Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.

ABSTRACT: Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival. Whole tumor gene expression profiling was conducted on tissue samples from 60 ovarian cancer patients, and characteristics and clinico-pathological features of the patients are provided. We used several steps to analyze the expression profiles of the samples to identify the genes whose expression values correlate with survival, recurrence and advanced disease stage. First, using hazard ratios from univariate Cox regression analysis, the top 200 survival-related genes were evaluated for intersection with the top 200 recurrence-related genes, and 44 genes were obtained. Second, we examined the 44 genes that met the criteria of fold-change values between advanced stage and early stage samples of greater than 2 or less than 0.5. Ultimately, 17 genes were identified. A heat map of the 17 genes is depicted in the associated publication. Gene ontology and pathway enrichment analyses of the 17 genes were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The major cellular component, biological process and molecular function of the 17 genes are associated with the extracellular region, intracellular signaling cascade, and protein binding and bridging, respectively. Two genes, COL11A1 and COL4A6, are involved in ECM-receptor interaction pathways. Notably, COL11A1 displayed the highest fold-change value in ovarian cancer disease progression; therefore, we selected COL11A1 for further experimental analysis.

ORGANISM(S): Homo sapiens

SUBMITTER: Tzu-Hao Chang

PROVIDER: E-GEOD-44104 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

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COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.

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