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Highlights: [•] We assessed concentration-dependent gene expression changes to four phthalates. [•] We derived a 97-gene set for toxicity potency ranking of phthalate monoesters. [•] The gene set describes and predicts developmental toxicity of phthalates. [•] Gene expression results are comparable to those using the classical ID50. [Copyright &y& Elsevier]

Abstract: Gene set analysis (GSA) incorporates biological information into statistical knowledge to identify gene sets differently expressed between two or more phenotypes. It allows us to gain an insight into the functional working mechanism of cells beyond the detection of differently expressed gene sets. In order to evaluate the competence of GSA approaches, three self-contained GSA approaches with different statistical methods were chosen; Category, Globaltest and Hotelling's T 2 together with their assayed power to identify the differences expressed via simulation and real microarray data. The Category does not take care of the correlation structure, while the other two deal with correlations. In order to perform these methods, R and Bioconductor were used. Furthermore, venous thromboembolism and acute lymphoblastic leukemia microarray data were applied. The results of three GSAs showed that the competence of these methods depends on the distribution of gene expression in a dataset. It is very important to assay the distribution of gene expression data before choosing the GSA method to identify gene sets differently expressed between phenotypes. On the other hand, assessment of common genes among significant gene sets indicated that there was a significant agreement between the result of GSA and the findings of biologists. [Copyright &y& Elsevier]

Abstract: The sequencing of the rice genome is one of the major achievements in plant science with direct impact on improving the staple food for half the world's population. The high-quality and precise map-based sequence of Oryza sativa ssp. japonica ‘Nipponbare’ provides a valuable resource for characterization of many biological processes with direct roles in agricultural productivity and offers great opportunities for comparative genomic studies among thousands of rice cultivars and between rice and other taxa. The most recently updated reference sequence, now referred to as Os-Nipponbare-Reference-IRGSP-1.0, consists of 37,869 loci including 35,679 protein-coding and 2190 non-protein-coding loci. The high-quality genome sequence and annotation of rice and Arabidopsis, which are widely accepted models for monocots and dicots, offer evidence on similarities and differences of the two major groups of higher plant species that could be used in understanding the most basic features that define a plant. The genus Oryza also includes a wide range of species of various genome sizes reflecting a diversity that could provide genetic resources for breeding improved cultivars. Comparative analysis of genome organization including the genes, intergenic regions and transposable elements within the genus Oryza may yield key insights into genome evolution, speciation and domestication. [Copyright &y& Elsevier]

The influence of adjacent non-tumor tissue characteristics on patient outcomes in head and neck squamous cell carcinoma (HNSC) remains unclear. In HNSC, subtype identification is generally tumor-based, and most prognosis-related studies focus on a single gene set. Here, we performed Gene Set Variation Analysis to comprehensively evaluate variations in diverse gene sets in tumor and non-tumor samples and converted a gene-centric matrix into a pathway-centric model. Three different prognostic subtypes correlated with previously identified subgroups, clinicopathologic features, risk factors, and tumor microenvironment (TME) were identified using the non-negative matrix factorization method. We also screened 2 and 11 gene sets from nontumor and tumor tissues, respectively based on representative gene sets. Interestingly, genes from nontumor gene sets were associated with serotonin secretion and P2Y receptors, while genes from tumor gene sets were associated with immunity and inflammation. The PGSscore was constructed to predict outcomes and immunotherapy responses. Low- and high- PGSscore groups showed significant differences in clinicopathological characteristics and TME. Our analysis indicates that the non-tumor tissue is indispensable for prognosis. PGSscore provides new avenue to evaluate overall survival and immunotherapy responses, and our method based on pathway-centric models can be extended to other diseases., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Background: The genetic and molecular basis for many intermediate and end stage phenotypes in model systems such as C. elegans and D. melanogaster has long been known to involve pleiotropic effects and complex multigenic interactions. Gene sets are groups of genes that contribute to multiple biological or molecular phenomena. They have been used in the analysis of large molecular datasets such as microarray data, Next Generation sequencing, and other genomic datasets to reveal pleiotropic and multigenic contributions to phenotypic outcomes. Many model systems lack species specific organized phenotype based gene sets to enable high throughput analysis of large molecular datasets. Results and discussion: Here, we describe two novel collections of gene sets in C. elegans and D. melanogaster that are based exclusively on genetically determined phenotypes and use a controlled phenotypic ontology. We use these collections to build genome-wide models of thousands of defined phenotypes in both model species. In addition, we demonstrate the utility of these gene sets in systems analysis and in analysis of gene expression-based molecular datasets and show how they are useful in analysis of genomic datasets connecting multigenic gene inputs to complex phenotypes. Conclusions: Phenotypic based gene sets in both C. elegans and D. melanogaster are developed, characterized, and shown to be useful in the analysis of large scale species-specific genomic datasets. These phenotypic gene set collections will contribute to the understanding of complex phenotypic outcomes in these model systems [ABSTRACT FROM AUTHOR]

