Prognostic significance of the tumor suppressor protein p53 gene in childhood acute lymphoblastic leukemia

Identification of a Seven-lncRNA-mRNA Signature for Recurrence and Prognostic Prediction in Relapsed Acute Lymphoblastic Leukemia Based on WGCNA and LASSO Analyses

Abnormal expressions of long noncoding RNAs (lncRNAs) and protein-encoding messenger RNAs (mRNAs) are important for the development of childhood acute lymphoblastic leukemia (ALL). This study developed an lncRNA-mRNA integrated classifier for the prediction of recurrence and prognosis in relapsed childhood ALL by using several transcriptome data. Weighted gene coexpression network analysis revealed that green, turquoise, yellow, and brown modules were preserved across the TARGET, GSE60926, GSE28460, and GSE17703 datasets, and they were associated with clinical relapse and death status. A total of 184 genes in these four modules were differentially expressed between recurrence and nonrecurrence samples. Least absolute shrinkage and selection operator analysis showed that seven genes constructed a prognostic signature (including one lncRNA: LINC00652 and six mRNAs: INSL3, NIPAL2, REN, RIMS2, RPRM, and SNAP91). Kaplan-Meier curve analysis observed that patients in the high-risk group had a significantly shorter overall survival than those of the low-risk group. Receiver operating characteristic curve analysis demonstrated that this signature had high accuracy in predicting the 5-year overall survival and recurrence outcomes, respectively. LINC00652 may function by coexpressing with the above prognostic genes (INSL3, SNAP91, and REN) and lipid metabolism-related genes (MIA2, APOA1). Accordingly, this lncRNA-mRNA-based classifier may be clinically useful to predict the recurrence and prognosis for childhood ALL. These genes represent new targets to explain the mechanisms for ALL.

Aptamer-Functionalized Three-Dimensional Carbon Nanowebs for Ultrasensitive and Free-Standing PDGF Biosensor

Research on flexible biosensors is mostly focused on their use in obtaining information on physical signals (such as temperature, heart rate, pH, and intraocular pressure). Consequently, there are hardly any studies on using flexible electronics for detecting biomolecules and biomarkers that cause diseases. In this study, we propose a flexible, three-dimensional carbon nanoweb (3DCNW)-based aptamer sensor to detect the platelet-induced growth factor (PDGF), which is an oncogenic biomarker. As a template for the 3D structure, poly(acrylonitrile) (PAN) nanowebs were synthesized using a facile electrospinning process. The PAN nanowebs were then subjected to chemical vapor deposition with copper powder. This was followed by Cu etching to generate carbon protrusions on the web surface. As an active site, PDGF-B binding aptamer was introduced on the 3DCNW surface to form biosensor electrodes. The 3DCNW-based aptasensor exhibited excellent sensitivity (down to 1.78 fM), with high selectivity, reversibility, and stability to PDGF-BB.