Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.
Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often produce MHC peptides with amino acid mutations, known as neoantigens, which evade T-cell recognition, leading to rapid tumor growth. In immunotherapies such as TCR-T and CAR-T, identifying these mutated MHC peptide sequences is crucial. Current mass spectrometry-based peptide identification methods primarily rely on database searching, which fails to detect mutated peptides not present in human databases. In this paper, we propose a novel workflow called NeoMS, designed to efficiently identify both non-mutated and mutated MHC-I peptides from mass spectrometry data. NeoMS utilizes a tagging algorithm to generate an expanded sequence database that includes potential mutated proteins for each sample. Furthermore, it employs a machine learning-based scoring function for each peptide-spectrum match (PSM) to maximize search sensitivity. Finally, a rigorous target-decoy approach is implemented to control the false discovery rates (FDR) of the peptides with and without mutations separately. Experimental results for regular peptides demonstrate that NeoMS outperforms four benchmark methods. For mutated peptides, NeoMS successfully identifies hundreds of high-quality mutated peptides in a melanoma-associated sample, with their validity confirmed by further studies.