The cellular origin of clear cell ovarian carcinoma (CCOC), a major histological subtype of ovarian carcinoma remains elusive. Here, we explored the candidate cellular origin and identify molecular subtypes using integrated genomic/epigenomic analysis. We performed whole exome-sequencing, microarray, and DNA methylation array in 78 CCOC samples according to the original diagnosis. The findings revealed that ARID1A and/or PIK3CA mutations were mutually exclusive with DNA repair related genes, including TP53, BRCA1, and ATM. Clustering of CCOC and other ovarian carcinomas (n = 270) with normal tissues from the fallopian tube, ovarian surface epithelium, endometrial epithelium, and pelvic peritoneum mesothelium (PPM) in a methylation array showed that major CCOC subtypes (with ARID1A and/or PIK3CA mutations) were associated with the PPM-lile cluster (n = 64). This cluster was sub-divided into three clusters: (1) mismatch repair (MMR) deficient with tumor mutational burden-high (n = 2), (2) alteration of ARID1A (n = 51), and (3) ARID1A wild-type (n = 11). The remaining samples (n = 14) were subdivided into (4) ovarian surface epithelium-like (n = 11) and (5) fallopian tube-like (considered as high-grade serous histotype; n = 3). Among these, subtypes (1-3) and others (4 and 5) were found to be associated with immunoreactive signatures and epithelial-mesenchymal transition, respectively. These results contribute to the stratification of CCOC into biological subtypes.

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

BACKGROUND: Fibulin-2 (FBLN2) is a secreted extracellular matrix (ECM) glycoprotein and has been identified in the mouse mammary gland, in cap cells of terminal end buds (TEBs) during puberty, and around myoepithelial cells during early pregnancy. It is required for basement membrane (BM) integrity in mammary epithelium, and its loss has been associated with human breast cancer invasion. Herein, we attempted to confirm the relevance of FBLN2 to myoepithelial phenotype in mammary epithelium and to assess its expression in molecular subtypes of human breast cancer.
METHODS: The relationship between FBLN2 expression and epithelial markers was investigated in pubertal mouse mammary glands and the EpH4 mouse mammary epithelial cell line using immunohistochemistry, immunocytochemistry, and immunoblotting. Human breast cancer mRNA data from the METABRIC and TCGA datasets from Bioportal were analyzed to assess the association of Fbln2 expression with epithelial markers, and with molecular subtypes. Survival curves were generated using data from the METABRIC dataset and the KM databases.
RESULTS: FBLN2 knockdown in mouse mammary epithelial cells was associated with a reduction in KRT14 and an increase in KRT18. Further, TGFβ3 treatment resulted in the upregulation of FBLN2 in vitro. Meta-analyses of human breast cancer datasets from Bioportal showed a higher expression of Fbln2 mRNA in claudin-low, LumA, and normal-like breast cancers compared to LumB, Her2 +, and Basal-like subgroups. Fbln2 mRNA levels were positively associated with mesenchymal markers, myoepithelial markers, and markers of epithelial-mesenchymal transition. Higher expression of Fbln2 mRNA was associated with better prognosis in less advanced breast cancer and this pattern was reversed in more advanced lesions.
CONCLUSIONS: With further validation, these observations may offer a molecular prognostic tool for human breast cancer for more personalized therapeutic approaches.

UNASSIGNED: Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score.
UNASSIGNED: Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses.
UNASSIGNED: Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups.
UNASSIGNED: The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.

