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Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers.

Lin CC, Jiang W, Mitra R, Cheng F, Yu H, Zhao Z - Sci Rep (2015)

Bottom Line: Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells.We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer.Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, USA.

ABSTRACT
Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN formed by TFs, miRNAs, and target genes. To increase the reliability of the proposed network and examine the regulation activity of TFs and miRNAs, we further incorporated the mRNA and miRNA expression profiles in seven cancer types using The Cancer Genome Atlas data. We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer. Interestingly, we identified one regulatory feedback loop between STAT1 and miR-155-5p that is consistently activated in all seven cancer types with its function to regulate tumor-related biological processes. Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

No MeSH data available.


Related in: MedlinePlus

Regulatory activity of TF and miRNA in cancers.The correlation patterns of four regulation types across the seven TCGA cancer types. The regulation types are labeled on the right-hand side. For each cancer type, the percentage of positive and negative regulations is shown as a function of the top 1% to 10% and 100% correlated GRNs. In each cancer type, the left and right sub-column represents the correlated GRN derived from normal and tumor samples, respectively. The asterisk shows the enrichment significance of positive or negative regulations with P < 0.05, as produced by Fisher′s exact test. The red (green) asterisks indicate that the positive (negative) regulations are significantly overrepresented.
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f1: Regulatory activity of TF and miRNA in cancers.The correlation patterns of four regulation types across the seven TCGA cancer types. The regulation types are labeled on the right-hand side. For each cancer type, the percentage of positive and negative regulations is shown as a function of the top 1% to 10% and 100% correlated GRNs. In each cancer type, the left and right sub-column represents the correlated GRN derived from normal and tumor samples, respectively. The asterisk shows the enrichment significance of positive or negative regulations with P < 0.05, as produced by Fisher′s exact test. The red (green) asterisks indicate that the positive (negative) regulations are significantly overrepresented.

Mentions: To impart regulation activity, we considered expression correlations between regulators and target genes. A positive or negative correlation hints to a potential activation or repression mediated by a regulator, respectively. We first categorized regulations into four types: 1) unidirectional TF to target (TFout), 2) unidirectional miRNA to target (miRout), 3) bidirectional TF to TF (BiTT), and 4) bidirectional TF to miRNA (BiTM). We observed that positive correlations are more prevalent than negative ones in TFout and BiTT regulations over all seven cancer types (Fig. 1, TFout and BiTT). Moreover, the proportions of positively correlated BiTT regulations are even larger than negative ones when compared to TFout regulations (Fig. 1, BiTT). Observations in the surveyed cancerous and paracancerous tissues suggest that activating regulations might prevail over TF regulation. Furthermore, they also imply that regulatory circuits between two TFs may mutually induce expression levels in each other. Unlike in TF regulations, no universal pan-cancer regulatory pattern was found in miRNA regulation (Fig. 1, miRout). This result is in accordance with the notion that miRNAs do not dominate gene expression regulation, and their regulations are likely disturbed by other regulators in cancers31519. As stated earlier, miRNAs have demonstrated their ability to fine-tune target gene expression in order to regulate molecular mechanisms in cells1011. In this way, miRNA regulations are critical to living cells, even if they might not dominate the regulation of target gene expression. Another relevant assumption is that miRNA regulation activity is easily affected by other co-regulators, i.e., context-dependent regulation activity315. On the other hand, like TFout and BiTT regulations, the positively correlated BiTM regulations are more frequently observed than the negatively correlated ones in most cases (Fig. 1, BiTM). Combined with the above investigations, the stronger positive regulation activity of BiTM may be attributed to the positive and dominant regulatory activity of TF and the fine-tuning regulation of miRNAs.


Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers.

Lin CC, Jiang W, Mitra R, Cheng F, Yu H, Zhao Z - Sci Rep (2015)

Regulatory activity of TF and miRNA in cancers.The correlation patterns of four regulation types across the seven TCGA cancer types. The regulation types are labeled on the right-hand side. For each cancer type, the percentage of positive and negative regulations is shown as a function of the top 1% to 10% and 100% correlated GRNs. In each cancer type, the left and right sub-column represents the correlated GRN derived from normal and tumor samples, respectively. The asterisk shows the enrichment significance of positive or negative regulations with P < 0.05, as produced by Fisher′s exact test. The red (green) asterisks indicate that the positive (negative) regulations are significantly overrepresented.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496795&req=5

f1: Regulatory activity of TF and miRNA in cancers.The correlation patterns of four regulation types across the seven TCGA cancer types. The regulation types are labeled on the right-hand side. For each cancer type, the percentage of positive and negative regulations is shown as a function of the top 1% to 10% and 100% correlated GRNs. In each cancer type, the left and right sub-column represents the correlated GRN derived from normal and tumor samples, respectively. The asterisk shows the enrichment significance of positive or negative regulations with P < 0.05, as produced by Fisher′s exact test. The red (green) asterisks indicate that the positive (negative) regulations are significantly overrepresented.
Mentions: To impart regulation activity, we considered expression correlations between regulators and target genes. A positive or negative correlation hints to a potential activation or repression mediated by a regulator, respectively. We first categorized regulations into four types: 1) unidirectional TF to target (TFout), 2) unidirectional miRNA to target (miRout), 3) bidirectional TF to TF (BiTT), and 4) bidirectional TF to miRNA (BiTM). We observed that positive correlations are more prevalent than negative ones in TFout and BiTT regulations over all seven cancer types (Fig. 1, TFout and BiTT). Moreover, the proportions of positively correlated BiTT regulations are even larger than negative ones when compared to TFout regulations (Fig. 1, BiTT). Observations in the surveyed cancerous and paracancerous tissues suggest that activating regulations might prevail over TF regulation. Furthermore, they also imply that regulatory circuits between two TFs may mutually induce expression levels in each other. Unlike in TF regulations, no universal pan-cancer regulatory pattern was found in miRNA regulation (Fig. 1, miRout). This result is in accordance with the notion that miRNAs do not dominate gene expression regulation, and their regulations are likely disturbed by other regulators in cancers31519. As stated earlier, miRNAs have demonstrated their ability to fine-tune target gene expression in order to regulate molecular mechanisms in cells1011. In this way, miRNA regulations are critical to living cells, even if they might not dominate the regulation of target gene expression. Another relevant assumption is that miRNA regulation activity is easily affected by other co-regulators, i.e., context-dependent regulation activity315. On the other hand, like TFout and BiTT regulations, the positively correlated BiTM regulations are more frequently observed than the negatively correlated ones in most cases (Fig. 1, BiTM). Combined with the above investigations, the stronger positive regulation activity of BiTM may be attributed to the positive and dominant regulatory activity of TF and the fine-tuning regulation of miRNAs.

Bottom Line: Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells.We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer.Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, USA.

ABSTRACT
Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN formed by TFs, miRNAs, and target genes. To increase the reliability of the proposed network and examine the regulation activity of TFs and miRNAs, we further incorporated the mRNA and miRNA expression profiles in seven cancer types using The Cancer Genome Atlas data. We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer. Interestingly, we identified one regulatory feedback loop between STAT1 and miR-155-5p that is consistently activated in all seven cancer types with its function to regulate tumor-related biological processes. Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.

No MeSH data available.


Related in: MedlinePlus