Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1242544 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Morphology-guided organoid classification reveals prognosis of oral cancer [dataset 2]
| PRJNA1244285 | ENA
Project description:Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a novel therapeutic strategy. We successfully established 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring were utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlated with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and Cisplatin combination therapies identified a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies.
2025-04-02 | GSE293371 | GEO
Project description:Oral cancer is an aggressive malignancy with a survival rate below 50% in advanced stages due to low mutation rates, lack of molecular subtypes, and limited treatment targets. This study presents a pioneering approach to classifying oral cancer subtypes based on the morphology of patient-derived organoids (PDOs) and proposes a novel therapeutic strategy. We successfully established 76 cancer and 81 normal PDOs. For cancer PDOs, both manual classification and AI-based scoring were utilized to categorize them into three distinct subtypes: normal-like, dense, and grape-like. These subtypes correlated with unique transcriptomic profiles, genetic mutations, and clinical outcomes, with patients harboring dense and grape-like organoids exhibiting poorer prognoses. Furthermore, drug response assessments of 14 single agents and Cisplatin combination therapies identified a synergistic treatment approach for resistant subtypes. This study highlights the potential of integrating morphology-based classification with genomic and transcriptomic analyses to refine oral cancer subtyping and develop effective treatment strategies.
2025-04-02 | GSE293370 | GEO
Project description:Epithelial-mesenchymal transition (EMT) is a continuum that includes epithelial, partial EMT (P-EMT) and mesenchymal states, each of which are associated with cancer progression, invasive capabilities and ultimately metastasis. We have employed a lineage traced sporadic model of pancreatic cancer to generate a murine organoid biobank from primary and secondary tumors, including sublines that have undergone P-EMT and complete EMT (C-EMT). Using an unbiased quantitative proteomics approach, we found that the morphology of the organoids could predict EMT state, with solid organoids associated with a C-EMT signature. We also observed that exogenous TGFb1 could induce a solid organoid morphology that was associated with changes in the S100 family of proteins and the formation of high-grade tumors. Our work reveals that S100A4 may represent a useful biomarker to predict EMT state, disease progression and outcome.
2023-02-24 | PXD030992 | Pride
Project description:Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing between proliferative and senescent cells. Recent research reveals that nuclear morphology can act as a predictive biomarker of senescence, suggesting an active role for the nucleus in driving senescence phenotypes. By employing deep learning algorithms to analyze nuclear morphology, accurate classification of cells as proliferative or senescent is achievable across various cell types and species both in vitro and in vivo. This quantitative imaging-based approach can be employed to establish links between senescence burden and clinical data, aiding in the understanding of age-related diseases, as well as assisting in disease prognosis and treatment response.
2025-04-07 | GSE293637 | GEO
Project description:Oral cancer is characterized by poor prognosis and low survival rate despite sophisticated surgical and radiotherapeutic modalities. Lymphatic metastasis of oral cancer is a complex process involving multiple post-transcriptional biological processes. Thus, feasible detection of cancer metastasis in plasma could be challenging and beneficial. We recruited patients with oral squamous cell carcinoma (OSCC), one of the common oral cancer types, and checked the lymphatic metastatic status and profiled small RNA in plasma samples.
2025-05-09 | GSE238204 | GEO
Project description:Diffuse Large B Cell Lymphoma (DLBCL) is the most common lymphoid malignancy in adults. Despite being considered a single disease, DLBCL presents with variable backgrounds in terms of morphology, genetics, and biological behavior, which results in heterogeneous outcomes among patients. Although new tools have been developed for the classification and management of patients, 40% of them still have primary refractory disease or relapse. In addition, multiple factors regarding the pathogenesis of this disease remain unclear and identification of novel biomarkers is needed. In this context, recent investigations point to microRNAs as useful biomarkers in cancer as well as important players in the development of the disease. However, regarding DLBCL, up to date, there is inconsistency in the data reported. Therefore, in this work, the main goals were to determine a microRNA set with utility as biomarkers for DLBCL diagnosis, classification, prognosis and treatment response. To achieve these goals, we analyzed microRNA expression in a cohort of 78 DLBCL samples at diagnosis and 17 controls using small RNA sequencing. This way, we were able to define new microRNA expression signatures for diagnosis, classification, treatment response and prognosis. In summary, our study remarks that microRNAs could play an important role as biomarkers in diagnosis, classification, treatment response and prognosis in DLBCL.
2023-10-18 | GSE185796 | GEO
Project description:Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective is to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.
2016-06-07 | GSE80683 | GEO
Project description:<p>Molecular imaging with 18F-fluorocholine PET/CT reveals two distinct imaging phenotypes for hepatocellular carcinoma (HCC) as a potential source of non-invasive insight into its molecular heterogeneity. Using gene set enrichment analyses, we found 18F-fluorocholine-avid tumors to be significantly enriched by genes comprising a subset of previously published HCC-related gene signatures. Significant gene sets included those from existing molecular classification systems for HCC as well as gene signatures predictive of clinical outcomes after tumor resection. PET/CT imaging using 18F-fluorocholine might therefore provide surrogate information about tumor molecular characteristics and prognosis in HCC.</p>
| phs001784 | dbGaP
Project description:Detecting circulating tumor cells from I-IV stage colorectal cancer patients pre-and post-operatively. Analyzing the morphology and biomarkers of CTCs and builting prognosis predicting model based on the morphology and biomarkers of CTCs. Verifying the prognosis model by the survival data.
| 2379533 | ecrin-mdr-crc