据推测，肿瘤组织微生物组是促进或抑制肿瘤获得某些癌症特征的能力的有利特征之一。这强调了它在致癌、癌症进展和治疗反应中的至关重要性。然而，表征肿瘤微生物组极具挑战性，因为它们的生物量低，并且控制实验室传播的污染物非常困难，而由于缺乏全面有效的计算方法来识别与癌症相关的独特或丰富的微生物种类，这进一步加剧了这种情况。在这里，我们利用 Ma (2024) 最近的计算框架，称为宏基因组比较 (MC) 框架 (MCF)，它可以检测治疗特异性、独特或丰富的 OMU（操作宏基因组单元）或 US/ES（独特/丰富的物种）当适应本研究时。我们应用 MCF 重新分析了四个肺癌组织微生物组数据集，其中包括来自肺腺癌 (LUAD)、肺鳞状细胞癌 (LUSC) 及其邻近正常组织 (NT) 对照的样本。我们的分析围绕三个不同的方案进行： 方案 I - 分别检测四个肺癌微生物组数据集中每一个的 US/ES；方案II-四个数据集的合并，然后在合并的数据集中检测US/ES；方案三——根据前两个方案的结果构造US/ES的并集和交集。生成的 US/ES 列表（包括丰富的微生物门）可能对开发肺癌风险评估的诊断和预后生物标志物、提高免疫疗法的疗效以及在肺癌研究中设计基于微生物组的新型疗法具有重要的生物医学价值。版权所有 © 2024 年。由爱思唯尔有限公司出版。

It is postulated that the tumor tissue microbiome is one of the enabling characteristics that can either promote or suppress the ability of tumors to acquire certain hallmarks of cancer. This underscores its critical importance in carcinogenesis, cancer progression, and therapy responses. However, characterizing the tumor microbiomes is extremely challenging because of their low biomass and severe difficulties in controlling laboratory-borne contaminants, which is further aggravated by lack of comprehensively effective computational approaches to identify unique or enriched microbial species associated with cancers. Here we take advantage of a recent computational framework by Ma (2024), termed metagenome comparison (MC) framework (MCF), which can detect treatment-specific, unique or enriched OMUs (operational metagenomic unit), or US/ES (unique/enriched species) when adapted for this study. We apply the MCF to reanalyze four lung cancer tissue microbiome datasets, which include samples from Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), and their adjacent normal tissue (NT) controls. Our analysis is structured around three distinct schemes: Scheme I-separately detecting the US/ES for each of the four lung cancer microbiome datasets; Scheme II-consolidation of the four datasets followed by detection of US/ES in the combined datasets; Scheme III-construction of the union and intersection sets of US/ES derived from the results of the preceding two schemes. The generated lists of US/ES, including enriched microbial phyla, likely hold significant biomedical value for developing diagnostic and prognostic biomarkers for lung cancer risk assessment, improving the efficacy of immunotherapy, and designing novel microbiome-based therapies in lung cancer research.Copyright © 2024. Published by Elsevier Ltd.