A new method of identifying glioblastoma subtypes and creation of corresponding animal models

Impact of endogenous viral elements on glioma clinical phenotypes by inducing OCT4 in the host

Introduction

Endogenous viral elements (EVEs) are viral sequences integrated within the host genome that can influence gene regulation and tumor development. While EVEs have been implicated in cancer, their role in regulating key transcription factors in glioblastoma (GBM) remains underexplored. This study investigates the relationship between EVEs and the activation of OCT4, a critical transcription factor in GBM progression.

Methods

We utilized CancerHERVdb and HervD Atlas databases to identify potential interactions between EVEs and key genes involved in GBM. Data from 273 GBM patient samples in the TCGA database were analyzed to examine the correlation between OCT4 expression and mutations in glioma-related genes. Furthermore, glioblastoma stem cells (GSCs) were assessed for the expression levels of OCT4 and SOX2, and Pearson correlation analysis was performed.

Results

Our analysis revealed that OCT4 is a pivotal gene activated by EVEs in GBM. OCT4 expression was significantly correlated with mutations in key glioma-associated genes. Higher OCT4 levels were associated with poorer patient prognosis, higher tumor grades, and older age. Additionally, GSCs exhibited elevated expression of both OCT4 and SOX2, with a positive correlation observed between these two genes in GBM patients.

Discussion

This study highlights the potential role of EVEs in driving GBM progression through the activation of OCT4. The findings emphasize the importance of OCT4 in GBM malignancy and suggest that targeting EVE-mediated pathways may provide new therapeutic approaches for GBM treatment.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma

Background

Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors.

Objective

Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study.

Methods

We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207).

Results

It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment.

Conclusions

By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.

Dynamic m6 A profiles reveal the role of YTHDC2-TLR2 signaling axis in Talaromyces marneffei infection

Talaromyces marneffei (TM) immune evasion is an important factor leading to the high mortality rate of Penicilliosis marneffei. N6 -methyladenosine (m6 A) plays important roles in host immune response to various pathogen infections, yet its role in TM and HIV/TM coinfection remains largely unexplored. Here we reported genome-wide transcriptional m6 A profiles of TM mono-infection and HIV/TM coinfection. Our finding revealed dynamic alterations in global m6 A levels and upregulation of the m6 A reader YTH N6 -methyladenosine RNA binding protein C2 (YTHDC2) in TM-infected macrophages. Knockdown of YTHDC2 in TM-infected cells showed an elevated expression of TLR2 through m6 A-dependence, along with upregulation of TNF-α and IL1-β. Overall, we characterized the m6 A profiles of the host and fungus before and after TM infection, and demonstrated that YTHDC2 mediates the key m6 A site of TLR2 to exert its function. These findings provide new insights into the underlying mechanisms and novel therapeutic approaches for TM diseases.

Enhancing nasopharyngeal carcinoma cell radiosensitivity by suppressing AKT/mTOR via CENP-N knockdown

OBJECTIVE

Investigating the impact of centromere protein N (CENP-N) on radiosensitivity of nasopharyngeal carcinoma (NPC) cells.

METHODS

Using immunohistochemistry and immunofluorescence to detect CENP-N expression in tissues from 35 patients with radiosensitive or radioresistant NPC. Assessing the effect of combined CENP-N knockdown and radiotherapy on various cellular processes by CCK-8, colony formation, flow cytometry, and Western blotting. Establishing a NPC xenograft model. When the tumor volume reached 100 mm3, a irradiation dose of 6 Gy was given, and the effects of the combined treatment were evaluated in vivo using immunofluorescence and Western blotting techniques.

RESULTS

The level of CENP-N was significantly reduced in radiosensitive tissues of NPC (p < 0.05). Knockdown of CENP-N enhanced NPC radiosensitivity, resulting in sensitizing enhancement ratios (SER) of 1.44 (5-8 F) and 1.16 (CNE-2Z). The combined treatment showed significantly higher levels of proliferation suppression, apoptosis, and G2/M phase arrest (p < 0.01) compared to either CENP-N knockdown alone or radiotherapy alone. The combined treatment group showed the highest increase in Bax and γH2AX protein levels, whereas the protein Cyclin D1 exhibited the greatest decrease (p < 0.01). However, the above changes were reversed after treatment with AKT activator SC79. In vivo, the mean volume and weight of tumors in the radiotherapy group were 182 ± 54 mm3 and 0.16 ± 0.03 g. The mean tumor volume and weight in the combined treatment group were 84 ± 42 mm3 and 0.04 ± 0.01 g.

CONCLUSION

Knockdown of CENP-N can enhance NPC radiosensitivity by inhibiting AKT/mTOR.

Identifying Potential Tumor Antigens and Antigens-Related Subtypes in Hepatocellular Carcinoma for mRNA Vaccine Development

Background

The mRNA vaccine has become a promising platform for cancer therapy. Lots of studies have been focusing on discovering novel potent cancer-associated antigens to develop mRNA vaccines against cancers. Besides, immunotyping shows the immune status, and immune microenvironment of immunotyping is related with therapeutic reaction. However, potential antigens for mRNA vaccines and immunotyping of liver hepatocellular carcinoma (LIHC) remain far from being understood.

Methods

In this study, we collected gene expression data and clinical information data from ICGC and TCGA databases. Using GEPIA2, we calculated differential expression genes and prognostic indices. We applied TIMER to calculate the correlation coefficient between immune infiltrating cells and each gene. Consensus cluster was used for immunotyping of LIHC.

Results

We uncovered four most potential candidates including PES1, MCM3, PPM1G, and KPNA2, which were all related with antigen-presenting cell (APC) infiltration and poor survival in LIHC in two independent datasets. Furthermore, three immune-related subtypes (IS1-IS3) of LIHC were identified. All these results were validated in two independent datasets. Furthermore, we validated our results in vitro.

Conclusions

The above candidates will be expected to be potential antigen genes for developing anti-LIHC mRNA vaccine, and furthermore, patients with IS2 and IS3 tumors are supposed to be appropriate for mRNA vaccine in LIHC.

Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.