Characterization of tumor immune microenvironment and cancer therapy for head and neck squamous cell carcinoma through identification of a genomic instability-related lncRNA prognostic signature - Jing, Lijun; Du, Yabing; Fu, Denggang (Frontiers in Genetics, 2022)
Head and neck squamous cell carcinoma (HNSCC) represent one of the most prevalent and malignant tumors of epithelial origins with unfavorable outcome. Increasing evidence has shown that dysregulated long non-coding RNAs (lncRNAs) correlate with tumorigenesis and genomic instability (GI), while the roles of GI-related lncRNAs in the tumor immune microenvironment (TIME) and predicting cancer therapy were still yet to clarified. In this study, transcriptome and somatic mutations profiles with clinical parameters were obtained from TCGA database. Patients were classified into GI-like and genomic stable (GS)-like groups according to the top 25% and bottom 25% cumulative counts of somatic mutations. Differentially expressed lncRNAs (DElncRNAs) between GI- and GS-like groups were identified as GI-related lncRNAs. These lncRNAs related coding genes were enriched in cancer-related KEGG pathways. Patients totaling 499 with clinical information were randomly divided into the training and validation sets. 18 DElncRNAs screened by univariate Cox regression analysis were associated with overall survival (OS) in the training set. GI-related lncRNAs signature that comprised of 10 DElncRNAs was generated through least absolute shrinkage and selection operator (Lasso)-Cox regression analysis. Patients in high-risk group have significantly decreased OS versus patients in low-risk group, which was verified in internal validation and entire HNSCC sets. Integrated HNSCC sets from GEO confirmed the notably survival stratification of the signature. Time-dependent receiver operating characteristic curve demonstrated that the signature was reliable. Additionally, the signature retained strong performance of OS prediction for patients with various clinicopathological features. Cell composition analysis showed high anti-tumor immunity in low-risk group which was evidenced by increased infiltrating CD8+ T cells, natural killer, and reduced cancer-associated fibroblasts, which was convinced by immune signatures analysis via ssGSEA algorithm. T helper/IFNγ signaling, co-stimulatory and co-inhibitory signatures were increased expression in low-risk group. Low-risk patients were predicted to be beneficial to immunotherapy, which was confirmed by patients with progressive disease had high risk scores versus complete remission patients. Furthermore, the drugs that might be sensitive to HNSCC were identified. In summary, the novel prognostic GILncRNAs signature provided a promising approach for characterizing the TIME and predicting therapeutic strategies for HNSCC patients.