The biodiversity Composition of Microbiome in Ovarian Carcinoma Patients

Bo Zhou, Chaoyang Sun, Jia Huang, Meng Xia, Ensong Guo, Na Li, Hao Lu, Wanying Shan, Yifan Wu, Yuan Li, Xiaoyan Xu, Danhui Weng, Li Meng, Junbo Hu, Qinglei Gao, Ding Ma, Gang Chen, Bo Zhou, Chaoyang Sun, Jia Huang, Meng Xia, Ensong Guo, Na Li, Hao Lu, Wanying Shan, Yifan Wu, Yuan Li, Xiaoyan Xu, Danhui Weng, Li Meng, Junbo Hu, Qinglei Gao, Ding Ma, Gang Chen

Abstract

Ovarian carcinoma is caused by multiple factors, but its etiology associated with microbes and infection is unknown. Using 16S rRNA high-throughput sequencing methods, the diversity and composition of the microbiota from ovarian cancer tissues (25 samples) and normal distal fallopian tube tissues (25 samples) were analyzed. High-throughput sequencing showed that the diversity and richness indexes were significantly decreased in ovarian cancer tissues compared to tissues from normal distal fallopian tubes. The ratio of the two phyla for Proteobacteria/Firmicutes was notably increased in ovarian cancer, which revealed that microbial composition change might be associated with the process of ovarian cancer development. In addition, transcriptome-sequencing (RNA-seq) analyses suggested that the transcriptional profiles were statistically different between ovarian carcinoma and normal distal fallopian tubes. Moreover, a set of genes including 84 different inflammation-associated or immune-associated genes, which had been named as the human antibacterial-response genes were also modulated expression. Therefore, we hypothesize that the microbial composition change, as a novel risk factor, may be involving the initiation and progression of ovarian cancer via influencing and regulating the local immune microenvironment of fallopian tubes except for regular pathways.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Comparison of the structures of the tissues microbiota of the ovarian cancer (C group) and normal distal fallopian tube. (A) Number of OTUs was statistically calculated between C group and N group. There was not statistically different between the two groups. (B) Shannon and (C) Simpson indices were used to estimate the level of diversity of the microbiota of C group and N group. (D) Rarefaction curves were used to estimate the richness of C group and N group at a 97% similarity level. The vertical axis shows the number of OTUs that were expected to be found after sampling the number of tags or sequences shown on the horizontal axis. (E) The Rank-Abundance Curve based on the results of OTUs analysis between C group and N group. N group exhibited the relative more abundant species than in C group. The horizontal axis presents the OUT rank, the vertical axis presents the relative abundance of the OUT rank. (F) Venn diagrams demonstrating 97% OUT cluster overlap identified in the tissues microbiota of C group and N group. (G) The differences in microbial communities among the C group, N group and environmental negative control (EN) as demonstrated by a principal co-ordinates analysis (3D-PCoA) plot based on the unweighted UniFrac metric. Data were shown as the mean and SEM.
Figure 2
Figure 2
LEfSe was performed to identify the most differentially abundant taxons between ovarian cancer tissues and normal distal fallopian tube tissues. (B)Taxonomic representation of statistically and biologically consistent differences between ovarian cancers and normal tissues displayed by a cladogram. (Red) ovarian cancer-enriched taxa; (Green) normal tissues-enriched taxa. The brightness of each dot is proportional to its effect size. Cladogram was calculated by LEfSe, a metagenome analysis approach which performs the liner discriminant analysis following the Wilcoxon sum-rank test to assess effect size of each differentially abundant taxon or OUT. (A,C) Histogram of the LDA scores for differentially abundant phyla and genera, respectively. Only taxa meeting an LDA significant threshold of 3.5 are shown. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 3
Figure 3
RT-qPCR was used to validate the microbial biomarker candidates in an independent samples and ROC curves constructions. (A) Proteobacteria was found significantly enriched in ovarian cancer tissues (p < 0.05) and the AUC was 0.671; (B) Firmicutes was found significantly decreased in ovarian cancer tissues (p < 0.05) and the AUC was 0.656; (C) Acinetobacter was found significantly increased in ovarian cancer tissues (p < 0.05) and the AUC was 0.691; (D) Lactococcus was found significantly decreased in ovarian cancer tissues (P < 0.05) and the AUC was 0.633; (E) Acinetobacter_lwoffii was found significantly increased in ovarian cancer tissues (p < 0.05) and the AUC was 0.608; (F) Lactococcus_piscium was found significantly decreased in ovarian cancer tissues (P < 0.05) and the AUC was 0.808.
