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Abstract Background Radiation-induced bystander effect (RIBE) can promote tumor metastasis contributing to the failure of radiotherapy for esophageal squamous cell carcinoma (ESCC). Aberrant expression of DJ-1 has been identified in ESCC; however, the relationship between DJ-1 and RIBE in ESCC remains unknown. Methods We detected DJ-1 in the serum and cell supernatants by enzyme-linked immunosorbent assay (ELISA) and evaluated tumor metastasis by phenotypic experiments in vivo and in vitro. RNA-seq, mass spectrometry, western blot (WB), immunoprecipitation (IP), and dual-luciferase reporter assays were performed to explore the underlying mechanisms. Results DJ-1 was highly expressed in the serum of patients with ESCC receiving radiotherapy and was significantly overexpressed in the medium of ESCC cells receiving irradiation. DJ-1 promoted tumor metastasis via the TGF-β1 pathway. Mechanistic studies revealed that DJ-1 bound to HSC70 to promote Smad3 phosphorylation and nuclear aggregation in a protein-interaction manner, which activated the transcription of Thrombospondin-1 (TSP1). Subsequently, the activation of TGF-β1 by TSP1 re-promoted Smad3 phosphorylation and nuclear aggregation, constituting a positive feedback loop to strengthen the metastasis of ESCC cells, which was effectively blocked by LY2109761 and LSKL. Moreover, higher levels of serum DJ-1 in patients with ESCC were related to a poorer prognosis of radiotherapy. Conclusions Irradiation can induce ESCC cells secreting DJ-1. Secreted DJ-1 enters bystander cells to initiate activation of the TGF-β1 pathway via the DJ-1/HSC70/Smad3 signaling axis. The TSP1/TGF-β1/Smad3 positive feedback pathway constitutes the core pathway that promotes ESCC metastasis. DJ-1 is a useful biomarker for predicting the efficacy of radiotherapy and a potential therapeutic target for reversing RIBE in ESCC. Graphical Abstract Schematic diagram showing the underlying mechanism that irradiation-induced secretion of DJ-1 accelerates the metastasis of bystander ESCC cells.

BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.ResultsAll UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.ConclusionWe successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

BackgroundLung adenocarcinoma (LUAD) is a leading malignancy and has a poor prognosis over the decades. LUAD is characterized by dysregulation of cell cycle. Immunotherapy has emerged as an ideal option for treating LUAD. Nevertheless, optimal biomarkers to predict outcomes of immunotherapy is still ill-defined and little is known about the interaction of cell cycle-related genes (CCRGs) and immunity-related genes (IRGs).MethodsWe downloaded gene expression and clinical data from TCGA and GEO database. LASSO regression and Cox regression were used to construct a differentially expressed CCRGs and IRGs signature. We used Kaplan-Meier analysis to compare survival of LUAD patients. We constructed a nomogram to predict the survival and calibration curves were used to evaluate the accuracy.ResultsA total of 61 differentially expressed CCRGs and IRGs were screened out. We constructed a new risk model based on 8 genes, including ACVR1B, BIRC5, NR2E1, INSR, TGFA, BMP7, CD28, NUDT6. Subgroup analysis revealed the risk model accurately predicted the overall survival in LUAD patients with different clinical features and was correlated with immune cells infiltration. A nomogram based on the risk model exhibited excellent performance in survival prediction of LUAD.ConclusionsThe 8 gene survival signature and nomogram in our study are effective and have potential clinical application to predict prognosis of LUAD.