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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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MSICKB: A Curated Knowledgebase for Exploring Molecular Heterogeneity and Biomarker Prioritization in Microsatellite

Yuxin Zhang¹, Xiaoyu Li^1,2, Xin Zheng¹

¹Department of Medical Oncology, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Computational and Structural Biotechnology Journal

|April 22, 2026

Summary

Microsatellite instability (MSI) is a key cancer marker. We created MSICKB, a database of MSI features across 31 cancer types, identifying 9 hub genes for improved biomarker discovery and cancer comparison.

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Last Updated: Apr 23, 2026

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Published on: December 9, 2015

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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

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Microsatellite instability (MSI) is a crucial molecular cancer phenotype.
Existing MSI data is fragmented, hindering cross-cancer analysis and biomarker prioritization.

Purpose of the Study:

To develop a comprehensive knowledgebase (MSICKB) for MSI-associated molecular and clinical features.
To enable systematic cross-cancer comparisons and guide biomarker discovery.

Main Methods:

Manually curated 1,382 MSI-related features from 492 publications across 31 cancer types.
Organized evidence into genetic alterations, clinicopathology, prognosis, and therapy.
Constructed a bipartite network of 99 MSI-associated genes and 13 cancer types.

Main Results:

Identified 9 hub genes (BRAF, CD274, KRAS, MLH1, MSH2, PTEN, RNF43, TGFBR2, TP53) with cross-cancer relevance.
Hub genes are enriched in mismatch repair, cancer signaling, and immune regulation pathways.
Hub genes showed significant mutation and expression differences in MSI-high vs. non-MSI-high tumors.

Conclusions:

MSICKB offers a structured framework for MSI evidence synthesis and cross-cancer comparison.
The identified hub genes are promising candidates for MSI biomarker development.
MSICKB facilitates systematic biomarker prioritization for clinical actionability.