Thoracic Cancer最新文献

Background: The emergence of chemoresistance markedly compromised the treatment efficiency of human cancer, including non-small cell lung cancer (NSCLC). In the present study, we aimed to explore the effects of ubiquitin-specific peptidase 22 (USP22) and murine double minute 2 (MDM2) in gefitinib resistance in NSCLC.

Methods: Immunohistochemistry (IHC) assay, quantitative real-time polymerase chain reaction (qRT-PCR) assay and western blot assay were carried out to determine the expression of USP22 and MDM2. Transwell assay and flow cytometry analysis were performed to evaluate cell migration and apoptosis. Cell Counting Kit-8 (CCK-8) assay was employed to assess gefitinib resistance. The phenomenon of ferroptosis was estimated by related commercial kits. The oxidized C11-BODIPY fluorescence intensity by C11-BODIPY staining. The relation between USP22 and MDM2 was analyzed by ubiquitination assay and co-immunoprecipitation (Co-IP) assay.

Results: USP22 was abnormally upregulated in NSCLC tissues and cells, and USP22 silencing markedly repressed NSCLC cell migration and facilitated apoptosis and ferroptosis. Moreover, our results indicated that ferroptosis could enhance the suppressive effect of gefitinib on NSCLC cells. Besides, USP22 overexpression enhanced gefitinib resistance and ferroptosis protection in NSCLC cells. Mechanically, USP22 stabilized MDM2 and regulated MDM2 expression through deubiquitination of MDM2. MDM2 deficiency partially restored the effects of USP22 on gefitinib resistance and ferroptosis in NSCLC cells. Of note, we validated the promotional effect of USP22 on gefitinib resistance in NSCLC in vivo through establishing the murine xenograft model.

Conclusion: USP22/MDM2 promoted gefitinib resistance and inhibited ferroptosis in NSCLC, which might offer a novel strategy for overcoming gefitinib resistance in NSCLC.

Background: Limited information is available regarding the impact of sarcopenia on the prognosis of antiangiogenic therapy in individuals with advanced non-small cell lung cancer (NSCLC). This study primarily sought to examine the prognostic significance of sarcopenia in individuals with advanced NSCLC undergoing antiangiogenic therapy.

Methods: We retrospectively enrolled all patients who met the inclusion and exclusion criteria from 2019 to 2021 at Nantong University Hospital. Patients were grouped according to the presence or absence of sarcopenia. After propensity score matching (PSM), progression-free survival (PFS), overall survival (OS), and adverse event rates were compared between the two groups. Factors associated with prognosis were screened using univariate and multivariate analyses.

Results: A total of 267 patients were included, with a total of 201 matched at baseline after PSM (77 in the sarcopenia group and 124 in the non-sarcopenia group). The sarcopenia group had lower PFS (p = 0.043) and OS (p = 0.011) than the non-sarcopenia group and a higher incidence of adverse events (p = 0.044). Multivariate analysis suggested that sarcopenia is an independent prognostic risk factor for OS in advanced NSCLC patients receiving antiangiogenic therapies (p = 0.009). Results of subgroup analyses showed some differences in the impact of sarcopenia on survival prognosis in populations with different characteristics.

Conclusion: Patients with advanced NSCLC with comorbid sarcopenia exhibit a worse prognosis when treated with antiangiogenic therapy, and preventing and ameliorating sarcopenia may lead to better survival outcomes in patients with advanced NSCLC.

Objective: This study aimed to evaluate the feasibility and performance of deep transfer learning (DTL) networks with different types and dimensions in differentiating thymomas from thymic cysts in a retrospective cohort.

Materials and methods: Based on chest-enhanced computed tomography (CT), the region of interest was delineated, and the maximum cross section of the lesion was selected as the input image. Five convolutional neural networks (CNNs) and Vision Transformer (ViT) were used to construct a 2D DTL model. The 2D model constructed by the maximum section (n) and the upper and lower layers (n - 1, n + 1) of the lesion was used for feature extraction, and the features were selected. The remaining features were pre-fused to construct a 2.5D model. The whole lesion image was selected for input and constructing a 3D model.

Results: In the 2D model, the area under curve (AUC) of Resnet50 was 0.950 in the training cohort and 0.907 in the internal validation cohort. In the 2.5D model, the AUCs of Vgg11 in the internal validation cohort and external validation cohort 1 were 0.937 and 0.965, respectively. The AUCs of Inception_v3 in the training cohort and external validation cohort 2 were 0.981 and 0.950, respectively. The AUC values of 3D_Resnet50 in the four cohorts were 0.987, 0.937, 0.938, and 0.905.

