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Journal of Ovarian Research (2024) 17:140 Journal of Ovarian Research  \n[https://doi.org/10.1186/s13048-024-01464-7](https://doi.org/10.1186/s13048-024-01464-7)  \nRESEARCH Open Access  \nThe intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma  \nHao Qin 1*†, Jie Liu2†, Yi Qu3,4,5,6, Yang-Yang Li7, Ya-Lan Xu8 and Yi-Fang Yan3,4,5,6*  \nAbstract  \nBackground Ovarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need.  \nMethods In this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores.  \nResults Survival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups.  \nConclusion Our findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.  \nKeywords Intratumoral microbiota, Ovarian serous cystadenocarcinoma, TCGA-OV, Microbial biomarker, Nomogram  \n†Hao Qin and Jie Liu contributed equally to this work.  \n*Correspondence: Hao Qin [haoqin@bjmu.edu.cn](haoqin@bjmu.edu.cn)[ ](haoqin@bjmu.edu.cn)Yi-Fang Yan  \n[yanyifangyanyf@hotmail.com](yanyifangyanyf@hotmail.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.  \nQin et al. Journal of Ovarian Research (2024) 17:140  \nIntroduction  \nOvarian cancer is one of the most common malignant tumors of the female reproductive system worldwide, with 313,959 new cases and 207,252 deaths recorded in 2020 [1]. The past decade has shown a trend of increasing annual incidence. Due to its insidious onset and lack of obvious symptoms in the early stages, 70% patients present with advanced disease at the time of clinical consultati","cbCaibBya7Yrfjyd","https://ap.wps.com/l/cbCaibBya7Yrfjyd","pdf",8208010,15,"English","# Introduction\n## Background and clinical need\n## Role of tumor microbiota and immune microenvironment\n# Materials and Methods\n## Sample cohort and microbial analysis\n## Cox regression and LASSO risk model\n## Risk stratification\n# Results\n## Survival differences by risk group\n## Nomogram construction\n## Immune infiltration and checkpoint gene expression\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"Ovarian serous cystadenocarcinoma is frequently detected at advanced stages, leading to poor outcomes. The study targets the need for biomarkers that can predict survival and immunotherapy efficacy for more personalized treatment.\"},{\"question\":\"How were microbial biomarkers identified?\",\"answer\":\"Microbial contents were analyzed in 453 tumor samples and 68 adjacent non-cancerous samples. Univariate Cox regression identified microorganisms linked to survival, and a prognostic risk score model was built using LASSO Cox regression.\"},{\"question\":\"What did the results show about survival and immune features?\",\"answer\":\"Patients in the low-risk group had higher overall survival. Immune cell infiltration and immune checkpoint gene expression were positively correlated with risk level, indicating stronger immune response signals in higher-risk groups.\"}]","The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma | PDF",1789979316,38]