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Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Related Experiment Video

Updated: Jun 20, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Tumor Heterogeneity in Gastrointestinal Cancer Based on Multimodal Data Analysis.

Dongmei Ai¹, Yang Du¹, Hongyu Duan²

¹School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.

|September 28, 2024

Summary

This study reveals three distinct gastrointestinal cancer subtypes by integrating diverse data. Key genes like MAGI2-AS3, MALAT1, and SPARC influence metastasis, aiding personalized treatment strategies.

Keywords:

cancer classification cancer imaging multiomics transcriptome profile tumor heterogeneity

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Last Updated: Jun 20, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

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Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Gastrointestinal cancers exhibit significant cellular heterogeneity, complicating single data model representation.
Understanding this diversity is crucial for effective diagnosis and treatment.

Purpose of the Study:

To investigate gastrointestinal cancer heterogeneity using a multimodal data integration approach.
To identify distinct tumor subtypes and associated biomarkers.

Main Methods:

Utilized a modified Canny algorithm for image edge detection, combined with mRNA, miRNA, and immune cell data.
Employed K-medoids for pre-clustering and spectral clustering for tumor subtype identification.
Applied Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Ontology (GO) for hub gene analysis.

Main Results:

Identified three distinct gastrointestinal tumor subtypes.
Pinpointed hub genes including MAGI2-AS3, MALAT1, and SPARC.
These genes demonstrated a differential impact on cancer cell metastasis and invasion.

Conclusions:

Multimodal data integration enhances understanding of gastrointestinal tumor heterogeneity.
Identified biomarkers can facilitate personalized medicine and targeted therapies.