Differences in the gut microbiota among patients with benign and malignant pulmonary nodules and healthy individuals

doi:10.3969/j.issn.1004-583X.2026.09.003

Abstract

Abstract:

Objective To preliminarily investigate differences in the gut microbiota and metabolic pathways among patients with malignant pulmonary nodules, benign pulmonary nodules, and healthy individuals. Methods This study was designed as a small-sample, exploratory investigation. A total of 31 patients with pulmonary nodules who visited Shanghai Tongji Hospital from August to December 2024 were enrolled and divided into a malignant pulmonary nodule group (n=23) and a benign pulmonary nodule group (n=8) according to nodule pathology. In addition, 14 healthy individuals were included as the healthy control group. 16S ribosomal RNA sequencing was used to analyze microbiota diversity, species abundance, community structure, and functional metabolic differences among the three groups. Results Alpha diversity analysis showed that, in terms of species richness, the abundance-based coverage estimator index differed significantly between the malignant and benign pulmonary nodule groups, as well as between the benign pulmonary nodule and healthy control groups (P<0.05). In terms of community evenness and diversity, both the malignant and benign pulmonary nodule groups had lower Shannon and Simpson indices than the healthy control group (P<0.05). Beta diversity analysis showed significant overall differences in community macrostructure among the three groups (P<0.05). Specifically, the malignant pulmonary nodule group demonstrated an independent clustering pattern deviating from healthy homeostasis, whereas the community structure of the benign pulmonary nodule group partially overlapped with and converged toward that of the healthy control group. Linear cliscriminant analysis effect size analysis showed that biomarkers across the three groups exhibited marked pathological cascade differentiation (LDA>3.0, P<0.05). The malignant pulmonary nodule group showed specific enrichment of opportunistic pathogenic bacteria, including the phylum Proteobacteria and its downstream taxaGammaproteobacteria, Enterobacteriaceae, and Escherichia-Shigella. In contrast, the benign pulmonary nodule group exhibited a probiotic-defense pattern characterized by enrichment of Actinobacteriota and Bifidobacterium. Spearman correlation network analysis indicated that microbial dysbiosis was not an isolated event, but was driven by strong antagonistic competition between opportunistic pathogens and core beneficial symbionts. Pairwise Kyoto encyclopedia of genes and genomes functional prediction revealed significant differences in multiple specific metabolic pathways among the three groups (P<0.05). Conclusion The gut microbiota composition differs significantly between patients with malignant pulmonary nodules and healthy individuals. Distinct metabolic pathways were also identified among the malignant pulmonary nodule, benign pulmonary nodule, and healthy control groups. These findings may provide novel biomarkers and strategies for the diagnosis and treatment of lung cancer.

Key words: lung neoplasms, benign pulmonary nodules, healthy controls, gut microbiota, 16S rRNA

CLC Number:

R734.2

Xie Shiliang, Wang Xinyu, Zhao Long, Sun Ruixin. Differences in the gut microbiota among patients with benign and malignant pulmonary nodules and healthy individuals[J]. Clinical Focus, 2026, 41(9): 794-806.

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URL: https://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2026.09.003

https://www.lchc.cn/EN/Y2026/V41/I9/794

Figures/Tables 13

Tab.1 Baseline characteristics of the three groups and clinical stratification of the malignant pulmonary nodule group

项目	恶性肺结节组 (n=23)	良性肺结节组 (n=8)	健康人群组 (n=14)	χ²/H值	P值
年龄(岁)	68.0(58.0, 74.0)^*	59.5(55.0, 65.5)	50.5(42.0, 61.8)	9.615	0.008
男性[例(%)]	13(56.5)	4(50.0)	7(50.0)	0.192	0.908
体质量指数(kg/m²)	24.02(21.18, 25.72)	24.87(21.05, 26.95)	23.84(23.37, 26.51)	1.004	0.606
吸烟史[例(%)]	5(21.7)	2(25.0)	4(28.6)	0.222	0.895
饮酒史[例(%)]	2(8.7)	1(12.5)	2(14.3)	0.294	0.863
合并基础疾病[例(%)]	9(39.1)	1(12.5)	3(21.4)	2.600	0.273
结节最大径[例(%)]
≤1.0 cm	11(47.8)	-	-	-	-
>1.0 cm	12(52.2)	-	-	-	-
分化程度[例(%)]
高分化	12(52.2)	-	-	-	-
中分化	7(30.4)	-	-	-	-
低分化	4(17.4)	-	-	-	-
有无淋巴结转移[例(%)]
有	0	-	-	-	-
无	23(100.0)	-	-	-	-