There is a significant difference in prognosis and response to chemotherapy between basal and classical subtypes of pancreatic ductal adenocarcinoma (PDAC). Further biomarkers are required to identify subtypes of PDAC. We selected candidate biomarkers via review articles. Correlations between these candidate markers and the PDAC molecular subtype gene sets were analyzed using bioinformatics, confirming the biomarkers for identifying classical and basal subtypes. Subsequently, 298 PDAC patients were included, and their tumor tissues were immunohistochemically stratified using these biomarkers. Survival data underwent analysis, including Cox proportional hazards modeling. Our results indicate that the pairwise and triple combinations of KRT5/KRT17/S100A2 exhibit a higher correlation coefficient with the basal-like subtype gene set, whereas the corresponding combinations of GATA6/HNF4A/TFF1 show a higher correlation with the classical subtype gene set. Whether analyzing unmatched or propensity-matched data, the overall survival time was significantly shorter for the basal subtype compared with the classical subtype (p < .001), with basal subtype patients also facing a higher risk of mortality (HR = 4.017, 95% CI 2.675-6.032, p < .001). In conclusion, the combined expression of KRT5, KRT17, and S100A2, in both pairwise and triple combinations, independently predicts shorter overall survival in PDAC patients and likely identifies the basal subtype. Similarly, the combined expression of GATA6, HNF4A, and TFF1, in the same manner, may indicate the classical subtype. In our study, the combined application of established biomarkers offers valuable insights for the prognostic evaluation of PDAC patients.

OBJECTIVE: Endometrial cancer (EC) is heterogeneous with respect to epidemiology, clinical course, histopathology and tumor biology. Recently, The Cancer Genome Atlas (TCGA) network has identified four molecular subtypes with distinct clinical courses by an integrated multi-omics approach. These subtypes are of critical importance in the clinical management of EC. However, determination of TCGA molecular subtypes requires a complex methodological approach that is resource intensive and difficult to implement in diagnostic routine procedures. In this context, Talhouk et al. reported the precise determination of modified subtypes based on molecular surrogates obtained by a two-method approach comprising immunohistochemistry and DNA-sequence analysis (Proactive Molecular Risk Classifier for Endometrial Cancer; ProMisE). In this study, we aimed to identify EC molecular subtypes in analogy to TCGA and ProMisE applying an innovative whole exome-sequencing (WES) based single-method approach.
METHODS: WES was performed in a cohort comprising N = 114 EC patients. WES data were analyzed using the oncology treatment decision support software MH Guide (Molecular Health, Heidelberg, Germany) and EC molecular subtypes in analogy to TCGA and ProMisE were determined. Results from both classifications were compared regarding their prognostic values using overall survival and progression-free survival analyses.
RESULTS: Applying a single-method WES-approach, EC molecular subtypes analogue to TCGA and ProMisE were identified in the study cohort. The surrogate marker-analogue classification precisely identified high-risk and low-risk EC, whereas the TCGA-analogue classification failed to obtain significant prognostic values in this regard.
CONCLUSIONS: Our data demonstrate that determination of EC molecular subtypes analogue to TCGA and ProMisE is feasible by using a single-method WES approach. Within our EC cohort, prognostic implications were only reliably provided by applying the surrogate marker-analogue approach. Designation of molecular subtypes in EC will be increasingly important in routine clinical practice. Thus, the single-method WES approach provides an important simple tool to tailor therapeutic decisions in EC.

UNASSIGNED: An important factor in the pathogenesis of polycystic ovary syndrome (PCOS) is chronic low-grade inflammation. However, the exact pathophysiology of PCOS is currently unknown, which makes clinical diagnosis and the development of effective treatments more difficult. We aimed to investigate the role of the inflammatory response in initiating and progressing PCOS.
UNASSIGNED: 13 control granulosa cell samples and 15 granulosa cell samples from patients with PCOS were obtained from the GSE102293, GSE34526, and GSE5850 datasets. The gene set variation analysis (GSVA) method was used to calculate the inflammatory response score. Subsequently, the genes associated with inflammation in the hub were identified using differential expression analysis and weighted gene co-expression network analysis (WGCNA). The findings were confirmed by analysis of independent datasets and examination of clinical samples by qRT-PCR analysis. A consensus cluster analysis was conducted to categorize the PCOS samples into subtypes related to inflammation. Functional enrichment and analysis of immune cell infiltration were conducted to explore the potential mechanisms involved. Additionally, the CMap database was utilized to predict potential drugs, and the results were confirmed through molecular docking.
UNASSIGNED: During the training cohort analysis, we identified five distinct genes (TGFBR2, ICAM3, WIPF1, SLC11A1, and NCF2) that could serve as potential diagnostic markers for PCOS. The expression levels of these genes were confirmed through validation in both the test set and clinical samples. In training cohort, two distinct inflammatory patterns (C1 and C2) were identified, and the C2 subtype exhibited activated immune- and inflammation-related pathways. Esmolol was shown to have potential as a drug to treat PCOS and it showed good results for molecular binding at TGFBR2, ICAM3, WIPF1, SLC11A1, and NCF2 proteins.
UNASSIGNED: Five diagnostic biomarkers and two inflammation-related molecular types associated with PCOS were identified, and esmolol was a potential drug for PCOS treatment. Our findings provided new diagnostic markers and potential small-molecule drugs for PCOS diagnosis and prevention.

