Identification of core differentially methylated genes in glioma

Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits.

TP53I13 promotes metastasis in glioma via macrophages, neutrophils, and fibroblasts and is a potential prognostic biomarker

Background

TP53I13 is a protein coding tumor suppression gene encoded by the tumor protein p53. Overexpression of TP53I13 impedes tumor cell proliferation. Nevertheless, TP53I13 role and expression in the emergence and progression of glioma (low-grade glioma and glioblastoma) are yet to be identified. Thus, we aim to use comprehensive bioinformatics analyses to investigate TP53I13 and its prognostic value in gliomas.

Methods

Multiple databases were consulted to evaluate and assess the expression of TP53I13, such as the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GeneMANIA, and Gene Expression Profiling Interactive. TP53I13 expression was further explored using immunohistochemistry (IHC) and multiplex immunohistochemistry (mIHC). Through Gene Set Enrichment Analysis (GSEA), the biological functions of TP53I13 and metastatic processes associated with it were studied.

Results

The expression of TP53I13 was higher in tumor samples compared to normal samples. In samples retrieved from the TCGA and CGGA databases, high TP53I13 expression was associated with poor survival outcomes. The analysis of multivariate Cox showed that TP53I13 might be an independent prognostic marker of glioma. It was also found that increased expression of TP53I13 was significantly correlated with PRS type, status, 1p/19q codeletion status, IDH mutation status, chemotherapy, age, and tumor grade. According to CIBERSORT (Cell-type Identification by Estimating Relative Subsets of RNA Transcript), the expression of TP53I13 correlates with macrophages, neutrophils, and dendritic cells. GSEA shows a close correlation between TP53I13 and p53 signaling pathways, DNA replication, and the pentose phosphate pathway.

Conclusion

Our results reveal a close correlation between TP53I13 and gliomas. Further, TP53I13 expression could affect the survival outcomes in glioma patients. In addition, TP53I13 was an independent marker that was crucial in regulating the infiltration of immune cells into tumors. As a result of these findings, TP53I13 might represent a new biomarker of immune infiltration and prognosis in patients with gliomas.

Identification and Validation of a Seizure-Free-Related Gene Signature for Predicting Poor Prognosis in Lower-Grade Gliomas

Background

Lower-grade gliomas (LGGs) patients presented seizure-free have a worse survival than those presented with seizures. However, the current knowledge on its potential value in LGGs remains scarce.

Purpose

This study aimed to identify a novel gene signature associated with seizures-free for predicting poor prognosis for LGGs patients.

Materials and Methods

The RNA expression and clinical information of LGGs patients were downloaded from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were screened out between LGGs patients presented seizures-free and seizures. The novel gene signature was constructed by Lasso and multivariate regression analyses for predicting prognosis in LGGs. Its prognostic value was assessed and validated by Kaplan-Meier analyses and receiver operating characteristic (ROC) curves. Multivariate regression analysis was applied to identify the independent prognostic value of the gene signature. Furthermore, bioinformatics analysis was performed to elucidate the molecular mechanisms.

Results

A total of 253 DEGs were screened out between LGG patients presented with seizures and free of seizures. A 5-gene signature (HIST1H4F, HORMAD2, LILRA3, PRSS33, and TBX20 genes) was constructed from these 253 DEGs. Kaplan-Meier analyses and ROC curves assessed and validated the good performance of the 5-gene signature in differentiating and predicting prognosis of high- and low-risk patients. Multivariate regression analysis determined the independent prognostic value of the 5-gene signature. According to bioinformatics analysis, DEGs were mainly enriched in biological processes related to positive regulation of transcription from RNA polymerase II promoter, G-protein coupled receptor signaling pathway, and pathways of cytokine-cytokine receptor interaction, chemokine signaling pathway.

Conclusion

Our findings suggested that the 5-gene signature might serve as a potential prognostic biomarker and provide guidance for the personalized LGGs management.

