Identification of a 20-Gene Expression-Based Risk Score as a Predictor of Clinical Outcome in Chronic Lymphocytic Leukemia Patients

Samra, Elias Bou; Klein, Bernard; Commes, Thérèse; Moreaux, Jérôme

doi:10.1155/2014/423174

Cited by 18 publications

(10 citation statements)

References 53 publications

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“…However, single molecular biomarkers often fail to predict the survival of gastric cancer due to their heterogeneity, while transcriptome-based classification includes redundant information. However, the multiple molecular biomarker-based model has been proven to be robust across datasets and has been implemented in cancer ( Bou Samra et al, 2014 ; Chang et al, 2014 ; Kim et al, 2014 ; Salazar et al, 2011 ; Wu et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

A gene expression-based risk model reveals prognosis of gastric cancer

Deng

Xiao

et al. 2018

PeerJ

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BackgroundThe prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence.MethodsIn this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores.ResultsThe patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9–26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9–101] months, p = 1.80e–5). Further validation was implemented in another three independent datasets (, , ). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified.ConclusionThe risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel.

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Section: Introductionmentioning

confidence: 99%

A gene expression-based risk model reveals prognosis of gastric cancer

Deng

Xiao

et al. 2018

PeerJ

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“…However, the clinical prognostic effect of single molecular biomarkers varies across datasets; whereas a multiple gene expression-based staging method is robust across datasets (7)(8)(9)(10). In the present study, a transcriptome-based risk score for the prediction of survival in patients with ER+ breast cancer treated with tamoxifen was developed using the Cox multivariate regression model.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic signature predicts the distant relapse in patients with ER+ breast cancer treated with tamoxifen for five years

Zhou

Guo

2017

Mol Med Report

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Tamoxifen is the most commonly used drug to treat estrogen receptor positive (ER+) breast cancer. However, many patients with ER+ breast cancer have experienced resistance and other adverse side effects following treatment with tamoxifen. Furthermore, clinical and pathological parameters have thus far failed to predict the efficiency of tamoxifen administration. Therefore, gene signature based models for the prediction of survival time of such patients are urgently needed. In the current study, gene expression levels and follow‑up information of samples from GSE17705 and GSE22219 databases were used to construct a risk score model based on Cox multivariate regression. The expression levels of 10 genes were included in the model: CCNB2, CCNA2, FOXD1, WSB2, RBPMS, CTDSP1, BIN3, SLBP, EPRS, FTO. The samples in the high‑risk group had a relative early distant relapse time period (median survival time of 3.75 years) compared with the patients in the low risk group (median survival time of 6.5 years, P<0.01). For further validation, a further two independent datasets (GSE26971, GSE58644) were assessed. The overall survival time period of patients with high‑risk scores in these datasets was significantly longer than those with low‑risk scores (P<0.01). Furthermore, the associations between clinical parameters and risk score were investigated, and it was revealed that the risk score was significantly correlated with tumor age, tumor stage and grade. In addition, a 5‑year survival nomogram was plotted in order to facilitate the utilization of risk score along with other clinical data. In summary, using the transcriptomic profile, a multi‑gene expression based risk score was developed and was revealed as being able to successfully predict the outcome of patients with ER+ breast cancer treated with tamoxifen for 5 years.

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“…Here, we observed that these three genes were highly associated with survival using a completely different methodological approach and the same cohort (GSE22762). Conversely, Bou et al [ 50 ] proposed a risk score combining with NRIP1 and TCF7 expression to identify high-risk patients. Therefore, the involvement of these genes in disease prognosis is significant.…”

Section: Discussionmentioning

confidence: 99%

Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia

2015

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BackgroundThe clinical course of chronic lymphocytic leukemia (CLL) is highly variable; some patients follow an indolent course, but others progress to a more advanced stage. The mutational status of rearranged immunoglobulin heavy chain variable (IGVH) genes in CLL is a feature that is widely recognized for dividing patients into groups that are related to their prognoses. However, the regulatory programs associated with the IGVH statuses are poorly understood, and markers that can precisely predict survival outcomes have yet to be identified.MethodsIn this study, (i) we reconstructed gene regulatory networks in CLL by applying an information-theoretic approach to the expression profiles of 5 cohorts. (ii) We applied master regulator analysis (MRA) to these networks to identify transcription factors (TFs) that regulate an IGVH mutational status signature. The IGVH mutational status signature was developed by searching for differentially expressed genes between the IGVH mutational statuses in numerous CLL cohorts. (iii) To evaluate the biological implication of the inferred regulators, prognostic values were determined using time to treatment (TTT) and overall survival (OS) in two different cohorts.ResultsA robust IGVH expression signature was obtained, and various TFs emerged as regulators of the signature in most of the reconstructed networks. The TF targets expression profiles exhibited significant differences with respect to survival, which allowed the definition of a reduced profile with a high value for OS. TCF7 and its targets stood out for their roles in progression.ConclusionTFs and their targets, which were obtained merely from inferred regulatory associations, have prognostic implications and reflect a regulatory context for prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2189-6) contains supplementary material, which is available to authorized users.

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Identification of a 20-Gene Expression-Based Risk Score as a Predictor of Clinical Outcome in Chronic Lymphocytic Leukemia Patients

Cited by 18 publications

References 53 publications

A gene expression-based risk model reveals prognosis of gastric cancer

A gene expression-based risk model reveals prognosis of gastric cancer

Transcriptomic signature predicts the distant relapse in patients with ER+ breast cancer treated with tamoxifen for five years

Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia

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