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Updated: Apr 19, 2026
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Identifying glioblastoma gene networks based on hypergeometric test analysis
Vasileios Stathias1, Chiara Pastori2, Tess Z Griffin3
1Department of Human Genetics & Genomics, University of Miami Miller School of Medicine, Miami, Florida, 33136, United States of America; Department of Psychiatry and Behavioral Sciences, Center for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, Florida, 33136, United States of America.
Abstract:
Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA). To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM). We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.
Insights
This study introduces a bioinformatics pipeline to find patient-specific cancer therapies by comparing individual tumor gene expression to The Cancer Genome Atlas (TCGA) database, aiding glioblastoma (GBM) research.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Personalized cancer therapy requires identifying specific genomic and transcriptional alterations in individual tumors.
- Analyzing single tumor samples poses statistical challenges due to limitations in standard analysis techniques.
Purpose of the Study:
- To develop a bioinformatics pipeline for identifying differentially expressed genes and networks in individual tumors.
- To compare tumor-specific gene expression profiles against a large cohort database (TCGA) for identifying potential therapeutic targets.
Main Methods:
- RNA sequencing was performed on individual glioblastoma (GBM) tumor samples.
- A bioinformatics pipeline was devised to filter sequencing results through The Cancer Genome Atlas (TCGA) database.
- Hypergeometric-based analysis of gene pairs was used to identify overrepresented gene networks.
Main Results:
- The pipeline successfully identified gene networks overrepresented in GBM samples compared to controls.
- Experimental validation confirmed the identified gene networks.
- The study demonstrates a novel workflow for uncovering patient-specific genes and networks.
Conclusions:
- This approach provides a viable method for identifying patient-specific genes and networks in glioblastoma (GBM) and potentially other cancers.
- The developed bioinformatics pipeline facilitates the discovery of targeted therapies for individual cancer patients.
- Comparing individual tumor data with large public databases like TCGA is effective for identifying clinically relevant molecular signatures.
