Intratumoral Bacteria Dysbiosis Is Associated with Human Papillary Thyroid Cancer and Correlated with Oncogenic Signaling Pathways

Shuang Yu, Yanqiang Ding, Xuejie Wang, Siu Kin Ng, Siting Cao, Weixin Liu, Zhuming Guo, Yubin Xie, Shubin Hong, Lixia Xu, Xiaoxing Li, Jie Li, Weiming Lv, Sui Peng, Yanbing Li, Joseph J.Y. Sung, Jun Yu, Haipeng Xiao

Engineering ›› 2023, Vol. 28 ›› Issue (9) : 179-192.

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Engineering ›› 2023, Vol. 28 ›› Issue (9) : 179-192. DOI: 10.1016/j.eng.2023.01.007
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Intratumoral Bacteria Dysbiosis Is Associated with Human Papillary Thyroid Cancer and Correlated with Oncogenic Signaling Pathways

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Abstract

Emerging evidence suggests that microbial dysbiosis plays vital roles in many human cancers. However, knowledge of whether the microbial community in thyroid tumor is related to tumorigenesis remains elusive. In this study, we aimed to explore the microbial community in thyroid tissues and its contribution to papillary thyroid cancer (PTC). In parallel, we performed microbial profiling and transcriptome sequencing in the tumor and adjacent normal tissues of a large cohort of 340 PTC and benign thyroid nodule (BTN) patients. Distinct microbial signatures were identified in PTC, BTN, and their adjacent non-tumor tissues. Intra-thyroid tissue bacteria were verified by means of bacteria staining, fluorescence in situ hybridization, and immunoelectron microscopy. We found that 17 bacterial taxa were differentially abundant in PTC compared with BTN, which included enrichment in PTC of the pathobionts Rhodococcus, Neisseria, Streptococcus, Halomonas, and Devosia, and depletion of the beneficial bacteria Amycolatopsis. These differentially abundant bacteria could differentiate PTC tumor tissues (PTC-T) from BTN tissues (BTN-T) with an area under the curve (AUC) of 81.66%. Microbial network analysis showed increased correlation strengths among the bacterial taxa in PTC-T in comparison with BTN-T. Immune-function-corresponding bacteria (i.e., Erwinia, Bacillus, and Acinetobacter) were found to be enriched in PTC with Hashimoto’s thyroiditis. Moreover, our integrative analysis revealed that the PTC-enriched bacteria had a positive association with key PTC-oncogenic pathway-related genes, including BRAF, KRAS, IRAK4, CTNNB1, PIK3CA, MAP3K7, and EGFR. In conclusion, our results suggest that intratumor bacteria dysbiosis is associated with the thyroid tumorigenesis and oncogenic signaling pathways of PTC.

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Papillary thyroid cancer / Benign thyroid nodule / Bacteria / Transcriptome / Hashimoto’s thyroiditis

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Shuang Yu, Yanqiang Ding, Xuejie Wang, Siu Kin Ng, Siting Cao, Weixin Liu, Zhuming Guo, Yubin Xie, Shubin Hong, Lixia Xu, Xiaoxing Li, Jie Li, Weiming Lv, Sui Peng, Yanbing Li, Joseph J.Y. Sung, Jun Yu, Haipeng Xiao. Intratumoral Bacteria Dysbiosis Is Associated with Human Papillary Thyroid Cancer and Correlated with Oncogenic Signaling Pathways. Engineering, 2023, 28(9): 179‒192 https://doi.org/10.1016/j.eng.2023.01.007