Figure 4
Figure 4
Functional analyses in six ovarian cancer tissues and seven normal distal fallopian tube tissues. (A) 2,625 related genes were up-regulated and 2,141 related genes were down-regulated in ovarian cancer tissues compared to normal distal fallopian tube tissues. DEG was shown via MA plot scattergram. (B) Enriched KEGG categories in ovarian cancer tissues at a false discovery rate of 5%. X axis represents Rich Factor, Y axis represents Pathway Name, Color represents Q value, the Dot represents the number of DEG (differential gene expression).

References

    1. Jemal A, et al. Cancer statistics, 2007. CA Cancer J Clin. 2007;57:43–66. doi: 10.3322/canjclin.57.1.43.
    1. Shih Ie M, Kurman RJ. Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol. 2004;164:1511–8. doi: 10.1016/S0002-9440(10)63708-X.
    1. Kurman RJ, Visvanathan K, Roden R, Wu TC. & Shih Ie, M. Early detection and treatment of ovarian cancer: shifting from early stage to minimal volume of disease based on a new model of carcinogenesis. Am J Obstet Gynecol. 2008;198:351–6. doi: 10.1016/j.ajog.2008.01.005.
    1. Kurman RJ, Shih IM. Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol Pathol. 2008;27:151–60.
    1. Vang R, Shih IM, Kurman RJ. Ovarian low-grade and high-grade serous carcinoma: pathogenesis, clinicopathologic and molecular biologic features, and diagnostic problems. Adv Anat Pathol. 2009;16:267–82. doi: 10.1097/PAP.0b013e3181b4fffa.
    1. Cho KR, Shih IM. Ovarian cancer. Annu Rev Pathol. 2009;4:287–313. doi: 10.1146/annurev.pathol.4.110807.092246.
    1. Bast RC, Jr, Hennessy B, Mills GB. The biology of ovarian cancer: new opportunities for translation. Nat Rev Cancer. 2009;9:415–28. doi: 10.1038/nrc2644.
    1. Crum CP, et al. The distal fallopian tube: a new model for pelvic serous carcinogenesis. Curr Opin Obstet Gynecol. 2007;19:3–9. doi: 10.1097/GCO.0b013e328011a21f.
    1. Kim J, et al. High-grade serous ovarian cancer arises from fallopian tube in a mouse model. Proc Natl Acad Sci USA. 2012;109:3921–6. doi: 10.1073/pnas.1117135109.
    1. Crum CP, et al. Lessons from BRCA: the tubal fimbria emerges as an origin for pelvic serous cancer. Clin Med Res. 2007;5:35–44. doi: 10.3121/cmr.2007.702.
    1. Jarboe EA, et al. Tubal and ovarian pathways to pelvic epithelial cancer: a pathological perspective. Histopathology. 2008;53:127–38. doi: 10.1111/j.1365-2559.2007.02938.x.
    1. Lee Y, et al. A candidate precursor to serous carcinoma that originates in the distal fallopian tube. J Pathol. 2007;211:26–35. doi: 10.1002/path.2091.
    1. Uemura N, et al. Helicobacter pylori infection and the development of gastric cancer. N Engl J Med. 2001;345:784–9. doi: 10.1056/NEJMoa001999.
    1. Clifford GM, Smith JS, Plummer M, Munoz N, Franceschi S. Human papillomavirus types in invasive cervical cancer worldwide: a meta-analysis. Br J Cancer. 2003;88:63–73. doi: 10.1038/sj.bjc.6600688.
    1. Zampino R, et al. Hepatocellular carcinoma in chronic HBV-HCV co-infection is correlated to fibrosis and disease duration. Ann Hepatol. 2015;14:75–82.
    1. Castellarin M, et al. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res. 2012;22:299–306. doi: 10.1101/gr.126516.111.
    1. Kostic AD, et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 2012;22:292–8. doi: 10.1101/gr.126573.111.
    1. Xuan C, et al. Microbial dysbiosis is associated with human breast cancer. PLoS One. 2014;9:e83744. doi: 10.1371/journal.pone.0083744.
    1. Urbaniak C, et al. Microbiota of human breast tissue. Appl Environ Microbiol. 2014;80:3007–14. doi: 10.1128/AEM.00242-14.
    1. Lin HW, et al. Risk of ovarian cancer in women with pelvic inflammatory disease: a population-based study. Lancet Oncol. 2011;12:900–4. doi: 10.1016/S1470-2045(11)70165-6.
    1. Idahl A, et al. Chlamydia trachomatis and Mycoplasma genitalium plasma antibodies in relation to epithelial ovarian tumors. Infect Dis Obstet Gynecol. 2011;2011:824627. doi: 10.1155/2011/824627.
    1. Carvalho JP, Carvalho FM. Is Chlamydia-infected tubal fimbria the origin of ovarian cancer? Med Hypotheses. 2008;71:690–3. doi: 10.1016/j.mehy.2008.06.028.
    1. Vaughan S, et al. Rethinking ovarian cancer: recommendations for improving outcomes. Nat Rev Cancer. 2011;11:719–25. doi: 10.1038/nrc3144.
    1. Plottel CS, Blaser MJ. Microbiome and malignancy. Cell Host Microbe. 2011;10:324–35. doi: 10.1016/j.chom.2011.10.003.
    1. Hummelen R, et al. Deep sequencing of the vaginal microbiota of women with HIV. PLoS One. 2010;5:e12078. doi: 10.1371/journal.pone.0012078.
    1. Dewhirst FE, et al. The human oral microbiome. J Bacteriol. 2010;192:5002–17. doi: 10.1128/JB.00542-10.
    1. Grice EA, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324:1190–2. doi: 10.1126/science.1171700.
    1. Wolfe AJ, et al. Evidence of uncultivated bacteria in the adult female bladder. J Clin Microbiol. 2012;50:1376–83. doi: 10.1128/JCM.05852-11.
    1. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature486, 207–14 (2012).
    1. Costello EK, et al. Bacterial community variation in human body habitats across space and time. Science. 2009;326:1694–7. doi: 10.1126/science.1177486.
    1. Beeckman DS, Vanrompay DC. Bacterial secretion systems with an emphasis on the chlamydial Type III secretion system. Curr Issues Mol Biol. 2010;12:17–41.
    1. Brown NF, Finlay BB. Potential origins and horizontal transfer of type III secretion systems and effectors. Mob Genet Elements. 2011;1:118–121. doi: 10.4161/mge.1.2.16733.
    1. Xenoulis PG, et al. Molecular-phylogenetic characterization of microbial communities imbalances in the small intestine of dogs with inflammatory bowel disease. FEMS Microbiol Ecol. 2008;66:579–89. doi: 10.1111/j.1574-6941.2008.00556.x.
    1. El Aidy S, et al. Gut bacteria-host metabolic interplay during conventionalisation of the mouse germfree colon. Isme j. 2013;7:743–55. doi: 10.1038/ismej.2012.142.
    1. Costello EK, Gordon JI, Secor SM, Knight R. Postprandial remodeling of the gut microbiota in Burmese pythons. Isme j. 2010;4:1375–85. doi: 10.1038/ismej.2010.71.
    1. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444:1022–3. doi: 10.1038/4441022a.
