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A gene sets approach for identifying prognostic gene signatures for outcome prediction.

ABSTRACT: BACKGROUND: Gene expression profiling is a promising approach to better estimate patient prognosis; however, there are still unresolved problems, including little overlap among similarly developed gene sets and poor performance of a developed gene set in other datasets. RESULTS: We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 pre-defined gene sets including gene ontology categories and pathways, we found many gene sets that were prognostic in most of the analyzed datasets. Those prognostic gene sets were related to biological processes such as cell cycle and proliferation and had additional prognostic values over conventional clinical parameters such as tumor grade, lymph node status, estrogen receptor (ER) status, and tumor size. We then estimated the prediction accuracy of each gene set by performing external validation using six large datasets and identified a gene set with an average prediction accuracy of 67.55%. CONCLUSION: A gene sets approach is an effective method to develop prognostic gene sets to predict patient outcome and to understand the underlying biology of the developed gene set. Using the gene sets approach we identified many prognostic gene sets in breast cancer.

SUBMITTER: Kim SY

PROVIDER: S-EPMC2364634 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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A gene sets approach for identifying prognostic gene signatures for outcome prediction.

Kim Seon-Young SY Kim Yong Sung YS

BMC genomics 20080416

<h4>Background</h4>Gene expression profiling is a promising approach to better estimate patient prognosis; however, there are still unresolved problems, including little overlap among similarly developed gene sets and poor performance of a developed gene set in other datasets.<h4>Results</h4>We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 ...[more]

PMID: 18416850

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Project description:BackgroundProstate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients' well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer.MethodsThe study employed an integrative multi-omics data analysis of the prostate cancer transcriptomic, CNV and methylation datasets to identify prognosis-related subtypes. Subtype-specific expression profiles of protein-coding genes (PCGs) and lncRNAs were determined. We analysed CNV patterns of lncRNAs across the genome to identify subtype-specific lncRNAs with CNV changes. LncRNAs exhibiting significant amplification or deletion and a positive correlation were designated CNV-deregulated lncRNAs. A prognostic risk score model was subsequently developed using these CNV-driven lncRNAs.ResultsSix molecular subtypes of prostate cancer were identified, demonstrating significant differences in prognosis (P = 0.034). The CNV profiles of subtype-specific lncRNAs were examined, revealing their correlation with CNV amplification or deletion. Six lncRNAs (CCAT2, LINC01593, LINC00276, GACAT2, LINC00457, LINC01343) were selected based on significant CNV amplifications or deletions using a rigorous univariate Cox proportional risk regression model. A robust risk score model was developed, stratifying patients into high-risk and low-risk categories. Notably, our prognostic model based on these six lncRNAs exhibited exceptional predictive capabilities for recurrence-free survival (RFS) in prostate cancer patients (P = 0.024).ConclusionsOur study successfully identified a prognostic risk score model comprising six CNV-driven lncRNAs that could potentially be prognostic biomarkers for prostate cancer. These lncRNA signatures are closely associated with RFS, providing promising prospects for improved patient prognostication and personalized therapeutic strategies for novel prostate cancer treatment.

Project description:BackgroundThe understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains.MethodsGene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO).ResultsIn The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA).ConclusionOur results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

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A gene sets approach for identifying prognostic gene signatures for outcome prediction.

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A gene sets approach for identifying prognostic gene signatures for outcome prediction.

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