Abstract
Background Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for the unlimited proliferation potential of the majority of cancers but the underlying regulatory processes are only partly understood.
Results We assembled regulator binding information from different sources to construct a generic human and mouse regulatory network. Advancing our “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.
Conclusion Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/network-modeling/MIPRIP. MIPRIP 2.0 identified both common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.
Background
Telomere repeats are lost at the 3’-end erosion during replication of linear chromosomes. If the telomeres become critically short senescence or apoptosis are induced. This process can thus act as a barrier towards unlimited proliferation and tumorigenesis [1]. Cancer cells circumvent this constraint by acquiring a telomere maintenance mechanism (TMM) [2]. In most instances they reactivate the reverse transcriptase telomerase via different pathways, which can extend the telomere repeats again [3, 4]. Human telomerase consists of the catalytic subunit TERT and the template RNA TERC (or hTR) [5]. TERC is constitutively expressed while the TERT gene is silenced in adult somatic cells [6, 7]. Germ and stem cells [7] as well as most tumor cells [2] express TERT so that telomerase is assembled. The mechanism of TERT activation in cancer cells appears to be highly variable between different cancer entities and numerous transcription factors (TFs) have been reported to be involved in this process [8]. The core region of the human TERT promoter is located between 330 bp upstream and 228 bp downstream of the transcription start site. This region comprises several TF binding sites, including binding sites with GC and E-box motifs [8]. Previous studies showed that TERT promoter mutations can induce its expression in cancer cells. TERT promoter mutations occur most frequently in bladder cancer (59%), cancers of the central nervous system (43%), melanoma skin cancer (29%) and follicular cell-derived thyroid cancer (10%) [9].
Here, we performed an in silico analysis of TERT regulation by using our previously developed “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) to predict TFs regulating the gene expression of TERT. MIPRIP was developed to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in Saccharomyces cerevisiae. In S. cerevisiae we uncovered novel regulators of telomerase expression, several of which affect histone levels or modifications [10]. A variety of other approaches have been developed which integrate regulatory information into an unified model of a gene regulatory network (GRN). Many of them infer TF acitvity using linear regression from gene expression profiles, a pre-defined network of TFs and their target genes [11-13], probabilistic models [14] or a reverse engineering approach that identifies regulator to target gene interactions from the pairwise mutual information of their gene expression pofiles [15]. It is noted that the activity of TFs frequently depends only partially on the gene expression of the TF itself but is rather modulated by post-translational modifications and protein stability. Hence, it is informative to infer the activity of a TF from the expression of its potential target genes [11, 16, 17]. In the present study, we have optimized our MIPRIP software and applied it to gene expression profiles of 19 different cancer types from The Cancer Genome Atlas (TCGA) to identify TFs regulating the TERT gene.
Results
Transcription factor binding information and network construction
We constructed a generic human regulatory network based on seven different repositories, mainly containing experimental validated binding information from ChIP based assays. In total, the generic network comprises 618,537 interactions of 1,160 regulators and 31,915 target genes. For TERT, we identified 75 putative regulators (Table S2) that originated mainly from the manual curated database MetaCore™ (60 out of 75). Our list of TERT regulators compares well to the TERT regulators described in the review by Ramlee et al. [8]. In total, 30 from our identified 75 regulators were also described by Ramlee et al. (Fisher’s Exact Test P=6.01E-23) and except of CTCF (Encode) all are listed in MetaCore™. Additionally, we assembled a generic gene regulatory network for mouse containing 93,140 interactions of 976 TFs and 15,728 target genes from three different databases. To focus on more reliable edges, entries were selected based on database reliability and co-occurrences.
Three different modes of a MIPRIP 2.0 analysis
MIPRIP 2.0 can be used to (i) predict the most important regulators of one group of samples (single-mode), (ii) identify significant regulators being different between two groups of samples (e.g. disease vs. control) (dual-mode) and (iii) can be applied to more than two groups (multi-mode). The newly developed multi-mode implementation is embedded in a statistical analysis pipeline and can be applied to more than two datasets or conditions to identify common but also condition-specific regulators (Fig. 1). Here, we applied the multi-mode MIPRIP 2.0 version to study the regulation of TERT across 19 different cancer types (described in the next section and Table S3) and employed the dual-mode to compare the regulation of melanoma samples with and without TERT promoter mutation.
