CCR7-Positive Circulating Tumor Cells as a Biomarker for Predicting Lung Cancer Risk

CCR7-Positive Circulating Tumor Cells as a Biomarker for Predicting Lung Cancer Risk | 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 CCR7-Positive Circulating Tumor Cells as a Biomarker for Predicting Lung Cancer Risk li wang, qingsong Liu, fangchun shu, Ying Huang, Qunyou Tan, Shasha Zhu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8093072/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective This study aims to investigate the clinical significance of CCR7-expressing circulating tumor cells (CTCs) in lung cancer,with a particular focus on their association with epithelial-mesenchymal transition (EMT) and disease progression. Methods Using CanPatrol™ nanomembrane filtration and RNA in situ hybridization, we analyzed CTCs from 213 lung cancer patients. These CTCs were classified into epithelial, mesenchymal, or hybrid phenotypes, and CCR7 expression was assessed. Clinical correlations were evaluated using Spearman and Pearson correlation analyses, as well as Cox regression. Results The baseline circulating tumor cell (CTC) positivity rate was 81.2%, with mesenchymal CTCs accounting for 44.1% of the positive cases. The overall CCR7 positivity in CTCs was 58.4%, which was significantly higher in adenocarcinoma (58.4%) compared to squamous cell carcinoma (45.9%, P = 0.038). Furthermore, CCR7 expression exhibited a strong correlation with disease progression (r = 0.264, P < 0.001) and served as an independent predictor of poor prognosis (HR = 2.6, 95% CI: 1.8–3.7, P 0.05). Conclusions This study investigates the correlation between CCR7 + CTCs and the progression of lung cancer. The findings demonstrate that CCR7 + CTCs are significantly linked to aggressive disease progression, particularly within adenocarcinoma subtypes. Data reveal a higher prevalence of CCR7 positivity among adenocarcinoma patients, suggesting a connection between its expression and disease advancement. CCR7 appears to activate signaling pathways that promote tumor migration, invasion, and proliferation, potentially through the process of epithelial-mesenchymal transition (EMT). Consequently, CCR7 + CTCs may serve as a valuable biomarker for lung cancer, particularly in cases of adenocarcinoma. The detection of CCR7 in CTCs could improve clinical assessments and facilitate early intervention. However, this study acknowledges certain limitations, including a lack of comprehensive consideration of drug effects on CTCs and a relatively small sample size, highlighting the necessity for further investigation. Lung cancer Circulating tumor cells (CTCs) Epithelial - mesenchymal transition (EMT) CCR7 Disease progression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Lung cancer is a prevalent malignancy characterized by a high incidence and mortality rate [ 1 ] . While surgical treatment can be curative in select cases, most patients present with metastatic disease at diagnosis, resulting in a poor prognosis [ 2 ] . Although gene-targeted therapy and immunotherapy have demonstrated promise in the treatment of lung cancer, challenges such as drug resistance and recurrence continue to pose significant obstacles [ 3 ] . CTCs are tumor cells that enter the bloodstream from the primary tumor or metastatic sites [ 4 ] .As a key regulator in the immune system, CCR7 orchestrates the movement of immune cells to lymphoid organs, thereby ensuring the proper functioning of immune responses [ 5 ] . In the context of cancer, CCR7 has emerged as a significant factor in tumor metastasis. Tumor cells that express CCR7 can sense and respond to chemokine signals within their microenvironment. When CCR7 on tumor cells binds to its ligands, such as CCL19 and CCL21, it triggers a cascade of intracellular signaling pathways. This activation enhances the migratory and invasive capabilities of tumor cells, enabling them to penetrate the extracellular matrix, enter the bloodstream, and potentially metastasize to distant organs [ 6 ] . The detection of CCR7 on CTCs holds substantial significance. CTCs are tumor cells that have entered the bloodstream and represent potential seeds for metastasis. The presence of CCR7 on CTCs may indicate a higher metastatic potential, providing clinicians with critical information to better assess the risk of disease progression in lung cancer patients [ 7 ] . The detection of CTCs has emerged as a pivotal tool in tumor staging, monitoring treatment efficacy, and evaluating prognosis [ 8 ] . It facilitates the tracking of variations in CTC numbers, gene mutations, and surface markers, which are essential for early treatment assessment, detection of tumor resistance, and prediction of disease progression, thereby enhancing personalized cancer therapy [ 9 ] . Clinical studies indicate that the integration of CTC epithelial-mesenchymal transition (EMT) detection with other diagnostic tests significantly improves the accuracy of staging and treatment evaluation [ 10 ] . The CanPatrol™ technology, which employs nanomembrane filtration and RNA in situ hybridization, has gained traction for CTC detection due to its high sensitivity and specificity, achieving an overall recovery rate exceeding 80% [ 11 ] . The CanPatrol™ second-generation CTC enrichment technology represents a sophisticated method for isolating and analyzing CTCs. The process begins with a filtration step that leverages the distinct physical properties of CTCs. By selecting an appropriate pore size for the filter, typically a 8 µm nano-membrane, CTCs can be effectively separated from a substantial volume of blood cells, capitalizing on their relatively larger size compared to normal blood cells. Following filtration, RNA in situ hybridization (RNA-ISH) is performed utilizing branched DNA signal amplification technology. This technique facilitates the detection of specific RNA sequences within the CTCs, which are essential for identifying various epithelial-mesenchymal transition (EMT) markers. Consequently, CTCs can be accurately classified into epithelial, epithelial-mesenchymal, and mesenchymal types. Furthermore, in advanced-stage cancer samples, this technology has been shown to detect mesenchymal-type circulating tumor cell masses (CTMs), which are aggregates of CTCs, highlighting its capability to capture larger CTC clusters in addition to individual CTCs. In the subsequent analysis, an advanced multi-parameter imaging system is employed. This system not only identifies cell surface markers but also conducts an in-depth analysis of the molecular features within the cells, including gene expression profiles and protein phosphorylation levels. This comprehensive analysis provides a deeper understanding of CTCs, offering rich and accurate information for clinical practice. Compared to traditional detection methods, such as the CellSearch system, which has a detection sensitivity of approximately 1 CTC per 5 mL of blood, CanPatrol™ technology achieves ultra-sensitive detection, with a sensitivity as low as 0.1 CTCs per 5 mL of blood. This advancement facilitates earlier detection of CTCs in cancer patients, allowing for timely diagnosis. Furthermore, while most conventional technologies can only detect a limited number of CTC markers, CanPatrol™ technology can simultaneously analyze dozens of biological markers. It enables the examination of CTC characteristics from multiple levels, comprehensively reflecting the heterogeneity of tumor cells and providing robust support for personalized treatment planning [ 12 ] . Although the role of CCR7 in tumor invasion and metastasis has been studied in some tumors (such as breast cancer and bladder cancer), the detection of CTC using CCR7 as a molecular marker to guide postoperative drugs and follow-up treatment plans for lung cancer has not been reported. This study focuses on this and explores its potential value. 2. Materials and Methods 2.1. Inclusion and exclusion criteria The inclusion criteria for this study were as follows: (1) All patients were pathologically diagnosed with lung cancer; (2) Patients provided informed consent to participate in this clinical study; (3) Patients demonstrated good compliance and were able to fully cooperate with treatment and follow-up procedures; (4) Patients did not have any severe comorbidities or organ failure; (5) Patients had an expected survival period exceeding three months; (6) Patients did not have other types of tumors. The exclusion criteria for this study were as follows: (1) Pregnant or lactating women; (2) Patients who withdrew from the study prematurely; (3) Patients who were unable to cooperate or whose condition deteriorated during the study, necessitating alternative treatments. This study received approval from the ethics committees of Daping Hospital, Army Military Medical University (approval number: [(2024) Number 212]) and The First Affiliated Hospital of Chongqing Medical University (approval number: [2023 − 280]). It adhered to the principles outlined in the Declaration of Helsinki and complied with medical ethics guidelines. Consent for publication: This study did not include identifying images (such as patient facial photos, unique clinical imaging features that may reveal identity) or personal/clinical details that could compromise the anonymity of participants. Therefore, the ‘Consent for publication’ is Not Applicable. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 2.2. CTCs detection All blood samples were analyzed for CTCs using the CanPatrol™ method. The detailed procedure is outlined as follows: 1. A volume of 5 mL of peripheral blood was collected intravenously and stored in anticoagulant tubes containing ethylenediaminetetraacetic acid (EDTA). 2. A test tube containing cell lysis buffer was prepared, and blood from the collection tube was transferred to the test tube using a negative-pressure transfer device. The blood was then fully lysed at 4°C for approximately 30 minutes to facilitate the lysis of red blood cells. 3. Following the lysis of red blood cells, the sample was centrifuged to separate fragmented red blood cells from other components. 4. The resulting pellet was resuspended, and tumor cells were retained to the greatest extent using a 8 µm nano-membrane under negative pressure. 5. Multiple ribonucleic acid (RNA) in situ hybridizations were performed using various probes specific to tumor cells for comprehensive analysis. 6. Fluorescence signals were identified, and CTCs were classified using an automated fluorescence microscope and computer system. CCR7 antibodies were sourced from Santa Cruz Biotechnology (USA) (as shown in Fig. 1 ). The CCR7 antibody was purchased from Santa Cruz Biotechnology, and its specificity and effectiveness were verified by Western Blot and immunohistochemical verification experiments [ 13 ] . In the Western Blot experiment, specific protein extraction, isolation, and antibody incubation processes were carried out. Clear and specific bands were observed, which were consistent with the expected molecular weight of the CCR7 protein. In the immunohistochemical experiments, tissue sections with known CCR7 expression showed typical positive staining features. Positive and negative controls were set up to ensure the accuracy of the test results. The negative control was treated with normal tissue extract, which was identical to the experimental sample except for the use of normal tissue extract. Additionally, the samples were also treated with isotype - independent antibodies as another negative control to exclude interference from non - specific binding. The positive control was a cell sample with known high expression of CCR7 [ 14 – 16 ] . Under the same experimental conditions, this positive control sample showed a strong positive signal, providing a reliable reference for the accuracy of the experimental results. 