Lymph Node Metastasis Of Non-small Cell Lung Cancer Research Articles

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph node metastasis is essential for improving the clinical management of NSCLC patients. In this research, the WGCNA algorithm was utilized to pinpoint genes linked to lymph node metastasis in NSCLC. A cluster analysis was carried out to investigate how these genes correlate with the prognosis and the outcomes of immunotherapy for NSCLC patients. Following this, diagnostic and prognostic models were created and validated through various machine learning methodologies. The random forest technique highlighted the importance of ARHGAP11A, leading to an in-depth examination of its role in NSCLC. By analyzing 78 tissue chip samples from NSCLC patients, the study confirmed the association between ARHGAP11A expression, patient prognosis, and lymph node metastasis. Finally, the influence of ARHGAP11A on NSCLC cells was assessed through cell function experiments. This research utilized the WGCNA technique to identify 25 genes that are related to lymph node metastasis, clarifying their connections with tumor invasion, growth, and the activation of stemness pathways. Cluster analysis revealed significant associations between these genes and lymph node metastasis in NSCLC, especially concerning immunotherapy and targeted treatments. A diagnostic system that combines various machine learning approaches demonstrated strong efficacy in forecasting both the diagnosis and prognosis of NSCLC. Importantly, ARHGAP11A was identified as a key prognostic gene associated with lymph node metastasis in NSCLC. Molecular docking analyses suggested that ARHGAP11A has a strong affinity for targeted therapies within NSCLC. Additionally, immunohistochemical assessments confirmed that higher levels of ARHGAP11A expression correlate with unfavorable outcomes for NSCLC patients. Experiments on cells showed that reducing ARHGAP11A expression can hinder the proliferation, metastasis, and stemness traits of NSCLC cells. This investigation reveals the novel insight that ARHGAP11A may function as a potential biomarker connected to lymph node metastasis in NSCLC. Moreover, reducing the expression of ARHGAP11A has demonstrated the ability to diminish tumor stemness characteristics, presenting a promising opportunity for improving treatment strategies for this condition.

Machine learning predictive models and risk factors for lymph node metastasis in non-small cell lung cancer

BackgroundThe prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.MethodsClinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.ResultsWe enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (https://wubopredict.shinyapps.io/dynnomapp/).ConclusionThe predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Head-to-head comparison of 18F-FDG PET/CT and 18F-FDG PET/MRI for lymph node metastasis staging in non-small cell lung cancer: a meta-analysis.

The current meta-analysis aimed to compare the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) with 18F-FDG PET/magnetic resonance imaging (MRI) in non-small cell lung cancer (NSCLC) lymph node metastasis staging. We searched the PubMed, Web of Science, and Embase databases for relevant articles between November 1992 and September 2022. Studies evaluating the head-to-head comparison of 18F-FDG PET/CT and 18F-FDG PET/MRI for lymph node metastasis in patients with NSCLC were included. The quality of each study was assessed using the Quality Assessment of Diagnostic Performance Studies-2 tool. The analysis includes six studies with a total of 434 patients. The pooled sensitivity of 18F-FDG PET/CT and 18F-FDG PET/MRI was 0.78 [95% confidence interval (CI): 0.59-0.90] and 0.84 (95% CI: 0.68-0.93), and the pooled specificity was 0.87 (95% CI: 0.72-0.94) and 0.87 (95% CI: 0.80-0.92), respectively. The accuracy of 18F-FDG PET/CT and 18F-FDG PET/MRI was 0.81 (95% CI: 0.71-0.90) and 0.84 (95% CI: 0.75-0.92), respectively. When the pre-test probability was set at 50%, the post-test probability for 18F-FDG PET/CT could increase to 85%, and the post-test probability for 18F-FDG PET/MRI could increase to 87%. 18F-FDG PET/CT and 18F-FDG PET/MRI have similar diagnostic performance in detecting lymph node metastasis in NSCLC. However, the results of this study were from a small sample study, and further studies with larger sample sizes are needed.

Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.

The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer.

