SNRPB is a mediator for cellular response to cisplatin in non-small-cell lung cancer

Small Nuclear Ribonucleoprotein Polypeptides B and B1 Promote Osteosarcoma Progression via Activating the Ataxia-Telangiectasia Mutated Signaling Pathway through Ribonucleotide Reductase Subunit M2

Osteosarcoma is a malignant bone tumor characterized by high metastatic potential and recurrence rates after therapy. The small nuclear ribonucleoprotein polypeptides B and B1 (SNRPB), core components of a spliceosome, exhibit up-regulation across several cancer types. However, the precise role of SNRPB in osteosarcoma progression remains poorly elucidated. Herein, SNRPB expression was explored in human osteosarcoma tissues and normal bone tissues by immunohistochemical staining, revealing a notable up-regulation of SNRPB in osteosarcoma, correlating with diminished survival rates. The in vitro loss-of-function experiments showed that SNRPB knockdown significantly suppressed the osteosarcoma cell proliferation and migration, as well as tubule formation of human umbilical vascular endothelial cells, while enhancing osteosarcoma cell apoptosis. Mechanistically, SNRPB promoted the transcription of ribonucleotide reductase subunit M2 via E2F transcription factor 1. Further rescue experiments indicated that ribonucleotide reductase subunit M2 was required for SNRPB-induced malignant behaviors in osteosarcoma. Additionally, the function of SNRPB in osteosarcoma cell growth and apoptosis was confirmed to be associated with ataxia-telangiectasia mutated (ATM) signaling pathway activation. In conclusion, these findings provide initial insights into the underlying mechanisms governing SNRPB-induced osteosarcoma progression, and we propose SNRPB as a novel therapeutic target in osteosarcoma management.

Construction and validation of a prognostic model for esophageal cancer based on prognostic-related RNA-binding protein

BACKGROUND

Construction of a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs) and preliminary evaluation of RBP function.

METHODS

RNA-seq data of ESCA was downloaded from The Cancer Genome Atlas database and mRNA was extracted to screen differentially expressed genes using R. After screening RBPs in differentially expressed genes, R packages clusterProfiler and pathview were used to analyze the RBPs for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway. Based on the prognosis-related RBPs, COX regression was used to establish the prognostic risk model of ESCA. Risk model predictive ability was assessed using calibration analysis, receiver operating characteristic curves, Kaplan-Meier curves, decision curve analysis, and Harrell consistency index (C-index). A nomogram was established by combining the risk model with clinicopathological features.

RESULTS

A total of 105 RBPs were screened from ESCA. A prognostic risk model consisting of 6 prognostic RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1) was constructed by COX regression analysis. The prognosis was worse in the high-risk group, and the receiver operating characteristic curve showed (area under the curve = 0.90) that the model better predicted patients' 5-year survival. In addition, 6 prognostic RBPs had good diagnostic power for ESCA. In addition, a total of 39 mRNAs were identified as predicted target molecules for DKC1.

CONCLUSION

ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1, as RBPs, are associated with the prognosis of ESCA, which may provide new ideas for targeted therapy of ESCA.

Identification of Novel Prognostic Long Non-coding RNAs in Lung Adenocarcinoma Using WGCNA Analysis

Long non-coding RNAs play crucial role in the tumorigenesis of lung adenocarcinoma (LUAD). However, the function of a large number of lncRNAs in LUAD has not been investigated yet. Weighted gene correlation network analysis (WGCNA) was applied to construct the co-expression module in the TCGA-LUAD cohort. Protein-protein interaction (PPI) network was used to explore the relationship of genes in the key module. The function of the key module on the prognosis in LUAD was analyzed using GO and KEGG analysis. Finally, we constructed the mRNA-lncRNA co-expression network in the key module to identify the hub lncRNAs that play crucial role in the prognosis in LUAD. The most highly expressed 2500 mRNAs and 2500 lncRNAs in the TCGA-LUAD cohort were clustered into 21 modules. After analyzing the correlation between the module and prognostic clinical traits, the Tan module, consisting of 130 genes, was selected as the key module on the prognosis in LUAD. And then, we found that genes in the key module were majorly enriched in ten multiple signaling pathways. Subsequently, we constructed the mRNA-lncRNA co-expression network based on the genes in the key module. Finally, we identified three lncRNAs and nineteen mRNAs that could be the promising prognostic biomarkers for LUAD. We identified three lncRNAs (MIR99AHG, ADAMTS9-AS2, and AC037459.2) and nineteen mRNAs as potential prognostic biomarkers in LUAD, which provided new insight for prognosis monitoring and therapy development in LUAD.

