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Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, is approved as a first-line therapy in advanced non-small cell lung carcinoma (NSCLC) patients 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 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.
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The upper mediastinal lymph nodes are a rare site of metastasis in early-stage cervical cancer, but they are a common site of metastasis in lung cancer. Notably, standard approaches for identifying the source of metastasis and subsequent treatment are currently lacking. The present study describes the case of a patient with primary lung adenocarcinoma harboring upper mediastinal lymphatic skip metastasis from cervical squamous cell carcinoma 2 years after a radical hysterectomy. During video-assisted thoracoscopic surgery, it was indicated that the patient had a tendency for metastasis to the upper mediastinal lymph nodes from the lung tumor. Pathological examination confirmed the presence of metastasis; however, it was confirmed to originate from cervical carcinoma, rather than lung adenocarcinoma. In conclusion, for patients with lung cancer and concurrent malignancies, metastatic lymph nodes discovered during surgery may originate from the previous malignancy. Surgical management of oligometastatic lymph nodes in the mediastinum can be a potential treatment option, albeit one that may necessitate the integration of adjuvant treatment modalities as warranted by the individual case.
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-Accurate lung tumor segmentation from Computed Tomography (CT) scans is crucial for lung cancer diagnosis. Since the 2D methods lack the volumetric information of lung CT images, 3D convolution-based and Transformer-based methods have recently been applied in lung tumor segmentation tasks using CT imaging. However, most existing 3D methods cannot effectively collaborate the local patterns learned by convolutions with the global dependencies captured by Transformers, and widely ignore the important boundary information of lung tumors. To tackle these problems, we propose a 3D boundary-guided hybrid network using convolutions and Transformers for lung tumor segmentation, named BGHNet. In BGHNet, we first propose the Hybrid Local-Global Context Aggregation (HLGCA) module with parallel convolution and Transformer branches in the encoding phase. To aggregate local and global contexts in each branch of the HLGCA module, we not only design the Volumetric Cross-Stripe Window Transformer (VCSwin-Transformer) to build the Transformer branch with local inductive biases and large receptive fields, but also design the Volumetric Pyramid Convolution with transformer-based extensions (VPConvNeXt) to build the convolution branch with multi-scale global information. Then, we present a Boundary-Guided Feature Refinement (BGFR) module in the decoding phase, which explicitly leverages the boundary information to refine multi-stage decoding features for better performance. Extensive experiments were conducted on two lung tumor segmentation datasets, including a private dataset (HUST-Lung) and a public benchmark dataset (MSD-Lung). Results show that BGHNet outperforms other state-of-the-art 2D or 3D methods in our experiments, and it exhibits superior generalization performance in both non-contrast and contrast-enhanced CT scans.
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Imageamento Tridimensional , Neoplasias Pulmonares , Tomografia Computadorizada por Raios X , Neoplasias Pulmonares/diagnóstico por imagem , Humanos , Tomografia Computadorizada por Raios X/métodos , Imageamento Tridimensional/métodos , Redes Neurais de ComputaçãoRESUMO
BACKGROUND: In tumor treatment, protein tyrosine kinase inhibitors (TKIs) have been extensively utilized. However, the efficacy of TKI is significantly compromised by drug resistance. Consequently, finding an effective solution to overcome TKI resistance becomes crucial. Reactive oxygen species (ROS) are a group of highly active molecules that play important roles in targeted cancer therapy including TKI targeted therapy. In this review, we concentrate on the ROS-associated mechanisms of TKI lethality in tumors and strategies for regulating ROS to reverse TKI resistance in cancer. MAIN BODY: Elevated ROS levels often manifest during TKI therapy in cancers, potentially causing organelle damage and cell death, which are critical to the success of TKIs in eradicating cancer cells. However, it is noteworthy that cancer cells might initiate resistance pathways to shield themselves from ROS-induced damage, leading to TKI resistance. Addressing this challenge involves blocking these resistance pathways, for instance, the NRF2-KEAP1 axis and protective autophagy, to promote ROS accumulation in cells, thereby resensitizing drug-resistant cancer cells to TKIs. Additional effective approaches inducing ROS generation within drug-resistant cells and providing exogenous ROS stimulation. CONCLUSION: ROS play pivotal roles in the eradication of tumor cells by TKI. Harnessing the accumulation of ROS to overcome TKI resistance is an effective and widely applicable approach.
