The prognostic value of tumor mutation burden (TMB) and its relationship with immune infiltration in breast cancer patients.

OBJECTIVE: Although the tumor mutation burden (TMB) was reported as a biomarker for immunotherapy of various cancers, whether it can effectively predict the survival prognosis in breast cancer patients remains unclear. In this study, the prognostic value of TMB and its correlation with immune infiltration were explored by using multigroup studies. METHODS: The somatic mutation data of 986 breast cancer patients were obtained from TCGA database. Breast cancer patients were divided into a low-TMB group and a high-TMB group according to the quartile of TMB scores. The differentially expressed genes (DEGs) were identified by the "limma" R program. The CIBERSORT algorithm was utilized to estimate the immune cell fraction of each sample. The TIMER database was utilized to evaluate the association between CNVs of immune genes and tumor immune cell infiltration and the prognostic value of the immune cells in breast cancer. RESULTS: In breast cancer, TP53, PIK3CA, TTN, CDH1 and other genes were the most important mutated genes. Higher survival rate of patients was found in the low-TMB group. Among the top 10 DEGs, three of them belong to the KRT gene family. GSEA enrichment analysis showed that MAPK, Hedgehog, mTOR, TGF-bate and GnRH signaling pathways were enriched in the low-TMB group. The infiltration levels of the most of immune cells were higher in the low-TMB group (P < 0.01). Higher expression of CCL18 and TRGC1 was correlated with poor prognosis. Breast cancer patients with CCL18 copy number variations, especially arm-level gains, showed significantly decreased immune cell infiltration. In the low B cell infiltration group, the survival prognosis of breast cancer patients was poor. CONCLUSIONS: TMB is a potential prognosis marker in breast cancer. Immune-related gene CCL18 and TRGC1 are biomarkers of poor prognosis while immune (B cell) infiltration is a biomarker of good prognosis.

Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis.

Background: Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses. Methods: Gene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape software platform. The expression of hub genes in NSCLC was validated through the GEPIA database. Kaplan-Meier plotter was used to analyse the survival rate with multivariate Cox regression. The expression of protein tyrosine kinase 2 (PTK2) in NSCLC and adjacent lung tissues was evaluated on the UALCAN database platform. Results: A total of 225 significant DEGs were obtained between NSCLC and adjacent lung tissues, containing 52 upregulated genes and 173 downregulated genes. The DEGs were clustered based on functions and signaling pathways that may be closely associated with NSCLC occurrence. A total of 174 DEGs were identified from the PPI network complex. Top 10 hub genes were selected by CytoHubba plugin. As independent predictors, seven genes (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with poor prognosis in NSCLC via multivariate Cox regression (P<0.01). Four genes (VWF, CAV1, ITGA8, COL1A1) were found to be significantly enriched in the focal adhesion pathway (P=1.04E-04) and to be upstream regulators of PTK2. PTK2 was upregulated in NSCLC and associated with poor survival prognosis in lung squamous cell carcinoma (LUSC). Conclusions: Taken together, the important genes and pathways in NSCLC were identified by using integrated bioinformatics analysis. PTK2 could be a key gene associated with the biological process of NSCLC formation and progression and a potential therapeutic target for NSCLC treatment.

Structural characterization and antioxidant effect of green alga Enteromorpha prolifera polysaccharide in Caenorhabditis elegans via modulation of microRNAs.

A water-soluble heteropolysaccharide was isolated and purified from Enteromorpha prolifera by DEAE-52 and Bio-Gel P-2 column chromatography. Fourier transform infrared spectroscopy (FTIR), high performance liquid chromatography (HPLC), multi-angle laser light scattering (MALLS), and nuclear magnetic resonance (NMR) spectroscopy were used to characterize the structure of E. prolifera polysaccharide degradation (EPP-1). Its anti-oxidative activity was determined in Caenorhabditis elegans via modulation of microRNAs. The average molecular weight of EPP-1 was 4.28 kDa. It contained six types of linkage units as →2)-ß-d-GlcpA-(1→, →3,6)-ß-d-Manp-(1→, →4)-α-d-Glcp-(1→, →6)-ß-d-Galp-(1→, ß-l-Rhap-(1→, and →4)-ß-d-GalpA-(1→. The mean lifespan, ultraviolet-induced oxidative stress, and thermotolerance in C. elegans were improved after treatment of EPP-1. Moreover, EPP-1 significantly increased the total superoxide dismutase levels and decreased the malondialdehyde levels in C. elegans. Intracellular reactive oxygen species accumulation and DNA damage were ameliorated by up-regulation of SKN-1 and DAF-16 expression through miR-48 and miR-51 miR-186 down-regulation. In vivo studies demonstrated that EPP-1 might be applied in functional foods as the antioxidative and anti-ageing ingredient.

Nutrient Properties and Nuclear Magnetic Resonance-Based Metabonomic Analysis of Macrofungi.

Many delicious and nutritional macrofungi are widely distributed and used in East Asian regions, considered as edible and medicinal foods. In this study, 11 species of dried and fresh, edible and medicinal macrofungi, Ganoderma amboinense, Agaricus subrufescens, Dictyophora indusiata, Pleurotus sajor-caju, Pleurotus ostreatus, Pleurotus geesteranu, Hericium erinaceus, Stropharia rugosoannulata, Pleurotus sapidus, Antrodia camphorata, and Lentinus edodes (Berk.) Sing, were investigated to determine the content of their nutritional components, including proteins, fat, carbohydrates, trace minerals, coarse cellulose, vitamins, and amino acids. The amino acid patterns and similarity of macrofungi were distinguished through principal component analysis and hierarchical cluster analyses, respectively. A total of 103 metabolic small molecules of macrofungi were identified by nuclear magnetic resonance spectroscopy and were aggregated by heatmap. Moreover, the macrofungi were classified by principal component analysis based on these metabolites. The results show that carbohydrates and proteins are two main components, as well as the nutritional ingredients, that differ among various species and varied between fresh and dried macrofungi. The amino acid patterns in L. edodes and A. subrufescens were different compared with that of the other tested mushrooms.