Dataset Information

Identification of a prognostic signature for old-age mortality by integrating genome-wide transcriptomic data with the conventional predictors: the Vitality 90+ Study

ABSTRACT: BACKGROUND: Prediction models for old-age mortality have generally relied upon conventional markers such as plasma-based factors and biophysiological characteristics. However, it is unknown whether the existing markers are able to provide the most relevant information in terms of old-age survival or whether predictions could be improved through the integration of whole-genome expression profiles. METHODS: We assessed the predictive abilities of survival models containing only conventional markers, only gene expression data or both types of data together in a Vitality 90+ study cohort consisting of n = 151 nonagenarians. The all-cause death rate was 32.5% (49 of 151 individuals), and the median follow-up time was 2.55 years. RESULTS: Three different feature selection models, the penalized Lasso and Ridge regressions and the C-index boosting algorithm, were used to test the genomic data. The Ridge regression model incorporating both the conventional markers and transcripts outperformed the other models. The multivariate Cox regression model was used to adjust for the conventional mortality prediction markers, i.e., the body mass index, frailty index and cell-free DNA level, revealing that 331 transcripts were independently associated with survival. The final mortality-predicting transcriptomic signature derived from the Ridge regression model was mapped to a network that identified nuclear factor kappa beta (NF-κB) as a central node. CONCLUSIONS: Together with the loss of physiological reserves, the transcriptomic predictors centered around NF-κB underscored the role of immunoinflammatory signaling, the control of the DNA damage response and cell cycle, and mitochondrial functions as the key determinants of old-age mortality.

ORGANISM(S): Homo sapiens

PROVIDER: GSE65218 | GEO | 2015/01/22

SECONDARY ACCESSION(S): PRJNA273853

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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.

Dataset Information

Identification of a prognostic signature for old-age mortality by integrating genome-wide transcriptomic data with the conventional predictors: the Vitality 90+ Study

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets