A 4-Gene Signature Associated With Recurrence in Low- and Intermediate-Risk Endometrial Cancer

HAGLR, A Long Non-coding RNA of Potential Tumor Suppressive Function in Clear Cell Renal Cell Carcinoma: Diagnostic and Prognostic Implications

Research works suggested the role of long non-coding RNAs (lncRNAs) in pathogenesis of clear cell renal cell carcinoma (ccRCC). lncRNA HAGLR is studied in several malignancies, but not in ccRCC. From The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, we analyzed molecular alterations of HAGLR and constructed a competitive endogenous RNA (ceRNA) network with related miRNAs and mRNAs. Gene Ontology analysis was done to identify important pathways enriched with HAGLR recovered mRNAs. Clinical importance of HAGLR and related mRNAs was assessed and, the impact of selected mRNA-encoding genes on tumor immune infiltration was studied using TIMER. HAGLR expression was reduced in ccRCC than in normal kidneys, and correlated significantly with gene promoter methylation. Low HAGLR level in tumors showed diagnostic potency, and was associated with clinicopathological parameters (stage/grade/metastasis) and poor patient survival. The HAGLR-associated ceRNA network constituted 13 miRNAs and 23 mRNAs differentially expressed in the TCGA-KIRC dataset. From HAGLR recovered mRNA-encoding genes, we developed a 5-gene (PAQR5, ARHGAP24, HABP4, PDLIM5, and RPS6KA2) prognostic signature in the training dataset and validated it in testing as well as entire datasets. The expression level of signature genes showed negative correlation with tumor infiltration of immune cells having adverse impact on ccRCC prognosis and also with tumor derived chemokines facilitating the infiltration. In conclusion, HAGLR seemed to play a tumor suppressive role in ccRCC. HAGLR and associated gene signature may have implementation in improving existing prognostic measure and developing effective immunotherapeutic strategies for ccRCC.

Transcriptomic analysis identifies four novel receptors potentially linking endometrial cancer with polycystic ovary syndrome and generates a transcriptomic atlas

Polycystic Ovary Syndrome (PCOS) is associated with a 3 to 4-fold increased risk of endometrial cancer (EC), but molecular mechanisms are unclear. Upregulation of the IGF1 gene in PCOS endometrium may increase EC risk, but this is uncertain. We aimed to investigate links between EC and PCOS, by analysing publicly available transcriptomic data. The NCBI Gene Expression Omnibus was used to identify relevant studies. Differentially expressed genes (DEGs) were identified and analysed using Metascape to identify pathways of interest. PCOS DEGs that encode proteins secreted into blood were identified using the Human Protein Atlas blood protein database. EC DEGs that are cellular receptors were identified using EcoTyper. These were intersected to identify which EC receptors interact with PCOS secreted proteins. Seven receptors were identified in EC but only PTPRF, ITGA2, ITGA3 and ITGB4 genes were expressed on epithelial cells. Pathway enrichment of these genes showed that the major and common pathway involved was that of the PI3K-AKT signalling pathway which was consistent with a link between PCOS and EC. However, IGF1 was down regulated in PCOS and EC. These findings hold significant promise for improving our understanding of mechanistic pathways leading to EC in PCOS.

Identification of key biomarkers related to epithelial-mesenchymal transition and immune infiltration in ameloblastoma using integrated bioinformatics analysis

OBJECTIVE

This study aimed to elucidate the underlying mechanisms of ameloblastoma (AM) through integrated bioinformatics analysis.

METHODS

We downloaded two microarrays of AMs from the GEO database and identified differentially expressed genes (DEGs) by integrated bioinformatics analysis. The enrichment analysis of DEGs was conducted to characterize GO and KEGG pathways. Protein-protein interaction (PPI) network and hub genes were screened via STRING and Cytoscape. CIBERSORT algorithm was utilized to analyze immune infiltration in AMs. We also verified the diagnostic and therapeutic value of hub genes.

RESULTS

Overall, 776 DEGs were identified in AMs through bioinformatics analysis. The function enrichment analysis shed light on pathways involved in AMs. Subsequently, we screened six hub genes via PPI network. Furthermore, we evaluated immune infiltration in AMs and found that macrophages may be participating in the progression of AMs. The upregulated expression of FN1 was related to the macrophages M2 polarization. Finally, ROC analysis indicated that six hub genes had high diagnostic value for AMs and 11 drugs interacted with upregulated hub genes were identified by screening the DGIdb database.

CONCLUSION

This study revealed the underlying mechanisms of pathogenesis and biological behavior of AMs and provided candidate targets for the diagnosis and treatment of AMs.