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Gene expression profiling of epithelial ovarian cancer by bulk RNA-seq

ABSTRACT: Gene expression profiling of epithelial ovarian cancer by bulk RNA-seq

PROVIDER: PRJNA783540 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR17037971.fastq.gz	Fastqsanger.gz
	SRR17037972.fastq.gz	Fastqsanger.gz
	SRR17037973.fastq.gz	Fastqsanger.gz
	SRR17037974.fastq.gz	Fastqsanger.gz
	SRR17037975.fastq.gz	Fastqsanger.gz

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Gene expression profiling of epithelial ovarian cancer by bulk RNA-seq

Project description:Ovarian cancer has one of the worst prognoses among gynecologic malignancies. About 60% of ovarian cancer cases are diagnosed at Stages III and IV, and their five-year survival rate is less than 30%. Ovarian cancer is classified into four major histological subtypes; serous, clear cell, endometrioid and mucinous. Ovarian clear cell carcinoma (OCCC) is less sensitive to conventional platinum-based chemotherapy and has worse prognosis than other subtypes. In this study, we attempted to identify novel molecular targets critical for the proliferation and tumorigenicity of OCCC using the CRISPR/Cas9 system, and identified some genes as important candidates. Some of these candedates were upregulated in ovarian cancer tissue, especially OCCC. Our results suggest that not only may be these candedates a promising diagnostic maker for OCCC, drugs against these candedates would be useful for the therapeutic treatment of OCCC.

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Project description:Protein biomarkers of early stages of disease are critical for managing the disease and improving outcomes. Unfortunately, discovery of protein biomarkers is hampered by the limited availability of assays for sensitive and reproducible quantification of proteins in complex biological matrices such as blood plasma. In the recent years, Selected Reaction Monitoring (SRM) has emerged as a robust mass spectrometric method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing protein biomarkers for epithelial ovarian cancer, and illustrate the extent to which SRM platform, combined with sound experimental design and statistical analysis, results in robust detection of predictive analytes. First, the biomarker development was guided by a discovery-driven proteomic effort to detect potential N-glycoprotein biomarker candidates in tissue of a genetically stable ovarian cancer mouse model. Next, 65 candidate markers were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive 5-protein signatures for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

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