Altered expression of TGF-β1 and TGF-βR2 in tissue samples compared to blood is associated with food habits and survival in esophageal squamous cell carcinoma

Talukdar, Jayasree; Kataki, Kangkana; Ali, Eyashin; Choudhury, Bikash Narayan; Baruah, Munindra Narayan; Bhattacharyya, Mallika; Bhattacharjee, Sahana; Medhi, Subhash

doi:10.1016/j.currproblcancer.2020.100617

Cited by 4 publications

(3 citation statements)

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“…TGF-β, a widely expressed cytokine in tumor microenvironments, is important in tumorigenesis, progression, and chemoresistance [ 49 ]. TGF-β1, as one of the most important isoforms of the TGFβ family [ 50 ], was increased in both tumor tissue and serum of ESCC patients [ 51 ]. TGF-β1 could regulate the chemoresistance of tumor cells through complex mechanisms, such as inducing epithelial-mesenchymal transition, increasing the expression of MDR-related genes, and activating Smad2/3 and FOXC1 pathways [ 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma

et al. 2023

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Background Esophageal squamous cell carcinoma (ESCC) is a deadly gastrointestinal malignancy, and chemotherapy resistance is a key factor leading to its poor prognosis. M2 tumor-associated macrophages (M2-TAMs) may be an important cause of chemoresistance in ESCC, but its exact mechanism is still unclear. Methods In order to study the role of M2-TAMs in ESCC chemoresistance, CCK-8, clone formation assay, flow cytometric apoptosis assay, qRT-PCR, western blotting, and serum-free sphere formation assays were used. In vivo animal experiments and human ESCC tissues were used to confirm the findings. Results In vitro and in vivo animal experiments, M2-TAMs reduced the sensitivity of ESCC cells to cisplatin. Mechanistically, M2-TAMs highly secreted TGF-β1 which activated the TGFβR1-smad2/3 pathway to promote and maintain the stemness characteristic of ESCC cells, which could inhibit the sensitivity to cisplatin. Using TGFβ signaling inhibitor SB431542 or knockdown of TGFβR1 could reverse the cisplatin resistance of ESCC cells. In 92 cases of human ESCC tissues, individuals with a high density of M2-TAMs had considerably higher levels of TGF-β1. These patients also had worse prognoses and richer stemness markers. Conclusion TGF-β1 secreted from M2-TAMs promoted and maintained the stemness characteristic to induce cisplatin resistance in ESCC by activating the TGFβ1-Smad2/3 pathway.

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Section: Discussionmentioning

confidence: 99%

M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma

et al. 2023

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“…Furthermore, TGF-β has been regarded as the marker that associated with prognosis of HNSCC patients [26-28], and which has been con rmed by KIM et al [29]. As the most abundant type in TGF-β family [21], TGF-β1 has been reported that it is related to the poor prognosis of many malignancies [21][22][23][24][25], including HNSCC. For example, the TGF-β1 expression in interstitial tissue of HNSCC is signi cantly increased [9], and the elevated TGF-β1 level in plasma is negatively correlated with prognosis of HNSCC patients with cetuximab treatment [30].…”