Background: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as " contrast data") in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). Results: To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset. Conclusion: Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/. [ABSTRACT FROM AUTHOR]

Abstract: Most “omics” experiments require comprehensive interpretation of the biological meaning of gene lists. To address this requirement, a number of gene set analysis (GSA) tools have been developed. Although the biological value of GSA is strictly limited by the breadth of the gene sets used, very few methods exist for simultaneously analysing multiple publically available gene set databases. Therefore, we constructed GeneSetDB (http://genesetdb.auckland.ac.nz/haeremai.html), a comprehensive meta-database, which integrates 26 public databases containing diverse biological information with a particular focus on human disease and pharmacology. GeneSetDB enables users to search for gene sets containing a gene identifier or keyword, generate their own gene sets, or statistically test for enrichment of an uploaded gene list across all gene sets, and visualise gene set enrichment and overlap using a clustered heat map. [Copyright &y& Elsevier]

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms. [Copyright &y& Elsevier]

ABSTRACT High-density single nucleotide polymorphism (SNP) microarrays have made large-scale genome-wide association studies (GWAS) and genomic selection (GS) feasible. Valuable insight into the genetic basis underlying complex polygenic traits will likely be gained by considering functionally related sets of genes simultaneously. SNPpath, a suite of computer-generated imagery-based web servers has been developed to automatically annotate and characterize cattle SNPs by enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms. The SNPpath allows users to navigate and analysis large SNP sets and is the only web server currently providing pathway annotations of cattle SNPs in National Center for Biotechnology Information's dbSNP database and three commercial platforms. Hence, we describe SNPpath and provide details of the query options, as well as biological examples of use. The SNPpath may be favorable for the analysis of combining SNP association analysis with pathway-driven gene set enrichment analysis and is freely available at . [ABSTRACT FROM AUTHOR]

Background: Genome-wide expression profiling using microarrays or sequence-based technologies allows us to identify genes and genetic pathways whose expression patterns influence complex traits. Different methods to prioritize gene sets, such as the genes in a given molecular pathway, have been described. In many cases, these methods test one gene set at a time, and therefore do not consider overlaps among the pathways. Here, we present a Bayesian variable selection method to prioritize gene sets that overcomes this limitation by considering all gene sets simultaneously. We applied Bayesian variable selection to differential expression to prioritize the molecular and genetic pathways involved in the responses to Escherichia coli infection in Danish Holstein cows. Results: We used a Bayesian variable selection method to prioritize Kyoto Encyclopedia of Genes and Genomes pathways. We used our data to study how the variable selection method was affected by overlaps among the pathways. In addition, we compared our approach to another that ignores the overlaps, and studied the differences in the prioritization. The variable selection method was robust to a change in prior probability and stable given a limited number of observations. Conclusions: Bayesian variable selection is a useful way to prioritize gene sets while considering their overlaps. Ignoring the overlaps gives different and possibly misleading results. Additional procedures may be needed in cases of highly overlapping pathways that are hard to prioritize. [ABSTRACT FROM AUTHOR]

