepwise selection identified six model formulas derived from various gene-sample combinations, all demonstrating significantly improved AUC values (Table 3 ). Notably, the methylation detection of a five-gene panel ( CDO1 , TAC1 , p16 , CDH13 , and APC ) in N2LNs yielded the optimal predictive performance, achieving an AUC greater than 0.82 (Supplemental Figure S1 ). Table 3 Univariable and stepwise analysis of recurrence according to gene methylation predictors. Univariable Stepwise OR (95% CI) p-value AUC OR (95% CI) p-value AUC Tumor 0.721 SOX17 2.04 (1.07–3.90) 0.030 p16 2.33 (1.34–4.05) 0.003 0.595 2.71 (1.49–4.93) 0.001 CDH13 1.97 (1.15–3.38) 0.013 0.584 1.90 (1.07–3.39) 0.028 APC 0.36 (0.21–0.61) < 0.001 0.626 0.25 (0.13–0.48) < 0.001 N1LN 0.700 CDO1 0.39 (0.23–0.67) < 0.001 0.613 0.39 (0.22–0.70) 0.002 TAC1 0.39 (0.22–0.69) 0.001 0.600 0.50 (0.27–0.92) 0.026 p16 5.24 (2.08–13.23) < 0.001 0.576 6.37 (2.41–16.83) < 0.001 N2LN 0.829 CDO1 0.34 (0.20–0.59) < 0.001 0.624 0.33 (0.17–0.66) 0.002 TAC1 0.48 (0.27–0.87) 0.015 0.572 0.55 (0.27–1.12) 0.099 p16 14.09 (5.58–35.61) < 0.001 0.661 17.65 (6.49–48.02) < 0.001 CDH13 3.43 (1.97–5.94) < 0.001 0.646 3.30 (1.73–6.29) < 0.001 APC 1.74 (1.02–2.97) 0.041 0.569 2.01 (1.04–3.87) 0.036 Tumor +N1 0.686 CDO1 0.41 (0.24–0.70) 0.001 0.609 0.38 (0.21–0.69) 0.002 TAC1 0.46 (0.26–0.81) 0.007 0.584 0.62 (0.34–1.13) 0.119 p16 4.87 (1.91–12.36) < 0.001 0.570 6.20 (2.33–16.50) < 0.001 Tumor +N2 0.761 CDO1 0.32 (0.18–0.55) < 0.001 0.635 0.34 (0.18–0.63) < 0.001 TAC1 0.52 (0.29–0.93) 0.028 0.565 p16 9.68 (3.77–24.83) < 0.001 0.620 12.19 (4.37–33.99) < 0.001 CDH13 2.84 (1.64–4.90) < 0.001 0.623 2.46 (1.34–4.53) 0.004 APC 0.61 (0.32–1.18) 0.142 Tumor +N1 + N2 0.709 CDO1 0.35 (0.20–0.60) < 0.001 0.629 0.34 (0.18–0.63) < 0.001 TAC1 0.40 (0.23–0.70) 0.001 0.603 0.56 (0.30–1.03) 0.060 p16 5.71 (2.14–15.23) < 0.001 0.574 7.62 (2.70-21.49) < 0.001 Model performance and validation A multivariate regression model incorporating clinical and methylation indicators was developed to evaluate predictive performance using AUC values and the Brier Score. The results indicated that the five-gene methylation detection in N2LNs, combined with clinical predictors, provided the optimal predictive efficacy for tumor recurrence (Table 4 ). Stepwise regression identified six candidate models. The Brier Score was calculated for each model, with the N2LN-Clinic model exhibiting the lowest (best) score of 0.136 (Table 4 ). Receiver operating characteristic (ROC) analysis demonstrated that the N2LN-Clinic model achieved the highest AUC of 0.873 among the six models (Fig. 1 A-F). Internal cross-validation was performed using four methods: leave-one-out cross-validation (LOOCV), leave-group-out cross-validation (LGOCV), 10-fold cross-validation, and bootstrap resampling (Table 4 ). The results consistently showed that the N2LN-Clinic model had the highest cross-validation accuracy across all methods. Based on comparisons of the Brier Score, AUC, and cross-validation accuracy, the N2LN-Clinic model was selected as the preferred predictive model. A calibration curve was plotted for the N2LN-Clinic model, and the Hosmer-Lemeshow test was conducted, yielding a p-value of 0.74 (Fig. 2 A). This indicates that the model closely aligns with the observed outcomes and has a good fit, demonstrating reliable predictive capability for tumor recurrence. Table 4 Performance and validation of six models identified by stepwise regression. Database Formula Brier Score AUC (95% CI) Accuracy LGOCV 10 fold CV LOOCV Bootstrap Tumor -Clinic p16 + CDH13 + APC + HOXA7 + Age + Invasion + Pathology 0.153 0.84 (0.79–0.89) 0.6479 0.7448 0.7479 0.7446 N1LN -Clinic CDO1 + TAC1 + p16 + Age + Tumor_location + Invasion + Pathology 0.153 0.83 (0.78–0.88) 0.7184 0.7564 0.7686 0.7567 N2LN -Clinic CDO1 + TAC1 + p16 + CDH13 + APC + Invasion + Pathology 0.136 0.87 (0.83–0.92) 0.7888 0.7846 0.7727 0.7849 Tumor + N1 -Clinic CDO1 + TAC1 + p16 + Age + Invasion + Pathology 0.162 0.81 (0.75–0.86) 0.7043 0.7484 0.7438 0.7501 Tumor + N2 -Clinic CDO1 + TAC1 + p16 + CDH13 + APC + Invasion + Pathology 0.145 0.85 (0.80–0.90) 0.7324 0.7729 0.7810 0.7761 Tumor + N1 + N2 -Clinic CDO1 + TAC1 + p16 + Age + Invasion + Pathology 0.159 0.82 (0.76–0.87) 0.7324 0.7521 0.7521 0.7517 Risk factors and predictive nomogram Based on the N2LN model, we developed a predictive nomogram that incorporates seven key variables: five gene methylation markers ( CDO1 , TAC1 , p16 , CDH13 , and APC ) and two clinical factors (invasion and differentiation) (Fig. 2 C). In this model, methylation of the CDO1 and TAC1 genes emerged as protective factors against tumor recurrence. In contrast, methylation of the p16 , CDH13 , and APC genes, along with the presence of pleural invasion and lower tumor differentiation, were identified as significant risk factors for recurrence. To illustrate the impact of these variables, a forest plot of the odds ratio (OR) was constructed (Fig. 2 D). This visualization highlights that methylation of CDO1 and TAC1 is associated with a decreased risk of recurrence. In contrast, methylation of p16 , CDH13 , and APC , as well as adverse clinical features like pleural invasion and lower differentiation grade, significantly increases the risk. The model emphasizes the multifactorial nature of recurrence risk, integrating genetic and clinical predictors to enhance predictive accuracy. Cumulative risk analysis and decision curve analysis We subsequently calculated cumulative risk curves for patients based on the seven variables included in the model, illustrating the contribution of each variable to the risk of patient recurrence (Fig. 3 A-G). It was observed that different variables contributed to the risk of recurrence to varying degrees. Among them, positive results for CDO1 and TAC1 had a negative contribution to the risk of recurrence (Fig. 3 A, B). Among the variables that contributed to recurrence risk, positive p16 showed a relatively more significant impact (Fig. 3 C). Regarding clinical variables, poorly differentiated pathological types contributed significantly to the risk of recurrence (Fig. 3 G). We calculated the total score for each patient based on the nomogram and divided the patients into high-risk and low-risk groups according to the median total score of 123 points (Fig. 2 C). The total score is calculated by summing the individual scores for each predictive variable, as indicated on the “Total Points” axis. A score of 123 represents the median total score across all patients. Patients with scores above 123 were classified into the high-risk group, while those with scores below 123 were assigned to the low-risk group. By plotting the cumulative risk curves for both groups, it was evident that the recurrence risk in the high-risk group was significantly higher than in the low-risk group (Fig. 3 H). The decision curve analysis (DCA) of the nomogram indicated that when the threshold probability ranges between 5% and 90%, using this nomogram to predict patient recurrence offers a net clinical benefit (Fig. 2 B). Predictive value of the model in stage IA2-A3 EGFRm NSCLC patients We further divided the dataset into two cohorts—stage IA2-IA3 patients and stage IB patients—to evaluate the model’s predictive performance in earlier-stage disease. The results indicated that while the model’s predictive efficacy slightly decreased in stage IA2-A3 patients, it maintained a high AUC value (Fig. 1 G and Table 5 ). It suggests that the predictive model possesses significant prognostic value for tumor recurrence even in stage IA2-IA3 patients. Table 5 Comparison of predictive value of the model between stage IA and stage IB patients. Cohort Brier Score AUC Accuracy LGOCV 10 fold CV LOOCV Bootstrap IA 0.133 0.8793 0.7879 0.7613 0.7321 0.7463 IB 0.123 0.8988 0.8947 0.8146 0.8077 0.8069 Discussion Current understanding suggests that tumor recurrence is intricately linked to inherent genetic mutations within the tumor and a broader spectrum of factors, including the systemic immune response and epigenetic mechanisms ( 32 ). Emerging evidence has shed light on forming a pre-metastatic niche, a critical precursor environment, which is intricately established before observable tumor metastasis ( 33 , 34 ). It highlights the need to look at tumor recurrence from a broader perspective, considering the overall regulatory processes in the body. In this study, using modified MOB-qMSP, we investigate the role of specific gene methylation in tumor tissues and pathologically negative lymph nodes, aiming to accurately identify high-risk patients for recurrence and guide postoperative adjuvant therapy. Our findings indicate that the specific gene methylation panel detected in N2 lymph nodes can effectively predict postoperative recurrence in stage IB EGFRm NSCLC patients and demonstrates excellent predictive performance in stage IA2-IA3 patients, highlighting this detection approach’s high specificity and stability. Previous studies demonstrated that the methylation of promoter regions in p16, CDH13, APC, and RASSF1A in patients with T1-2N0 NSCLC treated with curative intent is strongly associated with early recurrence, particularly when these molecular alterations are detected in both primary tumors and lymph nodes ( 18 ). Building on this research, our study expanded the gene detection panel and specifically targeted EGFRm NSCLC, a patient subgroup urgently needing postoperative recurrence biomarkers. Drawing from The Cancer Genome Atlas (TCGA) and previous studies ( 27 , 35 ), we also investigated four additional genes—CDO1, TAC1, SOX17, and HOXA7—known for their frequent methylation changes in NSCLC. Our findings confirmed previous results, demonstrating that patients with promoter methylation of p16, CDH13, and APC exhibited higher recurrence rates. However, unlike earlier studies, RASSF1A methylation did not retain its predictive value in our dataset. Moreover, we identified that a five-gene methylation panel—comprising CDO1, TAC1, p16, CDH13, and APC—detected in N2LN provided optimal predictive performance for postoperative recurrence. In our analysis, methylation status of individual genes yielded AUC values below 0.67 for predicting recurrence, highlighting that single-gene assays alone lack adequate sensitivity and specificity in an unstratified NSCLC cohort. This limitation likely stems from the marked heterogeneity inherent in non-small cell lung cancer, where methylation of a specific gene may be highly predictive in one tumor subtype yet negligible or irrelevant in another, thus constraining its general applicability. Furthermore, tumor recurrence involves complex interactions among multiple signaling pathways and diverse epigenetic alterations that cannot be adequately captured by single-marker approaches. In contrast, multi-gene methylation panels integrate signals from various pathways, enhancing both the robustness and predictive accuracy of recurrence assessments. Compared to earlier detection strategies ( 18 ), this panel requires more genes and does not require simultaneous tumor and lymph node testing. Instead, positive results in N2LN alone efficiently predict tumor recurrence, offering a streamlined yet highly effective approach to recurrence prediction. One possible explanation for this discrepancy could be the differences between the two DNA methylation detection methods—nested MSP and MOB. Previous studies have utilized nested MSP to detect DNA methylation across various biological samples, including fresh frozen tumor (FFT) tissues, FFPE samples, sputum, plasma, and other fluids ( 36 – 38 ). However, the limitations of this method have been well-recognized. With its 1.5- to 6-fold increase in DNA extraction efficiency, MOB significantly reduces PCR inhibitor carryover and enhances analytical sensitivity by over 25-fold. This improvement enables the detection of a greater number of DNA methylation signatures ( 24 , 27 , 39 ). Another potential factor could be our study’s relatively small patient cohort, which may have introduced bias in identifying methylation events. Nonetheless, the loss of predictive significance for RASSF1A further highlights the robust predictive value of p16, APC, and CDH13, reinforcing their importance from an alternative perspective. Our results further demonstrated the critical role of detecting molecular biomarkers in pathologically negative lymph nodes for predicting early recurrence in NSCLC. As a systemic disease, lung cancer often exhibits detectable molecular alterations at the earliest stages of tumorigenesis or metastasis. The methylation changes identified in histologically negative lymph nodes strongly suggest the presence of undetectable micrometastases or pre-metastatic niches. Current recurrence risk assessments, which predominantly rely on immunohistochemical analyses, may overlook rare malignant cells within a normal tissue background. In contrast, the MOB-qMSP assay is both straightforward and highly sensitive, enabling precise detection of gene methylation signals. In recent years, circulating tumor DNA (ctDNA) analysis has become increasingly significant in predicting tumor recurrence. MOB-qMSP has demonstrated superior sensitivity and specificity in detecting ctDNA methylation. It is a promising tool for monitoring specific gene methylation in ctDNA to predict postoperative recurrence of early-stage NSCLC. This represents a critical area for future research. Our study has several limitations. First, the relatively small sample size may have introduced biases. To minimize this, we included a large cohort of patients over an extended follow-up period. However, the sample size remained limited due to the low recurrence rates in stage IA-IB NSCLC after surgery and the need for long-term follow-up. We employed machine learning-based statistical and validation techniques to overcome this challenge, yielding more solid results. Second, due to the wide time range of the sample collection, discrepancies in clinical record collection, pathological diagnosis, and genetic mutation evaluation compared to current standards may have introduced potential bias. Third, we did not perform comprehensive large-panel gene mutation sequencing for all tumor samples, which may have omitted important co-occurring genetic mutations. Lastly, the genes in our study were selected based on prior research. It is possible that some genes with greater specificity were missed. This is the first study correlating a five-gene methylation panel in N2 lymph nodes with stage IA-IB EGFR-mutant NSCLC recurrence. Validation of these findings in larger, independent cohorts is crucial before this approach can be implemented in clinical practice. In conclusion, our study demonstrates that the methylation of five genes—CDO1, TAC1, p16, CDH13, and APC—in N2 lymph nodes represents a strong biomarker panel for predicting recurrence in stage IB EGFR-mutant NSCLC after curative resection. This approach also shows exceptional predictive accuracy for postoperative recurrence in stage IA2-IA3 EGFRm NSCLC, underscoring its significant potential for clinical application. Declarations Ethics approval and consent to participate The research protocol of this study was approved by the Institutional Review Board of the Second Xiangya Hospital. Written informed consent was waived due to its retrospective nature. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Information This study was funded by the Natural Science Foundation of Hunan Province (No. 2020SK53419 and 2021JJ30926), National Natural Science Foundation of China (No.82302449), the Scientific Research Launch Project for New Employees of the Second Xiangya Hospital of Central South University, and the Health Research Project of Hunan Provincial Health Commission (No: W20243115). Authors’ contributions Yunyi Li: Data Analysis, Visualization, Writing-Original Draft. Zhihui Yang: Data Curation, Analysis. Fang Wu: Data Curation. Qingchun Liang: Data Curation and formal analysis. James G. Herman: Methodology. Malcolm V. Brock: Methodology. Wenliang Liu: Project administration. Fenglei Yu: Funding acquisition, Resources. Xue He: Data Curation, Visualization, Conceptualization, Review & Editing the Draft. Chen Chen: Conceptualization, Writing-Review & Editing the draft. Acknowledgments The authors sincerely thank the multidisciplinary team (MDT) of thoracic oncology at the Second Xiangya Hospital of Central South University for caring for all the patients. References Zhong WZ, Wang Q, Mao WM, Xu ST, Wu L, Shen Y, et al. Gefitinib versus vinorelbine plus cisplatin as adjuvant treatment for stage II-IIIA (N1-N2) EGFR-mutant NSCLC (ADJUVANT/CTONG1104): a randomised, open-label, phase 3 study. Lancet Oncol. 2018;19(1):139-48. Herbst RS, Wu YL, John T, Grohe C, Majem M, Wang J, et al. Adjuvant Osimertinib for Resected EGFR-Mutated Stage IB-IIIA Non-Small-Cell Lung Cancer: Updated Results From the Phase III Randomized ADAURA Trial. J Clin Oncol. 2023;41(10):1830-40. Wu YL, Tsuboi M, He J, John T, Grohe C, Majem M, et al. Osimertinib in Resected EGFR-Mutated Non-Small-Cell Lung Cancer. N Engl J Med. 2020;383(18):1711-23. Tsuboi M, Herbst RS, John T, Kato T, Majem M, Grohe C, et al. Overall Survival with Osimertinib in Resected EGFR-Mutated NSCLC. N Engl J Med. 2023;389(2):137-47. Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, et al. Non-Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022;20(5):497-530. Passaro A, Leighl N, Blackhall F, Popat S, Kerr K, Ahn MJ, et al. ESMO expert consensus statements on the management of EGFR mutant non-small-cell lung cancer. Ann Oncol. 2022;33(5):466-87. Kamigaichi A, Mimae T, Tsubokawa N, Miyata Y, Adachi H, Shimada Y, et al. Risk Factors for Recurrence of Stage I Epidermal Growth Factor Receptor Mutated Lung Adenocarcinoma. Ann Thorac Surg. 2023. Saw SPL, Zhou S, Chen J, Lai G, Ang MK, Chua K, et al. Association of Clinicopathologic and Molecular Tumor Features With Recurrence in Resected Early-Stage Epidermal Growth Factor Receptor-Positive Non-Small Cell Lung Cancer. JAMA Netw Open. 2021;4(11):e2131892. Maeda R, Yoshida J, Ishii G, Hishida T, Nishimura M, Nagai K. Poor prognostic factors in patients with stage IB non-small cell lung cancer according to the seventh edition TNM classification. Chest. 2011;139(4):855-61. Jung HA, Lim J, Choi YL, Lee SH, Joung JG, Jeon YJ, et al. Clinical, Pathologic, and Molecular Prognostic Factors in Patients with Early-Stage EGFR-Mutant NSCLC. Clin Cancer Res. 2022;28(19):4312-21. Ito M, Miyata Y, Tsutani Y, Ito H, Nakayama H, Imai K, et al. Positive EGFR mutation status is a risk of recurrence in pN0-1 lung adenocarcinoma when combined with pathological stage and histological subtype: A retrospective multi-center analysis. Lung Cancer. 2020;141:107-13. Jiang Y, Lin Y, Fu W, He Q, Liang H, Zhong R, et al. The impact of adjuvant EGFR-TKIs and 14-gene molecular assay on stage I non-small cell lung cancer with sensitive EGFR mutations. EClinicalMedicine. 2023;64:102205. Luskin MR, Murakami MA, Manalis SR, Weinstock DM. Targeting minimal residual disease: a path to cure? Nat Rev Cancer. 2018;18(4):255-63. Zhang JT, Liu SY, Gao W, Liu SM, Yan HH, Ji L, et al. Longitudinal Undetectable Molecular Residual Disease Defines Potentially Cured Population in Localized Non-Small Cell Lung Cancer. Cancer Discov. 2022;12(7):1690-701. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;15(7):459-66. Dawson MA, Kouzarides T. Cancer epigenetics: from mechanism to therapy. Cell. 2012;150(1):12-27. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022;23(6):369-83. Brock MV, Hooker CM, Ota-Machida E, Han Y, Guo M, Ames S, et al. DNA methylation markers and early recurrence in stage I lung cancer. N Engl J Med. 2008;358(11):1118-28. Guo M, House MG, Hooker C, Han Y, Heath E, Gabrielson E, et al. Promoter hypermethylation of resected bronchial margins: a field defect of changes? Clin Cancer Res. 2004;10(15):5131-6. Chen K, Kang G, Zhang Z, Lizaso A, Beck S, Lyskjaer I, et al. Individualized dynamic methylation-based analysis of cell-free DNA in postoperative monitoring of lung cancer. BMC Med. 2023;21(1):255. Ettinger DS, Wood DE, Akerley W, Bazhenova LA, Borghaei H, Camidge DR, et al. Non-Small Cell Lung Cancer, Version 6.2015. Journal of the National Comprehensive Cancer Network : JNCCN. 2015;13(5):515-24. Lin MT, Mosier SL, Thiess M, Beierl KF, Debeljak M, Tseng LH, et al. Clinical validation of KRAS, BRAF, and EGFR mutation detection using next-generation sequencing. American journal of clinical pathology. 2014;141(6):856-66. Tsiatis AC, Norris-Kirby A, Rich RG, Hafez MJ, Gocke CD, Eshleman JR, et al. Comparison of Sanger sequencing, pyrosequencing, and melting curve analysis for the detection of KRAS mutations: diagnostic and clinical implications. J Mol Diagn. 2010;12(4):425-32. Bailey VJ, Zhang Y, Keeley BP, Yin C, Pelosky KL, Brock M, et al. Single-tube analysis of DNA methylation with silica superparamagnetic beads. Clinical chemistry. 