Gene Summary

Gene:SNRPE; small nuclear ribonucleoprotein polypeptide E
Aliases: SME, Sm-E, HYPT11, snRNP-E
Summary:The protein encoded by this gene is a core component of U small nuclear ribonucleoproteins, which are key components of the pre-mRNA processing spliceosome. The encoded protein plays a role in the 3' end processing of histone transcripts. This protein is one of the targets in the autoimmune disease systemic lupus erythematosus, and mutations in this gene have been associated with hypotrichosis. Several pseudogenes of this gene have been identified. [provided by RefSeq, Jun 2016]
Databases:OMIM, HGNC, Ensembl, GeneCard, Gene
Protein:small nuclear ribonucleoprotein E
Source:NCBIAccessed: 01 September, 2019


What does this gene/protein do?
Show (22)
Pathways:What pathways are this gene/protein implicaed in?
Show (1)

Cancer Overview

Research Indicators

Publications Per Year (1994-2019)
Graph generated 01 September 2019 using data from PubMed using criteria.

Literature Analysis

Mouse over the terms for more detail; many indicate links which you can click for dedicated pages about the topic.

Tag cloud generated 01 September, 2019 using data from PubMed, MeSH and CancerIndex

Specific Cancers (6)

Data table showing topics related to specific cancers and associated disorders. Scope includes mutations and abnormal protein expression.

Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).

Latest Publications: SNRPE (cancer-related)

Tapak L, Saidijam M, Sadeghifar M, et al.
Competing risks data analysis with high-dimensional covariates: an application in bladder cancer.
Genomics Proteomics Bioinformatics. 2015; 13(3):169-76 [PubMed] Free Access to Full Article Related Publications
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P<0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.

Xu W, Huang H, Yu L, Cao L
Meta-analysis of gene expression profiles indicates genes in spliceosome pathway are up-regulated in hepatocellular carcinoma (HCC).
Med Oncol. 2015; 32(4):96 [PubMed] Related Publications
Hepatocellular carcinoma (HCC) is among the commonest kind of malignant tumors, which accounts for more than 500,000 cases of newly diagnosed cancer annually. Many microarray studies for identifying differentially expressed genes (DEGs) in HCC have been conducted, but results have varied across different studies. Here, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus datasets, which covers five independent studies, containing 753 HCC samples and 638 non-tumor liver samples. We identified 192 DEGs that were consistently up-regulated in HCC vs. normal liver tissue. For the 192 up-regulated genes, we performed Kyoto Encyclopedia of Genes and Genomes pathway analysis. To our surprise, besides several cell growth-related pathways, spliceosome pathway was also up-regulated in HCC. For further exploring the relationship between spliceosome pathway and HCC, we investigated the expression data of spliceosome pathway genes in 15 independent studies in Nextbio database ( ). It was found that many genes of spliceosome pathway such as HSPA1A, SNRPE, SF3B2, SF3B4 and TRA2A genes which we identified to be up-regulated in our meta-analysis were generally overexpressed in HCC. At last, using real-time PCR, we also found that BUD31, SF3B2, SF3B4, SNRPE, SPINK1, TPA2A and HSPA1A genes are significantly up-regulated in clinical HCC samples when compared to the corresponding non-tumorous liver tissues. Our study for the first time indicates that many genes of spliceosome pathway are up-regulated in HCC. This finding might put new insights for people's understanding about the relationship of spliceosome pathway and HCC.