In this study, the BALB/c and Qs mouse responses to infection by the parasite Neospora caninum were investigated in order to identify host response mechanisms. Investigation was done using gene set (enrichment) analyses of microarray data. GSEA, MANOVA, Romer, subGSE and SAM-GS were used to study the contrasts Neospora strain type, Mouse type (BALB/c and Qs) and time post infection (6 hours post infection and 10 days post infection). The analyses show that the major signal in the core mouse response to infection is from time post infection and can be defined by gene ontology terms Protein Kinase Activity, Cell Proliferation and Transcription Initiation. Several terms linked to signaling, morphogenesis, response and fat metabolism were also identified. At 10 days post infection, genes associated with fatty acid metabolism were identified as up regulated in expression. The value of gene set (enrichment) analyses in the analysis of microarray data is discussed. [ABSTRACT FROM AUTHOR]

Abstract: Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis. [Copyright &y& Elsevier]

The article offers information on SNPknow, a CGI-based web server for functional annotation for cattle single nucleotide polymorphism (SNP) markers. The web server, which was developed to meet the immediate need for a framework of genome-wide association study (GAWAS), provides enrichment analysis for SNP microarray technology. The significance of SNPknow on the analysis of combining SNP association analysis with the gene enrichment analysis is mentioned.

We generated gene expression data from the tissues of 50 gastric cancer patients, and applied meta-analysis and gene set analysis to this data and three other stomach cancer gene expression data sets to define the gene expression changes in gastric tumors. By meta-analysis we identified genes consistently changed in gastric carcinomas, while gene set analysis revealed consistently changed biological themes. Genes and gene sets involved in digestion, fatty acid metabolism, and ion transport were consistently down-regulated in gastric carcinomas, while those involved in cellular proliferation, cell cycle, and DNA replication were consistently up-regulated. We also found significant differences between the genes and gene sets expressed in diffuse and intestinal type gastric carcinoma. By gene set analysis of cytogenetic bands, we identified many chromosomal regions with possible gross chromosomal changes (amplifications or deletions). Similar analysis of transcription factor binding sites (TFBSs), revealed transcription factors that may have caused the observed gene expression changes in gastric carcinomas, and we confirmed the overexpression of one of these, E2F1, in many gastric carcinomas by tissue array and immunohistochemistry. We have incorporated the results of our metaand gene set analyses into a web accessible database (http://human-genome.kribb.re.kr/stomach/). [ABSTRACT FROM AUTHOR]

Maternal effect genes (MEGs) encode factors (e.g., RNA) in the oocyte that control embryonic development prior to activation of the embryonic genome. Over 80 mammalian MEGs have been identified, including several that have been associated with phenotypes in humans. Maternal variation in MEGs is associated with a range of adverse outcomes, which, in humans, include hydatidiform moles, zygotic cleavage failure, and offspring with multi-locus imprinting disorders. In addition, data from both animal models and humans suggest that the MEGs may be associated with structural birth defects such as congenital heart defects (CHDs). To further investigate the association between MEGs and CHDs, we conducted gene-level and gene-set analyses of known mammalian MEGs (n = 82) and two common groups of CHDs: conotruncal heart defects and left ventricular outflow tract defects. We identified 14 candidate CHD-related MEGs. These 14 MEGs include three ( CDC20 , KHDC3L , and TRIP13 ) of the 11 known human MEGs, as well as one ( DNMT3A ) of the eight MEGs that have been associated with structural birth defects in animal models. Our analyses add to the growing evidence that MEGs are associated with structural birth defects, in particular CHDs. Given the large proportion of individuals with structural birth defects for whom etiology of their condition is unknown, further investigations of MEGs as potential risk factors for structural birth defects are strongly warranted., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data. [ABSTRACT FROM AUTHOR]

Although antibodies targeting the immune checkpoint protein programmed death-1 (PD-1) exert therapeutic effects in patients with primary or metastatic non-small cell lung cancer (NSCLC), the majority of patients exhibit partial or complete resistance to anti-PD1 treatment. Thus, the aim of the present study was to identify reliable biomarkers for predicting the response to anti-PD-1 therapy. The present study analyzed tumor specimens isolated from 24 patients (13 with primary and 11 with metastatic NSCLC) prior to treatment with approved PD1-targeting antibodies. The expression profile of 395 immune-related genes was examined using RNA immune-oncology panel sequencing. The results demonstrated that six immune-related differently expressed genes (DEGs), including HLA-F-AS1, NCF1, RORC, DMBT1, KLRF1 and IL-18, and five DEGs, including HLA-A, HLA-DPA1, TNFSF18, IFI6 and PTK7, may be used as single biomarkers for predicting the efficacy of anti-PD-1 treatment in patients with primary and with metastatic NSCLC, respectively. In addition, two DEG sets comprising either six (HLA-F-AS1, NCF1, RORC, DMBT1, KLRF and IL-18) or two (HLA-A and TNFSF18) DEGs as potential combination biomarkers for predicting the efficacy of anti-PD-1 therapy in patients with NSCLC. Patients with a calculated expression level of the DEG sets >6.501 (primary NSCLC) or >6.741 (metastatic NSCLC) may benefit from the anti-PD-1 therapy. Overall, these findings provided a basis for the identification of additional biomarkers for predicting the response to anti-PD-1 treatment. [ABSTRACT FROM AUTHOR]