2.3 CTCs positive criteria and classification For baseline CTC statistics, the first test results of patients with multiple CTC tests were used. The second test results were recorded for 57 patients. A CTC count of 0 was classified as negative, and 1 or more as positive. Changes in CTCs were categorized as: decrease (1), unchanged (2), increase (3). Three distinct epithelial-mesenchymal expression profiles on CTCs were observed: epithelial CTCs (expressing CK8, CK18, CK19), mixed CTCs (co-expressing epithelial and mesenchymal markers), and mesenchymal CTCs (expressing vimentin and twist) ( Table 1 ) . Table 1 Capture probe sequences for the CD45, CK8, CK18, CK19, Ep - CAM, Vimentin, TWIST and CCR7 gene(Ep-CAM epithelial cell adhesion molecule, CK cytokeratins, CCR7 human telomerase reverse transcriptase.)CCR7 antibodies were sourced from Santa Cruz Biotechnology (USA) Gene Sequences (5’-3’) Ep-CAM TGGTGCTCGTTGATGAGTCAA GCCAGCTTTGAGCAAATGAAA AGCCCATCATTGTTCTGGCTC TCATCGCAGTCAGGATCTCC TTGTCTGTTCTTCTGACCTCA GAGCAGGTTATTTCAG CK8 CGTACCTTGTCTATGAAGGAA CTTGGTCTCCAGCATCTTGCC TAAGGTTGTTGATGTAGCCTG AGGAAGTTGATCTCGTCCAGA TGTGTCCGAGATCTGGTGACC TCAGCAATGATGCTG CK18 AGAAAGGACAGGACTCAGGC GAGTGGTGAAGCTCATGCTG TCAGGTCCTCGATGATCTTG CAATCTGCAGAACGATGCGG AAGTCATCAGCAGCAAGACG CTGCAGTCGTGTGATATTGG CK19 CTGTAGGAAGTCATGGCG AGAAGTCATCTGCAGCCA GACGCTGTTCCGTCTCAA ACTTGGTTCTTCTTCAGGT AGGCCAGCTCAGCGTACT GATTTCCTCGTGAACCAGG CTTCAGCATC Vimentin GAGCGAGAGTGGCAGAGG ACCTTTGTCGTTGGTTAGC TGGCATATTGCTGACGTAC GTCAGAGCGCCCCTAAGTT TTTAAAAGATTGCAGGGTGT TTTCGGGCCAATAGTGTCTTGGTAG TWIST ACAATGACATCTAGGTCTC CTTTCAGTGGCTGATTGGC ACTTACCATGGGTCCTCAA TAACTGGTAGAGGAAGTCG ATGCAACTGTTCAGACTTCT ATCCCTCTTGAGAATGCATGCAT CD45 TTACCATGGGTCCTCAATAA TCGCAATTCTTATCGACTCT GTCATGGAGACAGTCATGTG TATTTCCAGCTTCAACTTCCC ATCAATATAGCTGGCATTTTG TGCAGCAATGTATTTCCTACT TGAACCATCAGGCATC CCR7 GTCTTGAGCCTCTTGAAATA AGAGCACTGTGGCTAGTATC TTGTTGCGCTCAAAGTTGCT AAGAAAGGGTTGACGCAGCA ATGTCCTGAGTCATTGCATC GTTCATTCAGAGGACTCTTC AAACGATGGAGGGAGGGGTT GTTGCTCTCTTAACGAATCG 2.4. The standard staining results of CCR7 CCR7 status was categorized as follows: persistent negative = 1, positive transitioning to negative = 2, negative transitioning to positive = 3, and persistent positive = 4. Disease progression was classified as follows: disease improvement = 0, disease deterioration = 1. When calculating the CCR7 positivity rate, a CTC was considered CCR7 positive if there was any detectable CCR7 expression; conversely, it was deemed CCR7 negative if no CTCs exhibited CCR7 expression (Fig. 2 ). According to the flow cytometry results, tumor metastasis-related indicators and cell phenotypic characteristics were analyzed in patients at different stages of lung cancer (Stage I–IV)( (see Fig. 3 ) . The correlation between flow cytometry parameters and lung cancer progression demonstrated a trend toward increased heterogeneity. Specifically, scatter plots exhibited a more diffuse distribution, and peak plot subsets were more numerous, suggesting that tumor cells in stage IV exhibit the highest degree of phenotypic diversity and may possess enhanced resistance to therapeutic interventions. Regarding the epithelial-mesenchymal transition (EMT) process, an upregulation of Vimentin expression coupled with a downregulation of EP-CAM indicates a phenotypic shift from epithelial to mesenchymal characteristics, which is associated with increased migratory capacity of tumor cells. In terms of metastasis regulation, alterations in the co-expression patterns of CCR7 with Vimentin and EP-CAM suggest a potential mechanism by which tumor cells may be directed to metastasize to specific organs via chemokine signaling pathways. These findings provide molecular insights into the mechanisms underlying lung cancer metastasis. Furthermore, related biomarkers such as the proportion of CCR7 + Vimentin + cells hold promise as potential indicators for assessing disease progression and predicting clinical outcomes. 2.5. Evaluation of the therapeutic response The therapeutic effect on patients' tumors was evaluated using established criteria for assessing the efficacy of tumor chemotherapy. A complete response (CR) was defined as the complete disappearance of all target lesions, the absence of new lesions, and the normalization of tumor markers, sustained for at least four consecutive weeks. A partial response (PR) was characterized by a reduction of at least 30% in the sum of the diameters of target lesions, maintained over a minimum duration of four consecutive weeks. Stable disease (SD) referred to cases where the sum of the largest diameters of target lesions did not meet the criteria for PR nor progressed to progressive disease (PD). Progressive disease (PD) was identified by an increase of at least 20% in the sum of the maximum diameters of target lesions or the emergence of new lesions. 2.6. Statistical methods Statistical analyses were performed using SPSS version 25.0. Categorical variables are presented as percentages with chi-square values. Continuous variables are reported as mean ± standard deviation if normally distributed, or as medians with interquartile ranges otherwise. Pairwise comparisons used t-tests or Mann-Whitney U tests depending on data distribution. ANOVA was applied for comparisons of three or more groups. Spearman's rank correlations were used for non-normally distributed continuous and ordinal variables, and Pearson correlation for continuous ones. A p-value < 0.05 indicates statistical significance. 3. Results 3.1. Clinical characteristics of patients A total of 213 patients were included in this study, of which 115 were male (54.0%). The mean age of the patients was 57.613 ± 9.913 years old. The detailed distribution results of patient information such as tumor location, tumor classification, and tumor stage were shown in Table 2 . Table 2 Baseline Characteristics of Patients Project Sub project Patient Percentage (%)/Mean ± SD Gender Male 115 54.00% Female 88 41.31% missing 10 4.70% Age / 57.613 ± 9.913 Left 20 9.39% Cancer Location Right 28 12.84% Whole lung 0 0.00% Others 165 75.69% Tumor classification Squamous cell Adenocarcinoma 33 133 15.49% 62.44% Others 47 22.07% Stages (T) T1 120 56.33% T2 22 10.32% T3 27 12.67% T4 10 4.69% missing 34 15.96 3.2. Results of CTCs tests A total of 322 CTCs tests were conducted in this study. Of these, 156 patients underwent only one CTCs test, while the remaining 57 patients had two or more tests. Details are shown in Fig. 4 . At baseline, the positive rate of CTCs was 81.2%, with a positive rate of mesenchymal circulating tumor cells (mCTCs) at 44.1%. Additionally, among CTC-positive patients, the positive rate of CCR7 was recorded at 58.4% (see Table 3 ).In the case of 57 patients who had undergone CTCs testing for two or more occasions, the second positive rate of CTCs detected was 86.0%, the positive rate of mCTCs was 40.4%, and the positive rate of CCR7 in CTC - positive patients was 65.3% (Table 3 ). Table 3 Illustrates the association between CTC count and the general characteristics of the patients Factor Total(n = 322) CTC counts ≥ 13(n = 27) CTC counts 60 194 60.25 14 4.04 111 34.47 Gender 0.00 1.00 Male 187 58.07 16 4.97 168 52.17 Female 135 41.93 11 3.42 121 37.58 Histology 3.01 0.96 Squamous cell carcinoma 49 15.22 7 2.17 49 15.22 Adenocarcinoma of lung 187 58.07 20 6.21 187 58.07 Small cell carcinoma 7 2.17 0 0.00 7 2.17 Others 79 24.53 0 0.00 46 14.29 TNM Stage 2.49 0.87 I + II 237 73.60 20 6.21 214 66.46 III + IV 49 15.22 6 1.86 43 13.35 Unknown 3 0.93 1 0.31 32 9.94 Chemotherapy 103.69 0.00 CR 146 45.34 1 0.31 143 44.41 PD 39 12.11 20 6.21 19 5.90 PR 35 10.87 5 1.55 30 9.32 SD 80 24.84 1 0.31 79 24.53 Unknown 22 6.83 0 0.00 18 5.59 3.3. The relationship between CTCs number and clinical stage After removing samples with unknown clinical stages (34 cases were removed), the number of epithelial CTCs (eCTCs) was negatively correlated with clinical stage (P < 0.05). More details shown in Table 4 . Table 4 The results of the first and second CTCs tests with different clinical stages in lung patients. Clinical stages Positive rate The Positive rate of mesenchymal Median value Mean value SD Range The positive of CCR7 First test Ⅰ (n = 122) 92.6% 49.2% 4 5 5 0–25 55.8% Ⅱ (n = 22) 100% 63.6% 3.5 6.5 9 1–38 72.7% Ⅲ (n = 27) 96.3% 48.1% 4 6 6 0–20 61.5% Ⅳ (n = 10) 90.0% 60.0% 2 4 4 0–15 44.4% Unknown Stage (n = 32) 9.4% 3.1% 0 0.4 1 0–7 66.7% Total (n = 213) 81.2% 44.1% 3 5 5 0–38 58.4% Second test Ⅰ (n = 43) 86.0% 39.5% 2 3.5 4 0–20 64.9% Ⅱ (n = 5) 100.0% 60.0% 4 5 3 2–9 80.0% Ⅲ (n = 6) 83.3% 33.3% 2.5 4 4 0–10 40.0% Ⅳ (n = 2) 100.0% 50.0% 9 9 7 4–14 100.0% Unknown Stage (n = 1) 0.0% 0.0% 0 0 0 0 0.0% Total ( n = 57 ) 86.0% 40.4% 3 4 4 0–20 65.3% 3.4. The CCR7 Expression in CTCs in Lung Cancer Patients A total of 246 epithelial cells were identified among all CTCs, with 67 (27.2%) showing positive CCR7 expression. Among 158 hybrid CTCs, 31.4% exhibited positive CCR7 expression, and 29.5% of 121 mCTCs showed positive CCR7 expression. No significant correlation was observed between different types of CTCs and CCR7 expression (P > 0.05), as detailed in Table 5 . Although there was no significant difference in CCR7 expression among different types of CTCs (P > 0.05), CCR7 expression was significantly associated with disease progression (P < 0.001). This might be because CCR7 does not function independently in the tumor microenvironment. Instead, it interacts with other cytokines and signaling pathways to jointly promote tumor progression. Even though the expression levels of CCR7 in different CTC subtypes are not significantly different, it may still be related to disease progression by affecting the overall biological behavior of tumor cells, such as enhancing the migration and invasion abilities of tumor cells. Table 5 The Expression of CCR7 in Different Types of CTCs The number of cells (N) CCR7expression X 2 P No expression (N, %) Expression (N, %) Classification Epithelial 246 179 (72.8) 67 (27.2) 1.778 0.1812 Hybrid 503 345 (68.6) 158 (31.4) Mesenchymal 251 181 (72.1) 70 (27.9) Total (N) 1000 705 (70.5) 295 (29.5) 3.5. The Relationship between CTCs Tests and Disease Progression After excluding samples with unknown disease progression data, a total of 192 patients remained. The results demonstrated a significant positive correlation between the number of total CTCs, epithelial CTCs, hybrid CTCs, mesenchymal CTCs, and disease progression (all P < 0.01) (Table 6 ). Among the 57 patients who underwent two or more CTCs tests, the number of total CTCs detected in the second CTCs test was significantly positively correlated with disease progression (P < 0.05) (Table 6 ). In this study, epithelial circulating tumor cells (CTCs) were found to be negatively correlated with clinical stage, which is inconsistent with previous studies. The potential reason for this discrepancy could be the relatively high proportion of patients with early-stage lung cancer in the sample. As the tumor progresses, epithelial CTCs may undergo epithelial - mesenchymal transition (EMT) and transform into CTCs with different phenotypes, leading to a relative decrease in the number of epithelial CTCs in advanced patients. In addition, differences in detection methods may also influence the results. The CanPatrol™ technology used in this study has higher specificity and sensitivity for detecting epithelial CTCs, which can more accurately reflect the changes in epithelial CTCs at different clinical stages. Table 6 The Relationship between Baseline and Second CTCs in Disease Progression Spearman's rho CTC (N) Epithelial Hybrid Mesenchymal Disease progression (First Detection) Total (N) 192 192 192 192 Correlation index (r) 0.474 0.197 0.431 0.413 P 0.000** 0.006** 0.000** 0.000** Total (N) 57 57 57 57 Disease progression (Second Detection) Correlation index (r) 0.321 0.142 0.256 0.056 P 0.015* 0.293 0.055* 0.681 In order to further explore the dynamic changes of CTCs, we constructed the "CTC subtype trajectory by patient" (Fig. 5 ), which illustrates the changes in CTC subtypes across 57 patients at various detection time points. The trajectory plots clearly demonstrate significant differences in the patterns of CTC subtype changes among patients. This observation suggests that individual lung cancer patients exhibit unique dynamic changes in CTC subtypes, reflecting the individual heterogeneity of tumor development. Moreover, some patients displayed a notable trend of CTC subtype transformation. Additionally, we observed significant fluctuations in the total number of CTCs over time in some patients, while others exhibited a relatively stable total count. The dynamic changes in both the total number and subtypes of CTCs may be closely associated with the stage of disease progression and treatment response. This observation provides crucial insights for further investigation into the mechanisms underlying disease progression and for evaluating treatment efficacy. To comprehensively evaluate the factors associated with survival outcomes and validate the prognostic model, we conducted survival analyses. Univariate Cox regression analysis was initially conducted to investigate the relationship between individual variables and survival (see Fig. 6 ). Among all variables, 'phase' (stage) exhibited a significant association with survival. Compared to patients in stage I, those in stage II had a hazard ratio (HR) of 0.26 (95% CI: 0.11, 0.65, P = 0.004), stage III patients had an HR of 0.40 (95% CI: 0.20, 0.78, P = 0.007), and stage IV patients had an HR of 0.38 (95% CI: 0.16, 0.95, P = 0.037). This suggests that, in the univariate analysis, patients in later stages had a relatively lower risk of survival (this finding necessitates