Additional file 1 of X chromosome rearrangement associated with premature ovarian insufficiency as diagnosed by molecular cytogenetic methods: a case report and review of the literature

Supplementary Material

Table_1_Investigating the shared genetic architecture between hypothyroidism and rheumatoid arthritis.docx

BackgroundThere is still controversy regarding the relationship between hypothyroidism and rheumatoid arthritis (RA), and there has been a dearth of studies on this association. The purpose of our study was to explore the shared genetic architecture between hypothyroidism and RA.MethodsUsing public genome-wide association studies summary statistics of hypothyroidism and RA, we explored shared genetics between hypothyroidism and RA using linkage disequilibrium score regression, ρ-HESS, Pleiotropic analysis under a composite null hypothesis (PLACO), colocalization analysis, Multi-Trait Analysis of GWAS (MTAG), and transcriptome-wide association study (TWAS), and investigated causal associations using Mendelian randomization (MR).ResultsWe found a positive genetic association between hypothyroidism and RA, particularly in local genomic regions. Mendelian randomization analysis suggested a potential causal association of hypothyroidism with RA. Incorporating gene expression data, we observed that the genetic associations between hypothyroidism and RA were enriched in various tissues, including the spleen, lung, small intestine, adipose visceral, and blood. A comprehensive approach integrating PLACO, Bayesian colocalization analysis, MTAG, and TWAS, we successfully identified TYK2, IL2RA, and IRF5 as shared risk genes for both hypothyroidism and RA.ConclusionsOur investigation unveiled a shared genetic architecture between these two diseases, providing novel insights into the underlying biological mechanisms and establishing a foundation for more effective interventions.</p

CA153 in Breast Secretions as a Potential Molecular Marker for Diagnosing Breast Cancer: A Meta Analysis

<div><p>Purpose</p><p>Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS.</p><p>Methods</p><p>Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency.</p><p>Results</p><p>This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614.</p><p>Conclusions</p><p>CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study.</p></div

The summary diagnostic indices of CA153 in BS for BC diagnosis exhibited in forest plots.

<p><b>(A)</b> sensitivity and specificity; <b>(B)</b> positive LR and negative LR; <b>(C)</b> DOR. These pooled indices indicate that CA153 in BS could be a useful indicator for the noninvasive diagnosis of BC. The individual index for each study is represented by circles, and the combined indices are shown as triangles.</p

The overall diagnostic performance of CA153 in BS, shown by SROC.

<p>Each circle represents a study. The SROC curve is symmetric and the AUC is 0.8614, suggesting a moderate diagnostic accuracy for BC.</p

A literature screening flow diagram and quality assessment schematic diagram for the included articles.

<p><b>(A)</b> A flow diagram of screening eligible studies. <b>(B)</b> Presentation of data quality evaluated with the QUADAS-2 tool, showing “risk of bias” and “concerns of applicability” of each eligible study (with risk of bias in the “flow and timing” domain).</p

Publication bias is exhibited with a funnel plot.

<p>Each point represents one study, and results demonstrate that publication bias does not exist.</p