Network Medicine of lncRNA-Driven Cell Fate in Cancer: Biomarkers for Predicting Drug Resistance

Drug resistance remains one of the main obstacles to effective cancer therapy, and it is increasingly clear that resistance is a cell fate decision. Long non-coding RNAs (lncRNAs) are central regulators of that decision, steering tumor cells between ferroptosis, apoptosis, pyroptosis, and survival through resistance. Substantial experimental work has mapped the metabolic and signaling machinery of these pathways in tumors, including regulators such as GPX4, FSP1, and ACSL4. What is still missing is a predictive, systems-level framework that can tell, before treatment, which fate a tumor cell will follow, and therefore which patients are likely to respond or to develop resistance. Such a framework has clear diagnostic and therapeutic value: it could yield biomarkers that stratify patients and identify targets for reprogramming resistant tumor cells toward cell death.Individual lncRNAs rarely act alone. They operate within dense regulatory circuits, including competing endogenous RNA (ceRNA) networks, chromatin-modifying complexes, and RNA-protein interactomes, which converge on iron metabolism, lipid peroxidation, mitochondrial permeability, inflammasome activation, and apoptotic signaling. Understanding these decision points requires integrative network modeling, machine learning, and multi-omics analysis rather than pathway-by-pathway characterization.This Research Topic is deliberately computational and translational. It welcomes original research, systematic reviews, and methods articles that apply systems biology, network medicine, machine learning, and multi-omics integration to:- Construct lncRNA-centered regulatory networks that link ceRNA circuits and RNA-protein interactomes to ferroptosis, apoptosis, and pyroptosis signaling- Develop predictive models or network-derived diagnostic and prognostic biomarkers that forecast drug resistance under therapeutic pressure- Characterize the topological and dynamical features (bistable switches, feedback loops, critical transitions) that define the switching point between ferroptotic, apoptotic, pyroptotic, and resistant states- Integrate transcriptomic, epigenomic, proteomic, and single-cell data to reconstruct patient- or tumor-specific decision landscapes- Prioritize lncRNA candidates as therapeutic targets or predictive resistance biomarkers using machine learning or network inference- Support in silico findings with validation, such as experimental perturbation studies, patient-derived datasets, or clinical outcome dataSubmissions based only on in silico analysis should include validating results. Studies focused on describing a single mechanistic axis, without a predictive, diagnostic, or therapeutic component, fall outside the scope of this collection. The collection is intended to complement the mechanistic and metabolic literature on ferroptosis in cancer by asking a distinct question: can lncRNA-driven network states be used to predict, ahead of time, which death or resistance pathway a tumor cell will follow, and can that prediction guide diagnosis and treatment? Topic editors: Shantanu Gupta, Katia Grillone Source: https://www.frontiersin.org/research-topics/86419/network-medicine-of-lncrna-driven-cell-fate-in-cancer-biomarkers-for-predicting-drug-resistance