Machine Learning-Based Immune Subgroup Classification of Solid Tumors Using RNA-Seq Data

Abstract

Accurate classification of tumor immune microenvironment (TIME) subgroups is critical for predicting immunotherapy response and informing personalized treatment strategies. While immune subgroups are known to correlate with immunotherapy efficacy and prognosis, the underlying microenvironmental factors remain incompletely understood. In this study, we developed a machine learning-based classification model using FPKM-normalized RNA-Seq data from 440 immune-related genes. The model, trained with the eXtreme Gradient Boosting (XGBoost) algorithm on 7,300 samples, achieved a macro-balanced accuracy of 0.959 and a macro-balanced F1 score of 0.908 on an independent test set of 1,826 samples.Notably, the model also identified a seventh, predominant subgroup that exhibits mixed characteristics of the six established TIME subgroups, offering a new perspective on tumor heterogeneity. To support clinical and research use, the model has been deployed as a user-friendly web interface with integrated visualization tools, including Principal Component Analysis (PCA) and T-distributed Stochastic Neighbor Embedding (t-SNE), for classification and exploratory analysis. This tool has the potential to enhance immunotherapy research and facilitate more precise treatment planning.