Joint Covariate Detection on Expression Profiles for Selecting Prognostic miRNAs in Glioblastoma

Sun, Chengqi; Zhao, Xudong

doi:10.1155/2017/3017948

Cited by 7 publications

(13 citation statements)

References 25 publications

(27 reference statements)

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“…After that, 48 unrelated articles were excluded by screening the title and abstract of these articles. At last, the full text of the remaining18 articles were carefully screened and only 9 articles (11 data sets) were included in our study [ 11 , 26 , 28 – 33 , 35 ]. Among these studies, 7 data sets were related to overall survival (OS) and 4 data sets were related to the disease-free survival (DFS).…”

Section: Resultsmentioning

confidence: 99%

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

Song

Zhang

et al. 2020

BioMed Research International

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Background. Several studies have focused on the prognostic role of microRNA 222 in glioma. But different conclusions were drawn by these studies. We aimed to systematically evaluate the role of microRNA 222 in glioma by conducting a meta-analysis. Methods. A systematic literature search until January 2020 was conducted in Web of Science, EMBASE, Cochrane Library, PubMed, and China National Knowledge Infrastructure. The general characteristics and relevant data of nine articles were extracted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were applied to evaluate the prognostic role of microRNA 222 in glioma. The primary outcomes were overall survival (OS) and disease-free survival (DFS). Results. Nine articles (11 data sets) with 1564 patients were included. We systematically evaluated the role of microRNA 222 for OS and DFS in glioma patients (HR for OS=1.72; 95% CI, 1.31-2.26; p=0.001; HR for DFS=1.02; 95% CI, 0.86-1.22; p=0.032). Subgroup analyses were performed according to the sources of patients, the types of the samples, the stages of the tumors, the methods for detecting the microRNA 222, and the sample size. No significant publication bias was found. Conclusion. In conclusion, our study provided evidence that a high expression of microRNA 222 was related to worse overall survival in glioma patients. However, given the limited study number, more high-quality studies are warranted in the future.

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Section: Resultsmentioning

confidence: 99%

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

Song

Zhang

et al. 2020

BioMed Research International

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“…The k -tuple with each component corresponding to a significant p value is regarded as a candidate feature associated with survival outcomes. More details can be seen in [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…Due to the limited effectiveness of current clinical diagnoses, expression profiles are utilized for informing variables, which are not only associated with the categories of patients with different survival risks but also consistent with survival time [ 1 ]. Commonly, Cox proportional hazards regression analysis is used to seek relevant variables considering the continuity of the patients’ survival outcomes with right censoring [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Bottom-up strategies with different constraints such as least-angle regression [ 8 ] and sparse kernel [ 9 ] are utilized for providing variables associated with survival time. To the best of our knowledge, we are the first to present joint covariate detection [ 1 ] that combines significant variables consistent with survival time and associated with the categories of patients. Other than individually significant variables, we concentrate on bottom-up enumeration of feature tuples, each component of which is either individually significant or not.…”

Section: Introductionmentioning

confidence: 99%

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JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles

Liu

Wang

et al. 2018

BMC Bioinformatics

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BackgroundSurvival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk and high-risk group of patients. Common features derived from the two aspects may provide variable candidates for prognosis of cancer.ResultsBased on the provided two-step feature selection strategy, we develop a joint covariate detection tool for survival analysis on tumor expression profiles. Significant features, which are not only consistent with survival time but also associated with the categories of patients with different survival risks, are chosen. Using the miRNA expression data (Level 3) of 548 patients with glioblastoma multiforme (GBM) as an example, miRNA candidates for prognosis of cancer are selected. The reliability of selected miRNAs using this tool is demonstrated by 100 simulations. Furthermore, It is discovered that significant covariates are not directly composed of individually significant variables.ConclusionsJoint covariate detection provides a viewpoint for selecting variables which are not individually but jointly significant. Besides, it helps to select features which are not only consistent with survival time but also associated with prognosis risk. The software is available at http://bio-nefu.com/resource/jcdsa.

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“…Studies show that miRs may be related to the prognosis of different cancers such as lung cancer, gastric cancer, and breast cancer [12–14]. Moreover, the relationships between different kinds of miRs, such as miRNA-15b [15, 16], 21 [17, 18], and 222 [19, 20], and prognosis of glioma have been investigated, while their results are conflicting due to the variability in study design, size of sample, or specimens. Additionally, no systematic review has been performed to explore the role of all pertinent miRs in evaluating glioma prognosis as a whole.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Significance of MicroRNAs in Glioma: A Systematic Review and Meta-Analysis

Zhang

Chen

Xue

et al. 2019

BioMed Research International

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Purpose. Different microRNAs (miRs) have been demonstrated to relate with the outcome of glioma patients, while the conclusions are inconsistent. We perform a meta-analysis to clarify the relationship between different miRs and prognosis of glioma. Methods. Related studies were retrieved from PubMed, Embase, and Cochrane Library. Pooled hazard ratios (HRs) of different miRs expression for survival and 95% confidence intervals (CIs) were calculated using random-effects model. Results. A total of 15 miRs with 4708 glioma patients were ultimately included. Increased expression of miR-15b (HR, 1.584; 95% CI, 1.199-2.092), 21 (HR, 1.591; 95% CI, 1.278-1.981), 148a (HR, 1.122; 95% CI, 1.023-1.231), 196 (HR, 1.877; 95% CI, 1.033-3.411), 210 (HR, 1.251; 95% CI, 1.010-1.550), and 221 (HR, 1.269; 95% CI, 1.054-1.527) or decreased expression of miR-106a (HR, 0.809; 95% CI, 0.655-0.998) and 124 (HR, 0.833; 95% CI, 0.729-0.952) was correlated with poor outcome of glioma patients. Conclusions. miR-15b, 21, 148a, 196, 210, 221, 106a, and 124 are valuable biomarkers for the prognosis of glioma which might be used in clinical settings.

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Joint Covariate Detection on Expression Profiles for Selecting Prognostic miRNAs in Glioblastoma

Cited by 7 publications

References 25 publications

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

Prognostic Role of MicroRNA 222 in Patients with Glioma: A Meta-analysis

JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles

Prognostic Significance of MicroRNAs in Glioma: A Systematic Review and Meta-Analysis

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