Colon cancer stem cells (CSC) identified by cell surface markers CD133, CD24, and CD44, have been shown to be involved with tumor formation, chemotherapy resistance, and the progression of metastatic disease. Using an in silico translational approach, we hypothesize that a combination of these CSC markers has prognostic value in a large cohort of patients with colorectal cancer. Clinicopathologic and RNA expression data from a total of 594 colorectal cancer (CRC) patients from TCGA were analyzed. The expression of CD133, CD24, and CD44 was individually defined as "high" or "low" based on the median expression. Disease specific survival (DSS) and overall survival (OS) were not associated with tumors that are CD133-high or CD44-high alone. Patients with CD24-high tumors have significantly better DSS (P<0.001) and OS (P = 0.043). CD24-high, CD44-high and CD133-high tumors were associated with significantly greater EGFR, KRAS and Ki67 expression (all P<0.001). CD133, CD24 and CD44-high tumors were independently enriched for conventional stemness-related signaling pathways such as Wnt/β-catenin and Hedgehog signaling pathways. There was no survival difference linked to CD133-high/CD44-low patients, but CD44-high/CD24-low patients have worse DSS (P = 0.005) compared with CD44-low/CD24-high patients. CD133-high/CD24-low tumors show significant negative enrichment of MYC targets, E2F targets, G2M checkpoint and mitotic spindle gene sets, suggesting less cell proliferation in these tumors. Patients with CD133-high/CD24-low tumors have worse DSS (P = 0.004) and OS (P = 0.044), and are more likely to have early and late recurrences. In conclusion, we demonstrated that CD133-high/CD24-low tumors may predict colorectal cancer prognosis., Competing Interests: None., (AJCR Copyright © 2021.)

Annexin A1 (ANXA1) is a calcium-dependent phospholipid-binding protein overexpressed in pancreatic cancer (PC). ANXA1 expression has been shown to take part in a wide variety of cancer biology, including carcinogenesis, cell proliferation, invasion, apoptosis, and metastasis, in addition to the initially identified anti-inflammatory effect in experimental settings. We hypothesized that ANXA1 expression is associated with cell proliferation and survival in PC patients. To test this hypothesis, we analyzed 239 PC patients in The Cancer Genome Atlas (TCGA) and GSE57495 cohorts. ANXA1 expression correlated with epithelial–mesenchymal transition (EMT) but weakly with angiogenesis in PC patients. ANXA1-high PC was significantly associated with a high fraction of fibroblasts and keratinocytes in the tumor microenvironment. ANXA1 high PC enriched multiple malignant gene sets, including hypoxia, tumor necrosis factor (TNF)-α signaling via nuclear factor-kappa B (NF-kB), and MTORC1, as well as apoptosis, protein secretion, glycolysis, and the androgen response gene sets consistently in both cohorts. ANXA1 expression was associated with TP53 mutation alone but associated with all KRAS, p53, E2F, and transforming growth factor (TGF)-β signaling pathways and also associated with homologous recombination deficiency in the TCGA cohort. ANXA1 high PC was associated with a high infiltration of T-helper type 2 cells in the TME, with advanced histological grade and MKI67 expression, as well as with a worse prognosis regardless of the grade. ANXA1 expression correlated with a sensitivity to gemcitabine, doxorubicin, and 5-fluorouracil in PC cell lines. In conclusion, ANXA1 expression is associated with EMT, cell proliferation, survival, and the drug response in PC. [ABSTRACT FROM AUTHOR]

