Nantong University Affiliated Hospital Licenses Metastatic Breast Cancer Prognostic Patent for RMB 50,000

Recently, an important invention patent for the precise prognostic assessment of metastatic breast cancer has entered the stage of achievement transformation and implementation. According to the Affiliated Hospital of Nantong UniversityJanuary 3, 2026The official public notice released, with the hospital as the patent'sSole Applicant (Patentee), intends to transfer its patented invention titled "A Prognostic Model Based on Immune-Related Genes for Patients with Metastatic Breast Cancer: Construction Method and Application”, granted in the form of a non-exclusive licenseIndustry Playersconduct application development. The proposed transaction price for this license is RMBRMB 50,000。

This patent was developed by Wang Qingqing, Zhu Huixia, Wang Chongyu, Xu Lijun, Yang Qinyi, and Xie Chenyu from the Affiliated Hospital of Nantong University. Its core lies in constructing a prognostic prediction model for metastatic breast cancer (MBC) based on immune-related genes. The main innovations are as follows: In terms of research perspective, it pioneers a focus on the “differences in the immune microenvironment between primary tumors and metastatic lesions,” directly addressing the core issue of high mortality in MBC. In terms of technical methodology, it integrates multiple public databases to screen out two key genes, CD28 and CFB, and constructs a quantitative risk model. In terms of clinical translation, it further develops a nomogram integrating genetic risk with clinical indicators, laying the tool foundation for the development of diagnostic products.

This patent proposes direct solutions to two severe and urgent real-world pain points in the diagnosis and treatment of metastatic breast cancer (MBC).

First, at the clinical level, there is a significant demand for precise prognostic prediction.Breast cancer is the most common malignant tumor among women worldwide, with distant metastasis being the leading cause of patient mortality. Despite continuous advancements in targeted therapies, immunotherapies, and other treatment modalities, the prognosis for patients with metastatic breast cancer (MBC) remains highly unfavorable. More challenging still is the substantial heterogeneity in survival outcomes observed among different MBC patients. Therefore, there is an urgent need in clinical practice for a tool that canBeyond Traditional Pathological Staging: Achieving Personalized Precision Predictiontools to help physicians differentiate between high-risk and low-risk patients, thereby enabling the formulation of more rational and targeted treatment strategies.

Secondly, at the research level, there is a critical gap in knowledge and application.Although the scientific community has recognized the central role of the tumor immune microenvironment in cancer metastasis, previous studies have mostly focused on primary tumors. For “Specific Differences in Immune Characteristics Between Primary and Metastatic Lesions of Breast Cancer”, and there is limited systematic research on how to leverage this discrepancy to construct prognostic models. This has resulted in many existing prognostic models lacking specific consideration of “metastasis,” a critical and often fatal step in disease progression.

This patent directly addresses the two major challenges mentioned above:Aims to Develop a Novel Prognostic Prediction Model for MBC Based on Differences in the Immune Microenvironment Between Primary and Metastatic Tumors, to bridge the translational gap between cutting-edge scientific understanding and clinically practical tools.

This patent establishes a framework fromFrom Scientific Hypothesis Validation to Clinical Tool Developmenta complete and rigorous technical closed loop. Its core innovations are specifically reflected in three progressive levels:

1. Innovation in Research Paradigm: Targeting “Metastasis-Specific” TargetsUnlike traditional research, this patent precisely sets the biological starting point for modeling atDifferences in the Immune Microenvironment Between Primary and Metastatic Breast Cancer Lesionsabove. By systematically comparing gene expression in paired samples from two independent datasets (GSE102818 and GSE43837), we first screened for immune-related differentially expressed genes (DEIRGs) that exhibited stable changes during metastasis. This design ensures that the cornerstone of model construction isSpecific Biological Signals Directly Associated with Metastatic Behavior, rather than a universal tumor expression profile.

