DOI: 10.5281/zenodo.18813869
title: DATASET from "A systemic immune signature stratifies early-stage breast cancer patients and reveals soluble IL- 2RA and PD-1 as independent prognostic biomarkers"

description: Title: Cytokine and Immune Biomarker Dataset in Breast Cancer Patients

General Overview

This dataset contains clinical, pathological, survival, and immunological data collected from a cohort of 279 breast cancer patients. The primary aim of the dataset is to support the investigation of associations between systemic immune mediators, tumor characteristics, and clinical outcomes, with special emphasis on cytokine profiles and immune checkpoint–related biomarkers.

The dataset integrates demographic variables, tumor pathological features, molecular subtype classification, survival outcomes, and quantitative measurements of a broad panel of circulating cytokines, chemokines, growth factors, and immune regulatory molecules.

Dataset Structure

The dataset includes 94 variables, organized into the following main categories:





Demographic and Clinical Variables





Patient identifier (anonymized ID)




Age at diagnosis




Categorized age variables




Clinical status and follow-up information






Tumor Pathology Characteristics





Histological type




Tumor size and categorized tumor size




Histological grade and grouped grade variables




Tumor necrosis and invasion parameters




Lymph node involvement




Tumor-infiltrating lymphocytes (TILs)




Molecular phenotype classification (including luminal vs non-luminal categories)




Proliferation index (Ki-67 and categorized versions)






Clinical Outcomes





Recurrence status




Disease-free survival (DFS / SLE)




Overall survival (OS / SG)




Current patient status






Immune and Cytokine ProfilingQuantitative measurements of multiple immune mediators, including:





Interleukins (e.g., IL-1, IL-2, IL-4, IL-5, IL-6, IL-10, IL-17 family, IL-23, IL-27, IL-33)




Interferons and inflammatory cytokines (e.g., IFN-γ, TNF-α)




Chemokines (e.g., MCP-1, IP-10, MIG, GROα, TARC)




Growth factors and angiogenic markers (e.g., VEGF, EGF, HGF, PDGF, SCF)




Immune checkpoint and regulatory molecules (e.g., CTLA-4, PD-1, PD-L1, PD-L2, TIM-3, LAG-3, Galectin-9)






Derived and Categorized Variables





Dichotomized biomarker variables generated for survival analyses




Categorized clinical predictors




Variables derived from ROC-based cutoff optimization for outcome analyses





Data Type and Format

All variables are numerically encoded. Continuous biomarker concentrations and clinical measurements coexist with categorical variables represented as numeric codes. Missing values are present in some biomarkers due to technical or sampling limitations.

Potential Applications

This dataset can be used for:





Biomarker discovery in oncology




Immune profiling and tumor microenvironment studies




Survival and prognostic modeling




Machine learning approaches for outcome prediction




Translational cancer immunology research



Ethical Considerations

Patient identifiers have been anonymized to preserve confidentiality. The dataset is intended exclusively for research and educational purposes in accordance with applicable ethical and data protection regulations.