UNASSIGNED: The heterogeneity and dynamic changes of endometrial cells have a significant impact on health as they determine the normal function of the endometrium during the menstrual cycle. Dysfunction of the endometrium can lead to the occurrence of various gynecological diseases. Therefore, deconvolution of immune microenvironment that drives transcriptional programs throughout the menstrual cycle is key to understand regulatory biology of endometrium.
UNASSIGNED: Herein, we comprehensively analyzed single-cell transcriptome of 59,397 cells across ten human endometrium samples and revealed the dynamic cellular heterogeneity throughout the menstrual cycle.
UNASSIGNED: We identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. Moreover, we inferred the cell type-specific transcription factor (TF) activities and linked critical TFs to transcriptional output of diverse immune cell types, highlighting the importance of transcriptional regulation in endometrium. Dynamic interactions between various types of cells in endometrium contribute to a range of biological pathways regulating differentiation of secretory. Integration of the molecular biomarkers identified in endometrium and bulk transcriptome of 535 endometrial cancers (EC), we revealed five RNA-based molecular subtypes of EC with highly intratumoral heterogeneity and different clinical manifestations. Mechanism analysis uncovered clinically relevant pathways for pathogenesis of EC.
UNASSIGNED: In summary, our results revealed the dynamic immune microenvironment of endometrium and provided novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma.

UNASSIGNED: To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).
UNASSIGNED: A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.
UNASSIGNED: The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163-33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172-38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760-29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588-8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145-133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689-56.740), and distortion (OR=11.471, 95 % CI: 2.250-58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180-52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303-161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915-182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040-31.987).
UNASSIGNED: Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.

UNASSIGNED: This study aimed to determine clinical and pathological factors that identify a pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).
UNASSIGNED: A retrospective, single-center study was conducted in women over the age of 18 who had been diagnosed with pathologically confirmed invasive breast cancer and who had received NAC between July 2016 and October 2021. Patient demographics, clinical, radiological, treatment, and pathological data were reviewed from the electronic hospital records. The primary outcome of interest was pCR, defined as the absence of residual invasive breast cancer in both the breast and axillary lymph nodes. Multivariable logistic regression analysis was used to identify factors associated with pCR.
UNASSIGNED: A total of 119 patients were included in the analysis. The distribution of age was 54.5 ± 11.5 years. pCR was observed in 33 (27.7%) patients. pCR for breast tissue was observed in 43 (36.1%) patients. There was no statistically significant relation between the clinical stage and pCR. Age, age at first labor, extent of disease in the breast, NAC completeness, clinical tumor size (cT) stage, clinical lymph node (cN) stage, and molecular subtype were analyzed in a multivariable model. Analysis showed that molecular subtype was the only independent factor related to pCR. pCR rates across molecular subtypes were: 8.7% in luminal-A, 10.8% in luminal-B, 54.5% in human epidermal growth factor receptor 2 (HER-2)-positive, 42.4% in luminal-B (HER-2 positive) and 46.7% in triple-negative. There was no statistically significant difference between luminal-A and luminal-B subgroups (odds ratio 1.15, 95% confidence interval, 0.19-9.35, p= 0.881). Despite the limited number of patients in HER2-positive and triple-negative groups, both demonstrated statistically significant higher odds compared to reference group.
UNASSIGNED: The presented study underscores the relevance of molecular subtypes in determining the response to neoadjuvant chemotherapy in breast cancer patients. Particularly HER2-positive and triple-negative subtypes may demonstrate more favorable response rates.