RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.
CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.
METHODS: The genome sequence was used as a reference to study gene expression during growth in a starved carbon (C) and nitrogen (N) environment with minimal sugar and sawdust as initial energy sources. This study was conducted to mimic possible limitations of the C-N nutrient sources during the growth of G. boninense in oil palm plantations.
RESULTS: Genome sequencing of an isolate collected from a palm tree in West Malaysia generated an assembly of 67.12 Mb encoding 19,851 predicted genes. Transcriptomic analysis from a time course experiment during growth in this starvation media identified differentially expressed genes (DEGs) that were found to be associated with 29 metabolic pathways. During the active growth phase, 26 DEGs were related to four pathways, including secondary metabolite biosynthesis, carbohydrate metabolism, glycan metabolism and mycotoxin biosynthesis. G. boninense genes involved in the carbohydrate metabolism pathway that contribute to the degradation of plant cell walls were up-regulated. Interestingly, several genes associated with the mycotoxin biosynthesis pathway were identified as playing a possible role in pathogen-host interaction. In addition, metabolomics analysis revealed six metabolites, maltose, xylobiose, glucooligosaccharide, glycylproline, dimethylfumaric acid and arabitol that were up-regulated on Day2 of the time course experiment.
CONCLUSIONS: This study provides information on genes expressed by G. boninense in metabolic pathways that may play a role in the initial infection of the host.
METHODS: Thirty-three cell death-associated genes were selected from a literature review. The "DESeq2" R package was used to identify differentially expressed cell death-associated genes between normal prostate tissue (GTEx) and prostate cancer tissue (TCGA) samples. Biological functional enrichment analysis of differentially expressed cell death genes was performed using R statistical software packages, such as "clusterProfiler," "org.Hs.eg.db," "enrichplot," "ggplot2," and "GOplot." Univariate Cox and LASSO Cox regression analyses were conducted to identify prognostic genes associated with the immune microenvironment using the "survival" package. Finally, a predictive model was established based on Gleason score, T stage, and cell death-associated genes.odel was established based on Gleason score, T stage, and cell death-associated genes.
RESULTS: Seventeen differentially expressed genes related to pyroptosis were screened out. Based on these differentially expressed genes, biological function enrichment analysis showed that they were related to pyroptosis of prostate cells. Based on univariate Cox and (LASSO) Cox regression analysis, four pyroptosis-related genes (CASP3, PLCG1, GSDMB, GPX4) were determined to be related to the prognosis of prostate cancer, and the immune correlation analysis of the four pyroptosis-related genes was performed. The expression of CASP3, PLCG1 and GSDMB was positively correlated with the proportion of immune cells, and the expression of GPX4 was negatively correlated with the proportion of immune cells. A predictive nomogram was established by combining Gleason score, T and pyroptosis genes. The nomogram was accompanied by a calibration curve and used to predict 1 -, 2 -, and 5-year survival in PAAD patients.
CONCLUSION: Cell death-associated genes (CASP3, PLCG1, GSDMB, GPX4) play crucial roles in modulating the immune microenvironment and can be used to predict the prognosis of prostate cancer.
DESCRIPTION: The hemibiotroph G. boninense establishes via root contact during early stage of colonization and subsequently kills the host tissue as the disease progresses. Information on the pathogenicity factors/genes that causes BSR remain poorly understood. In addition, the molecular expressions corresponding to G. boninense growth and pathogenicity are not reported. Here, six transcriptome datasets of G. boninense from two contrasting conditions (three biological replicates per condition) are presented. The first datasets, collected from a 7-day-old axenic condition provide an insight onto genes responsible for sustenance, growth and development of G. boninense while datasets of the infecting G. boninense collected from oil palm-G. boninense pathosystem (in planta condition) at 1 month post-inoculation offer a comprehensive avenue to understand G. boninense pathogenesis and infection especially in regard to molecular mechanisms and pathways. Raw sequences deposited in Sequence Read Archive (SRA) are available at NCBI SRA portal with PRJNA514399, bioproject ID.
OBJECTIVE: The main objective of the present study was to identify the cancer-related genes and gene pathways in the endometrium of healthy and cancer patients.
MATERIALS AND METHODS: Thirty endometrial tissues from healthy and type I EC patients were subjected to total RNA isolation. The RNA samples with good integrity number were hybridized to a new version of Affymetrix Human Genome GeneChip 1.0 ST array. We analyzed the results using the GeneSpring 9.0 GX and the Pathway Studio 6.1 software. For validation assay, quantitative real-time polymerase chain reaction was used to analyze 4 selected genes in normal and EC tissue.
RESULTS: Of the 28,869 genes profiled, we identified 621 differentially expressed genes (2-fold) in the normal tissue and the tumor. Among these genes, 146 were up-regulated and 476 were down-regulated in the tumor as compared with the normal tissue (P < 0.001). Up-regulated genes included the v-erb-a erythroblastic leukemia viral oncogene homolog 3 (ErbB3), ErbB4, E74-like factor 3 (ELF3), and chemokine ligand 17 (CXCL17). The down-regulated genes included signal transducer and activator transcription 5B (STAT5b), transforming growth factor A receptor III (TGFA3), caveolin 1 (CAV1), and protein kinase C alpha (PKCA). The gene set enrichment analysis showed 10 significant gene sets with related genes (P < 0.05). The quantitative polymerase chain reaction of 4 selected genes using similar RNA confirmed the microarray results (P < 0.05).
CONCLUSIONS: Identification of molecular pathways with their genes related to type I EC contribute to the understanding of pathophysiology of this cancer, probably leading to identifying potential biomarkers of the cancer.