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Abstract：
BACKGROUND: In the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD).
METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies.
RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways.
CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient\'s disease.
METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies.
RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways.
CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient\'s disease.
摘要：
背景：在肿瘤微环境（TME）中，缺氧和乳酸代谢之间存在双向关系，每个组件对另一个组件施加相互影响，形成了不可分割的联系。本研究的目的是通过合并与缺氧和乳酸代谢相关的基因来建立预后模型。该模型旨在作为预测患者预后的工具，包括生存率,免疫微环境的状态，以及肺腺癌（LUAD）患者对治疗的反应性。
方法：从癌症基因组图谱（TCGA）和基因表达综合（GEO）的综合存储库中获得对LUAD特异的转录组测序数据和患者临床信息。从一系列可访问的数据集组装了与缺氧和乳酸代谢有关的基因汇编。采用单变量和多变量Cox回归分析。额外的调查程序，包括肿瘤突变负荷（TMB），微卫星不稳定性（MSI），功能富集评估和估计，CIBERSORT,和TIDE算法，用于评估药物敏感性和预测免疫疗法的疗效。
结果：包含5个乳酸和缺氧相关基因（LHRGs）的新型预后标记，PKFP,SLC2A1,BCAN,CDKN3和ANLN,已建立。该模型表明，LHRG相关风险评分升高的LUAD患者的生存率显着降低。单变量和多变量Cox分析均证实，风险评分是总体生存率的可靠预后指标。免疫表型显示记忆CD4+T细胞浸润增加，与低风险患者相比，树突状细胞和NK细胞被归类为高风险类别。在高危人群中，肺腺癌驱动基因突变的可能性更高，MSI与风险评分相关.功能富集分析表明，在高风险组中，细胞周期相关途径占优势。而代谢途径在低危组更为普遍.此外，药物敏感性分析显示，高危人群对各种药物的敏感性增加，特别是PI3K-AKT的抑制剂，EGFR,和ELK通路。
结论：该预后模型整合了乳酸代谢和缺氧参数，提供关于生存的预测性见解，免疫细胞浸润和功能，以及LUAD患者的治疗反应性。该模型可以促进个性化治疗策略，根据每个患者疾病的独特分子特征定制干预措施。
方法：从癌症基因组图谱（TCGA）和基因表达综合（GEO）的综合存储库中获得对LUAD特异的转录组测序数据和患者临床信息。从一系列可访问的数据集组装了与缺氧和乳酸代谢有关的基因汇编。采用单变量和多变量Cox回归分析。额外的调查程序，包括肿瘤突变负荷（TMB），微卫星不稳定性（MSI），功能富集评估和估计，CIBERSORT,和TIDE算法，用于评估药物敏感性和预测免疫疗法的疗效。
结果：包含5个乳酸和缺氧相关基因（LHRGs）的新型预后标记，PKFP,SLC2A1,BCAN,CDKN3和ANLN,已建立。该模型表明，LHRG相关风险评分升高的LUAD患者的生存率显着降低。单变量和多变量Cox分析均证实，风险评分是总体生存率的可靠预后指标。免疫表型显示记忆CD4+T细胞浸润增加，与低风险患者相比，树突状细胞和NK细胞被归类为高风险类别。在高危人群中，肺腺癌驱动基因突变的可能性更高，MSI与风险评分相关.功能富集分析表明，在高风险组中，细胞周期相关途径占优势。而代谢途径在低危组更为普遍.此外，药物敏感性分析显示，高危人群对各种药物的敏感性增加，特别是PI3K-AKT的抑制剂，EGFR,和ELK通路。
结论：该预后模型整合了乳酸代谢和缺氧参数，提供关于生存的预测性见解，免疫细胞浸润和功能，以及LUAD患者的治疗反应性。该模型可以促进个性化治疗策略，根据每个患者疾病的独特分子特征定制干预措施。
