Systemic analysis of genome-wide expression profiles identified potential therapeutic targets of demethylation drugs for glioblastoma

Identification and validation of ferroptosis-related biomarkers and the related pathogenesis in precancerous lesions of gastric cancer

Using advanced bioinformatics techniques, we conducted an analysis of ferroptosis-related genes (FRGs) in precancerous lesions of gastric cancer (PLGC). We also investigated their connection to immune cell infiltration and diagnostic value, ultimately identifying new molecular targets that could be used for PLGC patient treatment. The Gene Expression Omnibus (GEO) and FerrDb V2 databases were used to identify FRGs. These genes were analysed via ClueGO pathways and Gene Ontology (GO) enrichment analysis, as well as single-cell dataset GSE134520 analysis. A machine learning model was applied to identify hub genes associated with ferroptosis in PLGC patients. Receiver Operating Characteristics (ROC) curve analysis was conducted to verify the diagnostic efficacy of these genes, and a PLGC diagnosis model nomogram was established based on hub genes. R software was utilized to conduct functional, pathway, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) on the identified diagnostic genes. Hub gene expression levels and survival times in gastric cancer were analysed using online databases to determine the prognostic value of these genes. MCPcounter and single-sample gene set enrichment analysis (ssGSEA) algorithms were used to investigate the correlation between hub genes and immune cells. Finally, noncoding RNA regulatory mechanisms and transcription factor regulatory networks for hub genes were mapped using multiple databases. Eventually, we identified 23 ferroptosis-related genes in PLGC. Enrichment analyses showed that ferroptosis-related genes were closely associated with iron uptake and transport and ferroptosis in the development of PLGC. After differential analysis using machine learning algorithms, we identified four hub genes in PLGC patients, including MYB, CYB5R1, LIFR and DPP4. Consequently, we established a ferroptosis diagnosis model nomogram. GSVA and GSEA mutual verification analysis helped uncover potential regulatory mechanisms of hub genes. MCPcounter and ssGSEA analysed immune infiltration in the disease and indicated that B cells and parainflammation played an important role in disease progression. Finally, we constructed noncoding RNA regulatory networks and transcription factor regulatory networks. Our study identified ferroptosis-related diagnostic genes and therapeutic targets for PLGC, providing novel insights and a theoretical foundation for research into the molecular mechanisms, clinical diagnosis, and treatment of this disease.

Identification and validation of hub genes for diabetic retinopathy

Background

Diabetic retinopathy (DR) is characterized by a gradually progressive alteration in the retinal microvasculature that leads to middle-aged adult acquired persistent blindness. Limited research has been conducted on DR pathogenesis at the gene level. Thus, we aimed to reveal novel key genes that might be associated with DR formation via a bioinformatics analysis.

Methods

The GSE53257 dataset from the Gene Expression Omnibus was downloaded for gene co-expression analysis. We identified significant gene modules via the Weighted Gene Co-expression Network Analysis, which was conducted by the Protein-Protein Interaction (PPI) Network via Cytoscape and from this we screened for key genes and gene sets for particular functional and pathway-specific enrichments. The hub gene expression was verified by real-time PCR in DR rats modeling and an external database.

Results

Two significant gene modules were identified. Significant key genes were predominantly associated with mitochondrial function, fatty acid oxidation and oxidative stress. Among all key genes analyzed, six up-regulated genes (i.e., SLC25A33, NDUFS1, MRPS23, CYB5R1, MECR, and MRPL15) were highly and significantly relevant in the context of DR formation. The PCR results showed that SLC25A33 and NDUFS1 expression were increased in DR rats modeling group.

Conclusion

Gene co-expression network analysis highlights the importance of mitochondria and oxidative stress in the pathophysiology of DR. DR co-expressing gene module was constructed and key genes were identified, and both SLC25A33 and NDUFS1 may serve as potential biomarker and therapeutic target for DR.

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients

BACKGROUND

Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice.

MATERIAL AND METHODS

Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness.

RESULTS

We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients.

CONCLUSION

We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.