转移是癌症患者治疗失败和预后不良的主要原因。本研究使用TCGA和GEO数据库下载的数据。鉴定差异表达的与转移相关的基因，并实施生物功能。进行Kaplan-Meier分析、单变量和多变量Cox回归分析以鉴定稳健的预后生物标志物，随后构建风险模型和示意图。进行基因集富集分析以鉴定低风险和高风险组富集的通路。在LSCC中，POLR2J3和MYH11被作为预后生物标志物并构建风险模型。经受试者工作特性曲线显示出风险模型的良好性能。通过校准和决策曲线证明了高准确性的示意图的构建。此外，我们通过RT-qPCR和免疫组化检测发现，与非转移组织相比，POLR2J3和MYH11在转移组织中表达显著增加。我们的研究在LSCC中确定了新的与转移相关的预后生物标志物，并构建了一个独特的示意图用于预测LSCC患者的预后。此外，我们探索了与转移相关的基因在调节LSCC中的相关机制。© 2023. Springer Nature Limited.

Metastasis is a major cause of treatment failure and poor outcomes in cancer patients. The data used in the current study was downloaded from TCGA and GEO databases. Differentially expressed metastasis-related genes were identified and the biological functions were implemented. Kaplan-Meier analysis univariate, and, multivariate Cox regression analyses were performed to identify robust prognostic biomarkers, followed by construction of the risk model and nomogram. Gene set enrichment analysis was performed to identify pathways enriched in low- and high-risk groups. POLR2J3 and MYH11 were treated as prognostic biomarkers in LSCC and the risk model was constructed. Receiver operating characteristic curves revealed the good performance of the risk model. A nomogram with high accuracy was constructed, as evidenced by calibration and decision curves. Moreover, we found that the expressions of POLR2J3 and MYH11 was significantly higher in metastasis tissues compared with those in non-metastasis tissues by RT-qPCR and IHC. Our study identified novel metastasis-related prognostic biomarkers in LSCC and constructed a unique nomogram for predicting the prognosis of LSCC patients. Moreover, we explored the related mechanisms of metastasis-related genes in regulating LSCC.© 2023. Springer Nature Limited.