Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A plasma miRNA-based classifier for small cell lung cancer diagnosis

ABSTRACT: A plasma miRNA-based classifier for small cell lung cancer diagnosis

PROVIDER: PRJNA1005199 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR25628900.fastq.gz	Fastqsanger.gz
	SRR25628901.fastq.gz	Fastqsanger.gz
	SRR25628865.fastq.gz	Fastqsanger.gz
	SRR25628866.fastq.gz	Fastqsanger.gz
	SRR25628867.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 37

Similar Datasets

A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]

Project description:Objectives: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

2023-10-31 | GSE240757 | GEO

A plasma miRNA-based classifier for small cell lung cancer diagnosis [NanoString]

2023-10-31 | GSE240758 | GEO

A plasma miRNA-based classifier for small cell lung cancer diagnosis [NanoString]

Project description:A plasma miRNA-based classifier for small cell lung cancer diagnosis [NanoString]

| PRJNA1005201 | ENA

A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]

Project description:A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]

| PRJNA1005200 | ENA

A plasma miRNA-based classifier for small cell lung cancer diagnosis.

Project description:IntroductionSmall cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses.MethodsWe profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients' clinical data. Finally, we applied the classifier on a validation dataset.ResultsWe determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group.DiscussionThis study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

| S-EPMC10585112 | biostudies-literature

A plasma miRNA-based classifier for small cell lung cancer diagnosis

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-10-31 | GSE240759 | GEO

Identification of a robust methylation classifier for cutaneous melanoma diagnosis

Project description:Identification of a robust methylation classifier for cutaneous melanoma diagnosis

| PRJNA494817 | ENA

Analysis of plasma small RNAs prior to breast cancer diagnosis

Project description:Analysis of plasma small RNAs prior to breast cancer diagnosis

| PRJNA1001369 | ENA

YangziChen2024 - Metabolic Machine Learning Predictor Model for Diagnosis of Gastric Cancer

Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models. I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.

2024-07-23 | MODEL2407230001 | BioModels

Diagnosis potential of subarachnoid haemorrhage using miRNA signatures isolated plasma-derived extracellular vesicles

Project description:Diagnosis potential of subarachnoid haemorrhage using miRNA signatures isolated plasma-derived extracellular vesicles

| PRJNA924332 | ENA