2. Innovation in Technical Pathways: Achieving Robust Modeling through the Integration of “Dry” and “Wet” ExperimentsThe patent employs a reproducible computational biology pipeline:

Multi-source data-driven: Innovatively integrating four public datasets with clearly defined roles: two for identifying differential targets, one (GSE124648) for internal training and validation, and another (TCGA-BRCA) for final external independent validation, thereby establishing a rigorous chain of evidence.

Multi-Level Analytical Screening: During the model construction phase, candidate genes wereUnivariate Cox Regression for Initial Screening, Protein-Protein Interaction (PPI) Network Functional Enrichment, and Multivariate Cox Regression for Final Model Constructionthree-step analysis. In particular, the incorporation of PPI network analysis enhanced the biological plausibility of the screening results.

Develop Specific Models: ultimately obtaining aCD28 and CFB Genesa concise prognostic model, along with a quantitative calculation formula:Risk Score = (-0.3047975 × CD28 Expression Level) + (-0.1628303 × CFB Expression Level). The model demonstrated stable predictive performance in both internal and external validation (e.g., the AUCs for 1-, 3-, and 5-year overall survival [OS] in the internal validation were 0.73, 0.75, and 0.68, respectively).

3. Innovation in Translational Application: Developing “Ready-to-Use” Clinical Decision Support ToolsThe patent’s innovation does not stop at algorithmic models; its most application-valuable output is the construction ofClinical Prognostic Nomogram. This tool integrates genomic risk scores with routine clinical indicators—Progesterone Receptor (PR) StatusandVisceral Metastasis Status——integration. Clinicians can directly calculate individualized 1-, 3-, and 5-year survival probabilities based on these three indicators using graphical tools, thereby transforming complex bioinformatics analysis results into intuitive references for clinical decision-making. Furthermore, the patent claims explicitly cover the use of CD28 and CFB as biomarkers.Test Kit, paving the way for subsequent productization and commercialization.

In summary, the core innovation of this patent lies in its successful translation of a cutting-edge biological perspective—specificity of the immune microenvironment in metastatic sites—into a “product prototype” with clear intellectual property protection and direct applicability to clinical practice, achieved through a rigorous data science methodology, thereby accomplishing the critical leap from scientific research to practical application.

Precision Prevention and Control of Breast Cancer Begins with Early Screening: A Field Characterized by Rapid Technological Evolution and Increasingly Precise Strategies. Current screening has established a system centered on imaging, progressively integrating risk stratification and artificial intelligence assistance.

Mainstream screening methods each have their own characteristics, among whichMammography (Molybdenum Target)is the only screening modality proven to reduce breast cancer mortality and serves as the cornerstone of screening programs worldwide. To overcome the limitations of traditional two-dimensional mammography inDense BreastsLimitations of Reduced Sensitivity,3D Tomosynthesis MammographyTechnology has emerged to significantly reduce missed and misdiagnoses caused by tissue overlap. For women with dense breasts, combinedBreast UltrasoundScreening has become an important complementary strategy that can further improve cancer detection rates. For individuals carrying BRCA gene mutations, etc.High-Risk Populations，Breast Magnetic Resonance Imagingis recommended due to its extremely high sensitivity.

Screening strategies are becoming increasingly refined. The latest “Expert Consensus on Breast Cancer Screening for Chinese Women with Dense Breasts (2025 Edition)” specifically emphasizes the need to conduct risk stratification and develop individualized combined screening protocols, taking into account the high prevalence of dense breast tissue among Chinese women. Meanwhile,Artificial IntelligenceTechnology is profoundly reshaping the screening landscape. A German real-world study published in Nature Medicine, involving more than 460,000 participants, demonstrated that AI-assisted image interpretation can significantly improve breast cancer detection rates.17.6%, while not increasing unnecessary recall, demonstrating significant application potential.

In summary, modern breast cancer screening is evolving from universal screening to risk stratification, from single-modality imaging to multimodal integration, and from manual interpretation to human-AI collaboration. The prognostic prediction model focused on in this patent adds value by enabling precise risk stratification for patients already diagnosed with metastatic breast cancer. This complements the screening phase aimed at “early detection,” together forming a critical component of precision management across the entire breast cancer care continuum.