Li Wenshu, Zhu Zixiang, Li Longyuan, Wu Xin, Li Jiaxuan, Zhou Yi, Gu Lingwen, Vittal Pranathi, Chen Zhouqing, Wang Zhong, Guo Lingchuan
Department of Pathology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, Jiangsu, China.
Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Front Oncol. 2025 Jun 18;15:1582068. doi: 10.3389/fonc.2025.1582068. eCollection 2025.
Low-grade gliomas (LGGs) exhibit diverse bacterial infiltrations. This study delves into the intricate relationship between microbial infiltration in glioma samples and tumor multi-omics characteristics, aiming to elucidate its impact on tumor behavior and patient prognosis.
We included low-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA) as analysis cohort and used LGG tumor samples from patients who underwent surgical treatment as validation cohort. For the TCGA samples, utilizing advanced machine learning algorithms, this study identified distinct patterns of bacterial infiltration within the LGG population and constructed a prognostically relevant intra-tumoral bacteria risk model (PRIBR Index). For the clinically derived samples, we performed 16S rRNA sequencing, bulk RNA sequencing, and proteomics analysis. Subsequently, the samples were stratified into high-risk and low-risk groups. We then explored clinical information, tumor microenvironment, methylation status, and sensitivity to targeted therapies between these groups to elucidate the impact of varying bacterial infiltration levels on glioma behavior.
A total of 32 common differentially expressed genes were identified between the TCGA-LGG samples and the clinical samples when comparing the high-risk and low-risk groups. The high-risk group demonstrated elevated bacterial infiltration levels, which were associated with increased infiltration of inflammatory factors. Patients in this group exhibited shorter survival periods, potentially attributable to the heightened expression of negative immune checkpoints. Predictive analysis for targeted drugs indicated that certain agents might achieve a lower half maximal inhibitory concentration (IC50) in the low-risk group compared to the high-risk group. Furthermore, while no significant differences were observed in tumor mutation burden or copy number variation between the two groups, the high-risk group showed increased methylation levels across multiple pathways.
These findings offer new insights into the biological characteristics of gliomas and provide novel avenues for exploring new therapeutic approaches, bringing fresh perspectives to the field of intra-tumoral bacteria.
低级别胶质瘤（LGGs）表现出多样的细菌浸润。本研究深入探讨胶质瘤样本中的微生物浸润与肿瘤多组学特征之间的复杂关系，旨在阐明其对肿瘤行为和患者预后的影响。
我们纳入来自癌症基因组图谱（TCGA）的低级别胶质瘤（LGG）样本作为分析队列，并使用接受手术治疗患者的LGG肿瘤样本作为验证队列。对于TCGA样本，本研究利用先进的机器学习算法，识别LGG群体中不同的细菌浸润模式，并构建了一个与预后相关的肿瘤内细菌风险模型（PRIBR指数）。对于临床来源的样本，我们进行了16S rRNA测序、全转录组测序和蛋白质组学分析。随后，将样本分为高风险组和低风险组。然后，我们探讨了这些组之间的临床信息、肿瘤微环境、甲基化状态以及对靶向治疗的敏感性，以阐明不同细菌浸润水平对胶质瘤行为的影响。
在比较高风险组和低风险组时，TCGA-LGG样本与临床样本之间共鉴定出32个常见的差异表达基因。高风险组显示细菌浸润水平升高，这与炎症因子浸润增加有关。该组患者生存期较短，可能归因于负性免疫检查点的表达增加。靶向药物的预测分析表明，与高风险组相比，某些药物在低风险组中可能达到更低的半数最大抑制浓度（IC50）。此外，虽然两组之间在肿瘤突变负荷或拷贝数变异方面未观察到显著差异，但高风险组在多个通路中显示出甲基化水平增加。
这些发现为胶质瘤的生物学特征提供了新的见解，并为探索新的治疗方法提供了新途径，为肿瘤内细菌领域带来了新的视角。