    1. Turnbaugh PJ, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31. doi: 10.1038/nature05414.
    1. Sengupta S, Muir JG, Gibson PR. Does butyrate protect from colorectal cancer? J Gastroenterol Hepatol. 2006;21:209–18. doi: 10.1111/j.1440-1746.2006.04213.x.
    1. Duncan SH, et al. Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol. 2007;73:1073–8. doi: 10.1128/AEM.02340-06.
    1. Scharlau D, et al. Mechanisms of primary cancer prevention by butyrate and other products formed during gut flora-mediated fermentation of dietary fibre. Mutat Res. 2009;682:39–53. doi: 10.1016/j.mrrev.2009.04.001.
    1. Polk DB, Peek RM., Jr. Helicobacter pylori: gastric cancer and beyond. Nat Rev Cancer. 2010;10:403–14. doi: 10.1038/nrc2857.
    1. Kostic AD, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 2013;14:207–15. doi: 10.1016/j.chom.2013.07.007.
    1. Shanmughapriya S, et al. Viral and bacterial aetiologies of epithelial ovarian cancer. Eur J Clin Microbiol Infect Dis. 2012;31:2311–7. doi: 10.1007/s10096-012-1570-5.
    1. Ku SC, Hsueh PR, Yang PC, Luh KT. Clinical and microbiological characteristics of bacteremia caused by Acinetobacter lwoffii. Eur J Clin Microbiol Infect Dis. 2000;19:501–5. doi: 10.1007/s100960000315.
    1. Beebe JL, Koneman EW. Recovery of uncommon bacteria from blood: association with neoplastic disease. Clin Microbiol Rev. 1995;8:336–56. doi: 10.1128/CMR.8.3.336.
    1. Chen Y, et al. Identification of host-immune response protein candidates in the sera of human oral squamous cell carcinoma patients. PLoS One. 2014;9:e109012. doi: 10.1371/journal.pone.0109012.
    1. Saraoui T, et al. Inhibition mechanism of Listeria monocytogenes by a bioprotective bacteria Lactococcus piscium CNCM I-4031. Food Microbiol. 2016;53:70–8. doi: 10.1016/j.fm.2015.01.002.
    1. Rutkowski MR, et al. Microbially driven TLR5-dependent signaling governs distal malignant progression through tumor-promoting inflammation. Cancer Cell. 2015;27:27–40. doi: 10.1016/j.ccell.2014.11.009.
    1. Round JL, et al. The Toll-like receptor 2 pathway establishes colonization by a commensal of the human microbiota. Science. 2011;332:974–7. doi: 10.1126/science.1206095.
    1. Li B, Yang R. Interaction between Yersinia pestis and the host immune system. Infect Immun. 2008;76:1804–11. doi: 10.1128/IAI.01517-07.
    1. Zhang SS, et al. Plasminogen activator Pla of Yersinia pestis utilizes murine DEC-205 (CD205) as a receptor to promote dissemination. J Biol Chem. 2008;283:31511–21. doi: 10.1074/jbc.M804646200.
    1. Banerjee S, et al. The ovarian cancer oncobiome. Oncotarget. 2017;8:36225–36245.
    1. Miles SM, Hardy BL, Merrell DS. Investigation of the microbiota of the reproductive tract in women undergoing a total hysterectomy and bilateral salpingo-oopherectomy. Fertil Steril. 2017;107:813–820.e1. doi: 10.1016/j.fertnstert.2016.11.028.
    1. Youssef N, et al. Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl Environ Microbiol. 2009;75:5227–36. doi: 10.1128/AEM.00592-09.
    1. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20. doi: 10.1128/AEM.01043-13.
    1. Wu X, et al. Comparison of the fecal microbiota of dholes high-throughput Illumina sequencing of the V3-V4 region of the 16S rRNA gene. Appl Microbiol Biotechnol. 2016;100:3577–86. doi: 10.1007/s00253-015-7257-y.
    1. Schloss PD, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41. doi: 10.1128/AEM.01541-09.
    1. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7. doi: 10.1128/AEM.00062-07.
    1. Segata N, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa