Explore the vast range of titles available.

Background Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). Results We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z -score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z -score, PLAGE and ssGSEA can be unstable when less data are available ( N S  < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy. Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis. The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD. The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy.

Background The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. Results In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method P athway A nalysis with D own-weighting of O verlapping G enes ( PADOG ). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. Conclusions PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/ or http://www.bioconductor.org .

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine–adenine–guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network‐based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein–protein interaction (PPI) network. These hub genes were typically down‐regulated. Additionally, DEGs‐transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG‐microRNA (miRNA) interactions (e.g. hsa‐miR‐124‐3p and has‐miR‐26b‐5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence‐specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16‐Epoxy‐25‐hydroxy‐17‐cheilanthen‐19,25‐olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Background Tumor immune microenvironment (TIME) plays a crucial role in cancer development. However, the prognostic significance of immune-related genes (IRGs) in thyroid cancer (THCA) is unclear. Methods The Cancer Genome Atlas (TCGA)-THCA dataset was downloaded. The CIBERSORT algorithm was used to determine immune cell infiltration and a Weighted Gene Co-expression Network Analysis (WGCNA) was executed to obtain immune cell-related genes. Univariate Cox analysis was performed to screen prognostic genes and THCA samples were categorized into different immune cell-related clusters. The correlations between clusters and THCA prognosis and clinical characteristics were explored. Differentially expressed genes (DEGs) between THCA and controls from TCGA-THCA were identified. Macrophage and lymphocyte abundances, IFN-γ, wound healing, and TGF-beta levels were determined using the single set gene set enrichment analysis (GSEA), and THCA samples were categorized into different immune-related clusters, and corresponding genes were obtained from WGCNA. DEGs, IRGs, and immune-related clusters genes were subjected to overlap analysis to obtain differentially expressed IRGs (DE-IRGs), and these were subjected to least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses to identify prognosis-related genes. THCA samples were divided into high/low-risk groups based on the median risk score. Furthermore, the prognostic model’s utility in predicting immunotherapy response was analyzed. The potential therapeutic drugs were obtained. The expression of the corresponding genes in 10 pairs of clinical specimens was evaluated and those of proteins were analyzed by immunofluorescence assay. Results TCGA-THCA samples were categorized into two immune cell-related clusters based on 141 prognostic immune cell-related genes. Significant differences in survival and clinical characteristics such as T Stage between clusters. In total, 16,648 DEGs between THCA and control samples were extracted. THCA samples were categorized into two immune-related clusters and were found to affect the prognosis and TIME of THCA. By using LASSO and multivariate Cox analyses for 88 DE-IRGs, three prognostic IRGs, namely FLNC, IL18, and MMP17 were identified. The TIDE score of the low-risk group was significantly lower than that of the other one, indicating that these samples were more responsive to immunotherapy. The 50% inhibitory concentration (IC50) of camptothecin, methotrexate, rapamycin, and others were notably different between the risk groups. Conclusion Based on bioinformatics analysis, we constructed an immune-related prognosis model for THCA, which is expected to provide new ideas for studies related to the prognosis and treatment of THCA.

IntroductionGut microbiota is now considered to be a hidden organ that interacts bidirectionally with cellular responses in numerous organs belonged to the immune, bone, and nervous systems. Here, we aimed to investigate the relationships between gut microbiota and complex diseases by utilizing multiple publicly available genome-wide association.Materials and methodsWe applied a novel microbiota-related gene set enrichment analysis approach to detect the associations between gut microbiota and complex diseases by processing genome-wide association studies (GWASs) data sets of six autoimmune diseases (including celiac disease (CeD), inflammatory bowel diseases (IBD), multiple sclerosis (MS), primary biliary cirrhosis (PBC), type 1 diabetes (T1D) and primary sclerosing cholangitis (PSC)) and osteoporosis (OP).ResultsThe family Oxalobacteraceae and genus Candidatus_Soleaferrea were found to be correlated with all of the six autoimmune diseases (FDR adjusted P < 0.05). Moreover, we observed that the six autoimmune diseases except PBC shared 3 overlapping features (including family Peptostreptococcaceae, order Gastranaerophilales and genus Romboutsia). For all of the six autoimmune diseases and BMDs (LS-BMD and TB-BMD), an association signal was observed for genus Candidatus_Soleaferrea (FDR adjusted P < 0.05). Notably, FA / FN-BMD shared the maximum number of overlapping microbial features (e.g., genus Ruminococcaceae_UCG009, Erysipelatoclostridium and Ruminococcaceae_UCG013).ConclusionOur study found that part of the gut microbiota could be novel regulators of BMDs and autoimmune diseases via the effects of its metabolites and may lead to a better understanding of the role played by gut microbiota in the communication of the microbiota-skeletal/immune-gut axis.

Spinal cord injury (SCI) is a neurological injury that can cause neuronal loss around the lesion site and leads to locomotive and sensory deficits. However, the underlying molecular mechanisms remain unclear. This study aimed to verify differential gene time-course expression in SCI and provide new insights for gene-level studies. We downloaded two rat expression profiles (GSE464 and GSE45006) from the Gene Expression Omnibus database, including 1 day, 3 days, 7 days, and 14 days post-SCI, along with thoracic spinal cord data for analysis. At each time point, gene integration was performed using “batch normalization.” The raw data were standardized, and differentially expressed genes at the different time points versus the control were analyzed by Gene Ontology enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis. A protein-protein interaction network was then built and visualized. In addition, ten hub genes were identified at each time point. Among them, Gnb5 , Gng8 , Agt , Gnai1 , and Psap lack correlation studies in SCI and deserve further investigation. Finally, we screened and analyzed genes for tissue repair, reconstruction, and regeneration and found that Anxa1 , Snap25 , and Spp1 were closely related to repair and regeneration after SCI. In conclusion, hub genes, signaling pathways, and regeneration genes involved in secondary SCI were identified in our study. These results may be useful for understanding SCI-related biological processes and the development of targeted intervention strategies.