Effects of immune inflammation in head and neck squamous cell carcinoma: Tumor microenvironment, drug resistance, and clinical outcomes - Zhu, Li; Wang, Yue; Yuan, Xingzhong; Ma, Yifei; Zhang, Tian; Zhou, Fangwei; Yu, Guodong (Frontiers in Genetics, 2022)
Abstract Numerous studies have confirmed the association between inflammation and malignancy and the involvement of inflammation-related regulators in the progression of head and neck squamous cell carcinoma (HNSCC). However, there is insufficient information about the predictive role of single-gene biomarkers, and more accurate prognostic models are needed. We downloaded HNSCC patients from the cancer genome atlas database and obtained the corresponding clinical data for data analysis, model construction, and differential gene expression analysis. Genes related to inflammatory factors were screened from published papers and intersected with differentially expressed genes to identify differentially expressed inflammatory factor-related genes. Subgroups were then typed based on the differentially expressed inflammatory factor-related genes. Then, a univariate/multivariate Cox regression algorithm was applied to identify 13 prognostic genes associated with inflammatory factors to construct a prognostic prediction model. The receiver operating characteristic curves indicated that the predictive efficacy of the model was significant. An enrichment analysis indicated that the high- and low-risk groups showed strong immune function differences. The CIBERSORT immune infiltration score showed that all 25 relevant and differentially expressed inflammatory factor genes were associated with immune function. As the risk score increased, specific immune function activation in the tumor tissue decreased, and this was associated with a poor prognosis. The association between the model genes and drug sensitivity (GSDC and CTRP) was analyzed using the GSCALite database. Our study showed that CXCL8, SCN1B, ITGA5, and IL1A were more sensitive to drugs, whereas RGS16, overall survival, and CCL5 were resistant to most drugs. We also screened for drug resistance between high- and low-risk groups. The differential drugs were talazoparib-1259, camptothecin-1003, vincristine-1818, azd5991-1720, mitoxantrone-1810, cdk9-5038-1709, teniposide-1809, docetaxel- 1819, nutlin-3a(-)-1047, and gemcitabine-1190. In addition, we detected the expression of prognostic genes in the pathological tissues, which verified that these genes could be used to predict prognoses. In conclusion, we propose a predictive model based on inflammation-related factors. It is a non-invasive predictive approach for genomic characterization that has shown satisfactory and effective performance in predicting patient survival outcomes and treatment responses. More interdisciplinary areas that combine medicine and electronics will be explored in the future.