Dataset Information

TMT-based quantitative proteomic profiling of brain tissue from multiparous and nulliparous 5xFAD mice

ABSTRACT: TMT-based quantitative proteomic profiling of brain tissue from multiparous and nulliparous 5xFAD mice

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Yang Renzhi

LAB HEAD: Hongsheng Zhang

PROVIDER: PXD064605 | Pride | 2026-06-08

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	Label_unadjDiff_mulitparity_con_all.xlsx	Xlsx
	PRIDE_Metadata_Table.csv	Csv
	Protein_Matrix_annotation.xlsx	Xlsx
	WAK20240927chenmn_wosp24320_60minTMT_1.raw	Raw
	WAK20240927chenmn_wosp24320_60minTMT_10.raw	Raw

Items per page:

1 - 5 of 26

Publications

Multiparity exacerbates Aβ accumulation and promotes cellular senescence in a mouse model of amyloidosis.

Yang Renzhi R He Ying Y Pan Youkai Y Geng Aoyi A Huang Fang F Guo Kai K Zhang Hongsheng H

Immunity & ageing : I & A 20260329 1

PMID: 41906135

Similar Datasets

Project description:The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17). Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: NARR, MLPH, S100A14, CK7) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.