Background Endoscopic submucosal dissection is effective for treating T1 early esophageal squamous cell carcinoma. However, there is a risk of lymph node metastasis . Standard radical surgery may be overtreatment for T1 ESCC. This trial was designed to clarify the efficacy of novel dual-scopy combined surgery in the treatment of T1 ESCC.Methods Between December 2021 and May 2025, 21 patients underwent dual-scopy combined surgery, 197 underwent ESD, and 213 underwent radical surgery for early (T1 stage) ESCC. This study included 63 patients who underwent ESD, dual-scopy combined surgery, and standard radical surgery (1:1:1) in the efficacy analysis. The patients' overall survival, recurrence-free survival, disease-specific survival, complications, and clinical outcomes were evaluated.Results There was no significant difference among the three groups in OS, RFS, and DSS. Complication rates were similar between the ESD and dual-scopy groups, both lower than in the radical surgery group. Compared to the radical surgery group, the ESD and dual-scopy groups demonstrated significantly shorter hospital stays and lower costs. Postoperative quality of life and nutritional levels were better in both the ESD and dual-scopy groups compared to radical surgery.Conclusions For T1-stage ESCC patients without clear metastasis, the novel dual-scopy combined surgery may achieve lesion resection and lymph node dissection without affecting patient survival. Moreover, it may reduce postoperative complications, hospitalization time, and costs, and potentially improve quality of life and nutritional status compared to standard radical surgery.Trial registration This study was registered as a clinical trial with the China Clinical Trial Registration Center (ChiCTR2100053603)

Osimertinib resistance poses a significant clinical challenge in treating non-small cell lung carcinoma (NSCLC) patients harboring EGFR-activating or T790M mutations, highlighting the urgent need to elucidate the underlying molecular mechanisms. In this study, we show that elevated USP20 expression drives osimertinib resistance and is associated with poor clinical outcomes in osimertinib-resistant NSCLC. Mechanistically, USP20 specifically interacts with PGAM1 and catalyzes the removal of K225-linked ubiquitin chains through its C154 catalytic site, thereby stabilizing PGAM1 to enhance glycolysis and promote osimertinib resistance. Importantly, through extensive virtual drug screening, we identified compound 89131-02-2 as a novel and selective inhibitor that targets the USP20 C154 catalytic site. Pharmacological inhibition of USP20 by 89131-02-2 effectively suppressed glycolysis and restored osimertinib sensitivity in functional assays. Our findings not only establish the USP20-PGAM1 axis as a key mediator of osimertinib response but also offer a potential therapeutic strategy to overcome resistance in NSCLC patients.

Background:Esophageal squamous cell carcinoma (ESCC) is highly prevalent in Asia. The management of T1b ESCC is challenging due to two competing concerns: the relatively low but prognostically significant risk of lymph node metastasis (LNM), and the considerable postoperative morbidity associated with radical esophagectomy. Based on the retrospective data and relevant studies, we pioneered a novel "dual-scopy combined surgery", as combining endoscopic submucosal dissection with the thoracoscopic lymph node sampling (ESD + TLNS) approach to achieve precise staging and esophagus preservation. Methods:In this prospective study, eligible T1b ESCC patients underwent the "dual-scopy combined surgery". We analyzed the baseline characteristics of nine participants and compared their perioperative and mid-to-long-term outcomes with those of patients undergoing radical esophagectomy. Results:Nine patients underwent the combined procedure. The mean number of lymph nodes sampled was 10.22±4.82, and all were negative on frozen section. Compared to a matched radical esophagectomy cohort (n=9), the ESD + TLNS group exhibited superior perioperative outcomes, including significantly shorter operative duration (135.38±48.00 vs. 372.11±80.07 min), reduced blood loss (43.75±15.98 vs. 174.44±77.32 mL), lower costs (¥65,691.35±49,477.91 vs. ¥108,392.38±14,441.56), and fewer major complications. There was no significant difference in recurrence during a mean follow-up of nearly 4 years. Conclusions:The "dual-scopy combined surgery" is safe and effective and lies in the realization of a transformation in the treatment paradigm. It represents a significant attempt to preserve the esophagus in the treatment of T1b esophageal cancer. This paradigm warrants validation in larger comparative studies.

