Decades of omics in lung cancer research: a bibliometric analysis and visualization from 2004 to 2024.

Background: Omics, encompassing genomics, transcriptomics, proteomics and metabolomics, plays a pivotal role in elucidating the molecular mechanisms underlying lung cancer and advancing precision oncology. While existing studies have primarily focused on the technical development and clinical efficacy of omics applications in cancer, there remains a notable gap in comprehensive assessments of the global research landscape. At different stages of lung cancer initiation, progression, and metastasis, genomics and transcriptomics predominantly reveal oncogenic alterations and dysregulated signaling networks, whereas proteomics and metabolomics capture functional protein dynamics and metabolic reprogramming that drive tumor growth and metastatic adaptation. Importantly, the integration of multi-omics data enables a systematic understanding of the crosstalk between genetic alterations, transcriptional regulation, protein expression, and metabolic remodeling throughout lung cancer evolution. This bibliometric analysis study aims to systematically evaluate scientific output, research trends and hotspots in omics-related lung cancer research. Methods: Relevant publications were retrieved from the Web of Science Core Collection (WoSCC) from January 1, 2004 to April 27, 2024. Bibliometric analyses and knowledge domain visualizations were conducted using VOSviewer (v1.6.20), CiteSpace (v6.3), R (v4.3.3), and Origin (2024). Results: A total of 19,087 publications were included, demonstrating sustained growth over two decades [2004-2024]. China contributed the largest volume of publications, whereas the USA showed higher citation impact and stronger influence in collaboration networks. Keyword co-occurrence and burst analyses illustrated that "expression", "lung cancer", "gene expression", "tumor microenvironment", "mutation" and "immunotherapy" are dominant and emerging themes. These findings indicate a clear shift from single-omics approaches and gene-centric investigations toward integrative multi-omics frameworks, with increasing emphasis on the tumor microenvironment (TME) and immunotherapy. The burst analysis of keywords also highlights the rising prominence of artificial intelligence (AI) and machine learning (ML), which have emerged as rapidly growing methodological backbones in recent years. Conclusions: Research on omics in lung cancer has rapidly evolved toward integrative, TME-focused and immunotherapy-oriented paradigms, with AI/ML serving as an enabling analytical infrastructure. This study underscores the critical role that omics in facilitating early detection, guiding personalized therapeutic strategies, and improving prognostic accuracy. The findings suggest that enhancing cross-disciplinary collaboration and accelerating the clinical translation of multi-omics data may help overcome current challenges in precision oncology.