Yingying Wu's research while affiliated with First Affiliated Hospital of China Medical University and other places

Oxidative stress refers to the process of reactive oxide species (ROS) increase in human body due to various factors, which leads to oxidative damage in human tissues. Current studies have confirmed that sustained oxidative stress is one of the distinctive features throughout the development of tumors. Numerous reports have shown that lncRNAs can regulate the process of oxidative stress through multiple pathways. However, the relationship between glioma-associated oxidative stress and lncRNAs is not clearly investigated. RNA sequencing data of GBM (glioblastoma) and LGG (low grade glioma) and corresponding clinical data were retrieved from the TCGA database. Oxidative stress related lncRNAs (ORLs) were identified by Pearson correlation analysis. Prognostic models for 6-ORLs were structured in the training cohort by univariate Cox regression analysis, multivariate Cox regression analysis and LASSO regression analysis. We constructed the nomogram and verified its predictive efficacy by Calibration curves and DCA decision curves. The biological functions and pathways of 6-ORLs-related mRNAs were inferred by Gene Set Enrichment Analysis. Immune cell abundance and immune function associated with risk score (RS) were estimated by ssGSEA, CIBERSORT and MCPcounter synthetically. External validation of the signature was completed using the CGGA-325 and CGGA-693 datasets. 6-ORLs signature—AC083864.2, AC107294.1, AL035446.1, CRNDE, LINC02600, and SNAI3-AS1—were identified through our analysis as being predictive of glioma prognosis. Kaplan–Meier and ROC curves indicated that the signature has a dependable predictive efficacy in the TCGA training cohort, validation cohort and CGGA-325/CGGA-693 test cohort. The 6-ORLs signature were verified to be independent prognostic predictors by multivariate cox regression and stratified survival analysis. Nomogram built with risk scores had strong predictive efficacy for patients' overall survival (OS). The outcomes of the functional enrichment analysis revealing potential molecular regulatory mechanisms for the 6-ORLs. Patients in the high-risk subgroup presented a significant immune microenvironment of macrophage M0 and cancer-associated fibroblast infiltration which was associated with a poorer prognosis. Finally, the expression levels of 6-ORLs in U87/U251/T98/U138 and HA1800 cell lines were verified by RT-qPCR. The nomogram in this study has been made available as a web version for clinicians. This 6-ORLs risk signature has the capabilities to predict the prognosis of glioma patients, assist in evaluating immune infiltration, and assess the efficacy of various anti-tumor systemic therapy regimens.

Background: The Unc-93 homolog B1 (UNC93B1) is a transmembrane protein that is associated with immune diseases such as influenza, herpes simplex encephalitis, and systemic lupus erythematosus; however, the role of UNC93B1 in cancer (including human breast cancer) is known less. The analysis of the association between UNC93B1 expression and breast cancer survival is also unclear. Methods: We used multiple online databases including Oncomine, GEPIA, bcGenExMiner v4.6 and PrognoScan, to conduct bioinformatics analysis of clinical parameters and survival data related to UNC93B1 in breast cancer patients. Results: It was found that UNC93B1 was expressed in different subtypes of breast cancer compared to normal tissues. Scarff-Bloom-Richardson (SBR) classification, Nottingham prognostic index (NPI), estrogen receptor (ER) negative, progesterone receptor (PR) negative epidermal growth factor receptor-2 (HER2) positive and lymph node positive are positively correlated with the UNC93B1 level. We found that increased expression of UNC93B1 was associated with worse relapse-free survival, disease-specific survival, and overall survival. We also confirmed the positive correlation between UNC93B1 and ALDH3B1 gene expression. Conclusion: The lower expression of UNC93B1 was correlated with better clinical prognostic parameters and clinical survival in breast cancer on the basis of the bioinformatic analysis.