Neoadjuvant immunochemotherapy (NAIC) induces tumor microenvironment remodeling in non-small cell lung cancer (NSCLC), presenting challenges for treatment response assessment. This study developed and validated a habitat radiomics approach for non-invasive prediction of tumor-infiltrating lymphocyte (TIL) status to evaluate NAIC response in NSCLC. This retrospective study enrolled 238 NSCLC patients following NAIC for clinical analysis, of which 201 patients met criteria for radiomics analysis. Patients were classified into TIL-positive and TIL-negative groups based on pathological assessment. Post-treatment computed tomography (CT) images were analyzed using K-means clustering to identify tumor habitat sub-regions for radiomic feature extraction. Seven machine learning algorithms were evaluated for TIL status prediction. Model interpretability was assessed through SHapley Additive exPlanations (SHAP) analysis. Single-cell RNA sequencing (scRNA-seq) data were analyzed to compare major pathological response (MPR) and non-MPR tumor microenvironments through cell type annotation, differentiation trajectory analysis, and intercellular communication network analysis. Pre-treatment neutrophil-to-lymphocyte ratio (NLR) showed association with pathological response in multivariable analysis. The radiomics cohort was randomly divided 7:3 into training (n = 140) and test (n = 61) sets. The Random Forest model achieved an area under the receiver operating characteristic curve (AUC) of 0.823 (95

Supplementary Table 11 Detection of SNVs and CNVs in HCC827OR and PC9OR

Background:Surgical resection remains the cornerstone of treatment for multiple primary lung cancer (MPLC). Given that the dominant lesion (DL) primarily determines the prognosis of patients with MPLC, accurate identification of the DL is crucial. This study aims to investigate the prognostic impact of preoperative misidentification of the DL on patients with MPLC. Methods:Patients with clinical stage I MPLC between January 2014 and December 2021 were retrospectively collected. The DL was preoperatively identified based on the mean diameter of lesions on computed tomography (CT) images, with postoperative confirmation via pathological examination results. Patients were categorized into the Inconsistent group (IG) if preoperative misidentification of the DL led to two scenarios: (I) a discrepancy between the actual surgical procedure performed and the optimal surgical approach retroactively determined based on postoperative assessment; or (II) prioritized resection of secondary lesions followed by staged resection of the DL. All remaining patients, in whom preoperative DL identification was consistent with postoperative confirmation and surgical procedures aligned with the DL-guided strategy, were assigned to the Consistent group (CG). Propensity score matching (PSM) was implemented to mitigate confounding effects from intergroup clinical characteristic variances. Overall survival (OS) and recurrence-free survival (RFS) were assessed, with prognostic factors evaluated using multivariate cox regression analysis. Results:From an initial cohort of 159 patients, 78 patients were selected through 1:2 PSM. All covariates demonstrated satisfactory balance between groups after matching, with no statistically significant differences observed. Five-year OS rates were 96.15% [95% confidence interval (CI): 91.7-98.3%] for IG and 98.08% (95% CI: 94.2-99.4%) for CG. Corresponding RFS rates were 62.86% (95% CI: 54.8-70.1%) and 91.54% (95% CI: 85.1-95.3%), respectively. Multivariable analysis identified inconsistent surgery [hazard ratio (HR) 5.341; 95% CI: 1.085-26.282; P=0.04] and receipt of postoperative adjuvant therapy (HR 5.613; 95% CI: 1.359-23.174; P=0.02) as independent risk factors of RFS. Conclusions:In patients with MPLC, preoperative misidentification of the DL may potentially affect prognosis due to the difference of surgical approach. Consequently, accurate preoperative identification of DLs is essential for optimizing surgical planning.

This study aimed to evaluate the clinical value of robot-assisted surgery combined with the total mesoesophageal excision (TME) for resectable esophageal cancer and to compare its advantages over conventional minimally invasive esophagectomy (MIE) and non-mesoesophageal esophagectomy. The study retrospectively analyzed data from 159 patients who underwent McKeown esophagectomy at 2 provincial tertiary hospitals (January 2019–March 2025). The patients were stratified into 4 groups based on surgical approach, including robot-assisted total mesoesophageal esophagectomy (RATME, n = 38), robot-assisted conventional minimally invasive esophagectomy (RAMIE, n = 37), video-assisted thoracoscopic total mesoesophageal esophagectomy (VATME, n = 42), and video-assisted minimally invasive esophagectomy (VAMIE, n = 42). The analysis compared baseline characteristics, perioperative data, and survival outcomes among groups. The RATME group had a significantly longer operative time than the other groups (P < 0.01). However, it demonstrated significant reductions in intraoperative blood loss and thoracic drainage volume within the first 48 h postoperatively (P < 0.05), together with a shorter postoperative hospital stay. Compared with the non-mesoesophageal group, the mesoesophageal group had significantly more harvested lymph nodes (P < 0.05) and a lower overall incidence of postoperative complications (P < 0.05). No statistically significant differences were observed in overall survival (OS) or disease-free survival (DFS) among the 4 groups. The incidence of recurrence and death events was lower in the RATME group. Robot-assisted total mesoesophageal esophagectomy (RATME) could be a safe technique. Integrating mesoesophagus theory with robotic surgery achieved superior perioperative outcomes, including reduced intraoperative bleeding, increased lymph nodes dissected, lower complication rates, and accelerated recovery, and it may bring about a better long-term outcome.

