Shutta, K. | Search bioRxiv

bioRxiv2025

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

cancer biology↗

bioRxiv2022

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