5.1 Main research hotspots5.1.1 Tumor immunity and cell biology research5.1.1.1 Double positive T cells

Double-positive T cells (DP T cells), characterized by the simultaneous expression of both CD4 and CD8 markers, have increasingly attracted attention in cancer phenomics research. DP T cells are not typically derived from immature thymic cells; instead, they are formed when single-positive (SP) T cells (CD4⁺ or CD8⁺ T cells) re-express the opposite coreceptor (CD8 or CD4) after T cell receptor (TCR) stimulation. This process induces SP T cells to acquire a new phenotype and functional characteristics, ultimately resulting in the formation of DP T cells [16]. Under certain pathological conditions, including cancer, DP T cells can abnormally proliferate and appear in peripheral blood or within the tumor microenvironment. DP T cells exhibit significant pro-inflammatory and metabolic characteristics in various pathological states. For instance, they express multiple pro-inflammatory cytokines at baseline, including IFNγ, TNFα, and IL-17, indicating that DP T cells may play a crucial role in inflammatory responses. Additionally, DP T cells express a range of metabolic markers such as GLUT1, HKII, CPT1a, and pS6, reflecting a highly active metabolic state that is likely linked to their pro-inflammatory function [17].

DP T cells possess dual functionality, being capable of both promoting inflammation and regulating immune responses, making them pivotal in various pathological conditions. Previous studies have demonstrated that the traditional Chinese medicine compound Ruyong Formula (RYF) [18]can reverse the negative effects associated with DP T cells, such as reducing the expression of thymic stromal lymphopoietin (TSLP), through the activation of the JAK2/STAT3/PI3K pathway. The activation of this pathway is closely related to the restoration of thymic function, suggesting that DP T cells may contribute to the therapeutic effects of RYF in breast cancer mouse models by modulating inflammation and immune function. DP T cells also have significant clinical implications. For example, in colorectal cancer (CRC) patients, high expression levels of DP T cells may be associated with poor responsiveness to anti-PD-1 immune checkpoint inhibitors. This suggests that DP T cells may play a critical role in the mechanisms underlying resistance or suboptimal efficacy of immune checkpoint inhibitors [19]. Table 5 presents additional findings on DP T cell phenotypes in cancer research. These discoveries underscore the importance of further investigating the functions of DP T cells, particularly in exploring their potential roles in tumor immunity and therapeutic resistance.

Table 5 DP T Cell Phenotypes in Cancer5.1.1.2 Natural killer cells

As a critical component of the innate immune system, Natural Killer (NK) cells possess a unique ability to recognize and kill tumor cells without the need for antigen presentation. NK cells exert their effector functions by detecting cells with abnormal expression or loss of MHC I molecules, playing a vital role in tumor immune surveillance [25]. The functionality of NK cells is closely linked to the distribution of their receptors, including inhibitory receptors (such as KIRs and NKG2A) and activating receptors (such as NKp30, NKp46, and NKG2D). Within the tumor microenvironment, tumor cells often evade immune attack by upregulating inhibitory ligands or secreting immunosuppressive factors, thereby impairing NK cell function [26].

In studies on neoadjuvant immunochemotherapy (NICT) for esophageal cancer (ESCA), NK cells, particularly CD16 + NK cells, were found to synergize with CD8 + T cells to enhance tumor cell killing through the secretion of IFN-γ. Phenomics technologies have helped identify the critical role of NK cells in recognizing and eliminating CD276 + tumor cells, especially following immune checkpoint inhibitor therapy [27]. It is crucial to further explore the impact of NK cells within the tumor microenvironment and their regulation of cancer progression.

In gastric cancer research, phenomics technologies have been instrumental in identifying and validating key NK cell-related genes, such as MAB21L2, ARPP21, and MUCL1. These genes have been utilized to construct prognostic models to predict patient survival rates and treatment responses [28]. Through phenomics analysis, researchers have also discovered that NK cell infiltration and activity within the tumor microenvironment are closely associated with the efficacy of immunotherapy. The antitumor activity of NK cells is significantly enhanced under the regulation of specific genes, such as Regnase-1. Studies have shown that deleting the Regnase-1 gene in NK cells increases their cytotoxicity and IFN-γ production, leading to a stronger antitumor effect in vivo [29]. Table 6 presents additional findings on NK cell phenotypes in cancer research. These discoveries underscore the importance of understanding NK cell dynamics within the tumor microenvironment and their potential as therapeutic targets in cancer treatment.

