# Global research trends and focus on biomarkers in lung cancer immunotherapy: a comprehensive bibliometric insight and visualization analysis (2001-2025)

**Authors:** Jiangbo He, Chaoyuan Liu, Fang Ma, Yiguang Zhou, Xianling Liu

PMC · DOI: 10.3389/fimmu.2026.1622573 · 2026-02-03

## TL;DR

This study maps global research trends in lung cancer immunotherapy biomarkers from 2001 to 2025, identifying growth phases, key contributors, and emerging research frontiers.

## Contribution

The first bibliometric analysis of biomarkers in lung cancer immunotherapy, revealing a shift from single-biomarker approaches to multi-omics integration.

## Key findings

- The field evolved through three phases: incubation (2003–2014), rapid expansion (2015–2022), and maturation (2023–2025).
- China led in research productivity, while the U.S. dominated in academic impact.
- Emerging research frontiers include genomics, gut microbiome, and multi-omics integration.

## Abstract

Immunotherapy has revolutionized the therapeutic paradigm for lung cancer, yet its clinical efficacy exhibits significant interpatient heterogeneity. The implementation of personalized immunotherapy critically depends on the precise identification of predictive biomarkers. However, this field is currently characterized by rapid expansion of research outputs alongside marked fragmentation of knowledge domains, while a systematic evaluation of scientific advancements and emerging frontiers remains conspicuously absent. This study aims to synthesize global research achievements to methodologically delineate the evolutionary trajectory of immunotherapy biomarkers in lung cancer, identify research hotspots, and forecast developmental trends.

A bibliometric analysis was conducted on 6,180 publications (2001–2025) from the Web of Science Core Collection. Advanced tools (Bibliometrix, VOSviewer, CiteSpace) were employed to analyze publication trends, country/institutional contributions, journal influence, author networks, keyword evolution, and citation dynamics.

The field progressed through incubation (2003–2014), rapid expansion (2015–2022), and maturation (2023–2025) phases, with a 28.87% annual growth rate. China led in productivity (9,394 publications), while the U.S. dominated academic impact (93,888 citations). Harvard University Harvard University emerged as the predominant contributor (532 publications). WANG Y was ranked first in the top 10 most prolific authors (97 publications) while RAMALINGAM SS (5,033 citations) was the most cited authors. CANCERS (319 papers; Q1) was the most published journal, while JOURNAL FOR IMMUNOTHERAPY OF CANCER (8,511 citations; Q1) was the most cited journal. Emerging frontiers encompassed genomics, gut microbiome, soluble PD-L1, and immune infiltration patterns, driven by multi-omics integration and artificial intelligence.

This study represents the first bibliometric analysis of biomarkers in lung cancer immunotherapy. Through scientometric visualization tools, our analysis delineates a paradigm shift from reductionist biomarker discovery to multidimensional integration and translational validation. Prospective advancements necessitate leveraging technological innovations, fostering international collaborative networks, and promoting multi-omics convergence. These efforts aim to facilitate a critical transition from population-based therapeutic strategies to precision-driven stratification, ultimately optimizing clinical outcomes and survival benefits for heterogeneous patient populations.

## Linked entities

- **Diseases:** lung cancer (MONDO:0005138)

