AKAP12 and IGFBP4 Are Prognostic Factors for Chronic Lymphocytic Leukemia

INTRODUCTION

The aim of this study was to develop a prognostic model for chronic lymphocytic leukemia (CLL).

METHODS

GEO2R was used to retrieve the gene expression data of CLL and normal B cells from the Gene Expression Omnibus (GEO; GSE22529 and GSE50006 datasets) database. Practical Extraction and Report Language was used to extract the gene expression and overall survival (OS) data of CLL patients from the Chronic Lymphocytic Leukemia - ES (CLLE-ES) project in the International Cancer Genome Consortium (ICGC) database. Cox regression with Lasso was used to create and validate a prognostic model for CLL.

RESULTS

A total of 267 genes exhibited differential expression between CLL and normal B cells. Cox univariate analysis identified 14 DEGs that correlated with OS. Lasso multivariate evaluation demonstrated that AKAP12 and IGFBP4 are independent prognostic factors for CLL. Kaplan-Meier survival analysis revealed a significant association between the estimated risk score and survival. The area under the receiver operating characteristic curve was calculated to be 0.97, indicating high predictive accuracy. In addition, high AKAP12 and IGFBP4 risk scores were associated with the high incidence of trisomy 12q.

CONCLUSION

Taken together, AKAP12 and IGFBP4 are independent prognostic factors for CLL.

Top-down stepwise refinement identifies coding and noncoding RNA-associated epigenetic regulatory maps in malignant glioma

With the emergence of the molecular era and retreat of the histology epoch in malignant glioma, it is becoming increasingly necessary to research diagnostic/prognostic/therapeutic biomarkers and their related regulatory mechanisms. While accumulating studies have investigated coding gene‐associated biomarkers in malignant glioma, research on comprehensive coding and noncoding RNA‐associated biomarkers is lacking. Furthermore, few studies have illustrated the cross‐talk signalling pathways among these biomarkers and mechanisms in detail. Here, we identified DEGs and ceRNA networks in malignant glioma and then constructed Cox/Lasso regression models to further identify the most valuable genes through stepwise refinement. Top‐down comprehensive integrated analysis, including functional enrichment, SNV, immune infiltration, transcription factor binding site, and molecular docking analyses, further revealed the regulatory maps among these genes. The results revealed a novel and accurate model (AUC of 0.91 and C‐index of 0.84 in the whole malignant gliomas, AUC of 0.90 and C‐index of 0.86 in LGG, and AUC of 0.75 and C‐index of 0.69 in GBM) that includes twelve ncRNAs, 1 miRNA and 6 coding genes. Stepwise logical reasoning based on top‐down comprehensive integrated analysis and references revealed cross‐talk signalling pathways among these genes that were correlated with the circadian rhythm, tumour immune microenvironment and cellular senescence pathways. In conclusion, our work reveals a novel model where the newly identified biomarkers may contribute to a precise diagnosis/prognosis and subclassification of malignant glioma, and the identified cross‐talk signalling pathways would help to illustrate the noncoding RNA‐associated epigenetic regulatory mechanisms of glioma tumorigenesis and aid in targeted therapy.