Dendritic quinary PtRhMoCoFe high-entropy alloy as a robust immunosensing nanoplatform for ultrasensitive detection of biomarker.

Recently, high-entropy alloys have superior physicochemical properties as compared to conventional alloys for their glamorous "cocktail effect". Nevertheless, they are scarcely applied to electrochemical immunoassays until now. Herein, uniform PtRhMoCoFe high-entropy alloyed nanodendrites (HEANDs) were synthesized by a wet-chemical co-reduction method, where glucose and oleylamine behaved as the co-reducing agents. Then, a series of characterizations were conducted to illustrate the synergistic effect among multiple metals and fascinating structural characteristics of PtRhMoCoFe HEANDs. The obtained high-entropy alloy was adopted to build a electrochemical label-free biosensor for ultrasensitive bioassay of biomarker cTnI. In the optimized analytical system, the resultant sensor exhibited a dynamic linear range of 0.0001-200 ng mL-1 and a low detection limit of 0.0095 pg mL-1 (S/N = 3). Eventually, this sensing platform was further explored in serum samples with satisfied recovery (102.0 %). This research renders some constructive insights for synthesis of high-entropy alloys and their expanded applications in bioassays and bio-devices.

Ultrasensitive PEC cytosensor for breast cancer cells detection and inhibitor screening based on plum-branched CdS/Bi2S3 heterostructures.

Breast cancer is the most common malignant tumor in women, which seriously threatens the life and health of patients. Therefore, facile and sensitive detection of human breast cancer cells is crucial for cancer diagnosis. In this work, plum-branched CdS/Bi2S3 heterostructures (CdS/Bi2S3 HSs) were synthesized under hydrothermal condition, whose photoelectrochemical (PEC) property and biocompatibility were scrutinously investigated. In parallel, a signal amplification strategy was designed based on immune recognition between epidermal growth factor receptor (EGFR) overexpressed on membrane of breast cancer cells MDA-MB-231 and its aptamer. Integration of the above together, a highly sensitive PEC cytosensor was developed for analysis of target MDA-MB-231 cells, exhibiting a wider linear range of 1 × 102 âˆ¼ 3 × 105 cells mL-1 with a limit of detection (LOD) down to 6 cells mL-1 (S/N = 3). Further, the biosensor was explored for anticancer drug (e.g., dacomitinib) screening by monitoring the variations in the PEC signals of the expressed EGFR upon drug stimulation. The obtained CdS/Bi2S3 HSs are identified as promising and feasible photoactive material for determination of cancer cells and drug screening in clinic and related research.

Identification of key candidate genes, pathways and related prognostic values in ER-negative/HER2-negative breast cancer by bioinformatics analysis.

PURPOSE: Breast cancer possesses different molecular expressions and biological behaviors. The purpose of this study was to identify the key genes, pathways, and related prognostic values in estrogen receptor (ER)-negative/human epidermal growth factor 2 (HER2)-negative breast cancer by bioinformatics analysis. METHODS: The mRNA expression profiles of GSE20194 and GSE23988 were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) were analyzed by GEO2R. A functional and pathway enrichment analysis of DEGs was conducted using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and a module analysis of the PPI network was conducted using Cytoscape software. Survival analysis of hub genes was analyzed using the Kaplan-Meier plotter online tool. RESULTS: 108 ER-negative/HER2-negative and 172 ER-positive/HER2-negative breast cancer samples were collected from the datasets GSE20194 and GSE23988. A total of 355 DEGs were identified in the ER-negative/HER2-negative samples, including 140 up-regulated and 215 down-regulated genes. The PPI network of DEGs consisted of 265 nodes and 648 edges. A significant module (12 nodes and 56 edges) was acquired from the PPI network of DEGs. Geneontology (GO) and pathway enrichment analysis demonstrated that this module was mainly related with transcription, cell proliferation, binding, and pathways in the PI3K-Akt signaling pathway. The high expression of CCNE1, KRT16, and MYBL2 was associated with worse relapse-free survival (RFS) and overall survival (OS) in ER-negative/HER2-negative breast cancer. CONCLUSIONS: An integrated bioinformatics analysis was utilized to discover key candidate genes and pathways in ER-negative/HER2-negative breast cancer. This can improve the understanding of molecular mechanisms and provide potential candidate genes for diagnosis, prognosis, and individualized therapy.

Poly (AT) deletion/insertion polymorphism of the XPC gene contributes to urinary system cancer susceptibility: a meta-analysis.

Numerous studies have investigated the association between xeroderma pigmentosum complementation group C (XPC) poly (AT) deletion/insertion (PAT -/+) polymorphism and cancer susceptibility; however, the findings are inconsistent. Therefore, we performed a meta-analysis based on 32 publications including 10,214 cases and 11,302 controls to acquire a more robust estimation of the relationship. We searched publications from MEDLINE, EMBASE and CBM which assessed the associations between XPC PAT -/+ polymorphism and cancer risk. We calculated pooled odds ratio (OR) and 95% confidence interval (CI) by using either fixed-effects or random-effects model. We found that individuals carrying the PAT +/+ genotype have significantly increased cancer risk (PAT +/+ vs. PAT -/-: OR=1.18, 95% CI=1.03-1.35 and recessive model: OR=1.19, 95% CI=1.06-1.33). Further stratification analysis showed a significantly increased risk for prostate cancer (PAT +/+ vs. PAT -/-: OR=2.20, 95% CI=1.39-3.48, recessive model: OR=2.07, 95% CI=1.33-3.23 and PAT + vs. PAT -: OR=1.39, 95% CI=1.12-1.71), bladder cancer (recessive model: OR=1.33, 95% CI=1.03-1.72), Caucasian ethnicity (recessive model: OR=1.21, 95% CI=1.02-1.43), population-based studies (recessive model: OR=1.23, 95% CI=1.05-1.43) and studies with relatively large sample size (PAT +/+ vs. PAT -/-: OR=1.18, 95% CI=1.04-1.35 and recessive model: OR=1.20, 95% CI=1.08-1.33). Despite some limitations, this meta-analysis established solid statistical evidence for the association between the XPC PAT +/+ genotype and cancer risk, especially for urinary system cancer, but this association warrants further validation in single large studies.