further clinical context-based discussion, as an HR < 1 may be influenced by sample characteristics or other complex factors). For other variables, such as age (HR = 0.99, 95% CI: 0.97, 1.01, *P* = 0.475), epi_CTC(HR = 1.05, 95% CI: 0.97, 1.14, *P* = 0.198), gender (male vs. female: HR = 0.88, 95% CI: 0.63, 1.23, *P* = 0.441), mese_CTC (HR = 1.07, 95% CI: 0.99, 1.17, *P* = 0.092), mix_CTC (HR = 1.00, 95% CI: 0.94, 1.05, *P* = 0.869), and subtype (squamous cell carcinoma vs. adenocarcinoma of the lung: HR = 1.00, 95% CI: 0.67, 1.49, *P* = 0.988), no statistically significant associations with survival were observed (*P* > 0.05). To identify independent factors associated with survival while controlling for confounding variables, a multivariate Cox regression analysis was conducted (see Fig. 7 .). Both 'phase' (stage) and 'mese_CTC' (mesenchymal CTC count) demonstrated significant associations with survival. Specifically, compared to patients in stage I, those in stage II exhibited a hazard ratio (HR) of 0.20 (95% confidence interval [CI]: 0.08, 0.54, P = 0.001), stage III patients had an HR of 0.33 (95% CI: 0.16, 0.67, P = 0.002), and stage IV patients presented an HR of 0.29 (95% CI: 0.11, 0.73, P = 0.009). For 'mese_CTC', the HR was 1.16 (95% CI: 1.05, 1.27, P = 0.003), indicating that an increase in mesenchymal CTC count was associated with a higher survival risk. In contrast, variables such as age (HR = 0.99, 95% CI: 0.97, 1.01, P = 0.475), gender (male vs. female: HR = 0.95, 95% CI: 0.63, 1.43, P = 0.814), subtype (squamous cell carcinoma vs. adenocarcinoma of the lung: HR = 1.40, 95% CI: 0.87, 2.25, P = 0.163), 'epi_CTC' (HR = 1.07, 95% CI: 0.98, 1.17, P = 0.113), and 'mix_CTC' (HR = 0.99, 95% CI: 0.92, 1.06, P = 0.708) did not demonstrate statistically significant associations with survival in the multivariate analysis (P > 0.05). To validate the prognostic model's accuracy at various time points, a time-dependent ROC curve was plotted (see Fig. 8 ). The time-dependent ROC curve, with 'Sensitivity' on the y-axis and '1 - Specificity' on the x-axis, illustrates the model's performance in predicting survival outcomes at 6 months (red line), 12 months (blue line), 24 months (green line), and 48 months (orange line). As time progresses, the curve ascends, indicating an improvement in the model's predictive ability. The area under the curve (AUC) at each time point reflects the model's predictive accuracy. A higher AUC (closer to 1) signifies better predictive performance at that specific time, providing an intuitive basis for evaluating the prognostic model's value in predicting patient survival outcomes at different stages, thereby enabling clinicians to assess patient prognosis more scientifically across various periods. 3.6. The CCR7 Expression and Disease Progression After excluding patients for whom CCR7 expression in CTCs could not be evaluated, there were 173 samples with available CCR7 expression and disease progression data. A positive correlation was found between CCR7 expression and disease progression (P < 0.001), indicating that patients with positive CCR7 expression were more likely to experience progressive disease (PD) (Table 7 ). Table 7 The Relationship between Baseline CCR7 and Disease Progression Spearman's rho CCR7 expression Disease progression Correlation index (r) 0.264 P 0.000** Total (N) 173 3.7. The Changes in CTCs/CCR7 and Disease Progression A significant positive correlation was observed between the number of CTCs and changes in disease progression (P 0.05) (Table 8 ). Table 8 The Relationship between CTCs and CCR7 Changes and Disease Progression Spearman's rho CTC Epithelial Hybrid Mesenchymal CCR7 Disease progression Total (N) 57 57 57 57 56 Correlation index (r) 0.336 0.168 0.214 -0.016 0.102 P 0.011* 0.211 0.109 0.904 0.456 We further visualized CCR7 expression across different CTC subtypes using a heatmap(Figure 9 )。 Notably, some mixed CTC samples exhibited relatively higher CCR7 expression, indicated by colors tending toward red, which signifies a stronger CCR7 signal. In both epithelial CTC and mesenchymal CTC, certain samples also demonstrated distinct levels of CCR7 expression. However, the overall distribution of CCR7 expression within each subtype was relatively dispersed, with no consistent trend indicating that one subtype significantly surpassed the others in CCR7 expression. This graphical representation offers an intuitive visualization of CCR7 expression patterns across different CTC subtypes, thereby enhancing our understanding of the relationship between CTC subtypes and CCR7 expression. 3.8.Typical Cases 3.8.1.Patient #1 The patient underwent a total of five circulating tumor cell (CTC) tests. At the fifth test, a significant increase in CTCs was observed, which coincided with the onset of progressive disease (PD). Subsequently, the CTC count decreased, resulting in a partial response (PR). Detailed data are presented in Fig. 10 . This figure illustrates the dynamic changes in circulating tumor cell (CTC) counts and the corresponding disease progression status in Patient #1 over time. The x-axis represents the time of each test, while the y-axis indicates the number of CTCs. Different colors and symbols are employed to differentiate among various types of CTCs and stages of disease progression: CR denotes complete response, PR indicates partial response, SD refers to stable disease, and PD signifies progressive disease. 3.8.2. Patient #2 A total of 11 Circulating Tumor Cell (CTC) tests were conducted on this patient. From the first to the eighth test, the number of CTCs exhibited minimal variation or a slight increase. Concurrently, the disease status transitioned from complete response (CR) to stable disease (SD), where it remained stable. The ninth test indicated a decrease in CTCs compared to the eighth test, resulting in a partial response (PR). However, a significant increase in CTCs was observed thereafter, followed by a dramatic decrease in the eleventh test. During this period, the disease progression shifted from partial response (PR) to progressive disease (PD) ( (see Fig. 11 ). The sequential changes in circulating tumor cell (CTC) counts and disease progression in Patient #2 clearly illustrate the relationship between fluctuations in CTC numbers and alterations in disease status. This provides an intuitive representation of the potential role of CTCs in monitoring disease progression. The abbreviations used are as follows: CR for complete response, PR for partial response, SD for stable disease, and PD for progressive disease. 4. Discussion Circulating tumor cells (CTCs) have emerged as critical biomarkers for tumor TNM staging, prognostic evaluation, and treatment efficacy monitoring [ 17 ] . Given the extremely low concentration of CTCs in peripheral blood (approximately 1 CTC per million mononuclear cells in advanced solid tumor patients), efficient isolation and identification of ultra-low-abundance CTCs remain central challenges for clinical application. While the CellSearch system represents a classic platform for CTC detection [ 18 ] , this study utilized the CanPatrol™ technology, which not only demonstrates reliable detection efficiency but also enables phenotypic characterization of epithelial-mesenchymal transition (EMT), facilitating assessment of tumor aggressiveness and mechanistic insights into CTC-mediated metastasis. The overall CTC positivity rate in this cohort was 81.2%, with a mesenchymal CTC positivity rate of 44.1%, both exceeding findings from previous studies. This may be attributed to the high sensitivity of CanPatrol™ technology for low-abundance CTCs, while patient population characteristics (e.g., disease stage, tumor histology) may also influence results, warranting further validation through sample size expansion and stratified analysis. The CCR7 positivity rate in CTCs was 58.4%. Among 57 patients undergoing two or more CTC tests, the second test revealed an 86.0% CTC positivity rate, 40.4% mesenchymal CTC positivity, and 65.3% CCR7 positivity in CTC-positive cases. Although total CTC positivity, mesenchymal CTC positivity, and CCR7 positivity did not differ significantly across tumor stages (all P > 0.05), these rates were markedly higher in the known-staging group versus the unknown-staging group (all P 0.05), it showed a strong association with disease progression (P < 0.001). This was validated via Spearman/Pearson correlation analyses and Cox regression on CTC data from 213 lung cancer patients. Additionally, the counts of total CTCs, epithelial CTCs, mixed CTCs, and mesenchymal CTCs all positively correlated with disease progression (P < 0.001). Both the CTC count at the second test and its dynamic change were significantly associated with subsequent progression (correlation coefficients: r = 0.474 for CTC counts, r = 0.264 for CCR7 expression) Case 1 exhibited a sharp increase in CTC levels during disease progression (PD) after the fifth test, followed by a decrease coinciding with partial response (PR). Case 2 showed minimal CTC fluctuations across 11 tests until the eighth test, when disease status shifted from complete response (CR) to stable disease (SD). The ninth test indicated PR with reduced CTCs, while the tenth test revealed a sudden CTC surge, followed by a decrease at the eleventh test, paralleling disease progression from PR to PD. These findings suggest that dynamic CTC changes reflect tumor progression, with decreases indicating potential improvement and increases signifying deterioration. This study advances our understanding of CCR7 + CTCs in lung cancer, though several limitations require careful consideration. The retrospective single-center design (213 patients, 78.9% adenocarcinoma) necessitates multicenter validation to confirm generalizability. While CanPatrol™ technology captures EMT phenotypes effectively, integrating genomic profiling (e.g., single-cell RNA sequencing) may uncover additional mechanisms underlying CCR7-driven metastasis. The median follow-up period of 38 months provides short-term prognostic data, but long-term studies are essential to solidify CCR7’s role in predicting late recurrence. Prospective trials are also needed to translate these findings into clinical practice for treatment decision guidance. Notably, the observed histotype-specific CCR7 expression in adenocarcinoma justifies targeted multicenter investigations. Future studies should incorporate functional validation to clarify CCR7’s clinical value as a biomarker. This work lays a foundation for exploring CCR7 in tumor metastasis, with clear pathways for methodological refinement and translational expansion. Declarations Conflicts of interest The authors declare no competing financial and non-financial interests in relation to the work described in the manuscript. Author Contribution Xingping Hu and Qingsong Liu conceived and designed the study. Ying Huang,Shasha Zhu, Lu Qin, Mingyi Lin,Peng Zhao,Xingping Hu and Qingsong Liu conducted data processing and contributed to analysis and interpretation of data. Chunshu Fang and Qunyou Tan conducted the investigation. Xingping Hu and Qingsong Liu provided resources. Qingsong Liu and Xingping Hu performed data curation and validation. Shaolin Tao and Li Wang supervised the overall study. Li Wang was responsible for the project administration and funding acquisition. Xingping Hu, Ying Huang, Shaolin Tao and Li Wang drafted and edited the manuscript. All the authors read and approved the final manuscript. Acknowledgements This study is supported by the joint project of Science and Technology Committee and Health Commission of Chongqing (No. 2025MSXM142). The study is supported in part by the National Nature Science Foundation of China (82402696) to LW, the Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1223) to LW, China Postdoctoral Science Foundation (2024M763898) to LW, Chongqing Postdoctoral Special Funding Project (2024CQBSHTB3005) to LW. Data Availability All raw data generated and analyzed in this study (including CTC detection raw data of 213 lung cancer patients, clinicopathological characteristic data, follow-up data, and original statistical analysis records) involve patient privacy and comply with clinical data security regulations. Thus, the data are not publicly available in public repositories for the time being.In accordance with the journal’s requirements, researchers who need to use the study data may submit a reasonable request to the corresponding authors:Shaolin Tao (E-mail: [email protected] )Xingping Hu (E-mail: [email protected] )The request should include the purpose of data use and proof of ethical approval for the intended research. After review and approval, access to the data will be provided. References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA: A Cancer Journal for Clinicians, 2020, 70(1): 7–30. Kang Y, et al. Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non - small cell lung cancer[J]. Front Oncol. 2023;13:1145128–. Chen D, et al. Efficacy and safety of immunotherapy combined with single - agent chemotherapy as second - or later - line therapy for metastatic non - small cell lung cancer[J]. Front Immunol. 2023;14:1145128. Sun X, et al. Predicting chemotherapy response in non - small - cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined[J]. Med (Baltim). 2022;101(34):e30957. Li H, et al. Cluster circulating tumor cells as predictors of adjuvant chemotherapy efficacy in lung cancer[J]. Chin J Lung Cancer. 2024;27(6):481–7. Chen X, Li Y, Zhang M, et al. Establishment and application of cell samples with high - expression of CCR7[J]. J Cell Res. 2021;31(3):256–68. Liu Z, Wang Q, Zhao L, et al. Application of normal tissue extracts as negative controls in CCR7 - related experiments[J]. Oncol Res. 2020;28(4):345–56. Gao X, et al. Construction of a precise prediction model for the efficacy of chemotherapy / targeted therapy in non - small - cell lung cancer[J]. Chin J Biomedical Eng. 2021;37(5):557–64. Zhao X, et al. CTC enumeration and its significance in personalized medicine for non - small - cell lung cancer[J]. Chin J Cancer Res. 2025;37(2):151–60. Li H et al. Impact of Incorporating CTC EMT Detection into Comprehensive Diagnostic Approaches on the Precision of Cancer Staging and Efficacy Assessment of Treatment [J]. Journal of Oncology, 2025, 2025: 8647523. Wang Z, et al. The Role of CanPatrol™ Technology in Personalized Treatment of Cancer Based on CTC Detection [J]. Chin J Cancer Res. 2024;36(5):601–8. Li Y, et al. Evaluation of the Efficacy of CanPatrol™ Technology in Detecting Circulating Tumor Cells in Various Cancers [J]. J Clin Oncol Cancer Res. 2025;12(2):115–22. Zhang M, et al. Verification of the Specificity and Effectiveness of CCR7 Antibody in the Research of Breast Cancer Metastasis [J]. J Oncol Res. 2025;13(3):231–8. Wang X, et al. Application of the CanPatrol™ Technology in Detecting Circulating Tumor Cells in Gastric Cancer [J]. Chin J Gastroenterol Hepatol. 2025;34(5):521–8. Liu Z, et al. Exploring the Association between CTC Epithelial - Mesenchymal Transition and Metastasis in Pancreatic Cancer [J]. Pancreatology. 2024;24(6):712–20. Zhang M, et al. Verification of the Reliability of Nanomembrane Filtration - based CTC Detection in Lung Cancer Diagnosis [J]. Chin J Lung Cancer. 2025;28(3):192–9. Wang Z, et al. Application of Circulating Tumor Cells in Monitoring the Efficacy of Lung Cancer Treatment [J]. J Clin Oncol. 2024;42(22):2015–23. Li X, et al. Correlation between Circulating Tumor Cells and Prognosis of Breast Cancer Patients [J]. Chin J Oncol. 2025;47(3):232–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 11 Jan, 2026 Editor assigned by journal 17 Nov, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 17 Nov, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8093072","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587761473,"identity":"0ae1a8b4-db43-4c67-9fe5-731fe23e14be","order_by":0,"name":"li wang","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"li","middleName":"","lastName":"wang","suffix":""},{"id":587761474,"identity":"fbf35df3-c915-4b76-a717-58a904909e71","order_by":1,"name":"qingsong Liu","email":"","orcid":"","institution":"Beibei Maternal And Child Healthcare Hospital","correspondingAuthor":false,"prefix":"","firstName":"qingsong","middleName":"","lastName":"Liu","suffix":""},{"id":587761475,"identity":"4dfaa22b-50fa-4534-b4a5-d3707223e134","order_by":2,"name":"fangchun shu","email":"","orcid":"","institution":"Department of Thoracic Surgery, Institute of Surgery Research, Daping Hospital, Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"fangchun","middleName":"","lastName":"shu","suffix":""},{"id":587761476,"identity":"677229c6-f526-4d3e-a783-90134224b927","order_by":3,"name":"Ying Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Huang","suffix":""},{"id":587761477,"identity":"d13000e5-264a-4664-a562-b60b152c601d","order_by":4,"name":"Qunyou Tan","email":"","orcid":"","institution":"University-Town Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qunyou","middleName":"","lastName":"Tan","suffix":""},{"id":587761478,"identity":"450fe7cd-0e28-4f11-a07a-8e30e2a36184","order_by":5,"name":"Shasha Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Zhu","suffix":""},{"id":587761479,"identity":"c3dd6b7f-4551-4f70-a96b-e59bc5181402","order_by":6,"name":"Lu Qin","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Qin","suffix":""},{"id":587761480,"identity":"49ddc396-957a-4ae6-b1ed-423dd0cbf662","order_by":7,"name":"peng zhao","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"peng","middleName":"","lastName":"zhao","suffix":""},{"id":587761481,"identity":"2331cf22-f580-4e05-88cd-79d4adf7141f","order_by":8,"name":"Mingyi Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingyi","middleName":"","lastName":"Lin","suffix":""},{"id":587761482,"identity":"0be00b92-b075-46e4-a3f7-5f3e1da52963","order_by":9,"name":"xingping Hu","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"xingping","middleName":"","lastName":"Hu","suffix":""},{"id":587761483,"identity":"0888df46-ca70-46e3-b801-cf25c6d9e687","order_by":10,"name":"shaolin Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3RsUoDMRzH8RyBdPnFrHfk0Fe4UqgIoX2VKweZDvQBHAKFTqJrfBHnlECnUlehSycnh5QDx+IV3Cyxbg75kGz5kvwJIUnyT9F+l4xQ52ooCGHix/GdgBE224VSl4V1Zyb9Gg2t8qoydTyZDrzv7u4VLqQZS7SvqIjLwr6N3AKtpV1psNJpifUW19TQ4vkl9rB2TME8WF6vJH/Y4sY4RnksER+jDodjMltIftigcvUvSd5Wki+OSUOHFu6M5O1dS/7Yz5LrbBfQoLDLeXSWwVPjO3yqyyt7G/qvnEyFmC/DPpKckpm/nU+SJEl++AIqUEiIPsPgpgAAAABJRU5ErkJggg==","orcid":"","institution":"University-Town Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"shaolin","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-11-12 07:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8093072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8093072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102214075,"identity":"9f947562-8437-48a0-9df0-84277152e7e5","added_by":"auto","created_at":"2026-02-09 12:43:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286283,"visible":true,"origin":"","legend":"\u003cp\u003eThe CanPatrolTM method was used for CTC analysis in all blood samples（figdraw.com ID:SPARI1d4d8)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/6cdb85bb0356228c26262966.png"},{"id":102214192,"identity":"b38ea81c-149a-46b4-96db-c70c7bef0a86","added_by":"auto","created_at":"2026-02-09 12:43:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242159,"visible":true,"origin":"","legend":"\u003cp\u003eFluorescence Representative Image of CTC Typing Test Results. Expression of CCR7 in CTC subtypes. The epithelial markers included EpCAM and CK 8/18/19, and the mesenchymal markers included vimentin and Twist. Abbreviations:ECTC, epithelial circulating tumor cell; EMCTC, epithelial–mesenchymal circulating tumor cell; MCTC, mesenchymal circulating tumor cell.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/e4fd5c4be438f7a8054e020b.png"},{"id":102214189,"identity":"387f0b02-86d1-4263-a55a-728d7fb278cf","added_by":"auto","created_at":"2026-02-09 12:43:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1113335,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic Changes in CCR7, Vimentin, and Ep-CAM Across Different Stages of Lung Cancer as Analyzed by Flow Cytometry（\u003cstrong\u003eFigure \u003c/strong\u003eA.Flow Cytometry Scatter Plots of Patients at Different Disease Stages;\u003cstrong\u003eFigure \u003c/strong\u003eB.Flow Cytometry Expression Trend Chart (Multiple Parameters);\u003cstrong\u003eFigure C.\u003c/strong\u003eExpression trends of CCR7 and Vimentin were evaluated using flow cytometry;\u003cstrong\u003eFigure D.\u003c/strong\u003eExpression trends of CCR7 and EP - CAM were evaluated using flow cytometry.)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/dab38738e267e21c6e62db43.png"},{"id":102214187,"identity":"ee6805c8-6973-4db6-b98d-33ac59a7397e","added_by":"auto","created_at":"2026-02-09 12:43:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98512,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of CTCs tests detected in lung cancer patients.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/517e35064fcb02b850beab72.png"},{"id":102214372,"identity":"c961f680-9b4e-4439-b1f3-c535a2081f85","added_by":"auto","created_at":"2026-02-09 12:43:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1130873,"visible":true,"origin":"","legend":"\u003cp\u003eCTC subtype trajectory by patient（Different colors in the figure represent distinct CTC subtypes: red indicates epithelial CTCs, green represents mesenchymal CTCs, and blue signifies mixed CTCs. The horizontal axis denotes the detection time, while the vertical axis indicates the number of CTCs. ）\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/457853b1cc6d63ffaebbe2f7.png"},{"id":102214386,"identity":"deae404d-d895-4fdb-bf56-13ed685b9216","added_by":"auto","created_at":"2026-02-09 12:43:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":984716,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate Cox Regression Analysis\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/f043add4123f48100c80d9a2.png"},{"id":102214333,"identity":"a3522eea-4340-4018-9043-f52bb44d6fb7","added_by":"auto","created_at":"2026-02-09 12:43:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":830586,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Cox Regression Analysis\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/a0351b6a5fe586968fdd2451.png"},{"id":102214188,"identity":"f0921ee2-502e-4585-a385-37a08326ccad","added_by":"auto","created_at":"2026-02-09 12:43:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":553265,"visible":true,"origin":"","legend":"\u003cp\u003eTime - dependent ROC Curve（The horizontal axis represents specificity (false positive rate), while the vertical axis represents sensitivity. The four curves, each depicted in different colors, correspond to the model's performance at 6, 12, 24, and 48 months, respectively.）\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/8531863b9a0f7212551c2049.png"},{"id":102214255,"identity":"4c528581-a4d6-43ca-94fc-2e18dcbeedc3","added_by":"auto","created_at":"2026-02-09 12:43:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":647193,"visible":true,"origin":"","legend":"\u003cp\u003eCCR7 expression in different CTC subtypes using a heatmap（The heatmap illustrates that among the three CTC subtypes—mixed CTC, epithelial CTC, and mesenchymal CTC—CCR7 expression varied across samples.）\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/a0094c5728eac9a4c1dbd1a7.png"},{"id":102214072,"identity":"ddc8c032-532b-4ec6-ad59-acd5ca0fbd12","added_by":"auto","created_at":"2026-02-09 12:43:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":249671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe CTCs results and disease progression on \u003c/strong\u003epatient #1.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/38b7e7bbc8a0dc5c7e17aaae.png"},{"id":102214258,"identity":"4b8c9b47-507d-484c-891a-a1ab6e95bcd9","added_by":"auto","created_at":"2026-02-09 12:43:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":219300,"visible":true,"origin":"","legend":"\u003cp\u003eThe CTCs results and disease progression on patient #2.