Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Identification of genomic regions and genes associated with subclinical ketosis in periparturient dairy cows.

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

Application of Graph Models to the Identification of Transcriptomic Oncometabolic Pathways in Human Hepatocellular Carcinoma.

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders.

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

Comprehensive proteomic profiling of lung adenocarcinoma: development and validation of an innovative prognostic model.

Background: Lung adenocarcinoma (LUAD), a global leading cause of cancer deaths, remains inadequately addressed by current protein biomarkers. Our study focuses on developing a protein-based risk signature for improved prognosis of LUAD. Methods: We employed the least absolute shrinkage and selection operator (LASSO)-COX algorithm on The Cancer Genome Atlas database to construct a prognostic model incorporating six proteins (CD49B, UQCRC2, SMAD1, FOXM1, CD38, and KAP1). The model's performance was assessed using principal component, Kaplan-Meier (KM), and receiver operating characteristic (ROC) analysis, indicating strong predictive capability. The model stratifies LUAD patients into distinct risk groups, with further analysis revealing its potential as an independent prognostic factor. Additionally, we developed a predictive nomogram integrating clinicopathologic factors, aimed at assisting clinicians in survival prediction. Gene set enrichment analysis (GSEA) and examination of the tumor immune microenvironment were conducted, highlighting metabolic pathways in high-risk genes and immune-related pathways in low-risk genes, indicating varied immunotherapy sensitivity. Validation through immunohistochemistry from the Human Protein Atlas (HPA) database and immunofluorescence staining of clinical samples was performed, particularly focusing on CD38 expression. Results: Our six-protein model (CD49B, UQCRC2, SMAD1, FOXM1, CD38, KAP1) effectively categorized LUAD patients into high and low-risk groups, confirmed by principal component, KM, and ROC analyses. The model showed high predictive accuracy, with distinct survival differences between risk groups. Notably, CD38, traditionally seen as protective, was paradoxically associated with poor prognosis in LUAD, a finding supported by immunohistochemistry and immunofluorescence data. GSEA revealed that high-risk genes are enriched in metabolic pathways, while low-risk genes align with immune-related pathways, suggesting better immunotherapy response in the latter group. Conclusions: This study presented a novel prognostic protein model for LUAD, highlighting the CD38 expression paradox and enhancing our understanding of protein roles in lung cancer progression. It offered new clinical tools for prognosis prediction and provided assistance for future lung cancer pathogenesis research.

irGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

Elucidating the molecular mechanisms of pterostilbene against cervical cancer through an integrated bioinformatics and network pharmacology approach.

Pterostilbene (PTE), a natural phenolic compound, has exhibited promising anticancer properties in the preclinical treatment of cervical cancer (CC). This study aims to comprehensively investigate the potential targets and mechanisms underlying PTE's anticancer effects in CC, thereby providing a theoretical foundation for its future clinical application and development. To accomplish this, we employed a range of methodologies, including network pharmacology, bioinformatics, and computer simulation, with specific techniques such as WGCNA, PPI network construction, ROC curve analysis, KM survival analysis, GO functional enrichment, KEGG pathway enrichment, molecular docking, MDS, and single-gene GSEA. Utilizing eight drug target prediction databases, we have identified a total of 532 potential targets for PTE. By combining CC-related genes from the GeneCards disease database with significant genes derived from WGCNA analysis of the GSE63514 dataset, we obtained 7915 unique CC-related genes. By analyzing the intersection of the 7915 CC-related genes and the 2810 genes that impact overall survival time in CC, we identified 690 genes as crucial for CC. Through the use of a Venn diagram, we discovered 36 overlapping targets shared by PTE and CC. We have constructed a PPI network and identified 9 core candidate targets. ROC and KM curve analyses subsequently revealed IL1B, EGFR, IL1A, JUN, MYC, MMP1, MMP3, and ANXA5 as the key targets modulated by PTE in CC. GO and KEGG pathway enrichment analyses indicated significant enrichment of these key targets, primarily in the MAPK and IL-17 signaling pathways. Molecular docking analysis verified the effective binding of PTE to all nine key targets. MDS results showed that the protein-ligand complex between MMP1 and PTE was the most stable among the nine targets. Additionally, GSEA enrichment analysis suggested a potential link between elevated MMP1 expression and the activation of the IL-17 signaling pathway. In conclusion, our study has identified key targets and uncovered the molecular mechanism behind PTE's anticancer activity in CC, establishing a firm theoretical basis for further exploration of PTE's pharmacological effects in CC therapy.

Prospective association of the infant gut microbiome with social behaviors in the ECHO consortium.

BACKGROUND: Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. METHODS: Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. RESULTS: Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. LIMITATIONS: The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. CONCLUSIONS: Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT).

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation.

DhuFAP: a platform for gene functional analysis in Dendrobium huoshanense.

