{"title":"肺癌脑转移患者的肠道微生物组与放疗反应相关","authors":"Fei Liang, Yichu Sun, Jing Yang, Ziqiang Shen, Guangfeng Wang, Jiangrui Zhu, Chong Zhou, Youyou Xia","doi":"10.3389/fcimb.2025.1562831","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the gut microbiome of lung cancer patients with brain metastases undergoing radiotherapy, identify key microorganisms associated with radiotherapy response, and evaluate their potential as biomarkers.</p><p><strong>Methods and materials: </strong>This study enrolled 55 newly diagnosed lung cancer patients with brain metastases. Fecal samples were collected before radiotherapy and analyzed by 16S rRNA sequencing to assess the gut microbiome's composition and function. Patients were categorized into response (n=28) and non-response (n=27) groups based on treatment efficacy, and α-diversity, β-diversity, and functional pathways were compared between them. Linear Discriminant Analysis Effect Size was used to identify microbial features associated with treatment efficacy. Logistic regression analyses were performed to evaluate the predictive capacity of clinical and microbial factors for treatment outcomes.</p><p><strong>Results: </strong>No significant difference in α-diversity was observed between the groups (P > 0.05), but β-diversity differed significantly (P = 0.036). Twelve characteristic microorganisms were identified in the response group, including <i>g_ Oscillibacter</i> and <i>g_ Blautia</i>, and nine in the non-response group, such as <i>f_ Desulfovibrionaceae</i> and <i>g_ Megamonas</i>. Metabolic pathways associated with treatment response included ketone body metabolism and pathways related to amyotrophic lateral sclerosis. Multivariate analysis identified <i>g_Flavonifractor</i> (odds ratio [OR] = 6.680, P = 0.004), <i>g_Negativibacillus</i> (OR = 3.862, P = 0.014), C-reactive protein (OR = 1.054, P = 0.017), and systemic inflammation response index (OR = 1.367, P = 0.043) as independent predictors of radiotherapy response. The nomogram and microbiome models achieved area under the curve (AUC) values of 0.935 and 0.866, respectively, demonstrating excellent predictive performance. Decision curve analysis further confirmed these models provided significant net benefits across risk thresholds.</p><p><strong>Conclusions: </strong>The composition and functional characteristics of the gut microbiome in lung cancer patients with brain metastases prior to radiotherapy are associated with therapeutic response and possess potential as predictive biomarkers. Further studies are warranted to validate these findings.</p>","PeriodicalId":12458,"journal":{"name":"Frontiers in Cellular and Infection Microbiology","volume":"15 ","pages":"1562831"},"PeriodicalIF":4.8000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931136/pdf/","citationCount":"0","resultStr":"{\"title\":\"Gut microbiome is associated with radiotherapy response in lung cancer patients with brain metastases.\",\"authors\":\"Fei Liang, Yichu Sun, Jing Yang, Ziqiang Shen, Guangfeng Wang, Jiangrui Zhu, Chong Zhou, Youyou Xia\",\"doi\":\"10.3389/fcimb.2025.1562831\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To investigate the gut microbiome of lung cancer patients with brain metastases undergoing radiotherapy, identify key microorganisms associated with radiotherapy response, and evaluate their potential as biomarkers.</p><p><strong>Methods and materials: </strong>This study enrolled 55 newly diagnosed lung cancer patients with brain metastases. Fecal samples were collected before radiotherapy and analyzed by 16S rRNA sequencing to assess the gut microbiome's composition and function. Patients were categorized into response (n=28) and non-response (n=27) groups based on treatment efficacy, and α-diversity, β-diversity, and functional pathways were compared between them. Linear Discriminant Analysis Effect Size was used to identify microbial features associated with treatment efficacy. Logistic regression analyses were performed to evaluate the predictive capacity of clinical and microbial factors for treatment outcomes.</p><p><strong>Results: </strong>No significant difference in α-diversity was observed between the groups (P > 0.05), but β-diversity differed significantly (P = 0.036). Twelve characteristic microorganisms were identified in the response group, including <i>g_ Oscillibacter</i> and <i>g_ Blautia</i>, and nine in the non-response group, such as <i>f_ Desulfovibrionaceae</i> and <i>g_ Megamonas</i>. Metabolic pathways associated with treatment response included ketone body metabolism and pathways related to amyotrophic lateral sclerosis. Multivariate analysis identified <i>g_Flavonifractor</i> (odds ratio [OR] = 6.680, P = 0.004), <i>g_Negativibacillus</i> (OR = 3.862, P = 0.014), C-reactive protein (OR = 1.054, P = 0.017), and systemic inflammation response index (OR = 1.367, P = 0.043) as independent predictors of radiotherapy response. The nomogram and microbiome models achieved area under the curve (AUC) values of 0.935 and 0.866, respectively, demonstrating excellent predictive performance. Decision curve analysis further confirmed these models provided significant net benefits across risk thresholds.