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Updated: Aug 6, 2026

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Sex differences in the cancer proteome
Chenghao Zhu1,2,3,4,5,6,7, Nicole Zeltser3,4,5,6,7, Jieun Oh3,4,5,6,7
1Cancer Genome and Epigenetics Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Objectives:
Proteins play a central role in cancer biology. They are the most common drug targets and biomarkers. Sex influences the proteome in many diseases ranging from neurological to cardiovascular. In cancer, sex is associated with incidence, progression and therapeutic response, as well as characteristics of the tumour genome and transcriptome. This study aimed to characterise the extent to which sex differences impact the cancer proteome.
Design:
Retrospective analysis of publicly available proteogenomic datasets.
Setting:
Clinical Proteomic Tumor Analysis Consortium cancer cohorts.
Participants:
A total of 1590 proteomes from eight cancer types.
Interventions:
Not applicable.
Main Outcome Measures:
Sex-differential protein abundance and its association with copy number aberrations, biological pathways and gene dependency.
Results:
We identified 901 genes with sex-differential protein abundance in adenocarcinomas of the lung and 20 genes across five other tumour types: squamous cell carcinoma of the lung, hepatocellular carcinomas, clear cell cancers of the kidney, adenocarcinomas of the pancreas and glioblastoma. A subset of these protein differences could be rationalised by sex-differential copy number aberrations. Pathway analysis showed that male-biased proteins in lung adenocarcinoma were enriched in MYC and E2F target pathways, and female-biased proteins were enriched in metabolic and stress-response pathways. These genes also exhibited stronger CRISPR gene dependency in cell lines derived from lung adenocarcinoma.
Conclusions:
These findings highlight the modest impact of sex on the cancer proteome, but the very limited power of existing proteomics cohorts for these analyses.
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