Background:Due to the widespread implementation of computed tomography (CT) in lung cancer screening, multifocal pulmonary nodules (MPNs) are increasingly detected. Given the importance of selecting preoperative dominant lesions (DLs) in the management of MPNs, this study evaluates the diagnostic accuracy of commonly used approaches for assessment of preoperative DLs. Methods:Patients who underwent surgical resection and CT for MPNs from May 2019 to September 2023 were retrospectively collected from a single center. The postoperative DLs were determined based on the pathology results. Four methods were employed to identify preoperative DLs including diameter, Mayo model, Brock model, and Peking University multiple pulmonary nodules malignancy prediction model (PKU-M model) and the predictive results of these methods were compared with postoperative DLs. Subgroup analysis was conducted based on the type of nodules. Results:A total of 999 patients with 2,285 nodules were included in this study. The accuracy of the proposed methods including diameter, Mayo model, Brock model, and PKU-M model for the assessment of preoperative DLs were 81.09%, 78.69%, 81.73%, and 78.77%, respectively. Compared to the pathology results, the Kappa values for the four methods were 0.62, 0.51, 0.59, and 0.51, respectively. Among the three subgroups, four methods applied in subsolid nodule group demonstrated the best performance with accuracy of 83.07%, 77.88%, 81.90% and 72.59%, respectively. Conclusions:Current assessment approaches for identifying preoperative DLs still have room for improvement and further studies are warranted to develop a more effective approach for the assessment of preoperative DLs.

Lung adenocarcinoma (LUAD) is one of the main causes of cancer-related mortality worldwide. Pathological risk factors such as spreading through air spaces, high-risk pathological subtypes, occult lymph nodes, and visceral pleural invasion have significant impact on patient prognosis. In recent years, there has been significant progress in the application of artificial intelligence (AI) technology, e.g., deep learning (DL), in medical image analysis and pathological diagnosis of lung cancer, offering novel approaches for predicting the aforementioned pathological risk factors. This article reviews recent advancements in AI-based analysis and prediction of pathological risk factors in lung adenocarcinoma, with a focus on the applications and limitations of DL models, focusing on studies aimed at improving diagnostic accuracy and efficiency for specific high-risk pathological subtypes. Finally, we summarize current challenges and future directions, emphasizing the need to expand dataset diversity and scale, improve model interpretability, and enhance the clinical applicability of AI models. This article aims to provide a reference for future research on the analysis and prediction of pathological risk factors of LUAD and to promote the development and application of AI, especially DL, in this field.

OBJECTIVES:This study aimed to develop a pretreatment CT-based multichannel predictor integrating deep learning features encoded by Transformer models for preoperative diagnosis of major pathological response (MPR) in non-small cell lung cancer (NSCLC) patients receiving neoadjuvant immunochemotherapy. MATERIAL AND METHODS:This multicenter diagnostic study retrospectively included 332 NSCLC patients from four centers. Pretreatment computed tomography images were preprocessed and segmented into region of interest cubes for radiomics modeling. These cubes were cropped into four groups of two-dimensional image modules. GoogLeNet architecture was trained independently on each group within a multichannel framework, with gradient-weighted class activation mapping and SHapley Additive exPlanations value for visualization. Deep learning features were carefully extracted and fused across the four image groups using the Transformer fusion model. After models training, model performance was evaluated via the area under the curve (AUC), sensitivity, specificity, F1 score, confusion matrices, calibration curves, decision curve analysis, integrated discrimination improvement, net reclassification improvement, and DeLong test. RESULTS:The dataset was allocated into training (n = 172, Center 1), internal validation (n = 44, Center 1), and external test (n = 116, Centers 2-4) cohorts. Four optimal deep learning models and the best Transformer fusion model were developed. In the external test cohort, traditional radiomics model exhibited an AUC of 0.736 [95% confidence interval (CI): 0.645-0.826]. The optimal deep learning imaging module showed superior AUC of 0.855 (95% CI: 0.777-0.934). The fusion model named Transformer_GoogLeNet further improved classification accuracy (AUC = 0.924, 95% CI: 0.875-0.973). CONCLUSION:The new method of fusing multichannel deep learning with the Transformer Encoder can accurately diagnose whether NSCLC patients receiving neoadjuvant immunochemotherapy will achieve MPR. Our findings may support improved surgical planning and contribute to better treatment outcomes through more accurate preoperative assessment.

