Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment

Abstract

Background. Nasopharyngeal carcinoma (NPC) is one of the most prevalent cancers with a poor prognosis. Immunotherapy, especially immune checkpoint blockade (ICB), is becoming a potential therapeutic choice for NPC patients. Thus, the identification of patients who could benefit from immunotherapy is clinically significant. Methods. The NPC expression profiles from GSE102349 were used to calculate the cell scores of the tumor microenvironment (TME). The consensus clustering method was utilized to identify the potential molecular subtypes among NPC samples. The hub genes were selected from subtype-specific genes by bioinformatics analysis. Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype. Results. In the present study, we identified two TME subtypes among NPC patients. Patients with the S1 subtype have higher levels of immune cells, immune checkpoint genes, and prognosis. Using expression data profiles of NPC patients, we constructed machine learning models for predicting TME subtypes of NPC patients. This model consists of 8 genes (LCK, CD247, FYN, ZAP70, SH2D1A, CD3D, CD3E, and CD3G). Among them, LCK, FYN, SH2D1A, and CD3D were associated with better prognoses. Among the two constructed models, SVM exhibited a higher area under curve (AUC) of 0.977, when compared with RF ( AUC = 0.966 ). The web server based on the constructed machine learning models will contribute to the identification of NPC patients likely to benefit from ICB therapies. Conclusions. This study identified NPC subtypes and provided an accurate model to select individuals who are most likely to respond to ICB.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

“…In the correlation analysis, we found that the 10 COMAR genes were positively correlated with immune checkpoint expression, such as PD-L1 and the IPS with anti-PD1 plus anti-CTLA4 or anti-PD1 along immunotherapy in both patient specimens and LUAD cell lines (Figure 8; Supplementary Figure S6A). Most of the genes in the COMAR model have also been reported to be positively correlated with PD-L1 expression and respond to ICB immunotherapy in multiple cancers (47,(88)(89)(90)(91)(92)(93), which was consistent with the results of our study. These suggested that the 10 COMAR genes might serve as potential targets for ICB immunotherapy.…”