利用肠道细菌组作为生物标志物优化粪便隐血试验 (FOBT) 大肠癌筛查

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Moumita Roy Chowdhury , Karina Gisèle Mac Si Hone , Karine Prévost , Philippe Balthazar , Mariano Avino , Mélina Arguin , Jude Beaudoin , Mandy Malick , Michael Desgagné , Gabriel Robert , Michelle Scott , Jean Dubé , Isabelle Laforest-Lapointe , Eric Massé
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引用次数: 0

摘要

背景大肠癌(CRC)是全球癌症死亡的主要原因。免疫化学粪便潜血试验(iFOBT)是最广泛使用的 CRC 筛查方法之一,它能从患者的粪便样本中检测出人类血红蛋白。虽然它能高效地检测出息肉和癌症等胃肠道病变患者的血液,但 iFOBT 的假阳性发现率很高。最近的研究表明,肠道细菌是一种有希望改善 CRC 诊断的无创生物标志物。在本研究中,我们利用接受结肠镜检查和筛查试验的患者残留的 iFOBT 检查了肠道细菌的组成。第一组和第二组分别为 "健康 "组,即患者没有便血或有便血但没有病变。第三组和第四组患者既有便血,又有癌前病变和癌变,分别被视为低级病变组和高级病变组。对 16S rRNA(V4 区)基因进行扩增,然后进行测序以及各种统计和生物信息分析。虽然与健康患者相比,3 组患者中的固着菌门成员有所增加,但放线菌门却有所减少。此外,与高级别病变患者相比,健康组中固醇菌门的 Blautia obeum 和 Anaerostipes hadrus 增加了,而放线菌门的 Collinsella aerofaciens 减少了。最后,在用于进行分析的 5 种机器学习算法中,弹性网(AUC > 0.7)和随机森林(AUC > 0.8)在区分健康患者和其他 3 个便血患者群体方面表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Fecal Occult Blood Test (FOBT) Colorectal Cancer Screening Using Gut Bacteriome as a Biomarker

Background

Colorectal cancer (CRC) is a major cause of cancer mortality in the world. One of the most widely used screening tests for CRC is the immunochemical fecal occult blood test (iFOBT), which detects human hemoglobin from patient's stool sample. Although it is highly efficient in detecting blood from patients with gastro-intestinal lesions, such as polyps and cancers, the iFOBT has a high rate of false positive discovery. Recent studies suggested gut bacteria as a promising noninvasive biomarker for improving the diagnosis of CRC. In this study, we examined the composition of gut bacteria using iFOBT leftover from patients undergoing screening test along with a colonoscopy.

Methods

After collecting data from more than 800 patients, we considered 4 groups for this study. The first and second groups were respectively “healthy” in which the patients had either no blood in their stool or had blood but no lesions. The third and fourth groups of patients had both blood in their stools with precancerous and cancerous lesions and considered either as low-grade and high-grade lesion groups, respectively. An amplification of 16S rRNA (V4 region) gene was performed, followed by sequencing along with various statistical and bioinformatic analysis.

Results

We analyzed the composition of the gut bacteriome at phylum, class, genus, and species levels. Although members of the Firmicute phylum increased in the 3 groups compared to healthy patients, the phylum Actinobacteriota was found to decrease. Moreover, Blautia obeum and Anaerostipes hadrus from the phylum Firmicutes were increased and Collinsella aerofaciens from phylum Actinobacteriota was found decreased when healthy group is compared to the patients with high-grade lesions. Finally, among the 5 machine learning algorithms used to perform our analysis, both elastic net (AUC > 0.7) and random forest (AUC > 0.8) performs well in differentiating healthy patients from 3 other patient groups having blood in their stool.

Conclusion

Our study integrates the iFOBT screening tool with gut bacterial composition to improve the prediction of CRC lesions.

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CiteScore
7.20
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