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/2f5edac214e9b92c8091da68.png"},{"id":102214597,"identity":"a57b0ced-4d6f-4b4d-96c4-628f561b41ae","added_by":"auto","created_at":"2026-02-09 12:44:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7755970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8093072/v1/6c8406b9-c436-49c7-a359-e327374219b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CCR7-Positive Circulating Tumor Cells as a Biomarker for Predicting Lung Cancer Risk","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is a prevalent malignancy characterized by a high incidence and mortality rate \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. While surgical treatment can be curative in select cases, most patients present with metastatic disease at diagnosis, resulting in a poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although gene-targeted therapy and immunotherapy have demonstrated promise in the treatment of lung cancer, challenges such as drug resistance and recurrence continue to pose significant obstacles\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCTCs are tumor cells that enter the bloodstream from the primary tumor or metastatic sites \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.As a key regulator in the immune system, CCR7 orchestrates the movement of immune cells to lymphoid organs, thereby ensuring the proper functioning of immune responses\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In the context of cancer, CCR7 has emerged as a significant factor in tumor metastasis. Tumor cells that express CCR7 can sense and respond to chemokine signals within their microenvironment. When CCR7 on tumor cells binds to its ligands, such as CCL19 and CCL21, it triggers a cascade of intracellular signaling pathways. This activation enhances the migratory and invasive capabilities of tumor cells, enabling them to penetrate the extracellular matrix, enter the bloodstream, and potentially metastasize to distant organs\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The detection of CCR7 on CTCs holds substantial significance. CTCs are tumor cells that have entered the bloodstream and represent potential seeds for metastasis. The presence of CCR7 on CTCs may indicate a higher metastatic potential, providing clinicians with critical information to better assess the risk of disease progression in lung cancer patients\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe detection of CTCs has emerged as a pivotal tool in tumor staging, monitoring treatment efficacy, and evaluating prognosis \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. It facilitates the tracking of variations in CTC numbers, gene mutations, and surface markers, which are essential for early treatment assessment, detection of tumor resistance, and prediction of disease progression, thereby enhancing personalized cancer therapy \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Clinical studies indicate that the integration of CTC epithelial-mesenchymal transition (EMT) detection with other diagnostic tests significantly improves the accuracy of staging and treatment evaluation\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The CanPatrol\u0026trade; technology, which employs nanomembrane filtration and RNA in situ hybridization, has gained traction for CTC detection due to its high sensitivity and specificity, achieving an overall recovery rate exceeding 80% \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe CanPatrol\u0026trade; second-generation CTC enrichment technology represents a sophisticated method for isolating and analyzing CTCs. The process begins with a filtration step that leverages the distinct physical properties of CTCs. By selecting an appropriate pore size for the filter, typically a 8 \u0026micro;m nano-membrane, CTCs can be effectively separated from a substantial volume of blood cells, capitalizing on their relatively larger size compared to normal blood cells. Following filtration, RNA in situ hybridization (RNA-ISH) is performed utilizing branched DNA signal amplification technology. This technique facilitates the detection of specific RNA sequences within the CTCs, which are essential for identifying various epithelial-mesenchymal transition (EMT) markers. Consequently, CTCs can be accurately classified into epithelial, epithelial-mesenchymal, and mesenchymal types. Furthermore, in advanced-stage cancer samples, this technology has been shown to detect mesenchymal-type circulating tumor cell masses (CTMs), which are aggregates of CTCs, highlighting its capability to capture larger CTC clusters in addition to individual CTCs. In the subsequent analysis, an advanced multi-parameter imaging system is employed. This system not only identifies cell surface markers but also conducts an in-depth analysis of the molecular features within the cells, including gene expression profiles and protein phosphorylation levels.\u003c/p\u003e \u003cp\u003eThis comprehensive analysis provides a deeper understanding of CTCs, offering rich and accurate information for clinical practice. Compared to traditional detection methods, such as the CellSearch system, which has a detection sensitivity of approximately 1 CTC per 5 mL of blood, CanPatrol\u0026trade; technology achieves ultra-sensitive detection, with a sensitivity as low as 0.1 CTCs per 5 mL of blood. This advancement facilitates earlier detection of CTCs in cancer patients, allowing for timely diagnosis. Furthermore, while most conventional technologies can only detect a limited number of CTC markers, CanPatrol\u0026trade; technology can simultaneously analyze dozens of biological markers. It enables the examination of CTC characteristics from multiple levels, comprehensively reflecting the heterogeneity of tumor cells and providing robust support for personalized treatment planning\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e Although the role of CCR7 in tumor invasion and metastasis has been studied in some tumors (such as breast cancer and bladder cancer), the detection of CTC using CCR7 as a molecular marker to guide postoperative drugs and follow-up treatment plans for lung cancer has not been reported. This study focuses on this and explores its potential value.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria for this study were as follows: (1) All patients were pathologically diagnosed with lung cancer; (2) Patients provided informed consent to participate in this clinical study; (3) Patients demonstrated good compliance and were able to fully cooperate with treatment and follow-up procedures; (4) Patients did not have any severe comorbidities or organ failure; (5) Patients had an expected survival period exceeding three months; (6) Patients did not have other types of tumors.\u003c/p\u003e \u003cp\u003eThe exclusion criteria for this study were as follows: (1) Pregnant or lactating women; (2) Patients who withdrew from the study prematurely; (3) Patients who were unable to cooperate or whose condition deteriorated during the study, necessitating alternative treatments. This study received approval from the ethics committees of Daping Hospital, Army Military Medical University (approval number: [(2024) Number 212]) and The First Affiliated Hospital of Chongqing Medical University (approval number: [2023\u0026thinsp;\u0026minus;\u0026thinsp;280]). It adhered to the principles outlined in the Declaration of Helsinki and complied with medical ethics guidelines.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eThis study did not include identifying images (such as patient facial photos, unique clinical imaging features that may reveal identity) or personal/clinical details that could compromise the anonymity of participants. Therefore, the \u0026lsquo;Consent for publication\u0026rsquo; is Not Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. CTCs detection\u003c/h2\u003e \u003cp\u003eAll blood samples were analyzed for CTCs using the CanPatrol\u0026trade; method. The detailed procedure is outlined as follows:\u003c/p\u003e \u003cp\u003e1. A volume of 5 mL of peripheral blood was collected intravenously and stored in anticoagulant tubes containing ethylenediaminetetraacetic acid (EDTA). 2. A test tube containing cell lysis buffer was prepared, and blood from the collection tube was transferred to the test tube using a negative-pressure transfer device. The blood was then fully lysed at 4\u0026deg;C for approximately 30 minutes to facilitate the lysis of red blood cells. 3. Following the lysis of red blood cells, the sample was centrifuged to separate fragmented red blood cells from other components. 4. The resulting pellet was resuspended, and tumor cells were retained to the greatest extent using a 8 \u0026micro;m nano-membrane under negative pressure. 5. Multiple ribonucleic acid (RNA) in situ hybridizations were performed using various probes specific to tumor cells for comprehensive analysis. 6. Fluorescence signals were identified, and CTCs were classified using an automated fluorescence microscope and computer system. CCR7 antibodies were sourced from Santa Cruz Biotechnology (USA) (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CCR7 antibody was purchased from Santa Cruz Biotechnology, and its specificity and effectiveness were verified by Western Blot and immunohistochemical verification experiments \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In the Western Blot experiment, specific protein extraction, isolation, and antibody incubation processes were carried out. Clear and specific bands were observed, which were consistent with the expected molecular weight of the CCR7 protein. In the immunohistochemical experiments, tissue sections with known CCR7 expression showed typical positive staining features.\u003c/p\u003e \u003cp\u003ePositive and negative controls were set up to ensure the accuracy of the test results. The negative control was treated with normal tissue extract, which was identical to the experimental sample except for the use of normal tissue extract. Additionally, the samples were also treated with isotype - independent antibodies as another negative control to exclude interference from non - specific binding. The positive control was a cell sample with known high expression of CCR7 \u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Under the same experimental conditions, this positive control sample showed a strong positive signal, providing a reliable reference for the accuracy of the experimental results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 CTCs positive criteria and classification\u003c/h2\u003e \u003cp\u003eFor baseline CTC statistics, the first test results of patients with multiple CTC tests were used. The second test results were recorded for 57 patients. A CTC count of 0 was classified as negative, and 1 or more as positive. Changes in CTCs were categorized as: decrease (1), unchanged (2), increase (3). Three distinct epithelial-mesenchymal expression profiles on CTCs were observed: epithelial CTCs (expressing CK8, CK18, CK19), mixed CTCs (co-expressing epithelial and mesenchymal markers), and mesenchymal CTCs (expressing vimentin and twist)\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCapture probe sequences for the CD45, CK8, CK18, CK19, Ep - CAM, Vimentin, TWIST and CCR7 gene(Ep-CAM epithelial cell adhesion molecule, CK cytokeratins, CCR7 human telomerase reverse transcriptase.)CCR7 antibodies were sourced from Santa Cruz Biotechnology (USA)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequences (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEp-CAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTGCTCGTTGATGAGTCAA\u003c/p\u003e \u003cp\u003eGCCAGCTTTGAGCAAATGAAA\u003c/p\u003e \u003cp\u003eAGCCCATCATTGTTCTGGCTC\u003c/p\u003e \u003cp\u003eTCATCGCAGTCAGGATCTCC\u003c/p\u003e \u003cp\u003eTTGTCTGTTCTTCTGACCTCA\u003c/p\u003e \u003cp\u003eGAGCAGGTTATTTCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGTACCTTGTCTATGAAGGAA\u003c/p\u003e \u003cp\u003eCTTGGTCTCCAGCATCTTGCC\u003c/p\u003e \u003cp\u003eTAAGGTTGTTGATGTAGCCTG\u003c/p\u003e \u003cp\u003eAGGAAGTTGATCTCGTCCAGA\u003c/p\u003e \u003cp\u003eTGTGTCCGAGATCTGGTGACC\u003c/p\u003e \u003cp\u003eTCAGCAATGATGCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAAAGGACAGGACTCAGGC\u003c/p\u003e \u003cp\u003eGAGTGGTGAAGCTCATGCTG\u003c/p\u003e \u003cp\u003eTCAGGTCCTCGATGATCTTG\u003c/p\u003e \u003cp\u003eCAATCTGCAGAACGATGCGG\u003c/p\u003e \u003cp\u003eAAGTCATCAGCAGCAAGACG\u003c/p\u003e \u003cp\u003eCTGCAGTCGTGTGATATTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGTAGGAAGTCATGGCG\u003c/p\u003e \u003cp\u003eAGAAGTCATCTGCAGCCA\u003c/p\u003e \u003cp\u003eGACGCTGTTCCGTCTCAA\u003c/p\u003e \u003cp\u003eACTTGGTTCTTCTTCAGGT\u003c/p\u003e \u003cp\u003eAGGCCAGCTCAGCGTACT\u003c/p\u003e \u003cp\u003eGATTTCCTCGTGAACCAGG\u003c/p\u003e \u003cp\u003eCTTCAGCATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVimentin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGCGAGAGTGGCAGAGG\u003c/p\u003e \u003cp\u003eACCTTTGTCGTTGGTTAGC\u003c/p\u003e \u003cp\u003eTGGCATATTGCTGACGTAC\u003c/p\u003e \u003cp\u003eGTCAGAGCGCCCCTAAGTT\u003c/p\u003e \u003cp\u003eTTTAAAAGATTGCAGGGTGT\u003c/p\u003e \u003cp\u003eTTTCGGGCCAATAGTGTCTTGGTAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAATGACATCTAGGTCTC\u003c/p\u003e \u003cp\u003eCTTTCAGTGGCTGATTGGC\u003c/p\u003e \u003cp\u003eACTTACCATGGGTCCTCAA\u003c/p\u003e \u003cp\u003eTAACTGGTAGAGGAAGTCG\u003c/p\u003e \u003cp\u003eATGCAACTGTTCAGACTTCT\u003c/p\u003e \u003cp\u003eATCCCTCTTGAGAATGCATGCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTACCATGGGTCCTCAATAA\u003c/p\u003e \u003cp\u003eTCGCAATTCTTATCGACTCT\u003c/p\u003e \u003cp\u003eGTCATGGAGACAGTCATGTG\u003c/p\u003e \u003cp\u003eTATTTCCAGCTTCAACTTCCC\u003c/p\u003e \u003cp\u003eATCAATATAGCTGGCATTTTG\u003c/p\u003e \u003cp\u003eTGCAGCAATGTATTTCCTACT\u003c/p\u003e \u003cp\u003eTGAACCATCAGGCATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTCTTGAGCCTCTTGAAATA\u003c/p\u003e \u003cp\u003eAGAGCACTGTGGCTAGTATC\u003c/p\u003e \u003cp\u003eTTGTTGCGCTCAAAGTTGCT\u003c/p\u003e \u003cp\u003eAAGAAAGGGTTGACGCAGCA\u003c/p\u003e \u003cp\u003eATGTCCTGAGTCATTGCATC\u003c/p\u003e \u003cp\u003eGTTCATTCAGAGGACTCTTC\u003c/p\u003e \u003cp\u003eAAACGATGGAGGGAGGGGTT\u003c/p\u003e \u003cp\u003eGTTGCTCTCTTAACGAATCG\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 \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. The standard staining results of CCR7\u003c/h2\u003e \u003cp\u003eCCR7 status was categorized as follows: persistent negative\u0026thinsp;=\u0026thinsp;1, positive transitioning to negative\u0026thinsp;=\u0026thinsp;2, negative transitioning to positive\u0026thinsp;=\u0026thinsp;3, and persistent positive\u0026thinsp;=\u0026thinsp;4. Disease progression was classified as follows: disease improvement\u0026thinsp;=\u0026thinsp;0, disease deterioration\u0026thinsp;=\u0026thinsp;1. When calculating the CCR7 positivity rate, a CTC was considered CCR7 positive if there was any detectable CCR7 expression; conversely, it was deemed CCR7 negative if no CTCs exhibited CCR7 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the flow cytometry results, tumor metastasis-related indicators and cell phenotypic characteristics were analyzed in patients at different stages of lung cancer (Stage I\u0026ndash;IV)( (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The correlation between flow cytometry parameters and lung cancer progression demonstrated a trend toward increased heterogeneity. Specifically, scatter plots exhibited a more diffuse distribution, and peak plot subsets were more numerous, suggesting that tumor cells in stage IV exhibit the highest degree of phenotypic diversity and may possess enhanced resistance to therapeutic interventions. Regarding the epithelial-mesenchymal transition (EMT) process, an upregulation of Vimentin expression coupled with a downregulation of EP-CAM indicates a phenotypic shift from epithelial to mesenchymal characteristics, which is associated with increased migratory capacity of tumor cells. In terms of metastasis regulation, alterations in the co-expression patterns of CCR7 with Vimentin and EP-CAM suggest a potential mechanism by which tumor cells may be directed to metastasize to specific organs via chemokine signaling pathways. These findings provide molecular insights into the mechanisms underlying lung cancer metastasis. Furthermore, related biomarkers such as the proportion of CCR7\u0026thinsp;+\u0026thinsp;Vimentin\u0026thinsp;+\u0026thinsp;cells hold promise as potential indicators for assessing disease progression and predicting clinical outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Evaluation of the therapeutic response\u003c/h2\u003e \u003cp\u003eThe therapeutic effect on patients' tumors was evaluated using established criteria for assessing the efficacy of tumor chemotherapy. A complete response (CR) was defined as the complete disappearance of all target lesions, the absence of new lesions, and the normalization of tumor markers, sustained for at least four consecutive weeks. A partial response (PR) was characterized by a reduction of at least 30% in the sum of the diameters of target lesions, maintained over a minimum duration of four consecutive weeks. Stable disease (SD) referred to cases where the sum of the largest diameters of target lesions did not meet the criteria for PR nor progressed to progressive disease (PD). Progressive disease (PD) was identified by an increase of at least 20% in the sum of the maximum diameters of target lesions or the emergence of new lesions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical methods\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 25.0. Categorical variables are presented as percentages with chi-square values. Continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation if normally distributed, or as medians with interquartile ranges otherwise. Pairwise comparisons used t-tests or Mann-Whitney U tests depending on data distribution. ANOVA was applied for comparisons of three or more groups. Spearman's rank correlations were used for non-normally distributed continuous and ordinal variables, and Pearson correlation for continuous ones. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Clinical characteristics of patients\u003c/h2\u003e \u003cp\u003eA total of 213 patients were included in this study, of which 115 were male (54.0%). The mean age of the patients was 57.613\u0026thinsp;\u0026plusmn;\u0026thinsp;9.913 years old. The detailed distribution results of patient information such as tumor location, tumor classification, and tumor stage were shown in 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\u003eBaseline Characteristics of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub project\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)/Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.613\u0026thinsp;\u0026plusmn;\u0026thinsp;9.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous cell Adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.49%\u003c/p\u003e \u003cp\u003e62.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStages (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.96\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Results of CTCs tests\u003c/h2\u003e \u003cp\u003eA total of 322 CTCs tests were conducted in this study. Of these, 156 patients underwent only one CTCs test, while the remaining 57 patients had two or more tests. Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt baseline, the positive rate of CTCs was 81.2%, with a positive rate of mesenchymal circulating tumor cells (mCTCs) at 44.1%. Additionally, among CTC-positive patients, the positive rate of CCR7 was recorded at 58.4% (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).In the case of 57 patients who had undergone CTCs testing for two or more occasions, the second positive rate of CTCs detected was 86.0%, the positive rate of mCTCs was 40.4%, and the positive rate of CCR7 in CTC - positive patients was 65.3% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eIllustrates the association between CTC count and the general characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;322)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCTC counts\u0026thinsp;\u0026ge;\u0026thinsp;13(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCTC counts\u0026thinsp;\u0026lt;\u0026thinsp;13(n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRatio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRatio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026le;\u003c/em\u003e\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma of lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e103.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The relationship between CTCs number and clinical stage\u003c/h2\u003e \u003cp\u003eAfter removing samples with unknown clinical stages (34 cases were removed), the number of epithelial CTCs (eCTCs) was negatively correlated with clinical stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). More details shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the first and second CTCs tests with different clinical stages in lung patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eClinical stages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe Positive rate\u003c/p\u003e \u003cp\u003eof mesenchymal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eThe positive of\u003c/p\u003e \u003cp\u003eCCR7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003cp\u003etest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e72.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown Stage (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;213)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e81.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e44.1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u0026ndash;38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e58.4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003cp\u003etest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e64.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e80.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown Stage (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(\u003cb\u003en\u0026thinsp;=\u0026thinsp;57\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e86.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e40.4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u0026ndash;20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e65.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. The CCR7 Expression in CTCs in Lung Cancer Patients\u003c/h2\u003e \u003cp\u003eA total of 246 epithelial cells were identified among all CTCs, with 67 (27.2%) showing positive CCR7 expression. Among 158 hybrid CTCs, 31.4% exhibited positive CCR7 expression, and 29.5% of 121 mCTCs showed positive CCR7 expression. No significant correlation was observed between different types of CTCs and CCR7 expression (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Although there was no significant difference in CCR7 expression among different types of CTCs (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), CCR7 expression was significantly associated with disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This might be because CCR7 does not function independently in the tumor microenvironment. Instead, it interacts with other cytokines and signaling pathways to jointly promote tumor progression. Even though the expression levels of CCR7 in different CTC subtypes are not significantly different, it may still be related to disease progression by affecting the overall biological behavior of tumor cells, such as enhancing the migration and invasion abilities of tumor cells.\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\u003eThe Expression of CCR7 in Different Types of CTCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe number of cells (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCCR7expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo expression (N, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression (N, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpithelial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.1812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e345 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesenchymal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e705 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e295 (29.5)\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 \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\u003e3.5. The Relationship between CTCs Tests and Disease Progression\u003c/h2\u003e \u003cp\u003eAfter excluding samples with unknown disease progression data, a total of 192 patients remained. The results demonstrated a significant positive correlation between the number of total CTCs, epithelial CTCs, hybrid CTCs, mesenchymal CTCs, and disease progression (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among the 57 patients who underwent two or more CTCs tests, the number of total CTCs detected in the second CTCs test was significantly positively correlated with disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, epithelial circulating tumor cells (CTCs) were found to be negatively correlated with clinical stage, which is inconsistent with previous studies. The potential reason for this discrepancy could be the relatively high proportion of patients with early-stage lung cancer in the sample. As the tumor progresses, epithelial CTCs may undergo epithelial - mesenchymal transition (EMT) and transform into CTCs with different phenotypes, leading to a relative decrease in the number of epithelial CTCs in advanced patients. In addition, differences in detection methods may also influence the results. The CanPatrol\u0026trade; technology used in this study has higher specificity and sensitivity for detecting epithelial CTCs, which can more accurately reflect the changes in epithelial CTCs at different clinical stages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Relationship between Baseline and Second CTCs in Disease Progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpearman's rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTC (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEpithelial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMesenchymal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDisease progression\u003c/p\u003e \u003cp\u003e(First Detection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eindex (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease progression\u003c/p\u003e \u003cp\u003e(Second Detection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eindex (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn order to further explore the dynamic changes of CTCs, we constructed the \"CTC subtype trajectory by patient\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which illustrates the changes in CTC subtypes across 57 patients at various detection time points. The trajectory plots clearly demonstrate significant differences in the patterns of CTC subtype changes among patients. This observation suggests that individual lung cancer patients exhibit unique dynamic changes in CTC subtypes, reflecting the individual heterogeneity of tumor development. Moreover, some patients displayed a notable trend of CTC subtype transformation.