Table 6 NK Cell Phenotypes in Cancer5.1.2 The application of phenotypic screening in drug discovery

Phenotypic screening has regained significant attention in drug development, particularly in the discovery of drugs targeting novel mechanisms in cancer. Although molecular target-driven drug discovery has become prominent, it has limitations in predicting therapeutic efficacy, and many cancers still lack effective targets. In this context, phenotypic screening has demonstrated unique advantages in the development of drugs for complex diseases, owing to advancements in technology, such as larger biological panels and improved experimental protocols [36].

One notable application of phenotypic screening is in the study of G-quadruplex (G4) structures. Traditional target-based screening methods have struggled to effectively identify G4 structures and their ligands, making phenotype-driven approaches particularly valuable. Through this strategy, researchers have developed several generations of naphthalene diimide (ND) ligands, ultimately discovering the highly effective QN-302, which has shown broad G4 inhibition potential in pancreatic and other cancers and has progressed to Phase I clinical trials [37]. This example highlights the immense potential of phenotypic screening in new drug discovery and the study of disease mechanisms [38].

Phenotypic screening also holds great promise in the discovery of anti-metastatic drugs. In studies of tumor-derived extracellular vesicles (TEVs)-induced epithelial-mesenchymal transition (EMT), researchers have developed a functional phenotypic screening platform based on organic electrochemical transistors (OECTs). This platform allows for continuous, non-invasive monitoring of the TEV-induced EMT process and the identification of potential anti-metastatic drugs [39]. In summary, phenotypic screening, as a strategy based on observing biological effects rather than relying solely on molecular targets, shows significant potential and research value in the development of drugs for complex diseases.

5.2 Future research trends5.2.1 Multi-omics data integration and precision medicine

The integration of multi-omics data and precision medicine has played a pivotal role in the advancement of phenomics and cancer research. By incorporating multi-layered biological data—such as genomics, transcriptomics, proteomics, and metabolomics—researchers can delve deeply into the heterogeneity and complexity of cancer, enabling a more precise identification of disease subtypes and their unique pathological mechanisms. This integrated approach not only uncovers the underlying mechanisms of tumorigenesis and progression but also offers new avenues for personalized therapies [40]. Rongjian Xu et al. [41] demonstrated the application and advantages of multi-omics approaches in non-small cell lung cancer (NSCLC), specifically lung adenocarcinoma (LUAD). Through multi-omics data integration, researchers were able to explore potential biomarkers and their associations with disease progression from various perspectives, including genomics, proteomics, and immunology, thus developing models to assess LUAD prognosis. Using R packages like DESeq2 and Limma, they analyzed differentially expressed genes (DEGs) and employed gene function and pathway enrichment analyses (GO and KEGG) to identify key genes, such as CENPL, DARS2, and PAICS, which serve as biomarkers with significant negative impacts on LUAD prognosis.

Additionally, Peng Liang et al. [42] employed an integration of microbiome, genome, transcriptome, and clinical data to reveal the microbiome-immune characteristics of LUAD subtypes, thereby providing a critical basis for future individualized treatments and anti-resistance strategies. Furthermore, Anna Aakula et al. [43] used multi-omics technologies—including functional genomics, transcriptomics, and bioinformatic analyses—to systematically evaluate the function of 1,129 miRNAs in prostate cancer (PCa) cell lines. They combined miRNA expression data from 188 human PCa tumor samples to predict and validate the target genes of these miRNAs. Their findings revealed that 14 miRNAs were aberrantly expressed in PCa, impacting cell proliferation. Among over 3,700 genes predicted to be regulated, seven genes, such as FLNC and MSRB3, were inversely correlated with their corresponding miRNAs and significantly associated with patients' progression-free survival in biochemical recurrence. These findings illuminate the critical role of miRNA-target gene networks in PCa proliferation and progression, offering potential therapeutic targets and personalized treatment strategies. Together, these discoveries hold essential implications for precise cancer diagnosis and therapy.

