Identification of long non-coding RNA biomarkers and signature scoring, with competing endogenous RNA networks- targeted drug candidates for recurrent implantation failure

Emerging role of lncRNAs in the etiology of recurrent implantation failure

Recurrent implantation failure (RIF) is a complex clinical entity with several molecular pathways contributing to its pathogenesis. Long non-coding RNAs (lncRNAs) have recently been found to affect the normal implantation, thus aberrant expression of these transcripts is involved in RIF. Altered expression of HOXA11-AS, NONHSAT193031.1, NONHSAT053761.2, NONHSAT083203.2, LUCAT1, PART1, TUNAR, LINC02190, lncSAMD11-1:1 and H19 has been reported in this condition. Moreover, polymorphisms within some lncRNAs have been shown to be associated with miscarriage/RIF. The current review article summarizes the recent data about the role of lncRNAs in RIF. This information would pave the way for identification of the molecular events in this context.

Identifying potential biomarkers for the diagnosis and treatment of IgA nephropathy based on bioinformatics analysis

BACKGROUND

IgA nephropathy (IgAN) has become the leading cause of end-stage renal disease in young adults. Nevertheless, the current diagnosis exclusively relies on invasive renal biopsy, and specific treatment is deficient. Thus, our study aims to identify potential crucial genes, thereby providing novel biomarkers for the diagnosis and therapy of IgAN.

METHODS

Three microarray datasets were downloaded from GEO official website. Differentially expressed genes (DEGs) were identified by limma package. GO and KEGG analysis were conducted. Tissue/organ-specific DEGs were distinguished via BioGPS. GSEA was utilized to elucidate the predominant enrichment pathways. The PPI network of DEGs was established, and hub genes were mined through Cytoscape. The CTD database was employed to determine the association between hub genes and IgAN. Infiltrating immune cells and their relationship to hub genes were evaluated based on CIBERSORT. Furthermore, the diagnostic effectiveness of hub markers was subsequently predicted using the ROC curves. The CMap database was applied to investigate potential therapeutic drugs. The expression level and diagnostic accuracy of TYROBP was validated in the cell model of IgAN and different renal pathologies.

RESULTS

A total of 113 DEGs were screened, which were mostly enriched in peptidase regulator activity, regulation of cytokine production, and collagen-containing extracellular matrix. Among these DEGs, 67 genes manifested pronounced tissue and organ specificity. GSEA analysis revealed that the most significant enriched gene sets were involved in proteasome pathway. Ten hub genes (KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, and ITGB2) were recognized. CTD showed a close connection between ALB, IGF, FN1 and IgAN. Immune infiltration analysis elucidated that IGF1, EGF, HRG, FN1, ITGB2, and TYROBP were closely associated with infiltrating immune cells. ROC curves reflected that all hub genes, especially TYROBP, exhibited a good diagnostic value for IgAN. Verteporfin, moxonidine, and procaine were the most significant three therapeutic drugs. Further exploration proved that TYROBP was not only highly expressed in IgAN, but exhibited high specificity for the diagnosis of IgAN.

CONCLUSIONS

This study may offer novel insights into the mechanisms involved in IgAN occurrence and progression and the selection of diagnostic markers and therapeutic targets for IgAN.

Three Oxidative Stress-Related Genes That Associate Endometrial Immune Cells Are Considered as Potential Biomarkers for the Prediction of Unexplained Recurrent Implantation Failure

Recurrent implantation failure (RIF) represents a new challenge in the field of assisted reproductive technology (ART). Considering the known effects of immune cell regulation on embryo implantation process, as well as our gene set variation analysis (GSVA) results that suggested the association between RIF and pathways of oxidative stress and immune responses, we hypothesized that oxidative stress- related genes (OSGs) associated with aberrant immunological factor may represent novel biomarkers for unexplained RIF. We therefore screened out the immune cell coexpressed OSGs by performing CIBERSORT, LM22 matrix and Pearson correlation, followed by constructing an OSG signature by least absolute shrinkage and selection operator (LASSO) regression. Three OSGs (AXL, SLC7A11 and UBQLN1) were then identified to establish a RIF risk signature, which showed high ability to discriminating RIF from fertile control. A nomogram was established, with a free online calculator for easier clinical application. Finally, Chilibot, protein-protein interaction analysis and BioGPS were sequentially applied for the investigation of functional relationships of these three genes with RIF and other OSGs, as well as their expression abundance across different human tissues. In conclusion, we identified an OSG signature that are relevant novel markers for the occurrence of unexplained RIF.