Pawel Kordowitzki

Background: Endometriosis-associated ovarian cancers (EAOCs), encompassing clear cell (CC) and endometrioid carcinomas (EC), constitute distinct biological entities yet lack robust biomarkers for precise classification, prognostication, and therapeutic decision-making in women. Therefore, we aimed to describe novel biomarkers. Methods: In this study, we conducted an integrated transcriptomic analysis, powered by machine learning, to discover novel consensus biomarkers and delineate cancer hallmark signatures specific to EC and CC. Drawing on gene expression profiles from EAOC specimens, we merged differential expression analysis with LASSO regression and Random Forest classification to generate a reliable biomarker panel that effectively distinguishes EC from CC. Kaplan–Meier survival analyses and mutation analyses have been performed for selected biomarker genes. Results: Novel biomarkers, among others, the genes RPS28, EPAS1, ALKBH2, and DCLRE1A, uncover extensive transcriptional alterations tied to hypoxia signaling, oxidative stress, DNA repair, and metabolic reprogramming. Gene Ontology and pathway enrichment analyses revealed synchronized upregulation of epithelial–mesenchymal transition, TNF-α/NF-κB signaling, oxidative stress, hypoxia, and KRAS signaling pathways. Conclusions: Our work establishes novel exploratory transcriptomic candidates for innovative consensus biomarkers, yielding novel diagnostic and prognostic insights into EAOC and supporting further study of subtype-associated expression programs. The current study was designed primarily as an integrative computational investigation aimed at identifying candidate genes and molecular pathways distinguishing CC from EC.

In this case series, four equines were diagnosed with cystitis, resulting in divergent clinical outcomes. Three animals achieved complete clinical resolution without recurrence, whereas one required euthanasia due to progressive clinical deterioration. All diagnoses were established through a dual-modality investigative protocol comprising cystoscopic evaluation and histopathological examination of bladder mucosal biopsies. The cohort exhibited considerable etiological heterogeneity. One case was classified as chronic idiopathic cystitis, while another was attributed to an opportunistic bacterial infection involving Facklamia spp. A third case demonstrated histopathological features consistent with mild chronic-active inflammation, characterized by mucosal hyperplasia and focal ulceration. Of particular pathological significance was one equine presented with marked epithelial denudation accompanied by pronounced submucosal edema, notably in the absence of appreciable inflammatory cellular infiltration. These characteristic morphological features and lesions observed in the horses share certain histopathological features with inflammatory bladder lesions described as interstitial cystitis in other species. However, these conclusions should be interpreted with caution, as the findings are based on only four horses.

Background: The malignant transformation of endometriosis into endometriosis-associated ovarian cancer (EAOC) is still of great interest among researchers. Despite the shared inflammatory and hormonal drivers, the cellular aging dynamics differentiating these two pathologies remain elusive. This study aimed to quantify biological aging using DNA methylation status and telomere length to identify distinct signatures associated with disease progression. Methods: We conducted a comparative analysis of multiple aging-related biomarkers in blood, including biological age, telomere length, and DNA methylation status, as well as specific longevity markers, across cohorts of age-matched patients with endometriosis and EAOC, compared with unaffected controls. Precision assessment utilized machine learning models to determine biological age deviation and methylation levels at specific CpG sites. Finally, the study employs a Random Forest machine learning classifier to assess the discriminative power of gene signatures, complemented by Gene Ontology enrichment. Results: Our analysis revealed a profound divergence in biological aging trajectories. Patients with endometriosis exhibited significant age acceleration (+ 16.92%) and maintained telomeres that were significantly longer (13.18 ± 0.46 kbp). Conversely, EAOC patients displayed a biologically younger profile compared to their chronological age (− 14.64%). These patients also demonstrated significant telomeric attrition (12.45 ± 0.72 kbp; p < 0.001), reflecting their extensive mitotic history and the rapid turnover of malignant cells. Age-related methylation was the most robust marker, distinguishing the groups with high statistical significance. Conclusions: This study provides novel evidence that the transition from endometriosis to EAOC involves a fundamental shift in biological age and in telomere dynamics. Identifying these divergent aging signatures offers a powerful tool for precision oncology, enabling the development of predictive models to identify patients at high risk of malignant transformation and facilitating personalized interceptive strategies based on biological, rather than chronological age.