Li Y, Liang C, Easterbrook S, et al.
Investigating the functional implications of reinforcing feedback loops in transcriptional regulatory networks.
Mol Biosyst. 2014; 10(12):3238-48 [PubMed] Related Publications
Transcription factors (TFs) and microRNAs (miRNAs) can jointly regulate transcriptional networks in the form of recurrent circuits or motifs. A motif can be divided into a feedforward loop (FFL) and a feedback loop (FBL). Incoherent FFLs have been the recent focus due to their potential to dampen gene expression noise in maintaining physiological norms. However, a cell is not only able to manage noise but also able to exploit it during development or tumorigenesis to initiate radical transformation such as cell differentiation or metastasis. A plausible mechanism may involve reinforcing FBLs (rFBLs), which amplify changes to a sufficient level in order to complete the state transition. To study the behaviour of rFBLs, we developed a novel theoretical framework based on biochemical kinetics. The proposed rFBL follows a parsimonious design, involving two TFs and two miRNAs. A simulation study based on our model suggested that a system with rFBLs is robust to only a certain level of fluctuation but prone to a complete paradigm shift when the change exceeds a threshold level. To investigate the natural occurrence of rFBLs, we performed a rigorous network motif analysis using a recently available TF/miRNA regulatory network from the Encyclopedia of DNA Elements (ENCODE). Our analysis suggested that the rFBL is significantly depleted in the observed network. Nonetheless, we identified 9 rFBL instances. Among them, we found a double-rFBL involving three TFs SUZ12/BCLAF1/ZBTB33 and three miRNAs miR-9/19a/129-5p, which together serve as an intriguing toggle switch between nerve development and telomere maintenance. Additionally, we investigated the interactions implicated in the rFBLs using expression profiles of cancer patients from The Cancer Genome Atlas (TCGA). Together, we provided a novel and comprehensive view of the profound impacts of rFBLs and highlighted several TFs and miRNAs as the leverage points for potential therapeutic targets in cancers due to their eminent roles in the identified rFBLs.

Akbari Moqadam F, Lange-Turenhout EA, Ariës IM, et al.
MiR-125b, miR-100 and miR-99a co-regulate vincristine resistance in childhood acute lymphoblastic leukemia.
Leuk Res. 2013; 37(10):1315-21 [PubMed] Related Publications
MicroRNA-125b (miR-125b), miR-99a and miR-100 are overexpressed in vincristine-resistant acute lymphoblastic leukemia (ALL). Cellular viability of ETV6-RUNX1-positive Reh cells significantly increased in presence of 9 ng/mL vincristine upon co-expression of miR-125b/miR-99a (91 ± 4%), miR-125b/miR-100 (93 ± 5%) or miR-125b/miR-99a/miR-100 (82 ± 17%) compared with miR-125b-transduced cells (38 ± 13%, P<0.05). Co-expression of these miRNAs resulted in downregulation of DNTT, NUCKS1, MALAT1, SNRPE, PNO1, SET, KIF5B, PRPS2, RPS11, RPL38 and RPL23A (fold-change 1.3-1.9, p<0.05). Similarly, 7 out of these genes are lower expressed in vincristine-resistant ALL cells of children (p<0.05). The concerted function of miR-125b in combination with miR-99a and/or miR-100 illustrates the complexity of vincristine-resistant pediatric ALL.

Quidville V, Alsafadi S, Goubar A, et al.
Targeting the deregulated spliceosome core machinery in cancer cells triggers mTOR blockade and autophagy.
Cancer Res. 2013; 73(7):2247-58 [PubMed] Related Publications
The spliceosome is a large ribonucleoprotein complex that guides pre-mRNA splicing in eukaryotic cells. Here, we determine whether the spliceosome could constitute an attractive therapeutic target in cancer. Analysis of gene expression arrays from lung, breast, and ovarian cancers datasets revealed that several genes encoding components of the core spliceosome composed of a heteroheptameric Sm complex were overexpressed in malignant disease as compared with benign lesions and could also define a subset of highly aggressive breast cancers. siRNA-mediated depletion of SmE (SNRPE) or SmD1 (SNRPD1) led to a marked reduction of cell viability in breast, lung, and melanoma cancer cell lines, whereas it had little effect on the survival of the nonmalignant MCF-10A breast epithelial cells. SNRPE or SNRPD1 depletion did not lead to apoptotic cell death but autophagy, another form of cell death. Indeed, induction of autophagy was revealed by cytoplasmic accumulation of autophagic vacuoles and by an increase in both LC3 (MAP1LC3A) protein conversion and the amount of acidic autophagic vacuoles. Knockdown of SNRPE dramatically decreased mTOR mRNA and protein levels and was accompanied by a deregulation of the mTOR pathway, which, in part, explains the SNRPE-dependent induction of autophagy. These findings provide a rational to develop new therapeutic agents targeting spliceosome core components in oncology.

