Understanding the mechanisms of cancers based on function sub-pathways

Liu, Wenbin; Xu, Peng; Bao, Zhenshen

doi:10.1016/j.compbiolchem.2018.11.011

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…Methods like the Pathifier algorithm are also excellent methods to infer pathway deregulation, but Pathifier uses standard deviation to assess the significance of pathways [8]. (3) Some other methods cannot be acquired, such as LEGO [17]. We followed the instructions for the R packages of the methods used to compare with SPACI to evaluate the significance of the pathways.…”

Section: E Methods Used To Compare With Spacimentioning

confidence: 99%

“…Ihnatova et al developed an R package that implemented seven topology-based pathway analysis methods [16]. Liu et al proposed a topological-based method that is used to find subpathways based on the final genes in a signaling pathway to help understand the mechanisms of diseases and increase the accuracy of signaling pathway analysis [17]. Pathway-Express (pe) introduced a term of perturbation factor by considering the type of interactions [12,18].…”

Section: Introductionmentioning

confidence: 99%

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Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions

Bao

Chen

et al. 2020

IEEE Access

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Signaling pathway analysis has become a routine task after differentially expressed gene (DEG) studies across disease conditions, drug treatments, or developmental stages. A signaling pathway can be represented by a graph that consists of Genes and interactions (the genetic regulation) between them. However, existing signaling pathway analysis methods ignore the strength variations of interactions in signaling pathways under different conditions. Here, we developed a novel method named SPACI (Signaling Pathway Analysis Combined with the strength variations of Interactions between genes in signaling pathways under different conditions) to improve signaling pathway analysis after DEG studies. To further evaluate the performance of SPACI, we compared SPACI with nine other methods by using a benchmark of 28 gene expression datasets in two standard measures: sensitivity and prioritization. The False positive rate (FPR) of SPACI was also compared with five methods. The results show that SPACI is the second-ranked method in terms of prioritization and the third-ranked method in terms of sensitivity. SPACI is the top method when compared in terms of the sum value of the two ranks. Also, the FPR of SPACI is modest compared with the classic methods. Furthermore, the strength variation of the interaction is demonstrated as coherent with the biological problem. The interactions with high strength variations under different conditions can help improve the discovery of the underlying biological information. The R package of SPACI can be accessed at https://github.com/ZhenshenBao/SPACI.

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Section: E Methods Used To Compare With Spacimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions

Bao

Chen

et al. 2020

IEEE Access

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“…Some genes can regulate miRNAs expression by binding to promoter regions; and in turn, the disrupted miRNAs can regulate other genes. These interactions provide a medium for miRNAs exerting epigenetic control upon cell cycle, apoptosis, proliferation and other crucial biologic processes [ 27 , 28 ]. As the imbalance between proliferation and apoptosis has been known as one of the fundamental features in tumorigenesis [ 29 , 30 ], understanding how miRNA impacts the two processes can shed light on deciphering tumorigenesis by answering the query: ‘what’s the role of miRNA in tumorigenesis?’ Mendell et al showed that miR-17 clustering was associated with cell proliferation and cell cycle dysregulation [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path

et al. 2020

BMC Bioinformatics

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Background MicroRNAs are a class of important small noncoding RNAs, which have been reported to be involved in the processes of tumorigenesis and development by targeting a few genes. Existing studies show that the imbalance between cell proliferation and apoptosis is closely related to the initiation and development of cancers. However, the impact of miRNAs on this imbalance has not been studied systematically. Results In this study, we first construct a cell fate miRNA-gene regulatory network. Then, we propose a systematical method for calculating the global impact of miRNAs on cell fate genes based on the shortest path. Results on breast cancer and liver cancer datasets show that most of the cell fate genes are perturbed by the differentially expressed miRNAs. Most of the top-identified miRNAs are verified in the Human MicroRNA Disease Database (HMDD) and are related to breast and liver cancers. Function analysis shows that the top 20 miRNAs regulate multiple cell fate related function modules and interact tightly based on their functional similarity. Furthermore, more than half of them can promote sensitivity or induce resistance to some anti-cancer drugs. Besides, survival analysis demonstrates that the top-ranked miRNAs are significantly related to the overall survival time in the breast and liver cancers group. Conclusion In sum, this study can help to systematically study the important role of miRNAs on proliferation and apoptosis and thereby uncover the key miRNAs during the process of tumorigenesis. Furthermore, the results of this study will contribute to the development of clinical therapy based miRNAs for cancers.

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“…For instance, Gene Graph Enrichment Analysis (GGEA) uses prior knowledge derived from directed gene regulatory networks (Geistlinger et al 2011). Liu et al (2019) used a subgraph method to take advantage of pathway topological information (Liu et al 2019). ROntoTools introduced a term of perturbation factor by considering the type of interactions to take the pathway topology into consideration (Tarca et al 2009;Voichita et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "Identifying disease-associated signaling pathways through a novel effector gene analysis (v0.1)"

Suderman¹

2020

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Signaling pathway analysis methods are commonly used to explain biological behaviors of disease cells. Effector genes typically decide functional attributes (associated with biological behaviors of disease cells) by abnormal signals they received. The signals that the effector genes receive can be quite different in normal versus disease conditions. However, most of current signaling pathway analysis methods do not take these signal variations into consideration. Methods. In this study, we developed a novel signaling pathway analysis method called signaling pathway functional attributes analysis (SPFA) method. This method analyzes the signal variations that effector genes received between two conditions (normal and disease) in different signaling pathways. Results. We compared the SPFA method to seven other methods across 33 Gene Expression Omnibus (GEO) datasets using three measurements: the median rank of target pathways , the median p-value of target pathways, and the percentages of significant pathways. The results confirmed that SPFA was the topranking method in terms of median rank of target pathways and the fourth best method in terms of median p-value of target pathways. SPFA's percentage of significant pathways was modest, indicating a good false positive rate and false negative rate. Overall, SPFA was comparable to the other methods. Our results also suggested that the signal variations calculated by SPFA could help identify abnormal functional attributes and parts of pathways. The SPFA R code and functions can be accessed at https://github.com/ZhenshenBao/SPFA.

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Understanding the mechanisms of cancers based on function sub-pathways

Cited by 8 publications

References 21 publications

Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions

Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions

A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path

Peer Review #2 of "Identifying disease-associated signaling pathways through a novel effector gene analysis (v0.1)"

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