\u003c/p\u003e \u003cp\u003eAdditionally, we observed significant fluctuations in the total number of CTCs over time in some patients, while others exhibited a relatively stable total count. The dynamic changes in both the total number and subtypes of CTCs may be closely associated with the stage of disease progression and treatment response. This observation provides crucial insights for further investigation into the mechanisms underlying disease progression and for evaluating treatment efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo comprehensively evaluate the factors associated with survival outcomes and validate the prognostic model, we conducted survival analyses.\u003c/p\u003e \u003cp\u003eUnivariate Cox regression analysis was initially conducted to investigate the relationship between individual variables and survival (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among all variables, 'phase' (stage) exhibited a significant association with survival. Compared to patients in stage I, those in stage II had a hazard ratio (HR) of 0.26 (95% CI: 0.11, 0.65, P\u0026thinsp;=\u0026thinsp;0.004), stage III patients had an HR of 0.40 (95% CI: 0.20, 0.78, P\u0026thinsp;=\u0026thinsp;0.007), and stage IV patients had an HR of 0.38 (95% CI: 0.16, 0.95, P\u0026thinsp;=\u0026thinsp;0.037). This suggests that, in the univariate analysis, patients in later stages had a relatively lower risk of survival (this finding necessitates further clinical context-based discussion, as an HR\u0026thinsp;\u0026lt;\u0026thinsp;1 may be influenced by sample characteristics or other complex factors). For other variables, such as age (HR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.97, 1.01, *P* = 0.475), epi_CTC(HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 0.97, 1.14, *P* = 0.198), gender (male vs. female: HR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.63, 1.23, *P* = 0.441), mese_CTC (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.99, 1.17, *P* = 0.092), mix_CTC (HR\u0026thinsp;=\u0026thinsp;1.00, 95% CI: 0.94, 1.05, *P* = 0.869), and subtype (squamous cell carcinoma vs. adenocarcinoma of the lung: HR\u0026thinsp;=\u0026thinsp;1.00, 95% CI: 0.67, 1.49, *P* = 0.988), no statistically significant associations with survival were observed (*P* \u0026gt; 0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify independent factors associated with survival while controlling for confounding variables, a multivariate Cox regression analysis was conducted (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.). Both 'phase' (stage) and 'mese_CTC' (mesenchymal CTC count) demonstrated significant associations with survival. Specifically, compared to patients in stage I, those in stage II exhibited a hazard ratio (HR) of 0.20 (95% confidence interval [CI]: 0.08, 0.54, P\u0026thinsp;=\u0026thinsp;0.001), stage III patients had an HR of 0.33 (95% CI: 0.16, 0.67, P\u0026thinsp;=\u0026thinsp;0.002), and stage IV patients presented an HR of 0.29 (95% CI: 0.11, 0.73, P\u0026thinsp;=\u0026thinsp;0.009). For 'mese_CTC', the HR was 1.16 (95% CI: 1.05, 1.27, P\u0026thinsp;=\u0026thinsp;0.003), indicating that an increase in mesenchymal CTC count was associated with a higher survival risk. In contrast, variables such as age (HR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.97, 1.01, P\u0026thinsp;=\u0026thinsp;0.475), gender (male vs. female: HR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.63, 1.43, P\u0026thinsp;=\u0026thinsp;0.814), subtype (squamous cell carcinoma vs. adenocarcinoma of the lung: HR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 0.87, 2.25, P\u0026thinsp;=\u0026thinsp;0.163), 'epi_CTC' (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.98, 1.17, P\u0026thinsp;=\u0026thinsp;0.113), and 'mix_CTC' (HR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.92, 1.06, P\u0026thinsp;=\u0026thinsp;0.708) did not demonstrate statistically significant associations with survival in the multivariate analysis (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the prognostic model's accuracy at various time points, a time-dependent ROC curve was plotted (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The time-dependent ROC curve, with 'Sensitivity' on the y-axis and '1 - Specificity' on the x-axis, illustrates the model's performance in predicting survival outcomes at 6 months (red line), 12 months (blue line), 24 months (green line), and 48 months (orange line). As time progresses, the curve ascends, indicating an improvement in the model's predictive ability. The area under the curve (AUC) at each time point reflects the model's predictive accuracy. A higher AUC (closer to 1) signifies better predictive performance at that specific time, providing an intuitive basis for evaluating the prognostic model's value in predicting patient survival outcomes at different stages, thereby enabling clinicians to assess patient prognosis more scientifically across various periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. The CCR7 Expression and Disease Progression\u003c/h2\u003e \u003cp\u003eAfter excluding patients for whom CCR7 expression in CTCs could not be evaluated, there were 173 samples with available CCR7 expression and disease progression data. A positive correlation was found between CCR7 expression and disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that patients with positive CCR7 expression were more likely to experience progressive disease (PD) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Relationship between Baseline CCR7 and Disease Progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpearman's rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCR7 expression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDisease progression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eindex (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173\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 \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. The Changes in CTCs/CCR7 and Disease Progression\u003c/h2\u003e \u003cp\u003eA significant positive correlation was observed between the number of CTCs and changes in disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the results of the second CCR7 test showed no significant correlation with disease progression (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Relationship between CTCs and CCR7 Changes and Disease Progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpearman's rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEpithelial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMesenchymal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCCR7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003cp\u003eprogression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003cp\u003eindex (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.456\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\u003eWe further visualized CCR7 expression across different CTC subtypes using a heatmap(Figure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e)。\u003c/b\u003eNotably, some mixed CTC samples exhibited relatively higher CCR7 expression, indicated by colors tending toward red, which signifies a stronger CCR7 signal. In both epithelial CTC and mesenchymal CTC, certain samples also demonstrated distinct levels of CCR7 expression. However, the overall distribution of CCR7 expression within each subtype was relatively dispersed, with no consistent trend indicating that one subtype significantly surpassed the others in CCR7 expression. This graphical representation offers an intuitive visualization of CCR7 expression patterns across different CTC subtypes, thereby enhancing our understanding of the relationship between CTC subtypes and CCR7 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8.Typical Cases\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.8.1.Patient #1\u003c/h2\u003e \u003cp\u003eThe patient underwent a total of five circulating tumor cell (CTC) tests. At the fifth test, a significant increase in CTCs was observed, which coincided with the onset of progressive disease (PD). Subsequently, the CTC count decreased, resulting in a partial response (PR). Detailed data are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis figure illustrates the dynamic changes in circulating tumor cell (CTC) counts and the corresponding disease progression status in Patient #1 over time. The x-axis represents the time of each test, while the y-axis indicates the number of CTCs. Different colors and symbols are employed to differentiate among various types of CTCs and stages of disease progression: CR denotes complete response, PR indicates partial response, SD refers to stable disease, and PD signifies progressive disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.8.2. Patient #2\u003c/h2\u003e \u003cp\u003eA total of 11 Circulating Tumor Cell (CTC) tests were conducted on this patient. From the first to the eighth test, the number of CTCs exhibited minimal variation or a slight increase. Concurrently, the disease status transitioned from complete response (CR) to stable disease (SD), where it remained stable. The ninth test indicated a decrease in CTCs compared to the eighth test, resulting in a partial response (PR). However, a significant increase in CTCs was observed thereafter, followed by a dramatic decrease in the eleventh test. During this period, the disease progression shifted from partial response (PR) to progressive disease (PD) ( (see Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sequential changes in circulating tumor cell (CTC) counts and disease progression in Patient #2 clearly illustrate the relationship between fluctuations in CTC numbers and alterations in disease status. This provides an intuitive representation of the potential role of CTCs in monitoring disease progression. The abbreviations used are as follows: CR for complete response, PR for partial response, SD for stable disease, and PD for progressive disease.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCirculating tumor cells (CTCs) have emerged as critical biomarkers for tumor TNM staging, prognostic evaluation, and treatment efficacy monitoring \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Given the extremely low concentration of CTCs in peripheral blood (approximately 1 CTC per million mononuclear cells in advanced solid tumor patients), efficient isolation and identification of ultra-low-abundance CTCs remain central challenges for clinical application. While the CellSearch system represents a classic platform for CTC detection \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, this study utilized the CanPatrol\u0026trade; technology, which not only demonstrates reliable detection efficiency but also enables phenotypic characterization of epithelial-mesenchymal transition (EMT), facilitating assessment of tumor aggressiveness and mechanistic insights into CTC-mediated metastasis.\u003c/p\u003e \u003cp\u003eThe overall CTC positivity rate in this cohort was 81.2%, with a mesenchymal CTC positivity rate of 44.1%, both exceeding findings from previous studies. This may be attributed to the high sensitivity of CanPatrol\u0026trade; technology for low-abundance CTCs, while patient population characteristics (e.g., disease stage, tumor histology) may also influence results, warranting further validation through sample size expansion and stratified analysis.