C77G in PTPRC (CD45) is no risk allele for ovarian cancer, but associated with less aggressive disease

Bioinformatics analysis of key biomarkers for bladder cancer

Bladder cancer (BC) is one of the most prevalent genitourinary cancers. Despite the growing research interest in BC, the molecular mechanisms underlying its carcinogenesis remain poorly understood. The microarray datasets GSE38264 and GSE61615 obtained from the Gene Expression Omnibus (GEO) database were analyzed and differentially expressed genes (DEGs) were identified, which were then verified using a dataset from The Cancer Genome Atlas (TCGA). By taking the intersection of the two microarray datasets, the common DEGs were identified and these were selected as candidate genes associated with BC. The DEGs were further subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and the protein-protein interaction network was constructed. Further module analysis was performed using STRING and Cytoscape. A total of 362 DEGs were identified, including 13 hub genes, and the GO analysis revealed that these genes were mainly enriched in extracellular matrix organization, positive regulation of cell proliferation, angiogenesis and peptidyl-tyrosine phosphorylation. The expression changes of PTPRC, PDGFRA, CASQ2, TGFBI, KLRD1 and MT1X in the different datasets indicated that these genes were involved in the development of BC. Next, the differential expression of these genes was verified in the TCGA dataset, and ultimately, these 13 genes were determined to be related to the occurrence and development of BC. Finally, the cancer tissues and adjacent tissues of patients with BC were collected and subjected to reverse transcription-quantitative PCR, the results of which were consistent with the bioinformatics prediction. The present findings provide several vital genes for the clinical diagnosis and treatment of BC.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

An Integrative Bioinformatics Analysis of the Potential Mechanisms Involved in Propofol Affecting Hippocampal Neuronal Cells

The aim of this study is to probe the possible molecular mechanisms underlying the effects of propofol on HT22 cells. HT22 cells treated with different concentrations were sequenced, and then the results of the sequencing were analyzed for dynamic trends. Expression pattern clustering analysis was performed to demonstrate the expression of genes in the significant trend modules in each group of samples. We first chose the genes related to the trend module for WGCNA analysis, then constructed the PPI network of module genes related to propofol treatment group, and screened the key genes. Finally, GSEA analysis was performed on the key genes. Overall, 2,506 genes showed a decreasing trend with increasing propofol concentration, and 1,871 genes showed an increasing trend with increasing propofol concentration. WGCNA analysis showed that among them, turquoise panel genes were negatively correlated with propofol treatment, and genes with Cor R >0.9 in the turquoise panel were selected for PPI network construction. The MCC algorithm screened a total of five key genes (CD86, IL10RA, PTPRC, SPI1, and ITGAM). GSEA analysis showed that CD86, IL10RA, PTPRC, SPI1, and ITGAM are involved in the PRION_DISEASES pathway. Our study showed that propofol sedation can affect mRNA expression in the hippocampus, providing new ideas to identify treatment of nerve injury induced by propofol anesthesia.

Effect of MHC Linked 7-Gene Signature on Delayed Hepatocellular Carcinoma Recurrence

Dysregulated immune response significantly affects hepatocellular carcinoma's (HCC) prognosis. Human Leukocyte Antigens are key in devising immune responses against HCC. Here, we investigated how HLAs modulate HCC development at the transcriptomic level. RNA-seq data of 576 patients from two independent cohorts was retrieved. The clinicopathological relevance of all HLA genes was investigated using Fisher-Exact, correlation, and Kaplan-Meier and cox regression survival tests. Clustering of ~800 immune-related genes against HLAs was completed using a ward-agglomerative method. Networks were generated using 40 HLA associated unique genes and hub genes were investigated. HLAs including HLA-DMA, HLA-DMB, HLA-DOA and HLA-DRB6 were associated with delayed recurrence in both discovery (204 HCC cases) and validation (372 HCC cases) cohorts. Clustering analyses revealed 40 genes associated with these four HLAs in both cohorts. A set of seven genes (NCF4, TYROBP, LCP2, ZAP70, PTPRC, FYN and WAS) was found co-expressed at gene-gene interaction level in both cohorts. Furthermore, survival analysis revealed seven HLA-linked genes as predictors of delayed recurrence. Multivariate analysis also predicted that mean expression of 7-gene is an independent predictor of delayed recurrence in both cohorts. We conclude that the expression of 7-gene signature may lead to improved patient prognosis. Further studies are required for consideration in clinical practice.

Upregulation Of Protein Tyrosine Phosphatase Receptor Type C Associates To The Combination Of Hashimoto's Thyroiditis And Papillary Thyroid Carcinoma And Is Predictive Of A Poor Prognosis

Introduction

PTC is not generally considered a lethal disease, but prone to recurrence as the prognosis. Hashimoto’s thyroiditis (HT) is an important factor that affects the prognosis of papillary thyroid carcinoma (PTC). It is crucial to find biomarkers to identify the combination of HT with PTC and to predict the prognosis.

Methods

RNASeq data from the Cancer Genome Atlas (TCGA) database was used to screen differentially expressed genes (DEGs) of PTC with HT via the edgeR package of R software version 3.3.0. Also, the DEGs were applied to the DAVID web-based tool to determine the enrichment of gene functions via Gene Ontology (GO) analysis and to identify associated pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. By constructing protein interaction networks within Cytoscape software, we screened candidate genes and explored possible relationships with the clinical phenotype of PTC. Finally, additional thyroid tissue samples were collected to verify the results above.

Results

After analyzing the RNA-Seq data of PTC patients from the Cancer Genomic Atlas, 497 differentially expressed PTC genes were found to be associated with HT, of which protein tyrosine phosphatase receptor type C (PTPRC), KIT, and COL1A1 were associated with tumor size and lymph node metastasis (p < 0.05). Verification of these results with another 30 thyroid tissues of clinical PTC patients revealed that the expression level of PTPRC in the PTC with HT group was higher than that in the PTC without HT group (p < 0.05) and the ROC curve showed a good discrimination (area under the curve = 0.846). However, the correlation with the clinical phenotype was not statistically significant (p > 0.05).

Discussion

These data suggest that upregulation of PTPRC enhances the incidence of HT associated with PTC and is also predictive of a poor prognosis.

CD45 in human physiology and clinical medicine

CD45 is an evolutionary highly conserved receptor protein tyrosine phosphatase exclusively expressed on all nucleated cells of the hematopoietic system. It is characterized by the expression of several isoforms, specific to a certain cell type and the developmental or activation status of the cell. CD45 is one of the key players in the initiation of T cell receptor signaling by controlling the activation of the Src family protein-tyrosine kinases Lck and Fyn. CD45 deficiency results in T- and B-lymphocyte dysfunction in the form of severe combined immune deficiency. It also plays a significant role in autoimmune diseases and cancer as well as in infectious diseases including fungal infections. The knowledge collected on CD45 biology is rather vast, but it remains unclear whether all findings in rodent immune cells also apply to human CD45. This review focuses on human CD45 expression and function and provides an overview on its ligands and role in human pathology.