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SNRPD1 Conveys Prognostic Value on Breast Cancer Survival and is Required for Anthracycline Sensitivity

Overview
Journal BMC Cancer
Publisher Biomed Central
Specialty Oncology
Date 2023 Apr 25
PMID 37098488
Authors
Affiliations
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Abstract

Background: Cancers harboring spliceosome mutations are highly sensitive to additional perturbations on the spliceosome that leads to the development of onco-therapeutics targeting the spliceosome and opens novel opportunities for managing aggressive tumors lacking effective treatment options such as triple negative breast cancers. Being the core spliceosome associated proteins, SNRPD1 and SNRPE have been both proposed as therapeutic targets for breast cancer management. Yet, their differences regarding their prognostic and therapeutic use as well as roles during carcinogenesis are largely unreported.

Methods: We conducted in silico analysis at gene expression and genetic levels to differentiate the clinical relevance of SNRPD1 and SNRPE, and explored their differential functionalities and molecular mechanistic associations with cancer in vitro.

Results: We showed that high SNRPD1 gene expression was prognostic of poor breast cancer survival whereas SNRPE was not. The SNRPD1 expression quantitative trait loci, rs6733100, was found independently prognostic of breast cancer survival using TCGA data. Silencing either SNRPD1 or SNRPE independently suppressed the growth of breast cancer cells, but decreased migration was only observed in SNRPD1-silenced cells. Knocking down SNRPD1 but not SNRPE triggers doxorubicin resistance in triple negative breast cancer cells. Gene enrichment and network analyses revealed the dynamic regulatory role of SNRPD1 on cell cycle and genome stability, and the preventive role of SNRPE against cancer stemness that may neutralize its promotive role on cancer cell proliferation.

Conclusion: Our results differentiated the functionalities of SNRPD1 and SNRPE at both prognostic and therapeutic levels, and preliminarily explained the driving mechanism that requires additional explorations and validations.

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