Whether lymph node metastasis in non-small cell lung cancer is critical to clinical decision-making. This study was to develop a non-invasive predictive model for preoperative assessing lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using radiomic features from chest CT images. In this retrospective study, 247 patients with resectable non-small cell lung cancer (NSCLC) were enrolled. These individuals underwent preoperative chest CT scans that identified lung nodules, followed by lobectomies and either lymph node sampling or dissection. We extracted both intratumoral and peritumoral radiomic features from the CT images, which were used as covariates to predict the lymph node metastasis status. By using ROC curves, Delong tests, Calibration curve, and DCA curves, intra-tumoral-peri-tumoral model performance were compared with models using only intratumoral features or clinical information. Finally, we constructed a model that combined clinical information and radiomic features to increase clinical applicability. This study enrolled 247 patients (117 male and 130 females). In terms of predicting lymph node metastasis, the intra-tumoral-peri-tumoral model (0.953, 95%CI 0.9272-0.9792) has a higher AUC compared to the intratumoral radiomics model (0.898, 95%CI 0.8553-0.9402) and the clinical model (0.818, 95%CI 0.7653-0.8709). The DeLong test shows that the performance of the Intratumoral and Peritumoral radiomics models is superior to that of the Intratumoral or clinical feature model (p <0.001). In addition, to increase the clinical applicability of the model, we combined the intratumoral-peritumoral model and clinical information to construct a nomogram. Nomograms still have good predictive performance. The radiomics-based model incorporating both peritumoral and intratumoral features from CT images can more accurately predict lymph node metastasis in NSCLC than traditional methods.

SUMOylation-triggered ALIX activation modulates extracellular vesicles circTLCD4-RWDD3 to promote lymphatic metastasis of non-small cell lung cancer

Lymph node (LN) metastasis is one of the predominant metastatic routes of non-small cell lung cancer (NSCLC) and is considered as a leading cause for the unsatisfactory prognosis of patients. Although lymphangiogenesis is well-recognized as a crucial process in mediating LN metastasis, the regulatory mechanism involving lymphangiogenesis and LN metastasis in NSCLC remains unclear. In this study, we employed high-throughput sequencing to identify a novel circular RNA (circRNA), circTLCD4-RWDD3, which was significantly upregulated in extracellular vesicles (EVs) from LN metastatic NSCLC and was positively associated with deteriorated OS and DFS of patients with NSCLC from multicenter clinical cohort. Downregulating the expression of EV-packaged circTLCD4-RWDD3 inhibited lymphangiogenesis and LN metastasis of NSCLC both in vitro and in vivo. Mechanically, circTLCD4-RWDD3 physically interacted with hnRNPA2B1 and mediated the SUMO2 modification at K108 residue of hnRNPA2B1 by upregulating UBC9. Subsequently, circTLCD4-RWDD3-induced SUMOylated hnRNPA2B1 was recognized by the SUMO interaction motif (SIM) of ALIX and activated ALIX to recruit ESCRT-III, thereby facilitating the sorting of circTLCD4-RWDD3 into NSCLC cell-derived EVs. Moreover, EV-packaged circTLCD4-RWDD3 was internalized by lymphatic endothelial cells to activate the transcription of PROX1, resulting in the lymphangiogenesis and LN metastasis of NSCLC. Importantly, blocking EV-mediated transmission of circTLCD4-RWDD3 via mutating SIM in ALIX or K108 residue of hnRNPA2B1 inhibited the lymphangiogenesis and LN metastasis of NSCLC in vivo. Our findings reveal a precise mechanism underlying SUMOylated hnRNPA2B1-induced EV packaging of circTLCD4-RWDD3 in facilitating LN metastasis of NSCLC, suggesting that EV-packaged circTLCD4-RWDD3 could be a potential therapeutic target against LN metastatic NSCLC.

An accurate prediction of negative lymph node metastasis with consideration of glucose metabolism in early-stage non-small cell lung cancer.

