Identification and analysis of novel endometriosis biomarkers via integrative bioinformatics

Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

PDLIM3 knockdown promotes ferroptosis in endometriosis progression via inducing Gli1 degradation and blocking Hedgehog signaling pathway

AIMS

Current evidence suggests that there is no completely effective method for endometriosis (EMS) without trauma due to diverse adverse effects. Reliable evidence illustrates that inhibiting ferroptosis is a potential strategy for EMS. We sufficiently verified that the expression of endogenous protein PDZ and LIM domain 3 (PDLIM3) was significantly increased in EMS.

METHODS

PDLIM3 knockdown reduced primary ectopic endometrial stromal cells' (EESCs) viability and migration, and elevated ferroptosis signaling indicators including Fe2+, malondialdehyde (MDA), and reactive oxygen species (ROS) in EESCs.

RESULTS

Mechanistic studies revealed that inhibition of PDLIM3 accelerated glioma-associated oncogene-1 (Gli1) degradation and further deactivated Hedgehog signaling. Gli1 inhibitor, GANT61, abrogated the impact of PDLIM3 deletion on EESC growth, migration, and ferroptosis. In vivo experiments suggested that PDLIM3 reduction repressed the growth of endometrial lesions. Likewise, repression of PDLIM3 promoted ferroptosis and attenuated Hedgehog signaling in endometrial lesions.

CONCLUSIONS

Collectively, silencing of PDLIM3 facilitates ferroptosis in EMS by inducing Gli1 degradation and blocking Hedgehog signaling. It may provide an alternative strategy for developing therapeutic agents of EMS in the future.

Identification of Prostaglandin I2 Synthase Rare Variants in Patients With Williams Syndrome and Severe Peripheral Pulmonary Stenosis

BACKGROUND

Peripheral pulmonary stenosis (PPS) is a condition characterized by the narrowing of the pulmonary arteries, which impairs blood flow to the lung. The mechanisms underlying PPS pathogenesis remain unclear. Thus, the aim of this study was to investigate the genetic background of patients with severe PPS to elucidate the pathogenesis of this condition.

METHODS AND RESULTS

We performed genetic testing and functional analyses on a pediatric patient with PPS and Williams syndrome (WS), followed by genetic testing on 12 patients with WS and mild-to-severe PPS, 50 patients with WS but not PPS, and 21 patients with severe PPS but not WS. Whole-exome sequencing identified a rare PTGIS nonsense variant (p.E314X) in a patient with WS and severe PPS. Prostaglandin I2 synthase (PTGIS) expression was significantly downregulated and cell proliferation and migration rates were significantly increased in cells transfected with the PTGIS p.E314X variant-encoding construct when compared with that in cells transfected with the wild-type PTGIS-encoding construct. p.E314X reduced the tube formation ability in human pulmonary artery endothelial cells and caspase 3/7 activity in both human pulmonary artery endothelial cells and human pulmonary artery smooth muscle cells. Compared with healthy controls, patients with PPS exhibited downregulated pulmonary artery endothelial prostaglandin I2 synthase levels and urinary prostaglandin I metabolite levels. We identified another PTGIS rare splice-site variant (c.1358+2T>C) in another pediatric patient with WS and severe PPS.

CONCLUSIONS

In total, 2 rare nonsense/splice-site PTGIS variants were identified in 2 pediatric patients with WS and severe PPS. PTGIS variants may be involved in PPS pathogenesis, and PTGIS represents an effective therapeutic target.

Chronic Pelvic Pain, Vulvar Pain Disorders, and Proteomics Profiles: New Discoveries, New Hopes

Chronic pelvic pain (CPP) is generally defined as non-cyclic pain perceived in the pelvic area that has persisted from three to six months or longer and is unrelated to pregnancy. The etiology of CPP is complex, multifactorial, with heterogeneous presentation, and includes several diseases such as endometriosis, adenomyosis, and interstitial cystitis/bladder pain syndrome. It may also be associated with sexual dysfunction, musculoskeletal disorders, and comorbid psychiatric symptoms. Vulvar pain disorders (VPDs) are typically categorized separately from chronic pelvic pain; among all VPDs, vulvodynia is a chronic vulvar pain of unknown etiology, lasting at least 3 months and that might be associated with other potentially linked factors. Proteomics represents a useful approach to study the proteome profiles of clinical samples. In this review, we have considered a selection of articles that have analyzed the protein abundance and novel protein species from various biological samples, including eutopic/ectopic endometrium, urine, serum, follicular, peritoneal fluid, and cervical mucus, potentially involved in the pathogenesis and progression of CPP and VPDs. These findings could represent valuable targets for paving the way for the differential diagnosis and therapeutic management of CPP and VDPs, thereby optimizing both the prevention and treatment of these conditions.

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

PURPOSE

Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches.

METHODS

Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software.

RESULTS

A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs.

CONCLUSION

In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.