Association of polymorphisms in survivin gene with the risk of hepatocellular carcinoma in Chinese han population: a case control study

Li, Yuhua; Wang, Jiaofeng; Jiang, Feng; Wang, Lin; Wang, Meng

doi:10.1186/1471-2350-13-1

Cited by 40 publications

(27 citation statements)

References 28 publications

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“…In recent decades, the prediction and ranking of disease-related miRNAs have received much attention16171819. Experimental verification method for the identification of disease-associated miRNAs is expensive and time-consuming2021.…”

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confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.

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confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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“…However, studies investigating the potential association of these SNPs in survivin gene with tumor risk have not yielded consistent results. Several studies have supported that risk for tumor is associated with these survivin SNPs [17], [19], [36], [38], while others have failed to find such an association [16], [32], [33], [48]. Therefore, we performed the present meta-analysis to clarify the relationship between these survivin SNPs and tumor risk.…”

Section: Discussionmentioning

confidence: 90%

“…As for survivin rs9904341, the ultimate analysis included four gastric cancer studies [28], [29], [30], [31], three hepatocellular cancer studies [20], [32], [33], three colorectal cancer studies [34], [35], [36], two esophageal [18], [37] and two bladder cancer studies [19], [21], as well as twelve studies of other tumors [16], [17], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47]. Overall, 8 studies used Caucasians, 17 used Asians, one used mixed populations.…”

Section: Resultsmentioning

confidence: 99%

“…For survivin rs17878467, the final analysis included three studies that used Asians [20], [21], [45] and one that used Caucasians [46]. Besides, there were three studies [17], [18], [33] and four studies [17], [20], [33], [45] that used Asians to investigate rs8073069 and rs1042489, respectively. Moreover, the genotype distributions among the controls of all included studies were consistent with HWE (Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Association of Survivin Polymorphisms with Tumor Susceptibility: A Meta-Analysis

Zhu

et al. 2013

PLoS ONE

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BackgroundThe survivin polymorphisms have been shown to confer genetic susceptibility to various tumors, but the results are inconsistent. In order to accomplish a more precise estimation of the relationship, a meta-analysis was performed.ResultsFor rs9904341, a significantly increased tumor risk was found in overall meta-analysis under C/C vs. G/G (OR = 1.40, 95% CI = 1.13–1.74, p = 0.002), dominant (OR = 1.18, 95% CI = 1.01–1.38, p = 0.039) and recessive (OR = 1.34, 95% CI = 1.13–1.58, p = 0.001) genetic models and Asians group. In subgroup analyses of tumor types, we found a significant association between this SNP and an increased risk of gastric, colorectal, bladder and other tumors as well as a decreased risk of hepatocellular cancer. For rs17878467, a significantly decreased tumor risk was identified in overall meta-analysis for allele contrast (T vs. C: OR = 0.69, 95% CI = 0.51–0.92, p = 0.012), C/T vs. C/C (OR = 0.61, 95% CI = 0.42–0.88, p = 0.009) and dominant (OR = 0.62, 95% CI = 0.43–0.88, p = 0.007) genetic models and Asians group. For rs2071214, we found a significant association between this SNP and an increased tumor risk in overall meta-analysis under G/G vs. A/A (OR = 1.51, 95% CI = 1.04–2.18, p = 0.029) and recessive (OR = 1.54, 95% CI = 1.07–2.22, p = 0.020) genetic models and Asians group. Besides, there was a significant association of rs8073069 with an increased tumor risk under recessive genetic model (OR = 1.37, 95% CI = 1.01–1.84, p = 0.040), while no significant association between rs1042489 and tumor risk was detected.ConclusionsThe survivin rs9904341 most likely contributed to increased susceptibility to tumor in Asians as well as to gastric, colorectal and bladder cancers. As for rs17878467, the T allele might be a protective factor for tumor, especially in Asians. Moreover, the survivin rs8073069 and rs2071214 seemed to be associated with an increased tumor risk in Asians, while there was no association between the survivin rs1042489 and tumor risk.

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“…On the other hand, off the three SNPs found using DCL α=108 , two of them are located on the noteworthy genes NISCH and PRKG1. Two previous studies have indicated that SNP rs6784615, found on NISCH, is associated with LOAD [34, 35]. Furthermore, many GWAS studies have found that PRKG1 is also strongly associated with Alzheimer’s disease [36, 37].…”

Section: Discussionmentioning

confidence: 99%

An algorithm for direct causal learning of influences on patient outcomes

Rathnam

Lee

Jiang

2017

Artificial Intelligence in Medicine

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Objective This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms. Method The DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-score based fast greedy search (FGS) algorithm, and the partial ancestral graphs algorithm fast causal inference (FCI). In addition, we extend our comparison of all five algorithms to both a real GWAS dataset and real breast cancer datasets over various time-points in order to observe how effective they are at predicting the causal influences of Alzheimer’s disease and breast cancer survival. Results DCL consistently outperforms FGS, PC, CPC, and FCI in discovering the parents of the target for the datasets simulated using a simple network. Overall, DCL predicts significantly more datasets correctly (McNemar’s test significance: p ≪ 0.0001) than any of the other algorithms for these network types. For example, when assessing overall performance (simple and complex network results combined), DCL correctly predicts approximately 1400 more datasets than the top FGS method, 1600 more datasets than the top CPC method, 4500 more datasets than the top PC method, and 5600 more datasets than the top FCI method. Although FGS did correctly predict more datasets than DCL for the complex networks, and DCL correctly predicted only a few more datasets than CPC for these networks, there is no significant difference in performance between these three algorithms for this network type. However, when we use a more continuous measure of accuracy, we find that all the DCL methods are able to better partially predict more direct causes than FGS and CPC for the complex networks. In addition, DCL consistently had faster runtimes than the other algorithms. In the application to the real datasets, DCL identified rs6784615, located on the NISCH gene, and rs10824310, located on the PRKG1 gene, as direct causes of late onset Alzheimer’s disease (LOAD) development. In addition, DCL identified ER category as a direct predictor of breast cancer mortality within 5 years, and HER2 status as a direct predictor of 10-year breast cancer mortality. These predictors have been identified in previous studies to have a direct causal relationship with their respective phenotypes, supporting the predictive power of DCL. When the other algorithms discovered predictors from the real datasets, these predictors were either also found by DCL or could not be supported by previous studies. ...

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Association of polymorphisms in survivin gene with the risk of hepatocellular carcinoma in Chinese han population: a case control study

Cited by 40 publications

References 28 publications

Network Consistency Projection for Human miRNA-Disease Associations Inference

Network Consistency Projection for Human miRNA-Disease Associations Inference

Association of Survivin Polymorphisms with Tumor Susceptibility: A Meta-Analysis

An algorithm for direct causal learning of influences on patient outcomes

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