Simple Summary: A biomarker to predict survival is a critical need in pancreatic cancer treatment. We hypothesized that a four-gene score, which was previously reported to reflect cell proliferation, can be used as a predictive biomarker for pancreatic cancer. A total of 954 patients were analyzed for both discovery and validation of the four-gene score from publicly available datasets for pancreatic cancer, in order to investigate the relationship between the score and clinical features of pancreatic cancer, such as metastasis, cancer aggressiveness, immune cell infiltration, patient survival, and resectability. We found that the score correlated with clinical aggressiveness in pancreatic cancer, and did so to a higher degree compared to breast cancer cohorts. We found that the four-gene score identified poor survival in pancreatic cancer, and has potential as a predictive biomarker of treatment response in metastatic pancreatic cancer, as well completion of a pathologically complete (R0) resection. Appropriately utilized, the four-gene score could be a valuable prognostic and predictive tool for pancreatic cancer in the future. Pathologically complete (R0) resection is essential for prolonged survival in pancreatic cancer. Survival depends not only on surgical technique, but also on cancer biology. A biomarker to predict survival is a critical need in pancreatic treatment. We hypothesized that this 4-gene score, which was reported to reflect cell proliferation, is a translatable predictive biomarker for pancreatic cancer. A total of 954 pancreatic cancer patients from multiple cohorts were analyzed and validated. Pancreatic cancer had the 10th highest median score of 32 cancers in The Cancer Genome Atlas (TCGA) cohort. The four-gene score significantly correlated with pathological grade and MKI67 expression. The high four-gene score enriched cell proliferation-related and cancer aggressiveness-related gene sets. The high score was associated with activation of KRAS, p53, transforming growth factor (TGF)-β, and E2F pathways, and with high alteration rate of KRAS and CDKN2A genes. The high score was also significantly associated with reduced CD8+ T cell infiltration of tumors, but with high levels of interferon-γ and cytolytic activity in tumors. The four-gene score correlated with the area under the curve of irinotecan and sorafenib in primary pancreatic cancer, and with paclitaxel and doxorubicin in metastatic pancreatic cancer. The high four-gene score was associated with significantly fewer R0 resections and worse survival. The novelty of the study is in the application of the four-gene score to pancreatic cancer, rather than the bioinformatics technique itself. Future analyses of inoperable lesions are expected to clarify the utility of our score as a predictive biomarker of systemic treatments. [ABSTRACT FROM AUTHOR]

Tumor infiltrating immune cells plays a critical role in cancer progression. Apoptosis is an autonomous cell death that counteracts tumor growth. To this end, we hypothesized that increased apoptosis in breast cancer is associated with immune cell killing. Apoptosis score of MSigDB Hallmark collection was used to analyze METABRIC cohort (n=1904) and TCGA (n=1069) as validation cohort. High apoptosis tumors enriched cancer promoting signaling pathways; hypoxia, KRAS, TGF-β, PI3K signaling, and was associated with low MKI67 expression and less cell proliferation gene sets, less homologous recombination defects, and less altered fraction. High apoptosis tumors also enriched angiogenesis and high infiltration of vascular endothelial cells, pericytes and stromal cells and significantly enriched inflammation and immune response-related gene sets and high infiltration of CD8, CD4 memory, dendritic cells, M1 and M2 macrophages and significant elevation of cytolytic activity and immune checkpoint molecules, consistently in both cohorts. In conclusion, breast cancer patients with high apoptosis are associated with angiogenesis, immune response, high immune cell infiltration and cytolytic activity. To the best of our knowledge, this is the first study to utilize in silico translational approach to demonstrate the clinical relevance of apoptosis in breast cancer patients in large cohorts., Competing Interests: None., (AJCR Copyright © 2021.)

