Comparison of the predictive capability of antral follicle count vs. the anti-Müllerian hormone for ovarian response in infertile women.

Objectives: The aim of this study was to compare the predictive capability of antral follicle count (AFC) and the anti-Müllerian hormone (AMH) on ovarian response in infertile women and to identify potential factors influencing retrieved oocytes. Methods: A total of 2585 infertile women who underwent in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles had been enrolled in this study. Spearman correlation was used to investigate the correlation between retrieved oocytes and AFC. Multiple linear regression analysis was used to study the parameters affecting the number of retrieved oocytes. Results: Spearman correlation and multiple linear regression analysis revealed that the oocyte retrieval number was positively correlated with AFC (r = 0.651, p < 0.001) and AMH (r = 0.566, p < 0.001) and negatively correlated with age (r = -0.425, p < 0.001) and regimen selection (r = -0.233 p < 0.001). There was no significant correlation between retrieved oocytes and BMI (p = 0.913). ROC analysis revealed that AFC was a better predictor of adverse effects than AMH, BMI, and age (AUC: 0.916 VS 0.791, 0.575, 0.752). Meanwhile, AFC and AMH were comparable in predicting high response (AUC = 0.731 and AUC = 0.733, respectively). Conclusions: This study showed that retrieved oocytes were positively correlated with serum AMH and AFC and negatively correlated with age and BMI. AFC had an ideal predictive performance in ovarian response prediction. The mechanism of the effect of AFC on ovarian response during controlled ovarian hyperstimulation (COH) needs to be further investigated.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma.

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.