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Background Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships. Results We created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/ ), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries. Conclusions The application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layers. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. Conclusions With multiGSEA we introduce a highly versatile tool for multi-omics pathway integration that minimizes previous restrictions in terms of omics layer selection, pathway database availability, organism selection and the mapping of omics feature identifiers. multiGSEA is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at bioconductor: https://bioconductor.org/packages/multiGSEA .

We aimed to elucidate the mechanism leading to polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA). PCOS is an endocrine disorder. Patients with RSA also have a high incidence rate of PCOS, implying that PCOS and RSA may share the same pathological mechanism. The single-cell RNA-seq datasets of PCOS (GSE168404 and GSE193123) and RSA GSE113790 and GSE178535) were downloaded from the Gene Expression Omnibus (GEO) database. Datasets of PSCO and RSA patients were retrieved from the Gene Expression Omnibus (GEO) database. The \"WGCNA\" package was used to determine the module eigengenes associated with the PCOS and RSA phenotypes and the gene functions were analyzed using the \"DAVID\" database. The GSEA analysis was performed in \"clusterProfiler\" package, and key genes in the activated pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Real-time quantitative PCR (RT-qPCR) was conducted to determine the mRNA level. Cell viability and apoptosis were measured by cell counting kit-8 (CCK-8) and flow cytometry, respectively. The modules related to PCOS and RSA were sectioned by weighted gene co-expression network analysis (WGCNA) and positive correlation modules of PCOS and RSA were all enriched in angiogenesis and Wnt pathways. The GSEA further revealed that these biological processes of angiogenesis, Wnt and regulation of cell cycle were significantly positively correlated with the PCOS and RSA phenotypes. The intersection of the positive correlation modules of PCOS and RSA contained 80 key genes, which were mainly enriched in kinase-related signal pathways and were significant high-expressed in the disease samples. Subsequently, visualization of these genes including PDGFC, GHR, PRLR and ITGA3 showed that these genes were associated with the PI3K-AKT signal pathway. Moreover, the experimental results showed that PRLR had a higher expression in KGN cells, and that knocking PRLR down suppressed cell viability and promoted apoptosis of KGN cells. This study revealed the common pathological mechanisms between PCOS and RSA and explored the role of the PI3K-AKT signaling pathway in the two diseases, providing a new direction for the clinical treatment of PCOS and RSA.

Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. \"Popularity\" is estimated according to each paper's citation counts, while \"performance\" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database (\"GSARefDB\") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

Estrogen receptor (ER) positive breast cancer (BC), the most abundant BC subtype, is notorious for poor response to neoadjuvant chemotherapy (NAC). The androgen receptor (AR) was reported to support estradiol-mediated ER activity in an in vitro system. Recently, ER-positive BC with fewer tumor infiltrating lymphocytes (TILs) was shown to have a better prognosis, opposite to the trend seen with ER-negative BC. We hypothesized that ER-positive BC with high expression of AR will have fewer TILs and an inferior response to NAC, but with a better prognosis. In both TCGA and METABRIC cohorts, AR expression was significantly higher in ER-positive BCs compared to ER-negatives (p < 0.001, p < 0.001, respectively) and it correlated with ER expression (R = 0.630, R = 0.509, respectively). In ER-positive tumors, AR high tumors enriched UV response down (NES = 2.01, p < 0.001), and AR low tumors enriched DNA repair (NES = −2.02, p < 0.001). AR high tumors were significantly associated with procancer regulatory T-cells, and AR low tumors were associated with anticancer immune cells, such as CD4, CD8, and Gamma-Delta T-cells and memory B-cells in ER-positive BC (p < 0.01). Further, cytolytic activity was significantly lower in AR high BC in both cohorts. Finally, AR high tumors had a significantly lower rate of attaining pathological complete response to NAC (GSE22358), but better survival. In conclusion, our results demonstrated that high AR has fewer tumor infiltrating lymphocytes as well as cytolytic activity and an inferior response to NAC, but better survival in ER-positive BC.

Annexin A1 (ANXA1) is a phospholipid-linked protein involved in inflammation, immune response, and mast cell reactivity. Recently, we reported that ANXA1 is associated with aggressive features of triple-negative breast cancer (TNBC); however, its clinical relevance remains controversial. We hypothesized that human TNBC with high expression of ANXA1 mRNA is associated with pro-cancerous immune cell infiltration, including mast cells, and with an aggressive phenotype. Clinical and RNA-seq data were obtained from The Cancer Genome Atlas (TCGA, n = 1079) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1904). TNBC patients had significantly higher levels of ANXA1 expression compared to the other subtypes in both TCGA and METABRIC cohorts (p < 0.001). ANXA1 protein expression was assessed by immunohistochemistry in Japanese TNBC patient cohort (n = 48), where 17 cases (35.4%) had positive ANXA1 staining, and their overall survival was significantly shorter compared with negative staining group (p = 0.008). The CIBERSORT algorithm was used to calculate immune cell infiltrations. ANXA1 high tumors were associated with activated mast cells and M2 macrophages (p > 0.01), but did not show any association with tumor heterogeneity nor cytolytic activity. High expression of ANXA1 group enriched inflammation, epithelial-to-mesenchymal transition (EMT), and angiogenesis-related genes in a gene set enrichment assay in both cohorts. To our knowledge, this is the first study to demonstrate that ANXA1 is associated with infiltration of mast cells and inflammation that is associated with the aggressive phenotype of TNBC, such as EMT and angiogenesis.

