Dataset Information

Proteome data from an external validation cohort using MRM-MS targeted proteomics assay

ABSTRACT: These are raw mass spectrometry files obtained by MRM from the external validation cohort, used to validate findings from the Guangzhou Nutrition and Health Study.

ORGANISM(S): Homo Sapiens

SUBMITTER: Jusheng Zheng

PROVIDER: PXD056935 | iProX | Fri Oct 18 00:00:00 BST 2024

REPOSITORIES: iProX

ACCESS DATA

Publications

Longitudinal serum proteome mapping reveals biomarkers for healthy ageing and related cardiometabolic diseases.

Tang Jun J Yue Liang L Xu Ying Y Xu Fengzhe F Cai Xue X Fu Yuanqing Y Miao Zelei Z Gou Wanglong W Hu Wei W Xue Zhangzhi Z Deng Kui K Shen Luqi L Jiang Zengliang Z Shuai Menglei M Liang Xinxiu X Xiao Congmei C Xie Yuting Y Guo Tiannan T Chen Yu-Ming YM Zheng Ju-Sheng JS

Nature metabolism 20250113 1

The blood proteome contains biomarkers of ageing and age-associated diseases, but such markers are rarely validated longitudinally. Here we map the longitudinal proteome in 7,565 serum samples from a cohort of 3,796 middle-aged and elderly adults across three time points over a 9-year follow-up period. We pinpoint 86 ageing-related proteins that exhibit signatures associated with 32 clinical traits and the incidence of 14 major ageing-related chronic diseases. Leveraging a machine-learning model ...[more]

PMID: 39805987

Similar Datasets

Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.

Dataset Information

Proteome data from an external validation cohort using MRM-MS targeted proteomics assay

Publications

Longitudinal serum proteome mapping reveals biomarkers for healthy ageing and related cardiometabolic diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets