Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

Alternative Splicing Factor Heterogeneous Nuclear Ribonucleoprotein U as a Promising Biomarker for Gastric Cancer Risk and Prognosis with Tumor-Promoting Properties

Gastric cancer (GC) is a major global health concern with poor outcomes. Heterogeneous nuclear ribonucleoprotein U (HNRNPU) is a multifunctional protein that participates in pre-mRNA packaging, alternative splicing regulation, and chromatin remodeling. Its potential role in GC remains unclear. In this study, the expression characteristics of HNRNPU were analyzed by The Cancer Genome Atlas data, Gene Expression Omnibus data, and then further identified by real-time quantitative PCR and immunohistochemistry using tissue specimens. From superficial gastritis, atrophic gastritis, and hyperplasia to GC, the in situ expression of HNRNPU protein gradually increased, and the areas under the curve for diagnosis of GC and its precancerous lesions were 0.911 and 0.847, respectively. A nomogram integrating HNRNPU expression, lymph node metastasis, and other prognostic indicators exhibited an area under the curve of 0.785 for predicting survival risk. Knockdown of HNRNPU significantly inhibited GC cell proliferation, migration, and invasion and promoted apoptosis in vitro. In addition, RNA-sequencing analysis showed that HNRNPU could affect alternative splicing events in GC cells, with functional enrichment analysis revealing that HNRNPU may exert malignant biological function in GC progression through alternative splicing regulation. In summary, the increased expression of HNRNPU was significantly associated with the development of GC, with a good performance in diagnosing and predicting the prognostic risk of GC. Functionally, HNRNPU may play an oncogenic role in GC by regulating alternative splicing.

Development and Validation of CXCR4 Nomogram-Based Immune Infiltration/Tumor Inflammation in Primary Glioblastoma

Glioblastoma (GBM) is a highly malignant and aggressive tumor with poor prognosis. Therefore, the discovery of new prognostic molecular markers is of great significance for clinical prognosis. The CXC chemokine receptor (CXCR) members play a key regulatory role in many cancers. In this study, we explore the clinical value and application of the CXCR members in primary glioblastoma. Two GBM datasets from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases were used to explore the relationship between differential expression of CXCRs and GBM subtypes as well as immune infiltration. C-X-C motif chemokine receptor 4 (CXCR4) was screened as an independent prognostic factor, and a nomogram and risk prediction model were developed and tested in the CGGA database using the TCGA database. Receiver operating curve (ROC) and decision curve analysis (DCA) found good accuracy and net benefit of the models. The correlation of CXCR4 with immune infiltration and tumor was analyzed using CancerSEA and TIMER. In in vitro experiments, we found that CXCR4 was significantly overexpressed in glioblastoma and was closely related to the inflammatory response of U251/U87 cells. CXCR4 is an excellent independent prognostic factor for glioblastoma and positively correlates with tumor inflammation.

Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BACKGROUND

Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated.

METHODS

HOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan-Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan-Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts.

RESULTS

HOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts.

CONCLUSION

HOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients.

DNA5mC Regulator-Mediated Molecular Clusters and Tumor Microenvironment Signatures in Glioblastoma

Growing evidence links DNA methylation to tumor immunity. The impact of DNA methylation (5 mC) on the microenvironment surrounding tumors and immunotherapy remains to be clarified. Through clustering gene expression of 20 DNA methylation regulators, this study aimed at systematically analyzing DNA methylation regulator patterns and tumor microenvironment characteristics of TCGA-GBM patients. Various subtypes of glioblastoma exhibit different tumor microenvironments and DNA methylation patterns. Each DNA methylation modification was then assigned a DNA methylation score (DMS). High DMS was associated with a good prognosis. In contrast, the low DMS group had a relatively low survival rate. A correlation was also found between high DMS and enhanced immunotherapy efficacy in two immune checkpoint blocking treatment cohorts. To conclude, identifying DNA methylation regulation patterns may prove critical to understanding glioblastoma progression and differentiation, as well as future therapeutic targets.