ObjectivesAlthough neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.MethodsThis study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.ResultsThe training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.ConclusionsBy extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

Rationale and Objectives: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. Materials and Methods: The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. Results: The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (RadDTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting Conclusion: Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection. (c) 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

ObjectivesTo investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.MethodsThis retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).ResultsIn the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.ConclusionThe quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Osimertinib, a third generation epidermal growth factor receptor tyrosine kinase inhibitor, is approved as a first-line therapy in patients with advanced non-small cell lung carcinoma (NSCLC) with EGFR-activating mutations or the T790M resistance mutation. However, the efficacy of osimertinib is limited due to acquired resistance, highlighting the need to elucidate resistance mechanisms to facilitate the development of improved treatment strategies. Here, we screened for significantly upregulated genes encoding protein kinases in osimertinib-resistant NSCLC cells and identified NUAK1 as a pivotal regulator of osimertinib resistance. NUAK1 was highly expressed in osimertinib-resistant NSCLC and promoted the emergence of osimertinib resistance. Genetic or pharmacological blockade of NUAK1 restored the sensitivity of resistant NSCLC cells to osimertinib in vitro and in vivo. Mechanistically, NUAK1 directly interacted with and phosphorylated nicotinamide adenine dinucleotide kinase (NADK) at serine 64 (S64), which mitigated osimertinib-induced accumulation of reactive oxygen species (ROS) and contributed to the acquisition of osimertinib resistance in NSCLC. Furthermore, virtual drug screening identified T21195 as an inhibitor of NADK-S64 phosphorylation, and T21195 synergized with osimertinib to reverse acquired resistance by inducing ROS accumulation. Collectively, these findings highlight the role of the NUAK1-NADK axis in governing osimertinib resistance in NSCLC and indicate the potential of targeting this axis as a strategy for circumventing resistance. Significance: Phosphorylation of NADK by NUAK1 diminishes ROS accumulation and confers resistance to osimertinib, identifying NUAK1-NADK signaling as a potential therapeutic target for improving the response to EGFR inhibition in lung cancer.

PurposeTo evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma.MethodsA cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores.ResultThe training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment.ConclusionsThe nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

Background A sleeve lobectomy is a routine operation in thoracic surgery. However, sleeve lobectomy is not only a complex operation, but also has the risk of anastomotic leakage and stenosis. We used bronchial flap to reconstruct the airway instead of sleeve lobectomy. The above disadvantages can be avoided because the bronchial flap reconstruction airway has no anastomosis. This technique has not previously been reported. This paper discusses the feasibility and safety of reconstructing the bronchus with the pedicle autogenous bronchus flap in lung cancer surgery. Methods During the operation, when the tumor tissue had invaded ≤1/3 of the circumference of the lobar bronchus, the bronchus wall was removed at least 5 mm away from the tumor, but the contralateral healthy bronchus wall was preserved. The healthy bronchial wall was made into a “tongue-shaped” pedicled autogenous bronchial flap, approximately the size of the bronchial defect, and the flap was turned up or down to repair the root defect of the bronchus. The patients were examined every 3 months after surgery by chest computed tomography (CT) to observe the re-expansion of lung and reconstruction of the bronchus, and analyze the incidence of bronchus stenosis and local recurrence. Results The lobar bronchus was successfully reconstructed with the pedicled autologous bronchial flap in 45 patients; 36 males and 9 females with an average age of 56.5 years. The diameters of the tumors ranged from 3–12 cm. The pathological examination results showed that the margin of bronchus was negative. There was no perioperative death or bronchopleural fistula. The bronchoscopy showed that the reconstructed bronchus healed well, and no atelectasis or bronchostenosis was found in the follow-up period. Conclusions This is the first report on the application of the pedicled autogenous bronchial flap being used to reconstruct the airway instead of a sleeve lobectomy in lung cancer surgery. In the radical resection of lung cancer, the operation can simplify the operation process, and reduce the risk of anastomotic leakage or stenosis. The operation is safe and feasible, and should be more widely used.

