Pan-cancer Analysis Reveals the Small Nuclear Ribonucleoprotein Polypeptide C (SNRPC) Its Significance in Human Cancers

Abstract BackgroundSNRPC is cloned on human chromosome 6p21.31, which encodes a special protein component of U1 snRNP granules. Although SNRPC played an important role in the pre-mRNA splicing starting and adjustment, but in the tumor biological function is still unknown.MethodThrough the pan-cancer analysis of SNRPC, and our data sets, phosphorylation and functional network analysis based on TCGA (Cancer Genome Map) and GEO (Integrated Gene Expression Database), also from the western blot, qRT-PCR and CCK-8, cloning-forming experiment, scratch experiment to prove SNRPC biological function.ResultsSNRPC is related to the regulation of RNA, shear, and protease signals and has an important effect on ovarian cancer prognosis. Through a series of biological information data mining and basic experiments, we found that SNRPC plays an important role in the proliferation and migration of ovarian cancer. SNRPC expression is positively correlated with the immersion of CD4+T cells, macrophages, and neutrophils (p< 0.05), as obtained through the TIMER database (Tumor Immunological Assessment Resources) database. ConclusionOur pan-cancer research provides SNRPC in different tumors, especially the relatively comprehensive understanding of the carcinogenic potential of ovarian cancer.

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Identification of Subpathway Signatures For Ovarian Cancer Prognosis by Integrated Analyses of High-Throughput miRNA and mRNA Expression

Cellular Physiology and Biochemistry ◽

10.1159/000485492 ◽

2017 ◽

Vol 44 (4) ◽

pp. 1325-1336 ◽

Cited By ~ 4

Author(s):

Songyu Tian ◽

Jiangtian Tian ◽

Xiuwei Chen ◽

Lianwei Li ◽

Yunduo Liu ◽

...

Keyword(s):

Ovarian Cancer ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Risk Scores ◽

Data Sets ◽

Global Risk ◽

Gene Expressions ◽

Validation Data ◽

Data Set ◽

Ovarian Cancer Prognosis

Background/Aims: Ovarian cancer (OC) causes more death and serious conditions than any other female reproductive cancers, and many expression signatures have been identified for OC prognoses. However, no significant overlap is found among signatures from different studies, indicating the necessity of signature identifications at the functional level. Methods: We performed an integrated analyses of miRNA and gene expressions to identify OC prognostic subpathways (pathway regions). Using The Cancer Genome Atlas data set, we identified core prognostic subpathways, and calculated subpathway risk scores using both miRNA and gene components. Finally, we performed global risk impact analyses to optimize core subpathways using the random walk algorithm. Results: Subpathway-level analyses displayed more robust results than the gene- and miRNA-level analyses. Moreover, we verified the advantage of core subpathways over the entire pathway-based results and their prognostic performance in two independent validation data sets. Based on the global impact score, 13 subpathway signatures were selected and a combined subpathway-based risk score was further calculated for OC patient prognoses. Conclusions: Overall, it was possible to systematically perform integrated analyses of the expression levels of miRNAs and genes to identify prognostic subpathways and infer subpathway risk scores for use in OC clinical applications.

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Ovarian cancer prognosis and soft computing

Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology ◽

10.1109/iembs.2002.1134387 ◽

2003 ◽

Author(s):

A.A. Odusanya

Keyword(s):

Ovarian Cancer ◽

Soft Computing ◽

Cancer Prognosis ◽

Ovarian Cancer Prognosis

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Common analgesics and ovarian cancer prognosis – Authors' reply

The Lancet Oncology ◽

10.1016/s1470-2045(18)30692-2 ◽

2018 ◽

Vol 19 (10) ◽

pp. e507

Author(s):

Melissa A Merritt ◽

Shelley S Tworoger

Keyword(s):

Ovarian Cancer ◽

Cancer Prognosis ◽

Ovarian Cancer Prognosis

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Worsening of ovarian cancer prognosis with age: an exploration of pharmacokinetics, body composition, and biology

Journal of Geriatric Oncology ◽

10.1016/s1879-4068(21)00338-6 ◽

2021 ◽

Vol 12 (8) ◽

pp. S6

Author(s):

M. Extermann ◽

C. Walko ◽

A. Mishra ◽

K. Thomas ◽

B. Cao ◽

...

Keyword(s):

Ovarian Cancer ◽

Body Composition ◽

Cancer Prognosis ◽

Ovarian Cancer Prognosis

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Identification of molecular marker associated with ovarian cancer prognosis using bioinformatics analysis and experiments

Journal of Cellular Physiology ◽

10.1002/jcp.27926 ◽

2019 ◽

Vol 234 (7) ◽

pp. 11023-11036 ◽

Cited By ~ 9

Author(s):

Ming‐Jun Zheng ◽

Xiao Li ◽

Yue‐Xin Hu ◽

Hui Dong ◽

Rui Gou ◽

...

Keyword(s):

Ovarian Cancer ◽

Molecular Marker ◽

Bioinformatics Analysis ◽

Cancer Prognosis ◽

Ovarian Cancer Prognosis

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miR-29c-3p regulates proliferation and migration in ovarian cancer by targeting KIF4A

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02088-z ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Songwei Feng ◽

Shanhui Luo ◽

Chenchen Ji ◽

Jia Shi

Keyword(s):

Ovarian Cancer ◽

Cell Line ◽

Cancer Prognosis ◽

Expression Level ◽

Luciferase Reporter ◽

Epithelial Cell Line ◽

Skov3 Cell ◽

Ovarian Carcinoma Cell Line ◽

And Migration ◽

Proliferation And Migration

Abstract Background Increasing evidence suggested that microRNA and kinesin superfamily proteins play an essential role in ovarian cancer. The association between KIF4A and ovarian cancer (OC) was investigated in this study. Methods We performed bioinformatics analysis in the GEO database to screen out the differentially expressed miRNAs (DEmiRNAs) associated with ovarian cancer prognosis. Upstream targeting prediction for KIF4A was acquired by using the mirDIP database. The potential regulatory factor miR-29c-3p for KIF4A was obtained from the intersection of the above all miRNAs. The prognosis of KIF4A and target-miRNA in OC was obtained in the subsequent analysis. qRT-PCR and Western blot detected KIF4A expression level in IOSE80 (human normal ovarian epithelial cell line). In the meantime, the gene expression level was detected in A2780, HO-8910PM, COC1, and SKOV3 cell lines (human ovarian carcinoma cell line). MTT and colony formation assays were used to detect cell proliferation of SKOV3 cell line. The following assays detected cell migration through the use of transwell and wound heal assays. Targeted binding relationship between KIF4A and miRNA was detected by using the dual-luciferase reporter assay. Results Both high expression of KIF4A and lower expression of miR-29c-3p could be used as biomarkers indicating poor prognosis in OC patients. Cellular function tests confirmed that when KIF4A was silenced, it inhibited the proliferation and migration of OC cells. In addition, 3′-UTR of KIF4A had a direct binding site with miR-29c-3p, which indicated that the expression of KIF4A could be regulated by miR-29c-3p. In subsequent assays, the proliferation and migration of OC cells were inhibited by the overexpression of miR-29c-3p. At the same time, rescue experiments also confirmed that the promotion of KIF4A could be reversed by miR-29c-3p. Conclusion In a word, our data revealed a new mechanism for the role of KIF4A in the occurrence and development of OC.

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