Specific gut microbiome signature predicts the early-stage lung cancer

ABSTRACT

Alterations of gut microbiota have been implicated in multiple diseases including cancer. However, the gut microbiota spectrum in lung cancer remains largely unknown. Here we profiled the gut microbiota composition in a discovery cohort containing 42 early-stage lung cancer patients and 65 healthy individuals through the 16S ribosomal RNA (rRNA) gene sequencing analysis. We found that lung cancer patients displayed a significant shift of microbiota composition in contrast to the healthy populations. To identify the optimal microbiota signature for noninvasive diagnosis purpose, we took advantage of Support-Vector Machine (SVM) and found that the predictive model with 13 operational taxonomic unit (OTU)-based biomarkers achieved a high accuracy in lung cancer prediction (area under curve, AUC = 97.6%). This signature performed reasonably well in the validation cohort (AUC = 76.4%), which contained 34 lung cancer patients and 40 healthy individuals. To facilitate potential clinical practice, we further constructed a ‘patient discrimination index’ (PDI), which largely retained the prediction efficiency in both the discovery cohort (AUC = 92.4%) and the validation cohort (AUC = 67.7%). Together, our study uncovered the microbiota spectrum of lung cancer patients and established the specific gut microbial signature for the potential prediction of the early-stage lung cancer.

KEYWORDS:

• Gut microbiota
• early-stage lung cancer
• biomarkers
• noninvasive diagnosis
• patient discrimination index

Acknowledgement

The authors gratefully acknowledge the support of SA-SIBS scholarship program.

Author Contributions

HBJ and JFC designed experiments and supervised the study. YJZ, ZYF, YX and JZ performed experiments and analyzed data. PZ, JJZ and GNJ collected patient fecal and provided the clinical information. RYG and HLQ provided the fecal from healthy control. ZYF preformed the bioinformatics analyses. SY, YY, SHW, CDL, SYC, HYH, and LH provided helpful comments. HBJ and JFC interpreted the results. HBJ, JFC, ZYF, and YJZ wrote the manuscript. All authors read and approved the final manuscript.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This work was supported by the National Basic Research Program of China (Grant 2017YFA0505500); Strategic Priority Research Program of the Chinese Academy of Sciences (Grants. XDB19020000, XDA12010101); National Natural Science Foundation of China (Nos. 31525016, 31930022, 31771476, 31830112, 81430066, 91731314, 31621003, 31601129, 81802279, 81602459, 81972172, 81790253, 81872312, 81871875, 81801868); Program of Shanghai Academic Research Leader (19XD1404200); China Postdoctoral Science Foundation Grant (2015M581673); National Ten Thousand Talents Program and Chinese Academy of Science Taiwan Young Scholar Visiting Program (2015TW1SB0001).