In the current analysis, a total of original pathways covering 6, human genes were collected, pathways from KEGG database called OPs. After mapping the protein ID to the gene symbol and eliminating repetitive interactions, the study ended up with , interactions involved in 16, genes. In an attempt to seek for the pathway cross-talks, the crosstalk analysis was performed for all the OPs. First, the background distribution of protein interaction of any 2 Ops was evaluated.
Specifically, each OP was randomized and all genes in a given OP were examined. If a gene did not have any interactions with other nodes, it was removed. If a gene had interactions with other genes, firstly the number of genes it interacted with was recorded, and then, a gene was randomly drown from the protein interaction dataset, which interacted with similar or the same number of genes, and this newly selected gene substituted the original pathway gene.
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Second, after randomizing both OPs, the number of protein interactions between any of the 2 pathways was calculated. For each OP, the common genes of the 2 OPs were eliminated and all the protein interactions between the 2 pathways were computed.
This step of randomization was repeated 10 rounds. The number of total interactions of the pairs of corresponding randomized pathways was obtained after 10, times of randomizations, and it was defined as the weight value. As documented, pathways with too few genes could not have enough biological content In light of this, the 20, genes obtained above were mapped to the OPs, and the OPs with less than 5 genes were abandoned to select more confident pathways. Subsequently, the remaining pathways were generated and named as AOPs. Large F-statistic value demonstrated a strong association, but on the other hand, small F-statistic value exhibited that the genes revealed minimal group-specific expression changes.
To testify this relationship, t test was used to amend log2-transformed F-statistics values. First, the weight values were calculated for pathway cross-talks. Then, the interaction analysis was performed on the genes in these AOPs. Otherwise, the weight values were calculated on the basis of the formula. Thus, the weights were defined as the sum of absolute value of difference of spearman correlation coefficient between 2 AOPs divided by G1x G2.
This randomization step was duplicated 10, times. The weight values obtained above were computed and named WICD. Topological centrality is helpful to extract essential molecules in complex networks Among the topological centralities, degree is an obvious index, which means the number of links of one node with its neighboring nodes To detect important cross-talks in BC, RankProd 21 was utilized, which is a simple, yet powerful meta-analysis tool to identify differentially expressed genes between 2 experimental conditions in many studies 22 , Recently, the biological impact of perturbation of pathways in genome wide gene expression experiments was suggested IF analysis obtains deeper level of statistical analysis, informed by more-specific biology than the exiting techniques.
Thus, the current study performed the IF analysis on the significant pathways. IFs of the significant pathways were computed using the following formula:. Based on the formula, the significant pathways with IFs greater than were considered as hub pathways. The cross-talks among hub pathways were regarded as hub cross-talks. Thus, the study firstly identified AOPs using the attract method. Based on these AOPs, cross-talks were extracted. Nevertheless, the edges where both pathways remarkably overlapped, and the gene members stood for analogical biology were eliminated.
Ultimately, a total of 25, cross-talks involved in nodes were obtained Figure 2. Nodes were the original pathways, edges stood for the cross-talks among pathways; yellow nodes denoted hub pathways. There were 25, cross-talks involved in nodes. Nodes were the attract original pathways AOPs ; edges stood for the cross-talks among them; yellow nodes were the hub pathways.
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Subsequently, the hub cross-talks among hub pathways were drawn and displayed in Figure 4. Nodes denoted hub pathways. Edges represented hub cross-talks among hub pathways.
The edge thickness was the correlated strength between 2 hub pathways. Currently, high-throughput technologies, such as gene microarray, are effective and standard means in biomedicine. Accordingly, a series of computational approaches are successfully employed to discover causal disease-associated genes 25 , However, to date, an unsolved problem is how to explain these genes in the biological contexts. A common solution is to utilize statistical method to detect pathways connected with the disease-associated genes Exhilaratingly, the identification of possible pathogenic pathways improves the accuracy when these pathways are utilized as biomarkers, compared with the gene-based methods.