Section: Discussionmentioning

confidence: 99%

Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma

Liu

et al. 2023

Preprint

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Purpose: TGF-β1 is an immunosuppressive gene that regulates a variety of activities relating to immune responses. However, the association between TGF-β1 expression and the survival rate of HNSCC patients remains unclear. This study is to explore that whether there is a connection between TGF-β1 expression and patients’ survival in HNSCC, and whether the TGF-β1 expression in HNSCC patients can be non-invasively predicted by CT-Based Radiomics. Materials and Methods:Transcriptional profiling data and clinical information were obtained from TCGA database, and then grouped basing on Cutoff value of TGF-β1 expression. 139 HNSCC patients (112 for training and 27 for validation) were selected basing on the completeness of enhanced arterial phase CT images. 3D Slicer software is used for image segmentation, and PyRadiomics package for extraction of radiomic features. The optimal features for establishing the corresponding gradient enhancement prediction models were obtained using mRMR_RFE algorithm and Repeat_LASSO algorithm. Conclusively, comprehensive performances of two models, including diagnostic efficacy, calibration and clinical practicability, were compared. Results: 483 patients were classified into two groups (high expression (n=333) and low expression (n=150)) basing on the cut-off of TGF-β1 expression (5.208), and then used for survival analysis. Kaplan-Meier curve showed that TGF-β1, as an independent risk factor, significantly decreased patients’ survival (p=<0.001). For construction of grdient enhancement prediction models, we respectively obtained two features-glrlm and ngtdm-and three radiation features-glrlm, first order _ 10percentile and gldm- using mRMR_RFE algorithm and Repeat_LASSO algorithm. The two established models showed strong predictive potentials in both training cohort and validation cohort. In training set, ROC curve shows that AUC of mRMR_RFE_GBM model is 0.911 and Repeat_LASSO_GBM model is 0.733. And it is statistically significant that AUC of mRMR_RFE_GBM model (0.911) is higher than Repeat_LASSO_GBM model (0.733); Likewise, in validation set, AUC of mRMR_RFE_GBM model is 0.849 and Repeat_LASSO_GBM model is 0.72. And the difference between two models in AUC value is not statistically significant (p=0.212). In addition, calibration curve shows high consistency between the predictive result and real value, and DCA diagram shows its good clinical practicability. Moreover, whether in training set or in validation set, there is no statistical difference in AUC values between mRMR_RFE_GBM model and LASSO_GBM model (p=0.443, p=0.912), indicating that the two models both fit well. Conclusion: TGF-β1 is an independent risk factor and significantly associated with poor prognosis. mRMR_RFE_GBM model and Repeat_LASSO_GBM model based on CT-Based Radiomics features can effectively and non-invasively predict TGF-β1 expression in HNSCC. Considering the efficacy of prediction, mRMR_RFE_GBM model is better for clinical application.

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“…Regarding the negative correlations between RORC expression and immunoinhibitors, RORC expression exhibited the strongest negative correlation with TGFB1 in ESCA. Recently, Talukdar et al demonstrated that TGFB1 expression influences the prognosis of ESCA ( Talukdar et al, 2021 ). Furthermore, several studies have reported that high TGFB1 expression is associated with poor prognosis in many cancers ( Liang et al, 2020 ; de Streel and Lucas, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

A comprehensive pancancer analysis reveals the potential value of RAR-related orphan receptor C (RORC) for cancer immunotherapy

Sun

et al. 2022

Front. Genet.

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Background: RAR-related orphan receptor C (RORC) plays an important role in autoimmune responses and inflammation. However, its function in cancer immunity is still unclear. Its potential value in cancer immunotherapy (CIT) needs to be further studied.Methods: Expression and clinical data for 33 cancers were obtained from UCSC-Xena. The correlation between RORC expression and clinical parameters was analyzed using the limma software package to assess the prognostic value of RORC. Timer2.0 and DriverDBv3 were used to analyze the RORC mutation and methylation profiles. RORC-associated signaling pathways were identified by GSEA. The correlations of RORC expression with tumor microenvironment factors were further assessed, including immune cell infiltration (obtained by CIBERSORT) and immunomodulators (in pancancer datasets from the Tumor-Immune System Interactions and Drug Bank [TISIDB] database). In addition, the correlations of RORC with four CIT biomarkers (tumor mutational burden, microsatellite instability, programmed death ligand-1, and mismatch repair) were explored. Furthermore, three CIT cohorts (GSE67501, GSE168204, and IMvigor210) from the Gene Expression Omnibus database and a previously published study were used to determine the association between RORC expression and CIT response.Results: RORC was differentially expressed in many tumor tissues relative to normal tissues (20/33). In a small number of cancers, RORC expression was correlated with age (7/33), sex (4/33), and tumor stage (9/33). Furthermore, RORC expression showed prognostic value in many cancers, especially in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and mesothelioma (MESO). The mutation rate of RORC in most cancer types was low, while RORC was hypermethylated or hypomethylated in multiple cancers. RORC was associated with a variety of biological processes and signal transduction pathways in various cancers. Furthermore, RORC was strongly correlated with immune cell infiltration, immunomodulators, and CIT

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Altered expression of TGF-β1 and TGF-βR2 in tissue samples compared to blood is associated with food habits and survival in esophageal squamous cell carcinoma

Cited by 4 publications

References 36 publications

M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma

M2 tumor-associated macrophage mediates the maintenance of stemness to promote cisplatin resistance by secreting TGF-β1 in esophageal squamous cell carcinoma

Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma

A comprehensive pancancer analysis reveals the potential value of RAR-related orphan receptor C (RORC) for cancer immunotherapy

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