Simple Summary: The G2M checkpoint pathway is a critical step in the cell cycle, which is one of the hallmarks in cancer. We investigated clinical relevance of the G2M pathway in pancreatic cancer, using gene set variation analysis (GSVA) with Hallmark G2M checkpoint gene set. High G2M score tumors enriched several cell proliferation gene sets as well as MKI67 expression, pathological grade, and proliferation score. Furthermore, the score was predictive of disease-specific survival in pancreatic cancer. High G2M tumor was associated with high mutation rate of KRAS and TP53, and significantly enriched these pathway gene sets, as well as high infiltration of Th2 cells. High G2M score consistently associated with worse overall survival in multiple cohorts, particularly in R1/2 resection, but not in R0. Pancreatic cancer is highly mortal due to uncontrolled cell proliferation. The G2M checkpoint pathway is an essential part of the cell cycle. We hypothesized that a high G2M pathway score is associated with cell proliferation and worse survival in pancreatic cancer patients. Gene set variation analysis using the Hallmark G2M checkpoint gene set was used as a score to analyze a total of 390 human pancreatic cancer patients from 3 cohorts (TCGA, GSE62452, GSE57495). High G2M score tumors enriched other cell proliferation genes sets as well as MKI67 expression, pathological grade, and proliferation score. Independent of other prognostic factors, G2M score was predictive of disease-specific survival in pancreatic cancer. High G2M tumor was associated with high mutation rate of KRAS and TP53 and significantly enriched these pathway gene sets, as well as high infiltration of Th2 cells. High G2M score consistently associated with worse overall survival in 3 cohorts, particularly in R1/2 resection, but not in R0. High G2M tumor in R1/2 highly enriched metabolic and cellular components' gene sets compared to R0. To our knowledge, this is the first study to use gene set variation analysis as a score to examine the clinical relevancy of the G2M pathway in pancreatic cancer. [ABSTRACT FROM AUTHOR]

Angiogenesis is one of the hallmarks of cancer. We hypothesized that intra-tumoral angiogenesis correlates with inflammation and metastasis in breast cancer patients. To test this hypothesis, we generated an angiogenesis pathway score using gene set variation analysis and analyzed the tumor transcriptome of 3999 breast cancer patients from The Cancer Genome Atlas Breast Cancer (TCGA-BRCA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), GSE20194, GSE25066, GSE32646, and GSE2034 cohorts. We found that the score correlated with expression of various angiogenesis-, vascular stability-, and sphingosine-1-phosphate (S1P)-related genes. Surprisingly, the angiogenesis score was not associated with breast cancer subtype, Nottingham pathological grade, clinical stage, response to neoadjuvant chemotherapy, or patient survival. However, a high score was associated with a low fraction of both favorable and unfavorable immune cell infiltrations except for dendritic cell and M2 macrophage, and with Leukocyte Fraction, Tumor Infiltrating Lymphocyte Regional Fraction and Lymphocyte Infiltration Signature scores. High-score tumors had significant enrichment for unfavorable inflammation-related gene sets (interleukin (IL)6, and tumor necrosis factor (TNF)α- and TGFβ-signaling), as well as metastasis-related gene sets (epithelial mesenchymal transition, and Hedgehog-, Notch-, and WNT-signaling). High score was significantly associated with metastatic recurrence particularly to brain and bone. In conclusion, using the angiogenesis pathway score, we found that intra-tumoral angiogenesis is associated with immune reaction, inflammation and metastasis-related pathways, and metastatic recurrence in breast cancer. [ABSTRACT FROM AUTHOR]

E2F transcription factors play critical roles in the cell cycle. Therefore, their activity is expected to reflect tumor aggressiveness and responsiveness to therapy. We scored 3905 tumors of nine breast cancer cohorts for this activity based on their gene expression for the Hallmark E2F targets gene set. As expected, tumors with a high score had an increased expression of cell proliferation-related genes. A high score was significantly associated with shorter patient survival, greater MKI67 expression, histological grade, stage, and genomic aberrations. Furthermore, metastatic tumors had higher E2F scores than the primary tumors from which they arose. Although tumors with a high score had greater infiltration by both pro- and anti-cancerous immune cells, they had an increased expression of immune checkpoint genes. Estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative cancer with a high E2F score achieved a significantly higher pathological complete response (pCR) rate to neoadjuvant chemotherapy. The E2F score was significantly associated with the expression of cyclin-dependent kinase (CDK)-related genes and strongly correlated with sensitivity to CDK inhibition in cell lines. In conclusion, the E2F score is a marker of breast cancer aggressiveness and predicts the responsiveness of ER-positive/HER2-negative patients to neoadjuvant chemotherapy and possibly to CDK and immune checkpoint inhibitors. [ABSTRACT FROM AUTHOR]

