Genetic variants of CYP4F12 gene are associated with glioma susceptibility

Integrative Analysis of Histone Acetylation Regulated CYP4F12 in Esophageal Cancer Development

Current therapeutic strategies for esophageal cancer (EC) patients have yielded limited improvements in survival rates. Recent research has highlighted the influence of drug metabolism enzymes on both drug response and EC development. Our study aims to identify specific drug metabolism enzymes regulated by histone acetylation and to elucidate its molecular and clinical features. CYP4F12 exhibited a notable upregulation subsequent to trichostatin A treatment as evidenced by RNA sequencing analysis conducted on the KYSE-150 cell line. The change in gene expression was associated with increased acetylation level of histone 3 K18 and K27 in the promoter. The regulation was dependent on p300. In silicon analysis of both The Cancer Genome Atlas esophageal carcinoma and GSE53624 dataset suggested a critical role of CYP4F12 in EC development, because CYP4F12 was downregulated in tumor tissues and predicted better disease-free survival. Gene ontology analysis has uncovered a robust correlation between CYP4F12 and processes related to cell migration, as well as its involvement in cytosine-mediated immune activities. Further investigation into the relationship between immune cells and CYP4F12 expression has indicated an increased level of B cell infiltration in samples with high CYP4F12 expression. CYP4F12 was also negatively correlated with the expression of inhibitory checkpoints. An accurate predictive nomogram model was established combining with clinical factors and CYP4F12 expression. In conclusion, CYP4F12 was crucial in EC development, and targeting CYP4F12 may improve the therapeutic efficacy of current treatment in EC patients. SIGNIFICANCE STATEMENT: CYP4F12 expression was downregulated in esophageal cancer (EC) patients and could be induced by trichostatin A. During EC development, CYP4F12 was linked to reduced cell migration and increased infiltration of B cells. CYP4F12 also is a biomarker as prognostic predictors and therapeutic guide in EC patients.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.