Bioinformatics analysis of potential core genes for glioblastoma

Screening lncRNAs essential for cardiomyocyte proliferation by integrative profiling of lncRNAs and mRNAs associated with heart development

BACKGROUND

The proliferation potential of mammalian cardiomyocytes declines markedly shortly after birth. Both long non-coding RNAs (lncRNAs) and mRNAs demonstrate altered expression patterns during cardiac development. However, the role of lncRNAs in the cell cycle arrest of cardiomyocytes remains inadequately understood.

METHOD

The expression pattern of lncRNAs and mRNAs was analyzed in mouse hearts exhibiting varying regenerative potentials on postnatal days (P) 1, 7, and 28. Weighted correlation network analysis (WGCNA) was employed to elucidate the co-expression relationship between lncRNAs and mRNAs. Protein-protein interaction (PPI) network was built using the STRING database, and hub lncRNAs were identified by CytoHubba. Molecular Complex Detection (MCODE) was used to screen core modules of the PPI network in Cytoscape. Upstream lncRNAs and miRNAs which may regulate mRNAs were predicted using miRTarBase and AnnoLnc2, respectively. Myocardial infarction (MI) was induced by ligation of the left anterior descending coronary artery.

RESULTS

Compared with the P1 heart, 618 mRNAs and 414 lncRNAs displayed. transcriptional changes in the P7 heart, while 2358 mRNAs and 1290 lncRNAs showed from P7 to P28. Gene Ontology (GO) analysis revealed that module 1 in the both comparisons was enriched in the mitotic cell cycle process. 2810408I11Rik and 2010110K18Rik were identified as hub lncRNAs and their effects on the proliferation of cardiomyocytes were verified in vitro. Additionally, four lncRNA-miRNA-mRNA regulatory axes were predicted to explain the mechanism by which 2810408I11Rik and 2010110K18Rik regulate cardiomyocyte proliferation. Notably, the overexpression of 2810408I11Rik enhances cardiomyocyte proliferation and heart regeneration in the adult heart following MI.

CONCLUSION

This study systematically analyzed the landscape of lncRNAs and mRNAs at P1, P7, and P28. These findings may enhance our understanding of the framework for heart development and could have significant implications for heart regeneration.

Novel DNA methylation-based epigenetic signatures in colorectal cancer from peripheral blood leukocytes

Colorectal cancer (CRC) is a multifactorial disease characterized by accumulation of multiple genetic and epigenetic alterations, transforming colonic epithelial cells into adenocarcinomas. Alteration of DNA methylation (DNAm) is a promising biomarker for predicting cancer risk and prognosis, but its role in CRC tumorigenesis is inconclusive. Notably, few DNAm studies have used pre-diagnostic peripheral blood (PB) DNA, causing difficulty in postulating the underlying biologic mechanism of CRC initiation. We conducted epigenome-wide association (EWA) scans in postmenopausal women from Women's Health Initiative (WHI) with their pre-diagnostic DNAm in PB leukocytes (PBLs) to prospectively evaluate CRC development. Our site-specific DNAm analyses across the genome adjusted for DNAm-age, leukocyte heterogeneities, as well as body mass index, diabetes, and insulin resistance. We validated 20 top EWA-CpGs in 2 independent CRC tissue datasets. Also, we detected differentially methylated regions (DMRs) associated with CRC, further mapped to transcriptomic profile, and finally conducted a Gene Set Enrichment Analysis. We detected multiple novel CpGs validated across WHI and tissue datasets. In particular, 2 CpGs (B4GALNT4cg10321339, SV2Bcg18144285) had the strongest effect on CRC risk. Results from our DMR scans contained MIR663cg06007966, which was also validated in EWA analyses. Also, we detected 1 methylome region in PEG10 of Chr7 shared across datasets. Our findings reflect both novel and well-established epigenomic and transcriptomic sites in CRC, warranting further functional validations. Our study contributes to better understanding of the complex interrelated mechanisms on the methylome underlying CRC tumorigenesis and suggests novel preventive DNAm-targets in PBLs for detecting at-risk individuals for CRC development.

SV2B/miR-34a/miR-128 axis as prognostic biomarker in glioblastoma multiforme

Glioblastoma (GBM) is a heterogenous primary brain tumour that is characterised with unfavourable patient prognosis. The identification of biomarkers for managing brain malignancies is of utmost importance. MicroRNAs (miRNAs) are small, non-coding RNAs implicated in cancer development. This study aimed to assess the prognostic significance of miRNAs and their gene targets in GBM. An in silico approach was employed to investigate the differentially expressed miRNAs in GBM. The most dysregulated miRNAs were identified and analysed via Sfold in association with their gene target. The candidate gene was studied via multi-omics approaches, followed by in vitro and in vivo experiments. The in silico analyses revealed that miR-128a and miR-34a were significantly downregulated within GBM. Both miRNAs displayed high binding affinity to the synaptic vesicle glycoprotein 2B (SV2B) 3' untranslated region (3'UTR). SV2B exhibited upregulation within brain regions with high synaptic activity. Significantly higher SV2B levels were observed in high grade brain malignancies in comparison to their normal counterparts. SV2B expression was observed across the cytoplasm of GBM cells. Our findings underscored the downregulated expression patterns of miR-128a and miR-34a, alongside the upregulation of SV2B in GBM suggesting the importance of the SV2B/miR-34a/miR-128 axis as a potential prognostic approach in GBM management.

