Dissecting the sources of gene expression variation in a pan-cancer analysis identifies novel regulatory mutations

Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung

Fetal adenocarcinoma of the lung (FLAC) is a rare form of lung adenocarcinoma and was divided into high-grade (H-FLAC) and low-grade (L-FLAC) subtypes. Despite the existence of some small case series studies, a comprehensive multi-omics study of FLAC has yet to be undertaken. In this study, we depicted the multi-omics landscapes of this rare lung cancer type by performing multi-regional sampling on 20 FLAC cases. A comparison of multi-omics profiles revealed significant differences between H-FLAC and L-FLAC in a multi-omic landscape. Two subtypes also showed distinct relationships between multi-layer intratumor heterogeneity (ITH). We discovered that a lower genetic ITH was significantly associated with worse recurrence-free survival and overall survival in FLAC patients, whereas higher methylation ITH in H-FLAC patients suggested a short survival. Our findings highlight the complex interplay between genetic and transcriptional heterogeneity in FLAC and suggest that different types of ITH may have distinct implications for patient prognosis.

Gastric precancerous lesions:occurrence, development factors, and treatment

Patients with gastric precancerous lesions (GPL) have a higher risk of gastric cancer (GC). However, the transformation of GPL into GC is an ongoing process that takes several years. At present, several factors including H.Pylori (Hp), flora imbalance, inflammatory factors, genetic variations, Claudin-4, gastric stem cells, solute carrier family member 26 (SLC26A9), bile reflux, exosomes, and miR-30a plays a considerable role in the transformation of GPL into GC. Moreover, timely intervention in the event of GPL can reduce the risk of GC. In clinical practice, GPL is mainly treated with endoscopy, acid suppression therapy, Hp eradication, a cyclooxygenase-2 inhibitor, aspirin, and diet. Currently, the use of traditional Chinese medicine (TCM) or combination with western medication to remove Hp and the use of TCM to treat GPL are common in Asia, particularly China, and have also demonstrated excellent clinical efficacy. This review thoroughly discussed the combining of TCM and Western therapy for the treatment of precancerous lesions as conditions allow. Consequently, this review also focuses on the causes of the development and progression of GPL, as well as its current treatment. This may help us understand GPL and related treatment.

Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer

A deep understanding of the complex interaction mechanism between the various cellular components in tumor microenvironment (TME) of lung adenocarcinoma (LUAD) is a prerequisite for understanding its drug resistance, recurrence, and metastasis. In this study, we proposed two complementary computational frameworks for integrating multi-source and multi-omics data, namely ImmuCycReg framework (single sample level) and L0Reg framework (population or subtype level), to carry out difference analysis between the normal population and different LUAD subtypes. Then, we aimed to identify the possible immune escape pathways adopted by patients with different LUAD subtypes, resulting in immune deficiency which may occur at different stages of the immune cycle. More importantly, combining the research results of the single sample level and population level can improve the credibility of the regulatory network analysis results. In addition, we also established a prognostic scoring model based on the risk factors identified by Lasso-Cox method to predict survival of LUAD patients. The experimental results showed that our frameworks could reliably identify transcription factor (TF) regulating immune-related genes and could analyze the dominant immune escape pathways adopted by each LUAD subtype or even a single sample. Note that the proposed computational framework may be also applicable to the immune escape mechanism analysis of pan-cancer.

The comprehensive expression and functional analysis of m6A modification "readers" in hepatocellular carcinoma

N6-methyladenosine (m6A) modification regulators are essential for the diagnosis and treatment of various cancers. However, the comprehensive analysis about roles of m6A "readers" in hepatocellular carcinoma (HCC) remains unclear. UALCAN, GEPIA2, HPA, Kaplan Meier plotter, cBioPortal, STRING WebGestalt, Metascape and TIMER 2.0 database and Cytoscape software were used to comprehensively analyze the bioinformatic data. We found that m6A "readers" were upregulated at the mRNA level and protein level in HCC patients. Highly expressed YTHDF1, IGF2BP3 and NKAP were positively correlated with advanced HCC stage and had a poor prognosis in OS and PFS. The gene alterations of m6A "readers" happened frequently, and YTHDF3 had the highest mutation rate. The function of m6A "readers" on HCC may be closely correlated with splicing related proteins (including HNRNP family, SNRP family, and SR family), metabolic process, protein binding and RNA splicing related signaling pathways. Moreover, although the correlation of YTHDF3 and CD8+ T cell infiltration, and the correlation of IGF2BP3 and infiltration of mast cells and CAF are negative, most m6A "readers" had a positive correlation with immune cells (including CD8+ T cell, CD4+ T cell, Tregs, B cell, neutrophil, monocyte, macrophage, myeloid dendritic cell, nature killer cell, mast cell, and CAF). Macrophages, CD4+ T cell, Treg, B cell, monocyte, and myeloid dendritic cell had a positively strong correlation (Rho>0.4) with most m6A "readers" (such as YTHDC1, YTHDC2, YTHDF1, IGF2BP3, HNRNPA2B1 and HNRNPC). In conclusion, by comprehensive analysis of m6A "readers", we found that they were involved in the prognosis of HCC, and m6A "readers" might regulate the development and progression of HCC by participating in some metabolism-related and RNA splicing-related signaling pathways as well as immune cell infiltration.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

