PO.BCS01.04 · 生物信息与计算
波多黎各头颈癌患者的部位特异性口腔微生物组模式
Site-specific oral microbiome patterns in Puerto Rican head and neck cancer patients
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：头颈癌(HNC)是全球第七大常见癌症，也是波多黎各第六大常见癌症。它还是波多黎各男性癌症死亡的第八大原因。了解该人群中HNC的发生和预后至关重要。口腔微生物组在HNC中发挥重要作用，此前的研究表明微生物失调可影响诊断和治疗。然而，关于波多黎各HNC患者的口腔微生物组模式及其在临床因素影响下的稳定性，人们知之甚少。因此，本研究的目的是确定波多黎各患者中与HNC相关的细菌群落。
方法：从79例经组织学确诊的波多黎各患者HNC肿瘤样本中提取基因组DNA，随后进行HPV基因分型，并采用16S rRNA基因扩增子测序进行微生物组分析。数据存入Qiita/Deblur进行质量控制和生物信息学分析。下游分析（包括α和β多样性、分类学表征和生物标志物分析）使用QIIME2、MicrobiomeAnalyst和随机森林进行。
结果：尽管不同解剖部位之间的β多样性相似，但在喉部和口咽部之间观察到显著差异(P = 0.041，FDR = 0.355)。与口咽、下咽和口腔相比，喉部始终表现出较低的丰富度和Shannon多样性。此外，随机森林模型对所分析的全部六个元数据变量（解剖位置、手术、HPV状态、放疗和化疗）均表现出较弱的判别能力(MDA < 0.05)。尽管来自多个门（包括Bacillota_A、Deinococcota、Pseudomonadota和Bacteroidota）以及多个属（如COE1、Duncaniella、Escherichia、Paramuribaculum和Aggregatibacter）都有贡献，但仍观察到这一现象。
结论：波多黎各HNC患者的口腔微生物组似乎具有很强的韧性，丰富度和多样性的显著变异主要与喉部解剖部位相关。为提高对总生存、癌症特异性生存和无病生存的预测准确性，未来的研究将系统地将微生物组组成与HNC的临床特征相关联。这将通过采用一系列基于生存的机器学习方法来实现，包括惩罚Cox回归(Elastic Net)、随机生存森林和梯度提升生存模型(XGBoost-Cox)。这项工作有望释放微生物组作为改善患者预后的有力工具的潜力。
查看英文原文 English abstract
Introduction: Head and neck cancers (HNCs) are the seventh most common cancer globally and the sixth most prevalent in Puerto Rico. They are also the eighth leading cause of cancer deaths among Puerto Rican men. Understanding HNC development and prognosis in this population is vital. The oral microbiome plays a significant role in HNC, as previous studies have shown that microbial dysbiosis can affect diagnosis and treatment. However, little is known about the oral microbiome patterns in Puerto Rican HNC patients and their stability in response to clinical factors. Therefore, the objective of this study is to determine the bacterial communities associated with HNC in Puerto Rican patients.
Methods: Genomic DNA was extracted from 79 histologically confirmed HNC tumor samples from Puerto Rican patients, followed by HPV genotyping and microbiome analyses using 16S rRNA gene amplicon sequencing. Data were deposited in Qiita/Deblur for quality control and bioinformatics analyses. Downstream analyses, including alpha and beta diversity, taxonomic characterization, and biomarker analyses, were conducted using QIIME2, MicrobiomeAnalyst, and Random Forest.
Results: Although beta diversity was similar across different anatomic sites, a significant difference was observed between the larynx and the oropharynx (P = 0.041, FDR = 0.355). The larynx consistently demonstrated lower richness and Shannon diversity compared to the oropharynx, hypopharynx, and oral cavity. Furthermore, Random Forest models showed weak discriminatory power (MDA < 0.05) for all six metadata variables analyzed: anatomic location, surgery, HPV status, radiotherapy, and chemotherapy. This was observed despite the contributions from various phyla, including Bacillota_A , Deinococcota, Pseudomonadota , and Bacteroidota , as well as genera such as COE1, Duncaniella, Escherichia, Paramuribaculum , and Aggregatibacter .
Conclusions: The oral microbiome of Puerto Rican HNC patients appears highly resilient, with significant variations in richness and diversity primarily linked to the laryngeal anatomical site. To enhance predictive accuracy for overall, cancer-specific, and disease-free survival, future studies will systematically correlate the microbiome's composition with clinical HNC characteristics. This will be accomplished by employing an array of survival-based machine learning methodologies, including penalized Cox regression (Elastic Net), Random Survival Forests, and Gradient Boosted Survival models (XGBoost-Cox). This work promises to unlock the microbiome's potential as a powerful tool for improving patient prognoses.
利益披露 Disclosure
G. N. García Quiñones, None..
J. Suárez Pérez, None..
O. Castro Ortíz, None..
M. Sánchez Vázquez, None..
J. Hernández Agosto, None..
F. Godoy Vitorino, None..
M. Martínez Ferrer, None.