Poor survival rates of squamous cell carcinomas (SCCs) are associated with high recurrence, metastasis, and late diagnosis, due in part to a limited number of reliable biomarkers. Thus, the identification of signatures improving the diagnosis of different SCC types is mandatory. Considering the relevant role of NAD+ metabolism in SCC chemoprevention and therapy, the study aimed at identifying new biomarkers based on NAD+ metabolism-related gene (NMRG) expression. Gene expression of 18 NMRGs and clinical-pathological information for patients with head and neck SCC (HNSCC), lung SCC (LuSCC), and cervix SCC (CeSCC) from The Cancer Genome Atlas (TCGA) were analyzed by several bioinformatic tools. We identified a 16-NMRG profile discriminating 3 SCCs from 3 non-correlated tumors. We found several genes for HNSCC, LuSCC, and CeSCC with high diagnostic power. Notably, three NMRGs were SCC-type specific biomarkers. Furthermore, specific signatures displayed high diagnostic power for several clinical-pathological characteristics. Analyzing tumor-infiltrating immune cell profiles and PD-1/PD-L1 levels, we found that NMRG expression was associated with suppressive immune microenvironment mainly in HNSCC. Finally, the evaluation of patient survival identified specific genes for HNSCC, LuSCC, and CeSCC with potential prognostic power. Therefore, our analyses indicate NAD+ metabolism as an important source of SCC biomarkers and potential therapeutic targets.
鳞状细胞癌(SCC)的低生存率与其高复发率、高转移率及诊断延迟相关,部分原因在于可靠的生物标志物数量有限。因此,必须寻找能够改善不同类型SCC诊断的特征标志物。鉴于NAD+代谢在SCC化学预防和治疗中的重要作用,本研究旨在基于NAD+代谢相关基因(NMRG)的表达识别新型生物标志物。通过多种生物信息学工具,我们分析了来自癌症基因组图谱(TCGA)的头颈鳞癌(HNSCC)、肺鳞癌(LuSCC)和宫颈鳞癌(CeSCC)患者的18个NMRG基因表达及临床病理信息。我们鉴定出一个由16个NMRG组成的特征谱,能够区分三种SCC与三种非相关肿瘤。研究发现多个对HNSCC、LuSCC和CeSCC具有高诊断效能的基因,其中三个NMRG可作为SCC亚型特异性生物标志物。此外,特定基因特征谱对多种临床病理特征展现出高诊断价值。通过分析肿瘤浸润免疫细胞特征及PD-1/PD-L1水平,发现NMRG表达与抑制性免疫微环境相关,尤其在HNSCC中表现显著。最后,通过患者生存评估确定了HNSCC、LuSCC和CeSCC中具有潜在预后价值的特异性基因。综上,我们的分析表明NAD+代谢是SCC生物标志物和潜在治疗靶点的重要来源。
NAD+ Metabolism-Related Gene Profile Can Be a Relevant Source of Squamous Cell Carcinoma Biomarkers