G protein-coupled estrogen receptor activates PI3K/AKT/mTOR signaling to suppress ferroptosis via SREBP1/SCD1-mediated lipogenesis.

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.

Promoting reactive oxygen species accumulation to overcome tyrosine kinase inhibitor resistance in cancer.

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.

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

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.

ERß promoted invadopodia formation-mediated non-small cell lung cancer metastasis via the ICAM1/p-Src/p-Cortactin signaling pathway.

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.

Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity.

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.

Stearoyl-CoA Desaturases1 Accelerates Non-Small Cell Lung Cancer Metastasis by Promoting Aromatase Expression to Improve Estrogen Synthesis.

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.

Effects of PFOA on the physicochemical properties of anaerobic granular sludge: Performance evaluation, microbial community and metagenomic analysis.

The impact of perfluorooctanoic acid (PFOA) on the anaerobic granular sludge was evaluated through a sequential batch experiment. Results showed that PFOA inhibited the chemical oxygen demand (COD) removal rate of the sludge and the dosage of 100 mg/L PFOA was more obvious. However, this negative effect would gradually weaken with the adaptation of microorganisms. For the 50 mg/L PFOA experimental group, the proteins content in the extracellular polymeric substances (EPS) of the anaerobic granular sludge increased from 1.53 mg/g to 3.65 mg/g. Meanwhile, PFOA inhibited the 2-(p-iodophenyl)-3-(p-nitrophenyl)-5-phenyltetrazolium chloride (INT) activity of the anaerobic granular sludge. Furthermore, 100 mg/L PFOA reduced the relative abundance of Proteobacteria by 5.99% and Longilinea by 1.11%. 100 mg/L PFOA mainly restricted COD removal by affecting the glycolysis process, with the abundances of glucokinase and pyruvate kinase reduced by 8% and 28.1%, respectively. Compared with the control group, the relative abundance of the methyl-coenzyme M reductase alpha subunit increased by 84%, respectively, under 100 mg/L PFOA.

METTL3 drives NSCLC metastasis by enhancing CYP19A1 translation and oestrogen synthesis.

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.

An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

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.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

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.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

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.

Prognostic roles of dysregulated METTL3 protein expression in cancers and potential anticancer value by inhibiting METTL3 function.

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.

USP7-mediated ERß stabilization mitigates ROS accumulation and promotes osimertinib resistance by suppressing PRDX3 SUMOylation in non-small cell lung carcinoma.

Osimertinib resistance is regarded as a major obstacle limiting survival benefits 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.

Diagnostic Value of GDF10 for the Tumorigenesis and Immune Infiltration in Lung Squamous Cell Carcinoma.

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.

Neoadjuvant chemotherapy with or without camrelizumab in resectable esophageal squamous cell carcinoma: the randomized phase 3 ESCORT-NEO/NCCES01 trial.

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 .

Estrogen and insulin-like growth factor 1 synergistically promote the development of lung adenocarcinoma in mice.

Estrogen receptor (ER) and insulin-like growth factor-1 receptor (IGF-1R) signaling are implicated in lung cancer progression. Based on their previous findings, the authors sought to investigate whether estrogen and IGF-1 act synergistically to promote lung adenocarcinoma (LADE) development in mice. LADE was induced with urethane in ovariectomized Kunming mice. Tumor-bearing mice were divided into seven groups: 17ß-estradiol (E2), E2+fulvestrant (Ful; estrogen inhibitor), IGF-1, IGF-1+AG1024 (IGF-1 inhibitor), E2+IGF-1, E2+IGF-1+Ful+AG1024 and control groups. After 14 weeks, the mice were sacrificed, and then the tumor growth was determined. The expression of ERα/ERß, IGF-1, IGF-1R and Ki67 was examined using tissue-microarray-immunohistochemistry, and IGF-1, p-ERß, p-IGF-1R, p-MAPK and p-AKT levels were determined based on Western blot analysis. Fluorescence-quantitative polymerase chain reaction was used to detect the mRNA expression of ERß, ERß2 and IGF-1R. Tumors were found in 93.88% (46/49) of urethane-treated mice, and pathologically proven LADE was noted in 75.51% (37/49). In the E2+IGF-1 group, tumor growth was significantly higher than in the E2 group (p < 0.05), the IGF-1 group (p < 0.05) and control group (p < 0.05). Similarly, the expression of ERß, p-ERß, ERß2, IGF-1, IGF-1R, p-IGF-1R, p-MAPK, p-AKT and Ki67 at the protein and/or mRNA levels was markedly higher in the ligand group than in the ligand + inhibitor groups (all p < 0.05). This study demonstrated for the first time that estrogen and IGF-1 act to synergistically promote the development of LADE in mice, and this may be related to the activation of the MAPK and AKT signaling pathways in which ERß1, ERß2 and IGF-1R play important roles.

