TGFβ1 in Cancer-Associated Fibroblasts Is Associated With Progression and Radiosensitivity in Small-Cell Lung Cancer

Zhang, Jiaqi; Qi, Jing; Wei, Hui; Lei, Yuanyuan; Yu, Hao; Liu, Ningbo; Zhao, Lujun; Wang, Ping

doi:10.3389/fcell.2021.667645

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Crosstalk between Slit2/Robo1 and Tgf‐β1/β‐catenin signaling has previously been identified in heart and breast tissue [ 58 , 59 ]. Tgf‐β1 and β‐catenin are important oncogenic drivers, and their levels inversely correlate with prognosis in many cancers, including SCLC [ 60 , 61 , 62 , 63 , 64 ]. However, an interaction between Slit2/Robo1 and Tgf‐β1/β‐catenin signaling in SCLC is untested.…”

Section: Resultsmentioning

confidence: 99%

Slit2/Robo1 signaling inhibits small‐cell lung cancer by targeting β‐catenin signaling in tumor cells and macrophages

et al. 2023

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Small‐cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with poor patient prognosis. However, the mechanisms that regulate SCLC progression and metastasis remain undefined. Here, we show that the expression of the slit guidance ligand 2 ( SLIT2 ) tumor suppressor gene is reduced in SCLC tumors relative to adjacent normal tissue. In addition, the expression of the SLIT2 receptor, roundabout guidance receptor 1 ( ROBO1 ), is upregulated. We find a positive association between SLIT2 expression and the Yes1 associated transcriptional regulator ( YAP1 )‐expressing SCLC subtype (SCLC‐Y), which shows a better prognosis. Using genetically engineered SCLC cells, adenovirus gene therapy, and preclinical xenograft models, we show that SLIT2 overexpression or the deletion of ROBO1 restricts tumor growth in vitro and in vivo . Mechanistic studies revealed significant inhibition of myeloid‐derived suppressor cells (MDSCs) and M2‐like tumor‐associated macrophages (TAMs) in the SCLC tumors. In addition, SLIT2 enhances M1‐like and phagocytic macrophages. Molecular analysis showed that ROBO1 knockout or SLIT2 overexpression suppresses the transforming growth factor beta 1 (TGF‐β1)/β‐catenin signaling pathway in both tumor cells and macrophages. Overall, we find that SLIT2 and ROBO1 have contrasting effects on SCLC tumors. SLIT2 suppresses, whereas ROBO1 promotes, SCLC growth by regulating the Tgf‐β1/glycogen synthase kinase‐3 beta (GSK3)/β‐catenin signaling pathway in tumor cells and TAMs. These studies indicate that SLIT2 could be used as a novel therapeutic agent against aggressive SCLC.

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Section: Resultsmentioning

confidence: 99%

Slit2/Robo1 signaling inhibits small‐cell lung cancer by targeting β‐catenin signaling in tumor cells and macrophages

et al. 2023

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“…Among these factors, TGF-β is particularly crucial; once produced and released by inflammatory cells (e.g., macrophages) or cancer cells in the tumor milieu [ 10 , 13 , 16 ], TGF-β acts on fibroblasts by transforming them into CAFs, which are important cells implicated in promoting tumor progression. It has been shown in resected squamous NSCLC tumor cells that MMP-2 CAFs expression is a negative prognostic sign [ 5 ] and, furthermore, high TGFβ levels in CAFs have been shown to be correlated to a negative regulation of growth and proliferation, and a better radiosensitivity and survival in SCLC [ 17 ]. In addition to TGF-β, reactive oxygen species (ROS), released by nicotinamide adenine dinucleotide phosphate 4 (NOX4) in LC cells, are also involved in LC tumorigenesis [ 10 ], thus contributing to the tumor microenvironment.…”

Section: Lung Cancer: Molecular Mechanismsmentioning

confidence: 99%

TGF-β as Predictive Marker and Pharmacological Target in Lung Cancer Approach

Ramundo

Palazzo

Aldieri

2023

Cancers

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Lung cancer (LC) represents the leading cause of cancer incidence and mortality worldwide. LC onset is strongly related to genetic mutations and environmental interactions, such as tobacco smoking, or pathological conditions, such as chronic inflammation. Despite advancement in knowledge of the molecular mechanisms involved in LC, this tumor is still characterized by an unfavorable prognosis, and the current therapeutic options are unsatisfactory. TGF-β is a cytokine that regulates different biological processes, particularly at the pulmonary level, and its alteration has been demonstrated to be associated with LC progression. Moreover, TGF-β is involved in promoting invasiveness and metastasis, via epithelial to mesenchymal transition (EMT) induction, where TGF-β is the major driver. Thus, a TGF-β-EMT signature may be considered a potential predictive marker in LC prognosis, and TGF-β-EMT inhibition has been demonstrated to prevent metastasis in various animal models. Concerning a LC therapeutic approach, some TGF-β and TGF-β-EMT inhibitors could be used in combination with chemo- and immunotherapy without major side effects, thereby improving cancer therapy. Overall, targeting TGF-β may be a valid possibility to fight LC, both in improving LC prognosis and cancer therapy, via a novel approach that could open up new effective strategies against this aggressive cancer.

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“…However, prognostic prediction of SCLC has proven to be enormously challenging. Despite conducting myriad studies on prognostic prediction of LS-SCLC, we have been unable to identify viable clinical factors for prognosis, such as peripheral blood inflammatory markers [ 4 ], pleural effusion [ 5 ], immune factors [ 6 ], and molecular subtypes [ 7 ], as none of them yielded satisfactory predictive outcomes. Thus, it is imperative to identify new prognostic prediction methods to enable informed clinical decision-making.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

Wu,

Zhou,

et al. 2024

BMC Cancer

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Background The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). Methods Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan–Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan–Meier analysis. Results A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). Conclusion Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

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TGFβ1 in Cancer-Associated Fibroblasts Is Associated With Progression and Radiosensitivity in Small-Cell Lung Cancer

Cited by 8 publications

References 44 publications

Slit2/Robo1 signaling inhibits small‐cell lung cancer by targeting β‐catenin signaling in tumor cells and macrophages

Slit2/Robo1 signaling inhibits small‐cell lung cancer by targeting β‐catenin signaling in tumor cells and macrophages

TGF-β as Predictive Marker and Pharmacological Target in Lung Cancer Approach

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

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