Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.
By screening specific genes related to the muscle growth of swine using cDNA microarray technology, a total of 5 novel genes (GF (growth factor) I, II, III, IV and V) were identified. Results of southern blotting to investigate the number of copies of these genes in the genome of swine indicated that GF I, GF III, and GF V existed as one copy and GF II, and GF IV existed as more than two copies. It was suggested that there are many isoforms of these genes in the genome of swine. Also, results of northern blotting to investigate whether these genes were expressed in grown muscle, using GF I, III, and V indicated that all the genes were much more expressed in the muscle of swine with body weight of 90 kg. Expression patterns of these genes in other organs, namely muscle and propagation and fat tissues, were investigated by extracting RNA from the tissues. These genes were not expressed in the propagation and fat tissues, but were expressed in the muscle tissue. To determine the mechanism of muscle growth, further studies should be preceded using the 3 specific genes related to muscle growth, that is GF I, III, and V.
The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.
Abstract Background Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. Here, we report for the first time, the molecular difference in the microbiome of oral and non-oral HNSCC. Methods Primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. Using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUST) and ANOVA-like differential expression (ALDEx2), we predicted bacterial metabolic contributions of oral rich and non-oral rich bacteria, common rich bacteria in two groups and their pathways. A Correlation analysis was performed between RNA expression data and common bacteria data and protein-protein interaction (PPI) analysis was performed using correlated genes. Finally, to find out unique microbial signatures, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene ontology (GO) analysis using the PPI results. Results The common microbiomes in oral and non-oral cancers were Fusobacterium, Treponema , and Selenomonas and Clostridium and Massilia , respectively. We found unique microbial signatures that positively and negatively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in bacterial infection pathways, while negative correlated genes were involved in HTLV-I infection, signal transduction, cell adhesion, and cancer-associated pathway. In non-oral cancer, positively correlated genes did not show any significant results, and negatively correlated genes showed results from focal adhesion pathway and regulation of actin cytoskeleton pathway. Conclusions These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.
Galectins are a group of animal lectins consisting of galectin-type carbohydrate recognition domains (CRD) with relatively minor domains.The biological properties of galectins include the regulation of inflammation, intercellular adhesion, cell differentiation and cell death.The diverse kinds of galectin suggest variety in their biological roles.Galectin-1 is released during adipocyte differentiation and is associated with fat which is one of the important factors for meat quality.To verify expression level, a 0.5 kb clone of galectin-1 was obtained from cDNA prepared from back fat tissue of a Sancheong Berkshire pig with good quality meat, and the galectin-1 gene identified.The deduced amino acid sequence of the galectin-1 gene was compared with those obtained from other species.By using RT-PCR and Real time-PCR, an attempt was made to determine the expression level of galectin-1 and to compare with various tissues (tenderloin and back fat) taken from pigs in different groups.Grouping of pigs was based on growth-stage (weighing 60, 80, and 110 kg) and the sub-speciation (Yorkshire and Sancheong Berkshire pigs).We attempted to determine influences of pig species, growth stages and tissue variations on the expression level of the galectin-l gene and it was revealed that the expression pattern of the galectin-1 gene was significantly different (p<0.01 or p<0.05).Galectin-1 genes were expressed more highly in the back fat tissues of pigs weighing 110 kg than in those weighing 60 kg or 80 kg.However, the lowest expression was seen in the tenderloin tissues of pigs weighing 110 kg.Sancheong Berkshire pigs showed higher expression of the galectin-1 gene compared to Yorkshire pigs.Accordingly, it is considered that the expression pattern of the galectin-1 gene influences the growth of back fat tissues and the pig speciation relationship.Previous studies suggested that different expression of galectin-1 genes represents variety among the breeds and is closely related to fat tissue growth, conjugation and catabolism.Further, this study suggests that the expression of galectin-1 at a specific growth stage and tissue contributes significantly to the overall meat quality of Sancheong Berkshire pigs.
Cutaneous melanoma is the most common cause of skin cancer-related deaths worldwide. There is an urgent need to identify prognostic biomarkers to facilitate decision-making for treatment of metastatic cutaneous melanoma. Gene expression microarrays and RNA-seq technology have recently improved or changed current prognostic and therapeutic strategies for several cancers. However, according to the current melanoma staging system, prognosis is almost entirely dependent on clinicopathological features. To identify novel prognostic biomarkers, we investigated gene expression and clinical data for patients with cutaneous melanoma from three cohorts of The Cancer Genome Atlas and Gene Expression Omnibus. Kaplan–Meier survival analysis using median values of each gene as cutoff value revealed that nine genes ( ABCC3 , CAPS2 , CCR6 , CDCA8 , CLU , DPF1 , PTK2B , SATB1 , and SYNE1 ) were statistically significant prognostic biomarkers of metastatic cutaneous melanoma in all three independent cohorts. Low expression of two genes ( CDCA8 and DPF1 ) and high expression of seven genes ( ABCC3 , CAPS2 , CCR6 , CLU , PTK2B , SATB1 , and SYNE ) were significantly associated with positive metastatic cutaneous melanoma prognoses. In conclusion, we suggest nine novel prognostic biomarkers for cutaneous metastatic melanoma.
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