MGSEA - a multivariate Gene set enrichment analysis

Autocrine phosphatase PDP2 inhibits ferroptosis by dephosphorylating ACSL4 in the Luminal A Breast Cancer

Phosphatases can dephosphorylate phosphorylated kinases, leading to their inactivation, and ferroptosis is a type of cell death. Therefore, our aim is to identify phosphatases associated with ferroptosis by analyzing the differentially expressed genes (DEGs) of the Luminal A Breast Cancer (LumABC) cohort from the Cancer Genome Atlas (TCGA). An analysis of 260 phosphatase genes from the GeneCard database revealed that out of the 28 DEGs with high expression, only the expression of pyruvate dehydrogenase phosphatase 2 (PDP2) had a significant correlation with patient survival. In addition, an analysis of DEGs using gene ontology, Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analysis revealed a significant variation in the expression of ferroptosis-related genes. To further investigate this, we analyzed 34 ferroptosis-related genes from the TCGA-LumABC cohort. The expression of long-chain acyl-CoA synthetase 4 (ACSL4) was found to have the highest correlation with the expression of PDP2, and its expression was also inversely proportional to the survival rate of patients. Western blot experiments using the MCF-7 cell line showed that the phosphorylation level of ACSL4 was significantly lower in cells transfected with the HA-PDP2 plasmid, and ferroptosis was correspondingly reduced (p < 0.001), as indicated by data from flow cytometry detection of membrane-permeability cell death stained with 7-aminoactinomycin, lipid peroxidation, and Fe2+. Immunoprecipitation experiments further revealed that the phosphorylation level of ACSL4 was only significantly reduced in cells where PDP2 and ACSL4 co-precipitated. These findings suggest that PDP2 may act as a phosphatase to dephosphorylate and inhibit the activity of ACSL4, which had been phosphorylated and activated in LumABC cells. Further experiments are needed to confirm the molecular mechanism of PDP2 inhibiting ferroptosis.

DEPDC1B-mediated USP5 deubiquitination of β-catenin promotes breast cancer metastasis by activating the wnt/β-catenin pathway

Breast cancer has become the malignant disease with the highest morbidity and mortality among female cancer patients. The prognosis of metastatic breast cancer is very poor, and the therapeutic effects still need to be improved. The molecular mechanism of breast cancer has not been fully clarified. Bioinformatics analysis was used to find the differentially expressed gene that affects the occurrence and development of breast cancer. Furthermore, scratch assays, Transwell assays, immunofluorescence, and Western blotting were used to determine the biological behavior of breast cancer cells affected by DEP domain-containing protein 1B (DEPDC1B). The molecular mechanism was investigated by mass spectrometry analysis, coimmunoprecipitation, and ubiquitin assays. Here, we found that DEPDC1B was highly expressed in breast cancer cells and tissues and was associated with lower overall survival (OS) in patients. We found that DEPDC1B interference significantly inhibited tumor invasion and migration in vitro and tumor metastasis in vivo. Mechanistically, DEPDC1B was first shown to activate the wnt/β-catenin signaling pathway as an oncogene in breast cancer cells. In addition, we also confirmed the interaction between DEPDC1B, ubiquitin-specific protease 5 (USP5), and β-catenin. Then, we found that DEPDC1B mediates the deubiquitination of β-catenin via USP5, which promotes cell invasion and migration. Our findings provide new insights into the carcinogenic mechanism of DEPDC1B, suggesting that DEPDC1B can be considered a potential therapeutic target for breast cancer.NEW & NOTEWORTHY By using bioinformatics analysis and the experimental techniques of cell biology and molecular biology, we found that DEP domain-containing protein 1B (DEPDC1B) can promote the invasion and migration of breast cancer cells and that DEPDC1B mediates the deubiquitination of β-catenin by ubiquitin-specific protease 5 (USP5), thus activating the wnt/β-catenin pathway. Our findings provide new insights into the carcinogenic mechanism of DEPDC1B, suggesting that DEPDC1B can be used as a potential therapeutic target for breast cancer.

Klarigi: Characteristic explanations for semantic biomedical data

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

WINNER: A network biology tool for biomolecular characterization and prioritization

Background and contribution

In network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

Results

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

Conclusion

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

A genomic instability-related lncRNA model for predicting prognosis and immune checkpoint inhibitor efficacy in breast cancer

Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan–Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.

leapR: An R Package for Multiomic Pathway Analysis

A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

Gene Set Analysis: Challenges, Opportunities, and Future Research

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits

Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits-subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year-olds, by conducting a methylome-wide association study (MWAS). We did not identify significant CpGs associated with SCDC. The most significant CpG site was cg14379490, on chromosome 9 (MWAS beta = - 1.78 ± 0.35, p value = 5.34 × 10 -7 ). Using methylation data for autism in peripheral tissues, we did not identify a significant concordance in effect direction of CpGs with p value < 10-4 in the SCDC MWAS (binomial sign test, p value > 0.5). In contrast, using methylation data for autism from post-mortem brain tissues, we identify a significant concordance in effect direction of CpGs with a p value < 10-4 in the SCDC MWAS (binomial sign test, p value = 0.004). Supporting this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain (one-sided Wilcoxon rank-sum test, p value = 6.22 × 10-5). Finally, integrating genome-wide association study (GWAS) data for autism (n = 46,350) with mQTL maps from cord-blood (n = 771), we demonstrate that mQTLs of CpGs associated with SCDC scores at p value thresholds of 0.01 and 0.005 are significantly shifted toward lower p values in the GWAS for autism (p < 5 × 10-3). We provide additional support for this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimer's disease. Our results highlight the shared cross-tissue methylation architecture of autism and autistic traits, and demonstrate that mQTLs associated with differences in DNA methylation associated with childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits.