Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

COADREAD_FDDXZLYY_20190823_ExpChip

ABSTRACT: colorectal cancer

TISSUE(S): ['colon']

DISEASE(S): ['colorectal Cancer']

SUBMITTER: Liyun Yuan

PROVIDER: OEX00013134 | NODE | 2021-06-03

REPOSITORIES: NODE

ACCESS DATA

Json Xml

Similar Datasets

Gastric Cancer Transcriptome

Project description:Gastric Cancer Transcriptome

| OEX00015796 | NODE

lung cancer 5hmc sequencing

Project description:lung cancer 5hmc sequencing

| OEX00020577 | NODE

colorectal cancer 5hmc sequencing

Project description:colorectal cancer 5hmc sequencing

| OEX00020517 | NODE

transcriptomic of breast cancer

Project description:transcriptomic of breast cancer

| OEX00026095 | NODE

transcriptomic of breast cancer

Project description:transcriptomic of breast cancer

| OEX00029441 | NODE

GNPS Systematic analyses of early-stage lung cancer by scRNA-seq and lipidomic reveals aberrant lipid metabolism as detection biomarkers

Project description:Lung cancer is the leading cause of cancer mortality and early detection is the key to improve survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of early-stage lung cancer and found lipid metabolism was broadly dysregulated in different cell types and glycerophospholipid metabolism is the most significantly altered lipid metabolism-related pathway. Untargeted lipidomics were detected in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we have identified nine lipids as the most important detection features and developed a LC-MS-based targeted assay utilizing multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low dose CT and a prospective clinical cohort containing 109 participants, this assay reached over 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization-mass spectrometry imaging assay confirmed the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. Thus, this method, designated as Lung Cancer Artificial Intelligence Detector (LCAID), may be used for early detection of lung cancer or large-scale screening of high-risk populations in cancer prevention.

2021-11-05 | MSV000088324 | GNPS