During pregnancy, there will be significant changes in endogenous hormones level, and pituitary tissues are susceptible to these changes. For women with pituitary tumor, changes in pituitary hormones during pregnancy will not only increase the risk of tumor growth, but also affect the health status of them and their fetuses. Different treatments or interventions during pregnancy can also affect the natural course of pregnancy in patients with pituitary tumors. The specificity of pregnancy status makes the diagnosis of pituitary tumors during pregnancy challenging, which leads to an increase in adverse pregnancy outcomes secondary to abnormal pituitary hormone levels such as prolactin, growth hormone, cortisol, etc.. Therefore, the diagnosis, treatments and follow-up of pituitary tumors during pregnancy are very important. This article focuses on analyzing the pregnancy characteristics and precautions of patients with various types of pituitary tumors, in order to provide guidance for the individualized management of such patients during pregnancy.
Objective:To explore the genetic etiology of fetuses with short femur length (FL) at the genome-wide level by chromosome microarray analysis (CMA) technique.Methods:Seventy-three pregnant women and their fetuses who had short FL by prenatal ultrasonography and underwent invasive prenatal diagnosis at Guangzhou Women and Children′s Medical Center from January 2019 to December 2020 were enrolled in the study. According to whether they were combined with other obvious structural developmental abnormalities, they were divided into isolated group (n=56) and complex group (n=17). Chromosomal karyotyping and CMA were performed, and results were analyzed by applying CHAS software and related bioinformatics methods. The procedures followed in this study were in accordance with the ethical standards set by the Human Experimentation Committee of Guangzhou Women and Children′s Medical Center, and were approved by this ethical committee (Approval No. [2019] 11600). Written informed consents were obtained form all pregnant women.Results:① Of these 56 fetuses in isolated group, 6 were combined with ultrasound soft index abnormalities; of these 17 fetuses in complex group, 6 were combined with neurological malformations, 6 with cardiovascular malformations, and 6 with malformations of other skeletal systems. ②The CMA results of 73 fetuses showed that 10 cases contained pathogenic/probably pathogenic copy number variants (CNV), with a total pathogenicity detection rate of 13.7% (10/73), the variants of uncertain significance (VUS) detection rate of 2.7% (2/73), and a benign CNV detection rate of 83.6% (61/73). ③Of the 44 short FL fetuses that underwent both chromosomal karyotyping and CMA testing, 34 were in isolated group and 10 were in complex group. Comparison of the rates of chromosomal karyotype abnormality (10.0%) and abnormal detection by CMA technique (30.0%) in the complex group showed no statistically significant difference (χ2=2.08, P=0.149). ④None of the 73 fetuses were found to contain the known pathogenic locus of FGFR3 gene. Of the 63 CMA-negative fetuses, 7 fetuses were highly suspected of having long-bone dysplasia due to prenatal ultrasonography (long bones of the limbs <3% of the children of the same gestational age), and further whole exome sequencing (WES) testing showed that the COL1A1 mutation (c.2519C>T; p. P840L) was detected in 1 fetus as VUS. ⑤Follow-up showed that 31 (42.5%) fetuses were delivered at term, 15 (48.4%) live births showed a growth rate of long below the 10th percentile of the same-age child in infancy, and other developmental assessments did not show any obvious abnormalities.Conclusions:CMA has a certain application value in prenatal diagnosis of fetuses with short FL and has a higher detection rate than chromosomal karyotyping. Therefore, it is suggested that CMA should be used as a first-line technique in the prenatal diagnosis of fetuses with short FL on prenatal ultrasound examination, especially when combined with other structural abnormalities.
