Differential methylation of promoters as a tool for histotype stratification in epithelial ovarian cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE326000 | GEO | 2026/03/31
REPOSITORIES: GEO
Similar Datasets
Project description:Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, yet clinical tools for diagnosis, prognosis and treatment remain limited, and molecular profiling of histotypes is lacking. Here, we leverage proteomic data to further stratify four main EOC histotypes, borderline, and benign tumors and identify novel prognostic and diagnostic biomarkers. Using proteomic data from 300 patient samples, we identified differentially abundant proteins (DAPs) such as SNCG, S100A1, VWA2, AGR2, CTH, and SPINK1 and biomarker panels to stratify the tissues. Enrichment of biological processes profiled histotypes and involvement of DAPs. Survival analysis identified candidate biomarkers predicting overall- and disease-specific survival with histotype-specificity. Of these, GLYR1, RPL12, GDPGP1, and POLR2M were associated with favorable outcomes, while SDF4, PPP3CC, EIF2AK2, and STX6 were linked to unfavorable outcomes. Collectively, these findings provide attributes spefici for histotypes for known and novel EOC biomarkers that may serve as new clinical tools for EOC treatment decisions and diagnosis.
2025-04-10 | MSV000097588 | MassIVE
Project description:Despite advances in cancer treatments, epithelial ovarian cancer (EOC) remains the leading cause of death among gynecologic cancers. EOC is stratified into five main histopathological subtypes: high-grade serous carcinoma (HGSC), clear cell carcinoma (CCC), mucinous carcinoma (MC), endometrioid carcinoma (EC), and low-grade serous carcinoma (LGSC). However, personalized treatment strategies and reliable biomarkers for all histotypes remain elusive. Building on our previous work with early-stage EOC, we aim to explore diagnostic and prognostic biomarkers in advanced-stage EOC, updated to the latest World Health Organization classification guidelines from 2020, using comprehensive transcriptomic profiling from total RNA sequencing of 146 EOC patients. Differential expression analysis identified top 9 histotype-specific gene panels for HGSC, CCC, MC, and EC, including S100A1 (HGSC), ARID3A (CCC), LGALS4 (MC), and SCUBE2 (EC). We also identified gene candidates associated with overall survival and disease-specific survival, reflecting both favorable (e.g., OTOF, EEF1E1-BLOC1S5, and STAC3) and unfavorable (e.g., SMOC1, GDPGP1, EPRS1) clinical outcome. Additionally, enrichment analysis revealed tumor progression-related pathways unique to each histotype, offering insights into molecular mechanisms underlying disease progression and potential therapeutic targets. These findings provide valuable insights into the molecular landscape of advanced-stage EOC, paving the way for more effective diagnostic and prognostic tools across diverse histotypes.
2025-10-01 | GSE295399 | GEO
Project description:Background
There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery.
Methods
We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues.
Results
We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues.
Conclusions
Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.
2024-03-01 | MSV000094219 | MassIVE
Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE260968 | GEO
Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261104 | GEO
Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261108 | GEO
Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261107 | GEO
Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into clinical practice, where routinely formalin-fixed paraffin-embedded (FFPE) samples are used, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and FFPE samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial difference in 5-year recurrence free survival (RFS) for CMS2 and CMS3 patients in stage III compared to II (CMS2 21% more recurrences, CMS3 31% more recurrences), while this difference is less pronounced for CMS1 and CMS4 with 11% and 9%, respectively. We posit NanoCMSer as a robust tool with potential applications to assess the CMS status of CRCs in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE262670 | GEO
Project description:Pancreatic cancer remains highly lethal, largely due to late diagnosis and limited efficacy of treatments. Improving first-line treatment selection and patient monitoring requires novel, non-invasive biomarkers beyond CA19-9 and imaging. This study investigates epigenetic biomarkers from liquid biopsy with prognostic and predictive potential in metastatic PDAC (mPDAC). Genome-wide methylation profiling of cell-free DNA (cfDNA) from healthy individuals and stage IV mPDAC patients identified thirteen gene-associated CpG sites with significantly altered methylation patterns. ddPCR validation confirmed consistent methylation differences in LAX1, NR3C1 and RCAN3 between healthy and patient groups. Elevated LAX1 and RCAN3 methylation and reduced NR3C1 methylation at diagnosis were associated with poor prognosis and correlated with high-risk circulating biomarker profiles, including CA19-9 levels, RAS MAF, cfDNA concentration, and cfDNA fragmentation. Notably, baseline NR3C1 methylation levels predicted response to first-line FOLFIRINOX-based treatment with an acceptable 75% sensitivity and a high specificity of 92.86%. These findings highlight the clinical significance of cfDNA methylation as a minimally invasive biomarker source, emphasizing LAX1, NR3C1 and RCAN3 as prognostic biomarkers in mPDAC. Specifically, baseline NR3C1 methylation emerges as a promising predictor of treatment response, supporting personalized therapeutic strategies in mPDAC.
2026-01-25 | GSE316251 | GEO
Project description:Previously, we have identified the RUNX2 gene as hypomethylated and overexpressed in post-chemotherapy (CT) primary cultures derived from epithelial ovarian cancer (EOC) patients, when compared to primary cultures derived from matched primary (prior to CT) tumors. However, we found no differences in the RUNX2 methylation in primary EOC tumors and EOC omental metastases, suggesting that DNA methylation-based epigenetic mechanisms have no impact on RUNX2 expression in advanced (metastatic) stage of the disease. Moreover, RUNX2 displayed significantly higher expression not only in metastatic tissue, but also in high-grade primary tumors and even in low malignant potential tumors. Knockdown of the RUNX2 expression in EOC cells led to sharp decrease of cell proliferation and significantly inhibited EOC cell migration and invasion. Gene expression profiling and consecutive network and pathway analyses confirmed these findings, as various genes and pathways known previously to be implicated in ovarian tumorigenesis, including EOC tumor invasion and metastasis, were found to be downregulated upon RUNX2 suppression, while a number of pro-apoptotic genes and some EOC tumor suppressor genes were induced. Taken together, our data are indicative for a strong oncogenic potential of the RUNX2 gene in EOC progression and suggest that RUNX2 might be a novel EOC therapeutic target. Further studies are needed to more completely elucidate the functional implications of RUNX2 and other members of the RUNX gene family in ovarian tumorigenesis.
2013-09-23 | GSE46477 | GEO