We aimed to identify risk factors in lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) and predict lymph node metastasis. A total of 416 patients with clinical stage IA2-3 NSCLC who underwent lobectomy and lymph node dissection between July 2016 and December 2020 at National Cancer Center Hospital East were included. Multivariable logistic regression was performed to develop a model for predicting lymph node metastasis. Leave-one-out cross-validation was performed to evaluate the developing prediction model, and sensitivity, specificity, and concordance statistics were calculated to evaluate its diagnostic performance. The formula for calculating the probability of pathological lymph node metastasis included SUVmax of the primary tumor and serum CEA level. The concordance statistics was 0.7452. When the cutoff value associated with the risk of incorrectly predicting pathological lymph node metastasis was 7.2%, the diagnostic sensitivity and specificity for predicting metastasis were 96.4% and 38.6%, respectively. We created a prediction model for lymph node metastasis in NSCLC by combining the SUVmax of the primary tumor and serum CEA levels, which showed a particularly strong association. This model is clinically useful as it successfully predicts negative lymph node metastasis in patients with clinical stage IA2-3 NSCLC.

The value on SUV-derived parameters assessed on 18F-FDG PET/CT for predicting mediastinal lymph node metastasis in non-small cell lung cancer

PurposeTo explore valuable predictors for mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC) patients, we analyzed the potential roles of standardized uptake value (SUV)-derived parameters from preoperative 18F-FDG PET/CT combined with clinical characteristics.MethodsData from 224 NSCLC patients who underwent preoperative 18F-FDG PET/CT scans in our hospital were collected. Then, a series of clinical parameters including SUV-derived features [SUVmax of mediastinal lymph node and primary-tumor SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] were evaluated. The best possible cutoff points for all measuring parameters were calculated using receiver operating characteristic curve (ROC) analysis. Predictive analyses were performed using a Logistic regression model to determine the predictive factors for mediastinal lymph node metastasis in NSCLC and lung adenocarcinoma patients. After multivariate model construction, data of another 100 NSCLC patients were recorded. Then, 224 patients and 100 patients were enrolled to validate the predictive model by the area under the receiver operating characteristic curve (AUC).ResultsThe mediastinal lymph node metastasis rates in 224 patients for model construction and 100 patients for model validation were 24.1% (54/224) and 25% (25/100), respectively. It was found that SUVmax of mediastinal lymph node ≥ 2.49, primary-tumor SUVmax ≥ 4.11, primary-tumor SUVpeak ≥ 2.92, primary-tumor SUVmean ≥ 2.39, primary-tumor MTV ≥ 30.88 cm3, and primary-tumor TLG ≥ 83.53 were more prone to mediastinal lymph node metastasis through univariate logistic regression analyses. The multivariate logistic regression analyses showed that the SUVmax of mediastinal lymph nodes (≥ 2.49: OR 7.215, 95% CI 3.326–15.649), primary-tumor SUVpeak (≥ 2.92: OR 5.717, 95% CI 2.094–15.605), CEA (≥ 3.94 ng/ml: OR 2.467, 95% CI 1.182–5.149), and SCC (< 1.15 ng/ml: OR 4.795, 95% CI 2.019–11.388) were independent predictive factors for lymph node metastasis in the mediastinum. It was found that SUVmax of the mediastinal lymph node (≥ 2.49: OR 8.067, 95% CI 3.193–20.383), primary-tumor SUVpeak (≥ 2.92: OR 9.219, 95% CI 3.096–27.452), and CA19-9 (≥ 16.6 U/ml: OR 3.750, 95% CI 1.485–9.470) were significant predictive factors for mediastinal lymph node metastasis in lung adenocarcinoma patients. The AUCs for the predictive value of the NSCLC multivariate model through internal and external validation were 0.833 (95% CI 0.769- 0.896) and 0.811 (95% CI 0.712–0.911), respectively.ConclusionHigh SUV-derived parameters (SUVmax of mediastinal lymph node and primary-tumor SUVmax, SUVpeak, SUVmean, MTV and TLG) might provide varying degrees of predictive value for mediastinal lymph node metastasis in NSCLC patients. In particular, the SUVmax of mediastinal lymph nodes and primary-tumor SUVpeak could be independently and significantly associated with mediastinal lymph node metastasis in NSCLC and lung adenocarcinoma patients. Internal and external validation confirmed that the pretherapeutic SUVmax of the mediastinal lymph node and primary-tumor SUVpeak combined with serum CEA and SCC can effectively predict mediastinal lymph node metastasis of NSCLC patients.

