Pathway enrichment analysis.

Top five KEGG pathway enrichment analysis.

Pathway enrichment analysis of differentially expressed genes (Top ten enriched pathways are shown).

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Network-based pathway enrichment analysis.

Gene set enrichment analysis: KEGG pathways significantly enriched in OCSC phenotype.

Sample datasets for testing CellEnrich package.

Therapeutic resistance remains a primary obstacle to curing cancer. Healthy cells exposed to genotoxic insult rapidly activate both p53-dependent and -independent non-genetic programs that pause the cell cycle and direct either DNA repair or apoptosis. Cancer cells exploit these same pathways as they respond to stresses induced by cancer therapies. In this study, we investigated a potential role for upstream stimulatory factor 1 (USF1) and USF2 in the p53-independent response of lymphoma cells to genotoxic therapy. We previously found that lymphocytes utilize the responsiveness of USF1 to double-stranded DNA breaks to coordinate T cell receptor beta (Tcrb) gene expression during V(D)J recombination. Here, microarray gene expression analysis of derivatives of the p53-deficient mouse B lymphoma cell line, M12, revealed that simultaneously depleting cells of both USF1 and USF2 altered the expression of 940 gene transcripts (>1.50-fold change, < 0.05 FDR), relative to cells expressing a scrambled control shRNA. Seven days after exposure to a single sublethal (5 Gy) dose of ionizing radiation, USF-depleted (USFKD) cells exhibited widespread and distinct transcriptional responses from those of irradiated controls (5035 and 5054 differentially expressed gene transcripts, respectively, with roughly half shared between both cell types). Gene ontology analyses revealed that USF knockdown induced numerous changes in the expression of genes critical for immune development and function while diminishing the responsiveness of genes linked to DNA damage pathways. Microarray findings were confirmed by RT-qPCR for a panel of genes responsive to USF knockdown and/or irradiation. These findings shed further light on transcriptional responses to ionizing radiation that manifest over time in transformed cells, identifying a novel p53-independent role in lymphocytic DNA damage stress responses for USF.

Curated Human Gene Signatures for Bioinformatics Research

The dataset provides gene set files in GMT format for Gene Set Enrichment Analysis (GSEA). The included files are from the Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb) and cover a variety of biological areas:

• c7.all.v2023.1.Hs.symbols.gmt: C7 (Immunologic Signatures) - A comprehensive collection of gene sets related to immune system processes.

• c2.cp.reactome.v2023.1.Hs.symbols.gmt: C2 CP (Canonical Pathways) - Gene sets representing well-established biological pathways from databases like Reactome.

• c5.go.bp.v2023.1.Hs.symbols.gmt: C5 GO (Biological Process) - Gene sets representing biological processes from the Gene Ontology.

• c5.go.mf.v2023.1.Hs.symbols.gmt: C5 GO (Molecular Function) - Gene sets representing molecular functions from the Gene Ontology.

• c5.go.cc.v2023.1.Hs.symbols.gmt: C5 GO (Cellular Component) - Gene sets representing cellular components from the Gene Ontology.

Description:

The dataset contains curated gene set collections from the Molecular Signatures Database (MSigDB) v2024.1. Included are key collections such as Reactome Pathways (C2), Gene Ontology (C5 BP, MF, CC), and Immunologic Signatures (C7). These datasets are invaluable for gene enrichment analyses, bioinformatics studies, and research on cellular processes.

These files can be used with GSEA to analyze gene expression data and identify enriched pathways and functions. Gene sets were obtained from the Molecular Signatures Database (MSigDB) [Subramanian et al., 2005]. The files are distributed under the Creative Commons Attribution 4.0 International License.

The dataset includes curated gene set collections from the Molecular Signatures Database (MSigDB) v2024.1, for gene enrichment analyses, pathway mapping, and immune system studies.

