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Project description:BackgroundMedulloblastoma (MB) is a malignant brain tumor in childhood. It comprises 4 subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts.MethodsWe used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis and electron microscopy to investigate intercellular communication in the MB tumor niche.ResultsTumor cells of the sonic hedgehog (SHH)-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from different MB subgroups or subtypes.ConclusionsWith EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.
| S-EPMC8041350 | biostudies-literature
Project description:Background: Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises four subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts. Methods: We used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis (NTA) and electron microscopy (EM), to investigate intercellular communication in the MB tumor niche. Results: Tumor cells of the SHH-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from otherwise very different MB subgroups or subtypes. Conclusions: With EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.
2020-12-11 | GSE159763 | GEO
Project description:Background: Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises four subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts. Methods: We used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis (NTA) and electron microscopy (EM), to investigate intercellular communication in the MB tumor niche. Results: Tumor cells of the SHH-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from otherwise very different MB subgroups or subtypes. Conclusions: With EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.
2020-12-11 | GSE127997 | GEO
Project description:An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma
| PRJNA671503 | ENA
Project description:(bulk RNAseq) An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma
| PRJNA670439 | ENA
Project description:(single-cell RNAseq) An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma
| PRJNA526024 | ENA
Project description:The prognosis of pancreatic ductal adenocarcinoma (PDAC) is one of the most dismal of all cancers and the median survival of PDAC patients is only 6-8 months after diagnosis. While decades of research effort have been focused on early diagnosis and understanding of molecular mechanisms, few clinically useful markers have been universally applied. To improve the treatment and management of PDAC, it is equally relevant to identify prognostic factors for optimal therapeutic decision-making and patient survival. Compelling evidence have suggested the potential use of extracellular vesicles (EVs) as non-invasive biomarkers for PDAC. The aim of this study was thus to identify non-invasive plasma-based EV biomarkers for the prediction of PDAC patient survival after surgery. Plasma EVs were isolated from a total of 258 PDAC patients divided into three independent cohorts (discovery, training and validation). RNA sequencing was first employed to identify differentially-expressed EV mRNA candidates from the discovery cohort (n = 65) by DESeq2 tool. The candidates were tested in a training cohort (n = 91) by digital droplet polymerase chain reaction (ddPCR). Cox regression models and Kaplan-Meier analyses were used to build an EV signature which was subsequently validated on a multicenter cohort (n = 83) by ddPCR. Transcriptomic profiling of plasma EVs revealed differentially-expressed mRNAs between long-term and short-term PDAC survivors, which led to 10 of the top-ranked candidate EV mRNAs being tested on an independent training cohort with ddPCR. The results of ddPCR enabled an establishment of a novel prognostic EV mRNA signature consisting of PPP1R12A, SCN7A and SGCD for risk stratification of PDAC patients. Based on the EV mRNA signature, PDAC patients with high risk displayed reduced overall survival (OS) rates compared to those with low risk in the training cohort (p = 0.014), which was successfully validated on another independent cohort (p = 0.024). Interestingly, the combination of our signature and tumour stage yielded a superior prognostic performance (p = 0.008) over the signature (p = 0.022) or tumour stage (p = 0.016) alone. It is noteworthy that the EV mRNA signature was demonstrated to be an independent unfavourable predictor for PDAC prognosis. This study provides a novel and non-invasive prognostic EV mRNA signature for risk stratification and survival prediction of PDAC patients.