Unfortunately, the majority of these methods do not consider the functional dependency among pathways, which may not be capable of generating a comprehensive view to the impact of the disease genes on pathways. Generally, pathways can influence each other via a phenomenon known as crosstalk, rather than acting independently 7. Thus, a novel method was introduced to detect dysregulated pathways in BC. Unlike the existing methods, the current study method considered the pathway interactions among pathways. These promising results suggested that the hub pathways indeed had cross-talks with each other, which further indicated the effectiveness of the proposed method.
Alterations in energy metabolism of the cell are regarded as the hallmark of cancer As already reported, cancer cells vitally need glucose similar to the metabolites for cancer cell growth In cancer cells, glucose is metabolized by aerobic glycolysis preferentially, and this phenomenon, known as the Warburg effect, is characterized by enhanced glycolysis generation Dong et al. Except being a striking characterization of cancer cell metabolism, the Warburg effect provides conditions favoring rapid proliferation and apoptosis resistance to cancer cells It is noteworthy that the Warburg effect, as a specific metabolic pathway, might be a promising strategy to inhibit tumor growth and relapse More significantly, a former study implicated that targeting the Warburg effect is a potentially novel therapeutic method in BC Evidence demonstrated that the accumulation of glucose caused by the constitutive activation of glycolysis leads to the HIF activation Consequently, the HIF activation increases the expression of many proteins that favor cancer progression, including apoptosis resistance, metastasis, invasion, and angiogenesis Another study reported that suppression of glycolysis was a potentially efficient approach to inhibit BC stem cell proliferation BC stem cells are responsible for regulating metastasis In addition, a previous study suggested that regulation of glycolysis and gluconeogenesis provides an insight into the therapeutic potential in hepatocarcinogenesis Another hub pathway, Alzheimer disease, was identified in the current study.
A link between neurodegeneration and cancer is plausible since these 2 diseases share several genes as well as biological pathways, such as inappropriate activation and misregulation of cell cycle Proteins including Pin1, which has a dual role in protein folding and cell cycle control, play an important part in the physiology of Alzheimer disease and cancer In addition, Driver et al.
In the light of these, the current study results supported the possibility of the relationship between Alzheimer disease and BC. The current study conducted analysis on BC based on PCNs via systematically integrating pathways information as well as PPI network data, for the first time.
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It was the main strong point of the current study. Overall, the current study successfully identified 4 hub pathways using this novel computational method. However, it should be considered that the current study had several limitations. The samples were downloaded from the open access database.
The microarray analysis was not performed on patients with BC. The current study analysis only employed bioinformatics methods to screen hub pathways to explore the pathogenic process of BC, yet the association between pathways and BC was not confirmed by animal experiments. It was the main weak point. Hence, the further understanding of the basic relationship between these pathways and BC is needed to illuminate the molecular mechanisms underlying BC. Despite these drawbacks, it is believed that the predicted pathways offer investigators valuable resources for better understanding of the BC mechanisms and also detecting potential biomarkers for early diagnose and therapy of BC.
Additionally, this method might provide analysis on other corresponding studies.
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These pathways might provide additional diagnostic and therapeutic targets for BC. McGuire S. World Cancer Report Adv Nutr. Triple-negative breast cancer. Breast Cancer Res. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers.
Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. Identification of biomarkers and pathway-related modules involved in ovarian cancer based on topological centralities. J BUON. Analysis and correction of crosstalk effects in pathway analysis.
Genome Res. Li Y, Agarwal P. A pathway-based view of human diseases and disease relationships. PLoS One. Analyzing cellular biochemistry in terms of molecular networks. Annu Rev Biochem.
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Network biology: understanding the cell's functional organization. Nat Rev Genet. Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue. Breast Cancer Res Treat. Histological grading of breast carcinomas: A study of interobserver agreement. Password required Remember me? Please enter your email address and click on the reset-password button.
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