Parrottkirkpatrick4840

DEAD‑box helicase 41 (DDX41) is an RNA helicase and accumulating evidence has suggested that DDX41 is involved in pre‑mRNA splicing during tumor development. However, the role of DDX41 in tumorigenesis remains unclear. In order to determine the function of DDX41, the human DDX41 gene was cloned and overexpressed in HeLa cells. The present study demonstrated that DDX41 overexpression inhibited proliferation and promoted apoptosis in HeLa cells. RNA‑sequencing analysis of the transcriptomes in overexpressed and normal control samples. DDX41 regulated 959 differentially expressed genes compared with control cells. Expression levels of certain oncogenes were also regulated by DDX41. DDX41 selectively regulated the alternative splicing of genes in cancer‑associated pathways including the EGFR and FGFR signaling pathways. selleck inhibitor DDX41 selectively upregulated the expression levels of five antigen processing and presentation genes (HSPA1A, HSPA1B, HSPA6, HLA‑DMB and HLA‑G) and downregulated other immune‑response genes in HeLa cells. Additionally, DDX41‑regulated oncogenes and antigen processing and presentation genes were associated with patient survival rates. Moreover, DDX41 expression was associated with immune infiltration in cervical and endocervical squamous cancer. The present findings showed that DDX41 regulated the cancer cell transcriptome at both the transcriptional and alternative splicing levels. The DDX41 regulatory network predicted the biological function of DDX41 in suppressing tumor cell growth and regulating cancer immunity, which may be important for developing anticancer therapeutics.We previously reported that Hedgehog (Hh) signal was enhanced in gallbladder cancer (GBC) and was involved in the induction of malignant phenotype of GBC. In recent years, therapeutics that target Hh signaling have focused on molecules downstream of smoothened (SMO). The three transcription factors in the Hh signal pathway, glioma‑associated oncogene homolog 1 (GLI1), GLI2, and GLI3, function downstream of SMO, but their biological role in GBC remains unclear. In the present study, the biological significance of GLI1, GLI2, and GLI3 were analyzed with the aim of developing novel treatments for GBC. It was revealed that GLI2, but not GLI1 or GLI3, was involved in the cell cycle‑mediated proliferative capacity in GBC and that GLI2, but not GLI1 or GLI3, was involved in the enhanced invasive capacity through epithelial‑mesenchymal transition. Further analyses revealed that GLI2 may function in mediating gemcitabine sensitivity and that GLI2 was involved in the promotion of fibrosis in a mouse xenograft model. Immunohistochemical staining of 66 surgically resected GBC tissues revealed that GLI2‑high expression patients had fewer numbers of CD3+ and CD8+ tumor‑infiltrating lymphocytes (TILs) and increased programmed cell death ligand 1 (PD‑L1) expression in cancer cells. These results suggest that GLI2, but not GLI1 or GLI3, is involved in proliferation, invasion, fibrosis, PD‑L1 expression, and TILs in GBC and could be a novel therapeutic target. The results of this study provide a significant contribution to the development of a new treatment for refractory GBC, which has few therapeutic options.Phospholipase C epsilon 1 (PLCE1) and the competing endogenous RNA (ceRNA) network are crucial for tumorigenesis and the progression of esophageal squamous cell carcinoma (ESCC). However, whether PLCE1 can regulate the ceRNA network in ESCC has not been clarified. In the present study, we aimed to identify the PLCE1‑regulated ceRNA network and further elucidate the regulatory mechanisms by which ESCC is promoted. Microarray analysis was used to identify differentially expressed lncRNAs (DELs) and differentially expressed genes (DEGs) from three pairs of samples of PLCE‑silenced Eca109 and control Eca109 cells. Next, the ceRNA regulatory network was established and visualized in Cytoscape, and functional enrichment analysis was performed to analyze DEGs from ceRNAs. Protein‑protein interaction (PPI) networks among the DEGs were established by the STRING database to screen hub genes. Kaplan‑Meier survival analysis was used to validate hub genes. Finally, PLCE1‑related hub gene/lncRNA/miRNA axes were also constre obtained based on the 4 hub genes, 13 DEmiRNAs, and 10 DELs. In conclusion, the PLCE1‑regulated ceRNA contributes to the onset and progression of ESCC and the underlying molecular mechanisms may provide insights into personalized prognosis and new therapies for ESCC patients.Radiotherapy (RT) followed by radical surgery is an effective standard treatment strategy for various types of cancer, including rectal cancer. The response to RT varies among patients, and the radiosensitivity of cancer cells determines the clinical outcome of patients. However, the application of RT to patients with radioresistant tumors may result in radiation‑induced toxicity without clinical benefits. Currently, there are no effective methods to predict the response to RT. The limitations of the methods currently used to evaluate tumor radiosensitivity, which are mainly based on clinical and radiological features, are low sensitivity and specificity. Non‑coding RNAs (ncRNAs) have emerged as a class of biomarkers for predicting radiosensitivity. In particular, the expression pattern of ncRNAs can predict the response to RT in patients with rectal cancer. Thus, ncRNAs may be used as potential biomarkers and therapeutic targets to improve the diagnosis and treatment outcome of patients with rectal cancer. In the present review, the current knowledge on the limitations of RT for rectal cancer and the association between ncRNA expression and sensitivity of rectal cancer to RT are presented. Additionally, the potential of ncRNAs as predictive biomarkers and therapeutic targets to mitigate resistance of rectal cancer to RT is discussed.Following the publication of this article, an interested reader drew to the authors' attention that, in Fig. 4 on p. 1913, the t-Akt panel in Fig. 4A looked unexpectedly similar to the β-actin panel in Fig. 4C. The authors were able to refer back to their original data, and realized that the Figure had been compiled incorrectly; essentially, the data for the t-Akt panel had been duplicated, and the data for the β-actin panel in Fig. 4C had not been included in the Figure as intended. The revised version of Fig. 4, showing the correct data for the β-actin panel in Fig. 4C, is shown opposite. This error did not have a significant impact on the results or the conclusions reported in this study. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this Corrigendum, and all of the authors agree to the publication of this Corrigendum. The authors sincerely apologize for this mistake, and regret any inconvenience this mistake has caused. [the original article was published in Oncology Reports 36 1909-1916, 2016; DOI 10.