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 71 (3) (2021), pp. 209-249. DOI: 10.3322/caac.21660
[2]
M.E. Cabanillas, D.G. McFadden, C. Durante. Thyroid cancer. Lancet, 388 (10061) (2016), pp. 2783-2795
[3]
Y.E. Nikiforov, R.R. Seethala, G. Tallini, Z.W. Baloch, F. Basolo, L.D. Thompson, et al. Nomenclature revision for encapsulated follicular variant of papillary thyroid carcinoma: a paradigm shift to reduce overtreatment of indolent tumors. JAMA Oncol, 2 (8) (2016), pp. 1023-1029. DOI: 10.1001/jamaoncol.2016.0386
[4]
M. Xing. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer, 13 (3) (2013), pp. 184-199. DOI: 10.1038/nrc3431
[5]
L. Lamartina, G. Grani, C. Durante, S. Filetti, D.S. Cooper. Screening for differentiated thyroid cancer in selected populations. Lancet Diabetes Endocrinol, 8 (1) (2020), pp. 81-88
[6]
L.M. Caronia, J.E. Phay, M.H. Shah. Role of BRAF in thyroid oncogenesis. Clin Cancer Res, 17 (24) (2011), pp. 7511-7517
[7]
J. Feng, F. Zhao, J. Sun, B. Lin, L. Zhao, Y. Liu, et al. Alterations in the gut microbiota and metabolite profiles of thyroid carcinoma patients. Int J Cancer, 144 (11) (2019), pp. 2728-2745. DOI: 10.1002/ijc.32007
[8]
D. Nejman, I. Livyatan, G. Fuks, N. Gavert, Y. Zwang, L.T. Geller, et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science, 368 (6494) (2020), pp. 973-980. DOI: 10.1126/science.aay9189
[9]
C.D. Link. Is there a brain microbiome?. Neurosci Insights, 16 (2021) 26331055211018709
[10]
A. Gnanasekar, G. Castaneda, A. Iyangar, S. Magesh, D. Perez, J. Chakladar, et al. The intratumor microbiome predicts prognosis across gender and subtypes in papillary thyroid carcinoma. Comput Struct Biotechnol J, 19 (2021), pp. 1986-1997
[11]
D. Dai, Y. Yang, Y. Yang, T. Dang, J. Xiao, W. Wang, et al. Alterations of thyroid microbiota across different thyroid microhabitats in patients with thyroid carcinoma. J Transl Med, 19 (1) (2021), p. 488
[12]
C.J. Liu, S.Q. Chen, S.Y. Zhang, J.L. Wang, X.D. Tang, K.X. Yang, et al. The comparison of microbial communities in thyroid tissues from thyroid carcinoma patients. J Microbiol, 59 (11) (2021), pp. 988-1001. DOI: 10.1007/s12275-021-1271-9
[13]
L. Yuan, P. Yang, G. Wei, X. Hu, S. Chen, J. Lu, et al. Tumor microbiome diversity influences papillary thyroid cancer invasion. Commun Biol, 5 (1) (2022), p. 864
[14]
P. Caturegli, A. de Remigis, N.R. Rose. Hashimoto thyroiditis: clinical and diagnostic criteria. Autoimmun Rev, 13 (4-5) (2014), pp. 391-397
[15]
S. Andrews. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics, Babraham Institute, Cambridge (2010)
[16]
E. Bolyen, J.R. Rideout, M.R. Dillon, N.A. Bokulich, C.C. Abnet, G.A. Al-Ghalith, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol, 37 (8) (2019), pp. 852-857. DOI: 10.1038/s41587-019-0209-9
[17]
D. Risso, J. Ngai, T.P. Speed, S. Dudoit. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol, 32 (9) (2014), pp. 896-902. DOI: 10.1038/nbt.2931
[18]
M.I. Love, W. Huber, S. Anders. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15 (12) (2014), p. 550
[19]
J. Friedman, E.J. Alm. Inferring correlation networks from genomic survey data. PLOS Comput Biol, 8 (9) (2012), p. e1002687. DOI: 10.1371/journal.pcbi.1002687
[20]
P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13 (11) (2003), pp. 2498-2504. DOI: 10.1101/gr.1239303
[21]
G.M. Douglas, V.J. Maffei, J.R. Zaneveld, S.N. Yurgel, J.R. Brown, C.M. Taylor, et al. PICRUSt 2 for prediction of metagenome functions. Nat Biotechnol, 38 (6) (2020), pp. 685-688. DOI: 10.1038/s41587-020-0548-6
[22]
R. Caspi, R. Billington, I.M. Keseler, A. Kothari, M. Krummenacker, P.E. Midford, et al. The MetaCyc database of metabolic pathways and enzymes—a 2019 update. Nucleic Acids Res, 48 (D1) (2020), pp. D445-D453. DOI: 10.1093/nar/gkz862
[23]
A. Kechin, U. Boyarskikh, A. Kel, M. Filipenko. cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing. J Comput Biol, 24 (11) (2017), pp. 1138-1143. DOI: 10.1089/cmb.2017.0096