The vast majority of breast cancer death is a result of metastasis. Thus, accurate identification of patients who are likely to have metastasis is expected to improve survival. The G2M checkpoint plays a critical role in cell cycle. We hypothesized that breast cancer tumors with high activity of G2M pathway genes are more aggressive and likely to metastasize. To test this, we used the single-sample gene set variation analysis method to calculate the score for the Hallmark G2M checkpoint pathway using gene expression data of a total of 4626 samples from 12 human breast cancer cohorts. As expected, a high G2M pathway score correlated with enriched tumor expression of other cell proliferation-related gene sets. The score was significantly associated with clinical aggressive features of tumors and patient survival in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Interestingly, a high G2M score of metastasis tumors was also significantly associated with worse survival. In primary as well as metastasis tumors with high scores, the infiltration of both pro- and anti-cancerous immune cells increased. Tumor G2M score was also associated with treatment response to systemic chemotherapy in ER-positive/HER2-negative cancer, and was predictive of response to cyclin-dependent kinase inhibition therapy. [ABSTRACT FROM AUTHOR]

Cataloging an accurate functional gene set for the Symbiodiniaceae species is crucial for addressing biological questions of dinoflagellate symbiosis with corals and other invertebrates. To improve the gene models of Fugacium kawagutii, we conducted high-throughput chromosome conformation capture (Hi-C) for the genome and Illumina combined with PacBio sequencing for the transcriptome to achieve a new genome assembly and gene prediction. A 0.937-Gbp assembly of F. kawagutii were obtained, with a N50 > 13 Mbp and the longest scaffold of 121 Mbp capped with telomere motif at both ends. Gene annotation produced 45,192 protein-coding genes, among which, 11,984 are new compared to previous versions of the genome. The newly identified genes are mainly enriched in 38 KEGG pathways including N-Glycan biosynthesis, mRNA surveillance pathway, cell cycle, autophagy, mitophagy, and fatty acid synthesis, which are important for symbiosis, nutrition, and reproduction. The newly identified genes also included those encoding O-methyltransferase (O-MT), 3-dehydroquinate synthase, homologous-pairing protein 2-like (HOP2) and meiosis protein 2 (MEI2), which function in mycosporine-like amino acids (MAAs) biosynthesis and sexual reproduction, respectively. The improved version of the gene set (Fugka_Geneset _V3) raised transcriptomic read mapping rate from 33% to 54% and BUSCO match from 29% to 55%. Further differential gene expression analysis yielded a set of stably expressed genes under variable trace metal conditions, of which 115 with annotated functions have recently been found to be stably expressed under three other conditions, thus further developing the "core gene set" of F. kawagutii. This improved genome will prove useful for future Symbiodiniaceae transcriptomic, gene structure, and gene expression studies, and the refined "core gene set" will be a valuable resource from which to develop reference genes for gene expression studies. [ABSTRACT FROM AUTHOR]

Angiogenesis is a cornerstone of cancer as it allows tumors to receive oxygen and nutrients. A high level of angiogenesis within a tumor may therefore be indicative of its aggressiveness. In this study, we examined this hypothesis in gastric cancer. Gene set variation analysis was used to measure the level of angiogenesis in tumors in 1,348 gastric cancer patients using the Hallmark&#95;angiogenesis gene set to score tumor transcriptomes. As we predicted, there was a significant correlation between angiogenesis score and expression of angiogenesis-related genes. The score moderately correlated with abundance of vessel-related stromal cells, fibroblasts and chondrocytes in the tumor microenvironment (TME). Tumors with high score had low infiltration of T helper type 1 and 2 cells but a greater infiltration of M1 macrophages and dendritic cells. They also had enriched expression of gene sets for coagulation, hypoxia, epithelial mesenchymal transition (EMT), and TGF-β signaling. High angiogenesis score was significantly associated with advanced AJCC stage and higher T- but not N-parameters in the TNM staging system. Patients with a high score also had shorter survival. In conclusion, bulk tumor transcriptome-based quantification of tumor angiogenesis using a computational algorithm may serve to identify patients with worse survival in gastric cancer., Competing Interests: None., (AJCR Copyright © 2021.)