Valles I, Pajares MJ, Segura V, et al.
Identification of novel deregulated RNA metabolism-related genes in non-small cell lung cancer.
PLoS One. 2012; 7(8):e42086 [PubMed] Free Access to Full Article Related Publications
Lung cancer is a leading cause of cancer death worldwide. Several alterations in RNA metabolism have been found in lung cancer cells; this suggests that RNA metabolism-related molecules are involved in the development of this pathology. In this study, we searched for RNA metabolism-related genes that exhibit different expression levels between normal and tumor lung tissues. We identified eight genes differentially expressed in lung adenocarcinoma microarray datasets. Of these, seven were up-regulated whereas one was down-regulated. Interestingly, most of these genes had not previously been associated with lung cancer. These genes play diverse roles in mRNA metabolism: three are associated with the spliceosome (ASCL3L1, SNRPB and SNRPE), whereas others participate in RNA-related processes such as translation (MARS and MRPL3), mRNA stability (PCBPC1), mRNA transport (RAE), or mRNA editing (ADAR2, also known as ADARB1). Moreover, we found a high incidence of loss of heterozygosity at chromosome 21q22.3, where the ADAR2 locus is located, in NSCLC cell lines and primary tissues, suggesting that the downregulation of ADAR2 in lung cancer is associated with specific genetic losses. Finally, in a series of adenocarcinoma patients, the expression of five of the deregulated genes (ADAR2, MARS, RAE, SNRPB and SNRPE) correlated with prognosis. Taken together, these results support the hypothesis that changes in RNA metabolism are involved in the pathogenesis of lung cancer, and identify new potential targets for the treatment of this disease.

Jia D, Wei L, Guo W, et al.
Genome-wide copy number analyses identified novel cancer genes in hepatocellular carcinoma.
Hepatology. 2011; 54(4):1227-36 [PubMed] Related Publications
UNLABELLED: A powerful way to identify driver genes with causal roles in carcinogenesis is to detect genomic regions that undergo frequent alterations in cancers. Here we identified 1,241 regions of somatic copy number alterations in 58 paired hepatocellular carcinoma (HCC) tumors and adjacent nontumor tissues using genome-wide single nucleotide polymorphism (SNP) 6.0 arrays. Subsequently, by integrating copy number profiles with gene expression signatures derived from the same HCC patients, we identified 362 differentially expressed genes within the aberrant regions. Among these, 20 candidate genes were chosen for further functional assessments. One novel tumor suppressor (tripartite motif-containing 35 [TRIM35]) and two putative oncogenes (hairy/enhancer-of-split related with YRPW motif 1 [HEY1] and small nuclear ribonucleoprotein polypeptide E [SNRPE]) were discovered by various in vitro and in vivo tumorigenicity experiments. Importantly, it was demonstrated that decreases of TRIM35 expression are a frequent event in HCC and the expression level of TRIM35 was negatively correlated with tumor size, histological grade, and serum alpha-fetoprotein concentration.
CONCLUSION: These results showed that integration of genomic and transcriptional data offers powerful potential for identifying novel cancer genes in HCC pathogenesis.

Li Z, Pützer BM
Spliceosomal protein E regulates neoplastic cell growth by modulating expression of cyclin E/CDK2 and G2/M checkpoint proteins.
J Cell Mol Med. 2008; 12(6A):2427-38 [PubMed] Free Access to Full Article Related Publications
Small nuclear ribonucleoproteins are essential splicing factors. We previously identified the spliceosomal protein E (SmE) as a downstream effector of E2F1 in p53-deficient human carcinoma cells. Here, we investigated the biological relevance of SmE in determining the fate of cancer and non-tumourigenic cells. Adenovirus-mediated expression of SmE selectively reduces growth of cancerous cells due to decreased cell proliferation but not apoptosis. A similar growth inhibitory effect for SmD1 suggests that this is a general function of Sm-family members. Deletion of Sm-motifs reveals the importance of the Sm-1 domain for growth suppression. Consistently, SmE overexpression leads to inhibition of DNA synthesis and G2 arrest as shown by BrdU-incorporation and MPM2-staining. Real-time RT-PCR and immunoblotting showed that growth arrest by SmE directly correlates with the reduction of cyclin E, CDK2, CDC25C and CDC2 expression, and up-regulation of p27Kip. Importantly, SmE activity was not associated with enhanced expression of other spliceosome components such as U1 SnRNP70, suggesting that the growth inhibitory effect of SmE is distinct from its pre-mRNA splicing function. Furthermore, specific inactivation of SmE by shRNA significantly increased the percentage of cells in S phase, whereas the amount of G2/M arrested cells was reduced. Our data provide evidence that Sm proteins function as suppressors of tumour cell growth and may have major implications as cancer therapeutics.