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Recent single-arm studies involving neoadjuvant camrelizumab, a PD-1 inhibitor, plus chemotherapy for resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) have shown promising results. This multicenter, randomized, open-label phase 3 trial aimed to further assess the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy followed by adjuvant camrelizumab, compared to neoadjuvant chemotherapy alone. A total of 391 patients with resectable thoracic LA-ESCC (T1b-3N1-3M0 or T3N0M0) were stratified by clinical stage (I/II, III or IVA) and randomized in a 1:1:1 ratio to undergo two cycles of neoadjuvant therapy. Treatments included camrelizumab, albumin-bound paclitaxel and cisplatin (Cam+nab-TP group; n = 132); camrelizumab, paclitaxel and cisplatin (Cam+TP group; n = 130); and paclitaxel with cisplatin (TP group; n = 129), followed by surgical resection. Both the Cam+nab-TP and Cam+TP groups also received adjuvant camrelizumab. The dual primary endpoints were the rate of pathological complete response (pCR), as evaluated by a blind independent review committee, and event-free survival (EFS), as assessed by investigators. This study reports the final analysis of pCR rates. In the intention-to-treat population, the Cam+nab-TP and Cam+TP groups exhibited significantly higher pCR rates of 28.0% and 15.4%, respectively, compared to 4.7% in the TP group (Cam+nab-TP versus TP: difference 23.5%, 95% confidence interval (CI) 15.1-32.0, P < 0.0001; Cam+TP versus TP: difference 10.9%, 95% CI 3.7-18.1, P = 0.0034). The study met its primary endpoint of pCR; however, EFS is not yet mature. The incidence of grade ≥3 treatment-related adverse events during neoadjuvant treatment was 34.1% for the Cam+nab-TP group, 29.2% for the Cam+TP group and 28.8% for the TP group; the postoperative complication rates were 34.2%, 38.8% and 32.0%, respectively. Neoadjuvant camrelizumab plus chemotherapy demonstrated superior pCR rates compared to chemotherapy alone for LA-ESCC, with a tolerable safety profile. Chinese Clinical Trial Registry identifier: ChiCTR2000040034 .
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Objectives: To 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. Methods: This 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). Results: In 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. Conclusion: The 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.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Terapia Neoadjuvante , Tomografia Computadorizada por Raios X , Humanos , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/terapia , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/patologia , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/terapia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Masculino , Feminino , Terapia Neoadjuvante/métodos , Pessoa de Meia-Idade , Estudos Retrospectivos , Idoso , Tomografia Computadorizada por Raios X/métodos , Resultado do Tratamento , Aprendizado de Máquina , Imunoterapia/métodos , Adulto , Resposta Patológica CompletaRESUMO
BACKGROUND: Many studies have demonstrated the relationship between METTL3 protein expression and clinical outcomes in various cancers and elucidated the mechanism by which METTL3 disrupts the behavior of cancer cells. Here, we attempted to define the prognostic value of METTL3 protein in patients with cancer via systematic analysis and explored the potential effect of inhibiting METTL3 using its specific inhibitor. METHODS: We searched PubMed, Embase, and the Web of Science databases for studies that elucidated the prognostic value of METTL3 protein expression in all cancer types and then calculated the pooled hazard ratios with 95% confidence intervals for the overall survival (OS) of all cancer types and subgroups. Data from The Cancer Genome Atlas dataset were used to study METTL3 mRNA expression in cancers. Further, the effects of a METTL3-specific inhibitor were studied in cancer cells via the colony formation assay, the cell proliferation assay, and apoptosis detection. RESULTS: Meta-analysis of the 33 cohorts in 32 studies (3666 patients in total) revealed that higher METTL3 protein expression indicated poor OS in the majority of cancers. Bioinformatics analysis of METTL3 mRNA expression and cancer prognosis did not show the extremely prominent prognostic value of METTL3 mRNA. Nevertheless, the METTL3-specific inhibitor attenuated cell proliferation and cell cloning formation and promoted apoptosis. CONCLUSIONS: METTL3 protein expression is associated with poor prognosis in most cancer types and could be a biomarker for OS. Further, METTL3 inhibition might be a potential treatment strategy for cancers.