| S-EPMC9896775 | biostudies-literature
Project description:BackgroundThe early diagnosis of lung adenocarcinoma (LUAD) is particularly challenging. Recent studies have reported that extracellular vesicles (EVs) include both small and long RNA. However, the profile and diagnosis-related significance of EV long RNA (exLR) profiles for early LUAD remain unclear.MethodsA case-control analysis was carried out involving 110 participants, including 64 stage I LUAD cases, 24 benign pulmonary nodule (BPN) cases, and 22 healthy controls (HCs). The analysis was performed on the plasma samples' exLR profile based on exLR sequencing. The d-signature was identified using the least absolute shrinkage and selection operator (LASSO) method and a training set (n=48), and validation was completed through use of an internal validation set (n=32) and an external validation set (n=30).ResultsA diagnostic signature (d-signature) encompassing 8 exLR markers (NFKBIA, NDUFB10, SLC7A7, ARPC5, SEPTIN9, HMGN1, H4C2, and lnc-PLA2G1B-2:3) was identified for the detection of LUAD. This d-signature exhibited a high level of accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.991 in the training group, 0.921 in the internal validation group, and 0.9 in the external validation group. Moreover, the d-signature could distinguish adenocarcinomas in situ (AIS) and minimally invasive adenocarcinomas (MIA) from the noncancerous controls (NCs), with AUCs of 0.934 and 0.909, respectively, in the combined cohorts.ConclusionsThis study initially characterized the plasma exLR profile of early LUAD and reported on an exLR-based diagnostic signature for the detection of LUAD. This d-signature could be a promising noninvasive biomarker for the early detection and routine screening of LUAD.
| S-EPMC9073734 | biostudies-literature
Project description:Identifying molecular characteristics that are associated with aggressive cancer phenotypes through gene expression profiling can help predict treatment responses and clinical outcomes. Claudins are deregulated in colorectal cancer (CRC). In CRC, increased claudin-1 expression results in epithelial-to-mesenchymal transition and metastasis, while claudin-7 functions as a tumor suppressor. In this study, we have developed a molecular signature based on claudin-1 and claudin-7 associated with poor patient survival and chemoresistance. This signature was validated using an integrated approach including publicly available datasets and CRC samples from patients who either responded or did not respond to standard-of-care treatment, CRC cell lines, and patient-derived rectal and colon tumoroids. Transcriptomic analysis from a patient dataset initially yielded 23 genes that were differentially expressed along with higher claudin-1 and decreased claudin-7. From this analysis, we selected a claudins-associated molecular signature including PIK3CA, SLC6A6, TMEM43, and ASAP-1 based on their importance in CRC. The upregulation of these genes and their protein products was validated using multiple CRC patient datasets, in vitro chemoresistant cell lines, and patient-derived tumoroid models. Additionally, blocking these genes improved 5-FU sensitivity in chemoresistant CRC cells. Our findings propose a new claudin-based molecular signature that associates with poor prognosis as well as characteristics of treatment-resistant CRC including chemoresistance, metastasis, and relapse.
| S-EPMC8466455 | biostudies-literature
Project description:Abstract Background Glioblastoma (GBM) is the most common malignant brain tumor and has a poor prognosis. Imaging findings at diagnosis and in response to treatment are nonspecific. Developing noninvasive assays to augment imaging would be helpful. Plasma extracellular vesicles (EVs) are a promising biomarker source for this. Here, we develop spectral flow cytometry techniques that demonstrate differences in bulk plasma EV phenotype between GBM patients and normal donors that could serve as the basis of a liquid biopsy. Methods Plasma EVs were stained for EV-associated tetraspanins (CD9/CD63/CD81), markers indicating cell of origin (CD11b/CD31/CD41a/CD45), and actin/phalloidin (to exclude cell debris). EVs were analyzed using spectral flow cytometry. Multiparametric analysis using t-distributed stochastic neighbor embedding (t-SNE) and self-organizing maps on flow cytometry data (FlowSOM) was performed comparing GBM and normal donor (ND) plasma EVs. Results Size exclusion chromatography plus spectral-based flow cytometer threshold settings enriched plasma EVs while minimizing background noise. GBM patients had increased CD9+, CD63+, CD81+, and myeloid-derived (CD11b+) EVs. Multiparametric analysis demonstrated distinct surface marker expression profiles in GBM plasma EVs compared to ND EVs. Fifteen plasma EV sub-populations differing in size and surface marker expression were identified, six enriched in GBM patients and two in normal donors. Conclusions Multiparametric analysis demonstrates that GBM patients have a distinct nonneoplastic plasma EV phenotype compared to ND. This simple rapid analysis can be performed without purifying tumor EVs and may serve as the basis of a liquid biopsy.
| S-EPMC10457026 | biostudies-literature