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Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

ABSTRACT: Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

PROVIDER: PRJNA1056027 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR27331502_1.fastq.gz	Fastqsanger.gz
	SRR27331502_2.fastq.gz	Fastqsanger.gz
	SRR27331503_1.fastq.gz	Fastqsanger.gz
	SRR27331503_2.fastq.gz	Fastqsanger.gz
	SRR27331504_1.fastq.gz	Fastqsanger.gz

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Project description:Breast carcinoma (BC) is the leading cause of death in women worldwide, making up 23% of all cancers in women, with 1.38 million new cases worldwide annually and responsible for 460,000 deaths. Despite the significant advances in the identification of molecular markers and different modalities of treatment in primary BC, the ability to predict the metastatic behavior in breast cancer is still limited. The purpose of this study was to help identify novel molecular markers associated with clinical outcome in a cohort of Brazilian BC patients. We generated global gene expression profiles from 24 patients with invasive ductal BC followed for â¥ 5-years, including 15 samples from patients classified as presenting good prognosis based on traditional markers and clinical criteria and 9 patients that developed metastasis. We identified a set of 58 differentially expressed genes (p â¤0.01) between groups of patients with good and poor prognosis. Up-regulation of B3GNT7, PPM1D, TNKS2, PHB and GTSE1 in patients with poor prognosis was confirmed by quantitative RT-PCR in an independent sample set from patients with BC (47 with good prognosis and 8 that presented metastasis). Expression of BAD protein was investigated by immunohistochemistry in 1276 BC samples and confirmed the reduced expression levels in metastatic cases observed in the oligoarray data. These findings point to novel prognostic markers that can distinguish breast carcinoma samples according to clinical course and progression of the disease. Global expression profiles from 38 ductal breast tumor patient samples were used to search for molecular signatures correlated with current prognostic markers. A subset of 24 cases comprising 15 patients that remained free of disease after surgery and 9 patients that developed metastasis was used to identify candidate biomarkers associated with metastatic progression. Candidates were subsequently validated in additional independent samples by RT-qPCR or immunohistochemistry.

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Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines

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