The association between ATP-binding cassette subfamily B member 1 (ABCB1) C3435T and C1236T polymorphisms and the risk for childhood acute lymphoblastic leukemia (ALL) is inconclusive. We conducted a meta-analysis of all published studies to determine the association of ABCB1 C3435T and C1236T polymorphisms and pediatric ALL risk. A systematic retrieval of relevant publications from the PubMed and Web of Science databases was performed. Data were calculated and statistical analysis was performed using STATA version 12.0 software. Metaanalysis results showed no significant association between C3435T polymorphism and pediatric ALL risk (TT vs. CC: odds ratio [OR] = 1.20, 95% confidence interval [CI] = 0.95-1.52; CT vs. CC: OR = 1.00, 95% CI = 0.82-1.23; the dominant model: OR = 1.07, 95% CI = 0.89-1.29; the recessive model: OR = 1.17, 95% CI = 0.84-1.62). Similarly, there was no association found for the C1236T polymorphism (TT vs. CC: OR = 1.18, 95% CI= 0.82-1.70; CT vs. CC: OR = 1.08, 95% CI = 0.80-1.45; the dominant model: OR = 1.10, 95% CI= 0.83-1.46; the recessive model: OR = 0.98, 95% CI = 0.61-1.58). Similar results were observed in the subgroup analyses on ethnicity and Hardy-Weinberg equilibrium. The present meta-analysis found no evidence for ABCB1 C3435T and C1236T polymorphisms as risk factors for pediatric ALL.
A type of sorafenib- (SOR-) loaded long-circulating nanoliposome was constructed, and the targeting performance and antitumor effects of the prepared liposome were evaluated in the present study. Polyethylene glycol- (PEG-) modified long-circulating nanoliposomes (LC-NPs) were designed and prepared using reverse evaporation, and the LC-NPs were used for delivering sorafenib (LC-PEG-SOR-NPs). Then, the anti-VEGFR antibody as a targeting moiety was chemically coupled with LC-PEG-SOR-NPs to form liver cancer-targeted nanoliposomes (anti-VEGFR-LC-PEG-SOR-NPs). The drug entrapment and loading efficiency were measured. And the cancer-targeting performance and therapeutic efficiency were evaluated both in vitro and in vivo. The anti-VEGFR-LC-PEG-SOR-NPs with an average of 119.8±4.2 nm showed a uniform spherical structure. The drug entrapment and loading efficiency were 92.5% and 18.5%, respectively. The killing efficiency of anti-VEGFR-LC-PEG-SOR-NPs was up to 18% after incubating with liver cancer cells for 72 h. Furthermore, the anti-VEGFR-LC-PEG-SOR-NPs could actively target at the tumor region and could efficiently inhibit tumor growth with negligible side effects. This newly designed nanoliposomes had desirable dispersibility, high drug entrapment efficiency, tumor targeting and therapeutic efficiency, and good safety. As a biocompatible nanocomposite, it was promising to become a novel and useful tumor-targeting nanodrug for liver cancer therapy.
This study is aimed at screening prognostic biomarkers in cholangiocarcinoma (CHOL) based on competitive endogenous RNA (ceRNA) regulatory network analysis. Microarray data for lncRNAs, mRNA, and miRNAs were downloaded from the GEO and TCGA databases. Differentially expressed RNAs (DERs) were identified in CHOL and normal liver tissue samples. WGCNA was used to identify disease-related gene modules. By integrating the information from the starBase and DIANA-LncBasev2 databases, we constructed a ceRNA network. Survival analysis was performed, and a prognostic gene-based prognostic score (PS) model was generated. The correlation between gene expression and immune cell infiltration or immune-related feature genes was analyzed using TIMER. Finally, real-time quantitative PCR (RT-qPCR) was used to verify the expression of the 10 DERs with independent prognosis. A large cohort of DERs was identified in the CHOL and control samples. The ceRNA network consisted of 6 lncRNAs, 2 miRNAs, 90 mRNAs, and 98 nodes. Ten genes were identified as prognosis-related genes, and a ten-gene signature PS model was constructed, which exhibited a good prognosis predictive ability for risk assessment of CHOL patients (AUC value = 0.975). Four genes, ELF4, AGXT, ABCG2, and LDHD, were associated with immune cell infiltration and closely correlated with immune-related feature genes (CD14, CD163, CD33, etc.) in CHOL. Additionally, the consistency rate of the RT-qPCR results and bioinformatics analysis was 80%, implying a relatively high reliability of the bioinformatic analysis results. Our findings suggest that the ten-signature gene PS model has significant prognostic predictive value for patients with CHOL. These four immune-related DERs are involved in the progression of CHOL and may be useful prognostic biomarkers for CHOLs.
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