BACKGROUND: Dendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense. RESULT: To address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS). CONCLUSION: DhuFAP ( www.gzybioinformatics.cn/DhuFAP ) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain.

bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Identifying COVID-19 subtypes by single-sample gene set enrichemnt analysis and providing guidance for sensitive drug selection.

This study aimed at using single-sample gene set enrichment analysis scores to cluster naso/pharyngeal swab specimen samples from coronavirus disease 2019 (COVID-19) patients into two clusters. One cluster with higher fractions of immune cells and more active inflammatory-related pathways was called the Immunity-High (Immunity-H) group, and the other one was called the Immunity-Low group. We explored impacts of the method on COVID-19 treatment. First, given that the Immunity-H group was mainly enriched in inflammatory-related pathways and had higher fractions of inflammatory cells, the Immunity-H group may obtain more curative effects from anti-inflammatory treatment. Second, we searched some hot genes from the PubMed platform that had been studied by researchers and found these genes upregulated in the Immunity-H group, so we speculated the Immunity-H group and Immunity-Low group may have different curative effects from drugs targeting these genes. Finally, we screened out hub genes for the Immunity-H group and predicted potential drugs for these hub genes by a public data set (http://dgidb.genome.wustl.edu). These hub genes are significantly upregulated in the Immunity-H group and neutrophils so that the Immunity-H group may obtain different treatment results from potential drugs compared with the Immunity-Low group. Therefore, the cluster method may provide help in drug development and administration for COVID-19 patients.

Loss of chromosome Y in regulatory T cells.

BACKGROUND: Mosaic loss of chromosome Y (LOY) in leukocytes is the most prevalent somatic aneuploidy in aging humans. Men with LOY have increased risks of all-cause mortality and the major causes of death, including many forms of cancer. It has been suggested that the association between LOY and disease risk depends on what type of leukocyte is affected with Y loss, with prostate cancer patients showing higher levels of LOY in CD4 + T lymphocytes. In previous studies, Y loss has however been observed at relatively low levels in this cell type. This motivated us to investigate whether specific subsets of CD4 + T lymphocytes are particularly affected by LOY. Publicly available, T lymphocyte enriched, single-cell RNA sequencing datasets from patients with liver, lung or colorectal cancer were used to study how LOY affects different subtypes of T lymphocyte. To validate the observations from the public data, we also generated a single-cell RNA sequencing dataset comprised of 23 PBMC samples and 32 CD4 + T lymphocytes enriched samples. RESULTS: Regulatory T cells had significantly more LOY than any other studied T lymphocytes subtype. Furthermore, LOY in regulatory T cells increased the ratio of regulatory T cells compared with other T lymphocyte subtypes, indicating an effect of Y loss on lymphocyte differentiation. This was supported by developmental trajectory analysis of CD4 + T lymphocytes culminating in the regulatory T cells cluster most heavily affected by LOY. Finally, we identify dysregulation of 465 genes in regulatory T cells with Y loss, many involved in the immunosuppressive functions and development of regulatory T cells. CONCLUSIONS: Here, we show that regulatory T cells are particularly affected by Y loss, resulting in an increased fraction of regulatory T cells and dysregulated immune functions. Considering that regulatory T cells plays a critical role in the process of immunosuppression; this enrichment for regulatory T cells with LOY might contribute to the increased risk for cancer observed among men with Y loss in leukocytes.

Spinster Homologue 2 Expression Correlates With Improved Patient Survival in Hepatocellular Carcinoma Despite Association With Lymph-Angiogenesis.

Background: Spinster homologue 2 (SPNS2) is a transporter of sphingosine-1-phosphate (S1P), a bioactive lipid linked to cancer progression. We studied the link between SPNS2 gene expression, tumor aggressiveness, and outcomes in patients with hepatocellular carcinoma (HCC). Methods: Gene expression in patients with HCC was analyzed from the Cancer Genome Atlas (TCGA) (n = 350) and GSE76427 (n = 115) as a validation cohort, as well as liver tissue cohort GSE6764 (n = 75). Results: High-SPNS2 HCC was significantly associated with high level of lymph-angiogenesis-related factors. SPNS2 expression was significantly higher in normal liver and early HCC versus advanced HCC (P < 0.02). High SPNS2 levels enriched immune response-related gene sets; inflammatory, interferon (IFN)-α, IFN-γ responses, and tumor necrosis factor (TNF)-α, interleukin (IL)-6/Janus kinase/signal transducer and activator of transcription (JAK/STAT3) signaling, complement and allograft rejection, but did not significantly infiltrate specific immune cells nor cytolytic activity score. High-SPNS2 HCC enriched tumor aggravating pathway gene sets such as KRAS (Kirsten rat sarcoma virus) signaling, but inversely correlated with Nottingham histological grade, MKI67 (marker of proliferation Ki-67) expression, and cell proliferation-related gene sets. Further, high-SPNS2 HCC had significantly high infiltration of stromal cells, showing that low-SPNS2 HCC is highly proliferative. Finally, high-SPNS2 HCC was associated with better disease-free, disease-specific, and overall survival (P = 0.031, 0.046, and 0.040, respectively). Conclusions: Although SPNS2 expression correlated with lymph-angiogenesis and other cancer-promoting pathways, it also enriched immune response. SPNS2 levels were higher in normal liver compared to HCC, and inversely correlated with cancer cell proliferation and better survival. SPNS2 expression may be beneficial in HCC patients despite detrimental in-vitro effects.

