Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC

Whole transcriptome expression profiles in kidney samples from rats with hyperuricaemic nephropathy.

Hyperuricaemic nephropathy (HN) is a common clinical complication of hyperuricaemia (HUA) and poses a huge threat to human health. Hence, we aimed to prospectively investigate the dysregulated genes, pathways and networks involved in HN by performing whole transcriptome sequencing using RNA sequencing. Six kidney samples from HN group (n = 3) and a control group (n = 3) were obtained to conduct RNA sequencing. To disclose the relevant signalling pathways, we conducted the analysis of differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A competitive endogenous RNA (ceRNA) network was established to reveal the interactions between lncRNAs, circRNAs, mRNAs and miRNAs and investigate the potential mechanisms of HN. Ultimately, 2250 mRNAs, 306 lncRNAs, 5 circRNAs, and 70 miRNAs were determined to be significantly differentially expressed in the HN group relative to the control group. We further authenticated 8 differentially expressed (DE)-ncRNAs by quantitative real-time polymerase chain reaction, and these findings were in accordance with the sequencing results. The analysis results evidently showed that these DE-ncRNAs were significantly enriched in pathways related to inflammatory reaction. In conclusion, HUA may generate abnormal gene expression changes and regulate signalling pathways in kidney samples. Potentially related genes and pathways involved in HN were identified