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OBJECTIVE: Lung squamous cell carcinoma (LUSC) is associated with a low survival rate. Evidence suggests that bone morphogenetic proteins (BMPs) and their receptors (BMPRs) play crucial roles in tumorigenesis and progression. However, a comprehensive analysis of their role in LUSC is lacking. Our study aimed to explore the relationship between BMPs/BMPRs expression levels and the tumorigenesis and prognosis of LUSC. METHODS: The "R/Limma" package was utilized to analyze the differential expression characteristics of BMPs/BMPRs in LUSC, using data from TCGA, GTEx, and GEO databases. Concurrently, the "survminer" packages were employed to investigate their prognostic value and correlation with clinical features in LUSC. The core gene associated with LUSC progression was further explored through weighted gene correlation network analysis (WGCNA). LASSO analysis was conducted to construct a prognostic risk model for LUSC. Clinical specimens were examined by immunohistochemical analysis to confirm the diagnostic value in LUSC. Furthermore, based on the tumor immune estimation resource database and tumor-immune system interaction database, the role of the core gene in the tumor microenvironment of LUSC was explored. RESULTS: GDF10 had a significant correlation only with the pathological T stage of LUSC, and the protein expression level of GDF10 decreased with the tumorigenesis of LUSC. A prognostic risk model was constructed with GDF10 as the core gene and 5 hub genes (HRASLS, HIST1H2BH, FLRT3, CHEK2, and ALPL) for LUSC. GDF10 showed a significant positive correlation with immune cell infiltration and immune checkpoint expression. CONCLUSION: GDF10 might serve as a diagnostic biomarker reflecting the tumorigenesis of LUSC and regulating the tumor immune microenvironment to guide more effective treatment for LUSC.
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Carcinoma Pulmonar de Células não Pequenas , Carcinoma de Células Escamosas , Neoplasias Pulmonares , Humanos , Carcinogênese/genética , Carcinoma de Células Escamosas/diagnóstico , Carcinoma de Células Escamosas/genética , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/genética , Pulmão , Microambiente Tumoral/genética , Fator 10 de Diferenciação de CrescimentoRESUMO
PURPOSE: To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. METHODS: A 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. RESULT: The 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. CONCLUSIONS: The 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.
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BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. The sex differences in the occurrence and fatality rates of non-small cell lung cancer (NSCLC), along with its association with estrogen dependence, suggest that estrogen receptors (ERs) contribute to the development of NSCLC. However, the influence of G protein-coupled estrogen receptor (GPER1) on NSCLC remains to be determined. Escape from ferroptosis is one of the hallmarks of tumor discovered in recent years. In this context, the present study evaluated whether GPER1 promotes NSCLC progression by preventing ferroptosis, and the underlying mechanism through which GPER1 protects against ferroptosis was also explored. METHODS: The effects of GPER1 on the cytotoxicity of H2O2, the ferroptosis inducer RSL3, and Erastin were assessed using the CCK8 assay and plate cloning. Lipid peroxidation levels were measured based on the levels of MDA and BODIPY™581/591C11. GPER1 overexpression and knockdown were performed and G1 was used, and the expression of SCD1 and PI3K/AKT/mTOR signaling factors was measured. Immunofluorescence analysis and immunohistochemistry were performed on paired specimens to measure the correlation between the expression of GPER1 and SCD1 in NSCLC tissues. The effect of GPER1 on the cytotoxicity of cisplatin was measured in vitro using the CCK8 assay and in vivo using xenograft tumor models. RESULTS: GPER1 and G1 alleviated the cytotoxicity of H2O2, reduced sensitivity to RSL3, and impaired lipid peroxidation in NSCLC tissues. In addition, GPER1 and G1 promoted the protein and mRNA expression of SCD1 and the activation of PI3K/AKT/mTOR signaling. GPER1 and SCD1 expression were elevated and positively correlated in NSCLC tissues, and high GPER1 expression predicted a poor prognosis. GPER1 knockdown enhanced the antitumor activity of cisplatin in vitro and in vivo. CONCLUSION: GPER1 prevents ferroptosis in NSCLC by promoting the activation of PI3K/AKT/mTOR signaling, thereby inducing SCD1 expression. Therefore, treatments targeting GPER1 combined with cisplatin would exhibit better antitumor effects.
