Signaling pathway impact analysis by incorporating the importance and specificity of genes (SPIA-IS)

Fang, Hongyuan; Li, Xianbin; Zan, Xiangzhen; Shen, Liangzhong; Ma, Rogawski; Liu, Wenbin

doi:10.1016/j.compbiolchem.2017.09.009

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Then we construct a regulatory network of all the cell fate genes based on the KEGG pathway and map the significantly differentially expressed miRNAs on it based on the miRNA-gene interactions. Motivated by the signaling propagation idea in SPIA [45,46], we calculate the perturbation of these miRNAs to the downstream cell fate genes by the shortest regulatory paths. Figure 6 shows the main workflow of this study.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Functional link enrichment of gene ontology or gene sets (LEGO) measures gene weights in a gene set according to its relative association with genes inside and outside the gene set in a functional association network (Dong et al, 2016). Fang et al (2017) proposed an improved SPIA method called SPIA-IS that measured and assigned the importance as the average output degree of the gene in the pathway.…”

Section: Introductionmentioning

confidence: 99%

Identifying disease-associated signaling pathways through a novel effector gene analysis

Bao

Zhang

et al. 2020

PeerJ

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Background 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 vs. 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 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 top-ranking 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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“…Functional link enrichment of gene ontology or gene sets (LEGO) measures gene weights in a gene set according to its relative association with genes inside and outside the gene set in a functional association network (Dong et al 2016). Fang et al (2017) proposed an improved SPIA method called SPIA-IS that measured and assigned the importance as the average output degree of the gene in the pathway (Fang et al 2017).…”

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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Signaling pathway impact analysis by incorporating the importance and specificity of genes (SPIA-IS)

Cited by 11 publications

References 24 publications

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

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

Identifying disease-associated signaling pathways through a novel effector gene analysis

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

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