Applying MIPRIP 2.0 to identify regulators of TERT across different cancers
We selected 19 different cancer types from TCGA for which more than 100 primary tumor samples were available. For each cancer type, we set up a regulatory model for TERT by using a ten-times three-fold cross-validation. We calculated different models by restricting the numbers of maximal regulators from 1 up to 10 resulting in 300 models per cancer type. The performance of the models was estimated by the correlation between the predicted and the measured gene expression value (of TERT in the expression data). For most of the cancer types, the performance was r = 0.4 or better (Fig 2a). For cervical (CESC), ovary (OV) and melanoma skin (SKCM) cancer the performance was distinctively lower. The highest performance was found for testicular germ cell cancer (TGCT) (r = 0.75) and thymoma (THYM) (r = 0.7), which also showed the highest TERT expression over all cancer types (Fig. 2b). The lowest TERT expression was found in breast (BRCA), pancreas (PAAD) and prostate (PRAD) cancer. The expression of TERT in melanoma skin cancer was comparable to most of the other cancer types, but the performance of the models was the worst (r = 0.1) (Fig. 2a). As common regulators of TERT across all cancer types, we identified nine regulators: the two paired box proteins PAX5 and PAX8, the E2F factors 2 and 4, AR, BATF, SMARCB1, TAF1 and MXI1 (Table 1). To validate our results in silico, we queried Pubmed articles for the identified regulators. We identified 21 out of 1,002 TERT articles for our identified regulators which was a significant enrichment for our hits (p = 0.013, Tables S4 and S5).
Applying the dual-mode MIPRIP analysis to melanoma skin cancer
Melanoma skin cancer was the first cancer type for which a high frequency of TERT promoter mutations was discovered, mainly in two hotspot C>T mutations at position 124 bp and 146 bp upstream of the translational start codon [18, 19]. The TERT promoter mutation status was available for 115 samples of the melanoma dataset. As described in the previous section, we obtained the lowest performance of our regulatory models for melanoma samples. Considering this and the high rate of TERT promoter mutations we divided the dataset into samples with and without TERT promoter mutation in order to improve our predictions. We applied the MIPRIP 2.0 dual-mode to the separated datasets. This resulted in a list of 12 and 17 TFs which were significantly more often used in the models for the samples with and without TERT promoter mutation. AR, E2F1, JUND and ETS1 were the most significant regulators in the samples with TERT promoter mutation, while HMGA2, HIF1, RUNX2 and TAL1 were most significant in the samples without TERT promoter mutation (Table 2). To validate that ETS1 is a key regulator in the samples with TERT promoter mutation, we investigated published microarray data from experiments in which ETS1 was knocked down in melanoma cells with TERT promoter mutation [20]. Indeed, TERT expression was lower in the ETS1 knockdown sample compared to controls (fold change: 0.82).
In summary, splitting up the melanoma dataset into two pre-defined cancer subgroups with and without the TERT promoter mutations led to more reliable modelling results (r = 0.3). Thus, dual-mode MIPRIP 2.0 is well suited to identify this specific regulation.
Comparison with ISMARA
We compared our results with results from the well-established tool ISMARA [11]. Similar to MIPRIP, ISMARA identifies the activity of regulators based on their target genes [11]. In contrast to MIPRIP, target genes are inferred from motif binding information, and not directly from ChIP experiments. In ISMARA, TF activities are calculated for each sample alone and then averaged over the samples of each group (TERT promoter mutated versus wild-type). ISMARA identified twenty TFs for the TERT promoter (Supplementary Table S6), but only SIN3A, MAZ and WT1 overlapped with the MIPRIP 2.0 results. Remarkably, ETS1 was not found by ISMARA.
Discussion
In the present study we have advanced our software package “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) for application to human and mouse cells. For this, we selected known regulator binding information to construct a generic network linking TFs to their potential target genes. The interactions between TFs and their targets organize as a scale-free network comprising hubs as central regulators [24]. The TFs with the highest score in our generic human regulatory network were MYC (sum of es: 12,607), YY1 (sum of es: 10,642.25) and CTCF (sum of es: 9,574.50). This might reflect the role of these TFs as master regulators that recruit chromatin modifying co-factors and remodel the chromatin structure in the case of MYC [25] or mediate structural interactions between enhancers and promoters as reported for CTCF and YY1 [26].