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Carcinoma Pulmonar de Células não Pequenas , Ferroptose , Neoplasias Pulmonares , Humanos , Feminino , Masculino , Carcinoma Pulmonar de Células não Pequenas/genética , Proteínas Proto-Oncogênicas c-akt/metabolismo , Neoplasias Pulmonares/metabolismo , Fosfatidilinositol 3-Quinases/metabolismo , Cisplatino/farmacologia , Lipogênese , Peróxido de Hidrogênio/farmacologia , Peróxido de Hidrogênio/metabolismo , Serina-Treonina Quinases TOR/metabolismo , Receptores Acoplados a Proteínas G/genética , Receptores Acoplados a Proteínas G/metabolismo , Estrogênios , Receptores de Estrogênio/metabolismo , Proteínas de Ligação ao GTP , Estearoil-CoA Dessaturase/metabolismoRESUMO
BACKGROUND: METTL3 plays a significant role as a catalytic enzyme in mediating N6-methyladenosine (m6A) modification, and its importance in tumour progression has been extensively studied in recent years. However, the precise involvement of METTL3 in the regulation of translation in non-small cell lung cancer (NSCLC) remains unclear. RESULTS: Here we discovered by clinical investigation that METTL3 expression is correlated with NSCLC metastasis. Ablation of METTL3 in NSCLC cells inhibits invasion and metastasis in vitro and in vivo. Subsequently, through translatomics data mining and experimental validation, we demonstrated that METTL3 enhances the translation of aromatase (CYP19A1), a key enzyme in oestrogen synthesis, thereby promoting oestrogen production and mediating the invasion and metastasis of NSCLC. Mechanistically, METTL3 interacts with translation initiation factors and binds to CYP19A1 mRNA, thus enhancing the translation efficiency of CYP19A1 in an m6A-dependent manner. Pharmacological inhibition of METTL3 enzymatic activity or translation initiation factor eIF4E abolishes CYP19A1 protein synthesis. CONCLUSIONS: Our findings indicate the crucial role of METTL3-mediated translation regulation in NSCLC and reveal the significance of METTL3/eIF4E/CYP19A1 signaling as a promising therapeutic target for anti-metastatic strategies against NSCLC.
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OBJECTIVES: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS: ⢠The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. ⢠The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. ⢠Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Área Sob a Curva , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/terapia , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/terapia , Terapia Neoadjuvante , Radiômica , Estudos RetrospectivosRESUMO
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 (Rad-DTL) 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 high-risk pathologic pulmonary nodules. 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.
