Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Swarm intelligence-enhanced detection of non-small cell lung cancer using tumor-educated platelets

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE89843 | GEO | 2017/08/15

SECONDARY ACCESSION(S): PRJNA353588

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management

Project description:We report RNA-seq data of 766 blood platelet samples, including 399 Non-Small Cell Lung Cancer (NSCLC) patients from different stages (I-IV) and 367 asymptomatic individuals (Controls). Our data explains how tumor-educated platelet (TEP)-derived spliced RNA can serve as a biomarker for minimally-invasive clinical blood tests on assisting the detection and management of lung cancer patients.

2023-06-12 | GSE207586 | GEO

Tumor-educated platelet RNA for the detection and (pseudo)progression monitoring of glioblastoma

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

RNA-sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA

Project description:We report RNA-sequencing data of 80 tumor-educated blood platelet (TEP) samples isolated from 39 patients with lower-grade glioma (LGG) and 41 healthy donors (HD). This dataset can be employed as input for the thromboSeq source code (available via GitHub: https://github.com/MyronBest/) to reproduce the thromboSeq drylab pipeline.

2019-03-20 | GSE107868 | GEO

RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass and molecular pathway cancer diagnostics

2015-10-30 | GSE68086 | GEO

RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass and molecular pathway cancer diagnostics

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

Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures [CITE-seq]

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

Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures [RNA-seq]

2024-10-16 | GSE271072 | GEO

Detection and localization of early- and late-stage cancers using tumor-educated platelet RNA

Project description:We report RNA-sequencing data of 2351 blood platelet samples, including 1628 patients with stage I-IV cancer (of which 18 different tumor types), 390 asymptomatic controls and 333 symptomatic controls. This dataset highlights the ability of TEP RNA-based 'liquid biopsy' diagnostics for the detection and localization of early- and late-stage cancers.

2022-08-05 | GSE183635 | GEO

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

Project description:Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.

| S-EPMC6381325 | biostudies-literature

Word sense disambiguation using hybrid swarm intelligence approach.

Project description:Word sense disambiguation (WSD) is the process of identifying an appropriate sense for an ambiguous word. With the complexity of human languages in which a single word could yield different meanings, WSD has been utilized by several domains of interests such as search engines and machine translations. The literature shows a vast number of techniques used for the process of WSD. Recently, researchers have focused on the use of meta-heuristic approaches to identify the best solutions that reflect the best sense. However, the application of meta-heuristic approaches remains limited and thus requires the efficient exploration and exploitation of the problem space. Hence, the current study aims to propose a hybrid meta-heuristic method that consists of particle swarm optimization (PSO) and simulated annealing to find the global best meaning of a given text. Different semantic measures have been utilized in this model as objective functions for the proposed hybrid PSO. These measures consist of JCN and extended Lesk methods, which are combined effectively in this work. The proposed method is tested using a three-benchmark dataset (SemCor 3.0, SensEval-2, and SensEval-3). Results show that the proposed method has superior performance in comparison with state-of-the-art approaches.

| S-EPMC6301655 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data