Background The purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses. Results Here we present SetRank, an advanced GSEA algorithm which is able to eliminate many false positive hits. The key principle of the algorithm is that it discards gene sets that have initially been flagged as significant, if their significance is only due to the overlap with another gene set. The algorithm is explained in detail and its performance is compared to that of other methods using objective benchmarking criteria. Furthermore, we explore how sample source bias can affect the results of a GSEA analysis. Conclusions The benchmarking results show that SetRank is a highly specific tool for GSEA. Furthermore, we show that the reliability of results can be improved by taking sample source bias into account. SetRank is available as an R package and through an online web interface.

Acute myocardial infarction (AMI) has the characteristics of sudden onset, rapid progression, poor prognosis, and so on. Therefore, it is urgent to identify diagnostic and prognostic biomarkers for it. Cuproptosis is a new form of mitochondrial respiratory-dependent cell death. However, studies are limited on the clinical significance of cuproptosis-related genes (CRGs) in AMI. In this study, we systematically assessed the genetic alterations of CRGs in AMI by bioinformatics approach. The results showed that six CRGs (LIAS, LIPT1, DLAT, PDHB, MTF1, and GLS) were markedly differentially expressed between stable coronary heart disease (stable_CAD) and AMI. Correlation analysis indicated that CRGs were closely correlated with N6-methyladenosine (m6A)-related genes through R language “corrplot” package, especially GLS was positively correlated with FMR1 and MTF1 was negatively correlated with HNRNPA2B1. Immune landscape analysis results revealed that CRGs were closely related to various immune cells, especially GLS was positively correlated with T cells CD4 memory resting and negatively correlated with monocytes. Kaplan–Meier analysis demonstrated that the group with high DLAT expression had a better prognosis. The area under curve (AUC) certified that GLS had good diagnostic value, in the training set (AUC = 0.87) and verification set (ACU = 0.99). Gene set enrichment analysis (GSEA) suggested that GLS was associated with immune- and hypoxia-related pathways. In addition, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, competing endogenous RNA (ceRNA) analysis, transcription factor (TF), and compound prediction were performed to reveal the regulatory mechanism of CRGs in AMI. Overall, our study can provide additional information for understanding the role of CRGs in AMI, which may provide new insights into the identification of therapeutic targets for AMI.

Gene set enrichment analysis (GSEA) tools can identify biological insights within gene expression-based studies. Although their statistical performance has been compared, the downstream biological implications that arise when choosing between the range of pairwise or single sample forms of GSEA methods remain understudied. We compare the statistical and biological results obtained from various pre-ranking methods/options for pairwise GSEA, followed by a stand-alone comparison of GSEA, single sample GSEA (ssGSEA) and gene set variation analysis (GSVA). Pairwise GSEA and fGSEA provide similar results when deployed using a range of gene pre-ranking methods. However, pairwise GSEA can overgeneralise biological enrichment, as when the most statistically significant signatures were assessed using single sample approaches, there was a complete absence of biological distinction between these groups. To avoid these issues, we developed a new dualGSEA tool, which provides users with multiple statistics and visuals to aid interpretation of results. This new tool removes the possibility of users inadvertently interpreting statistical findings as equating to biological distinction between samples within groups-of-interest. dualGSEA provides a more robust basis for discovery research, one which allows user to compare both statistical significance alongside biological distinctions in their data.

Ginseng rusty root symptoms (GRS) is a primary disease of ginseng, which seriously decreases the yield and quality of ginseng and causes enormous losses to ginseng production. GRS prevention and control is still challenging due to its unclear etiology. In this study, the phloem tissue of healthy Panax ginseng (AG), the nonred tissue of the phloem epidermis around the lesion (BG), and the red lesion site tissue of GRS (CG) were extracted for mRNA transcriptomic analysis; 35,958 differentially expressed genes (DEGs) were identified and were associated with multiple stress resistance pathways, reactive oxygen species (ROS), and iron ion binding. Further study showed that the contents of O2•‐, H2O2, and malondialdehyde (MDA) were significantly increased in BG and CG tissues. Under anaerobic conditions caused by excessive soil moisture, the overproduction of ROS destroys cell membranes, simultaneously converting Fe2+ to Fe3+ and depositing it in the cell wall, which results in GRS, as evidenced by the success of the GRS induction test.