Single-Cell RNA Sequencing Reshapes Liver Cancer Research, Global Analysis Reveals

Liver cancer remains one of the world’s most formidable malignancies, and a sweeping new bibliometric analysis has now mapped, with unprecedented precision, how a revolutionary technology has transformed the scientific assault against it. The study, published in Holistic Integrative Oncology, systematically examined nearly a thousand publications spanning nearly a decade of research applying single-cell RNA sequencing to liver cancer, and its findings reveal a field that has matured from descriptive cell cataloging into a dynamic, translation-oriented discipline poised to deliver new therapies.

The research team, led by Yi Zheng, Qianrong Wang, Qiong Zhang, and Hong-Mei Zhang of the Department of Clinical Oncology at Xijing Hospital, The Fourth Military Medical University in Xi’an, China, retrieved publications from the Web of Science Core Collection covering January 1, 2017 through December 31, 2025. After rigorous screening that excluded purely technical papers, early-access items, and retracted publications, 979 studies—933 articles and 46 reviews—formed the analytical core. These publications, spread across 267 journals and authored by 7,509 researchers, collectively cited 33,183 references, a testament to the field’s rapid expansion and deep intellectual roots.

The analytical toolkit itself reflects the computational sophistication that single-cell biology now demands. The team employed Bibliometrix 5.0 to chart publication and citation trends, CiteSpace 6.1R6 for keyword evolution and interdisciplinary knowledge mapping, and VOSviewer 1.6.20 for constructing co-authorship and collaboration networks, supplemented by R and Python for visualization. Keyword synonyms were manually standardized—merging terms such as hepatocellular carcinoma, HCC, and liver cancer into unified categories—while dual-map overlays traced how knowledge flows between research domains, revealing citation trajectories from molecular biology and immunology into genetics, and from clinical medicine into health sciences.

The headline finding is a dramatic acceleration in output. Publications and citations rose steeply between 2017 and 2020 before continuing upward at a more moderate pace, a trajectory the authors characterize as typical of an emerging field transitioning from early adoption to maturity. Geographically, the landscape is strikingly concentrated: China leads in both publication volume and citations, amassing 12,576 citations compared with 2,689 for the United States and 1,073 for France. Even more remarkable, all ten of the most prolific institutions are Chinese, with Fudan University alone producing 287 articles—29.3 percent of the total—followed by Sun Yat-sen University with 192 and Zhejiang University with 139. The United States remains China’s primary international partner, anchoring a powerful trans-Pacific research axis, while secondary collaborations link Japan with Canada, Australia with Germany, and Spain with Italy.

Within China, the analysis uncovered a pronounced regional imbalance. Research activity clusters in the southeastern coastal and central regions—Shanghai, Guangdong, Zhejiang, and Hunan—while western and northeastern provinces contribute far fewer studies. The authors attribute this pattern to two interlocking factors: the endemicity of hepatitis B virus in southeastern China, which drives a correspondingly high liver cancer burden and attracts greater funding, and the uneven distribution of biomedical research infrastructure. This concentration underscores, they argue, the need for intranational collaborative networks that connect established hubs with high-burden regions that have historically lacked research capacity.

At the individual level, a small cadre of investigators anchors the field. Fan Jia emerges as the most prolific contributor with 25 publications, followed by Zhou Jian with 24 and Gao Qiang with 12, and these same researchers serve as central hubs in the co-authorship network, connecting multiple research groups. Among the most cited authors, Zhang Zemin leads with 3,187 citations, ahead of Zhang Mingqi with 3,138 and Hu Xuedan with 1,217. The journal landscape is equally revealing: Frontiers in Immunology published the most articles at 80, while Cell accumulated the highest citation count at 2,174, followed by the Journal of Hepatology with 2,016 and Nature with 1,495. Core journals fostering collaboration include the Journal of Hepatology, Nature Communications, and Frontiers in Immunology.

The intellectual foundations of the field trace back to a handful of landmark studies. The most cited publication, with 1,558 citations, is the 2017 Cell paper by Zheng and colleagues, ‘Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing,’ which established the first comprehensive single-cell transcriptomic atlas of T cells in hepatocellular carcinoma and exposed the profound heterogeneity and dysfunctional states of tumor-infiltrating immune cells. The second most cited work, Zhang and colleagues’ 2019 Cell study on the dynamic immune landscape of hepatocellular carcinoma, identified LAMP3-positive dendritic cells as migratory regulators of lymphocyte crosstalk. Aizarani and colleagues’ 2019 Nature paper, which constructed a comprehensive human liver cell atlas and discovered a TROP2-intermediate progenitor population with bipotent organoid-forming capacity, rounds out the foundational trio. Co-citation analysis further shows the field rests on a dual foundation: deep exploration of cancer-associated fibroblasts and stromal biology, exemplified by a heavily cited 2017 Annual Review of Pathology review, coupled with adoption of key computational tools such as SCENIC, the single-cell regulatory network inference and clustering method published in Nature Methods.

