Background: Colon adenocarcinoma (COAD) is a malignant and lethal tumor in digestive system and distance metastasis lead to poor prognosis. The metastasisspecific ceRNAs (competitive endogenous RNAs) and tumor-infiltrating immune cells might associate with tumor prognosis and distance metastasis. Nonetheless, few studies have concentrated on ceRNAs and Immune cells in COAD. Methods: The gene expression profile and clinical information of COAD were downloaded from TCGA and divided into two groups: primary tumors with or without distance metastasis. We applied comprehensive bioinformatics methods to analyze differential expression genes (DEGs) related to metastasis and establish the ceRNA networks. The Cox analysis and Lasso regression were utilized to screen the pivotal genes and prevent overfitting. Based on them, the prognosis prediction nomograms were established. The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was then applied to screen significant tumor immune-infiltrating cells associated with COAD metastasis and established another prognosis prediction model. Ultimately, co-expression analysis was applied to explore the relationship between key genes in ceRNA networks and significant immune cells. Multiple databases and preliminary clinical specimen validation were used to test the expressions of key biomarkers at the cellular and tissue levels. Results: We explored 1 significantly differentially expressed lncRNA, 1 significantly differentially expressed miRNA, 8 survival-related immune-infiltrating cells, 5 immune cells associated with distance metastasis. Besides, 3 pairs of important biomarkers associated with COAD metastasis were also identified: T cells follicular helper and hsa-miR-125b-5p (R = −0.200, P < 0.001), Macrophages M0 and hsa-miR-125b-5p (R = 0.170, P < 0.001) and Macrophages M0 and FAS (R = −0.370,

Background The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC. Methods The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA). Results The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage. Conclusion A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.

Aim: Inflammation and apoptosis are main pathological processes that lead to the development of hyperuricemic nephropathy (HN). This study aims to explore whether baicalin (BA) and baicalein (BAI) can relieve the damage through PI3K/AKT/NF-κB signal pathway and provide more reliable and precise evidence for the treatment of HN.Methods: HN mice were induced by yeast extract with potassium oxonate (PO), and HK-2 cells were induced by monosodium urate (MSU). Molecular docking, western blot, q-PCR, and other methods were used to explore the changes of various indicators in HN mice and HK-2 cells.Results: Molecular docking results showed that BA and BAI had good binding ability with PI3K, AKT, p65 and IκBα. BA and BAI significantly ameliorated the levels of renal function, decreased the p-PI3K, p-AKT and p-p65 expression, down-regulated the BAX/BCL2 and CASP3, and blunted the mRNA levels of tumour necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-18 in both renal tissue of HN mice and HK-2 cells induced by MSU. BA and BAI also decreased the oxidative stress level of MSU-induced HK-2 cells.Conclusion: BA and BAI were confirmed to attenuate HN through alleviating renal inflammatory and apoptosis in cells and tissues by inhibiting PI3K/AKT/NF-κB pathway. BA and BAI were expected to be developed as new anti-HN drugs.

Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

To design a new electrochemical horseradish peroxidase (HRP) biosensor with excellent analytical performance, black phosphorene (BP) nanosheets and single-walled carbon nanotubes (SWCNTs) nanocomposites were used as the modifier, with a carbon ionic liquid electrode (CILE) as the substrate electrode. The SWCNTs-BP nanocomposite was synthesized by a simple in situ mixing procedure and modified on the CILE surface by the direct casting method. Then HRP was immobilized on the modified electrode with Nafion film. The electrocatalysis of this electrochemical HRP biosensor to various targets was further explored. Experimental results indicated that the direct electrochemistry of HRP was realized with a pair of symmetric and quasi-reversible redox peaks appeared, which was due to the presence of SWCNTs-BP on the surface of CILE, exhibiting synergistic effects with high electrical conductivity and good biocompatibility. Excellent electrocatalytic activity to trichloroacetic acid (TCA), sodium nitrite (NaNO2), and hydrogen peroxide (H2O2) were realized, with a wide linear range and a low detection limit. Different real samples, such as a medical facial peel solution, the soak water of pickled vegetables, and a 3% H2O2 disinfectant, were further analyzed, with satisfactory results, further proving the potential practical applications for the electrochemical biosensor.