The MIPRIP 2.0 framework with its new multi-mode was applied to dissect the regulation of the telomerase protein subunit TERT across 19 different cancer types, yielding nine TFs being common to TERT regulation across all cancer types. These regulators together showed a significant enrichment in Pubmed entries for TERT. Five TFs (PAX5, PAX8, AR, E2F2 and E2F4) have been described previously as TERT regulators. PAX5 has two and PAX8 four binding sites at the TERT transcription start site that induce activation of TERT transcription and their function in telomerase regulation has been validated [27, 28]. The androgen receptor (AR) belongs to the class of nuclear receptors and is a repressor of TERT expression [29]. The E2F2 and E2F4 factors bind to the E2 recognition motif and are involved in cell cycle processes and DNA damage response [30] and regulate TERT transcription in human B-cell lymphoma [31, 32]. In addition to these five TFs, we here identified BATF, SMARCB1, TAF1 and MXI1 as novel TERT regulators across cancer entities that to our knowledge, so far, have not been described in the literature as TERT regulators. Accordingly, we suggest these as potential candidates for future investigations on the mechanism of TERT reactivation in cancer cells.
The best performance of the MIPRIP 2.0 multi-mode analysis was observed for thymoma and testicular germ cell cancer, which showed also the highest TERT expression. The worst performance was observed for melanoma skin cancer, even though TERT expression was not particularly low. As described in the literature, cutaneous melanoma skin cancer patients have a high rate of TERT promoter mutations, being responsible for an upregulation of TERT by enabling a further binding site of TFs from the ETS family [18, 19].
Using MIPRIP 2.0 in the dual-mode after dividing the melanoma dataset into cancer samples with and without TERT promoter mutation improved the results considerably. We identified ETS1 as a highly significant regulator for TERT in tumors with TERT promoter mutation. To further validate this finding we analyzed publicly available expression data of an ETS1 siRNA knockdown experiment in a melanoma cell line with TERT promoter mutation and found a downregulation of TERT compared to controls. In line with this finding, ETS binding together with the activation of the non-canonical NFkB signaling pathway through the co-activator p52 enhances the promoter activity of TERT [33].
Besides ETS1, we predicted AR, E2F1 and JUND as the most significant regulators in melanoma patients with a TERT promoter mutation. AR and E2F were also predicted as common TERT regulators in our multi-mode MIPRIP analysis. A recent study showed that an inhibition of E2F1 leads to increased cell death in melanoma cells, even if they are resistant to BRAF-inhibitors [34]. These results indicate that E2F1 is an interesting therapeutic target for melanoma. According to our predictions, E2F1 regulates samples with a TERT promoter mutation. As E2F1 is a TERT repressor [30], an inhibition of E2F1 may be more efficient in samples without TERT promoter mutation.
For melanoma samples without TERT promoter mutation, we predicted HMGA2, HIF1, RUNX2 and TAL1 as the most significant regulators. HMGA2 is a member of the high-mobility group of AT-hook proteins, which are expressed during embryonic development [35] as well as in different tumors (e.g. squamous cell carcinoma and malignant melanoma [36]). While only a few samples showed a TERT promoter mutation [36], it is still unclear if there is an association between HMGA2 expression and TERT promoter mutations. According to our predictions, we suggest that TERT regulation by HMGA2 and TERT promoter mutations are mutually exclusive, which has to be validated in future experiments. In this case study, we observed that splitting up the datasets into subtypes led to an increased performance of the regulatory models and was necessary to break down the relevant regulatory processes. Melanoma patients with TERT promoter mutation show decreased survival rates [37]. Hence, identifying subtype specific regulatory mechanisms may support risk stratification by employing the identified regulators as biomarkers. In addition, such predictions may pave the way for a personalized therapy by developing drugs specifically interfering with the detected TFs.
Using the specific application of known ETS binding site mutations in the TERT promoter of melanoma samples as a case study, we compared the results from MIPRIP 2.0 with ISMARA. The overlap between our results and ISMARA was very low. ISMARA did not retrieve ETS1 as distinct TERT regulator of samples with the promoter mutation.
Besides the advancement with the three different modes and the possibility of weighted edges, MIPRIP 2.0 allows to extend the model by including information about gene copy number, DNA methylation, miRNA expression and binding, and additional variables e.g. related to further epigenetic regulation.
