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Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.
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GenReP: An Ensemble Model for Predicting TP53 in Response to Pharmaceutical Compounds
TP53 is a tumor-suppressor gene involved in regulating apoptosis, DNA repair, and genomic stability. Mutations in TP53 are implicated in approximately half of all detected cancers, including breast, lung, colorectal, and ovarian cancers, making it a significant target for therapeutic interventions. Many pharmaceutical drugs aim to restore TP53 function, and there is a need for predictive tools to assess how compounds may affect TP53 expression. In this study, we propose a new ensemble machine-learning model to predict the direction of TP53 relative gene expression in response to pharmaceutical compounds. Our model utilizes molecular fingerprints, descriptors, and scaffold-based features extracted from SMILES representations of compounds concatenated into a single feature vector. Trained using our newly generated benchmark dataset based on the Connectivity Map (CMap) database and addressing class imbalance with the Synthetic Minority Over-sampling Technique (SMOTE), our model achieves 62.9%, 93.9%, 40.3%, and 0.39 in terms of accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC), respectively. As the first-of-its-kind TP53 gene regulation prediction, our study serves as a convincing proof-of-concept that paves the way for future investigation. GenReP as a stand-alone predictor, its source code, and our newly generated benchmark dataset are publicly available.
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- Award ID(s):
- 2152059
- PAR ID:
- 10682446
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Molecules
- Volume:
- 31
- Issue:
- 4
- ISSN:
- 1420-3049
- Page Range / eLocation ID:
- 739
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/molecules31040739