2010;56(6):1022-5. Chen C, Huang X, Yin W, Peng M, Wu F, Wu X, et al. Ultrasensitive DNA hypermethylation detection using plasma for early detection of NSCLC: a study in Chinese patients with very small nodules. Clin Epigenetics. 2020;12(1):39. Keeley B, Stark A, Pisanic TR, 2nd, Kwak R, Zhang Y, Wrangle J, et al. Extraction and processing of circulating DNA from large sample volumes using methylation on beads for the detection of rare epigenetic events. Clin Chim Acta. 2013;425(C):169-75. Hulbert A, Jusue Torres I, Stark A, Chen C, Rodgers K, Lee B, et al. Early Detection of Lung Cancer using DNA Promoter Hypermethylation in Plasma and Sputum. Clinical cancer research : an official journal of the American Association for Cancer Research. 2016. Yin W, Wang X, Li Y, Wang B, Song M, Hulbert A, et al. Promoter hypermethylation of cysteine dioxygenase type 1 in patients with non-small cell lung cancer. Oncol Lett. 2020;20(1):967-73. Genome Bioinformatics Group of UC Santa Cruz. UCSC Genome Bioinformatics 2015 [Available from: http:// genome.uscs.edu. Brandes JC, Carraway H, Herman JG. Optimal primer design using the novel primer design program: MSPprimer provides accurate methylation analysis of the ATM promoter. Oncogene. 2007;26(42):6229-37. Untergrasser A CI, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG Primer3web 2012 [Available from: http://primer3.ut.ee/. Guidry K, Vasudevaraja V, Labbe K, Mohamed H, Serrano J, Guidry BW, et al. DNA Methylation Profiling Identifies Subgroups of Lung Adenocarcinoma with Distinct Immune Cell Composition, DNA Methylation Age, and Clinical Outcome. Clin Cancer Res. 2022;28(17):3824-35. Gong Z, Li Q, Shi J, Wei J, Li P, Chang CH, et al. Lung fibroblasts facilitate pre-metastatic niche formation by remodeling the local immune microenvironment. Immunity. 2022;55(8):1483-500 e9. Kong J, Tian H, Zhang F, Zhang Z, Li J, Liu X, et al. Extracellular vesicles of carcinoma-associated fibroblasts creates a pre-metastatic niche in the lung through activating fibroblasts. Mol Cancer. 2019;18(1):175. Wrangle J, Machida EO, Danilova L, Hulbert A, Franco N, Zhang W, et al. Functional identification of cancer-specific methylation of CDO1, HOXA9, and TAC1 for the diagnosis of lung cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2014;20(7):1856-64. Licchesi JD, Herman JG. Methylation-specific PCR. Methods in molecular biology. 2009;507:305-23. Belinsky SA, Klinge DM, Dekker JD, Smith MW, Bocklage TJ, Gilliland FD, et al. Gene promoter methylation in plasma and sputum increases with lung cancer risk. Clinical cancer research : an official journal of the American Association for Cancer Research. 2005;11(18):6505-11. Scher MB, Elbaum MB, Mogilevkin Y, Hilbert DW, Mydlo JH, Sidi AA, et al. Detecting DNA methylation of the BCL2, CDKN2A and NID2 genes in urine using a nested methylation specific polymerase chain reaction assay to predict bladder cancer. The Journal of urology. 2012;188(6):2101-7. Keeley B, Stark A, Pisanic TR, 2nd, Kwak R, Zhang Y, Wrangle J, et al. Extraction and processing of circulating DNA from large sample volumes using methylation on beads for the detection of rare epigenetic events. Clinica chimica acta; international journal of clinical chemistry. 2013;425:169-75. Additional Declarations No competing interests reported. Supplementary Files SupplementalFigureS1.jpg Supplemental Figure S1. ROC curves of univariable and multivariable logistic regression analyses for six combinations. (A-F) ROC curves of univariable logistic regression of gene methylation across various samples and combinations. (G, H) ROC curves of multivariable logistic regression analyses of gene methylation for six combinations. SupplementalTableS1S2S3.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Clinical Epigenetics → Version 1 posted Editorial decision: Accepted 12 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Editor assigned by journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 17 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5808504","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446543190,"identity":"7b415cd9-25b4-4384-ad3f-505975672a84","order_by":0,"name":"Yunyi Li","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yunyi","middleName":"","lastName":"Li","suffix":""},{"id":446543191,"identity":"084ccf17-ded0-46c9-b213-8bf684013c64","order_by":1,"name":"Zhihui Yang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Yang","suffix":""},{"id":446543193,"identity":"8969e8b7-63bb-4748-a761-846a63119010","order_by":2,"name":"Fang Wu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wu","suffix":""},{"id":446543197,"identity":"b9bf21b0-2330-484d-ba83-09d149b73d77","order_by":3,"name":"Qingchun Liang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qingchun","middleName":"","lastName":"Liang","suffix":""},{"id":446543199,"identity":"3e78ebbd-cdc1-4279-ae7c-999ec4b4e193","order_by":4,"name":"James G. 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(A-F) \u003c/strong\u003eROC curves of six model developed by clinical and methylation indicators to predict recurrence for all patients. The N2LN-Clinic model achieved the highest AUC of 0.873 among the six models.\u003cstrong\u003e (G, H) \u003c/strong\u003eROC curves of N2LN-Clinic model in IA2-A3 stage patients and IB stage patients. The N2LN-Clinic model in IA2-A3 stage patients maintained a high AUC value (0.879) even though it was lower than the AUC value of N2LN-Clinic model in IB stage patients (0.899).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/6e76391325b3cd9e80d0c541.jpg"},{"id":82045730,"identity":"4155985b-d4da-4051-b50c-263aea2a505a","added_by":"auto","created_at":"2025-05-06 09:38:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":503936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance of the predictive model. (A) \u003c/strong\u003eCalibration curve and Hosmer-Lemeshow test of the N2LN-Clinic model. The p-value of 0.74 indicates that the model closely aligns with the observed outcomes and fits well.\u003cstrong\u003e (B) \u003c/strong\u003eDecision curve analysis (DCA) curve of the N2LN-Clinic model. The net clinical benefit is positive when the threshold probability ranges between 5% and 90%.\u003cstrong\u003e (C) \u003c/strong\u003eNomogram of the N2LN-Clinic model. Methylation of the CDO1 and TAC1 genes were protective factors against tumor recurrence. Methylation of the p16, CDH13, and APC genes, along with the presence of pleural invasion and lower tumor differentiation, were risk factors for recurrence.\u003cstrong\u003e (D) \u003c/strong\u003eForest plot of the odds ratio (OR) of the N2LN-Clinic model. Methylation of CDO1 and TAC1 decreases the risk of recurrence. Methylation of p16, CDH13, and APC, as well as adverse clinical features like pleural invasion and lower differentiation grade, significantly increases the risk.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/0a8a52e80bd35025d494558c.jpg"},{"id":82045749,"identity":"e560d0df-3363-4135-beea-39a3c6dffe21","added_by":"auto","created_at":"2025-05-06 09:38:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":565644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative risk curves. (A-G) \u003c/strong\u003eCumulative risk curves for patients based on the seven variables included in the model. Positive CDO1 and TAC1 had a negative contribution to the risk of recurrence. Positive p16 showed a relatively more significant contribution to recurrence risk. Poorly differentiated pathological types contributed significantly to the risk of recurrence.\u003cstrong\u003e (H) \u003c/strong\u003eCumulative risk curve for the patients of the high-risk group and the low-risk group. The recurrence risk in the high-risk group was significantly higher than in the low-risk group.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/55a5e3ab3c4ae0c25fdadae4.jpg"},{"id":84242749,"identity":"958337c3-ef8b-460d-9554-46265a0684fb","added_by":"auto","created_at":"2025-06-09 16:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3339758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/0573b845-4584-4141-a406-2b02885ebb86.pdf"},{"id":82045732,"identity":"185dfb25-cbcb-4b25-9a1f-537ad850517e","added_by":"auto","created_at":"2025-05-06 09:38:15","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":509408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure S1. ROC curves of univariable and multivariable logistic regression analyses for six combinations. (A-F) \u003c/strong\u003eROC curves of univariable logistic regression of gene methylation across various samples and combinations.\u003cstrong\u003e (G, H) \u003c/strong\u003eROC curves of multivariable logistic regression analyses of gene methylation for six combinations.\u003c/p\u003e","description":"","filename":"SupplementalFigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/8cc064b341aead642efdcc27.jpg"},{"id":82048380,"identity":"3a2b28a4-3630-4d8d-ba6c-8e034ab820ca","added_by":"auto","created_at":"2025-05-06 09:46:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27430,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1S2S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5808504/v1/4e170abd82f7fa0ce923f30d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of DNA methylation on the recurrence risk of stage I non-small cell lung cancer with EGFR mutations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent studies have demonstrated that patients with stage IB-IIIA non-small cell lung cancer (NSCLC) harboring EGFR mutations (EGFRm) can significantly benefit from adjuvant therapy with EGFR-TKIs (Tyrosine Kinase Inhibitors) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Notably, the use of third-generation EGFR-TKIs has been associated with a marked reduction in post-surgical recurrence, alongside notable improvements in both Progression-Free Survival (PFS) and Overall Survival (OS) for this patient group (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consequently, many clinical guidelines now advocate for incorporating EGFR-TKIs in the adjuvant treatment regimen for NSCLC patients exhibiting EGFR mutations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nevertheless, there remains controversy in clinical practice about using EGFR-TKI adjuvant therapy for patients with stage IB EGFRm NSCLC, as many with stage IB disease can achieve excellent prognosis with surgery alone (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The potential for overtreatment with adjuvant targeted therapy in stage IB EGFRm NSCLC cases has garnered increasing clinical attention. Current practice leans towards reserving postoperative EGFR-TKI therapy for stage IB EGFRm NSCLC patients exhibiting specific \u0026ldquo;high-risk factors.\u0026rdquo; These mainly include visceral pleural invasion, vascular invasion, lymphatic invasion, the presence of lung neuroendocrine tumors, and micropapillary histological patterns (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It merits emphasis that these high-risk factors currently in use originated from the conventional chemotherapy era, aimed at identifying patients at increased risk of postoperative recurrence in stage IB NSCLC. These factors are not specifically tailored to EGFR-mutant NSCLC. Consequently, the relevance and efficacy of applying these risk factors to direct EGFR-TKI adjuvant therapy in the modern therapeutic context remains an ongoing clinical debate.\u003c/p\u003e \u003cp\u003eContemporary investigations have focused on delineating specific risk factors associated with tumor recurrence in stage I EGFRm NSCLC. These studies highlight variables including advanced stage within the category, presence of micropapillary subtype, pleural invasion, high-grade histological subtypes, and lymphovascular invasion as critical predictors of recurrence (\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A novel study has investigated the potential of a 14-gene expression-based model to forecast postoperative recurrence in patients with stage I EGFRm NSCLC (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, the role of minimal residual disease (MRD) as a predictive biomarker is gaining traction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Recent evidence suggests a stark contrast in recurrence rates between postoperative MRD-negative and MRD-positive patients, with the latter showing notable benefits from EGFR-TKI adjuvant therapy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Despite these promising developments, the quest for effective and precise molecular biomarkers for adjuvant therapy guidance in early-stage EGFRm NSCLC is still hindered by challenges such as tumor heterogeneity, the sensitivity of detection methodologies, and an evolving comprehension of the disease, particularly in stage I NSCLC.\u003c/p\u003e \u003cp\u003eDNA methylation, a key epigenetic mechanism, regulates gene expression reversibly without altering the genetic code, thereby modulating cellular functions (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Previous studies have identified that distinctive DNA methylation patterns in clinically negative lymph nodes and bronchial margins can predict postoperative recurrence in stage I NSCLC (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The assessment of DNA methylation in circulating tumor DNA (ctDNA) from blood samples has shown efficacy in monitoring NSCLC post-surgery (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Expanding upon these insights, our study zeroes in on stage I EGFRm NSCLC, employing modified MOB-qMSP method to examine promotor methylation of 8 genes (CDO1, TAC1, p16, SOX17, HOXA7, RASSF1A, CDH13, and APC) in both tumor tissues and pathologically negative lymph nodes. The goal is to evaluate the predictive value of DNA methylation markers in predicting post-surgical recurrence in stage IB EGFRm NSCLC and to investigate their potential applicability in stage IA cases, hoping to provide precise guidance for postoperative EGFR-TKI adjuvant therapy in stage I EGFRm NSCLC patients.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe patients enrolled in this retrospective cohort analysis were conducted at the Second Xiangya Hospital of Central South University from January 2011 to December 2020. Eligible patients were aged 18 years or older and had a histopathologically confirmed diagnosis of stage IA\u0026ndash;IB NSCLC (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). All patients harbored sensitizing EGFR mutations (exon 19 deletion or L858R) and underwent curative surgical resection. Clinicopathological data were available for all participants. Patients without disease progression had a minimum postoperative follow-up of 5 years, whereas those who experienced disease progression were followed until the time of progression within the 5 years. The following patients were excluded: those who underwent incomplete surgical resection, had a previous malignancy within the past 5 years, or had received prior systemic antitumor therapies, including chemotherapy, radiotherapy, or targeted therapy.\u003c/p\u003e \u003cp\u003eAll patients were staged according to the revised eighth edition of TNM guidelines classification criteria (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), including the histologic status of the tumor and regional lymph nodes (N1LNs) and mediastinal lymph nodes (N2LNs) that were resected en bloc from levels II, IV, VII, VIII, and IX on the right side and levels IV, V, VI, VII, VIII, and IX on the left side. Tumor differentiation was evaluated by experienced pathologists in the Department of Pathology at the Second Xiangya Hospital of Central South University, based on hematoxylin and eosin (H\u0026amp;E) staining assessed under light microscopy. All patients had negative surgical margins, with no macroscopic or microscopic residual disease. Clinical characteristics\u0026mdash;including demographics, smoking history, pulmonary function tests, and comorbidities\u0026mdash;were collected from clinical records. Tumor size was obtained from pathological reports.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePreparation of tumor and lymph-node specimens\u003c/h3\u003e\n\u003cp\u003eThe distribution of tumors and lymph nodes and the number of samples per patient did not significantly differ between the recurrence and non-recurrence groups. Formalin-fixed, paraffin-embedded (FFPE) tumor and lymph node specimens were collected and assigned study-specific coded identifiers to maintain blinding of investigators throughout the study. All tumor specimens were histologically confirmed as stage I NSCLC and contained more than 50% tumor cells. All lymph node specimens were histologically negative for metastatic involvement.\u003c/p\u003e\n\u003ch3\u003eGene mutation determination\u003c/h3\u003e\n\u003cp\u003eThe DNA extraction was performed using the QIAamp DNA FFPE Tissue Kit (Qiagen, USA), following the manufacturer\u0026rsquo;s instructions. Detection of EGFR mutations within exons 18 to 21 (codons 688\u0026ndash;875) was carried out using Sanger sequencing, as previously described (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDNA methylation analysis using modified MOB-qMSP\u003c/h3\u003e\n\u003cp\u003eDNA extraction from FFPE samples was performed using the MOB method, which enables DNA extraction and bisulfite conversion in a single tube via silica supermagnetic beads, as previously described (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This approach yields a 1.5- to 5-fold improvement in extraction efficiency compared to conventional techniques (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). We optimized the protocol for tissue samples based on the previously established plasma protocol (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), involving tissue incubation with proteinase K and Buffer AL at 55\u0026deg;C overnight. Subsequent steps followed the standard MOB procedure.\u003c/p\u003e \u003cp\u003eGenomic sequences for the target genes and 1,000 bases upstream were obtained from the UCSC Genome Browser website (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Primers and hybridization probes for methylation analysis were designed using Primer3 (v4.0.0) based on these sequences (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), with all primer and probe sequences listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Quantitative real-time methylation-specific PCR (qMSP) was performed and normalized to a β-actin (ACTB) control assay. Each reaction consisted of a 25 \u0026micro;L PCR mixture containing 2 \u0026micro;L of bisulfite-converted DNA, primers, probe, fluorescein reference dye, dNTPs, and Platinum Taq\u0026reg; DNA Polymerase, as previously described (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Amplification was carried out using an ABI StepOnePlus Real-Time PCR system, with all samples analyzed in triplicate under the following thermocycling conditions: 95\u0026deg;C for 5 minutes; 40 cycles of 95\u0026deg;C for 15 seconds, 60\u0026deg;C for 1 minute, and 72\u0026deg;C for 30 minutes. The 2\u003csup\u003e\u0026minus;ΔCT\u003c/sup\u003e was calculated for each methylation detection replicate relative to the mean Ct for β-Actin (ACTB). For undetected replicates (N.D.), a Ct value of 100 was assigned to yield a near-zero 2\u003csup\u003e\u0026minus;ΔCT\u003c/sup\u003e value. The mean 2\u003csup\u003e\u0026minus;ΔCT\u003c/sup\u003e value was calculated using the formula described in previous study (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDemographic, methylation, and genotype variables were summarized by case-control status with percentages for categorical variables and means and standard deviations for continuous variables. Differences in demographic variables between cases and controls were assessed with Fisher\u0026rsquo;s exact test for categorical variables and the Wilcoxon rank sum test for continuous variables. The Pack-years of cigarette smoking were defined as the average number of packs smoked per day multiplied by the number of years of smoking. Former smokers were defined as those individuals who had quit smoking one year or more at the time of surgery.