## Full-text entities

- **Genes:** EGFR (epidermal growth factor receptor) [NCBI Gene 1956] {aka ERBB, ERBB1, ERRP, HER1, NISBD2, NNCIS}, PIK3CB (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta) [NCBI Gene 5291] {aka P110BETA, PI3K, PI3KBETA, PIK3C1}, CD274 (CD274 molecule) [NCBI Gene 29126] {aka ADMIO5, B7-H, B7H1, PD-L1, PDCD1L1, PDCD1LG1}, CX3CR1 (C-X3-C motif chemokine receptor 1) [NCBI Gene 1524] {aka CCRL1, CMKBRL1, CMKDR1, GPR13, GPRV28, V28}, IL6 (interleukin 6) [NCBI Gene 3569] {aka BSF-2, BSF2, CDF, HGF, HSF, IFN-beta-2}, CTLA4 (cytotoxic T-lymphocyte associated protein 4) [NCBI Gene 1493] {aka ALPS5, CD, CD152, CELIAC3, CTLA-4, GRD4}, ALK (ALK receptor tyrosine kinase) [NCBI Gene 238] {aka ALK1, CD246, NBLST3}, MTOR (mechanistic target of rapamycin kinase) [NCBI Gene 2475] {aka FRAP, FRAP1, FRAP2, RAFT1, RAPT1, SKS}, TIGIT (T cell immunoreceptor with Ig and ITIM domains) [NCBI Gene 201633] {aka VSIG9, VSTM3, WUCAM}, CD8A (CD8 subunit alpha) [NCBI Gene 925] {aka CD8, CD8alpha, IMD116, Leu2, p32}, CD274 (CD274 molecule) [NCBI Gene 574058] {aka PDL1}, LAG3 (lymphocyte activating 3) [NCBI Gene 3902] {aka CD223}, FCGR3A (Fc gamma receptor IIIa) [NCBI Gene 2214] {aka CD16-II, CD16A, FCG3, FCGR3, FCRIIIA, FcGRIIIA}, STING1 (stimulator of interferon response cGAMP interactor 1) [NCBI Gene 340061] {aka ERIS, MITA, MPYS, NET23, SAVI, STING}, SLA-1 (MHC class I antigen 1) [NCBI Gene 100037293] {aka PD1, SLA-1a, sla-}, SPDL1 (spindle apparatus coiled-coil protein 1) [NCBI Gene 54908] {aka CCDC99}, FCRL4 (Fc receptor like 4) [NCBI Gene 83417] {aka CD307d, FCRH4, IGFP2, IRTA1}, FGFBP2 (fibroblast growth factor binding protein 2) [NCBI Gene 83888] {aka HBP17RP, KSP37}, MT2A (metallothionein 2A) [NCBI Gene 4502] {aka MT-2, MT-II, MT2}, AKT1 (AKT serine/threonine kinase 1) [NCBI Gene 207] {aka AKT, PKB, PKB-ALPHA, PRKBA, RAC, RAC-ALPHA}, FCRL5 (Fc receptor like 5) [NCBI Gene 83416] {aka BXMAS1, CD307, CD307e, FCRH5, IRTA2, PRO820}, PDCD1 (programmed cell death 1) [NCBI Gene 5133] {aka ADMIO4, AIMTBS, CD279, PD-1, PD1, SLEB2}
- **Diseases:** breast cancer (MESH:D001943), MSI-H (MESH:D000848), ONCOLOGY (MESH:D000072716), deaths (MESH:D003643), CL (MESH:D002971), TS (MESH:D005879), IMMUNOLOGY (MESH:D007154), lung adenocarcinoma (MESH:D000077192), pneumonia (MESH:D011014), squamous cell carcinoma (MESH:D002294), NSCLC (MESH:D002289), RESEARCH (MESH:D014947), inflammation (MESH:D007249), melanoma (MESH:D008545), ES-SCLC (MESH:D055752), Lung Cancer (MESH:D008175), adenocarcinoma (MESH:D000230), CANCER (MESH:D009369)
- **Chemicals:** camrelizumab (MESH:C000631724), durvalumab (MESH:C000613593), Nivolumab (MESH:D000077594), F-18-FDG (-), pembrolizumab (MESH:C582435), Etoposide (MESH:D005047), atezolizumab (MESH:C000594389), Ipilimumab (MESH:D000074324)
- **Species:** gut metagenome (species) [taxon 749906], Homo sapiens (human, species) [taxon 9606]

## Figures

9 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12909568/full.md

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Source: https://tomesphere.com/paper/PMC12909568