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Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer.

Chou WC, Cheng AL, Brotto M, Chuang CY - BMC Genomics (2014)

Bottom Line: Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs.This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan. cychuang@mx.nthu.edu.tw.

ABSTRACT

Background: Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.

Results: ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.

Conclusions: The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.

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Related in: MedlinePlus

WGCNA analysis on the large-scale microarray datasets. (A) Dendrogram showing relationship for the topological overlap of genes and their relationship to modules, which are color-coded. (B) Graphic depiction of the blue-color module (Mblue), green-color module (Mgreen), turquoise-color module (Mturquoise) and yellow-color module (Myellow) using Cytoscape. For each viewing module, pairs of genes with the highest intramodular topological overlap are illustrated, with each link corresponding to a topology overlap measure (TOM) between the connected nodes.
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Figure 2: WGCNA analysis on the large-scale microarray datasets. (A) Dendrogram showing relationship for the topological overlap of genes and their relationship to modules, which are color-coded. (B) Graphic depiction of the blue-color module (Mblue), green-color module (Mgreen), turquoise-color module (Mturquoise) and yellow-color module (Myellow) using Cytoscape. For each viewing module, pairs of genes with the highest intramodular topological overlap are illustrated, with each link corresponding to a topology overlap measure (TOM) between the connected nodes.

Mentions: In the initial analysis, we identified 3,920 genes having significant expression difference between subjects with cancer and subjects without cancer by applying a 1% FDR (Additional file2: Table S1). These endometrial cancer-related genes were used to reconstruct the EC-associated co-expression network (module) and identify a number of modules of high co-expression genes. As shown in Figure 2, these modules are significantly enriched for biologically important processes that are relevant to cancer, including cell-cycle regulation, antigen processing, immune response, and cell adhesion (Table 1). Among the modules, yellow-colored module (Myellow) that specifically corresponds to clinical information of ECs, showed a high Pearson correlation with phenotypic characteristics of ECs including grade (r = 0.44, Bonferroni-adjusted p-value = 1.2E−16), type (r = 0.34, Bonferroni-adjusted p-value = 6.3E−9) and stage (r = 0.31, Bonferroni-adjusted p-value = 2.1E−7) in ECs. The blue-colored module (Mblue) was only significantly correlated with the stage of ECs (r = 0.42, Bonferroni-adjusted p-value = 6.0E−19). By contrast, other modules showed a much lower correlation with the phenotypic characteristics of ECs. Interestingly, the Myellow module was significantly enriched for cell-cycle regulation (Bonferroni-adjusted p-value = 1.2E−31). Conversely, Mblue gene ontology categories included antigen processing (Bonferroni-adjusted p-value = 8.7E−12) and the citric acid (tricarboxylic acid; TCA) cycle (Bonferroni-adjusted p-value = 4.5E−12).


Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer.

Chou WC, Cheng AL, Brotto M, Chuang CY - BMC Genomics (2014)

WGCNA analysis on the large-scale microarray datasets. (A) Dendrogram showing relationship for the topological overlap of genes and their relationship to modules, which are color-coded. (B) Graphic depiction of the blue-color module (Mblue), green-color module (Mgreen), turquoise-color module (Mturquoise) and yellow-color module (Myellow) using Cytoscape. For each viewing module, pairs of genes with the highest intramodular topological overlap are illustrated, with each link corresponding to a topology overlap measure (TOM) between the connected nodes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4234489&req=5

Figure 2: WGCNA analysis on the large-scale microarray datasets. (A) Dendrogram showing relationship for the topological overlap of genes and their relationship to modules, which are color-coded. (B) Graphic depiction of the blue-color module (Mblue), green-color module (Mgreen), turquoise-color module (Mturquoise) and yellow-color module (Myellow) using Cytoscape. For each viewing module, pairs of genes with the highest intramodular topological overlap are illustrated, with each link corresponding to a topology overlap measure (TOM) between the connected nodes.
Mentions: In the initial analysis, we identified 3,920 genes having significant expression difference between subjects with cancer and subjects without cancer by applying a 1% FDR (Additional file2: Table S1). These endometrial cancer-related genes were used to reconstruct the EC-associated co-expression network (module) and identify a number of modules of high co-expression genes. As shown in Figure 2, these modules are significantly enriched for biologically important processes that are relevant to cancer, including cell-cycle regulation, antigen processing, immune response, and cell adhesion (Table 1). Among the modules, yellow-colored module (Myellow) that specifically corresponds to clinical information of ECs, showed a high Pearson correlation with phenotypic characteristics of ECs including grade (r = 0.44, Bonferroni-adjusted p-value = 1.2E−16), type (r = 0.34, Bonferroni-adjusted p-value = 6.3E−9) and stage (r = 0.31, Bonferroni-adjusted p-value = 2.1E−7) in ECs. The blue-colored module (Mblue) was only significantly correlated with the stage of ECs (r = 0.42, Bonferroni-adjusted p-value = 6.0E−19). By contrast, other modules showed a much lower correlation with the phenotypic characteristics of ECs. Interestingly, the Myellow module was significantly enriched for cell-cycle regulation (Bonferroni-adjusted p-value = 1.2E−31). Conversely, Mblue gene ontology categories included antigen processing (Bonferroni-adjusted p-value = 8.7E−12) and the citric acid (tricarboxylic acid; TCA) cycle (Bonferroni-adjusted p-value = 4.5E−12).

Bottom Line: Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs.This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan. cychuang@mx.nthu.edu.tw.

ABSTRACT

Background: Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.

Results: ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.

Conclusions: The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.

Show MeSH
Related in: MedlinePlus