Objective:This study aims to apply gene-based machine learning to screen and identify key genes (hub genes) related to bronchopulmonary dysplasia (BPD) in preterm infants, providing theoretical insights into the pathogenesis of BPD.Methods:We obtained gene microarray dataset GSE32472, including 68 BPD preterm infants (research group) and 43 non-BPD preterm infants born during the same period (control group) from the Gene Expression Omnibus (GEO) database. Using Weighted Gene Co-expression Network Analysis (WGCNA), we screened the hub gene set from color modules. Through least absolute shrinkage and selection operator(LASSO) regression analysis based on penalty values (λ values), we calculated coefficients for each gene and selected candidate hub genes for BPD in preterm infants. Random forest analysis results were used to screen the top 10 hub genes in the color module of BPD in preterm infants. After taking the intersection of the genes selected through LASSO regression and random forest analysis, we identified 6 hub genes for BPD.Results:① Through the " WGCNA" package in R software (version 4.1.3), we achieved a scale-free topology fit index slightly above 0.9, setting the soft-thresholding power to 18 for scale independence. This allowed us to identify and cluster hub gene modules of BPD, yielding 11 characteristic hub gene color modules. Further analysis revealed that genes in the yellow module showed significant correlation with the incidence of BPD in preterm infants. ②Using the " glmnet" package in R software, we performed LASSO regression analysis on 189 genes in the yellow module, achieving L1 regularization parameter estimation and variable selection. Most of the model parameter regression coefficients tended to zero, effectively avoiding overfitting of the training data. We employed ten-fold cross-validation to validate the model of the dataset of candidate hub genes for BPD. The prediction error of the model was minimized when it included 41 candidate hub genes, corresponding to a λ value of 0.0114. ③With the " randomForest" package in R software, our random forest analysis of the 189 genes in the yellow module showed that ten candidate genes (SPON1, TMEM204, CD28, ICOS, LOC100996619, NOL9, GCSAM, UBASH3A, CCNI2, AQP3) had an importance score of over 1.0 in the analysis of candidate hub genes for BPD, significantly surpassing other candidate genes. ④In the GSE32472 gene microarray dataset, six hub genes for BPD (SPON1, TMEM204, CD28, ICOS, LOC100996619, NOL9) were identified in the yellow module related to BPD, following LASSO regression analysis and random forest analysis.Conclusions:By constructing a co-expression hub gene regulatory network for BPD and selecting six BPD-related hub genes based on machine learning algorithms, we have laid a theoretical foundation for exploring the pathogenesis of BPD and potential treatment targets.
Lymphatic vascular infiltration (LVSI) is one of the prognostic factors for patients with endometrial cancer(EC), but LVSI mainly depends on pathological analysis of surgical specimens, which has certain limitations. In recent years, preliminary progress has been made in the prediction of LVSI, such as molecular level detection, including DNA, RNA and related protein detection, which can help to predict LVSI status. Predictive models developed from clinical data can also aid in clinical judgment LVSI in patients with EC. The author intends to review the latest research progresses on molecular prediction, immunohistochemistry prediction, and related risk model prediction of LVSI status in patients with EC, with the aim of providing reference for clinical diagnosis and treatment planning of EC.
In the 21st century, development of high-throughput sequencing technologies and molecular biology research techniques and methods for non-coding RNA (ncRNA) have further promoted the discovery of ncRNA. Epigenetically related ncRNA include microRNAs (miRNA), long non-coding RNA (lncRNA) and circular RNA (circRNA), etc., however, these ncRNA are not act directly as carriers of genetic information, but involved in life activities of granulosa cells (GC)and oocytes by regulating expression of genes such as BMP15 and GDF9. ncRNA plays an important regulatory role in GC proliferation and apoptosis, oocyte development and maturation, and may play a role in regulating disease progression in pathogenesis of polycystic ovary syndrome (PCOS) related diseases. The authors intend to present the latest research progresses on ncRNA in follicular development and maturation, aiming to explore the role of ncRNA in follicular development and its mechanism, which may provide new ideas for prevention, early diagnosis and treatment of POCS and other related reproductive diseases. The authors intend to present the latest research progress on the role of ncRNA in follicular development and maturation, and its mechanism, aiming at providing new ideas for prevention, early diagnosis and treatment of POCS and other related reproductive system diseases.