Screening and identification of differentially expressed microRNAs in diffuse large B-cell lymphoma based on microRNA microarray

Diffuse large B-cell lymphoma (DLBCL) is the most common type of B-cell non-Hodgkin lymphoma in adults and the pathogenesis of DLBCL is multifactorial and complex. Understanding the molecular mechanisms involved in DLBCL is important to identify new therapeutic targets. The present study aimed to screen and identify differentially expressed microRNAs (miRNAs/miRs) between diffuse large B-cell lymphoma (DLBCL) and control [lymph node reactive hyperplasia (LRH)] groups, and to investigate whether miRNAs associated with DLBCL could serve as potential therapeutic targets. In total, 5 DLBCL experimental samples and 5 control samples were obtained from fresh patient tissues. Firstly, the fresh samples were analyzed using miRNA microarray to identify differentially expressed miRNAs. Next, three databases (TargetScan, microRNA.org and PITA) were used to predict by intersection the potential target genes of the 204 differential miRNAs identified, and a Venn diagram of the results was performed. Subsequently, the target genes of differential miRNAs were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Finally, to validate the miRNA microarray data, reverse transcription-quantitative PCR (RT-qPCR) was performed for 8 differentially expressed miRNAs (miR-193a-3p, miR-19a-3p, miR-19b-3p, miR-370-3p, miR-1275, miR-490-5p, miR-630 and miR-665) using DLBCL and LRH fresh samples. In total, 204 miRNAs exhibited differential expression, including 105 downregulated and 54 upregulated miRNAs. The cut-off criteria were set as P≤0.05 and fold-change ≥2. A total of 7,522 potential target genes for the 204 miRNAs were predicted. Potential target genes were enriched in the following pathways: ‘Cancer’, ‘MAPK signaling pathway’, ‘regulation of actin cytoskeleton’, ‘focal adhesion’, ‘endocytosis’, ‘Wnt signaling pathway’, ‘axon guidance’, ‘calcium signaling pathway’ and ‘PI3K/AKT signaling pathway’. A total of 8 miRNAs were validated by RT-qPCR, and 4 miRNAs (miR-19b-3p, miR-193a-3p, miR-370-3p and miR-490-5p) exhibited low expression levels in DLBCL (P<0.05), while miR-630 was highly expressed in DLBCL (P<0.05). Overall, the present study screened 204 differentially expressed miRNAs and analyzed the expression levels of 8 differentially expressed miRNAs in DLBCL. These differentially expressed miRNAs may serve as therapeutic targets for improvement of therapeutic efficacy in DLBCL in the future.

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC

Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC.

Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes.

Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients.

Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Correlation among VEGFR3 gene promoter methylation, protein overexpression, and clinical pathology in early gastric cancer

BACKGROUND

The occurrence and development of gastric cancer is a multi-factor, multi-stage, multi-gene abnormal accumulation process. Both genetic and epigenetic mechanisms play an important role in the molecular mechanism of gastric cancer. DNA methylation is one of the most studied epigenetic expression mechanisms. To study the correlation between gene promoter methylation status and protein expression of vascular endothelial growth factor receptor 3 (VEGFR3), as well as their association with clinicopathological features in early gastric cancer (EGC) cases.

METHODS

Immunohistochemical analysis and methylation-specific PCR (MSP) were used to detect the expression of VEGFR3 protein and methylation status of the VEGFR3 promoter in 50 cases of EGC and their paired normal gastric mucosa tissues. The level of DNA methylation of the VEGFR3 promoter, in situ VEGFR3 protein expression, and the clinicopathological characteristics of EGC patients were statistically analyzed.

RESULTS

The positive rate of VEGFR3 protein expression in EGC tumor tissue (60%) was significantly higher than that in the normal gastric mucosa (10%). The detectable methylation frequency of VEGFR3 promoter in EGC tumor tissue (48%) was significantly lower than that in the normal gastric mucosa (85%). As anticipated, the methylation level of the VEGFR3 gene promoter was negatively associated with the overexpression of VEGFR3 protein. In addition, methylation status of the VEGFR3 gene promoter was positively correlated with lymph node metastasis in EGC patients (P<0.05), but was not linked to patients' gender, age, tumor size, degree of differentiation, or tumor invasion depth (P>0.05).

CONCLUSIONS

Hypomethylation of the VEGFR3 gene promoter is one of the major mechanisms underlying VEGFR3 gene overexpression in EGC tumor tissues and is related to lymph node metastasis in EGC patients. DNA methylation of VEGFR3 is expected to become a molecular diagnostic and prognostic biomarker for EGC.