Miao Xiangwan, Wang Hao, Fan Cui, Song QianQian, Ding Rui, Wu Jichang, Hu Haixia, Chen Kaili, Ji Peilin, Wen Qing, Shi Minmin, Ye Bin, Fu Da, Xiang Mingliang
Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
Cancer Cell Int. 2023 Aug 11;23(1):164. doi: 10.1186/s12935-023-03014-5.
Systemic chemotherapy is the first-line therapeutic option for head and neck squamous cell carcinoma (HNSCC), but it often fails. This study aimed to develop an effective prognostic model for evaluating the therapeutic effects of systemic chemotherapy.
This study utilized CRISPR/cas9 whole gene loss-of-function library screening and data from The Cancer Genome Atlas (TCGA) HNSCC patients who have undergone systemic therapy to examine differentially expressed genes (DEGs). A lipid metabolism-related clustered polygenic model called the lipid metabolism related score (LMRS) model was established based on the identified functionally enriched DEGs. The prediction efficiency of the model for survival outcome, chemotherapy, and immunotherapy response was evaluated using HNSCC datasets, the GEO database and clinical samples.
Screening results from the study demonstrated that genes those were differentially expressed were highly associated with lipid metabolism-related pathways, and patients receiving systemic therapy had significantly different prognoses based on lipid metabolism gene characteristics. The LMRS model, consisting of eight lipid metabolism-related genes, outperformed each lipid metabolism gene-based model in predicting outcome and drug response. Further validation of the LMRS model in HNSCCs confirmed its prognostic value.
In conclusion, the LMRS polygenic prognostic model is helpful to assess outcome and drug response for HNSCCs and could assist in the timely selection of the appropriate treatment for HNSCC patients. This study provides important insights for improving systemic chemotherapy and enhancing patient outcomes.
全身化疗是头颈部鳞状细胞癌（HNSCC）的一线治疗选择，但常常失败。本研究旨在开发一种有效的预后模型，用于评估全身化疗的治疗效果。
本研究利用CRISPR/cas9全基因功能丧失文库筛选以及来自接受全身治疗的癌症基因组图谱（TCGA）HNSCC患者的数据，以检测差异表达基因（DEG）。基于鉴定出的功能富集DEG，建立了一种名为脂质代谢相关评分（LMRS）模型的脂质代谢相关聚类多基因模型。使用HNSCC数据集、GEO数据库和临床样本评估该模型对生存结局、化疗和免疫治疗反应的预测效率。
该研究的筛选结果表明，差异表达的基因与脂质代谢相关途径高度相关，接受全身治疗的患者基于脂质代谢基因特征具有显著不同的预后。由八个脂质代谢相关基因组成的LMRS模型在预测结局和药物反应方面优于每个基于脂质代谢基因的模型。在HNSCC中对LMRS模型的进一步验证证实了其预后价值。
总之，LMRS多基因预后模型有助于评估HNSCC的结局和药物反应，并可协助为HNSCC患者及时选择合适的治疗方法。本研究为改进全身化疗和提高患者结局提供了重要见解。