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Adenocarcinoma de Pulmão , Aprendizado Profundo , Neoplasias Pulmonares , Nódulos Pulmonares Múltiplos , Humanos , Radiômica , Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Neoplasias Pulmonares/diagnóstico por imagem , Estudos RetrospectivosRESUMO
Osimertinib resistance is regarded as a major obstacle limiting survival beneï¬ts for patients undergoing treatment of epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). However, the underlying mechanisms of acquired resistance remain unclear. In this study, we report that estrogen receptor ß (ERß) is highly expressed in osimertinib-resistant NSCLC and plays a pivotal role in promoting osimertinib resistance. We further identified ubiquitin-specific protease 7 (USP7) as a critical binding partner that deubiquitinates and upregulates ERß in NSCLC. ERß promotes osimertinib resistance by mitigating reactive oxygen species (ROS) accumulation. We found that ERß mechanistically suppresses peroxiredoxin 3 (PRDX3) SUMOylation and thus confers osimertinib resistance onto NSCLC. Furthermore, we provide evidence showing that depletion of ERß induces ROS accumulation and reverses osimertinib resistance in NSCLC both in vitro and in vivo. Thus, our results demonstrate that USP7-mediated ERß stabilization suppresses PRDX3 SUMOylation to mitigate ROS accumulation and promote osimertinib resistance, suggesting that targeting ERß may be an effective therapeutic strategy to overcome osimertinib resistance in NSCLC.
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Acrilamidas , Carcinoma Pulmonar de Células não Pequenas , Indóis , Neoplasias Pulmonares , Pirimidinas , Humanos , Compostos de Anilina/farmacologia , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Resistencia a Medicamentos Antineoplásicos , Receptor beta de Estrogênio , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Mutação , Peroxirredoxina III/uso terapêutico , Inibidores de Proteínas Quinases/farmacologia , Espécies Reativas de Oxigênio , Sumoilação , Peptidase 7 Específica de UbiquitinaRESUMO
Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Animais , Camundongos , Prognóstico , Adenocarcinoma de Pulmão/genética , Nomogramas , Estrogênios/genética , Neoplasias Pulmonares/genéticaRESUMO
In non-small cell lung cancer (NSCLC), the heterogeneity promotes drug resistance, and the restricted expression of programmed death-ligand 1 (PD-L1) limits the immunotherapy benefits. Based on the mechanisms related to translation regulation and the association with PD-L1 of methyltransferase-like 3 (METTL3), the novel small-molecule inhibitor STM2457 is assumed to be useful for the treatment of NSCLC. We evaluated the efficacy of STM2457 in vivo and in vitro and confirmed the effects of its inhibition on disease progression. Next, we explored the effect of STM2457 on METTL3 and revealed its effects on the inhibition of catalytic activity and upregulation of METTL3 protein expression. Importantly, we described the genome-wide characteristics of multiple omics data acquired from RNA sequencing, ribosome profiling, and methylated RNA immunoprecipitation sequencing data under STM2457 treatment or METTL3 knockout. We also constructed a model for the regulation of the translation of METTL3 and PD-L1. Finally, we found PD-L1 upregulation by STM2457 in vivo and in vitro. In conclusion, STM2457 is a potential novel suppressor based on its inhibitory effect on tumor progression and may be able to overcome the heterogeneity based on its impact on the translatome. Furthermore, it can improve the immunotherapy outcomes based on PD-L1 upregulation in NSCLC.
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In a previous study, our research group observed that estrogen promotes the metastasis of non-small cell lung cancer (NSCLC) through the estrogen receptor ß (ERß). Invadopodia are key structures involved in tumor metastasis. However, it is unclear whether ERß is involved in the promotion of NSCLC metastasis through invadopodia. In our study, we used scanning electron microscopy to observe the formation of invadopodia following the overexpression of ERß and treatment with E2. In vitro experiments using multiple NSCLC cell lines demonstrated that ERß can increase the formation of invadopodia and cell invasion. Mechanistic studies revealed that ERß can upregulate the expression of ICAM1 by directly binding to estrogen-responsive elements (EREs) located on the ICAM1 promoter, which in turn can enhance the phosphorylation of Src/cortactin. We also confirmed these findings in vivo using an orthotopic lung transplantation mouse model, which validated the results obtained from the in vitro experiments. Finally, we examined the expressions of ERß and ICAM1 using immunohistochemistry in both NSCLC tissue and paired metastatic lymph nodes. The results confirmed that ERß promotes the formation of invadopodia in NSCLC cells through the ICAM1/p-Src/p-Cortactin signaling pathway.