C2: Canonical Pathways

Possible Use Cases:

• Identifying key molecular pathways related to gene expression changes in disease states
• Mapping drug interactions and metabolic pathways
• Conducting pathway-centric analyses of transcriptomic data

C5: Gene Ontology (GO) Gene Sets

• Biological Process (BP): Gene sets describing high-level biological objectives (e.g., cell cycle regulation, apoptosis).
• Molecular Function (MF): Gene sets defining activities at the molecular level (e.g., enzyme binding, receptor activity).
• Cellular Component (CC): Gene sets describing cellular structures where gene products are active (e.g., nucleus, mitochondria).

Possible Use Cases:
- Annotation and interpretation of transcriptomic data
- Discovery of functional roles for specific genes
- Enrichment analyses to find key biological activities

C7: Immunologic Signature Gene Sets

Possible Use Cases:
- Immunological studies focusing on disease responses
- Vaccine development and response evaluation
- Discovery of immune-related biomarkers

Citations

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. https://doi.org/10.1073/pnas.0506580102

Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems, 1(6), 417–425. PMC4707969

No Endorsement Statement:

The inclusion of gene sets from MSigDB in this dataset does not imply endorsement by the original authors or associated research groups. These datasets are redistributed under CC BY 4.0 for research purposes with proper attribution.

Key Features:
- Hallmark Reactome pathways
- GO terms (Biological Processes, Molecular Functions, Cellular Components)
- C7 Immunologic gene sets

License: CC BY 4.0

https://www.gsea-msigdb.org/gsea/license_terms_list.jsp

https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp

Important: No endorsement by original authors or institutions.

Comparison of NASFinder results with pathway enrichment analysis of dataset GSE1004.

1genetic associations were reported in or near genes in this pathway by genome wide association studies.2KEGG pathway ID (http://www.genome.jp/kegg/).Pathway enrichment analysis performed with miRSystem and mirPATH2.

Complete table for pathway and functional enrichment analysis for gene-based mutational burden analysis.

Information for Fig 3, detailing the Gene Set Enrichment Analysis (GSEA) databases used for pathway analyses.

KEGG pathway enrichment analysis.

Bubble plots exhibited the top-ranked GSEA results for the top-ranked KEGG pathways in (A) recurrent POP compared with primary POP uterosacral ligaments, and (B) recurrent POP compared with non-POP uterosacral ligaments. The classic GSEA plots showed that both adipose- and inflammation-related pathways were activated in the two contrast matrices for (C) recurrent POP vs.vs primary POP uterosacral ligaments, and (D) recurrent POP vs.vs non-POP uterosacral ligaments. Heatmaps showed hierarchical clustering of ssGSEA scores of KEGG pathways differentially enriched in (E) recurrent POP vs.vs primary POP uterosacral ligaments and (F) recurrent POP vs.vs non-POP uterosacral ligaments. (G) Spearman correlation analysis of pathway ssGSEA scores revealed that PPAR signaling pathways was strongly associated with adipose- and inflammation-related pathways in non-POP, primary POP and recurrent POP uterosacral ligaments. GSEA, g: Gene set enrichment analysis; ssGSEA, the s: Single-sample gene set enrichment analysis; POP, p: Pelvic organ prolapses; NES, n: Normalized enrichment scores.

MetaCore™ enrichment pathway analysis.

DESeq2 differential abundance output for genes with q < 0.05 and |log₂ fold change| > 1 in cancer vs control plasma samples:

• differentialabundance_pancancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)
• differentialabundance_threecancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls)
  • Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.

Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):

• customgenesets.txt: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data.
  • Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set
• GSEA_pancancer.txt & GSEA_threecancer.txt: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively
  • Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in customgenesets.txt); pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls

Enrichment analysis results for list-B genes from GeneSCF and DAVID 6.7 using KEGG as a reference database. (XLS 112 kb)

ID correspondence table and information on the SRA Runs. The correspondences among Entrez Gene ID, Kegg Gene ID, and Ensemble Gene ID are shown in Table 1. Information (Run ID, Experiment ID, Study ID, sample tissue and cultivar) on the SRA Runs used to construct the gene expression matrix is shown in Table 2. (XLSX 1015 kb)

Background Gene Lists Used for Gene Ontology and KEGG Pathway Enrichment Analysis.