Based on a systematic review and meta-analysis of articles published in PubMed, Embase, Cochrane, and Web of Science, we identified nine articles that provide evidence of the relationship between persistent organic pollutants and hyperuricemia. Our researchers assess the quality of the included studies and their risk of bias using the recommended method and tool. This study uses meta-analyses of the random effects of each exposure and outcome to estimate combined odds ratios (ORs) and 95% confidence intervals (CIs). We found that the risk of hyperuricemia was strongly associated with three perfluorinated compounds, PFNA, PFOA, and PFOS, with the OR(95%CI) of 1.26 (1.07-1.47), 1.44(1.15-1.79), and 1.23(1.01-1.50) respectively. We also found a weak association between two other perfluorinated compounds, PFDA and PFHxS. Other than that, the summary ORs (95% CIs) of incident hyperuricemia were 2.34 (1.79-3.08) for DDT, 3.25(2.40-4.39) for DDE, 2.57 (1.37-4.81) for PCBs and 3.05(2.22-4.19) in trans-nonanchlor. Therefore, DDT and its breakdown product, DDE, PCBs, and trans-nonanchlor have also been linked with an increased risk of hyperuricemia in humans. This study finds that persistent organic pollutant is a critical factor for hyperuricemia, and further studies in specific regions will be considered in the future.

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.