Traditional Chinese Medicine (TCM) classifies people into distinct constitutional types based on observable physical and health characteristics. One of these types, the Yang-Deficiency Constitution (YDC), is associated with cold intolerance, fatigue, low energy, and reduced immune function. Clinical observations have long suggested that people with this constitution may be more susceptible to certain diseases, including cancer.
This study investigated whether Yang-Deficiency Constitution has a biological basis that can be identified in kidney cancer tissue using modern genomic tools. The researchers analyzed large RNA sequencing datasets from patients with clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer, to find genes and biological pathways associated with YDC.
The approach integrated bulk RNA sequencing, which measures average gene activity across thousands of cells in a tumor sample, with single-cell RNA sequencing (scRNA-seq), which reveals gene activity in individual cells. This combination provided both a broad view of YDC-related gene expression patterns and a detailed map of which cell types carry those patterns.
The ultimate goal was to identify genes that connect TCM constitutional type to the behavior of the immune system within kidney tumors, potentially uncovering new therapeutic targets that could be addressed by both conventional treatments and herbal medicine compounds.
The tumor immune microenvironment (TME) refers to all the non-cancer cells that surround and interact with tumor cells, including immune cells, fibroblasts, blood vessel cells, and other structural components. The composition and activity of the TME profoundly influence whether the immune system can recognize and attack cancer, and whether immunotherapy treatments will be effective.
Yang-Deficiency Constitution in TCM theory is characterized by insufficient Yang energy, which governs warmth, activity, and defensive function. Modern researchers have begun exploring whether this corresponds to measurable biological differences in immune system activity, metabolic function, or gene expression patterns.
Clear cell RCC was chosen as the focus because it is the most immunologically active of the kidney cancer subtypes. Immunotherapy with checkpoint inhibitors has become a standard treatment for advanced ccRCC, but not all patients respond. Understanding the immune landscape of ccRCC and how it relates to constitutional factors could help identify which patients are most likely to benefit.
The study used two major ccRCC datasets: the TCGA-KIRC cohort from The Cancer Genome Atlas and the E-MTAB-1980 dataset from the ArrayExpress repository. These publicly available datasets provided gene expression and clinical information for hundreds of patients, giving the analysis adequate statistical power to identify robust YDC-associated signals.
The first step was to identify Yang-Deficiency Constitution-related genes through a comprehensive literature search of TCM genomics studies. The researchers compiled a list of genes previously reported to be differentially expressed in individuals classified as having a YDC compared to neutral constitutions. This literature-derived gene set served as the biological foundation for the analysis.
Using these YDC-related genes, the researchers applied a technique called ssGSEA (single sample gene set enrichment analysis) to score each patient's tumor sample for YDC activity. Patients were then divided into high-YDC and low-YDC groups based on their scores, and differences in survival outcomes and immune cell composition were compared between groups.
To identify the most important YDC-related genes specifically within ccRCC, the team applied several machine learning and statistical filtering methods including LASSO regression, SVM-RFE (support vector machine recursive feature elimination), and random forest. Each method independently ranked genes by their importance, and the overlap between rankings from different methods was used to select a final set of high-confidence signature genes.
Single-cell RNA sequencing data from ccRCC tumors was then used to map where the selected signature genes were expressed at the cellular level. This step revealed which specific cell types within the tumor carry the YDC-associated gene activity, providing insight into the biological mechanisms linking constitutional type to tumor behavior.
Molecular docking simulations were performed to identify natural compounds from TCM herbs that could interact with key signature proteins. This step connected the genomic findings back to traditional herbal medicine by exploring whether plant-derived molecules might therapeutically target the biological pathways identified in the analysis.
The final analysis identified a set of seven high-confidence YDC-associated genes in ccRCC: MXD3, PLCB2, CCDC88B, DEF6, IFNG, TBC1D10C, and PLEKHN1. Patients with high expression of these genes showed significantly worse overall survival compared to patients with low expression, indicating that the YDC gene signature captures clinically meaningful biological information.
