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Multi-omics profiling reveals key signaling pathways in ovarian cancer controlled by STAT3.


ABSTRACT: Inhibiting STAT3 signaling reduces tumor progression, metastasis and chemoresistance, however the precise molecular mechanism has not been fully delineated in ovarian cancer. METHODS:In this study, we generated STAT3 knockout (KO) ovarian cancer cell lines. Effects of STAT3 KO on cell proliferation, migration and spheroid formation were assessed in vitro and effects on in vivo tumor growth were tested using several tumor xenograft models. We used multi-omic genome-wide profiling to identify multi-level (Bru-Seq, RNA-Seq, and MS Proteomic) expression signatures of STAT3 KO ovarian cancer cells. RESULTS:We observed that deletion of STAT3 blocked cell proliferation and migration in vitro and suppressed tumor growth in mice. Deletion of STAT3 transcriptionally suppressed key genes involved in EMT, cell cycle progression, E2F signaling, and altered stemness markers. Notably, KO of STAT3 resulted in modulation of the expression of other STAT family members. CONCLUSION:Our study presents a rich, multi-faceted summary of the molecular mechanisms impacted by STAT3 deletion and provides new insight for STAT3's potential as a therapeutic target in ovarian cancer.

SUBMITTER: Lu T 

PROVIDER: S-EPMC6735387 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Multi-omics profiling reveals key signaling pathways in ovarian cancer controlled by STAT3.

Lu Tiangong T   Bankhead Armand A   Ljungman Mats M   Neamati Nouri N  

Theranostics 20190728 19


Inhibiting STAT3 signaling reduces tumor progression, metastasis and chemoresistance, however the precise molecular mechanism has not been fully delineated in ovarian cancer.<h4>Methods</h4>In this study, we generated <i>STAT3</i> knockout (KO) ovarian cancer cell lines. Effects of <i>STAT3</i> KO on cell proliferation, migration and spheroid formation were assessed <i>in vitro</i> and effects on <i>in vivo</i> tumor growth were tested using several tumor xenograft models. We used multi-omic gen  ...[more]

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