Conclusions: The DTL model based on multiple different dimensions can be used as a highly sensitive and specific tool for the non-invasive differential diagnosis of thymomas and thymic cysts to assist clinicians in decision-making.

Background: Superior outcomes have been obtained for neoadjuvant treatment with immune checkpoint inhibitors (ICI) plus chemotherapy over neoadjuvant chemotherapy alone, especially in patients with high programmed cell death ligand 1 (PD-L1) expression. However, it is not always possible to obtain sufficient tumor specimens for biomarker testing before surgery. In this study, we explored clinical factors that can predict high PD-L1 expression.

Methods: We retrospectively enrolled 340 lung cancer patients who received pulmonary resection between 2014 and 2023 and who had PD-L1 expression data. Chi-squared tests and logistic regression analyses were used to identify clinical factors associated with high PD-L1 status.

Results: Univariable and multivariable analyses revealed that smoking, high maximum standardized uptake value (SUVmax) of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT), and high plasma fibrinogen are independent predictors of high PD-L1 expression. A predictive score for high PD-L1 expression (ranging from 0 to 3) was developed based on these parameters. Notably, only 5% of patients with a score of 0 exhibited high PD-L1 expression, whereas this proportion increased to 53% for patients with a score of 3.

Conclusion: These results showed that plasma fibrinogen, smoking history, and SUVmax are predictors of high PD-L1 expression, providing a basis for identifying patients expected to benefit from neoadjuvant ICI treatment.

Background: Thymoma is a primary tumor of the thymus, commonly located in the anterior mediastinum. Most thymomas are benign or low-grade malignant, but they can invade surrounding organs or metastasize. The primary treatment for thymoma is surgical resection. Traditional methods involve open thoracotomy, but it is traumatic, with slow recovery and many complications. In recent years, with the development of thoracoscopic techniques, thoracoscopic total thymectomy has gradually become the preferred method for small size thymomas due to its minimally invasive, safe, and effective.

Methods: This paper introduces a thoracoscopic extend thymectomy technique, the subxiphoid video-assisted thoracoscopic extend thymectomy with sternal suspension. This method involves placing hooks at the upper and lower ends of the sternum to suspend the sternum upward, increasing the thoracic cavity space and facilitating thoracoscopic operations. This research reviews the clinical data of 59 patients with early-stage thymomas treated with this technique at our center since 2020 and analyzes the perioperative therapeutic efficacy and safety. It also compares the outcomes with those of 17 patients who underwent thoracoscopic approaches.

Results: The results show that subxiphoid video-assisted thoracoscopic total thymectomy with sternal suspension is an innovative and effective surgical method, achieving the same tumor eradication as other thoracic surgeries. The flexible switching of observation ports provides a more comprehensive surgical field, reduces surgical trauma and complications, and improves the surgical outcomes and quality of life for patients.

Background: Although de novo metastatic breast cancer (dnMBC) is acknowledged as a heterogeneous disease, the current staging systems do not distinguish between patients within the M1 or stage IV category. This study aimed to refine the M1 category and prognostic staging for dnMBC to enhance prognosis prediction and guide the choice of locoregional treatment.

Methods: We selected patients with dnMBC from the SEER database (2010-2019), grouping them into training (N = 8048) and internal validation (N = 3450) cohorts randomly at a 7:3 ratio. An independent external validation cohort (N = 660) was enrolled from dnMBC patients (2010-2023) treated in three hospitals. Nomogram-based risk stratification was employed to refine the M1 category and prognostic stage, incorporating T/N stage, histologic grade, subtypes, and the location and number of metastatic sites. Both internal and external validation sets were used for validation analyses.

Results: Brain, liver, or lung involvement and multiple metastases were independent prognostic factors for overall survival (OS). The nomogram-based stratification effectively divided M1 stage into three groups: M1a (bone-only involvement), M1b (liver or lung involvement only, with or without bone metastases), and M1c (brain metastasis or involvement of both liver and lung, regardless of other metastatic sites). Only subtype and M1 stage were included to define the final prognostic stage. Significant differences in OS were observed across M1 and prognostic subgroups. Patients with the M1c stage benefited less from primary tumor surgery in comparison with M1a stage.

Conclusion: Subdivision of the M1 and prognostic stage could serve as a supplement to the current staging guidelines for dnMBC and guide locoregional treatment.