Assessing Immune Microenvironment in TCGA-LUAD via CIBERSORTx Using Single-Cell Derived Signature Matrix and ESTIMATE Algorithm

Lung cancer (LC) remains a significant global health concern, affecting millions worldwide each year. Tumor-infiltrating immune cells (TIICs) play a crucial role in Lung Cancer progression and prognosis, with various immune cell types infiltrating the tumor microenvironment. Traditional methods like immunohistochemistry and flow cytometry have limitations in accurately profiling TIIC subtypes. However, recent advancements in single-cell RNA sequencing and computational algorithms like CIBERSORTx offer a promising approach for characterizing TIICs in bulk tumor samples. In this study, we undertook the validation of the signature matrix comprising 14 distinct immune cell types and subtypes, which was originally derived from PBMC single-cell RNA-seq data, in our previous work (Verma, 2024). The positive controls included 8 bulk RNA-seq samples of whole blood and specific immune cell bulk RNA-seq samples, while the negative control comprised neuroblastoma cell lines lacking immune content. Subsequently, we applied this signature matrix to deconvolute TCGA-LUAD data (n = 598), and assessed tumor purity and immune-stromal content using the ESTIMATE algorithm. Our findings indicate that the signature matrix accurately reflected flow cytometry-derived fractions, supported by correlation analysis. Specifically, the second positive control and negative control accurately reflected immune and non-immune sample fractions, respectively, further validating the efficacy of our approach. This study also provide insights into the invasion of immunocytes in lung adenocarcinoma and highlight the potential of computational tools like CIBERSORTx and ESTIMATE in characterizing the immune microenvironment of LC.