Role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters and Extracellular Volume Fraction as Predictors of Lung Cancer Subtypes and Lymph Node Status in Non-Small-Cell Lung Cancer Patients.

Objective: The aim of this study is to determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based quantitative parameters and the extracellular volume fraction (ECV) can differentiate small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC), squamous-cell carcinoma (SCC) from adenocarcinoma (Adeno-Ca), and NSCLC with lymph node metastasis from NSCLC without lymph node metastasis. Materials and methods: We prospectively enrolled patients with lung cancer (41 Adeno-Ca, 29 SCC, and 23 SCLC) who underwent DCE-MRI and enhanced T1 mapping prior to histopathological confirmation. Quantitative parameters based on DCE-MRI and ECV based on T1 mapping were compared between SCLC and NSCLC patients, between SCC and Adeno-Ca patients, and between NSCLC patients with and without lymph node metastasis. The area under the receiver-operating characteristic curve (AUC) was used to evaluate the diagnostic performance of each parameter. Spearman rank correlation was used to clarify the associations between ECV and DCE-MRI-derived parameters. Results: Ktrans, Kep, Ve, and ECV all performed well in differentiating SCLC from NSCLC (AUC > 0.729). Ktrans showed the best performance in differentiating SCC from Adeno-Ca (AUC = 0.836). ECV could differentiate NSCLCs with and without lymph node metastases (AUC = 0.764). ECV showed a significant positive correlation with both Ktrans and Ve. Conclusions: Ktrans is the most promising imaging parameter to differentiate SCLC from NSCLC, and Adeno-Ca from SCC. ECV was helpful in detecting lymph node metastasis in NSCLC. These imaging parameters may help guide the selection of lung cancer treatment.

Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC

PurposeThe goal of this study was to investigate whether the combined PET/CT radiomic features of the primary tumor and lymph node could predict lymph node metastasis (LNM) of resectable non-small cell lung cancer (NSCLC) in stage T2-4.MethodsThis retrospective study included 192 NSCLC patients who underwent tumor and node dissection between August 2016 and December 2017 and underwent 18F-fluorodeoxyglucose (18F-FDG) PET/CT scanning 1–3 weeks before surgery. In total, 192 primary tumors (> 3 cm) and 462 lymph nodes (LN > 0.5 cm) were analyzed. The pretreatment clinical features of these patients were recorded, and the radiomic features of their primary tumor and lymph node were extracted from PET/CT imaging. The Spearman’s relevance combined with the least absolute shrinkage and selection operator was used for radiomic feature selection. Five independent machine learning models (multi-layer perceptron, extreme Gradient Boosting, light gradient boosting machine, gradient boosting decision tree, and support vector machine) were tested as classifiers for model development. We developed the following three models to predict LNM: tumor PET/CT-clinical (TPC), lymph PET/CT-clinical (LPC), and tumor and lymph PET/CT-clinical (TLPC). The performance of the models and the clinical node (cN) staging was evaluated using the ROC curve and confusion matrix analysis.ResultsThe ROC analysis showed that among the three models, the TLPC model had better predictive clinical utility and efficiency in predicting LNM of NSCLC (AUC = 0.93, accuracy = 85%; sensitivity = 0.93; specificity = 0.75) than both the TPC model (AUC = 0.54, accuracy = 50%; specificity = 0.38; sensitivity = 0.59) and the LPC model (AUC = 0.82, accuracy = 70%; specificity = 0.41; sensitivity = 0.92). The TLPC model also exhibited great potential in predicting the N2 stage in NSCLC (AUC = 0.94, accuracy = 79%; specificity = 0.64; sensitivity = 0.91).ConclusionThe combination of CT and PET radiomic features of the primary tumor and lymph node showed great potential for predicting LNM of resectable T2-4 NSCLC. The TLPC model can non-invasively predict lymph node metastasis in NSCLC, which may be helpful for clinicians to develop more rational therapeutic strategies.

Correlation Study on the Expression of INSR, IRS-1, and PD-L1 in Nonsmall Cell Lung Cancer.