Conclusions
We here introduced our new MIPRIP 2.0 framework and applied it to predict TERT regulators in a pan-cancer analysis. Some of the common TFs identified like PAX5, PAX8, AR, E2F2 and E2F4 have been previously described as TERT regulators. Others like BATF, SMARCB1, TAF1 and MXI1 are novel. It will be exciting to test experimentally whether they are linked to a TMM phenotype. Furthermore, the predicted TERT regulators were compared in melanoma samples with wild-type vs mutated TERT promoters. In this manner, we validated that a change of TF targets, in this case for TFs from the ETS family, was captured by MIPRIP 2.0. The software package is available on our website [22] and github [23] together with the generic human or mouse regulatory network and example datasets. It can be applied to a large variety of datasets to investigate the role of TF mediated gene regulation of a gene of interest in the context of diseases or response to a variety of conditions that include treatment.
Methods
Gene expression data
We downloaded publicly available transcriptome expression data (RNA-Seq) of all cancer types with more than 100 primary tumor samples from the TCGA Genome Data Analysis Center (GDAC) of the Broad Institute [38]. For these datasets the usage restriction has been lifted according to the TCGA publication guidelines from December 21, 2015 [39]. The pre-processed transcriptomic data with log2 transformed RSEM [40] normalized values were downloaded for 19 different cancer types listed in Table S1. In each cancer type, genes with more than 25% missing entries and low varying genes (standard deviation ≤ 0.5) were filtered out. Furthermore, we performed a z-score transformation for each gene across each cancer dataset.
Assembling transcription factor binding information into a generic human and mouse gene regulatory network
We assembled a comprehensive set of putative regulators for each gene by compiling TF binding information in human cells from seven different data repositories comprising (i) MetaCore™[41] with annotated “direct”, “indirect” and “unspecific” interactions, (ii) the ChIP Enrichment Analysis (ChEA) database [42], (iii) chromatin immunoprecipitation data from the ENCODE project (http://www.genome.gov/Encode/), (iv) human ChIP-seq and ChIP-ChIP data from hmCHIP [43], (v) experimentally verified interactions from the Human Transcriptional Regulation Interactions database (HTRIdb) [44], (vi) ChIP-seq data for long non-coding RNA and microRNA genes from ChIPBase [45] and (vii) the method of Total Binding Affinity (TBA) [46]. TBA estimates the binding probability of a TF to the whole range of a gene’s promoter. Only TBA values with a stringency cutoff of a score ≥ 1.5 were selected. All these repositories were used to compute the generic network of TFs and their target genes. An interaction between a TF t and a target gene i was considered if it was listed
in MetaCore™and labelled as direct, or listed in Encode,
in at least two out of MetaCore™ (labelled as indirect), ChEA, TBA (score≥1.5) or HTRI, or
in ChIPBase and hmChIP.
The different repositories were not equally incorporated due to the fact, that some repositories were more reliable than others. Because the interactions from MetaCore™based on literature reports and were manually curated, MetaCore’s direct interactions (MCdirti, activation, inhibition or unspecific) were weighted by a factor of 2, while MetaCore’s indirect interactions (MCindirti, activation, inhibition or unspecific) were weighted by a factor of 1. Entries from cheati, htriti and tbati were also weighted by a factor of 1, interactions from Encode (encti) by 0.5. A factor of 0.25 was used for interactions found in hmChIP (hmti) and ChIPbase (chipti). This led to the overall edge strength score esti: with and
In total the generic network (version 1.0) comprises 618,537 non-zero entries for 1,160 TFs and 31,915 target genes.
Similarly, a comprehensive set of putative regulators for each gene was assembled for mouse by compiling TF binding information from MetaCore™, ChEA and ENCODE containing TF binding information for mouse. Additionally, we added two more databases, ECRBase and TfactS. ECRBase is based on alignments of evolutionary conserved TF binding sites [47]. TfactS [48] contains interaction information inferred from the regulation of TFs from gene expression data of experimentally well-characterized target genes listed in TRED [49], TRRD [50], PAZAR [51] and NFIregulomeDB [52]. Interaction information of TF t and target gene i from MetaCore™ (MCdirti) labelled as direct was weighted by 2. If an interaction was listed in two out of (a) MetaCore™ indirect (MCindirti), (b) ChEA (cheati) and (c) ECRbase (ecrbaseti), it was weighted by 1 (for each source). A listed mouse ENCODE entry (encti) was weighted by 0.5. The interactions of TfactS (tfacsti) were considered to have weaker evidence and were weighted by 0.25. This led to the overall edge strength score mesti for mouse: with and
In total the generic mouse network (version 1.0) comprises 93,140 non-zero entries for 976 TFs and 15,728 target genes.
