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. 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.
ObjectiveThis study aimed to construct a prognostic model for rectal adenocarcinomas based on immune-related long noncoding RNAs (lncRNAs) and verify its prediction efficiency.MethodsTranscript data and clinical data of rectal adenocarcinomas were downloaded from The Cancer Genome Atlas (TCGA) database. Perl software (strawberry version) and R language (version 3.6.1) were used to analyze the immune-related genes and immune-related lncRNAs of rectal adenocarcinomas, and the differentially expressed immune-related lncRNAs were screened according to the criteria |log2FC|> 1 and P < 0.05. The key immune-related lncRNAs were screened using single-factor Cox regression analysis and lasso regression analysis. Multivariate Cox regression analysis was performed to construct an immune-related lncRNA prognostic model using the risk scores. Next, we evaluated the effectiveness of the model through Kaplan-Meier (K-M) survival analysis, ROC curve analysis, and independent prognostic analysis of clinical features. In addition, prognostic biomarkers of immune-related lncRNAs in the model were analyzed by K-M survival analysis.ResultsIn this study, we obtained gene expression profile matrices of 89 rectal adenocarcinomas and 2 paracancerous specimens from TCGA database and applied immunologic signatures to these transcripts. Through R and Perl software analysis, we obtained 847 immune-related lncRNAs and 331 protein-encoded immune-related genes in rectal adenocarcinomas. Eight important immune-related lncRNAs related to the prognosis of rectal adenocarcinomas were identified using univariate Cox regression and lasso regression analysis. Furthermore, four immune-related lncRNAs were identified as prognostic markers of rectal adenocarcinomas via multivariate Cox regression analysis. The prognostic risk model was as follows: risk score = (-4.084) * expression LINC01871 + (3.112) * expression AL158152.2 + (7.616) * expression PXN-AS1 + (-0.867) * expression HCP5. The independent prognostic effect of the rectal adenocarcinoma risk score model was revealed through K-M analysis, ROC curve analysis, and univariate, and multivariate Cox regression analysis (P = 0.035). LINC01871 (P = 0.006), PXN-AS1 (P = 0.008), and AL158152.2 (P= 0.0386) were closely correlated with the prognosis of rectal adenocarcinomas through the K-M survival analysis.ConclusionWe constructed a prognostic model of rectal adenocarcinomas based on four immune-related lncRNAs by analyzing the data based on TCGA database, with high prediction accuracy. We also identified two biomarkers with poor prognosis (PXN-AS1 and AL158152.2) and one biomarker with good prognosis (LINC01871).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.