Objective To study the expression and correlation of insulin receptor (INSR), insulin receptor substrate-1 (IRS-1), and programmed cell death ligand-1 (PD-L1) in nonsmall cell lung cancer (NSCLC). Methods 45 lung cancer tissues and 30 adjacent normal tissues of NSCLC patients diagnosed in the Second Affiliated Hospital of Shandong First Medical University from June 2019 to August 2020 were selected. The expressions of INSR, IRS-1, and PD-L1 proteins in tumor tissues and adjacent tissues of NSCLC were detected by immunohistochemical staining. Results The expression of INSR and IRS-1 in NSCLC was significantly higher than that in adjacent normal lung tissue (P < 0.05). INSR expression had statistical significance with the degree of pathological differentiation of nonsmall cell carcinoma (P = 0.031), but had no significant association with age, gender, pathological type, TNM stage, and lymph node metastasis status (P > 0.05). There was no significant correlation between IRS-1 positive expression and NSCLC patients' age, gender, pathological typing, degree of differentiation, TNM stage, and lymph node metastasis (P > 0.05). PD-L1 positive expression was correlated with lymph node metastasis of NSCLC (P = 0.028), while there was no significant correlation with gender, age, pathological type, TNM stage, and pathological differentiation degree of NSCLC patients (P > 0.05). Spearman correlation analysis showed that PD-L1 protein expression had a significant positive correlation with IRS-1 protein expression (r = 0.373), but was not correlated with the expression of INSR protein. Conclusion IRS-1 may be involved in the regulation of PD-L1 expression and mediate the occurrence of tumor immune escape, which is expected to become a new target for NSCLC immunotherapy and provide new clinical evidence for immunosuppressive therapy.

Correlation between the Expression of VEGF and Ki67 and Lymph Node Metastasis in Non-small-Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Background Lymph node metastasis is the most common and important way of metastasis in NSCLC and is also the most important factor affecting lung cancer stage and prognosis. It is very important to analyze the relationship between the expression of vascular endothelial growth factor (VEGF) and Ki67 and lymph node metastasis (LNM) in non-small-cell lung cancer (NSCLC). Methods We searched the PubMed, EMBASE, and Cochrane Library and conducted meta-analyses using the R meta-package. Relative risk (RR) with a 95% confidence interval (95% CI) was the main indicator. Results Totally, 18 studies were considered eligible, with 4521 patients, including 1518 LNM-positive patients and 3033 LNM-negative patients. The incidence of LNM in Ki67-negative patients was lower than that in Ki67-positive patients (RR = 0.66, 95% CI: 0.44, 0.98). The incidence of LNM in VEGF-A-negative patients was lower than that in VEGF-A-positive patients (RR = 0.64, 95% CI: 0.49, 0.83). The incidence of LNM in VEGF-C negative patients was lower than that in VEGF-C positive patients (RR = 0.68, 95% CI: 0.53, 0.88). The incidence of LNM in VEGF-D negative and positive patients were of no significant differences (RR = 0.84, 95% CI: 0.61, 1.14). Conclusion The high expression of Ki67, VEGF-A, and VEGF-C significantly increases the risk of lymph node metastasis in NSCLC, while the VEGF-D expression has no correlation with lymph node metastasis. The expression levels of Ki67, VEGF-A, and VEGF-C show a good potential for lymph node metastasis prediction.

The diagnostic value of PET/CT for the lymph node metastasis in Asian patients with non-small cell lung cancer: A meta-analysis.

To evaluate the accuracy of positron emission tomography/computed tomography (PET/CT) in the diagnosis of lymph node metastasis in non-small cell lung cancer (NSCLC) by a method of meta-analysis. A comprehensive search of PubMed, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Embase data bases was conducted to collect literature about PET/CT diagnosing lymph node metastasis of NSCLC up to December 1, 2021. Stata 15.0 software was used for the calculation of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). The publication bias was evaluated by Deeks' funnel plot. A total of 25 studies were enrolled, including 2,458 patients with NSCLC. The pooled sensitivity of PET/CT for diagnosing lymph node metastasis in NSCLC was 0.68 (95%CI: 0.61-0.75), the pooled specificity being 0.93 (95%CI: 0.89-0.95). Likelihood ratio syntheses gave an overall PLR of 9.4 (95%CI: 6.3-13.9), and NLR of 0.34 (95%CI: 0.28-0.41). The pooled DOR was 28 (95%CI: 19-40). The summary receiver operating characteristic curve showed the area under the curve of 0.88 (95%CI: 0.84-0.90). Positron emission tomography/CT has a good value in the diagnosis of lymph node metastasis of NSCLC, with specificity excellent. Positron emission tomography/CT can be used as one of the main imaging diagnosis methods for lymph node metastasis in patients with NSCLC.