SNRPB promotes the progression of hepatocellular carcinoma via regulating cell cycle, oxidative stress, and ferroptosis

Small Nuclear Ribonucleoprotein Polypeptides B and B1 (SNRPB) have been linked to multiple human cancers. However, the mechanism of SNRPB in hepatocellular carcinoma (HCC) and whether SNRPB has a synergistic effect with sorafenib in the treatment of HCC remain unclear. In this study, bioinformatic analysis found that SNRPB was an independent prognostic factor for HCC that exerted a critical effect on the progression of HCC. SNRPB was linked with immune checkpoints, cell cycle, oxidative stress and ferroptosis in HCC. Single cell sequencing analysis found that HCC cell subset with high expression of SNRPB, accounted for a higher proportion in HCC cells with higher stages, had higher expression levels of the genes which promote cell cycle, inhibit oxidative stress and ferroptosis, and had higher cell cycle score, lower oxidative stress score and ferroptosis score. Single-sample gene set enrichment analysis (ssGSEA) analysis found that 17 oxidative stress pathways and 68 oxidative stress-ferroptosis related genes were significantly correlated with SNRPB risk scores. SNRPB knockdown induced cell cycle G2/M arrest and restrained cell proliferation, while downregulated the expression of CDK1, CDK4, and CyclinB1. The combined treatment (SNRPB knockdown+sorafenib) significantly inhibited tumor growth. In addition, the expression of SLC7A11, which is closely-related to ferroptosis, decreased significantly in vitro and in vivo. Therefore, SNRPB may promote HCC progression by regulating immune checkpoints, cell cycle, oxidative stress and ferroptosis, while its downregulation inhibits cell proliferation, which enhances the therapeutic effect of sorafenib, providing a novel basis for the development of HCC therapies.

RNA splicing alterations in lung cancer pathogenesis and therapy

RNA splicing alterations are widespread and play critical roles in cancer pathogenesis and therapy. Lung cancer is highly heterogeneous and causes the most cancer-related deaths worldwide. Large-scale multi-omics studies have not only characterized the mutational landscapes but also discovered a plethora of transcriptional and post-transcriptional changes in lung cancer. Such resources have greatly facilitated the development of new diagnostic markers and therapeutic options over the past two decades. Intriguingly, altered RNA splicing has emerged as an important molecular feature and therapeutic target of lung cancer. In this review, we provide a brief overview of splicing dysregulation in lung cancer and summarize the recent progress on key splicing events and splicing factors that contribute to lung cancer pathogenesis. Moreover, we describe the general strategies targeting splicing alterations in lung cancer and highlight the potential of combining splicing modulation with currently approved therapies to combat this deadly disease. This review provides new mechanistic and therapeutic insights into splicing dysregulation in cancer.

Significance of Spliceosome-Related Genes in the Prediction of Prognosis and Treatment Strategies for Lung Adenocarcinoma

BACKGROUND

The leading cause of cancer-related fatalities globally is lung cancer; lung adenocarcinoma (LUAD) is the most common histological type in it. The spliceosome plays an important role in a majority of malignancies. However, it is yet unclear how spliceosome-related genes affect patients with LUAD in terms of treatment course and prognosis.

METHODS

Spliceosome-related genes were assessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to obtain clinical information and gene expression in patients with LUAD. A spliceosome-related gene signature and prognostic model were constructed by using the least absolute shrinkage and selection operator (LASSO), time-dependent receiver operating characteristic (ROC), and nomogram. Immune infiltrate levels, mutation analysis, and pathway enrichment were predicted potential mechanisms of the signature by using single-sample gene set enrichment analysis (ssGSEA), Gene Set Cancer Analysis (GSCA) database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) database. Then, a protein-protein interaction (PPI) network and transcription factor- (TF-) hub gene and drug mining network were also established by Cytoscape software.

RESULTS

Firstly, we constructed a prognostic model for 11 spliceosome signature genes. Based on the prognostic risk score, we stratified patients with LUAD into high- and low-risk groups. The high- and low-risk groups were closely related to the OS, tumor immune infiltration level, immune checkpoint molecules, and tumor mutation burden (TMB) of LUAD patients. Based on PPI networks, we also predict relevant TF genes that may regulate signature prognostic genes. Finally, drugs including oxaliplatin, arsenic trioxide, cisplatin, and sunitinib were excavated for the treatment of the 11 spliceosome signature genes in LUAD patients.

CONCLUSION

In conclusion, this study is the first to explore the importance of spliceosome-related genes in the prognosis and treatment of LUAD. Through our study, we have innovatively provided potential prognosis genes and new therapeutic drug targets for the treatment of LUAD patients.