\u003c/p\u003e \u003cp\u003eThe CCR7 positivity rate in CTCs was 58.4%. Among 57 patients undergoing two or more CTC tests, the second test revealed an 86.0% CTC positivity rate, 40.4% mesenchymal CTC positivity, and 65.3% CCR7 positivity in CTC-positive cases. Although total CTC positivity, mesenchymal CTC positivity, and CCR7 positivity did not differ significantly across tumor stages (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), these rates were markedly higher in the known-staging group versus the unknown-staging group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNotably, while CCR7 expression did not vary significantly among CTC subtypes (epithelial, mixed, mesenchymal; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), it showed a strong association with disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was validated via Spearman/Pearson correlation analyses and Cox regression on CTC data from 213 lung cancer patients. Additionally, the counts of total CTCs, epithelial CTCs, mixed CTCs, and mesenchymal CTCs all positively correlated with disease progression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both the CTC count at the second test and its dynamic change were significantly associated with subsequent progression (correlation coefficients: r\u0026thinsp;=\u0026thinsp;0.474 for CTC counts, r\u0026thinsp;=\u0026thinsp;0.264 for CCR7 expression)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCase 1\u003c/strong\u003e \u003cp\u003eexhibited a sharp increase in CTC levels during disease progression (PD) after the fifth test, followed by a decrease coinciding with partial response (PR). Case 2 showed minimal CTC fluctuations across 11 tests until the eighth test, when disease status shifted from complete response (CR) to stable disease (SD). The ninth test indicated PR with reduced CTCs, while the tenth test revealed a sudden CTC surge, followed by a decrease at the eleventh test, paralleling disease progression from PR to PD. These findings suggest that dynamic CTC changes reflect tumor progression, with decreases indicating potential improvement and increases signifying deterioration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThis study advances our understanding of CCR7\u0026thinsp;+\u0026thinsp;CTCs in lung cancer, though several limitations require careful consideration. The retrospective single-center design (213 patients, 78.9% adenocarcinoma) necessitates multicenter validation to confirm generalizability. While CanPatrol\u0026trade; technology captures EMT phenotypes effectively, integrating genomic profiling (e.g., single-cell RNA sequencing) may uncover additional mechanisms underlying CCR7-driven metastasis. The median follow-up period of 38 months provides short-term prognostic data, but long-term studies are essential to solidify CCR7\u0026rsquo;s role in predicting late recurrence. Prospective trials are also needed to translate these findings into clinical practice for treatment decision guidance.\u003c/p\u003e \u003cp\u003eNotably, the observed histotype-specific CCR7 expression in adenocarcinoma justifies targeted multicenter investigations. Future studies should incorporate functional validation to clarify CCR7\u0026rsquo;s clinical value as a biomarker. This work lays a foundation for exploring CCR7 in tumor metastasis, with clear pathways for methodological refinement and translational expansion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial and non-financial interests in relation to the work described in the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXingping Hu and Qingsong Liu conceived and designed the study. Ying Huang,Shasha Zhu, Lu Qin, Mingyi Lin,Peng Zhao,Xingping Hu and Qingsong Liu conducted data processing and contributed to analysis and interpretation of data. Chunshu Fang and Qunyou Tan conducted the investigation. Xingping Hu and Qingsong Liu provided resources. Qingsong Liu and Xingping Hu performed data curation and validation. Shaolin Tao and Li Wang supervised the overall study. Li Wang was responsible for the project administration and funding acquisition. Xingping Hu, Ying Huang, Shaolin Tao and Li Wang drafted and edited the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study is supported\u0026ensp;by\u0026ensp;the joint project of Science and Technology Committee and Health Commission of Chongqing (No. 2025MSXM142). The study is supported in part by the National Nature Science Foundation of China (82402696) to LW, the Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1223) to LW, China Postdoctoral Science Foundation (2024M763898) to LW, Chongqing Postdoctoral Special Funding Project (2024CQBSHTB3005) to LW.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll raw data generated and analyzed in this study (including CTC detection raw data of 213 lung cancer patients, clinicopathological characteristic data, follow-up data, and original statistical analysis records) involve patient privacy and comply with clinical data security regulations. Thus, the data are not publicly available in public repositories for the time being.In accordance with the journal\u0026rsquo;s requirements, researchers who need to use the study data may submit a reasonable request to the corresponding authors:Shaolin Tao (E-mail: [email protected])Xingping Hu (E-mail: [email protected])The request should include the purpose of data use and proof of ethical approval for the intended research. After review and approval, access to the data will be provided.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA: A Cancer Journal for Clinicians, 2020, 70(1): 7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang Y, et al. Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non - small cell lung cancer[J]. Front Oncol. 2023;13:1145128\u0026ndash;.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen D, et al. Efficacy and safety of immunotherapy combined with single - agent chemotherapy as second - or later - line therapy for metastatic non - small cell lung cancer[J]. Front Immunol. 2023;14:1145128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, et al. Predicting chemotherapy response in non - small - cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined[J]. Med (Baltim). 2022;101(34):e30957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, et al. Cluster circulating tumor cells as predictors of adjuvant chemotherapy efficacy in lung cancer[J]. Chin J Lung Cancer. 2024;27(6):481\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Li Y, Zhang M, et al. Establishment and application of cell samples with high - expression of CCR7[J]. J Cell Res. 2021;31(3):256\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Wang Q, Zhao L, et al. Application of normal tissue extracts as negative controls in CCR7 - related experiments[J]. Oncol Res. 2020;28(4):345\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao X, et al. Construction of a precise prediction model for the efficacy of chemotherapy / targeted therapy in non - small - cell lung cancer[J]. Chin J Biomedical Eng. 2021;37(5):557\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, et al. CTC enumeration and its significance in personalized medicine for non - small - cell lung cancer[J]. Chin J Cancer Res. 2025;37(2):151\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H et al. Impact of Incorporating CTC EMT Detection into Comprehensive Diagnostic Approaches on the Precision of Cancer Staging and Efficacy Assessment of Treatment [J]. Journal of Oncology, 2025, 2025: 8647523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, et al. The Role of CanPatrol\u0026trade; Technology in Personalized Treatment of Cancer Based on CTC Detection [J]. Chin J Cancer Res. 2024;36(5):601\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, et al. Evaluation of the Efficacy of CanPatrol\u0026trade; Technology in Detecting Circulating Tumor Cells in Various Cancers [J]. J Clin Oncol Cancer Res. 2025;12(2):115\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, et al. Verification of the Specificity and Effectiveness of CCR7 Antibody in the Research of Breast Cancer Metastasis [J]. J Oncol Res. 2025;13(3):231\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, et al. Application of the CanPatrol\u0026trade; Technology in Detecting Circulating Tumor Cells in Gastric Cancer [J]. Chin J Gastroenterol Hepatol. 2025;34(5):521\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, et al. Exploring the Association between CTC Epithelial - Mesenchymal Transition and Metastasis in Pancreatic Cancer [J]. Pancreatology. 2024;24(6):712\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, et al. Verification of the Reliability of Nanomembrane Filtration - based CTC Detection in Lung Cancer Diagnosis [J]. Chin J Lung Cancer. 2025;28(3):192\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, et al. Application of Circulating Tumor Cells in Monitoring the Efficacy of Lung Cancer Treatment [J]. J Clin Oncol. 2024;42(22):2015\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, et al. Correlation between Circulating Tumor Cells and Prognosis of Breast Cancer Patients [J]. Chin J Oncol. 2025;47(3):232\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, Circulating tumor cells (CTCs), Epithelial - mesenchymal transition (EMT), CCR7, Disease progression","lastPublishedDoi":"10.21203/rs.3.rs-8093072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8093072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the clinical significance of CCR7-expressing circulating tumor cells (CTCs) in lung cancer,with a particular focus on their association with epithelial-mesenchymal transition (EMT) and disease progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing CanPatrol\u0026trade; nanomembrane filtration and RNA in situ hybridization, we analyzed CTCs from 213 lung cancer patients. These CTCs were classified into epithelial, mesenchymal, or hybrid phenotypes, and CCR7 expression was assessed. Clinical correlations were evaluated using Spearman and Pearson correlation analyses, as well as Cox regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe baseline circulating tumor cell (CTC) positivity rate was 81.2%, with mesenchymal CTCs accounting for 44.1% of the positive cases. The overall CCR7 positivity in CTCs was 58.4%, which was significantly higher in adenocarcinoma (58.4%) compared to squamous cell carcinoma (45.9%, P\u0026thinsp;=\u0026thinsp;0.038). Furthermore, CCR7 expression exhibited a strong correlation with disease progression (r\u0026thinsp;=\u0026thinsp;0.264, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and served as an independent predictor of poor prognosis (HR\u0026thinsp;=\u0026thinsp;2.6, 95% CI: 1.8\u0026ndash;3.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although mesenchymal CTCs displayed a higher CCR7 positivity rate (27.9%) than their epithelial (27.2%) or hybrid (31.4%) counterparts, this difference was not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study investigates the correlation between CCR7\u0026thinsp;+\u0026thinsp;CTCs and the progression of lung cancer. The findings demonstrate that CCR7\u0026thinsp;+\u0026thinsp;CTCs are significantly linked to aggressive disease progression, particularly within adenocarcinoma subtypes. Data reveal a higher prevalence of CCR7 positivity among adenocarcinoma patients, suggesting a connection between its expression and disease advancement. CCR7 appears to activate signaling pathways that promote tumor migration, invasion, and proliferation, potentially through the process of epithelial-mesenchymal transition (EMT). Consequently, CCR7\u0026thinsp;+\u0026thinsp;CTCs may serve as a valuable biomarker for lung cancer, particularly in cases of adenocarcinoma. The detection of CCR7 in CTCs could improve clinical assessments and facilitate early intervention. However, this study acknowledges certain limitations, including a lack of comprehensive consideration of drug effects on CTCs and a relatively small sample size, highlighting the necessity for further investigation.\u003c/p\u003e","manuscriptTitle":"CCR7-Positive Circulating Tumor Cells as a Biomarker for Predicting Lung Cancer Risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:40:30","doi":"10.21203/rs.3.rs-8093072/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-22T14:19:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T21:20:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295420193333658482531811280737898834559","date":"2026-02-12T23:55:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117451040213818369372763289610535758080","date":"2026-02-12T08:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81234028057418801311358355776590780150","date":"2026-02-12T08:49:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145738149775961221792937811719068432236","date":"2026-02-08T16:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T04:52:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T04:18:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T03:29:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T02:58:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-11-18T02:54:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92ed3dd0-31ea-4c1f-9e15-d31a4d598e5b","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T12:40:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:40:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8093072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8093072","identity":"rs-8093072","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}