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

Inhibiting STAT3 signaling reduces tumor progression, metastases 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. Effect of STAT3 KO on cell proliferation, migration and spheroid formation was assessed in vitro and effect on in vivo tumor growth was 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. Overall design: Cells were lysed with TRIzol® Reagent (ThermoFisher Scientific) at room temperature. RNA was further purified with DirectZol kit (Zymo Research, Irvine, CA). RNA quality was assessed using the TapeStation (Agilent Technologies, Santa Clara, CA). Samples with RINs (RNA Integrity Numbers) of 8 or greater were prepared with TruSeq Stranded mRNA Library Prep (Illumina) per the supplier’s protocol with 1g of RNA and 12 cycles of PCR amplification. Libraries were checked for size on the TapeStation and quantified using the Kapa Biosystems library quantification kit (Illumina). The libraries were barcoded, pooled and sequenced on the HiSeq 4000 at the University of Michigan DNA Sequencing Core using 50bp single-end 50bp (OVCAR3 and OVCAR8) and paired-end 50bp (SKOV3) sequencing. Reads were mapped to GRCh38 using STAR v2.5.2 [69] and gene quantifications were calculated using Cufflinks v2.2.1 [70] to quantify refGene annotations. Gene read counts calculated using featureCounts [71] v1.6.1 were used to evaluate differential expression using DESeq2 v1.18.1 [72]. For OVCAR3 and OVCAR8, genes were considered significantly differentially expressed with a mean FPKM > 0.5 and absolute fold change > 1.5 and FDR adjusted p-value 0.5 and absolute log2 fold change > 1.5 and FDR adjusted p-value < 0.05. All gene readouts where required to be mappable to both an HGNC and Entrez identifier to be considered for gene set enrichment analyses. Please note: Majority of paper figures are based on OVCAR3=OV3,OVCAR8=OV8, and SKOV3 with CAS9 control.

抑制信号转导与转录激活因子3（STAT3）信号通路可延缓肿瘤进展、抑制肿瘤转移并逆转化疗耐药，但卵巢癌中其确切分子机制尚未完全阐明。
方法：本研究构建了STAT3基因敲除（KO）卵巢癌细胞系。体外实验评估STAT3敲除对细胞增殖、迁移及球体形成的影响，体内实验则通过多种肿瘤异种移植模型检测其对肿瘤生长的作用。本研究采用多组学全基因组谱分析，鉴定STAT3敲除卵巢癌细胞的多维度表达特征，涵盖Bru-Seq、RNA-Seq及质谱蛋白质组学（MS Proteomic）三个层面。
总体实验设计：使用TRIzol®试剂（ThermoFisher Scientific）在室温下裂解细胞，随后通过DirectZol试剂盒（Zymo Research，美国加利福尼亚州欧文市）进一步纯化RNA。利用TapeStation（Agilent Technologies，美国加利福尼亚州圣克拉拉市）评估RNA质量，选取RNA完整性指数（RIN）≥8的样本，按照TruSeq链特异性mRNA文库制备试剂盒（Illumina）的标准流程，以1μg总RNA为起始材料，经12轮PCR扩增构建文库。文库的片段大小通过TapeStation检测，并用Kapa Biosystems文库定量试剂盒（Illumina）进行定量。将文库进行条码标记、混合后，于密歇根大学DNA测序核心实验室使用HiSeq 4000平台完成测序：其中OVCAR3与OVCAR8细胞系采用50bp单端测序方案，SKOV3细胞系采用50bp双端测序方案。
测序数据分析：使用STAR v2.5.2软件将测序读段比对至GRCh38参考基因组，采用Cufflinks v2.2.1软件对refGene注释进行基因定量；利用featureCounts v1.6.1计算基因读段计数，随后通过DESeq2 v1.18.1进行差异表达分析。针对OVCAR3与OVCAR8细胞系，当平均FPKM>0.5、绝对折叠变化>1.5且错误发现率（FDR）校正后P值<0.05时，判定为显著差异表达基因；针对SKOV3细胞系，判定标准为平均FPKM>0.5、绝对log₂折叠变化>1.5且FDR校正后P值<0.05。所有基因读数需同时匹配人类基因命名委员会（HGNC）与Entrez基因标识符，方可用于基因集富集分析。
备注：本文多数图表基于OVCAR3（简称OV3）、OVCAR8（简称OV8）及携带CAS9空载体对照的SKOV3细胞系数据绘制。