The Function, Role and Process of DDX58 in Heart Failure and Human Cancers

Abstract

BackgroundHeart failure (HF) is the most common outcome of cardiovascular disease, and an increasing number of patients with heart failure die from noncardiac causes, such as cancer. Epidemiological data suggest that ischemic cardiomyopathy–induced HF (ischemic HF) may be associated with an increased incidence of cancer. This study aimed to investigate the possible mechanisms of the association between ischemic HF and cancer, as well as potential therapeutic targets.MethodsWeighted gene co-expression network analysis was performed to analyze the correlations between phenotypes and gene modules using immune cells as phenotypes. Differential analysis was then performed to screen differentially expressed genes (DEGs) in ischemic HF and normal control samples. The macrophage-related Brown module was identified as the key module, and immune-related DEGs were obtained by taking the intersection of the Brown module, DEGs, and immune-related genes using a Venn diagram. DDX58 was identified as the key gene using a protein–protein interaction network and expression analyses and validated using immunohistochemistry. Kaplan–Meier survival analysis was performed to analyze the correlation between DDX58 expression and tumor prognosis. Spearman correlation analysis was performed to assess the correlation between DDX58 expression and immune cell infiltration.ResultsDDX58 was identified as a key immune-related gene associated with ischemic HF and was highly expressed in most cancer types. The survival analysis revealed a significant negative correlation between high DDX58 expression and prognosis in multiple tumor types. Moreover, DDX58 expression was significantly associated with immune cell infiltration and immune checkpoint gene expression in many cancer types.ConclusionDDX58 is a key immune-related gene in ischemic HF and may play a crucial role in the relationship between ischemic HF and cancer. Pan-cancer analysis suggests that DDX58 is a promising clinical prognostic marker for most cancers and may be a therapeutic target for cancer patients and ischemic HF patients at an increased risk of cancer.

DDX58 deficiency leads to doxorubicin chemotherapy resistance by hindering Type I IFN signalling-mediated apoptosis in TNBC

“…Previous studies have shown that DDX58 is a proto-oncogene, an important immune-related gene closely related to immune cell in ltration, and its high expression might promote the occurrence of cancer [29]. Similarly, our results indicated that the expression of DDX58 in tumour samples was signi cantly higher than that in normal samples and boosted the number of monoclonal formations and cell viabilities in TNBC cells.…”

Establishment of nucleic acid sensing pathways‐based model in predicting response to immunotherapy and targeted drug in hepatitis virus‐related hepatocellular carcinoma

“…Previous literature has disclosed that there are two prerequisites for satisfying response to immunotherapy, sufficient immune infiltration and abundant expression of immune checkpoint. 31,32 Therefore, screening immune characters through NAS-based scores lays the foundation for predicting response to immunotherapy. [33][34][35][36] To be noted, the transcriptomic data and qPCR assay revealed that S100A9 was rare in hepatoma cells.…”

“…Overall, the NAS‐activated subgroup was characterized by a significant infiltration of immune cells but increased expression of immune checkpoint‐related genes. Previous literature has disclosed that there are two prerequisites for satisfying response to immunotherapy, sufficient immune infiltration and abundant expression of immune checkpoint 31,32 . Therefore, screening immune characters through NAS‐based scores lays the foundation for predicting response to immunotherapy.…”

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

“…Altered expression of ATOH8 [364], STAT1 [365], ARG1 [366], TLR4 [367], VNN1 [368], ABCA1 [369], IFIH1 [370], PTGS2 [371], F2RL1 [289], CYP2D6 [372], PDK4 [373], RNF213 [374], JAK2 [375], NOTCH2 [376], PDGFC (platelet derived growth factor C) [377], TLR2 [378], CYP1B1 [379], IL1RN [380], GCH1 [381], EGR1 [382], HIF1A [383], PLA2G7 [384], CCR2 [385], GAB1 [386], VEGFA (vascular endothelial growth factor A) [387], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [388], OXR1 [389], IRF9 [390], FMR1 [391], LDLR (low density lipoprotein receptor) [392], SIRT1 [393], NOD2 [394], ATP13A3 [395], VCAN (versican) [396], FGL2 [397], TET2 [398], KDM6A [399], KLHL2 [400], CAVIN1 [401], TNFRSF4 [402], PF4 [403], VEGFB (vascular endothelial growth factor B) [330], CCR7 [404], PRDX2 [405], HSPB1 [406], TCF4 [407], MRPL4 [408], PHF14 [409], TRPM4 [410], AQP3 [411] and LGMN (legumain) [412] were observed to be associated with the progression of hypertension. Recent studies have shown that altered expression of DDX58 [413], STAT1 [414], TLR4 [415], CYP2D6 [416], JAK2 [417], TLR2 [418], DUSP6 [419], HDAC9 [420], LATS2 [421], CA2 [150], HIPK3 [422], CCR2 [423], GAB1 [120], UFL1 [424], OGT (O-linked N-acetylglucosamine (GlcNAc) transferase) [425], PRKAR1A [265], SIRT1 [426], FGL2 [427], TET2 [428], ASCC2 [429], BIN1 [430], HSPB1 […”