Modeling TERT regulation
We optimized our previously developed “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) software. MIPRIP 2.0 can be used for one set of samples (single-mode), can be applied to compare the regulatory processes between two sets of samples (dual-mode), and for multiple datasets, to identify the most common and condition specific regulators (multi-mode) (Fig. 1). The basic idea of MIPRIP is to identify the most relevant regulators of a particular target gene by predicting the target gene’s expression using a linear model in which the covariates are all potential regulators putatively binding to its promoter. The gene expression value of the TERT gene is predicted for each sample by the following model: where β0 is an additive offset, T the number of all regulators for which TERT promoter binding information is available, βt is the optimization parameter for regulator t, esti is the edge strength score between regulator t and its putative target gene i and acttk the activity of regulator t in sample k. If gene i was reported to be a target of regulator t, the edge weight was higher than 0. Instead of using the gene expression value of a regulator, we calculate an activity value acttk for each regulator and each sample based on the expression of all its putative target genes gik by
The activity is the cumulative effect of a regulator on all its target genes, normalized by the sum of all target genes. To calculate the activity value, we excluded the expression value of the gene of interest (TERT) itself. A linear regression is performed based on Mixed Integer Linear Programming (MILP). MILP has advantages over lasso regression model, as in MILP based regression, the error penalties are linear (L1 regression) and not quadratic which avoids over-emphasizing outliers. Furthermore, MILP enables using binary on-off switches for each beta coefficient to limit the number of beta coefficients [for details, see [17]]. All linear equations are optimized using the Gurobi optimizer [53] (version 6.0-7.01) to minimize the difference between the measured transcript level (from the gene expression matrix) gi,k and the predicted gene expression value. This equals to minimizing the error terms eik in
Because MILP cannot handle absolute values, the absolute values were transformed into two inequalities for each gene i and sample k,
To avoid overfitting, for each dataset we constructed models constraining the number of regulators starting with one regulator up to 10 regulators. A ten-times threefold cross-validation was performed yielding 300 models for each dataset. The correlation between the measured and the predicted gene expression values from the models indicates the prediction performance.
Single-mode MIPRIP 2.0 analysis
The single-mode analysis was developed to predict a list of regulators best explaining the gene expression profile of the target gene of interest (TERT) for all samples of a dataset for a single condition or disease. The single mode has no additional statistical analysis beyond the linear modelling. The results are simply the frequency of regulators over all cross-validation runs, prioritized by their usages.
Dual-mode MIPRIP 2.0 analysis
As a case study, we applied the dual-mode analysis to the skin cutaneous melanoma (SKCM) dataset. The SKCM dataset was divided into two subgroups based on the TERT promoter mutation (based on the analysis of [54]). In total, the status of the TERT promoter was available for 115 samples (primary and metastatic samples). One subgroup (n=74) comprised samples with a TERT promoter mutation, the other subgroup comprised samples with the according wild-type of the TERT promoter (n=41). With these two subgroups we performed a dual-mode analysis by calculating the linear models using the same parameters as described above. Significant regulators between the two subgroups were determined by a two-sided Fisher’s exact test, testing an enrichment of a TF to be in a model of the first or the second condition based on their distribution in the different models, followed by multiple testing correction using the Benjamini-Hochberg method [55]. The stringency cutoff was set to P=0.01.
Multi-mode MIPRIP 2.0 analysis
The multi-mode analysis was developed to predict (1) a list of regulators best explaining the gene expression of the gene of interest (TERT) across all conditions (in our case tumor types), and (2) for each specific condition, in contrast to all other conditions. We prioritized the regulators as follows. For each condition, we listed how often each regulator was selected by the optimizer resulting in a count table. With these distributions we performed a one-sided Wilcoxon Test for each regulator in the list to identify regulators which were selected significantly more often in one of the conditions compared to all other conditions yielding the condition specific. Significance values (p-values) were corrected for multiple testing [55]. To identify the most common TERT regulators within all conditions (of all 19 cancer types), a rank product test was performed based on the ranks from the counts of each condition. A permutation-based estimation was used to determine if the rank product value was higher than an observed value from a random distribution. We then counted how often the rank product values in the permutations were below or equal to the observed value, which led to an averaged expected value (E-value) [56].
