A5082466576- Cancer, Hypoxia, and Metabolism
- Metabolism, Diabetes, and Cancer
- RNA modifications and cancer
- Cardiovascular Function and Risk Factors
- Chronic Lymphocytic Leukemia Research
- Mitochondrial Function and Pathology
- Autophagy in Disease and Therapy
- Epigenetics and DNA Methylation
- Pancreatic and Hepatic Oncology Research
- Cancer-related Molecular Pathways
- Advanced Breast Cancer Therapies
- Glycosylation and Glycoproteins Research
- Health and Wellbeing Research
- Sarcoma Diagnosis and Treatment
- Adipose Tissue and Metabolism
- Pancreatic function and diabetes
- RNA Research and Splicing
- interferon and immune responses
- Diverse Approaches in Healthcare and Education Studies
- Electromagnetic Fields and Biological Effects
- Ultrasound and Hyperthermia Applications
Center for Cancer Research
2021-2023
National Cancer Institute
2021-2023
Ajou University
2022
National Institutes of Health
2021
Understanding functional interactions between cancer mutations is an attractive strategy for discovering unappreciated pathways and developing new combination therapies to improve personalized treatment. However, distinguishing driver gene pairs from passenger remains challenging. Here, we designed integrated omics approach identify by leveraging genetic interaction analyses of top mutated breast genes the proteomics interactome data their encoded proteins. This identified that PIK3CA...
The absence of prominent, actionable genetic alternations in osteosarcomas (OS) implies that transcriptional and epigenetic mechanisms significantly contribute to the progression this life-threatening form cancer. Therefore, identification potential events promote survival OS cells could be key devising targeted therapeutic approaches for OS. We have previously shown RUNX2 is a transcription factor (TF) essential cell survival. Unfortunately, network or circuitry regulated by still largely...
The CBFB gene is frequently mutated in several types of solid tumors. Emerging evidence suggests that a tumor suppressor breast cancer. However, our understanding the suppressive function remains incomplete. Here, we analyze genetic interactions between mutations and other highly genes human cancer datasets find TP53 are mutually exclusive, suggesting functional association p53. Integrated genomic studies reveal TAp73 common transcriptional target cooperates with p53 to maintain expression,...
<p>This file contains supplementary figure 1-9.</p>
<p>This is supplementary Table 1.</p>
<p>This is supplementary Table 3.</p>
<p>This is supplementary Table 3.</p>
<p>This is supplementary Table 4.</p>
<p>This is supplementary Table 4.</p>
<p>This is supplementary Table 2.</p>
<p>This is supplementary Table 2.</p>
<div>Abstract<p>Understanding functional interactions between cancer mutations is an attractive strategy for discovering unappreciated pathways and developing new combination therapies to improve personalized treatment. However, distinguishing driver gene pairs from passenger remains challenging. Here, we designed integrated omics approach identify by leveraging genetic interaction analyses of top mutated breast genes the proteomics interactome data their encoded proteins. This...
<p>This is supplementary Table 1.</p>
<p>This file contains supplementary figure 1-9.</p>
<div>Abstract<p>Understanding functional interactions between cancer mutations is an attractive strategy for discovering unappreciated pathways and developing new combination therapies to improve personalized treatment. However, distinguishing driver gene pairs from passenger remains challenging. Here, we designed integrated omics approach identify by leveraging genetic interaction analyses of top mutated breast genes the proteomics interactome data their encoded proteins. This...
<div>Abstract<p>Understanding functional interactions between cancer mutations is an attractive strategy for discovering unappreciated pathways and developing new combination therapies to improve personalized treatment. However, distinguishing driver gene pairs from passenger remains challenging. Here, we designed integrated omics approach identify by leveraging genetic interaction analyses of top mutated breast genes the proteomics interactome data their encoded proteins. This...
<p>This file contains supplementary figure 1-9.</p>
<p>This is supplementary Table 4.</p>
<p>This is supplementary Table 3.</p>
<p>This is supplementary Table 1.</p>