IFNG, which encodes the protein interferon-gamma, was among the most biologically significant genes identified. Interferon-gamma is a critical immune signaling molecule that activates immune cells and influences how tumors interact with the immune system. Its presence in the YDC signature provides a direct molecular link between constitutional type and immune function.
Single-cell analysis showed that the YDC signature genes were particularly active in myeloid cells and T cells within the tumor microenvironment. High YDC scores were associated with increased infiltration by immunosuppressive immune cell types, which suppress anti-tumor immunity. This helps explain why YDC patients might have worse outcomes, as an immunosuppressive microenvironment limits the body's ability to fight cancer.
The high-YDC group also showed different patterns of expression across pathways involved in immune checkpoint regulation, inflammatory signaling, and oxidative stress response. These differences suggest multiple biological mechanisms through which YDC status influences cancer behavior, and they highlight potential points of therapeutic intervention.
Baicalein is a flavonoid compound extracted from the root of Scutellaria baicalensis, a plant used extensively in traditional Chinese medicine for its anti-inflammatory properties. Molecular docking simulations showed that baicalein binds strongly to the IFNG protein, one of the central genes in the YDC signature.
The binding interaction between baicalein and IFNG was analyzed computationally to assess its stability and specificity. The results showed favorable binding energy and specific contact with key amino acid residues in the IFNG protein structure, suggesting that baicalein could modulate IFNG activity in a biologically meaningful way.
This finding connects the genomic analysis back to TCM practice in a concrete way. If baicalein can modulate IFNG signaling in kidney cancer cells, it may help address the immunosuppressive microenvironment associated with high YDC scores, potentially enhancing the effectiveness of immune-based treatments for ccRCC.
The researchers emphasize that these findings are computational and require experimental validation in cell lines, animal models, and eventually clinical studies before baicalein can be considered a treatment candidate. However, the molecular docking results provide a scientifically grounded hypothesis for future laboratory investigation.
Because the YDC signature genes are concentrated in immune cells within the tumor microenvironment, the researchers examined whether YDC scores could predict response to immunotherapy. Immunotherapy with checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 antibodies has transformed treatment for advanced ccRCC, but only a subset of patients achieve durable responses.
Analysis of immune checkpoint gene expression across YDC groups showed that high-YDC patients had elevated expression of several checkpoint molecules including PD-L1. This suggests an immunosuppressive rather than immunoactive microenvironment, which paradoxically might reduce response to checkpoint inhibitors despite providing potential targets.
The findings also have implications for drug sensitivity prediction. Using the GDSC (Genomics of Drug Sensitivity in Cancer) database, the researchers predicted differential drug sensitivity between high- and low-YDC groups, identifying specific chemotherapy and targeted therapy agents that might be more effective in each group.
For patients and oncologists, these results suggest that integrating constitutional assessment into treatment planning might eventually help identify patients most likely to benefit from specific treatment strategies. This represents a novel form of personalized medicine that incorporates traditional diagnostic frameworks into modern oncology decision-making.
This study provides the first multi-omics evidence that Yang-Deficiency Constitution corresponds to a distinct biological state in kidney cancer tissue. By integrating TCM classification with contemporary genomics, single-cell analysis, and machine learning, the researchers have established a framework for scientific investigation of constitutional medicine.
The identification of specific signature genes and their association with immune cell composition opens new avenues for both diagnosis and therapy. These genes could serve as biomarkers to identify patients who might benefit from strategies aimed at remodeling the tumor immune microenvironment, whether through conventional immunotherapy or herbal medicine compounds like baicalein.
The study acknowledges important limitations. The classification of patients into YDC groups was based on gene expression data rather than direct constitutional assessment by a TCM practitioner, which introduces potential imprecision. Future studies should include prospective cohorts with both formal TCM constitutional diagnosis and molecular profiling to better validate these findings.
For the broader scientific community, this work represents a model for how traditional medicine systems can be rigorously investigated using modern tools. Rather than dismissing TCM as purely empirical or non-scientific, this approach identifies testable biological hypotheses rooted in traditional classification, potentially unlocking new treatment strategies that complement or enhance conventional cancer care.