Search

We reconstructed gene regulatory networks for 80 TCGA and 37 DKFZ leiomyosarcoma samples, and used these networks as input to PORCUPINE (Principal Components Analysis to Obtain Regulatory Contributions Using Pathway-based Interpretation of Network Estimates) method to identify pathways driving leiomyosarcoma heterogeneity. In short, PORCUPINE combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. This repository contains following: “rse_gene.RData” - A RangedSummarizedExperiment-class object for the TCGA RNA-seq data. “LMS_37_readCount.txt”- Raw expression count data for 37 DKFZ-LMS samples. This file contains a 57,820 by 38 dataframe, where the first column is gene ID. “GN_ensemblID_symbol.txt”- A 55,476 by 2 dataframe where the first column is Ensemble ID and the second column is gene symbol, corresponding to features in “LMS_37_readCount.txt”. “preprocessing_and_normalization.R” - R script with preprocessing and normalization workflow of the data. “prior.txt” - Prior information on potential regulatory interactions, obtained from scanning known TF motifs to promoter regions in the human genome. This prior network was previously published in Lopes-Ramos et al. 2021 Cancer Research (PMID 34493595). This file contains a 11,151,077 by 3 dataframe, where the first column is the transcription factor's gene IDs, the second column is the target gene IDs and third column shows the presence (1) or absence (0) of a motif of a TF in a promoter region of a gene. “exp.txt” - Gene expression data, contains a 17,899 by 11,322 dataframe including normalized expression data for each sample. The first column is a gene ID. “ppi.txt” – protein-protein interactions between TFs obtained from StringDb (https://string-db.org/), as in Lopes-Ramos et al. 2021 (PMID 34493595). The file contains a 80,037 by 3 dataframe with three columns, where the first two columns contain protein IDs and the third column contains a score for each interaction. “80_tcga_lms_net.RData” - patient-specific gene regulatory networks for 80 TCGA leiomyosarcoma samples. This file contains a 11,151,077 by 80 dataframe that includes edge weights for each sample. Edge order corresponds to edge order in the edges.RData file. “37_dkfz_lms_net.RData” - patient-specific gene regulatory networks for 37 DKFZ leiomyosarcoma samples. This file contains a 11,151,077 by 37 dataframe that includes edge weights for each sample. Edge order corresponds to edge order in the edges.RData file. “edges.RData” - regulatory edge information, includes a 11,151,077 by 3 dataframe with three columns: reg (the transcription factor's gene IDs), tar (the target gene IDs), prior (information from “prior” network). "PORCUPINE.zip" - PORCUPINE R package, This work was supported by the Norwegian Research Council, Helse Sør-Øst, and University of Oslo through the Centre for Molecular Medicine Norway (187615), the Norwegian Research Council (313932), Familien Blix Fond, as well as the Emmy Noether Programme Grant from the German Research Foundation (DFG, No. CH 2302/1-1).

We collected studies in glioblastoma that included survival thresholds through a PubMed search (search term: glioblastoma short-term prognostic signature). As can be seen from this table, a wide range of survival thresholds is used, with thresholds for short-term survival ranging between 7-24 months, and thresholds for long-term survival between 13-36 months after initial diagnosis. The threshold of 1.7yrs, which we chose for our comparison in the discovery datasets, corresponds to 20 months, which lies right in between these two ranges.

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer.Significance:Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Smooth scatterplot visualizing the correlation between edge weights in the condition-specific network modeled on discovery dataset 1 and the condition-specific network modeled on discovery dataset 2. Pearson correlation coefficient (R) and p-value (p) are indicated in the plot.

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78–100] survival compared with 61% (95% CI, 45–82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538–47. ©2018 AACR.

Extensive clinical features for the dataset included in the study.

Overall survival analysis based on chemotherapy treatment.

Purpose: High-grade osteosarcoma is a malignant primary bone tumor with a peak incidence in adolescence. Overall survival (OS) of patients with resectable metastatic disease is approximately 20%. The exact mechanisms of development of metastases in osteosarcoma remain unclear. Most studies focus on tumor cells, but it is increasingly evident that stroma plays an important role in tumorigenesis and metastasis. We investigated the development of metastasis by studying tumor cells and their stromal context.Experimental Design: To identify gene signatures playing a role in metastasis, we carried out genome-wide gene expression profiling on prechemotherapy biopsies of patients who did (n = 34) and patients who did not (n = 19) develop metastases within 5 years. Immunohistochemistry (IHC) was performed on pretreatment biopsies from 2 additional cohorts (n = 63 and n = 16) and corresponding postchemotherapy resections and metastases.Results: A total of 118/132 differentially expressed genes were upregulated in patients without metastases. Remarkably, almost half of these upregulated genes had immunological functions, particularly related to macrophages. Macrophage-associated genes were expressed by infiltrating cells and not by osteosarcoma cells. Tumor-associated macrophages (TAM) were quantified with IHC and associated with significantly better overall survival (OS) in the additional patient cohorts. Osteosarcoma samples contained both M1- (CD14/HLA-DRα positive) and M2-type TAMs (CD14/CD163 positive and association with angiogenesis).Conclusions: In contrast to most other tumor types, TAMs are associated with reduced metastasis and improved survival in high-grade osteosarcoma. This study provides a biological rationale for the adjuvant treatment of high-grade osteosarcoma patients with macrophage activating agents, such as muramyl tripeptide. Clin Cancer Res; 17(8); 2110–9. ©2011 AACR.

Supplementary Figures S1-S4; Supplementary Tables S1-S5.

