Abstract
OBJECTIVE: This bibliometric analysis systematically examined cancer research literature (2015-2024), focusing on advancements driven by machine learning, multi-omics, and artificial intelligence (AI). METHODS: CiteSpace 6.3.R1 was used to construct co-citation, keyword co-occurrence, and collaboration networks, with knowledge graph optimization conducted via the g-index (k = 10) and LRF = 3.0 parameters. RESULTS: The publication volume demonstrated a biphasic growth. From 2015 to 2019, the average annual growth rate was 45%, increasing to 65% post-2020. By 2024, publications reached 916, a 25.4-fold increase since 2015. This trend coincided with expanding policy support and technological advances, though direct causality remains to be established. Moreover, core journals (e.g., Nature, Cell, Cancer Research) sustained substantial influence via high centrality values (mean = 0.28), fostering cross-domain knowledge exchange. Meanwhile, emerging journals such as PLOS ONE (burst strength = 56.16) mirrored open science trends and advances in proteomic technologies. The technological landscape unfolded through three phases: metabolomics dominance (2015-2017, MS burst = 16.83), AI integration (2018-2021, ML centrality = 0.11), and precision medicine expansion (2022-2024, scRNA-seq growing 182% annually). In most of the clusterings, the emerging technology of artificial intelligence is involved, notably in studies predicting drug responses. CONCLUSIONS: Overall, these bibliometric findings indicate that AI-integrated multi-omics constitutes an emerging focus in studies modeling the tumor microenvironment. However, further empirical research is warranted to evaluate its actual clinical or biological relevance.