SNRPB promotes cell cycle progression in thyroid carcinoma via inhibiting p53

Papillary thyroid carcinoma (PTC) accounts for more than 80% of all thyroid carcinoma cases. Small nuclear ribonucleoprotein polypeptides B and B1 (SNRPB) has been indicated to be carcinogenic in several cancers; however, its function and mechanism in PTC are unclarified. Real time quantitative polymerase chain reaction and western blotting revealed the upregulation of SNRPB and downregulation of tumor protein p53 in PTC tissues compared with the normal tissues. Flow cytometry and western blotting displayed that SNRPB silencing induced cell cycle arrest at G1 phase and suppressed the expression levels of Cyclin family proteins in PTC cells. In vivo experiments suggested that SNRPB silencing inhibited PTC tumor growth in mice. Bioinformatics analysis revealed that the expression of SNRPB and cell cycle-associated genes in thyroid carcinoma tissues is positively correlated. Immunofluorescence staining and co-immunoprecipitation demonstrated that SNRPB directly interacted with p53 and suppressed its expression in PTC cells. In conclusion, SNRPB facilitates cell cycle progression in PTC by inhibiting p53 expression.

The oncogenic role of SNRPB in human tumors: A pan-cancer analysis

Purpose: The purpose of this study was to explore the oncogenic role of small nuclear ribonucleoprotein polypeptides B and B1 (SNRPB) in human tumors.

Materials and methods: Study cases were acquired from The Cancer Genome Atlas database, the Gene Expression Omnibus database, The Human Protein Atlas, and the Clinical Proteomic Tumor Analysis Consortium. We then used the R package and several online tools to analyze and visualize the role of SNRPB across tumors.

Results: We found that the expression of SNRPB was significantly increased in 28 of 33 tumors, and higher expression was observed in late pathological and TNM stages. Significantly decreased levels of SNRPB promoter methylation were observed in 12 tumors. SNRPB was found to be a risk factor for decreased overall survival in 10 tumors (p < 0.05), a risk factor for decreased disease-specific survival in 8 tumors (p < 0.05), and a risk factor for decreased progression-free interval in 7 tumors (p < 0.05). The PPI network of SNRPB and the top 100 coexpressed genes revealed that CDK1, CDC6, AURKB, CCNB1, CCNA2, and CDC45 were the most closely interacting genes across tumors. The GO and KEGG enrichment analyses revealed that SNRPB and the above genes were mainly enriched with respect to functions in cell cycle-related genetic material replication, assembly, and distribution. SNRPB was significantly associated with immune cell infiltration and the expression of immunomodulation-related genes in several but not all tumors.

Conclusion and limitations: The expression of SNRPB was significantly elevated in almost all tumors, and the decreased promoter methylation level may contribute to the elevated expression of SNRPB. SNRPB may facilitate the progression of pathological and TNM stages and is a risk factor for unfavorable prognosis across tumors. However, our research was based on data obtained from public databases, without further validation of our findings at the cellular and animal levels. Therefore, further studies are needed to clarify the oncogenic mechanism of SNRPB and its potential as a therapeutic target.

SNRPD1/E/F/G Serve as Potential Prognostic Biomarkers in Lung Adenocarcinoma

Objectives: Sm proteins (SNRPB/D1/D2/D3/E/F/G), involved in pre-mRNA splicing, were previously reported in the tumorigenesis of several cancers. However, their specific role in lung adenocarcinoma (LUAD) remains obscure. Our study aims to feature abnormal expressions and mutations of genes for Sm proteins and assess their potential as therapeutic targets via integrated bioinformatics analysis.

Methods: In this research, we explored the expression pattern and prognostic worth of genes for Sm proteins in LUAD across TCGA, GEO, UALCAN, Oncomine, Metascape, David 6.8, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via univariate and multivariate cox regression analysis. Meanwhile, their expression patterns were validated by RT-qPCR. Gene mutations and co-expression of genes for Sm proteins were analyzed by the cBioPortal database. The PPI network for Sm proteins in LUAD was visualized by the STRING and Cytoscape. The correlations between genes for Sm proteins and immune infiltration were analyzed by using the “GSVA” R package.

Results: Sm proteins genes were found upregulated expression in both LUAD tissues and LUAD cell lines. Moreover, highly expressed mRNA levels for Sm proteins were strongly associated with short survival time in LUAD. Genes for Sm proteins were positively connected with the infiltration of Th2 cells, but negatively connected with the infiltration of mast cells, Th1 cells, and NK cells. Importantly, Cox regression analysis showed that high SNRPD1/E/F/G expression were independent risk factors for the overall survival of LUAD.

Conclusion: Our study showed that SNRPD1/E/F/G could independently predict the prognostic outcome of LUAD and was correlated with immune infiltration. Also, this report laid the foundation for additional exploration on the potential treatment target’s role of SNRPD1/E/F/G in LUAD.