Systematic literature query
A Pubmed [57] search was performed for the identified common TERT regulators from the multi-mode MIPRIP 2.0 analysis. A combination of all regulator gene symbols from Table 1 was queried together with “TERT” and the terms “telomerase”, “human” and “regulation”: (E2F4 OR AR OR PAX5 OR E2F2 OR BATF OR PAX8 OR SMARCB1 OR MXI1 OR TAF1) AND TERT AND telomerase AND human AND regulation. The received number of articles was compared to a query without the identified regulators, and to the same two queries without the gene symbol of TERT yielding the according background counts. A Fisher’s exact test was applied to test if the identified common regulators were significantly more often found together with TERT than without TERT (Supplementary Table S5).
TF perturbation experiments
We investigated TERT expression upon ETS1 knockdown for validating our result of the dual-mode case study. Wang et al. have previously performed siRNA mediated knockdown of 45 TF and signaling molecules in the melanoma cell line A375. The gene expression of cells with the knockdown (1 sample per knockdown), untreated (3 replicates) and siRNA control treated (3 replicates) cells was measured by microarrays (Affymetrix GeneChip Human Genome U133 Plus 2.0) 48 h after transfection [20]. RMA-normalized expression data of the perturbation experiments was downloaded from Gene Expression Omnibus (GSE31534). Affy probe-ids were mapped to gene symbols using BioMart [58] and multiple affy probe-ids for the same gene were averaged. A fold change was calculated for TERT upon ETS1 knockdown compared to control.
Comparison of MIPRIP 2.0 with ISMARA
We compared our MIPRIP 2.0 results with the results from the “Integrated Motif Activity Response Analysis” (ISMARA) tool. ISMARA predicts regulatory interactions between the TFs and the target genes based on TF binding motifs [11]. For the SKCM data from TCGA only preprocessed data was available. Hence, ISMARA could not be used via the web portal. Therefore, the ISMARA analysis was performed based on the FPKM values (downloaded from the GDC portal [59], June 2018) by the developers using default settings.
Declarations
Acknowledgements
We thank the members of the CancerTelSys consortium for fruitful discussions on telomere maintenance mechanisms in cancer and Mikhail Pachkov for the ISMARA analysis. We further would like to thank Ashwini Kumar Sharma for his instructions how to download TCGA data and proofreading of the manuscript.
Funding
This work was supported by the project CancerTelSys (01ZX1302B, 01ZX1602B) in the e:Med program and the project CSCC (01EO1002, 01EO1502) of the German Federal Ministry of Education and Research (BMBF). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Competing interests
The authors declare that they have no competing interests.
Availability of data and material
The R-package ‘MIPRIP2’ and the generic human/mouse regulatory networks can be downloaded from https://www.leibniz-hki.de/en/miprip.html or https://github.com/network-modeling/MIPRIP. All datasets analyzed in this study are available at the GDAC (http://gdac.broadinstitute.org/) or the GDC (https://portal.gdc.cancer.gov/) portal.
Authors’ contributions
PAM, OM, KT developed the Mixed Integer linear model, PAM implemented the MIPRIP 2.0 tool and performed the analysis. KT, AV and KA constructed the human and mouse generic gene regulatory network. PAM and KR wrote the manuscript with contributions from RK. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
List of abbreviations
- MIPRIP
- Mixed Integer linear Programming based Regulatory Interaction Predictor
- TMM
- telomere maintenance mechanism
- TF
- transcription factor
- GRN
- gene regulatory network
- ChIP
- Chromatin immunoprecipitation
- TCGA
- The Cancer Genome Atlas
- SKCM
- skin cutaneous melanoma
- OV
- ovarian serous cystadenocarcinoma
- CESC
- cervical cancer
- THYM
- thymoma
- TGCT
- testicular germ cell cancer
- PRAD
- prostate adenocarcinoma
- PAAD
- pancreatic ductal adenocarcinoma
- BRCA
- breast cancer
- ISMARA
- Integrated Motif Activity Response Analysis
- EMSA
- electrophoretic shift assay
- MILP
- Mixed Integer Linear Programming
- GDAC
- Genome Data Analysis Center
- RSEM
- accurate transcript quantification from RNA-Seq data with or without a reference genome
- ChEA
- ChIP Enrichment Analysis
- HTRIdb
- Human Transcriptional Regulation Interactions database
- TBA
- total binding affinity
- es
- edge strength
- FPKM
- fragments per kilobase of exon model per million reads mapped
- E-value
- expected value
- TERT
- Telomerase reverse transcriptase
- AR
- androgen receptor
- ETS
- E-twenty six