The diagnostic value of metabolic, morphological and heterogeneous parameters of 18F-FDG PET/CT in mediastinal lymph node metastasis of non-small cell lung cancer.

To investigate the value of PET/CT metabolic, morphological and heterogeneous parameters in the diagnosis of 18F-FDG positive mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC). A total of 156 patients with pathologically diagnosed NSCLC and underwent 18F-FDG PET/CT scans were enrolled in this study. Mediastinal lymph nodes with 18F-FDG uptake greater than the mediastinum were analyzed. The metabolic parameters of maximum and mean standardized uptake value (SUVmax, SUVmean), SUVratio (node SUVmax/mediastinum SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), morphological parameters of maximum short diameter (Dmin), CT values and metabolic heterogeneity parameter of coefficient of variation (COV) were measured. The performance of each parameter and their combinations for diagnosis of lymph node metastasis was evaluated through receiver operating characteristic (ROC) curves and binary logistic regression analysis. There were 206 lymph nodes with pathological evidence included in the study, including 103 metastatic and 103 nonmetastatic nodes. The SUVmax, SUVmean, SUVratio, TLG, COV and Dmin of metastatic lymph nodes were significantly higher/greater than those in nonmetastatic ones (P < 0.05). ROC curve analysis revealed that the combination of SUVratio, Dmin and COV showed the highest diagnostic efficacy among all single and combined parameters, the area under the curve (AUC) was 0.907 (P = 0.000), these three parameters all increased the risk of lymph node metastasis, with odds ratios of 1.848, 1.293 and 1.258, respectively (all P < 0.05). Heterogeneity parameter was helpful for the accurate distinction of mediastinal lymph node metastasis in NSCLC. The combination of the SUVratio, Dmin and COV could improve the diagnostic accuracy. Multiple-parameters analysis plays an important complementary role in the diagnosis of lymph node metastasis.

Driver and novel genes correlated with metastasis of non-small cell lung cancer: A comprehensive analysis

Although mutations of genes are crucial events in tumorigenesis and development, the association between gene mutations and lung cancer metastasis is still largely unknown. The goal of this study is to identify driver and novel genes associated with non-small cell lung cancer (NSCLC) metastasis. Candidate genes were identified using a novel comprehensive analysis, which was based on bioinformatics technology and meta-analysis. Firstly, EGFR, KRAS, ALK, TP53, BRAF and PIK3CA were identified as candidate driver genes. Further meta-analysis identified that EGFR (Pooled OR 1.33, 95% CI 1.19, 1.50; P < .001) and ALK (Pooled OR 1.52, 95% CI 1.22, 1.89; P < .001) mutations were associated with distant metastasis of NSCLC. Besides, ALK (Pooled OR 2.40, 95% CI 1.71, 3.38; P < .001) mutation was associated with lymph node metastasis of NSCLC. In addition, thirteen novel gene mutations were identified to be correlated with NSCLC metastasis, including SMARCA1, GGCX, KIF24, LRRK1, LILRA4, OR2T10, EDNRB, NR1H4, ARID4A, PRKCI, PABPC5, ACAN and TLN1. Furthermore, elevated mRNA expression level of SMARCA1 and EDNRB was associated with poor overall survival in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. Additionally, pathway and protein-protein interactions network analyses found the two genes were correlated with epithelial-mesenchymal transition process. In conclusion, mutations of EGFR and ALK were significantly correlated with NSCLC metastasis. In addition, thirteen novel genes were identified to be associated with NSCLC metastasis, especially SMARCA1 in LUAD and EDNRB in LUSC.