Association of preoperative serum IGF- I concentration with clinicopathological parameters in patients with non-small cell lung cancer.

Insulin-like growth factor-I (IGF-I) is a mitogenic and anti-apoptotic factor. Serum IGF-I concentration is related to some cancer risk and tumor progression. The aim of this research was to study the association of preoperative serum IGF-I concentration with clinicopathological parameters and prognosis of non-small cell lung cancer (NSCLC). Preoperative serum IGF-I concentration was measured in 80 consecutive patients with NSCLC who underwent radical lung cancer resection, and 45 patients with benign pulmonary lesion (BPL) by using enzyme linked immunosorbent assay (ELISA). The results showed that the serum IGF-I concentration was elevated and correlated with clinicopathological parameters and overall survival (OS) in NSCLC patients. Serum IGF-I concentration was significantly higher in patients with NSCLC than in those with BPL. The IGF-I concentrations were significantly higher in NSCLC patients with ≥T2, N1-3, and in IIIA-IV but not in those with

Effects and translatomics characteristics of a small-molecule inhibitor of METTL3 against non-small cell lung cancer.

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.

Bazedoxifene-induced ROS promote mitochondrial dysfunction and enhance osimertinib sensitivity by inhibiting the p-STAT3/SOCS3 and KEAP1/NRF2 pathways in non-small cell lung cancer.

Although the advent of osimertinib has brought revolutionary changes to the treatment landscape of non-small cell lung cancer (NSCLC) patients, acquired resistance remains a major obstacle limiting long-term survival benefits for the treatment of cancer. The purpose of this study was to examine the mechanisms involved in the ability of bazedoxifene to synergistically enhance osimertinib sensitivity, which will aid in delaying and overcoming osimertinib resistance to improve patient outcomes. Here, we found that osimertinib increased the production of reactive oxygen species (ROS), promoted mitochondrial fission, diminished mitochondrial membrane potential, and activated cell apoptosis. Moreover, the p-STAT3/suppressor of cytokine signaling 3 (SOCS3) and KEAP1/NRF2 signaling pathways were activated to scavenge ROS and promote osimertinib resistance. Mechanistically, SOCS3 can directly bind to KEAP1 to prevent the degradation of NRF2, resulting in the activation of an NRF2-dependent transcriptional program. Furthermore, the osimertinib-induced mitochondrial dysfunction and apoptosis were enhanced by bazedoxifene, thereby delaying and overcoming osimertinib resistance by inhibiting these pathways in vitro and in vivo. These findings identified a new critical link in the p-STAT3/SOCS3 pathway, KEAP1/NRF2 pathway, mitochondrial dysfunction, and osimertinib resistance. The present study demonstrated that bazedoxifene can be used for delaying or overcoming osimertinib resistance in NSCLC.

Critical Roles of METTL3 in Translation Regulation of Cancer.

Aberrant translation, a characteristic feature of cancer, is regulated by the complex and sophisticated RNA binding proteins (RBPs) in the canonical translation machinery. N6-methyladenosine (m6A) modifications are the most abundant internal modifications in mRNAs mediated by methyltransferase-like 3 (METTL3). METTL3 is commonly aberrantly expressed in different tumors and affects the mRNA translation of many oncogenes or dysregulated tumor suppressor genes in a variety of ways. In this review, we discuss the critical roles of METTL3 in translation regulation and how METTL3 and m6A reader proteins in collaboration with RBPs within the canonical translation machinery promote aberrant translation in tumorigenesis, providing an overview of recent efforts aiming to 'translate' these results to the clinic.