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Introduction: Breast cancer (BRCA) has the highest incidence among female malignancies, and the prognosis for these patients remains poor. Materials and Methods: In this study, core modules and central genes related to BRCA were identified through a weighted gene co-expression network analysis (WGCNA). Gene expression profiles and clinical data of GSE25066 were obtained from the Gene Expression Omnibus (GEO) database. The result was validated with RNA-seq data from The Cancer Genome Atlas (TCGA) and Oncomine database. The top 30 key module genes with the highest intramodule connectivity were selected as the core genes (R 2 = 0.40). Results: According to TCGA and Oncomine datasets, seven genes were selected as candidate hub genes. Following further experimental verification, four hub genes (FAM171A1, NDFIP1, SKP1, and REEP5) were retained. Conclusion: We identified four hub genes as candidate biomarkers for BRCA. These hub genes may provide a theoretical basis for targeted therapy against BRCA.

Three different types of feedstocks and their biochars were used to remove Cr(III), Cd(II), Cu(II) and Pb(II) ions from a mixture of multiple heavy metals. The effect of the initial concentration of heavy metals in solution has been analysed, and kinetics modelling and a comparison of the adsorption capacity of such materials have been performed to elucidate the possible adsorption mechanisms. The results show that the adsorption capacity is dependent on the type of feedstock and on the pyrolysis conditions. The adsorption capacity of the biomass types is ranked as follows: FO (from sewage sludge)>> LO > ZO (both from agriculture biomass waste)>> CO (from wood biomass waste). Biochars, which are the product of the pyrolysis of feedstocks, clearly improve the adsorption efficiency in the case of those derived from wood and agricultural biomasses. Complexation and cation exchange have been found to be the two main adsorption mechanisms in systems containing multiple heavy metals, with cation exchange being the most significant. The pore structure of biomass/biochar cannot be neglected when investigating the adsorption mechanism of each material. All the disposal biomasses presented here are good alternatives for heavy metal removal from wastewaters.

Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module ( R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.