Perhaps the most compelling narrative emerges from the keyword evolution analysis, which documents a clear three-phase conceptual progression. The initial phase was descriptive, dominated by terms like gene expression, intratumor heterogeneity, and transcriptome, as researchers cataloged the complete cellular repertoire of liver tumors and identified previously unappreciated malignant subpopulations with stem-like properties. The second phase shifted to the tumor microenvironment, with surging interest in T cells, macrophages, cancer-associated fibroblasts, and immunosuppression—work that dismantled the simplistic M1/M2 macrophage dichotomy and revealed exhausted CD8-positive T cells and regulatory T cells as drivers of immune evasion. The current frontier, marked by burst keywords including cellular crosstalk, growth factors, extracellular vesicles, and gene regulatory networks, moves from static snapshots to dynamic models of intercellular communication, as computational tools infer ligand-receptor interactions and map the signaling circuits that orchestrate angiogenesis, metastasis, and drug resistance.

These insights are already flowing into clinical translation. Single-cell RNA sequencing has refined the understanding of immune checkpoints beyond PD-1, revealing co-inhibitory and co-stimulatory receptors on specific T cell subsets and paving the way for rational combination immunotherapies, such as TIGIT and PD-1 co-blockade. The identification of pro-tumorigenic myeloid populations, including TREM2-positive macrophages that suppress CD8-positive T cell infiltration after transarterial chemoembolization, has opened targets for myeloid-directed therapies such as CSF1R and CD47 blockers. Distinct cancer-associated fibroblast subtypes, some of which determine immunotherapy efficacy—such as POSTN-positive fibroblasts—can now be selectively targeted or reprogrammed, with FAP inhibitors advancing through clinical testing for advanced solid cancers. The technology has also exposed vulnerabilities in therapy-resistant subclones: targeting PPAR-gamma counteracts tumor adaptation to immune checkpoint blockade, while the SNRPB-CCNB1 axis, which promotes progression and cisplatin resistance through lipid metabolism reprogramming, offers a route to augment chemotherapy sensitivity. Cell-type-specific gene signatures derived from single-cell data can deconvolve bulk RNA sequencing from patient biopsies, yielding prognostic tools such as a senescence-related gene signature and a 57-gene matrix stiffness signature that aid patient stratification and personalized treatment planning.

The authors are candid about the field’s remaining obstacles. Tissue dissociation of fibrotic livers can introduce transcriptional stress artifacts, and the loss of spatial context limits interpretation of cellular interactions. Batch effects across datasets and the sheer dimensionality of single-cell data complicate integration and noise discrimination, while high costs restrict large-scale cohort studies and the absence of standardized protocols hampers reproducibility. Emerging solutions—spatial transcriptomics, fixed-cell technologies, and machine learning-based batch correction—begin to address these limitations. The bibliometric analysis itself carries caveats: reliance on a single English-language database may underrepresent non-English contributions, and citation counts do not necessarily reflect clinical significance. Notably, the dual-map overlay revealed sparse representation from the physics, materials, and chemistry domains, suggesting untapped potential for interdisciplinary integration. Looking forward, the authors call for accelerated functional validation of single-cell-identified targets in preclinical models, standardized computational pipelines for inferring intercellular communication networks, and dedicated funding programs to broaden access. What began as cellular cartography, the analysis concludes, has evolved into a sophisticated discipline deconstructing the dynamic tumor ecosystem—one that offers a clear path toward more personalized and effective therapies for a disease that urgently needs them.

Subject of Research: Bibliometric analysis of single-cell RNA sequencing research in liver cancer

Article Title: Bibliometric analysis of research on liver cancer and single‑cell RNA sequencing: evolutionary trends, opportunities, and future perspectives

Article References: Zheng, Y., Wang, Q., Zhang, Q., & Zhang, H.-M. (2026). Bibliometric analysis of research on liver cancer and single‑cell RNA sequencing: evolutionary trends, opportunities, and future perspectives. Holistic Integrative Oncology, 5(1), Article 65. https://doi.org/10.1007/s44178-026-00285-6

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00285-6

Keywords: single-cell RNA sequencing, liver cancer, hepatocellular carcinoma, tumor microenvironment, bibliometric analysis, cancer-associated fibroblasts, tumor-associated macrophages, gene regulatory networks, immunotherapy, spatial transcriptomics, cellular crosstalk, biomarkers