Tamura K, Furihata M, Tsunoda T, et al.
Molecular features of hormone-refractory prostate cancer cells by genome-wide gene expression profiles.
Cancer Res. 2007; 67(11):5117-25 [PubMed] Related Publications
One of the most critical issues in prostate cancer clinic is emerging hormone-refractory prostate cancers (HRPCs) and their management. Prostate cancer is usually androgen dependent and responds well to androgen ablation therapy. However, at a certain stage, they eventually acquire androgen-independent and more aggressive phenotype and show poor response to any anticancer therapies. To characterize the molecular features of clinical HRPCs, we analyzed gene expression profiles of 25 clinical HRPCs and 10 hormone-sensitive prostate cancers (HSPCs) by genome-wide cDNA microarrays combining with laser microbeam microdissection. An unsupervised hierarchical clustering analysis clearly distinguished expression patterns of HRPC cells from those of HSPC cells. In addition, primary and metastatic HRPCs from three patients were closely clustered regardless of metastatic organs. A supervised analysis and permutation test identified 36 up-regulated genes and 70 down-regulated genes in HRPCs compared with HSPCs (average fold difference > 1.5; P < 0.0001). We observed overexpression of AR, ANLN, and SNRPE and down-regulation of NR4A1, CYP27A1, and HLA-A antigen in HRPC progression. AR overexpression is likely to play a central role of hormone-refractory phenotype, and other genes we identified were considered to be related to more aggressive phenotype of clinical HRPCs, and in fact, knockdown of these overexpressing genes by small interfering RNA resulted in drastic attenuation of prostate cancer cell viability. Our microarray analysis of HRPC cells should provide useful information to understand the molecular mechanism of HRPC progression and to identify molecular targets for development of HRPC treatment.

Maeshima AM, Maeshima A, Kawashima O, Nakajima T
K-ras gene point mutation in neogenetic lesions of subpleural fibrotic lesions: either an early genetic event in lung cancer development or a non-specific genetic change during the inflammatory reparative process.
Pathol Int. 1999; 49(5):411-8 [PubMed] Related Publications
In the present study, K-ras mutation was investigated in 156 neogenetic epithelia that appeared in the lesion of subpleural fibrosis in order to elucidate the close relationship of lung cancer development with pulmonary interstitial pneumonia. The neogenetic epithelia were histologically subclassified into six types: (i) ciliated bronchial epithelium (CBE); (ii) squamous metaplastic epithelium (SME); (iii) cuboidal immature epithelium (CIE); (iv) stratified immature epithelium (SIE); (v) mucus cell epithelium (MCE); and (vi) intestinal metaplastic epithelium (IME). K-ras mutation was detected in 9.6% of neogenetic epithelia overall; 21% of CIE, 12% of SIE, 16% of SME, but not in other types of neogenetic epithelia. Immunohistochemically, CIE and SIE frequently expressed surfactant apoprotein and SME was characteristic to carcinoembryonic antigen expression. According to Ki-67 immunostain, CIE, SIE and SME are likely to grow faster than other histological types of epithelia. K-ras mutation was seen exclusively in codon 12 with predominant G to A and G to C substitutions without any G to T transversions, results which are somewhat different to previous studies in lung cancers. The present study clearly demonstrated that K-ras mutation appeared in certain histological types of neogenetic epithelia, but raised the question of whether K-ras mutation in neogenetic epithelia during the inflammatory reparative process might be an early genetic event in lung carcinogenesis.

Stanford DR, Kehl M, Perry CA, et al.
The complete primary structure of the human snRNP E protein.
Nucleic Acids Res. 1988; 16(22):10593-605 [PubMed] Free Access to Full Article Related Publications
The snRNP E protein is one of four "core" proteins associated with the snRNAs of the U family (U1,U2,U4,U5, and U6). Screening of a human teratoma cDNA library with a partial cDNA for a human autoimmune antigen resulted in the isolation of a cDNA clone containing the entire coding region of this snRNP core protein. Comparison of the 5' end of this cDNA with the sequences of two processed pseudogenes and primer extension data suggest that the cDNA is nearly full length. The longest open reading frame in this clone codes for a basic 92 amino acid protein which is in perfect agreement with amino acid sequence data obtained from purified E protein. The predicted sequence of this protein reveals no extensive similarity to other snRNP proteins, but contains regions of similarity to a eukaryotic ribosomal protein.

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Cite this page: Cotterill SJ. SNRPE, Cancer Genetics Web: Accessed:

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