AbstractBackgroundAdvanced non‐small cell lung cancer (NSCLC) accounts for a high proportion of lung cancer cases. Targeted therapy improve the survival in these patients, but acquired drug resistance will inevitably occur. If tumor downstaging is achieved after targeted therapy, could surgical resection before drug resistance improve clinical benefits for patients with advanced NSCLC? Here, we conducted a clinical trial showing that for patients with advanced driver gene mutant NSCLC who did not progress after targeted therapy, salvage surgery (SS) could improve progression‐free survival (PFS). Herein, we retrospectively reviewed our former clinical trial and thoracic cancer database in our medical institutions.MethodsWe identified patients with advanced driver gene mutant NSCLC treated with targeted therapy plus SS or targeted therapy alone in our former clinical trial and our thoracic cancer database from July 2016 to July 2019. PFS was compared between the targeted therapy plus SS group and the targeted therapy only group using the log‐rank test.ResultsWe identified 73 patients with driver gene mutant NSCLC who were treated with targeted therapy and 18 treated with targeted therapy plus SS.Among the 18 patients treated with targeted therapy plus SS, there were no obvious perioperative complications and deaths. Targeted therapy followed by SS resulted in a significantly longer PFS compared with targeted therapy alone (23.4 months VS 12.9 months, p = 0.0004).ConclusionsSalvage surgery after tumor downstaging is a promising therapeutic strategy for some patients with advanced (stage IIIB–IV) NSCLC and may offer a new therapeutic option for multidisciplinary comprehensive treatment of lung cancer.

Key Points Question Is there any difference in the safety of neoadjuvant chemoradiotherapy (nCRT) followed by minimally invasive esophagectomy (MIE) for locally advanced esophageal squamous cell carcinoma (ESCC) compared with that of neoadjuvant chemotherapy (nCT) followed by MIE? Findings In this multicenter randomized clinical trial of 264 patients with ESCC, overall morbidity rates were 47% in the nCRT group and 43% in nCT group, which was not significantly different. Meaning This trial shows that the safety of nCRT followed by MIE is similar to that of nCT for the treatment of locally advanced ESCC.

BackgroundBone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies.MethodsThis study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis.ResultsBMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145).ConclusionBMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.

Background An increasing number of original studies suggest that estrogen receptor beta (ERβ) expression may be related to non-small cell lung cancer (NSCLC) prognosis; however, the evidence remains inconclusive and conflicting. We aimed to systematically evaluate the expression and prognostic value of ERβ in NSCLC, and to explain the inconsistency between ERβ protein and mRNA level. Methods PubMed, Embase, and Web of Science databases were searched for studies (published before October 6, 2020) reporting the prognostic value of ERβ protein expression in NSCLC. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) were calculated. Transcriptome and survival data of lung adenocarcinoma patients were obtained from public databases for differential expression and survival analyses. Immunohistochemistry (IHC) was performed to examine the ERβ protein expression in 39 NSCLC patients. Western blotting and RT-qPCR were performed to analyze ERβ expression in two paired NSCLC and normal adjacent tissue samples. The effect of methyltransferase-like 13 (METTL3) on ERβ expression was investigated in a lung cancer cell line. Results Meta-analysis of 23 studies with a total of 3744 patients demonstrated that high protein expression of overall ERβ and cytoplasmic ERβ indicated poor OS (HR: 1.05, 95% CI: 1.00 to 1.10; HR: 1.48, 95% CI: 1.13 to 1.95) in NSCLC. For lung adenocarcinoma especially, high protein expression of both overall/cytoplasmic ERβ and nuclear ERβ suggested poor OS (HR: 1.54, 95% CI: 1.05 to 2.25; HR: 1.36, 95% CI: 1.03 to 1.80). Bioinformatics analysis indicated the expression of ERβ mRNA was not associated with the prognosis of lung adenocarcinoma. Analysis of public databases showed that ERβ mRNA is not highly expressed in tumor tissues, however, IHC results revealed that ERβ protein is highly expressed in NSCLC tissues. We validated this inconsistency in ERβ expression in paired tumors and normal adjacent tissues from patients. Moreover, METTL3 knockdown in the A549 cell line downregulated ERβ protein expression but not ERβ mRNA expression. Conclusions Our study elucidated the inconsistency between ERβ protein and mRNA expression levels and their prognostic values. The results indicated that METTL3-driven enhanced translation in NSCLC may cause this inconsistency.