The prognostic value of USP14 and PSMD14 expression in non- small cell lung cancer

BackgroundUbiquitin specific peptidase 14 (USP14) and proteasome 26S subunit, non-ATPase 14 (PSMD14) are two deubiquitinases that are closely related to the human 19S proteasome. These are highly expressed in various types of cancers and are associated with prognosis. However, the expression, clinicopathological features, and prognostic relevance of these two deubiquitinases remain unclear in patients with non-small cell lung cancer (NSCLC). Moreover, the correlation between the expression of these two deubiquitinases in NSCLC has not been reported.MethodsIn this study, the expression of USP14 and PSMD14 in NSCLC tissues and adjacent non-tumor tissues were examined by immunohistochemical staining. The association of these two deubiquitinases with the clinicopathological features and overall survival (OS) of patients with NSCLC was evaluated meanwhile.ResultsThe expression of USP14 and PSMD14 was upregulated in NSCLC tissues compared with adjacent non-tumor tissues. High expression of both these deubiquitinases was positively correlated with the TNM stage of NSCLC. In addition, PSMD14 was positively correlated with lymph node metastasis in NSCLC. The survival analysis showed that elevated levels of USP14 or PSMD14 were associated with poorer survival of NSCLC patients compared with low expression of USP14 or PSMD14. Cox regression analysis indicated that TNM stage, USP14, and PSMD14 were independent prognostic factors for OS in NSCLC.ConclusionsThis study demonstrated that USP14 and PSMD14 may play important roles in the progression of NSCLC, especially when they are expressed simultaneously at elevated levels. Thus, USP14 and PSMD14 may be potential novel biomarkers and therapeutic targets for the prognosis and treatment of patients with NSCLC.

Driver Gene Mutations and Risk of Lymph Node Metastasis in Non-Small Cell Lung Cancer

Non-Small Cell Lung Cancer (NSCLC) lymph node status is closely related to its diagnosis, treatment, and prognosis. The lymph node status is an important basis for formulating clinical treatment strategies of NSCLC, therefore comprehensive and profound understanding of risk factors for lymph node metastasis is essential. There are many known factors for lymph node metastasis in NSCLC, such as pathological subtypes, tumor size, tumor location. Meanwhile, whether the mutation of the driver gene affects the lymph node metastasis is still lacking enough research. This article aims to elaborate the relationship between NSCLC driver gene mutation and lymph node metastasis, from NSCLC lymph node metastasis-related risk factors, driver genes and lymph node metastasis, metastatic lymph node mutation status analysis and detection these several aspects to summarize the latest research progress in NSCLC driver gene mutation and lymph node metastasis risk. It also fully explained the correlation between driver gene mutations and NSCLC tumor biological behaviors such as lymph node metastasis.

Repression of the AURKA-CXCL5 axis induces autophagic cell death and promotes radiosensitivity in non-small-cell lung cancer.

Aurora kinase A (AURKA) regulates apoptosis and autophagy in various diseases and has shown promising clinical effects. Nevertheless, the complex regulatory mechanism of AURKA and autophagy in non-small-cell lung cancer (NSCLC) radiosensitivity remains to be elucidated. Here, we showed that AURKA was upregulated in NSCLC cell lines and tissues and that AURKA overexpression was significantly related to a poor prognosis, tumor stage and lymph node metastasis in NSCLC. Interestingly, AURKA expression was significantly increased after 8Gy radiotherapy. Silencing of AURKA enhanced radiosensitivity and impaired migration and invasion in vivo and in vitro. Mechanistically, we determined that CXCL5, a member of the chemokine family, was a key downstream effector of AURKA, and the phenotype induced by AURKA silencing was partly due to CXCL5 inhibition. We further demonstrated that the AURKA-CXCL5 axis played an essential role in NSCLC autophagy and that the activation of cytotoxic autophagy attenuated the malignant biological behavior of NSCLC cells mediated by AURKA-CXCL5. In general, we revealed the role of the AURKA-CXCL5 axis and autophagy in regulating the sensitivity of NSCLC cells to radiotherapy, which may provide potential therapeutic targets and new strategies for combatting NSCLC resistance to radiotherapy.

