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Identification of Biomarker and Co-Regulatory Motifs in Lung Adenocarcinoma Based on Differential Interactions.

Zhao N, Liu Y, Chang Z, Li K, Zhang R, Zhou Y, Qiu F, Han X, Xu Y - PLoS ONE (2015)

Bottom Line: Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma.A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival.The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

ABSTRACT
Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

No MeSH data available.


Related in: MedlinePlus

Flow diagram of this work.Detection of differential interaction genes. The gene expression profiles were separated into disease and control groups. The co-expressed gene pairs were then obtained from Pearson correlation coefficient (PCC). The gene pairs were mapped to global protein-protein interaction network (PPIN) to obtain specific PPINs. Finally, the two specific PPINs were merged to a differential interaction network (DIN) to detect DIGs. (B) Excavation of the three co-regulatory motifs. MiRNA&TF co-regulated relationship were introduced to our DIGs to construct a miRNA&TF synergistic regulatory network. Three kinds of motifs (triplet, crosstalk, and joint) were mined in the network. (C) Identification of biomarker. Key molecules were detected from the motifs. Functional enrichment and survival analysis were applied to gain biomarker.
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pone.0139165.g005: Flow diagram of this work.Detection of differential interaction genes. The gene expression profiles were separated into disease and control groups. The co-expressed gene pairs were then obtained from Pearson correlation coefficient (PCC). The gene pairs were mapped to global protein-protein interaction network (PPIN) to obtain specific PPINs. Finally, the two specific PPINs were merged to a differential interaction network (DIN) to detect DIGs. (B) Excavation of the three co-regulatory motifs. MiRNA&TF co-regulated relationship were introduced to our DIGs to construct a miRNA&TF synergistic regulatory network. Three kinds of motifs (triplet, crosstalk, and joint) were mined in the network. (C) Identification of biomarker. Key molecules were detected from the motifs. Functional enrichment and survival analysis were applied to gain biomarker.

Mentions: The basic idea of lung adenocarcinoma-related gene detection is to obtain disease/control specific PPINs through the overlap of co-expression and interaction, and by predicting the lung adenocarcinoma-related genes through differences between interactions of disease and control samples. First, an expression profile was divided into disease and control samples. Then, co-expressed gene pairs were calculated according to the Pearson correlation coefficient (γ≥0.75 and p≤0.05) for the two groups, respectively. The p-value was computed by transforming the correlation to create a t statistic having n-2 degrees of freedom, where n is the number of rows of data. At the same time, a global PPIN was constructed based on the human PPI data. Subsequently, co-expressed genes were mapped to global PPIN to obtain specific PPINs for the disease and control groups, respectively. Finally, the two specific PPINs were compared to detect lung adenocarcinoma-related genes. The common genes (DIGs) of the two specific PPINs were extracted if they had different interaction partners in the two networks (Fig 5A). These DIGs have different interactions under normal and disease conditions. We assume that they are potential lung adenocarcinoma-related genes.


Identification of Biomarker and Co-Regulatory Motifs in Lung Adenocarcinoma Based on Differential Interactions.

Zhao N, Liu Y, Chang Z, Li K, Zhang R, Zhou Y, Qiu F, Han X, Xu Y - PLoS ONE (2015)

Flow diagram of this work.Detection of differential interaction genes. The gene expression profiles were separated into disease and control groups. The co-expressed gene pairs were then obtained from Pearson correlation coefficient (PCC). The gene pairs were mapped to global protein-protein interaction network (PPIN) to obtain specific PPINs. Finally, the two specific PPINs were merged to a differential interaction network (DIN) to detect DIGs. (B) Excavation of the three co-regulatory motifs. MiRNA&TF co-regulated relationship were introduced to our DIGs to construct a miRNA&TF synergistic regulatory network. Three kinds of motifs (triplet, crosstalk, and joint) were mined in the network. (C) Identification of biomarker. Key molecules were detected from the motifs. Functional enrichment and survival analysis were applied to gain biomarker.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4581687&req=5

pone.0139165.g005: Flow diagram of this work.Detection of differential interaction genes. The gene expression profiles were separated into disease and control groups. The co-expressed gene pairs were then obtained from Pearson correlation coefficient (PCC). The gene pairs were mapped to global protein-protein interaction network (PPIN) to obtain specific PPINs. Finally, the two specific PPINs were merged to a differential interaction network (DIN) to detect DIGs. (B) Excavation of the three co-regulatory motifs. MiRNA&TF co-regulated relationship were introduced to our DIGs to construct a miRNA&TF synergistic regulatory network. Three kinds of motifs (triplet, crosstalk, and joint) were mined in the network. (C) Identification of biomarker. Key molecules were detected from the motifs. Functional enrichment and survival analysis were applied to gain biomarker.
Mentions: The basic idea of lung adenocarcinoma-related gene detection is to obtain disease/control specific PPINs through the overlap of co-expression and interaction, and by predicting the lung adenocarcinoma-related genes through differences between interactions of disease and control samples. First, an expression profile was divided into disease and control samples. Then, co-expressed gene pairs were calculated according to the Pearson correlation coefficient (γ≥0.75 and p≤0.05) for the two groups, respectively. The p-value was computed by transforming the correlation to create a t statistic having n-2 degrees of freedom, where n is the number of rows of data. At the same time, a global PPIN was constructed based on the human PPI data. Subsequently, co-expressed genes were mapped to global PPIN to obtain specific PPINs for the disease and control groups, respectively. Finally, the two specific PPINs were compared to detect lung adenocarcinoma-related genes. The common genes (DIGs) of the two specific PPINs were extracted if they had different interaction partners in the two networks (Fig 5A). These DIGs have different interactions under normal and disease conditions. We assume that they are potential lung adenocarcinoma-related genes.

Bottom Line: Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma.A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival.The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.

ABSTRACT
Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

No MeSH data available.


Related in: MedlinePlus