\u003c/p\u003e \u003cp\u003eUsing R Project (version 4.3.2) for data analysis and modeling, we employed stepwise regression to select variables and logistic regression to establish the predictive model. Stepwise regression was performed using the \u0026ldquo;MASS\u0026rdquo; package in the R Project. Brier score calculation, calibration curve, and nomogram were conducted using the \u0026ldquo;rms\u0026rdquo; package. ROC analysis was carried out using the \u0026ldquo;pROC\u0026rdquo; package. ROC curves were plotted using the \u0026ldquo;modEvA\u0026rdquo; package. Cross-validation was performed using the \u0026ldquo;caret\u0026rdquo; package. The Hosmer-Lemeshow test was conducted using the \u0026ldquo;ResourceSelection\u0026rdquo; package. Decision curve analysis (DCA) was performed using the \u0026ldquo;rmda\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristic\u003c/h2\u003e \u003cp\u003eA total of 242 patients with IA2-IB EGFRm NSCLC who underwent curative surgical resection were included in the study. Among these patients, 86 constituted the recurrence (Rec) group, while 156 formed the non-recurrence (Non-Rec) group. Baseline characteristics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no statistically significant differences between the two groups regarding gender, age, smoking history, tumor recurrence location, tumor size, TNM stage, or the presence of chronic obstructive pulmonary disease (COPD). In contrast, significant differences were observed in tumor size, invasion, and differentiation between the Rec and Non-Rec groups.\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\u003eComparison of recurrence and non-recurrence groups using univariate and multivariate analyses of clinical predictors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Rec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;242)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.44\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.56 (0.26\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55 (0.17\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.52 (0.12\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194 (80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45 (0.18\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37 (0.10\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.47 (0.62\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.77 (0.62\u0026ndash;5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.36\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.91 (0.29\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.22\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.33 (0.30\u0026ndash;5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68 (0.29\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.73 (0.25\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.54\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.41 (0.58\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInvasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121 (77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.14 (1.00-9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.44 (0.68\u0026ndash;8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3.15 (1.73\u0026ndash;5.76)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e7.38 (1.68\u0026ndash;32.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNM stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.44\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.53 (0.14\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21 (0.62\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28 (0.04\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifferentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh_differ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMod_differ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.31 (1.08\u0026ndash;4.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.57 (1.13\u0026ndash;5.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor_differ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e21.60 (7.07\u0026ndash;65.97)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e29.50 (8.55-101.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\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\u003e\u003cb\u003eFPT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211 (87.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37 (0.63\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.04 (0.35\u0026ndash;3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFollow-up results\u003c/h3\u003e\n\u003cp\u003eThe median follow-up duration was 56.6 months, with the last assessment conducted on December 31, 2022. Among the patients in the Rec group, the median time to recurrence was 22.5 months. The most common sites of tumor recurrence were the lungs and local regions (34 cases, 43.6%), followed by other distant or multiple metastases (25 cases, 32.1%), brain metastases (9 cases, 11.5%), bone metastases (8 cases, 10.3%), and liver metastases (2 cases, 2.6%).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk of cancer recurrence according to clinical predictors\u003c/h2\u003e \u003cp\u003eTo identify clinical factors associated with tumor recurrence, we conducted univariate and multivariate logistic regression analyses on nine clinical variables: age, gender, smoking status, tumor location, invasion status, tumor size, TNM stage, tumor differentiation, and pulmonary function tests (PFT). In the univariate analysis, \u0026lsquo;invasion\u0026rsquo; and \u0026lsquo;differentiation\u0026rsquo; emerged as significant predictors of tumor recurrence. These associations remained robust in the multivariate analysis, confirming that both \u0026lsquo;invasion\u0026rsquo; and \u0026lsquo;differentiation\u0026rsquo; are independent risk factors for tumor recurrence (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene methylation patterns across sample types\u003c/h2\u003e \u003cp\u003eWe investigated the promoter methylation status of eight genes across various sample types to determine their association with tumor recurrence. Significant differences in methylation rates were observed between the Rec and Non-Rec groups. In tumor tissues, p16, CDH13, and APC methylation rates showed statistically significant in the Rec group compared to the Non-Rec group. In N1LNs, \u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003eTAC1\u003c/b\u003e, and \u003cb\u003ep16\u003c/b\u003e showed statistically significant methylation rates in the Rec group compared to the Non-Rec group. Similarly, in N2LNs, significant differences were found in the methylation of \u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003ep16\u003c/b\u003e, and \u003cb\u003eCDH13\u003c/b\u003e between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential gene methylation patterns across sample types.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eN1LNs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eN2LNs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Rec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-Rec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNon-Rec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e41 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e44 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123 (78.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e115 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e42 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e32 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e30 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122 (78.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e124 (79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e56 (65.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOX17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e70 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e37 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e86 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e149 (95.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69 (80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e150 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e55 (64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRASSF1A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55 (64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCDH13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54 (62.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e111 (71.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e36 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e45 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e50 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e51 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHOXA7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79 (91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e144 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e78 (90.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8 (9.3%)\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 next analyzing genes that were methylation-positive in both tumor tissues and N1LNs, \u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003eTAC1\u003c/b\u003e, and \u003cb\u003ep16\u003c/b\u003e exhibited significantly higher methylation rates in the Rec group than in the Non-Rec group. For genes methylation-positive in both tumor tissues and N2LNs, significant differences were observed in \u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003ep16\u003c/b\u003e, and \u003cb\u003eCDH13\u003c/b\u003e. Notably, among genes that were methylation-positive across tumor tissues, N1LNs and N2LNs, \u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003eTAC1\u003c/b\u003e, and \u003cb\u003ep16\u003c/b\u003e consistently demonstrated statistically significant differences (Supplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results suggest that the methylation status of specific genes\u0026mdash;particularly \u003cb\u003eCDO1, p16, TAC1\u003c/b\u003e, and \u003cb\u003eCDH13\u003c/b\u003e\u0026mdash;in tumor tissues and lymph nodes may be valuable biomarkers for predicting tumor recurrence in patients with EGFRm NSCLC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the detection combination of gene methylation for predicting cancer recurrence\u003c/h2\u003e \u003cp\u003eWe further explored the predictive performance of different gene methylation combinations for tumor recurrence and sought to determine the optimal combination. Univariate logistic regression analyses were conducted on all investigated indicators to identify statistically significant differences, calculating odds ratios (OR) and areas under the receiver operating characteristic curve (AUC). The results indicated that single-gene methylation detection was ineffective in predicting tumor recurrence in tumor tissues, N1LNs, N2LNs, and various combinations of these samples. This was evidenced by AUC values below 0.67, even for indicators with statistically significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Further multivariate regression analysis using stepwise selection identified six model formulas derived from various gene-sample