Purpose: We aim to elucidate the impact of endometriomas on oocyte developmental competence and identify genetic alterations that may influence ICSI success rates in women of different ages. Methods: In this retrospective study, two groups of patients have been analyzed: 108 women with a history of severe endometriosis (stages III and IV), who underwent OPU and ICSI after endometriosis surgery, and a control group, built out of 108 women with no endometriosis history. The Turku Endometriosis Database was used for differential gene expression analysis. Results: The number of aspirated oocytes (endometrioma group: 2.75 ± 0.32 vs. control 10.32 ± 0.54) was significantly (p < 0.0001) lower in patients with endometrioma excision, and the number of generated blastocysts was significantly (p < 0.0001) lower when compared to patients without endometriosis (endometrioma group: 1.09 ± 0.13 vs. control group: 3.76 ± 0.23). Additionally, AMH levels were significantly lower (p < 0.001) in the endometriosis-affected group below the age of 35 years (1.65 ± 0.15) than in the unaffected control group (2.99 ± 0.27) within the same age group. The mRNA expression of the following genes related to oxidative stress was significantly (p < 0.05) upregulated in the endometriosis group compared with healthy controls: DES, P2RX1, FOS, SRF, RASD2, SLITRK3, and TEAD3. Gene Ontology and pathway enrichment analyses demonstrated a significant (p < 0.0001) enrichment of oxidative stress–associated biological processes and signaling pathways, including responses to reactive oxygen species and hypoxia, HIF-1, MAPK, PI3K–Akt, NF-κB, and apoptotic signaling, supporting oxidative stress as a central molecular mechanism underlying the observed transcriptomic alterations. Conclusions: In conclusion, this retrospective study provides a holistic perspective on the impact of endometrioma and its oxidative stress effect on oocyte developmental rates and ICSI outcomes across different age groups.

Background: Given the challenges in early detection and diagnosis, understanding the molecular underpinnings of endometrioid ovarian cancer (EOC) is crucial for improving patient outcomes. This multi-level study provides a new perspective on EOC, focusing on the expression of ARID1a (BAF250a) and RIF1. Methods: This study evaluates patient cohorts with EOC through semi-quantitative immunohistochemical staining of BAF250a (protein encoded by ARID1a) and RIF1 proteins alongside mutations that influence the gene expression of ARID1a and RIF1. Besides survival analyses, platinum- and taxane-based treatment responsiveness with regard to ARID1a and RIF1 expression has been analyzed using an online available database. Results: Histological and immunohistochemical analysis of clinical samples revealed a significant reciprocal alteration in protein expression, characterized by a marked reduction in the tumor suppressor BAF250a (p < 0.0001) and a concomitant elevation of RIF1 (p < 0.0001) in EOC compared to controls. Tumors harboring mutations in BRCA1 exhibited significantly (p = 2.82 × 10⁻⁴) lower ARID1a expression levels compared with corresponding wild-type tumors, whereas LAMB3-mutant tumors showed a significant (p = 5.16 × 10⁻³) upregulation of RIF1 mRNA expression. Conclusions: In conclusion, our study offers a new perspective, emphasizing that EOC is a distinct clinical and molecular entity. We demonstrated the expression patterns of ARID1a/BAF250a and RIF1 in EOC, establishing their potential relevance in the context of tumor biology and malignant transformation.

Breast cancer has emerged as the preeminent global health crisis in oncology, currently standing as the most frequently diagnosed malignancy among women worldwide. Establishing novel predictive biomarkers is paramount to truly personalize treatment approaches, minimize unnecessary toxicity, and significantly improve long-term outcomes for patients with breast cancer. Breast cancer transcriptomic datasets were retrieved from the Gene Expression Omnibus and processed through standardized normalization procedures. Mutation-driven regulation of SYTL4 expression, treatment response to trastuzumab, cancer hallmark enrichment, and survival associations were evaluated using established bioinformatic tools and enrichment analysis based on integrated cancer hallmark gene sets. Additionally, DNA methylation profiles were analyzed. Herein, it is shown that SYTL4 mRNA expression is significantly (p = 2.01 × 10⁻⁴) diminished in breast cancer bearing BRCA1 mutations, suggesting a mechanistic interplay between BRCA1-driven genomic instability and SYTL4-regulated signaling cascades. Kaplan–Meier survival analysis demonstrated that elevated SYTL4 mRNA expression is significantly associated with improved overall survival in HER2-positive breast cancer patients (HR = 0.72; p = 0.034). Consistently, SYTL4 expression was significantly higher in patients who responded to trastuzumab therapy, supporting its potential as a biomarker of therapeutic response. Epigenetic analysis further revealed significant differential DNA methylation of SYTL4 between tumor and unaffected control tissues (p < 2.2 × 10⁻¹⁶), with region-specific hypomethylation in tumor regulatory regions. KEGG pathway and cancer hallmark enrichment analyses indicated that genes with prominent methylation changes are involved in cytokine signaling, growth factor pathways, and extracellular matrix remodeling, with the strongest associations observed for hallmarks related to genome instability, replicative immortality, resisting cell death, and metabolic reprogramming. In summary, we present that the gene SYTL4 is a prospective biomarker for survival and trastuzumab treatment responsiveness. Our observations posit that SYTL4 expression may signify a biological milieu conducive to sustained HER2 reliance and amplified therapeutic vulnerability.