Changes of TIZ expression in epithelial ovarian cancer cells

A combined biomarker panel shows improved sensitivity and specificity for detection of ovarian cancer

Background

Combined biomarkers can improve the sensitivity and specificity of ovarian cancer (OC) diagnosis and effectively predict patient prognosis. This study explored the diagnostic and prognostic values of serum CCL18 and CXCL1 antigens combined with C1D, FXR1, ZNF573, and TM4SF1 autoantibodies in OC.

Methods

CCL18 and CXCL1 monoclonal antibodies and C1D, FXR1, ZNF573, and TM4SF1 antigens were coated with microspheres. Logistic regression was used to construct a serum antigen‐antibody combined detection model; receiver‐operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of the model; and the Kaplan‐Meier method and Cox regression models were used for survival analysis to evaluate the prognosis of OC. Data from The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) projects and online survival analysis tools were used to evaluate prognostic genes for OC. The CIBERSORT immune score was used to explore the factors influencing prognosis and their relationship with tumor‐infiltrating immune cells.

Results

The levels of each index in the blood samples of patients with OC were higher than those of the other groups. The combined detection model has higher specificity and sensitivity in the diagnosis of OC, and its diagnostic efficiency is better than that of CA125 alone and diagnosing other malignant tumors. CCL18 and TM4SF1 may be factors affecting the prognosis of OC, and CCL18 may be related to immune‐infiltrating cells.

Conclusions

The serum antigen‐antibody combined detection model established in this study has high sensitivity and specificity for the diagnosis of OC.

Role of oxidative stress in Retinitis pigmentosa: new involved pathways by an RNA-Seq analysis

Retinitis pigmentosa (RP) is a very heterogeneous inherited ocular disorder group characterized by progressive retinal disruption. Retinal pigment epithelium (RPE) degeneration, due to oxidative stress which arrests the metabolic support to photoreceptors, represents one of the principal causes of RP. Here, the role of oxidative stress in RP onset and progression was analyzed by a comparative whole transcriptome analysis of human RPE cells, treated with 100 µg/ml of oxLDL and untreated, at different time points. Experiment was thrice repeated and performed on Ion Proton^TM sequencing system. Data analysis, including low quality reads trimming and gene expression quantification, was realized by CLC Genomics Workbench software. The whole analysis highlighted 14 clustered "macro-pathways" and many sub-pathways, classified by selection of 5271 genes showing the highest alteration of expression. Among them, 23 genes were already known to be RP causative ones (15 over-expressed and 8 down-expressed), and their enrichment and intersection analyses highlighted new 77 candidate related genes (49 over-expressed and 28 down-expressed). A final filtering analysis then highlighted 29 proposed candidate genes. This data suggests that many new genes, not yet associated with RP, could influence its etiopathogenesis.

Identification of six serum antigens and autoantibodies for the detection of early stage epithelial ovarian carcinoma by bioinformatics analysis and liquid chip analysis

The early detection of ovarian cancer is critical for improving the prognosis of patients, but there are currently insufficient tumor biomarkers for early detection owing to their low diagnostic sensitivity and specificity. The aim of the present study was to investigate the use of the serum antigens C-C motif chemokine ligand 18 and C-X-C motif chemokine ligand 1, and autoantibodies C1D, transmembrane 4 L six family member 1, zinc finger protein 675 and fragile X mental retardation 1 autosomal homolog 1, for the early screening of epithelial ovarian cancer (EOC). The expression of these sex genes/proteins in ovarian cancer and normal ovarian tissue was examined, and the potential functions of the six genes/proteins in ovarian cancer were analyzed by bioinformatics. Finally, these data were verified in clinical samples, and the multi-analyte suspension array method was compared with the ELISA method. Taken together, these data indicated that these six genes/proteins may serve as potential biomarkers for the early detection of EOC.