combinations, all demonstrating significantly improved AUC values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, the methylation detection of a five-gene panel (\u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003eTAC1\u003c/b\u003e, \u003cb\u003ep16\u003c/b\u003e, \u003cb\u003eCDH13\u003c/b\u003e, and \u003cb\u003eAPC\u003c/b\u003e) in N2LNs yielded the optimal predictive performance, achieving an AUC greater than 0.82 (Supplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\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\u003eUnivariable and stepwise analysis of recurrence according to gene methylation predictors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eStepwise\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOX17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04 (1.07\u0026ndash;3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33 (1.34\u0026ndash;4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.71 (1.49\u0026ndash;4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.97 (1.15\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.90 (1.07\u0026ndash;3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.21\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25 (0.13\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN1LN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.23\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39 (0.22\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.22\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.27\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.24 (2.08\u0026ndash;13.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.37 (2.41\u0026ndash;16.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN2LN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34 (0.20\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33 (0.17\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.27\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55 (0.27\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.09 (5.58\u0026ndash;35.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.65 (6.49\u0026ndash;48.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.43 (1.97\u0026ndash;5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.30 (1.73\u0026ndash;6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74 (1.02\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.01 (1.04\u0026ndash;3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+N1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.24\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38 (0.21\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.26\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.34\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.87 (1.91\u0026ndash;12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.20 (2.33\u0026ndash;16.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+N2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.18\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34 (0.18\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.29\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.68 (3.77\u0026ndash;24.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.19 (4.37\u0026ndash;33.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.84 (1.64\u0026ndash;4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.46 (1.34\u0026ndash;4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61 (0.32\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+N1\u0026thinsp;+\u0026thinsp;N2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.20\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34 (0.18\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.23\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56 (0.30\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.71 (2.14\u0026ndash;15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.62 (2.70-21.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel performance and validation\u003c/h2\u003e \u003cp\u003eA multivariate regression model incorporating clinical and methylation indicators was developed to evaluate predictive performance using AUC values and the Brier Score. The results indicated that the five-gene methylation detection in N2LNs, combined with clinical predictors, provided the optimal predictive efficacy for tumor recurrence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Stepwise regression identified six candidate models. The Brier Score was calculated for each model, with the N2LN-Clinic model exhibiting the lowest (best) score of 0.136 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Receiver operating characteristic (ROC) analysis demonstrated that the N2LN-Clinic model achieved the highest AUC of 0.873 among the six models (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-F). Internal cross-validation was performed using four methods: leave-one-out cross-validation (LOOCV), leave-group-out cross-validation (LGOCV), 10-fold cross-validation, and bootstrap resampling (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results consistently showed that the N2LN-Clinic model had the highest cross-validation accuracy across all methods. Based on comparisons of the Brier Score, AUC, and cross-validation accuracy, the N2LN-Clinic model was selected as the preferred predictive model. A calibration curve was plotted for the N2LN-Clinic model, and the Hosmer-Lemeshow test was conducted, yielding a p-value of 0.74 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This indicates that the model closely aligns with the observed outcomes and has a good fit, demonstrating reliable predictive capability for tumor recurrence.\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\u003ePerformance and validation of six models identified by stepwise regression.\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=\"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=\"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\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrier\u003c/p\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLGOCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 fold CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOOCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBootstrap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003cp\u003e-Clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep16\u0026thinsp;+\u0026thinsp;CDH13\u0026thinsp;+\u0026thinsp;APC\u0026thinsp;+\u0026thinsp;HOXA7\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.79\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1LN\u003c/p\u003e \u003cp\u003e-Clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDO1\u0026thinsp;+\u0026thinsp;TAC1\u0026thinsp;+\u0026thinsp;p16\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Tumor_location\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN2LN\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e-Clinic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCDO1\u0026thinsp;+\u0026thinsp;TAC1\u0026thinsp;+\u0026thinsp;p16\u0026thinsp;+\u0026thinsp;CDH13\u0026thinsp;+\u0026thinsp;APC\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.83\u0026ndash;0.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.7888\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.7846\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.7727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.7849\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u0026thinsp;+\u0026thinsp;N1\u003c/p\u003e \u003cp\u003e-Clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDO1\u0026thinsp;+\u0026thinsp;TAC1\u0026thinsp;+\u0026thinsp;p16\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.75\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u0026thinsp;+\u0026thinsp;N2\u003c/p\u003e \u003cp\u003e-Clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDO1\u0026thinsp;+\u0026thinsp;TAC1\u0026thinsp;+\u0026thinsp;p16\u0026thinsp;+\u0026thinsp;CDH13\u0026thinsp;+\u0026thinsp;APC\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.80\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u0026thinsp;+\u0026thinsp;N1\u0026thinsp;+\u0026thinsp;N2\u003c/p\u003e \u003cp\u003e-Clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDO1\u0026thinsp;+\u0026thinsp;TAC1\u0026thinsp;+\u0026thinsp;p16\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Invasion\u0026thinsp;+\u0026thinsp;Pathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7517\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 \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors and predictive nomogram\u003c/h2\u003e \u003cp\u003eBased on the N2LN model, we developed a predictive nomogram that incorporates seven key variables: five gene methylation markers (\u003cb\u003eCDO1\u003c/b\u003e, \u003cb\u003eTAC1\u003c/b\u003e, \u003cb\u003ep16\u003c/b\u003e, \u003cb\u003eCDH13\u003c/b\u003e, and \u003cb\u003eAPC\u003c/b\u003e) and two clinical factors (invasion and differentiation) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In this model, methylation of the \u003cb\u003eCDO1\u003c/b\u003e and \u003cb\u003eTAC1\u003c/b\u003e genes emerged as protective factors against tumor recurrence. In contrast, methylation of the \u003cb\u003ep16\u003c/b\u003e, \u003cb\u003eCDH13\u003c/b\u003e, and \u003cb\u003eAPC\u003c/b\u003e genes, along with the presence of pleural invasion and lower tumor differentiation, were identified as significant risk factors for recurrence. To illustrate the impact of these variables, a forest plot of the odds ratio (OR) was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This visualization highlights that methylation of \u003cb\u003eCDO1\u003c/b\u003e and \u003cb\u003eTAC1\u003c/b\u003e is associated with a decreased risk of recurrence. In contrast, methylation of \u003cb\u003ep16\u003c/b\u003e, \u003cb\u003eCDH13\u003c/b\u003e, and \u003cb\u003eAPC\u003c/b\u003e, as well as adverse clinical features like pleural invasion and lower differentiation grade, significantly increases the risk. The model emphasizes the multifactorial nature of recurrence risk, integrating genetic and clinical predictors to enhance predictive accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCumulative risk analysis and decision curve analysis\u003c/h2\u003e \u003cp\u003eWe subsequently calculated cumulative risk curves for patients based on the seven variables included in the model, illustrating the contribution of each variable to the risk of patient recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-G). It was observed that different variables contributed to the risk of recurrence to varying degrees. Among them, positive results for CDO1 and TAC1 had a negative contribution to the risk of recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Among the variables that contributed to recurrence risk, positive p16 showed a relatively more significant impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Regarding clinical variables, poorly differentiated pathological types contributed significantly to the risk of recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). We calculated the total score for each patient based on the nomogram and divided the patients into high-risk and low-risk groups according to the median total score of 123 points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The total score is calculated by summing the individual scores for each predictive variable, as indicated on the \u0026ldquo;Total Points\u0026rdquo; axis. A score of 123 represents the median total score across all patients. Patients with scores above 123 were classified into the high-risk group, while those with scores below 123 were assigned to the low-risk group. By plotting the cumulative risk curves for both groups, it was evident that the recurrence risk in the high-risk group was significantly higher than in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). The decision curve analysis (DCA) of the nomogram indicated that when the threshold probability ranges between 5% and 90%, using this nomogram to predict patient recurrence offers a net clinical benefit (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePredictive value of the model in stage IA2-A3 EGFRm NSCLC patients\u003c/h2\u003e \u003cp\u003eWe further divided the dataset into two cohorts\u0026mdash;stage IA2-IA3 patients and stage IB patients\u0026mdash;to evaluate the model\u0026rsquo;s predictive performance in earlier-stage disease. The results indicated that while the model\u0026rsquo;s predictive efficacy slightly decreased in stage IA2-A3 patients, it maintained a high AUC value (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It suggests that the predictive model possesses significant prognostic value for tumor recurrence even in stage IA2-IA3 patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of predictive value of the model between stage IA and stage IB patients.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrier Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGOCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 fold CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOOCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBootstrap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrent understanding suggests that tumor recurrence is intricately linked to inherent genetic mutations within the tumor and a broader spectrum of factors, including the systemic immune response and epigenetic mechanisms (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Emerging evidence has shed light on forming a pre-metastatic niche, a critical precursor environment, which is intricately established before observable tumor metastasis (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). It highlights the need to look at tumor recurrence from a broader perspective, considering the overall regulatory processes in the body. In this study, using modified MOB-qMSP, we investigate the role of specific gene methylation in tumor tissues and pathologically negative lymph nodes, aiming to accurately identify high-risk patients for recurrence and guide postoperative adjuvant therapy. Our findings indicate that the specific gene methylation panel detected in N2 lymph nodes can effectively predict postoperative recurrence in stage IB EGFRm NSCLC patients and demonstrates excellent predictive performance in stage IA2-IA3 patients, highlighting this detection approach\u0026rsquo;s high specificity and stability.\u003c/p\u003e \u003cp\u003ePrevious studies demonstrated that the methylation of promoter regions in p16, CDH13, APC, and RASSF1A in patients with T1-2N0 NSCLC treated with curative intent is strongly associated with early recurrence, particularly when these molecular alterations are detected in both primary tumors and lymph nodes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Building on this research, our study expanded the gene detection panel and specifically targeted EGFRm NSCLC, a patient subgroup urgently needing postoperative recurrence biomarkers. Drawing from The Cancer Genome Atlas (TCGA) and previous studies (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), we also investigated four additional genes\u0026mdash;CDO1, TAC1, SOX17, and HOXA7\u0026mdash;known for their frequent methylation changes in NSCLC. Our findings confirmed previous results, demonstrating that patients with promoter methylation of p16, CDH13, and APC exhibited higher recurrence rates. However, unlike earlier studies, RASSF1A methylation did not retain its predictive value in our dataset. Moreover, we identified that a five-gene methylation panel\u0026mdash;comprising CDO1, TAC1, p16, CDH13, and APC\u0026mdash;detected in N2LN provided optimal predictive performance for postoperative recurrence. In our analysis, methylation status of individual genes yielded AUC values below 0.67 for predicting recurrence, highlighting that single-gene assays alone lack adequate sensitivity and specificity in an unstratified NSCLC cohort. This limitation likely stems from the marked heterogeneity inherent in non-small cell lung cancer, where methylation of a specific gene may be highly predictive in one tumor subtype yet negligible or irrelevant in another, thus constraining its general applicability. Furthermore, tumor recurrence involves complex interactions among multiple signaling pathways and diverse epigenetic alterations that cannot be adequately captured by single-marker approaches. In contrast, multi-gene methylation panels integrate signals from various pathways, enhancing both the robustness and predictive accuracy of recurrence assessments.\u003c/p\u003e \u003cp\u003eCompared to earlier detection strategies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), this panel requires more genes and does not require simultaneous tumor and lymph node testing. Instead, positive results in N2LN alone efficiently predict tumor recurrence, offering a streamlined yet highly effective approach to recurrence prediction.\u003c/p\u003e \u003cp\u003eOne possible explanation for this discrepancy could be the differences between the two DNA methylation detection methods\u0026mdash;nested MSP and MOB. Previous studies have utilized nested MSP to detect DNA methylation across various biological samples, including fresh frozen tumor (FFT) tissues, FFPE samples, sputum, plasma, and other fluids (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). However, the limitations of this method have been well-recognized. With its 1.5- to 6-fold increase in DNA extraction efficiency, MOB significantly reduces PCR inhibitor carryover and enhances analytical sensitivity by over 25-fold. This improvement enables the detection of a greater number of DNA methylation signatures (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Another potential factor could be our study\u0026rsquo;s relatively small patient cohort, which may have introduced bias in identifying methylation events. Nonetheless, the loss of predictive significance for RASSF1A further highlights the robust predictive value of p16, APC, and CDH13, reinforcing their importance from an alternative perspective.\u003c/p\u003e \u003cp\u003eOur results further demonstrated the critical role of detecting molecular biomarkers in pathologically negative lymph nodes for predicting early recurrence in NSCLC. As a systemic disease, lung cancer often exhibits detectable molecular alterations at the earliest stages of tumorigenesis or metastasis. The methylation changes identified in histologically negative lymph nodes strongly suggest the presence of undetectable micrometastases or pre-metastatic niches. Current recurrence risk assessments, which predominantly rely on immunohistochemical analyses, may overlook rare malignant cells within a normal tissue background. In contrast, the MOB-qMSP assay is both straightforward and highly sensitive, enabling precise detection of gene methylation signals. In recent years, circulating tumor DNA (ctDNA) analysis has become increasingly significant in predicting tumor recurrence. MOB-qMSP has demonstrated superior sensitivity and specificity in detecting ctDNA methylation. It is a promising tool for monitoring specific gene methylation in ctDNA to predict postoperative recurrence of early-stage NSCLC. This represents a critical area for future research.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, the relatively small sample size may have introduced biases. To minimize this, we included a large cohort of patients over an extended follow-up period. However, the sample size remained limited due to the low recurrence rates in stage IA-IB NSCLC after surgery and the need for long-term follow-up. We employed machine learning-based statistical and validation techniques to overcome this challenge, yielding more solid results. Second, due to the wide time range of the sample collection, discrepancies in clinical record collection, pathological diagnosis, and genetic mutation evaluation compared to current standards may have introduced potential bias. Third, we did not perform comprehensive large-panel gene mutation sequencing for all tumor samples, which may have omitted important co-occurring genetic mutations. Lastly, the genes in our study were selected based on prior research. It is possible that some genes with greater specificity were missed. This is the first study correlating a five-gene methylation panel in N2 lymph nodes with stage IA-IB EGFR-mutant NSCLC recurrence. Validation of these findings in larger, independent cohorts is crucial before this approach can be implemented in clinical practice.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that the methylation of five genes\u0026mdash;CDO1, TAC1, p16, CDH13, and APC\u0026mdash;in N2 lymph nodes represents a strong biomarker panel for predicting recurrence in stage IB EGFR-mutant NSCLC after curative resection. This approach also shows exceptional predictive accuracy for postoperative recurrence in stage IA2-IA3 EGFRm NSCLC, underscoring its significant potential for clinical application.