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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

DIGs and the synergistic regulatory network are reliable.(A) The Venn diagram of DIGs and DEGs. The red circle represents DEGs and the blue circle represents DIGs. The amount of DEGs is quite smaller than DIGs. The overlap of them is significant. (B) The heatmap of samples (GSE10072) hierarchical clustering by 1,791 DIGs. The bar on the top of the heatmap indicates the group the samples really below to. Red represents disease and blue represents control. The sample orders under the heatmap corresponding to the orders in S5 Table. The 1,791 DIGs separate disease and control groups well. (C) Degree distributions of the synergistic regulatory network and each subnet. The large diagram indicates the degree distribution of synergistic regulatory network. Three insets from top to bottom, from left to right represent degree distributions of three subnets, triplet, crosstalk, and joint, respectively. They all met the requirement of scale-free network.
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pone.0139165.g001: DIGs and the synergistic regulatory network are reliable.(A) The Venn diagram of DIGs and DEGs. The red circle represents DEGs and the blue circle represents DIGs. The amount of DEGs is quite smaller than DIGs. The overlap of them is significant. (B) The heatmap of samples (GSE10072) hierarchical clustering by 1,791 DIGs. The bar on the top of the heatmap indicates the group the samples really below to. Red represents disease and blue represents control. The sample orders under the heatmap corresponding to the orders in S5 Table. The 1,791 DIGs separate disease and control groups well. (C) Degree distributions of the synergistic regulatory network and each subnet. The large diagram indicates the degree distribution of synergistic regulatory network. Three insets from top to bottom, from left to right represent degree distributions of three subnets, triplet, crosstalk, and joint, respectively. They all met the requirement of scale-free network.

Mentions: In order to further verify the accuracy of our results, we identified DEGs for the three profiles using the SAM [16] method (SAMR package) and took them as a combined unit. As shown in Fig 1A, the number of DEGs (4,686) was far greater than the number of DIGs (1,791), although a significant overlap (hypergeometric test p-value = 2.49 x 10-28) was noted between them. We also applied SAM and CoXpress [17] methods to GSE31547. They were chosen as they are mature methods representing differential expression and differential co-expression, respectively. We obtained 34 lung cancer-related genes from COSMIC as reference. A total of 834 genes were identified by our method, containing 11 COSMIC genes, while the results for SAM and CoXpress are only 1/1241 and 7/2216, respectively (S6 Table).


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)

DIGs and the synergistic regulatory network are reliable.(A) The Venn diagram of DIGs and DEGs. The red circle represents DEGs and the blue circle represents DIGs. The amount of DEGs is quite smaller than DIGs. The overlap of them is significant. (B) The heatmap of samples (GSE10072) hierarchical clustering by 1,791 DIGs. The bar on the top of the heatmap indicates the group the samples really below to. Red represents disease and blue represents control. The sample orders under the heatmap corresponding to the orders in S5 Table. The 1,791 DIGs separate disease and control groups well. (C) Degree distributions of the synergistic regulatory network and each subnet. The large diagram indicates the degree distribution of synergistic regulatory network. Three insets from top to bottom, from left to right represent degree distributions of three subnets, triplet, crosstalk, and joint, respectively. They all met the requirement of scale-free network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581687&req=5

pone.0139165.g001: DIGs and the synergistic regulatory network are reliable.(A) The Venn diagram of DIGs and DEGs. The red circle represents DEGs and the blue circle represents DIGs. The amount of DEGs is quite smaller than DIGs. The overlap of them is significant. (B) The heatmap of samples (GSE10072) hierarchical clustering by 1,791 DIGs. The bar on the top of the heatmap indicates the group the samples really below to. Red represents disease and blue represents control. The sample orders under the heatmap corresponding to the orders in S5 Table. The 1,791 DIGs separate disease and control groups well. (C) Degree distributions of the synergistic regulatory network and each subnet. The large diagram indicates the degree distribution of synergistic regulatory network. Three insets from top to bottom, from left to right represent degree distributions of three subnets, triplet, crosstalk, and joint, respectively. They all met the requirement of scale-free network.
Mentions: In order to further verify the accuracy of our results, we identified DEGs for the three profiles using the SAM [16] method (SAMR package) and took them as a combined unit. As shown in Fig 1A, the number of DEGs (4,686) was far greater than the number of DIGs (1,791), although a significant overlap (hypergeometric test p-value = 2.49 x 10-28) was noted between them. We also applied SAM and CoXpress [17] methods to GSE31547. They were chosen as they are mature methods representing differential expression and differential co-expression, respectively. We obtained 34 lung cancer-related genes from COSMIC as reference. A total of 834 genes were identified by our method, containing 11 COSMIC genes, while the results for SAM and CoXpress are only 1/1241 and 7/2216, respectively (S6 Table).

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