Xu Cheng

Identification of Five m6A-Related lncRNA Genes as Prognostic Markers for Endometrial Cancer Based on TCGA Database

Background. Due to difficulties involved in its early diagnosis and adequate prognostication, uterine corpus endometrial carcinoma (UCEC) is one of the most serious threats to human health, with the five-year survival rate being as low as roughly 60%. The discovery of specific biomarkers that serve as prognosticators of UCEC is of great significance. The role of N6-methyladenosine- (m6A-) related long noncoding RNAs (lncRNAs) in the pathogenesis of UCEC remains undefined. In this study, we explored the expression profiles of m6A-related lncRNAs of patients with UCEC and identified novel prognostic markers for UCEC. Methods. Gene expression and clinical data were extracted from The Cancer Genome Atlas. Coexpression analysis was performed to identify m6A-related lncRNAs, which were entered into univariate Cox regression models for evaluating the prognosis of UCEC. Clusters of UCEC patients and enrichment pathways were identified using consistent data clustering and gene set enrichment analysis (GSEA). A risk score model was established, and Kaplan–Meier analysis was conducted for investigating overall survival (OS) across two patient groups (high risk and low risk). Lastly, the relationship between the risk score and the cell content of 22 types of immune cells, clusters, age, programmed cell death 1 ligand-1 (PD-L1) expression level, immune score, and pathological grade was analyzed. Results. We identified a total of 2084 lncRNAs associated with m6A, of which 32 lncRNAs were prognostically relevant. Two clusters (clusters 1 and 2) of patients with UCEC were defined; patients in cluster 1 were found to have significantly higher pathological grades and shorter overall survival time compared to those in cluster 2. GSEA showed that “MITOTIC SPINDLE and other pathways” were more enriched in cluster 1. Five major lncRNAs associated with m6A were screened out, and risk score modeling was used for UCEC prognosis prediction. High risk scores were associated with a shorter OS. The risk score was also verified as an independent prognostic indicator for UCEC and was related to immune cell infiltration levels. Finally, we observed a higher pathological grade and greater levels of PD-L1 in the high-risk group than in the low-risk group of patients. Conclusions. m6A-related lncRNAs play an important role in UCEC progression. The risk-based model constructed from the five key m6A-related lncRNAs was implicated in immune cell infiltration and can potentially be an accurate prognosticator for UCEC.

Screening and Discovery of New Potential Biomarkers and Small Molecule Drugs for Cervical Cancer: A Bioinformatics Analysis

Background: Cervical cancer (CC) is the second most common type of malignant tumor survival rate is low in advanced stage, metastatic, and recurrent CC patients. This study aimed at identifying potential genes and drugs for CC diagnosis and targeting therapies. Methods: Three GEO mRNA microarray datasets of CC tissues and non-cancerous tissues were analyzed for differentially expressed genes (DEGs) by limma package. GO (Gene Ontologies) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to explore the relationships between the DEGs. Protein-protein interaction (PPI) of these genes was established by the STRING database. MCODE was used for screening significant modules in the PPI networks to select hub genes. Biochemical mechanisms of the hub genes were investigated with Metascape. GEPIA database was used for validating the core genes. According to these DEGs, molecular candidates for CC were recognized from the CMAP database. Results: We identified 309 overlapping DEGs in the 2 tissue-types. Pathway analysis revealed that the DEGs were involved in cell cycle, DNA replication, and p53 signaling. PPI networks between overlapping DEGs showed 68 high-connectivity DEGs that were chosen as hub genes. The GEPIA database showed that the expression levels of RRM2, CDC45, GINS2, HELLS, KNTC1, MCM2, MYBL2, PCNA, RAD54 L, RFC4, RFC5, TK1, TOP2A, and TYMS in CC tissues were significantly different from those in the healthy tissues and were significantly relevant to the OS of CC. We found 10 small molecules from the CMAP database that could change the trend of gene expression in CC tissues, including piperlongumine and chrysin. Conclusions: The 14 DEGs identified in this study could serve as novel prognosis biomarkers for the detection and forecasting of CC. Small molecule drugs like piperlongumine and chrysin could be potential therapeutic drugs for CC treatment.