Ontology highlight
ABSTRACT:
PROVIDER: PRJNA703002 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We developed classifiers for the detection of (early) breast cancer based on tumor educated platelets (TEPs).
| EGAS00001006821 | EGA
Project description:We report RNA-sequencing data of 779 blood platelet samples, including 402 tumor-educated platelet (TEP) samples collected from patients with non-small cell lung cancer (NSCLC). In addition, we report RNA-sequencing data of blood platelets isolated from 377 individuals without reported cancer, but not excluding individuals with inflammatory conditions. This dataset highlights the ability of TEP RNA-based 'liquid biopsy' diagnostics in patients with NSCLC.
2017-08-15 | GSE89843 | GEO
Project description:We aimed to investigate the diagnostic potential of tumor educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into training cohort (23 patients and 27 healthy individuals) and validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2 and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found keratin and collagen protein families, and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics.
2022-06-01 | GSE197514 | GEO
Project description:We report RNA-sequencing data of 805 blood platelet samples, including 240 tumor-educated platelet (TEP) samples collected from patients with glioblastoma and 126 TEP samples collected from patients with brain metastases. In addition, we report RNA-sequencing data of blood platelets isolated from 353 asymptomatic controls and 86 individuals with multiple sclerosis. This dataset highlights the ability of TEP RNA-based 'liquid biopsy' diagnostics for the detection and (pseudo)progression monitoring of glioblastoma.
2020-10-22 | GSE156902 | GEO
Project description:Platelets can change their expression of mRNA and miRNAs when educated by tumors. In this assay we assesed the diferential expression of miRNA between healthy individuals and patients suffering pancreatic cancer (PDAC) Platelets form 4 healthy donors, 4 PDAC patient blood and 4 PDAC patient pancreatic drainage were collected to asses differential expression
2022-05-17 | GSE203122 | GEO
Project description:We report RNA-sequencing data of 283 blood platelet samples, including 228 tumor-educated platelet (TEP) samples collected from patients with six different malignant tumors (non-small cell lung cancer, colorectal cancer, pancreatic cancer, glioblastoma, breast cancer and hepatobiliary carcinomas). In addition, we report RNA-sequencing data of blood platelets isolated from 55 healthy individuals. This dataset highlights the ability of TEP RNA-based 'liquid biopsies' in patients with several types with cancer, including the ability for pan-cancer, multiclass cancer and companion diagnostics.
2015-10-30 | GSE68086 | GEO
Project description:Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrate that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We use CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrate that a significant proportion of transcripts in the platelet transcriptome are derived from non-platelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with non-platelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.
2024-10-16 | GSE271073 | GEO
Project description:Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrate that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We use CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrate that a significant proportion of transcripts in the platelet transcriptome are derived from non-platelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with non-platelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.
2024-10-16 | GSE271072 | GEO
Project description:We report RNA-sequencing data of 283 blood platelet samples, including 228 tumor-educated platelet (TEP) samples collected from patients with six different malignant tumors (non-small cell lung cancer, colorectal cancer, pancreatic cancer, glioblastoma, breast cancer and hepatobiliary carcinomas). In addition, we report RNA-sequencing data of blood platelets isolated from 55 healthy individuals. This dataset highlights the ability of TEP RNA-based 'liquid biopsies' in patients with several types with cancer, including the ability for pan-cancer, multiclass cancer and companion diagnostics. Blood platelets were isolated from whole blood in purple-cap BD Vacutainers containing EDTA anti-coagulant by standard centrifugation. Total RNA was extracted from the platelet pellet, subjected to cDNA synthesis and SMARTer amplification, fragmented by Covaris shearing, and prepared for sequencing using the Truseq Nano DNA Sample Preparation Kit. Subsequently, pooled sample libraries were sequenced on the Illumina Hiseq 2500 platform. All steps were quality-controlled using Bioanalyzer 2100 with RNA 6000 Picochip, DNA 7500 and DNA High Sensitivity chips measurements. For further downstream analyses, reads were quality-controlled using Trimmomatic, mapped to the human reference genome using STAR, and intron-spanning reads were summarized using HTseq. The processed data includes 285 samples (columns) and 57736 ensemble gene ids (rows). The supplementary data file (TEP_data_matrix.txt) contains the intron-spanning read counts, after data summarization by HTseq.
2015-10-30 | E-GEOD-68086 | biostudies-arrayexpress
Project description:Swarm intelligence-enhanced detection of non-small cell lung cancer using tumor-educated platelets
| PRJNA353588 | ENA