A novel anoikis-related gene signature predicts prognosis in patients with sepsis and reveals immune infiltration

Sepsis is a common acute and severe medical condition with a high mortality rate. Anoikis, an emerging form of cell death, plays a significant role in various diseases. However, the role of anoikis in sepsis remains poorly understood. Based on the datasets from Gene Expression Omnibus and anoikis-related genes from GeneCards, the differentially expressed anoikis-related genes (DEARGs) were identified. Based on hub genes of DEARGs, a novel prognostic risk model was constructed, and the pattern of immune infiltration was investigated by CIBERSORT algorithm. And small molecule compounds targeting anoikis in sepsis were analyzed using Autodock. Of 23 DEARGs, CXCL8, CFLAR, FASLG and TP53 were significantly associated with the prognosis of sepsis (P < 0.05). Based on the prognostic risk model constructed with these four genes, high-risk population of septic patients had significant lower survival probability than low-risk population (HR = 3.30, P < 0.001). And the level of CFLAR was significantly correlated with the number of neutrophils in septic patients (r = 0.54, P < 0.001). Moreover, tozasertib had low binding energy with CXCL8, CFLAR, FASLG and TP53, and would be a potential compound for sepsis. Conclusively, our results identified a new prognostic model and potential therapeutic molecular for sepsis, providing new insights on mechanism and treatment of sepsis.

Synaptic components are required for glioblastoma progression in Drosophila

Glioblastoma (GB) is the most aggressive, lethal and frequent primary brain tumor. It originates from glial cells and is characterized by rapid expansion through infiltration. GB cells interact with the microenvironment and healthy surrounding tissues, mostly neurons and vessels. GB cells project tumor microtubes (TMs) contact with neurons, and exchange signaling molecules related to Wingless/WNT, JNK, Insulin or Neuroligin-3 pathways. This cell to cell communication promotes GB expansion and neurodegeneration. Moreover, healthy neurons form glutamatergic functional synapses with GB cells which facilitate GB expansion and premature death in mouse GB xerograph models. Targeting signaling and synaptic components of GB progression may become a suitable strategy against glioblastoma. In a Drosophila GB model, we have determined the post-synaptic nature of GB cells with respect to neurons, and the contribution of post-synaptic genes expressed in GB cells to tumor progression. In addition, we document the presence of intratumoral synapses between GB cells, and the functional contribution of pre-synaptic genes to GB calcium dependent activity and expansion. Finally, we explore the relevance of synaptic genes in GB cells to the lifespan reduction caused by GB advance. Our results indicate that both presynaptic and postsynaptic proteins play a role in GB progression and lethality.

Glioblastoma (GB) is the most frequent and aggressive type of brain tumor. It is originated from glial cells that expand and proliferate very fast in the brain. GB cells infiltrate and establish cell to cell communication with healthy neurons. Currently there is no effective treatment for GB and these tumors result incurable with an average survival of 16 months after diagnosis. Here we used a Drosophila melanogaster model to search for genetic suppressors of GB progression. The results show that genes involved in the formation of synapses are required for glial cell number increase, expansion of tumoral volume and premature death. Among these synaptic genes we found that post-synaptic genes that contribute to Neuron-GB interaction which validate previous findings in human GB. Moreover, we found electro dense structures between GB cells that are compatible with synapses and that expression of pre-synaptic genes, including brp, Lip-α and syt 1, is required for GB progression and aggressiveness. These results suggest a contribution of synapses between GB cells to disease progression, named as intratumoral synapses.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.

METHODS

We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.

RESULTS

We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.

CONCLUSION

We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Glioblastoma is an aggressive malignant tumor of the brain and spinal cord. Due to the blood-brain barrier, the accessibility of its treatments still remains significantly challenging. Unfortunately, the recurrence rates of glioblastoma upon surgery are very high too. Hence, understanding the molecular drivers of disease progression is valuable. In this study, we aimed to investigate the molecular drivers responsible for glioblastoma progression and identify valid biomarkers. Three microarray expression profiles GSE90604, GSE50601, and GSE134470 containing healthy and glioblastoma-affected samples revealed overlapping differentially expressed genes (DEGs). The interrelational pathway enrichment analysis elucidated the halt of cell cycle checkpoints and activation of signaling pathways and led to the identification of 6 predominant hub genes. Validation of hub genes in comparison with The Cancer Genome Atlas datasets identified the potential biomarkers of glioblastoma. The study evaluated two significantly upregulated genes, SPARC (secreted protein acidic and rich in cysteine) and VIM (vimentin) for glioblastoma. The genes CACNA1E (calcium voltage-gated channel subunit alpha1 e), SH3GL2 (SH3 domain-containing GRB2-like 2, endophilin A1), and DDN (dendrin) were identified as under-expressed genes as compared to the normal and pan-cancer tissues along with prominent putative prognostic biomarker potentials. The genes DDN and SH3GL2 were found to be upregulated in the proneural subtype, while CACNA1E in the mesenchymal subtype of glioblastoma exhibits good prognostic potential. The mutational analysis also revealed the benign, possibly, and probably damaging substitution mutations. The correlation between the DEG and survival in glioblastoma was evaluated using the Kaplan-Meier plots, and VIM had a greater life expectancy of 60.25 months. Overall, this study identified key candidate genes that might serve as predictive biomarkers for glioblastoma.

Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

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.