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

CXCL10 and its receptor in patients with chronic hepatitis B and their ability to predict HBeAg seroconversion during antiviral treatment with TDF.

The serum chemokine C-X-C motif ligand-10 (CXCL10) and its unique receptor (CXCR3) may predict the prognosis of patients with chronic hepatitis B (CHB) treated with tenofovir disoproxil fumarate (TDF). Nevertheless, there are few reports on the profile of CXCL10 and CXCR3 and their clinical application in HBeAg (+) CHB patients during TDF antiviral therapy. CXCL10 and CXCR3 were determined in 118 CHB patients naively treated with TDF for at least 96 weeks at baseline and at treatment weeks 12 and 24. In addition, gene set enrichment analysis was used to examine the associated dataset from Gene Expression Omnibus and explore the gene sets associated with HBeAg seroconversion (SC). The change of CXCL10 (ΔCXCL10, baseline to 48-week TDF treatment) and CXCR3 (ΔCXCR3) is closely related to the possibility of HBeAg SC of CHB patients under TDF treatment. Immunohistochemical analysis of CXCL10/CXCR3 protein in liver tissue shows that there is a significant difference between paired liver biopsy samples taken before and after 96 weeks of successful TDF treatment of CHB patients (11 pairs) but no significance for unsuccessful TDF treatment (14 pairs). Multivariate Cox analysis suggests that the ΔCXCL10 is an independent predictive indicator of HBeAg SC, and the area under the receiver operating characteristic curve of the ΔCXCL10 in CHB patients is 0.8867 (p < 0.0001). Our results suggest that a lower descending CXCL10 level is associated with an increased probability of HBeAg SC of CHB patients during TDF therapy. Moreover, liver tissue CXCL10 might be involved in the immunological process of HBeAg SC.

Benchmarking enrichment analysis methods with the disease pathway network.

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Platelet RNA sequencing reveals profile of caffeic acid affecting hemostasis in mice.

Background: Caffeic acid (CA) is a naturally occurring phenolic compound with diverse pharmacologic properties. CA plays a crucial role in hemostasis by increasing platelet count. However, the mechanism by which CA regulates platelets to promote hemostasis remains unclear. Objectives: We aim to identify the potential target pathways and genes by which CA regulates platelets to promote hemostasis. Methods: We performed RNA sequencing (RNA-seq) analysis of mouse platelet pools in both the CA-gavaged group and phosphate-buffered saline-gavaged group. Results: The 12,934 expressed transcripts had been annotated after platelet RNA-seq. Compared with the phosphate-buffered saline group, 987 differentially expressed genes (DEGs) were identified, of which 466 were downregulated and 521 were upregulated in CA group. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome gene set enrichment analysis demonstrated that upregulated DEGs were enriched in the pathways of hemostasis, platelet activation, signaling, aggregation, and degranulation. Moreover, Kyoto Encyclopedia of Genes and Genomes and Reactome gene set enrichment analysis revealed that 5 of the 25 cosignificantly upregulated DEGs were essential in CA-mediated platelet regulation to promote hemostasis. Conclusion: Our findings of platelet RNA-seq analysis demonstrate that CA regulates the gene expression of hemostasis and platelet activation-related pathways to increase platelet count and promote hemostasis. It will also provide reference molecular resources for future research on the function and mechanism by which CA regulates platelets to promote hemostasis.

Integrated Multi-Omics Analysis and Validation in Yeast Model of Amyotrophic Lateral Sclerosis.

Transcriptomics is a complex process that involves raw data extraction, normalization, differential gene expression, and analysis. The Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) is a repository of experimental datasets. Amyotrophic lateral sclerosis (ALS) datasets are deposited by various scientists and research investigators to expand the horizon of scientific knowledge. R-statistical tools are the most common ways for conducting these kinds of studies. The first step is the identification of appropriate datasets. Since the raw data is available in a variety of formats, a large array of software is used for extraction and analysis. Normalization is conducted for the datasets using NetworkAnalyst. Differential analysis is further conducted on the normalized data to identify significantly enriched genes. The significant genes are then grouped into pathways. The results were validated using yeast model of ALS in which the yeast is transformed with ALS plasmids encoding genes associated with ALS. The resulting GFP-tagged protein aggregates are imaged using fluorescence microscopy and subsequently validated using filter retardation assay and quantified using ImageJ software. Functional role of different genes is studied using metabolite treatment and knockout studies.