Mapping the rapid growth of multi-omics in tumor immunotherapy: Bibliometric evidence of technology convergence and paradigm shifts

Author(s): Huijing Dong1; Xinmeng Wang1; Yumin Zheng1; Jia Li1; Zhening Liu1; Aolin Wang1; Yulei Shen1; Daixi Wu1; Huijuan Cui2
Source: PMCID: PMC12026087
Original Article
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The article demonstrates that multi-omics research in tumor immunotherapy has grown rapidly since 2019, with China leading in publication volume but showing limited international collaboration. Early research focused on immune checkpoint blockade, while recent trends emphasize machine learning, multi-omics integration, and lncRNA, reflecting a shift toward predictive modeling and biomarker discovery. Multi-omics approaches have enabled the development of immune infiltration-based prognostic models and identified metabolic and spatial biomarkers, such as oxidative phosphorylation in melanoma and ENPP1 in Ewing sarcoma, which may guide therapeutic strategies. Overall, the study provides a systematic framework for tracking technological convergence and emerging frontiers, highlighting the need for longitudinal omics monitoring, AI-driven integration, and enhanced international collaboration to optimize precision-driven tumor immunotherapy.

ABSTRACT

This study aims to fill the knowledge gap in systematically mapping the evolution of omics-driven tumor immunotherapy research through a bibliometric lens. While omics technologies (genomics, transcriptomics, proteomics, metabolomics)provide multidimensional molecular profiling, their synergistic potential with immunotherapy remains underexplored in large-scale trend analyses. A comprehensive search was conducted using the Web of Science Core Collection for literature related to omics in tumor immunotherapy, up to August 2024. Bibliometric analyses, conducted using R version 4.3.3, VOSviewer 1.6.20, and Citespace 6.2, examined publication trends, country and institutional contributions, journal distributions, keyword co-occurrence, and citation bursts. This analysis of 9,494 publications demonstrates rapid growth in omics-driven tumor immunotherapy research since 2019, with China leading in output (63% of articles) yet exhibiting limited multinational collaboration (7.9% vs. the UK’s 61.8%). Keyword co-occurrence and citation burst analyses reveal evolving frontiers: early emphasis on “PD-1/CTLA-4 blockade” has transitioned toward “machine learning,” “multi-omics,” and “lncRNA,” reflecting a shift to predictive modeling and biomarker discovery. Multi-omics integration has facilitated the development of immune infiltration-based prognostic models, such as TIME subtypes, which have been validated across multiple tumor types, which inform clinical trial design (e.g. NCT06833723). Additionally, proteomic analysis of melanoma patients suggests that metabolic biomarkers, particularly oxidative phosphorylation and lipid metabolism, may stratify responders to PD-1 blockade therapy. Moreover, spatial omics has confirmed ENPP1 as a potential novel therapeutic target in Ewing sarcoma. Citation trends underscore clinical translation, particularly mutation-guided therapies. Omics technologies are transforming tumor immunotherapy by enhancing biomarker discovery and improving therapeutic predictions. Future advancements will necessitate longitudinal omics monitoring, AI-driven multi-omics integration, and international collaboration to accelerate clinical translation. This study presents a systematic framework for exploring emerging research frontiers and offers insights for optimizing precision-driven immunotherapy.

Author Affiliations

1China-Japan Friendship Clinical Medical College, Beijing University of Chinese Medicine, Beijing, China.; 2Department of Integrative Oncology, China-Japan Friendship Hospital, Beijing, China.

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