TTN是皮肤黑素瘤(SKCM)中最常见的突变基因。肿瘤突变负荷(TMB)可以产生调节T细胞识别的新抗原,这将显著影响患者的预后。TTN基因具有较长的编码序列和大量的突变位点,这使得SKCM患者产生更高的TMB,并可能影响免疫反应。已发现具有TTN突变的SKCM患者的总生存期(OS)明显高于野生型患者。然而,TTN突变对SKCM免疫微环境的影响尚未得到充分研究。这里,我们系统地探讨了TTN突变状态与免疫应答之间的关系和可能的机制。我们首先揭示了TTN突变的SKCM与四个免疫相关的生物学过程显着相关。接下来,发现TTN突变和野生型SKCM患者之间差异表达的115个免疫基因显着影响SKCM患者的OS。然后,我们使用LASSO回归分析筛选了四个免疫相关基因(CXCL9,PSMB9,CD274和FCGR2A),并构建了TTN突变相关免疫预后模型(TM-IPM),以区分具有高或低风险的SKCM患者预后不良。独立于多种临床特征。低危组SKCM高表达大量免疫相关基因,和这些基因的功能富集分析表明,该组参与多种免疫过程和途径。此外,TM-IPM与其他临床病理参数构建的列线图可以为临床医生提供预测工具。此外,我们发现CD8+T细胞在低危组显著富集.低危组免疫检查点的表达水平高于高危组。此外,低危组对化疗药物的反应高于高危组,这可能与低危组的长期生存有关。总的来说,我们使用4种免疫相关基因构建并验证了TM-IPM,并从免疫学角度分析了TM-IPM预测患者预后和免疫治疗反应的潜在机制.
TTN is the most commonly mutated gene in skin cutaneous melanoma (SKCM). Tumor mutational burden (TMB) can generate new antigens that regulate the recognition of T cells, which will significantly affect the prognosis of patients. The
TTN gene has a long coding sequence and a high number of mutant sites, which allows SKCM patients to produce higher TMB and may influence the immune response. It has been found that the overall survival (OS) of SKCM patients with
TTN mutation was significantly higher than that of wild-type patients. However, the effect of TTN mutation on the immune microenvironment of SKCM has not been fully investigated. Here, we systematically explored the relationship and potential mechanisms between
TTN mutation status and the immune response. We first revealed that TTN mutated SKCM were significantly associated with four immune-related biological processes. Next, 115 immune genes differentially expressed between
TTN mutation and wild-type SKCM patients were found to significantly affect the OS of SKCM patients. Then, we screened four immune-related genes (CXCL9, PSMB9, CD274, and FCGR2A) using LASSO regression analysis and constructed a TTN mutation-associated immune prognostic model (TM-IPM) to distinguish the SKCM patients with a high or low risk of poor prognosis, independent of multiple clinical characteristics. SKCM in the low-risk group highly expressed a large number of immune-related genes, and functional enrichment analysis of these genes showed that this group was involved in multiple immune processes and pathways. Furthermore, the nomogram constructed by TM-IPM with other clinicopathological parameters can provide a predictive tool for clinicians. Moreover, we found that CD8+ T cells were significantly enriched in the low-risk group. The expression level of immune checkpoints was higher in the low-risk group than in the high-risk group. Additionally, the response to chemotherapeutic agents was higher in the low-risk group than in the high-risk group, which may be related to the long survival in the low-risk group. Collectively, we constructed and validated a TM-IPM using four immune-related genes and analyzed the potential mechanisms of TM-IPM to predict patient prognosis and response to immunotherapy from an immunological perspective.