Tab.2 Comparison of serum biomarker levels between patients with adenocarcinoma and non-adenocarcinoma

病理分型	例数	CEA(μg/L)	NSE(μg/L)	CYFRA21-1(μg/L)	SCCA(μg/L)
腺癌	18	2.885(2.145, 4.660)	12.000(11.200, 16.650)	2.280(1.768, 2.700)	1.105(0.843, 1.458)
非腺癌	5	2.410(0.970, 2.740)	11.000(9.130, 13.100)	3.370(2.880, 4.170)	2.110(0.820, 3.070)
Z值		-1.342	-0.895	-1.715	-1.006
P值		0.180	0.371	0.086	0.314

Tab.3 Comparison of serum biomarker levels among patients with lung cancer at different pathological stages

病理分期	例数	CEA(μg/L)	NSE(μg/L)	CYFRA21-1(μg/L)	SCCA(μg/L)
Ⅰ期	19	2.890(2.160, 4.620)	11.600(10.800, 16.100)	2.310(1.835, 2.765)	1.080(0.805, 1.535)
Ⅱ期	4	1.745(1.052, 2.493)	12.050(10.430, 16.950)	3.770(2.765, 4.452)	1.620(1.052, 2.430)
Z值		-1.886	-1.785	-1.460	-0.284
P值		0.062	0.074	0.144	0.776

Fig.1 Alpha diversity indices of the three groups a. ACE index; b. Shannon index; c. Simpson index.Note: The x-axis represents the group, and the y-axis represents the corresponding diversity index value. The black lines above the plots indicate a statistically significant difference between the two groups (P<0.05), whereas black lines labeled NS indicate no statistically significant difference between the two groups (P>0.05)

Fig.2 PCoA analysis of the three groups a. PCoA based on unweighted UniFrac distance; b. PCoA based on weighted UniFrac distance.Note: The x-axis represents the first principal component, and the y-axis represents the second principal component. The percentages indicate the contribution of the corresponding principal components to sample variation. Each dot represents a sample; different colors represent different groups; and the ellipse indicates the 95% confidence region of each group

Fig.3 LEfSe analysis of the malignant pulmonary nodule group and the healthy control group a. LDA score histogram (with |LDA score| >3 as the threshold); the x-axis indicates the LDA scores of each taxon, and the y-axis indicates the taxa with statistically significant intergroup differences; longer bars indicate more significant differences; b. Evolutionary cladogram, with the inner-to-outer rings representing taxa from phylum to genus; the diameter of each circle at different taxonomic levels is proportional to its relative abundance

Fig.4 LEfSe analysis of the benign pulmonary nodule group and the healthy control group: a. LDA score histogram; b. Evolutionary cladogram

Fig.5 LEfSe analysis of the malignant pulmonary nodule group and the benign pulmonary nodule group a. LDA score histogram; b. Evolutionary cladogram

Fig.6 Correlation heatmap at the genus level Note: The x-axis and y-axis list the genus names of various bacteria, and the color bar on the right indicates values ranging from -1 to 1, representing the correlation between two genera. Values closer to -1 indicate a stronger negative correlation, whereas values closer to 1 indicate a stronger positive correlation. ***: P<0.01

Fig.7 Differential analysis of KEGG pathways between the malignant pulmonary nodule group and the healthy control group Note: The x-axis represents the relative abundance percentage of metabolic pathways, and the y-axis represents the pathway names. The figure shows differences under the 95% confidence interval; the farther from the center line, the greater the difference

Fig.8 Differential KEGG pathways between the malignant pulmonary nodule group and the benign pulmonary nodule group

Fig.9 Differential KEGG pathways between the benign pulmonary nodule group and the healthy control group

Fig.10 Clinical information heatmap Note: The x-axis represents bacterial genera, and the y-axis represents related clinical information, including core clinical variables such as body measurements, disease stage, clinical behavior, tumor morphology, and age. A correlation coefficient closer to 1 (higher red saturation) indicates a stronger positive correlation, whereas a correlation coefficient closer to -1 (higher blue saturation) indicates a stronger negative correlation. *:P<0.05, **: P<0.01

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