Investigation on Potential Correlation Between Small Nuclear Ribonucleoprotein Polypeptide A and Lung Cancer

The Continuous Adaptive Challenge Played by Arboviruses: An In Silico Approach to Identify a Possible Interplay between Conserved Viral RNA Sequences and Host RNA Binding Proteins (RBPs)

Climate change and globalization have raised the risk of vector-borne disease (VBD) introduction and spread in various European nations in recent years. In Italy, viruses carried by tropical vectors have been shown to cause viral encephalitis, one of the symptoms of arboviruses, a spectrum of viral disorders spread by arthropods such as mosquitoes and ticks. Arboviruses are currently causing alarm and attention, and the World Health Organization (WHO) has released recommendations to adopt essential measures, particularly during the hot season, to restrict the spreading of the infectious agents among breeding stocks. In this scenario, rapid analysis systems are required, because they can quickly provide information on potential virus-host interactions, the evolution of the infection, and the onset of disabling clinical symptoms, or serious illnesses. Such systems include bioinformatics approaches integrated with molecular evaluation. Viruses have co-evolved different strategies to transcribe their own genetic material, by changing the host's transcriptional machinery, even in short periods of time. The introduction of genetic alterations, particularly in RNA viruses, results in a continuous adaptive fight against the host's immune system. We propose an in silico pipeline method for performing a comprehensive motif analysis (including motif discovery) on entire genome sequences to uncover viral sequences that may interact with host RNA binding proteins (RBPs) by interrogating the database of known RNA binding proteins, which play important roles in RNA metabolism and biological processes. Indeed, viral RNA sequences, able to bind host RBPs, may compete with cellular RNAs, altering important metabolic processes. Our findings suggest that the proposed in silico approach could be a useful and promising tool to investigate the complex and multiform clinical manifestations of viral encephalitis, and possibly identify altered metabolic pathways as targets of pharmacological treatments and innovative therapeutic protocols.

Splicing factor SNRPA associated with microvascular invasion promotes hepatocellular carcinoma metastasis through activating NOTCH1/Snail pathway and is mediated by circSEC62/miR-625-5p axis

Microvascular invasion (MVI) is a crucial risk factor related to the metastasis of hepatocellular carcinoma (HCC), but the underlying mechanisms remain to be revealed. Characterizing the inherent mechanisms of MVI may aid in the development of effective treatment strategies to improve the prognosis of HCC patients with metastasis. Through the Gene Expression Omnibus (GEO) database, we identified that small nuclear ribonucleoprotein polypeptide A (SNRPA) was related to MVI in HCC. SNRPA was overexpressed in MVI-HCC and correlated with poor patient survival. Mechanistically, SNRPA promoted the epithelial-mesenchymal transition (EMT)-like process for HCC cells to accelerate metastasis by activating the NOTCH1/Snail pathway in vitro and in vivo. Importantly, circSEC62 upregulated SNRPA expression in HCC cells via miR-625-5p sponging. Taking these results together, our study identified a novel regulatory mechanism among SNRPA, miR-625-5p, circSEC62 and the NOTCH1/Snail pathway in HCC, which promoted metastasis of HCC and may provide effective suggestions for improving the prognosis of HCC patients with metastasis.

Elevated Small Nuclear Ribonucleoprotein Polypeptide an Expression Correlated With Poor Prognosis and Immune Infiltrates in Patients With Hepatocellular Carcinoma

Background

Elevated Small Nuclear Ribonucleoprotein Polypeptide A (SNRPA) can enhance tumor cell growth and proliferation in various cancers. However, rarely studies focus on the comprehensive analysis of SNRPA in hepatocellular carcinoma (HCC).

Methods

TCGA and GEO databases were used to analyze the mRNA expression of SNRPA in HCC. Protein expression of SNAPA was validated using immunohistochemistry. Stably transfected HCC cells were used to investigate the role of SNRPA in the progression of HCC. The functional enrichment analysis was utilized for the biological function prediction. The CIBERSORT and ssGSEA algorithms were used to evaluate the composition of the tumor microenvironment and immunocyte infiltration ratio.

Results

The SNRPA expression was upregulated in HCC and positively correlated with tumor stage and grade. SNRPA overexpression were independent risk factors for poor overall survival (OS) and recurrence-free survival (RFS). In patients with early-stage disease, low alpha-fetoprotein expression, and better differentiation, SNRPA still exhibited the excellent prognostic value. Knockdown of SNRPA inhibited the proliferation and migration while promoting the apoptosis of HCC cells. Higher methylation of the CpG site cg16596691 correlated with longer OS in HCC patients. Genes co-expressed with SNRPA were overexpressed in HCC and correlated with shorter OS. The GO and KEGG enrichment analysis showed that SNRPA expression was related to mRNA splicing, spliceosome signaling. GSEA demonstrated that the main enrichment pathway of SNRPA-related differential genes was spliceosome signaling, cell cycle signaling, P53 signaling pathway, T cell receptor signaling pathway, natural killer cell-mediated signaling. CIBERSORT and ssGSEA algorithm revealed that SNRPA was mainly associated with the higher proportion of CD8+T cells, T cells follicular helper, T cells regulatory, Macrophages M0, and the lower proportion of T cells CD4 memory resting, NK cells resting, Monocytes, and Mast cells resting.

Conclusion

Elevated SNRPA enhances tumor cell proliferation and correlated with poor prognosis and immune infiltrates in patients with HCC.

Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer. Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman's rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan-Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets. Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines. Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.