Mapping Metabolic Reprogramming in Lung and Breast Cancer Through Integrative Bioinformatics
Nosayba Al-Damook, Molham Sakkal, Mostafa Khair, Walaa K. Mousa, Rose Ghemrawi
Abstract
Metabolic reprogramming is central to cancer biology, enabling tumor cells to sustain rapid proliferation, resist stress, and adapt to therapy. However, these alterations are highly heterogeneous across cancer types, and current treatments rarely exploit subtype-specific metabolic vulnerabilities. To address this gap, we developed a unified bioinformatics framework that integrates transcriptomic profiling (UALCAN), drug–gene interactions (DGIdb), gene–disease associations (Open Targets), pathway enrichment (Enrichr), and protein–protein interaction networks (STRING/Cytoscape).
Introduction
Cancer cells survive and proliferate by fundamentally reshaping their metabolism. They increase glucose uptake and favor glycolysis even in oxygen-rich conditions, accelerate lipid and nucleotide biosynthesis, reroute amino acid metabolism, and enhance antioxidant defenses. These metabolic alterations supply the energy and building blocks necessary for rapid growth while enabling resistance to stress and therapy. Importantly, such reprogramming is not uniform across cancers.
Materials and Methods:
For each gene and cancer type, the “Survival Analysis” module in UALCAN was used to compare overall survival between high-expression and low/medium-expression patient groups. UALCAN automatically assigns samples to these categories based on normalized transcript expression. Kaplan–Meier plots and corresponding log-rank test p-values were retrieved directly from the platform. All survival curves, p-values, and group stratifications reported in the study were generated by UALCAN’s built-in survival analysis tool.
Discussion:
Our integrative analysis revealed that cancer metabolism is not a uniform process but rather exhibits striking cancer type–specific rewiring, with LUAD, LSCC, BRCA, and MET500 each adopting distinct metabolic programs that shape their progression and therapeutic vulnerabilities.
In this study, we systematically profiled metabolic gene expression across LUAD, LSCC, BRCA, and metastatic tumors using UALCAN-based transcriptomic analysis, revealing both conserved and context-specific signatures.
Citation: Al-Damook N, Sakkal M, Khair M, Mousa WK, Ghemrawi R (2026) Mapping metabolic reprogramming in lung and breast cancer through integrative bioinformatics. PLoS One 21(6): e0350628. https://doi.org/10.1371/journal.pone.0350628
Editor: Mohammad Zeeshan Najm, Apeejay Stya University, INDIA
Received: August 29, 2025; Accepted: May 15, 2026; Published: June 4, 2026.
Copyright: © 2026 Al-Damook et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This study was supported by internal funding from Al Ain University, United Arab Emirates (Grant No. Ph2025-6-102), awarded to R.G. There was no additional external funding received for this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