</p><p><strong>Conclusions: </strong>The composition and functional characteristics of the gut microbiome in lung cancer patients with brain metastases prior to radiotherapy are associated with therapeutic response and possess potential as predictive biomarkers. Further studies are warranted to validate these findings.</p>\",\"PeriodicalId\":12458,\"journal\":{\"name\":\"Frontiers in Cellular and Infection Microbiology\",\"volume\":\"15 \",\"pages\":\"1562831\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931136/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Cellular and Infection Microbiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fcimb.2025.1562831\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Cellular and Infection Microbiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fcimb.2025.1562831","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的:研究肺癌脑转移放疗患者的肠道微生物组,确定与放疗反应相关的关键微生物,并评估其作为生物标志物的潜力。方法和材料:本研究纳入55例新诊断的肺癌脑转移患者。放疗前收集粪便样本,通过16S rRNA测序分析肠道微生物组的组成和功能。根据治疗效果将患者分为缓解组(n=28)和无缓解组(n=27),比较α-多样性、β-多样性及功能通路。使用线性判别分析效应大小来确定与治疗效果相关的微生物特征。采用Logistic回归分析来评估临床和微生物因素对治疗结果的预测能力。结果:α-多样性组间差异无统计学意义(P < 0.05), β-多样性组间差异有统计学意义(P = 0.036)。反应组鉴定出12种特征微生物,包括g_ Oscillibacter和g_ Blautia;无反应组鉴定出9种特征微生物,包括f_ Desulfovibrionaceae和g_ Megamonas。与治疗反应相关的代谢途径包括酮体代谢和肌萎缩侧索硬化症相关的代谢途径。多因素分析发现,g_flavonoids ifractor(比值比[OR] = 6.680, P = 0.004)、g_Negativibacillus(比值比[OR] = 3.862, P = 0.014)、c -反应蛋白(比值比= 1.054,P = 0.017)和全身炎症反应指数(比值比= 1.367,P = 0.043)是放疗反应的独立预测因子。nomogram和microbiome模型的曲线下面积(area under The curve, AUC)分别为0.935和0.866,具有较好的预测效果。决策曲线分析进一步证实,这些模型在风险阈值上提供了显著的净收益。结论:肺癌脑转移患者放疗前肠道微生物组的组成和功能特征与治疗反应相关,具有作为预测性生物标志物的潜力。需要进一步的研究来证实这些发现。
Gut microbiome is associated with radiotherapy response in lung cancer patients with brain metastases.
Purpose: To investigate the gut microbiome of lung cancer patients with brain metastases undergoing radiotherapy, identify key microorganisms associated with radiotherapy response, and evaluate their potential as biomarkers.
Methods and materials: This study enrolled 55 newly diagnosed lung cancer patients with brain metastases. Fecal samples were collected before radiotherapy and analyzed by 16S rRNA sequencing to assess the gut microbiome's composition and function. Patients were categorized into response (n=28) and non-response (n=27) groups based on treatment efficacy, and α-diversity, β-diversity, and functional pathways were compared between them. Linear Discriminant Analysis Effect Size was used to identify microbial features associated with treatment efficacy. Logistic regression analyses were performed to evaluate the predictive capacity of clinical and microbial factors for treatment outcomes.
Results: No significant difference in α-diversity was observed between the groups (P > 0.05), but β-diversity differed significantly (P = 0.036). Twelve characteristic microorganisms were identified in the response group, including g_ Oscillibacter and g_ Blautia, and nine in the non-response group, such as f_ Desulfovibrionaceae and g_ Megamonas. Metabolic pathways associated with treatment response included ketone body metabolism and pathways related to amyotrophic lateral sclerosis. Multivariate analysis identified g_Flavonifractor (odds ratio [OR] = 6.680, P = 0.004), g_Negativibacillus (OR = 3.862, P = 0.014), C-reactive protein (OR = 1.054, P = 0.017), and systemic inflammation response index (OR = 1.367, P = 0.043) as independent predictors of radiotherapy response. The nomogram and microbiome models achieved area under the curve (AUC) values of 0.935 and 0.866, respectively, demonstrating excellent predictive performance. Decision curve analysis further confirmed these models provided significant net benefits across risk thresholds.
Conclusions: The composition and functional characteristics of the gut microbiome in lung cancer patients with brain metastases prior to radiotherapy are associated with therapeutic response and possess potential as predictive biomarkers. Further studies are warranted to validate these findings.
期刊介绍:
Frontiers in Cellular and Infection Microbiology is a leading specialty journal, publishing rigorously peer-reviewed research across all pathogenic microorganisms and their interaction with their hosts. Chief Editor Yousef Abu Kwaik, University of Louisville is supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
Frontiers in Cellular and Infection Microbiology includes research on bacteria, fungi, parasites, viruses, endosymbionts, prions and all microbial pathogens as well as the microbiota and its effect on health and disease in various hosts. The research approaches include molecular microbiology, cellular microbiology, gene regulation, proteomics, signal transduction, pathogenic evolution, genomics, structural biology, and virulence factors as well as model hosts. Areas of research to counteract infectious agents by the host include the host innate and adaptive immune responses as well as metabolic restrictions to various pathogenic microorganisms, vaccine design and development against various pathogenic microorganisms, and the mechanisms of antibiotic resistance and its countermeasures.