Up-Frameshift Suppressor 3 as a prognostic biomarker and correlated with immune infiltrates: A pan-cancer analysis

Multiple Myeloma Cells with Increased Proteasomal and ER Stress Are Hypersensitive to ATX-101, an Experimental Peptide Drug Targeting PCNA

Objectives: To examine the regulatory role of PCNA in MM, we have targeted PCNA with the experimental drug ATX-101 in three commercial cell lines (JJN3, RPMI 1660, AMO) and seven in-house patient-derived cell lines with a more primary cell-like phenotype (TK9, 10, 12, 13, 14, 16 and 18) and measured the systemic molecular effects. Methods: We have used a multi-omics untargeted approach, measuring the gene expression (transcriptomics), a subproteomics approach measuring mainly signalling proteins and proteins in complex with these (signallomics) and quantitative metabolomics. These results are supplemented with traditional analysis, e.g., viability, Western and ELISA analysis. Results: The sensitivity of the cell lines to ATX-101 varied, including between three cell lines derived from the same patient at different times of disease. A trend towards increased sensitivity to ATX-101 during disease progression was detected. Although with different sensitivities, ATX-101 treatment resulted in numerous changes in signalling and metabolite pools in all cell lines. Transcriptomics and signallomics analysis of the TK cell lines revealed that elevated endogenous expression of ribosomal genes, elevated proteasomal and endoplasmic reticulum (ER) stress and low endogenous levels of NAD+ and NADH were associated with ATX-101 hypersensitivity. ATX-101 treatment further enhanced the ER stress, reduced primary metabolism and reduced the levels of the redox pair GSH/GSSG in sensitive cells. Signallome analysis suggested that eleven proteins (TPD52, TNFRS17/BCMA, LILRB4/ILT3, TSG101, ZNRF2, UPF3B, FADS2, C11orf38/SMAP, CGREF1, GAA, COG4) were activated only in the sensitive MM cell lines (TK13, 14 and 16 and JJN3), and not in nine other cancer cell lines or in primary monocytes. These proteins may therefore be biomarkers of cells with activated proteasomal and ER stress even though the gene expression levels of these proteins were not elevated. Interestingly, carfilzomib-resistant cells were at least as sensitive to ATX-101 as the wild-type cells, suggesting both low cross-resistance between ATX-101 and proteasome inhibitors and elevated proteasomal stress in carfilzomib-resistant cells. Conclusions: Our multi-omics approach revealed a vital role of PCNA in regulation of proteasomal and ER stress in MM.

Unveiling the role of UPF3B in hepatocellular carcinoma: Potential therapeutic target

RNA-binding proteins can regulate nucleotide metabolism and gene expression. UPF3B regulator of nonsense mediated mRNA decay (UPF3B) exhibits dysfunction in cancers. However, its role in the progression of hepatocellular carcinoma (HCC) is still insufficiently understood. Here, we found that UPF3B was markedly upregulated in HCC samples and associated with adverse prognosis in patients. UPF3B dramatically promoted HCC growth both in vivo and in vitro. Mechanistically, UPF3B was found to bind to PPP2R2C, a regulatory subunit of PP2A, boosting its mRNA degradation and activating the PI3K/AKT/mTOR pathway. E2F transcription factor 6 (E2F6) directly binds to the UPF3B promoter to facilitate its transcription. Together, the E2F6/UPF3B/PPP2R2C axis promotes HCC growth through the PI3K/AKT/mTOR pathway. Hence, it could be a promising therapeutic target for treating HCC.

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

Purpose

Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis.

Methods

The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics.

Results

We developed a risk model based on four NMD-related genes (PABPC1, RPL8, SMG5, and UPF3B) and validated it using GSE54236, GSE116174, and GSE76427 data. The model effectively distinguished high- and low-risk groups corresponding to unfavorable and favorable HCC outcomes. Its prognostic prediction accuracy was confirmed through time-dependent ROC analysis, and clinical-use nomograms with calibration curves were developed. Single-cell RNA sequencing results indicated significantly higher expression of SMG5 and UPF3B in tumor cells. Knockdown of SMG5 and UPF3B inhibited HCC cell proliferation, invasion, and migration, while affecting cell-cycle progression and apoptosis. In vivo, UPF3B knockdown delayed tumor growth and increased immune cell infiltration.

Conclusion

Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.

HnRNPR-mediated UPF3B mRNA splicing drives hepatocellular carcinoma metastasis

INTRODUCTION

Abnormal alternative splicing (AS) contributes to aggressive intrahepatic invasion and metastatic spread, leading to the high lethality of hepatocellular carcinoma (HCC).

OBJECTIVES

This study aims to investigate the functional implications of UPF3B-S (a truncated oncogenic splice variant) in HCC metastasis.

METHODS

Basescope assay was performed to analyze the expression of UPF3B-S mRNA in tissues and cells. RNA immunoprecipitation, and in vitro and in vivo models were used to explore the role of UPF3B-S and the underlying mechanisms.

RESULTS

We show that splicing factor HnRNPR binds to the pre-mRNA of UPF3B via its RRM2 domain to generate an exon 8 exclusion truncated splice variant UPF3B-S. High expression of UPF3B-S is correlated with tumor metastasis and unfavorable overall survival in patients with HCC. The knockdown of UPF3B-S markedly suppresses the invasive and migratory capacities of HCC cells in vitro and in vivo. Mechanistically, UPF3B-S protein targets the 3'-UTR of CDH1 mRNA to enhance the degradation of CDH1 mRNA, which results in the downregulation of E-cadherin and the activation of epithelial-mesenchymal transition. Overexpression of UPF3B-S enhances the dephosphorylation of LATS1 and the nuclear accumulation of YAP1 to trigger the Hippo signaling pathway.

CONCLUSION

Our findings suggest that HnRNPR-induced UPF3B-S promotes HCC invasion and metastasis by exhausting CDH1 mRNA and modulating YAP1-Hippo signaling. UPF3B-S could potentially serve as a promising biomarker for the clinical management of invasive HCC.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma

BACKGROUND

Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC.

METHODS

The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC.

RESULTS

Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis.

CONCLUSION

We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.