5.2.2 Development of novel biosensing platforms

The rapid advancement of novel biosensing platforms has substantially propelled the application of phenomics technologies in cancer research, establishing a solid foundation for future investigations. Specifically, innovative platforms such as microfluidic chips, high-throughput imaging, single-cell multi-omics, and mass spectrometry have provided critical support for cancer phenomics research [44]. Recent progress in molecular biology, microfluidics, and bioinformatics now allows the examination of thousands or even millions of tumor cells at the single-cell level. This high-dimensional and multifaceted analysis integrates genomic, transcriptomic, epigenomic, and proteomic data, enabling researchers to dissect tumor heterogeneity, the intricate interactions among tumor cells, immune cells, and stromal cells, and the evolutionary pathways of tumors [45].

For instance, Renata Szydlak et al. [46] employed a microfluidic chip platform to capture normal skin cells (melanocytes and keratinocytes) and melanoma cells under low-shear flow conditions on surfaces coated with Dolichos biflorus (DBA) and Maackia Amurensis (MAL) lectins. This study highlighted microfluidic technology’s potential to differentiate tumor from normal cells. Additionally, Vasileios Papalazarou et al. [47] utilized a high-throughput imaging platform with RNA interference screening technology to study solute carrier (SLC) family proteins responsible for serine transport in colorectal cancer cells. Their findings indicated that targeting SLC6A14 and SLC25A15, or SLC12A4, significantly reduced serine uptake and proliferation in colorectal cancer cells, particularly in cells with impaired serine synthesis. This underscores the pivotal role of high-throughput imaging in cancer phenomics.

Despite these promising laboratory advances, translating these technologies into clinical applications remains challenging. Ensuring high throughput and sensitivity while addressing technical complexities and application limitations are pressing issues that need resolution [48]. Future research must prioritize the optimization of these technologies and facilitate their clinical translation to expand the use of phenomics in cancer research. In conclusion, while novel biosensing platforms have already begun to transform cancer phenomics, continuous refinement and efforts toward clinical application will be essential for maximizing their impact on cancer research and therapeutic development.

5.3 Limitations

While this study offers valuable insights into the development trends and research hotspots of cancer phenomics through bibliometric analysis, several limitations should be noted. First, the study primarily utilizes data from the Web of Science Core Collection database. This reliance may exclude pertinent literature published in other databases or non-indexed journals, potentially limiting the comprehensiveness of the findings. Second, bibliometric analysis depends heavily on the volume of available publications and citation counts, which may result in the underrepresentation of high-quality, recently published studies that have not yet accrued substantial citations.

Moreover, the inherent constraints of bibliometric analysis, such as the dependence on specific keywords and their standardization, may influence the accuracy and completeness of the results. Misrepresentation of emerging topics may occur if relevant terminology varies or is not widely adopted. Additionally, while cancer phenomics shows considerable promise, it remains an evolving field with several technologies and methodologies still in early stages. The clinical applicability and translational potential of these approaches require further validation, and the path to routine clinical use may be challenged by real-world complexities.

Consequently, the conclusions drawn from this study should be viewed with caution, taking these limitations into account. Further empirical studies and validation efforts will be essential to strengthen the findings and to extend the insights provided by this analysis into practical clinical frameworks.

5.4 Conclusion

This study systematically uncovers the hotspots and development trends in the emerging field of cancer phenomics through bibliometric analysis. The findings underscore phenomics’ growing impact in cancer research, particularly in understanding tumor complexity and heterogeneity, developing novel therapeutic strategies, and advancing personalized treatment. As technology continues to evolve, the integration of phenomics with other omics fields is anticipated to propel cancer research forward, providing deeper insights and novel approaches for precision medicine, ultimately aiming to improve patient outcomes.

However, this study also draws attention to certain limitations within current research, underscoring the need for further investigations to validate and build upon these findings. Continued research is essential to refine phenomic methodologies, enhance their clinical applicability, and fully realize their potential. In conclusion, the field of cancer phenomics shows significant promise in advancing precision diagnosis and treatment, offering renewed hope and potential breakthroughs for patients battling cancer.