Decoding LncRNA in COPD: Unveiling Prognostic and Diagnostic Power and Their Driving Role in Lung Cancer Progression

Abstract

Chronic obstructive pulmonary disease (COPD) and lung cancer represent formidable challenges in global health, characterized by intricate pathophysiological mechanisms and multifaceted disease progression. This comprehensive review integrates insights from diverse perspectives to elucidate the intricate roles of long non-coding RNAs (lncRNAs) in the pathogenesis of COPD and lung cancer, focusing on their diagnostic, prognostic, and therapeutic implications. In the context of COPD, dysregulated lncRNAs, such as NEAT1, TUG1, MALAT1, HOTAIR, and GAS5, emerge as pivotal regulators of genes involved in the disease pathogenesis and progression. Their identification, profiling, and correlation with the disease severity present promising avenues for prognostic and diagnostic applications, thereby shaping personalized disease interventions. These lncRNAs are also implicated in lung cancer, underscoring their multifaceted roles and therapeutic potential across both diseases. In the domain of lung cancer, lncRNAs play intricate modulatory roles in disease progression, offering avenues for innovative therapeutic approaches and prognostic indicators. LncRNA-mediated immune responses have been shown to drive lung cancer progression by modulating the tumor microenvironment, influencing immune cell infiltration, and altering cytokine production. Their dysregulation significantly contributes to tumor growth, metastasis, and chemo-resistance, thereby emphasizing their significance as therapeutic targets and prognostic markers. This review summarizes the transformative potential of lncRNA-based diagnostics and therapeutics for COPD and lung cancer, offering valuable insights into future research directions for clinical translation and therapeutic development.

Keywords

• chronic obstructive pulmonary disease (COPD)
• long non-coding RNAs (lncRNAs)
• molecular pathogenesis
• inflammation
• lung cancer
• diagnostic biomarkers