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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Updated: Jun 3, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Nonlinear Modeling Reveals Novel Associations Between Genetically Predicted Protein Levels and Pancreatic Cancer

Jingjing Zhu1,2,3, Chong Wu4, Omeed Moaven5

  • 1Department of Interdisciplinary Oncology and Department of Genetics, LSU-LCMC Health Cancer Center, School of Medicine, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA.

Molecular Carcinogenesis
|June 2, 2026
PubMed
Summary
This summary is machine-generated.

This study explored nonlinear protein associations with pancreatic cancer risk, identifying three novel protein biomarkers (APOF, CCL15, CHIT1) beyond previously known ones. Findings may improve pancreatic cancer (PDAC) understanding and risk assessment.

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Area of Science:

  • Genetics
  • Oncology
  • Biomarker Discovery

Background:

  • Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer with a significant public health impact.
  • Understanding PDAC etiology is crucial for developing effective prevention and treatment strategies.
  • Previous studies identified PDAC risk-associated proteins using linear models, but nonlinear associations remain underexplored.

Purpose of the Study:

  • To investigate nonlinear associations between genetically predicted plasma protein concentrations and PDAC risk.
  • To identify novel protein biomarkers for PDAC using advanced statistical methods.

Main Methods:

  • Employed a nonlinear modeling approach combining two-stage sliced inverse regression (2SIR) and adjusted inverse regression (AIR).
  • Integrated blood proteome and genome data from the INTERVAL study (n=3301) with a large PDAC genome-wide association study (8275 cases, 6723 controls).

Main Results:

  • Identified 25 genetically predicted proteins associated with PDAC risk after multiple comparison correction.
  • Confirmed 22 previously reported proteins and discovered three novel proteins: APOF, CCL15, and CHIT1.
  • Found literature support for the potential roles of novel proteins in PDAC development.

Conclusions:

  • Accounting for nonlinear relationships is vital for uncovering novel PDAC risk proteins.
  • The identified novel proteins (APOF, CCL15, CHIT1) warrant further investigation for their role in PDAC pathogenesis.
  • Findings may enhance understanding of PDAC and inform future therapeutic and risk assessment strategies.