{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:15:51Z","timestamp":1791944151674,"version":"4.3.4"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and materials<\/jats:title><jats:p>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\u2019s composition and function. Patients were categorized into response (n=28) and non-response (n=27) groups based on treatment efficacy, and \u03b1-diversity, \u03b2-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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>No significant difference in \u03b1-diversity was observed between the groups (P &amp;gt; 0.05), but \u03b2-diversity differed significantly (P = 0.036). Twelve characteristic microorganisms were identified in the response group, including <jats:italic>g_ Oscillibacter<\/jats:italic> and <jats:italic>g_ Blautia<\/jats:italic>, and nine in the non-response group, such as <jats:italic>f_ Desulfovibrionaceae<\/jats:italic> and <jats:italic>g_ Megamonas<\/jats:italic>. Metabolic pathways associated with treatment response included ketone body metabolism and pathways related to amyotrophic lateral sclerosis. Multivariate analysis identified <jats:italic>g_Flavonifractor<\/jats:italic> (odds ratio [OR] = 6.680, P = 0.004), <jats:italic>g_Negativibacillus<\/jats:italic> (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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2025.1562831","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T06:48:33Z","timestamp":1741589313000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Gut microbiome is associated with radiotherapy response in lung cancer patients with brain metastases"],"prefix":"10.3389","volume":"15","author":[{"given":"Fei","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqiang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangrui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyou","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Gene Ontology Consortium. 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