喉癌（LC）是头颈部常见的恶性肿瘤，其特点是术后复发率高，复发后治疗难度大，严重影响患者的生活质量。临床实践中迫切需要有效的生物标志物来预测LC复发风险并指导个性化治疗计划的制定。本研究采用生物信息学方法探索LC复发的潜在生物标志物，重点关注关键基因，探讨其在LC复发中的功能和作用机制。旨在为LC的临床诊断、预后评估和靶向治疗提供新的视角和证据。分析Gene Expression Omnibus数据库中GSE25727数据集的基因表达谱，检测肿瘤之间的差异表达基因（DEG）术后复发和非复发早期 LC 患者的组织。还进行了基因本体论（GO）和京都基因和基因组百科全书（KEGG）途径分析。使用相互作用基因/蛋白质检索搜索工具（STRING）数据库开发了蛋白质-蛋白质相互作用（PPI）网络和转录因子（TF）-DEG-microRNA（miRNA）网络，并使用分子复合物选择关键基因检测（MCODE）插件。进行基因集富集分析（GSEA）以研究关键基因的可能机制。对 83 例 LC 患者的临床数据进行回顾性分析。采用免疫组织化学染色检测LC肿瘤组织中关键基因的转录水平以及影响术后复发的因素。GSE25727数据集中共鉴定出248个上调和34个下调的DEG。 PPI 网络分析确定了一个重要模块和五个候选基因（即 RRAGA、SLC38A9、WDR24、ATP6V1B1 和 LAMTOR3）。 TF-DEG-miRNA网络的构建表明ATP6V1B1可能由一个TF调控并与17个miRNA相互作用。 KEGG 和 GSEA 分析表明，ATP6V1B1 可能通过参与促炎和促纤维化介质、谷胱甘肽代谢、基质金属蛋白酶、免疫调节和淋巴细胞相互作用来影响 LC 复发。研究纳入的83例LC患者的复发率为19.3%（16/83）。免疫组化结果表明ATP6V1B1在复发性LC患者中高表达。单因素和多因素Logistic回归分析显示，肿瘤分期T3（P=0.04）、肿瘤分期T4（P=0.01）和ATP6V1B1高表达（P=0.02）是LC患者手术治疗后复发的危险因素。通过生物信息学筛选确定的关键基因和信号通路为了解LC发病机制的潜在机制提供了见解。 ATP6V1B1可能通过削弱免疫表型促进LC复发。我们的研究结果为LC.2024转化癌症研究的临床诊断和治疗策略的进一步研究提供了理论基础。版权所有。

Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC.Gene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence.A total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., RRAGA, SLC38A9, WDR24, ATP6V1B1, and LAMTOR3). The construction of the TF-DEG-miRNA network indicated that ATP6V1B1 might be regulated by one TF and interact with 17 miRNAs. The KEGG and GSEA analyses suggested that ATP6V1B1 may influence LC recurrence through the involvement of pro-inflammatory and pro-fibrotic mediators, glutathione metabolism, matrix metalloproteinases, immune regulation, and lymphocyte interactions. The recurrence rate of the 83 LC patients included in the study was 19.3% (16/83). The immunohistochemistry results indicated that ATP6V1B1 was highly expressed in patients with recurrent LC. The univariate and multivariate logistic regression analyses revealed that tumor stage T3 (P=0.04), tumor stage T4 (P=0.01), and a high expression of ATP6V1B1 (P=0.02) were risk factors for recurrence after surgical treatment in LC patients.The key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC. ATP6V1B1 may promote the recurrence of LC by weakening the immune phenotype. Our findings provide a theoretical basis for further research into clinical diagnostics and treatment strategies for LC.2024 Translational Cancer Research. All rights reserved.