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Podossomos , Animais , Camundongos , Carcinoma Pulmonar de Células não Pequenas/patologia , Linhagem Celular Tumoral , Cortactina/metabolismo , Receptor beta de Estrogênio/genética , Receptor beta de Estrogênio/metabolismo , Estrogênios/metabolismo , Neoplasias Pulmonares/patologia , Invasividade Neoplásica/patologia , Podossomos/metabolismo , Podossomos/patologia , Transdução de SinaisRESUMO
Metastases contribute to the low survival rate of non-small cell lung cancer (NSCLC) patients. Targeting lipid metabolism for anticancer therapies is attractive. Accumulative evidence shows that stearoyl-CoA desaturases1 (SCD1), a key enzyme in lipid metabolism, enables tumor metastasis and the underlying mechanism remains unknown. In this study, immunohistochemical staining of 96 clinical specimens showed that the expression of SCD1 was increased in tumor tissues (p < 0.001). SCD1 knockdown reduced the migration and invasion of HCC827 and PC9 cells in transwell and wound healing assays. Aromatase (CYP19A1) knockdown eliminated cell migration and invasion caused by SCD1 overexpression. Western blotting assays demonstrated that CYP19A1, along with ß-catenin protein levels, was reduced in SCD1 knocked-down cells, and estrogen concentration was reduced (p < 0.05) in cell culture medium measured by enzyme-linked immunosorbent assay. SCD1 overexpression preserving ß-catenin protein stability was evaluated by coimmunoprecipitation and Western blotting. The SCD1 inhibitor A939572, and a potential SCD1 inhibitor, grape seed extract (GSE), significantly inhibited cell migration and invasion by blocking SCD1 and its downstream ß-catenin, CYP19A1 expression, and estrogen concentration. In vivo tumor formation assay and a tail vein metastasis model indicated that knockdown of SCD1 blocked tumor growth and metastasis. In conclusion, SCD1 could accelerate metastasis by maintaining the protein stability of ß-catenin and then promoting CYP19A1 transcription to improve estrogen synthesis. SCD1 is expected to be a promised therapeutic target, and its novel inhibitor, GSE, has great therapeutic potential in NSCLC.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Estearoil-CoA Dessaturase , Humanos , Aromatase/genética , beta Catenina/genética , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Linhagem Celular Tumoral , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Estearoil-CoA Dessaturase/metabolismo , Metástase NeoplásicaRESUMO
Background: Centromere proteins (CENPs) form a large protein family. Sixteen proteins in this family are positioned at the centromere throughout the cell cycle. The overexpression of CENPs is common in many cancers and predicts a poor prognosis. However, a comprehensive analysis of CENPs expression has not been conducted, and their clinical significance in lung adenocarcinoma (LUAD) is unclear. Methods: We investigated the expression differences of the CENP family in LUAD using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) cohorts. Kaplan-Meier curve survival analysis was performed to assess their independent prognostic values. We then tested 5 clinical LUAD specimens by quantitative real time polymerase chain reaction (qRT-PCR). The risk model was constructed with least absolute shrinkage and selection operator (LASSO). Cox regression analyses were carried out to determine independent prognostic indicators. Weighted gene coexpression network analysis (WGCNA) was employed to define the coexpression networks. Results: The messenger RNA (mRNA) expression of 15 differential CENP proteins was higher in LUAD than in normal lung tissues. Among them, 10 CENP proteins had significant prognostic value. The risk model comprising CENPF, CENPU, CENPM, CENPH, and CENPW showed a significant correlation [hazard ratio (HR) 1.75, 95% confidence interval (CI): 1.3-2.35; P=2e-04]. However, the prognostic accuracy was not strong [1-year survival: area under curve (AUC) 0.63; 3-year survival: AUC 0.62; 5-year survival: AUC 0.6]. The qRT-PCR results showed that the 5 CENPs were upregulated in LUAD tissues compared to in normal lung tissues. A total of 441 hub genes coexpressed with the 5 CENPs were identified. Conclusions: CENPF, CENPU, CENPM, CENPH, and CENPW have prognostic values and may be potential targets for LUAD treatment.