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Abstract：
BACKGROUND: Osteosarcoma (OS), the most common primary malignant bone tumor, predominantly affects children and young adults and is characterized by high invasiveness and poor prognosis. Despite therapeutic advancements, the survival rate remains suboptimal, indicating an urgent need for novel biomarkers and therapeutic targets. This study aimed to investigate the prognostic significance of LGMN expression and immune cell infiltration in the tumor microenvironment of OS.
METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression.
RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN\'s role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches.
CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.
METHODS: We performed an integrative bioinformatics analysis utilizing the GEO and TARGET-OS databases to identify differentially expressed genes (DEGs) associated with LGMN in OS. We conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to explore the biological pathways and functions. Additionally, we constructed protein-protein interaction (PPI) networks, a competing endogenous RNA (ceRNA) network, and applied the CIBERSORT algorithm to quantify immune cell infiltration. The diagnostic and prognostic values of LGMN were evaluated using the area under the receiver operating characteristic (ROC) curve and Cox regression analysis. Furthermore, we employed Consensus Clustering Analysis to explore the heterogeneity within OS samples based on LGMN expression.
RESULTS: The analysis revealed significant upregulation of LGMN in OS tissues. DEGs were enriched in immune response and antigen processing pathways, suggesting LGMN\'s role in immune modulation within the TME. The PPI and ceRNA network analyses provided insights into the regulatory mechanisms involving LGMN. Immune cell infiltration analysis indicated a correlation between high LGMN expression and increased abundance of M2 macrophages, implicating an immunosuppressive role. The diagnostic AUC for LGMN was 0.799, demonstrating its potential as a diagnostic biomarker. High LGMN expression correlated with reduced overall survival (OS) and progression-free survival (PFS). Importantly, Consensus Clustering Analysis identified two distinct subtypes of OS, highlighting the heterogeneity and potential for personalized medicine approaches.
CONCLUSIONS: Our study underscores the prognostic value of LGMN in osteosarcoma and its potential as a therapeutic target. The identification of LGMN-associated immune cell subsets and the discovery of distinct OS subtypes through Consensus Clustering Analysis provide new avenues for understanding the immunosuppressive TME of OS and may aid in the development of personalized treatment strategies. Further validation in larger cohorts is warranted to confirm these findings.
摘要：
背景：骨肉瘤（OS），最常见的原发性恶性骨肿瘤，主要影响儿童和年轻人，其特点是高侵袭性和不良预后。尽管治疗进展，存活率仍然不理想，这表明迫切需要新的生物标志物和治疗靶点。本研究旨在探讨LGMN表达和免疫细胞浸润在OS肿瘤微环境中的预后意义。
方法：我们利用GEO和TARGET-OS数据库进行了综合生物信息学分析，以鉴定OS中与LGMN相关的差异表达基因（DEG）。我们进行了基因本体论(GO)，京都基因和基因组百科全书（KEGG），和基因集富集分析（GSEA）探索生物学途径和功能。此外,我们构建了蛋白质-蛋白质相互作用(PPI)网络，竞争内源性RNA(ceRNA)网络，并应用CIBERSORT算法量化免疫细胞浸润。使用受试者工作特征（ROC）曲线下面积和Cox回归分析评估LGMN的诊断和预后价值。此外，我们采用共识聚类分析来探索基于LGMN表达的OS样本内的异质性。
结果：分析显示OS组织中LGMN显著上调。DEGs在免疫应答和抗原加工途径中富集,提示LGMN在TME内的免疫调节中的作用。PPI和ceRNA网络分析提供了有关LGMN的调控机制的见解。免疫细胞浸润分析表明LGMN高表达与M2巨噬细胞丰度增加之间存在相关性，暗示了免疫抑制的作用.LGMN的诊断AUC为0.799，证明了其作为诊断生物标志物的潜力。高LGMN表达与降低的总生存期(OS)和无进展生存期(PFS)相关。重要的是,共识聚类分析确定了两种不同的操作系统亚型，强调个性化医疗方法的异质性和潜力。
结论：我们的研究强调了LGMN在骨肉瘤中的预后价值及其作为治疗靶点的潜力。LGMN相关免疫细胞亚群的鉴定和通过共识聚类分析发现不同的OS亚型为理解OS的免疫抑制性TME提供了新的途径，并可能有助于制定个性化治疗策略。需要在更大的队列中进一步验证以确认这些发现。
方法：我们利用GEO和TARGET-OS数据库进行了综合生物信息学分析，以鉴定OS中与LGMN相关的差异表达基因（DEG）。我们进行了基因本体论(GO)，京都基因和基因组百科全书（KEGG），和基因集富集分析（GSEA）探索生物学途径和功能。此外,我们构建了蛋白质-蛋白质相互作用(PPI)网络，竞争内源性RNA(ceRNA)网络，并应用CIBERSORT算法量化免疫细胞浸润。使用受试者工作特征（ROC）曲线下面积和Cox回归分析评估LGMN的诊断和预后价值。此外，我们采用共识聚类分析来探索基于LGMN表达的OS样本内的异质性。
结果：分析显示OS组织中LGMN显著上调。DEGs在免疫应答和抗原加工途径中富集,提示LGMN在TME内的免疫调节中的作用。PPI和ceRNA网络分析提供了有关LGMN的调控机制的见解。免疫细胞浸润分析表明LGMN高表达与M2巨噬细胞丰度增加之间存在相关性，暗示了免疫抑制的作用.LGMN的诊断AUC为0.799，证明了其作为诊断生物标志物的潜力。高LGMN表达与降低的总生存期(OS)和无进展生存期(PFS)相关。重要的是,共识聚类分析确定了两种不同的操作系统亚型，强调个性化医疗方法的异质性和潜力。
结论：我们的研究强调了LGMN在骨肉瘤中的预后价值及其作为治疗靶点的潜力。LGMN相关免疫细胞亚群的鉴定和通过共识聚类分析发现不同的OS亚型为理解OS的免疫抑制性TME提供了新的途径，并可能有助于制定个性化治疗策略。需要在更大的队列中进一步验证以确认这些发现。