Core promoter mutation contributes to abnormal gene expression in bladder cancer

BACKGROUND

Bladder cancer is one of the most mortal cancers. Bladder cancer has distinct gene expression signature, highlighting altered gene expression plays important roles in bladder cancer etiology. However, the mechanism for how the regulatory disorder causes the altered expression in bladder cancer remains elusive. Core promoter controls transcriptional initiation. We hypothesized that mutation in core promoter abnormality could cause abnormal transcriptional initiation thereby the altered gene expression in bladder cancer.

METHODS

In this study, we performed a genome-wide characterization of core promoter mutation in 77 Spanish bladder cancer cases.

RESULTS

We identified 69 recurrent somatic mutations in 61 core promoters of 62 genes and 28 recurrent germline mutations in 20 core promoters of 21 genes, including TERT, the only gene known with core promoter mutation in bladder cancer, and many oncogenes and tumor suppressors. From the RNA-seq data from bladder cancer, we observed  altered expression of the core promoter-mutated genes. We further validated the effects of core promoter mutation on gene expression by using luciferase reporter gene assay. We also identified potential drugs targeting the core promoter-mutated genes.

CONCLUSIONS

Data from our study highlights that core promoter mutation contributes to bladder cancer development through altering gene expression.

Drivers of dynamic intratumor heterogeneity and phenotypic plasticity

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network

Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.

The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome

BACKGROUND

To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome.

METHODS

This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM.

RESULTS

PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions.

CONCLUSIONS

Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.

A Multi-Source Data Fusion Framework for Revealing the Regulatory Mechanism of Breast Cancer Immune Evasion

For precision medicine, there is an enormous need to understand the immune evasion mechanism of tumor development, especially when tumor heterogeneity significantly affects the effect of immunotherapy. Recognizing the subtypes of breast cancer based on the immune-related genes helps to understand the immune escape pathways dominated by different subtypes, so as to implement effective treatment measures for different subtypes. For that, we used non-negative matrix factorization and consistent clustering algorithm on The Cancer Genome Atlas RNA-seq breast cancer data and recognized 4 subtypes according to the curated immune-related genes. Then, we conducted differential expression analysis between each subtype of breast cancer and normal tissue of RNA-seq data from non-cancer individuals collected by the Genotype-Tissue Expression to find out subtype-related immune genes. After that, we carried out correlation analysis between copy number variants (CNV) and mRNA of immune genes and investigated the regulatory mechanism of the immune genes, which cannot be explained by CNV based on ATAC-seq data. The experimental results reveal that CDH1 and PVRL2 are potential for immune evasion in all 4 subgroups. The expression variations of CDH1 can be mainly explained by its CNV, while the expression variation of PVRL2 is more likely regulated by transcript factors.

Highly diversified core promoters in the human genome and their effects on gene expression and disease predisposition

Background

Core promoter controls transcription initiation. However, little is known for core promoter diversity in the human genome and its relationship with diseases. We hypothesized that as a functional important component in the genome, the core promoter in the human genome could be under evolutionary selection, as reflected by its highly diversification in order to adjust gene expression for better adaptation to the different environment.

Results

Applying the “Exome-based Variant Detection in Core-promoters” method, we analyzed human core-promoter diversity by using the 2682 exome data sets of 25 worldwide human populations sequenced by the 1000 Genome Project. Collectively, we identified 31,996 variants in the core promoter region (− 100 to + 100) of 12,509 human genes (https://dbhcpd.fhs.um.edu.mo). Analyzing the rich variation data identified highly ethnic-specific patterns of core promoter variation between different ethnic populations, the genes with highly variable core promoters, the motifs affected by the variants, and their involved functional pathways. eQTL test revealed that 12% of core promoter variants can significantly alter gene expression level. Comparison with GWAS data we located 163 variants as the GWAS identified traits associated with multiple diseases, half of these variants can alter gene expression.

Conclusion

Data from our study reals the highly diversified nature of core promoter in the human genome, and highlights that core promoter variation could play important roles not only in gene expression regulation but also in disease predisposition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07222-5.

Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors

Impacts of genetic and non-genetic intra-tumor heterogeneity (ITH) on tumor phenotypes and evolvability remain debated. We analyze ITH in lung squamous cell carcinoma at the levels of genome, transcriptome, and tumor-immune interactions and histopathological characteristics by multi-region bulk and single-cell sequencing. Genomic heterogeneity alone is a weak indicator of intra-tumor non-genetic heterogeneity at immune and transcriptomic levels that impact multiple cancer-related pathways, including those related to proliferation and inflammation, which in turn contribute to intra-tumor regional differences in histopathology and subtype classification. Tumor subclones have substantial differences in proliferation score, suggestive of non-neutral clonal dynamics. Proliferation and other cancer-related pathways also show intra-tumor regional differences, sometimes even within the same subclones. Neo-epitope burden negatively correlates with immune infiltration, indicating immune-mediated purifying selection on somatic mutations. Taken together, our observations suggest that non-genetic heterogeneity is a major determinant of heterogeneity in histopathological characteristics and impacts evolutionary dynamics in lung cancer.

Cellular State Transformations Using Deep Learning for Precision Medicine Applications

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., n=1), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies.

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We present the Transcriptome State Perturbation Generator (TSPG) application

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We apply TSPG to The Cancer Genome Atlas data to perturb gene expression states

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TSPG was used to learn patient-specific (n = 1) gene expression tumor alterations

Deep learning has shown tremendous success in image and natural language processing; however, attempts to apply the tools of machine learning to better understanding biological systems are still in the stage of early adoption. We propose a novel deep-learning tool that can be used to process samples of RNA-sequencing data. By applying the Transcriptome State Perturbation Generator to human samples, we show that deep learning derives insight into the gene expression shifts required for transition between two biological conditions (e.g., normal versus tumor). RNA-sequencing data derived from a single patient's tumor were analyzed using this tool to determine gene expression aberrations specific to that patient's tumor. As medicine shifts from cohort-based population studies to individual-based precision treatments, our example demonstrates that deep learning is a powerful ally in the quest to understand how complex biological systems have shifted for a single patient.

Dissecting expression profiles of gastric precancerous lesions and early gastric cancer to explore crucial molecules in intestinal-type gastric cancer tumorigenesis

Intestinal‐type gastric cancer (IGC) has a clear and multistep histological evolution. No studies have comprehensively explored gastric tumorigenesis from inflammation through low‐grade intraepithelial neoplasia (LGIN) and high‐grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC). We sought to investigate the characteristics participating in IGC tumorigenesis and identify related prognostic information within the process. RNA expression profiles of 94 gastroscopic biopsies from 47 patients, including gastric precancerous lesions (GPL: LGIN and HGIN), EGC, and paired controls, were detected by Agilent Microarray. During IGC tumorigenesis from LGIN through HGIN to EGC, the number of activity‐changed tumor hallmarks increased. LGIN and HGIN had similar expression profiles when compared to EGC. We observed an increase in the stemness of gastric epithelial cells in LGIN, HGIN, and EGC, and we found 27 consistent genes that might contribute to dedifferentiation, including five driver genes. Remarkably, we perceived that the immune microenvironment was more active in EGC than in GPL, especially in the infiltration of lymphocytes and macrophages. We identified a five‐gene signature from the gastric tumorigenesis process that could independently predict the overall survival and disease‐free survival of GC patients (log‐rank test: p < 0.0001), and the robustness was verified in an independent cohort (n > 300) and by comparing with two established prognostic signatures in GC. In conclusion, during IGC tumorigenesis, cancer‐like changes occur in LGIN and accumulate in HGIN and EGC. The immune microenvironment is more active in EGC than in LGIN and HGIN. The identified signature from the tumorigenesis process has robust prognostic significance for GC patients. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Predicting functional variants in enhancer and promoter elements using RegulomeDB

Here we present a computational model, Score of Unified Regulatory Features (SURF), that predicts functional variants in enhancer and promoter elements. SURF is trained on data from massively parallel reporter assays and predicts the effect of variants on reporter expression levels. It achieved the top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" challenge. We also show that features queried through RegulomeDB, which are direct annotations from functional genomics data, help improve prediction accuracy beyond transfer learning features from DNA sequence-based deep learning models. Some of the most important features include DNase footprints, especially when coupled with complementary ChIP-seq data. Furthermore, we found our model achieved good performance in predicting allele-specific transcription factor binding events. As an extension to the current scoring system in RegulomeDB, we expect our computational model to prioritize variants in regulatory regions, thus help the understanding of functional variants in noncoding regions that lead to disease.

Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis

In this work, we propose iProFun, an integrative analysis tool to screen for proteogenomic functional traits perturbed by DNA copy number alterations (CNAs) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. Compared with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types, and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNAs/methylations to global/phospho protein abundances may imply posttranslational regulations.We applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and identified CNAs and methylations of 500 and 121 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 117 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A network analysis of these 117 genes revealed the known oncogene AKT1 as a key hub node interacting with many of the rest. In addition, iProFun identified one gene, BIN2, whose DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances. These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.

Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing.

OMICfpp: a fuzzy approach for paired RNA-Seq counts

BACKGROUND

RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it.

RESULTS

We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz .

CONCLUSIONS

Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.