Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.01.09.523362

BRDriver: Breast Cancer Driver Gene Predictor

Perera, R.
View original source ↗Export metadata ↓

Abstract

The breast cancer mortality rate is high in developing countries as the early detection of cancer is deficient in patients. The identification of the genes that drive cancer is one of the major approaches in the early detection of breast cancer. Several computational tools have been developed to predict the cancer driver genes. However, there is no gold-standard method for identifying breast cancer driver genes. Therefore, this study aims to develop a model to predict high-confidence breast cancer diver genes using already-developed computational tools and already-produced breast cancer data. Primary breast cancer data were retrieved from the Cancer Genome Atlas Program (TCGA). Here we use twenty-seven different gene prediction tools that calculate each genes effect and variant in the primary gene dataset. The primary dataset feeds as the input for each tool. The results retrieved from each tool are recorded as the secondary dataset. The latest breast cancer driver gene set was retrieved from DriverDBv3 and included as the target attribute. Training and testing subsets were selected using k-fold cross-validation from the secondary dataset. Attributes from the secondary dataset were ranked according to their correlation with breast cancer driver genes. The ranked data were trained using different supervised machine-learning algorithms. The attributes and the learning algorithm which produced the highest classification accuracy were selected to build the new model, BReast cancer Driver (BRDriver). The new model BRDriver achieved 0.999 area under the curve and 0.999 classification accuracy on breast invasive carcinoma data. TP53 was the most predicted breast cancer drive gene (n=246) predicted by BRDriver. Interestingly, our BRDriver model predicted CDKN2A and NFE2L2 genes as new breast cancer driver genes. Further in vivo and in vitro studies are required to determine whether these genes are indeed cancer-driver genes. Many computational tools developed to identify the cancer driver genes and variants. A few methodologies were developed to combine these tools and increase efficiency for detecting breast cancer driver genes. Our BRDriver overcomes the difficulties and produces high-confidence breast cancer driver genes.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Perera, R.. 2023-01-10. BRDriver: Breast Cancer Driver Gene Predictor. https://doi.org/10.1101/2023.01.09.523362

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