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RNA-Seq and Network Analysis Revealed Interacting Pathways in TGF-β-Treated Lung Cancer Cell Lines.

Li Y, Rouhi O, Chen H, Ramirez R, Borgia JA, Deng Y - Cancer Inform (2015)

Bottom Line: Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample.With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically.We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA.

ABSTRACT
Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.

No MeSH data available.


Related in: MedlinePlus

Upregulated network in cancer, tissue development, and hematological disease. IPA network legend is on the right side.
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f5-cin-suppl.5-2014-129: Upregulated network in cancer, tissue development, and hematological disease. IPA network legend is on the right side.

Mentions: The up- and downregulated genes used for pathway analysis were also used for network analysis to find biological networks associated with those genes. Network analysis showed strong association of modulated gene expressions with known cancer networks, represented by high network scores. These scores are negative log-transformed P-values of the probability that a particular network is associated with the focus genes by chance. Table 4 lists a selection of five cancer-related gene networks modulated in these two cell lines, with three upregulated and the other two downregulated. The network of pathways involved in cellular movement, organismal injury and abnormalities, and cancer was observed to be significantly upregulated with TGF-β treatment (P-value = 5.82 × 10−11), as shown in Figure 4. A total of 36 unique genes were involved in this network (Table 4), with most of them upregulated in both cell lines after TGF-β treatment. Similarly, the network of pathways related to cancer, tissue development, and hematological disease was also upregulated (P = 9.54 × 10−7), with 35 genes involved (Table 4 and Fig. 5). There were 36 genes involved in the downregulated network of dermatological diseases and conditions, cancer, and neurological disease (P-value = 2.84E-14), and 35 genes in the network of cellular growth and proliferation, tissue development, and organ morphology, which was also observed to be downregulated (P-value = 3.81 × 10−6) in the two NSCLC cell lines after TGF-β treatment (Table 4, Figs. 6 and 7). As with the presentation of the findings from the canonical pathway analysis, we also separated the findings of the network analysis, listing the findings for the A549 cell line separately from the H358 cell line in order to permit an appreciation of the differences in events ongoing in the two cell lines. For the A549 cells, the networks commonly known to promote adoption of an embryonic phenotype and angiogenesis were noted in addition to those typically associated with an EMT; however, the inactivation of systems controlling cellular morphology and cellular adhesion was also observed (Supplementary Table 6). The H358 cells appeared to have networks activated that similarly promoted cellular locomotion and angiogenesis, and also display the inhibition of cellular proliferation, which is commonly associated with EMT (Supplementary Table 7).


RNA-Seq and Network Analysis Revealed Interacting Pathways in TGF-β-Treated Lung Cancer Cell Lines.

Li Y, Rouhi O, Chen H, Ramirez R, Borgia JA, Deng Y - Cancer Inform (2015)

Upregulated network in cancer, tissue development, and hematological disease. IPA network legend is on the right side.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4384765&req=5

f5-cin-suppl.5-2014-129: Upregulated network in cancer, tissue development, and hematological disease. IPA network legend is on the right side.
Mentions: The up- and downregulated genes used for pathway analysis were also used for network analysis to find biological networks associated with those genes. Network analysis showed strong association of modulated gene expressions with known cancer networks, represented by high network scores. These scores are negative log-transformed P-values of the probability that a particular network is associated with the focus genes by chance. Table 4 lists a selection of five cancer-related gene networks modulated in these two cell lines, with three upregulated and the other two downregulated. The network of pathways involved in cellular movement, organismal injury and abnormalities, and cancer was observed to be significantly upregulated with TGF-β treatment (P-value = 5.82 × 10−11), as shown in Figure 4. A total of 36 unique genes were involved in this network (Table 4), with most of them upregulated in both cell lines after TGF-β treatment. Similarly, the network of pathways related to cancer, tissue development, and hematological disease was also upregulated (P = 9.54 × 10−7), with 35 genes involved (Table 4 and Fig. 5). There were 36 genes involved in the downregulated network of dermatological diseases and conditions, cancer, and neurological disease (P-value = 2.84E-14), and 35 genes in the network of cellular growth and proliferation, tissue development, and organ morphology, which was also observed to be downregulated (P-value = 3.81 × 10−6) in the two NSCLC cell lines after TGF-β treatment (Table 4, Figs. 6 and 7). As with the presentation of the findings from the canonical pathway analysis, we also separated the findings of the network analysis, listing the findings for the A549 cell line separately from the H358 cell line in order to permit an appreciation of the differences in events ongoing in the two cell lines. For the A549 cells, the networks commonly known to promote adoption of an embryonic phenotype and angiogenesis were noted in addition to those typically associated with an EMT; however, the inactivation of systems controlling cellular morphology and cellular adhesion was also observed (Supplementary Table 6). The H358 cells appeared to have networks activated that similarly promoted cellular locomotion and angiogenesis, and also display the inhibition of cellular proliferation, which is commonly associated with EMT (Supplementary Table 7).

Bottom Line: Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample.With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically.We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA.

ABSTRACT
Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.

No MeSH data available.


Related in: MedlinePlus