Increased expression of the RNA-binding motif protein 47 predicts poor prognosis in non-small-cell lung cancer

RBM47 inhibits hepatocellular carcinoma progression by targeting UPF1 as a DNA/RNA regulator

The mechanisms by which the tumor behaviors of hepatocellular carcinoma (HCC) support growth and metastasis remain largely unknown, and it has become increasingly apparent that molecular dysregulation is of considerable importance for cellular signaling pathways. Recently, RNA-binding motif protein 47 (RBM47) has been suggested to function as a tumor regulator by acting as an RNA binding protein (RBP), but its role in HCC remains ambiguous. Here, in HCC, we identified that RBM47 had an inhibitory influence on tumor behaviors in vitro and accordingly suppressed the growth and metastasis of xenograft tumors in vivo. Additionally, RBM47 was verified to positively regulate Upframeshift 1 (UPF1), which is a crucial protein involved in the nonsense-mediated RNA decay (NMD) process and was previously determined to be an HCC suppressor. Mechanistically, the stability of UPF1 mRNA was demonstrated to be enhanced with its 3’UTR bound by RBM47, which acted as an RNA binding protein. Meanwhile, RBM47 was also proven to promote the transcription of UPF1 as a transcription factor. Taken together, we concluded that RBM47 functioned as a tumor suppressor by upregulating UPF1, acting as a DNA/RNA binding protein at the transcriptional and posttranscriptional levels.

RNA binding motif 47 (RBM47): emerging roles in vertebrate development, RNA editing and cancer

RNA-binding proteins (RBPs) are critical players in the post-transcriptional regulation of gene expression and are associated with each event in RNA metabolism. The term 'RNA-binding motif' (RBM) is assigned to novel RBPs with one or more RNA recognition motif (RRM) domains that are mainly involved in the nuclear processing of RNAs. RBM47 is a novel RBP conserved in vertebrates with three RRM domains whose contributions to various aspects of cellular functions are as yet emerging. Loss of RBM47 function affects head morphogenesis in zebrafish embryos and leads to perinatal lethality in mouse embryos, thereby assigning it to be an essential gene in early development of vertebrates. Its function as an essential cofactor for APOBEC1 in C to U RNA editing of several targets through substitution for A1CF in the A1CF-APOBEC1 editosome, established a new paradigm in the field. Recent advances in the understanding of its involvement in cancer progression assigned RBM47 to be a tumor suppressor that acts by inhibiting EMT and Wnt/[Formula: see text]-catenin signaling through post-transcriptional regulation. RBM47 is also required to maintain immune homeostasis, which adds another facet to its regulatory role in cellular functions. Here, we review the emerging roles of RBM47 in various biological contexts and discuss the current gaps in our knowledge alongside future perspectives for the field.

Elevated expression of the RNA-binding motif protein 43 predicts poor prognosis in esophageal squamous cell carcinoma

RNA-binding proteins (RBPs) play crucial roles in the post-transcriptional regulation of mRNA during numerous physiological and pathological processes, including tumor genesis and development. However, the role of RNA-binding motif protein 43 (RBM43) in esophageal squamous cell carcinoma (ESCC) has not been reported so far. The current study was the first to evaluate RBM43 protein expression by immunohistochemistry (IHC) in an independent cohort of 207 patients with ESCC, to explore its potential prognostic value and clinical relevance in ESCC. The results indicated that RBM43 protein levels were significantly elevated in ESCC tissues and increased RBM43 expression was associated with age and N categories. In addition, ESCC patients with high expression of RBM43 had shorter overall survival (OS) and disease‐free survival (DFS) than those with low RBM43 expression. Furthermore, when survival analyses were conducted at different clinical stages, overexpression of RBM43 was significantly correlated with shortened survival in patients with ESCC at early stages (TNM stage I–II and N0 stage). Cox regression analysis further proved that high RBM43 expression was an independent predictor of poor prognosis in ESCC patients. In conclusion, increased expression of RBM43 is correlated with malignant attributes to ESCC and predicts unfavorable prognosis, suggesting an effective prognostic biomarker and potential therapeutic target for ESCC.

Artificial Intelligence Applied to in vitro Gene Expression Testing (IVIGET) to Predict Trivalent Inactivated Influenza Vaccine Immunogenicity in HIV Infected Children

The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions [med, stim or Δ (stim-med)]. Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. These data show how a predictive bioinformatic model applied to transcriptional analysis deriving from in-vitro stimulated lymphocytes subsets may predict poor or protective vaccination immune response in vulnerable populations, such as HIV-infected individuals. Future studies on larger cohorts are needed to validate such strategy in the context of vaccination trials.