[24]
H. Li, R. Durbin. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25 (14) (2009), pp. 1754-1760. DOI: 10.1093/bioinformatics/btp324
[25]
Y. Liao, G.K. Smyth, W. Shi. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30 (7) (2014), pp. 923-930. DOI: 10.1093/bioinformatics/btt656
[26]
Z. Gu, R. Eils, M. Schlesner. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32 (18) (2016), pp. 2847-2849. DOI: 10.1093/bioinformatics/btw313
[27]
G. Yu, L.G. Wang, Y. Han, Q.Y. He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 16 (5) (2012), pp. 284-287. DOI: 10.1089/omi.2011.0118
[28]
W. Kühler. Bacterial cell wall. J.M. Ghuysen, R. Hakenbeck (Eds.), New comprehensive biochemistry, Elsevier Science, Amsterdam (1994)
[29]
C. Whitfield, M.S. Trent. Biosynthesis and export of bacterial lipopolysaccharides. Annu Rev Biochem, 83 (1) (2014), pp. 99-128. DOI: 10.1146/annurev-biochem-060713-035600
[30]
R.I. Amann, B.J. Binder, R.J. Olson, S.W. Chisholm, R. Devereux, D.A. Stahl. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol, 56 (6) (1990), pp. 1919-1925. DOI: 10.1128/aem.56.6.1919-1925.1990
[31]
G.H. Wu, H.J. Shi, M.T. Che, M.Y. Huang, Q.S. Wei, B. Feng, et al. Recovery of paralyzed limb motor function in canine with complete spinal cord injury following implantation of MSC-derived neural network tissue. Biomaterials, 181 (2018), pp. 15-34
[32]
N. Segata, J. Izard, L. Waldron, D. Gevers, L. Miropolsky, W.S. Garrett, et al. Metagenomic biomarker discovery and explanation. Genome Biol, 12 (6) (2011), p. R60
[33]
J.J. Farrell, L. Zhang, H. Zhou, D. Chia, D. Elashoff, D. Akin, et al. Variations of oral microbiota are associated with pancreatic diseases including pancreatic cancer. Gut, 61 (4) (2012), pp. 582-588. DOI: 10.1136/gutjnl-2011-300784
[34]
C.N. D’Alessandro-Gabazza, C. Méndez-García, O. Hataji, S. Westergaard, F. Watanabe, T. Yasuma, et al. Identification of halophilic microbes in lung fibrotic tissue by oligotyping. Front Microbiol, 9 (2018), p. 1892
[35]
O. Alexeyev, J. Bergh, I. Marklund, C. Thellenberg-Karlsson, F. Wiklund, H. Grönberg, et al. Association between the presence of bacterial 16S RNA in prostate specimens taken during transurethral resection of prostate and subsequent risk of prostate cancer (Sweden). Cancer Causes Control, 17 (9) (2006), pp. 1127-1133. DOI: 10.1007/s10552-006-0054-2
[36]
H. Zhang, Y. Chang, Q. Zheng, R. Zhang, C. Hu, W. Jia. Altered intestinal microbiota associated with colorectal cancer. Front Med, 13 (4) (2019), pp. 461-470. DOI: 10.1007/s11684-019-0695-7
[37]
L. Frattaruolo, M. Fiorillo, M. Brindisi, R. Curcio, V. Dolce, R. Lacret, et al. Thioalbamide, a thioamidated peptide from Amycolatopsis alba, affects tumor growth and stemness by inducing metabolic dysfunction and oxidative stress. Cells, 8 (11) (2019), p. 1408. DOI: 10.3390/cells8111408
[38]
M.G. Langille, J. Zaneveld, J.G. Caporaso, D. McDonald, D. Knights, J.A. Reyes, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol, 31 (9) (2013), pp. 814-821. DOI: 10.1038/nbt.2676
[39]
C. Resende de Paiva, C. Grønhøj, U. Feldt-Rasmussen, C. von Buchwald. Association between Hashimoto’s thyroiditis and thyroid cancer 64,628 in patients. Front Oncol, 7 (2017), p. 53
[40]
J.U. Lee, S. Huang, M.H. Lee, S.E. Lee, M.J. Ryu, S.J. Kim, et al. Dual specificity phosphatase 6 as a predictor of invasiveness in papillary thyroid cancer. Eur J Endocrinol, 167 (1) (2012), pp. 93-101
[41]
B. Ma, R. Shi, S. Yang, L. Zhou, N. Qu, T. Liao, et al. DUSP4/MKP 2 overexpression is associated with BRAF(V600E) mutation and aggressive behavior of papillary thyroid cancer. Onco Targets Ther, 9 (2016), pp. 2255-2263
[42]
A.M. Gaweł, M. Ratajczak, E. Gajda, M. Grzanka, A. Paziewska, M. Cieślicka, et al. Analysis of the role of FRMD 5 in the biology of papillary thyroid carcinoma. Int J Mol Sci, 22 (13) (2021), p. 6726. DOI: 10.3390/ijms22136726
[43]