THE INFLAMMATORY STATUS AND LYMPH NODE METASTASES IN NON-SMALL CELL LUNG CANCER

Introduction. The development of inflammation is characterized by changes in blood hematology parameters and indices. Various inflammatory parameters are used to assess the inflammatory status (IS) during cancer treatment. Recent studies have revealed a relationship between tumor progression and the presence of chronic inflammation. Consequently, there have been many attempts to predict the risk of tumor recurrence and distant metastases, as well as patient’s survival assessing the various inflammatory markers. The relationship between IS parameters and lymph node metastasis remains poorly understood in non-small cell lung cancer (NSCLC).Material and Methods. The prospective study included 35 patients with NSCLC (T1–4N0–2M0). Seventeen patients received 2–3 cycles of neoadjuvant chemotherapy (NAC). A leukocyte formula was determined in the peripheral blood and inflammatory indices, such as neutrophils to lymphocytes ratio (NLR), platelets to lymphocytes ratio (PLR), lymphocytes to monocytes ratio (LMR) and systemic immuno-inflammatory index (SII) were calculated. In addition, the concentrations of fibrinogen, C-reactive protein (CRP) and cortisol were evaluated.Results. NAC alone did not significantly change the parameters of patients’ IS. Lymph node metastases were associated with changes in parameters indicating the enhanced IS. In the group of patients who did not receive NAC, lymph node metastasis was associated with fibrinogen blood levels (cut-off value 5.35 g/L), PLR index value (cut-off value 7.18) and cortisol blood concentration (cut-off value 414 nmol/mL). The confidence level was χ2 =10.118; р=0.018. In the group of patients who received NAC, lymph node metastasis was associated with the leukocyte count (cut-off value 7.1×109 /L), PLR index value (cut-off value is 7.18) and CRP blood concentration (cut-off value is 8.5 mg/L). The confidence level was χ2 =8.193; р=0.042.Conclusion. Risk of lymph node metastasis in NSCLC is associated with IS. Parameters of IS can be used to predict the risk of lymph node metastases.

Correlation study between flash dual source CT perfusion imaging and regional lymph node metastasis of non-small cell lung cancer

BackgroundTo explore the correlation of flash dual source computed tomography perfusion imaging (CTPI) and regional lymph node metastasis of non-small cell lung cancer (NSCLC), and to evaluate the value of CT perfusion parameters in predicting regional lymph node metastasis of NSCLC.Methods120 consecutive patients with NSCLC confirmed by postoperative histopathology were underwent flash dual source CT perfusion imaging in pre-operation. The CT perfusion parameters of NSCLC, such as blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability (PMB) were obtained by the image post-processing. Then microvessel density (MVD), luminal vascular number (LVN), luminal vascular area (LVA) and luminal vascular perimeter (LVP) of NSCLC were counted by immunohistochemistry. These cases were divided into group A (patients with lymph node metastasis, 58 cases) and group B (patients without lymph node metastasis, 62 cases) according to their pathological results. The CT perfusion parameters and the microvessel parameters were contrastively analysed between the two groups. Receiver operating characteristic (ROC) curve was used to assess the diagnostic efficiency of CT perfusion parameters in predicting regional lymph node metastasis of NSCLC in pre-operation.ResultsGroup A presented significantly lower LVA, BF and higher MTT, PMB than Group B (P < 0.05), while BV, LVN, LVP and MVD were no significant difference (P > 0.05). Correlation analysis showed that BF was correlated with LVA and LVP (P < 0.05), while BV, MTT and PMB were not correlated with LVN, LVA and LVP (P > 0.05). All the perfusion parameters were not correlated with MVD. According to the ROC curve analysis, when BF < 85.16 ml/100 ml/min as a cutoff point to predict regional lymph node metastasis of NSCLC, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value were 60.8, 81.7, 71.5, 75.6 and 69.5% respectively.ConclusionFlash dual source CT perfusion imaging can non-invasively indicate the luminal vascular structure of tumor and BF can be used as one of the important indexes in predicting regional lymph node metastasis of NSCLC in pre-operation.