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol of this study was approved by the Institutional Review Board of the Second Xiangya Hospital. Written informed consent was waived due to its retrospective nature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\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 Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Natural Science Foundation of Hunan Province (No. 2020SK53419 and 2021JJ30926), National Natural Science Foundation of China (No.82302449), the Scientific Research Launch Project for New Employees of the Second Xiangya Hospital of Central South University, and the Health Research Project of Hunan Provincial Health Commission (No: W20243115).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunyi Li: Data Analysis, Visualization, Writing-Original Draft. Zhihui Yang: Data Curation, Analysis. Fang Wu: Data Curation. Qingchun Liang: Data Curation and formal analysis. James G. Herman: Methodology. Malcolm V. Brock: Methodology. Wenliang Liu: Project administration. Fenglei Yu: Funding acquisition, Resources. Xue He: Data Curation, Visualization, Conceptualization, Review \u0026amp; Editing the Draft. Chen Chen: Conceptualization, Writing-Review \u0026amp; Editing the draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the multidisciplinary team (MDT) of thoracic oncology at the Second Xiangya Hospital of Central South University for caring for all the patients.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhong WZ, Wang Q, Mao WM, Xu ST, Wu L, Shen Y, et al. 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Nat Rev Genet. 2022;23(6):369-83.\u003c/li\u003e\n\u003cli\u003eBrock MV, Hooker CM, Ota-Machida E, Han Y, Guo M, Ames S, et al. DNA methylation markers and early recurrence in stage I lung cancer. N Engl J Med. 2008;358(11):1118-28.\u003c/li\u003e\n\u003cli\u003eGuo M, House MG, Hooker C, Han Y, Heath E, Gabrielson E, et al. Promoter hypermethylation of resected bronchial margins: a field defect of changes? Clin Cancer Res. 2004;10(15):5131-6.\u003c/li\u003e\n\u003cli\u003eChen K, Kang G, Zhang Z, Lizaso A, Beck S, Lyskjaer I, et al. Individualized dynamic methylation-based analysis of cell-free DNA in postoperative monitoring of lung cancer. BMC Med. 2023;21(1):255.\u003c/li\u003e\n\u003cli\u003eEttinger DS, Wood DE, Akerley W, Bazhenova LA, Borghaei H, Camidge DR, et al. Non-Small Cell Lung Cancer, Version 6.2015. Journal of the National Comprehensive Cancer Network : JNCCN. 2015;13(5):515-24.\u003c/li\u003e\n\u003cli\u003eLin MT, Mosier SL, Thiess M, Beierl KF, Debeljak M, Tseng LH, et al. Clinical validation of KRAS, BRAF, and EGFR mutation detection using next-generation sequencing. American journal of clinical pathology. 2014;141(6):856-66.\u003c/li\u003e\n\u003cli\u003eTsiatis AC, Norris-Kirby A, Rich RG, Hafez MJ, Gocke CD, Eshleman JR, et al. Comparison of Sanger sequencing, pyrosequencing, and melting curve analysis for the detection of KRAS mutations: diagnostic and clinical implications. J Mol Diagn. 2010;12(4):425-32.\u003c/li\u003e\n\u003cli\u003eBailey VJ, Zhang Y, Keeley BP, Yin C, Pelosky KL, Brock M, et al. Single-tube analysis of DNA methylation with silica superparamagnetic beads. Clinical chemistry. 2010;56(6):1022-5.\u003c/li\u003e\n\u003cli\u003eChen C, Huang X, Yin W, Peng M, Wu F, Wu X, et al. Ultrasensitive DNA hypermethylation detection using plasma for early detection of NSCLC: a study in Chinese patients with very small nodules. Clin Epigenetics. 2020;12(1):39.\u003c/li\u003e\n\u003cli\u003eKeeley B, Stark A, Pisanic TR, 2nd, Kwak R, Zhang Y, Wrangle J, et al. Extraction and processing of circulating DNA from large sample volumes using methylation on beads for the detection of rare epigenetic events. Clin Chim Acta. 2013;425(C):169-75.\u003c/li\u003e\n\u003cli\u003eHulbert A, Jusue Torres I, Stark A, Chen C, Rodgers K, Lee B, et al. Early Detection of Lung Cancer using DNA Promoter Hypermethylation in Plasma and Sputum. Clinical cancer research : an official journal of the American Association for Cancer Research. 2016.\u003c/li\u003e\n\u003cli\u003eYin W, Wang X, Li Y, Wang B, Song M, Hulbert A, et al. Promoter hypermethylation of cysteine dioxygenase type 1 in patients with non-small cell lung cancer. Oncol Lett. 2020;20(1):967-73.\u003c/li\u003e\n\u003cli\u003eGenome Bioinformatics Group of UC Santa Cruz. UCSC Genome Bioinformatics 2015 [Available from: http:// genome.uscs.edu.\u003c/li\u003e\n\u003cli\u003eBrandes JC, Carraway H, Herman JG. Optimal primer design using the novel primer design program: MSPprimer provides accurate methylation analysis of the ATM promoter. Oncogene. 2007;26(42):6229-37.\u003c/li\u003e\n\u003cli\u003eUntergrasser A CI, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG Primer3web 2012 [Available from: http://primer3.ut.ee/.\u003c/li\u003e\n\u003cli\u003eGuidry K, Vasudevaraja V, Labbe K, Mohamed H, Serrano J, Guidry BW, et al. DNA Methylation Profiling Identifies Subgroups of Lung Adenocarcinoma with Distinct Immune Cell Composition, DNA Methylation Age, and Clinical Outcome. Clin Cancer Res. 2022;28(17):3824-35.\u003c/li\u003e\n\u003cli\u003eGong Z, Li Q, Shi J, Wei J, Li P, Chang CH, et al. 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Gene promoter methylation in plasma and sputum increases with lung cancer risk. Clinical cancer research : an official journal of the American Association for Cancer Research. 2005;11(18):6505-11.\u003c/li\u003e\n\u003cli\u003eScher MB, Elbaum MB, Mogilevkin Y, Hilbert DW, Mydlo JH, Sidi AA, et al. Detecting DNA methylation of the BCL2, CDKN2A and NID2 genes in urine using a nested methylation specific polymerase chain reaction assay to predict bladder cancer. The Journal of urology. 2012;188(6):2101-7.\u003c/li\u003e\n\u003cli\u003eKeeley B, Stark A, Pisanic TR, 2nd, Kwak R, Zhang Y, Wrangle J, et al. Extraction and processing of circulating DNA from large sample volumes using methylation on beads for the detection of rare epigenetic events. Clinica chimica acta; international journal of clinical chemistry. 2013;425:169-75.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"non-small cell lung cancer, recurrence, DNA methylation, EGFR mutation, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-5808504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5808504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent studies have demonstrated that patients with stage IB-IIIA non-small cell lung cancer (NSCLC) harboring EGFR mutations (EGFRm) can significantly benefit from adjuvant therapy with EGFR-TKIs. Nevertheless, there is remains controversial in clinical practice about the use of EGFR-TKI adjuvant therapy for patients with stage IB EGFRm NSCLC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted at the Second Xiangya Hospital of Central South University. From January 2011 to December 2020, completely resected stage IA-IB NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. FFPE tumor and lymph node specimens were collected and subjected to the 8-gene methylation panel using modified MOB-qMSP approach. We employed stepwise regression to select variables and logistic regression to establish the predictive model. Cross-validation and decision curve analysis were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 242 patients with IA2-IB EGFRm NSCLC were included in the study. Among these patients, 86 constituted the recurrence (Rec) group, while 156 formed the non-recurrence (Non-Rec) group. Through stepwise logistic regression, seven crucial feature variables were identified, including five gene methylation variables (CDO1, TAC1, p16, CDH13, APC) and two clinical variables (tumor invasion and differentiation). The ROC analysis revealed an AUC of 0.873 for the model with these seven variables. Internal cross-validation (CV) demonstrated a model accuracy exceeding 77%. The nomogram and decision curve analysis (DCA) underscored the clinical utility of the model. We calculated the total score for each patient based on the nomogram and divided the patients into high-risk and low-risk groups. The cumulative risk curves for both groups evidenced that the recurrence risk in the high-risk group was significantly higher than in the low-risk group. We further divided the dataset into two cohorts\u0026mdash;stage IA2-IA3 patients and stage IB patients. The model maintained a high AUC value (0.879) in stage IA2-A3 patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study demonstrates that the methylation of five genes\u0026mdash;CDO1, TAC1, p16, CDH13, and APC\u0026mdash;in N2 lymph nodes represents a strong biomarker panel for predicting recurrence in stage IB EGFRm NSCLC after curative resection. This approach also shows exceptional predictive accuracy for postoperative recurrence in stage IA2-IA3 EGFRm NSCLC.\u003c/p\u003e","manuscriptTitle":"Impact of DNA methylation on the recurrence risk of stage I non-small cell lung cancer with EGFR mutations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 09:38:10","doi":"10.21203/rs.3.rs-5808504/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-05-12T06:38:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T15:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284432269376984615364829670475934462245","date":"2025-04-28T07:52:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136001375211006427996834724372743551172","date":"2025-04-22T20:05:47+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-22T16:01:29+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T12:03:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T05:50:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2025-04-17T08:01:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55598a43-324d-4112-af35-1c954fd751df","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:06:49+00:00","versionOfRecord":{"articleIdentity":"rs-5808504","link":"https://doi.org/10.1186/s13148-025-01899-9","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2025-06-02 15:57:36","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2025-05-06 09:38:10","video":"","vorDoi":"10.1186/s13148-025-01899-9","vorDoiUrl":"https://doi.org/10.1186/s13148-025-01899-9","workflowStages":[]},"version":"v1","identity":"rs-5808504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5808504","identity":"rs-5808504","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}