H.Y. Jiang, S. Najmeh, G. Martel, E. MacFadden-Murphy, R. Farias, P. Savage, et al. Activation of the pattern recognition receptor NOD 1 augments colon cancer metastasis. Protein Cell, 11 (3) (2020), pp. 187-201. DOI: 10.1007/s13238-019-00687-5
[44]
D. Deglnnocenti, C. Alberti, G. Castellano, A. Greco, C. Miranda, M.A. Pierotti, et al. Integrated ligand-receptor bioinformatic and in vitro functional analysis identifies active TGFA/EGFR signaling loop in papillary thyroid carcinomas. PLoS One, 5 (9) (2010), p. e12701
[45]
Y. Hosono, T. Yamaguchi, E. Mizutani, K. Yanagisawa, C. Arima, S. Tomida, et al. MYBPH, a transcriptional target of TTF-1, inhibits ROCK1, and reduces cell motility and metastasis. EMBO J, 31 (2) (2012), pp. 481-493. DOI: 10.1038/emboj.2011.416
[46]
T. Zhan, G. Ambrosi, A.M. Wandmacher, B. Rauscher, J. Betge, N. Rindtorff, et al. MEK inhibitors activate Wnt signalling and induce stem cell plasticity in colorectal cancer. Nat Commun, 10 (1) (2019), p. 2197
[47]
S. Kang, B. Kim, H.S. Kang, G. Jeong, H. Bae, H. Lee, et al. SCTR regulates cell cycle-related genes toward anti-proliferation in normal breast cells while having pro-proliferation activity in breast cancer cells. Int J Oncol, 47 (5) (2015), pp. 1923-1931. DOI: 10.3892/ijo.2015.3164
[48]
W. Liu, X. Zhang, H. Xu, S. Li, H.C. Lau, Q. Chen, et al. Microbial community heterogeneity within colorectal neoplasia and its correlation with colorectal carcinogenesis. Gastroenterology, 160 (7) (2021), pp. 2395-2408
[49]
J. Zhang, F. Zhang, C. Zhao, Q. Xu, C. Liang, Y. Yang, et al. Dysbiosis of the gut microbiome is associated with thyroid cancer and thyroid nodules and correlated with clinical index of thyroid function. Endocrine, 64 (3) (2019), pp. 564-574. DOI: 10.1007/s12020-018-1831-x
[50]
K. Moriyama, C. Ando, K. Tashiro, S. Kuhara, S. Okamura, S. Nakano, et al. Polymerase chain reaction detection of bacterial 16S rRNA gene in human blood. Microbiol Immunol, 52 (7) (2008), pp. 375-382. DOI: 10.1111/j.1348-0421.2008.00048.x
[51]
X. Zhou, J. Li, J. Guo, B. Geng, W. Ji, Q. Zhao, et al. Gut-dependent microbial translocation induces inflammation and cardiovascular events after ST-elevation myocardial infarction. Microbiome, 6 (1) (2018), p. 66
[52]
A.K. Criss, H.S. Seifert. A bacterial siren song: intimate interactions between Neisseria and neutrophils. Nat Rev Microbiol, 10 (3) (2012), pp. 178-190. DOI: 10.1038/nrmicro2713
[53]
D.M. Weinstock, A.E. Brown. Rhodococcus equi: an emerging pathogen. Clin Infect Dis, 34 (10) (2002), pp. 1379-1385
[54]
E. Cekanaviciute, B.B. Yoo, T.F. Runia, J.W. Debelius, S. Singh, C.A. Nelson, et al. Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models. Proc Natl Acad Sci USA, 114 (40) (2017), pp. 10713-10718. DOI: 10.1073/pnas.1711235114
[55]
A. Sałkowska, K. Karaś, I. Karwaciak, A. Walczak-Drzewiecka, M. Krawczyk, M. Sobalska-Kwapis, et al. Identification of novel molecular markers of human Th17 cells. Cells, 9 (7) (2020), p. 1611. DOI: 10.3390/cells9071611
[56]
L. Fu, J. Song, C. Wang, S. Fu, Y. Wang. Bifidobacterium infantis potentially alleviates shrimp tropomyosin-induced allergy by tolerogenic dendritic cell-dependent induction of regulatory T cells and alterations in gut microbiota. Front Immunol, 8 (2017), p. 1536
[57]
M. Wang, S. Yin, Q. Qin, Y. Peng, Z. Hu, X. Zhu, et al. Stenotrophomonas maltophilia inhibits host cellular immunity by activating PD-1/PD-L 1 signaling pathway to induce T-cell exhaustion. Mol Immunol, 130 (2021), pp. 37-48
[58]
B.A. McKelvey, C.B. Umbricht, M.A. Zeiger. Telomerase reverse transcriptase (TERT) regulation in thyroid cancer: a review. Front Endocrinol, 11 (2020), p. 485
[59]
V. Fernández-García, S. González-Ramos, P. Martín-Sanz, F. García-Del Portillo, J.M. Laparra, L. Boscá. NOD 1 in the interplay between microbiota and gastrointestinal immune adaptations. Pharmacol Res, 171 (2021), Article 105775
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