PDF file - 173K, Analysis of HDMX-S mRNA expression in the NCI60. Cells were examined for HDMX-FL and HDMX-S mRNA expression

PDF file - 89K

PDF file - 565K, Nutlin-3 response in osteosarcoma cell lines. Treated osteosarcoma cells were analyzed by Western blotting and FACS analysis

PDF file - 51K, High HDMX-S/HDMX-FL ratios, but not p53 gene mutation associates with worse outcome in soft tissue sarcoma patients

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. We presentSCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single cell/nuclei RNA-seq data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found thatSCORPIONoutperforms 12 other gene regulatory network reconstruction techniques. Using supervised experiments, we show thatSCORPIONcan accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrateSCORPION’s scalability to population-level analyses using a single-cell RNA-seq atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences detected bySCORPIONbetween tumor regions are consistent across population cohorts, as well as with our understanding of disease progression and elucidate phenotypic regulators that may impact patient survival.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties., Graphical Abstract Graphical AbstractModeling gene regulation across human conditions integrates cancer tissues and cell lines, small molecules and normal tissue networks.

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. Significance: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large-scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence-profiled 524 American Joint Committee on Cancer Stage I-III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co-mutated, and an absence of co-occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole-genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma-associated dependency. © 2022 The Authors. The Journal of Pathology published by John WileySons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand “the zoo” to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present ‘retriever,’ a tool that extracts robust disease-specific transcriptional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML. The proliferation of molecular biology and bioinformatics tools necessary to generate huge quantities of immune receptor data has not been matched by frameworks that allow easy data analysis. The authors present immuneML, an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires.

We recently developed LIONESS, a method to estimate sample-specific networks based on the output of an aggregate network reconstruction approach. In this manuscript, we describe how to apply LIONESS to different network reconstruction algorithms and data types. We highlight how decisions related to data preprocessing may affect the output networks, discuss expected outcomes, and give examples of how to analyze and compare single sample networks.

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance.gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain-specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods.

Mucosal melanoma is a rare and poorly characterized subtype of human melanoma. Here we perform a cross-species analysis by sequencing tumor-germline pairs from 46 primary human muscosal, 65 primary canine oral and 28 primary equine melanoma cases from mucosal sites. Analysis of these data reveals recurrently mutated driver genes shared between species such as NRAS, FAT4, PTPRJ, TP53 and PTEN, and pathogenic germline alleles of BRCA1, BRCA2 and TP53. We identify a UV mutation signature in a small number of samples, including human cases from the lip and nasal mucosa. A cross-species comparative analysis of recurrent copy number alterations identifies several candidate drivers including MDM2, B2M, KNSTRN and BUB1B. Comparison of somatic mutations in recurrences and metastases to those in the primary tumor suggests pervasive intra-tumor heterogeneity. Collectively, these studies suggest a convergence of some genetic changes in mucosal melanomas between species but also distinctly different paths to tumorigenesis., Mucosal melanoma is a rare melanoma subtype that is poorly characterised. Here, the authors sequenced human, canine, and equine melanoma samples and performed a cross-species analysis, which revealed candidate driver genes, recurrent copy number alterations in regions syntenic between species, extensive intra-tumour heterogeneity and potential germline predisposing alleles

B- and T-cell receptor (immune) repertoires can represent an individual’s immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters (e.g., clonal diversity, germline usage). Here, we introduce immuneREF: a quantitative multi-dimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. immuneREF is implemented in an R package and was validated based on detection sensitivity of immune repertoires with known similarities and dissimilarities. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2400 datasets from individuals with varying immune states (healthy, [autoimmune] disease and infection [Covid-19], immune cell population). Importantly we discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF implements population-wide analysis of immune repertoire similarity and thus enables the study of the adaptive immune response across health and disease states., Support was provided from The Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO WorldLeading Research Community (to VG), UiO:LifeSciences Convergence Environment Immunolingo (to VG and GKS), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Research Council of Norway FRIPRO project (#300740, to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG and GKS), a Norwegian Cancer Society grant (#215817, to VG), and Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre) (to GKS), Swiss National Science Foundation (Project 31003A to S.T.R), the Norwegian Research Council, Helse Sør-Øst, and the University of Oslo through the Centre for Molecular Medicine Norway (#187615 to MLK).

MotivationCharacterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. ResultsHere, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and ImplementationrPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB. Contactdaniecos@uio.no Supplementary informationCode to replicate the case example and figure 1 is available at https://github.com/dosorio/rPanglaoDB O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/446161v1_fig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@b4e67forg.highwire.dtl.DTLVardef@88a46aorg.highwire.dtl.DTLVardef@e25681org.highwire.dtl.DTLVardef@19d5bb7_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Characterization of the fibrocytes transcriptome. (A) Identification of cells expressing marker genes that differentiate fibrocytes identity from macrophages and fibroblasts (CD34, ACTA2, FN1, Collagen V, FAP and SIRPA). (B) Cross validation of the identified cells expressing CD34+, ACTA2+, COL5A1+, COL5A2+, COL5A3+, FN1+, FAP+, SIRPA+, PTPRC+, MME+, and SEMA7A+ by kernel density estimation through the Nebulosa package. (C) Volcano plot displaying the differential expression between fibrocytes and the fibroblasts collected in the same merged samples. (D) Enrichment of the prostaglandin biosynthesis and regulation pathway using GSEA through the fgsea package. (E) Enrichment of the prostaglandin biosynthesis and regulation pathway using ssGSEA through the GSVA package. C_FIG