Dataset Information

Dataset for quantitative phospho-proteomics analysis of a serial hepatoma cell lines with increasing invasion and metastasis potential

ABSTRACT: Here, we used isobaric tags for relative and absolute quantitation (iTRAQ) based quantitative phospho-proteomics approach to identify biomarkers associated with hepatoma recurrence/metastasis in hepatoma cell lines with increasing metastasis ability.

ORGANISM(S): Homo Sapiens

SUBMITTER: Xiaolong Liu

PROVIDER: PXD015569 | iProX | Wed Sep 25 00:00:00 BST 2019

REPOSITORIES: iProX

ACCESS DATA

Publications

Dataset for quantitative phospho-proteomics analysis of a serial hepatoma cell lines with increasing invasion and metastasis potential.

Xing Xiaohua X Yuan Hui H Sun Ying Y Ke Kun K Dong Xiuqing X Chen Hui H Liu Xiaolong X Zhao Bixing B Huang Aimin A

Data in brief 20191014

Hepatoma is one of the most common malignant tumor, and most patients have very poor prognosis. Early prediction and intervention of the hepatoma recurrence/metastasis are the most effective way to improve the patients' clinical outcomes. Here, we used isobaric tags for relative and absolute quantitation (iTRAQ) based quantitative phospho-proteomics approach to identify biomarkers associated with hepatoma recurrence/metastasis in hepatoma cell lines with increasing metastasis ability. In total, ...[more]

PMID: 31709285

Similar Datasets

Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.

Dataset Information

Dataset for quantitative phospho-proteomics analysis of a serial hepatoma cell lines with increasing invasion and metastasis potential

Publications

Dataset for quantitative phospho-proteomics analysis of a serial hepatoma cell lines with increasing invasion and metastasis potential.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets