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Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

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Vascular calcification frequently occurs in the process of chronic kidney disease, atherosclerosis and aging, resulting in an increased prevalence of cardiovascular events. Piperlongumine (PLG) is a natural product isolated from Piper longum L. Here, we aimed to explore the effect of PLG in high calcium- and phosphate-induced vascular calcification and the associated mechanism. Flow cytometry assays showed that PLG at concentrations <10 μM did not promote vascular smooth muscle cells (VSMCs) apoptosis, and PLG at concentrations >2.5 μM inhibited VSMCs proliferation. Thus, 2.5 μM PLG was selected for subsequent experiments. Alizarin red staining and ALP activity assays showed that PLG inhibited calcium deposition of VSMCs treated with high calcium/phosphate medium. PLG also decreased the expression of osteogenic genes and proteins, including Runx2, Bmp2, and OPN, as determined by qRT-PCR and western blotting. In a vitamin D-induced aortic calcification mouse model, a 5 mg/kg dose of PLG decreased calcium deposition in the aortic wall as well as Runx2 expression. With regard to the mechanism, we found that the levels of P53 mRNA and protein in both VSMCs and mouse aortic tissues were decreased in the calcification models, and we observed that PLG preserved the levels of P53 and its downstream gene PTEN. Concurrent treatment of VSMCs with P53 ShRNA and PLG blunted the anti-calcific effect of PLG. In conclusion, PLG attenuates high calcium/phosphate-induced vascular calcification by upregulating P53/PTEN signaling in VSMCs. PLG may act as a promising herbal extract for the clinical management of vascular calcification.

Excessive manganese (Mn) exposure may adversely affect the central nervous system, and cause an extrapyramidal disorder known as manganism. The glutamine (Gln)/glutamate (Glu)–γ-aminobutyric acid (GABA) cycle and thyroid hormone system may be involved in Mn-induced neurotoxicity. However, the effect of Mn on the Gln/Glu–GABA cycle in the serum has not been reported. Herein, the present study aimed to investigate the effects of sub-acute Mn exposure on the Gln/Glu–GABA cycle and thyroid hormones levels in the serum of rats, as well as their relationship. The results showed that sub-acute Mn exposure increased serum Mn levels with a correlation coefficient of 0.733. Furthermore, interruption of the Glu/Gln–GABA cycle in serum was found in Mn-exposed rats, as well as thyroid hormone disorder in the serum via increasing serum Glu levels, and decreasing serum Gln, GABA, triiodothyronine (T3) and thyroxine (T4) levels. Additionally, results of partial correlation showed that there was a close relationship between serum Mn levels and the detected indicators accompanied with a positive association between GABA and T3 levels, as well as Gln and T4 levels in the serum of Mn-exposed rats. Unexpectedly, there was no significant correlation between serum Glu and the serum T3 and T4 levels. In conclusion, the results demonstrated that both the Glu/Gln–GABA cycle and thyroid hormone system in the serum may play a potential role in Mn-induced neurotoxicity in rats. Thyroid hormone levels, T3 and T4, have a closer relationship with GABA and Gln levels, respectively, in the serum of rats.

BackgroundImmune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS.MethodsWe obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.ResultsThe merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.ConclusionFive immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, and MORF4L2) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

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