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Table 1 The main findings of the human oncobiome studies in ovarian adenocarcinoma

From: The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

Sample type and size Method Changes to the microbiome and other observations Ref.
Changes to the vaginal and cervicovaginal microbiomes
176 women with epithelial ovarian cancer, 115 healthy controls, and 69 controls with benign gynecological conditions (aged 18–87 years) 16S RNA sequencing Cervicovaginal bacterial communities’ poor in Lactobacillus spp. (Type O) were more prevalent in ovarian cancer patients compared to controls. The type O community was more prevalent in BRCA (1/2) mutation carriers. Associations were stronger in younger patients (< 40 yrs. of age) Nené et al. 2019
117 women with ovarian cancer and 171 age- and ethnicity-matched population-based control subjects Serovar D of chlamydia elementary bodies (EB) and IgG antibodies to CHSP60-1 ELISA assay The probability of having ovarian cancer was 90% greater in women with the highest, compared with the lowest levels of Chlamydia-EB antibodies. There was also a monotonic trend in ovarian cancer risk associated with CHSP60-1 Ness et al. 2003
Changes to the upper genital tract of women microbiome
25 samples from the proximal fallopian tube, fimbriae, and ovary Sequencing the V1-V2 region of the 16S gene on the Ion Torrent platform The composition of the microbiome from healthy individuals and the ovarian cancer patients in the upper genital tract were different Brewster et al. 2016
25 ovarian cancer tissues and 25 normal distal fallopian tube tissues Illumina sequencing of the V3-V4 hypervariable regions of the 16S rDNA genes Decreased diversity and species richness in ovarian cancer
Clads or species upregulated in ovarian cancer:
Proteobacteria, Acinetobacter, Sphingomonas, Methylobacterium spp.
Clads or species downregulated in ovarian cancer:
Firmicutes, Candidate_division_TM7, Acidobacteria, Candidate_division_OD1, Lactococcus spp., Acinetobacter lwoffii, Lactococcus piscium
Lactococcus piscium abundance can be used for diagnostics
Zhou et al. 2019a
Changes to the ovarian microbiome
Six women with ovarian cancer and ten women with a noncancerous ovarian condition (three patients with uterine myoma and seven patients with uterine adenomyosis) IHC for LPS;
Deep sequencing of the V3-V4 16S rDNA region
Decreasing trends in species number, Shannon Index, Simpson Index, and Evenness Index in the ovarian cancer group
Clads or species upregulated in ovarian cancer:
Aquificae, Planctomycetes, Gemmata obscuriglobus, Halobacteroides halobius, and Methyloprofundus sedimenti
Clads or species downregulated in ovarian cancer:
Crenarchaeota
The relative abundance of Anoxynatronum sibiricum may be associated with the tumor stage. Methanosarcina vacuolata may be used to diagnose ovarian cancer
Wang et al. 2020
99 ovarian cancer samples (primary and recurrent), 20 matched (tissue adjacent to the tumor deemed non-cancerous by pathological analysis) samples, and 20 unmatched control samples PathoChip, a microarray followed by probe capture and Illumina sequencing Differential expression of viruses (Nodaviridae, Parvoviridae), Proteobacteria (Azorhizobium, Escherichia, Firmicutes, Clostridium), fungi (Alternaria, Malassezia, Mucor, Trichosporon), and parasites (Acanthamoeba, Naegleria, Taenia, Trichinella) between the cancer and matched control groups Banerjee et al. 2017
39 tissue samples from cancerous or healthy ovaries (mean age, 55 ± 15 years; range 40 to 70 years) Chlamydia and human papillomavirus DNA was assessed in PCR reactions Ovarian cancer patients had a higher prevalence of Chlamydia or HPV Shanmughapriya et al. 2012
18,116 samples across 10,481 patients and 33 types of cancer (including ovarian cancer) from the TCGA compendium of whole-genome sequencing (WGS; n = 4,831) and whole-transcriptome sequencing (RNA-seq; n = 13,285) studies in silico approach Fusobacteria (Bacteroides, Gram-negative) count in tumors was higher compared to healthy, untransformed tissues (Poore et al. 2020) Poore et al. 2020
Changes to the peritoneal microbiome
Peritoneal fluid from 10 ovarian cancer patients and 20 patients with benign ovarian masses (age ≥ 30) 16S RNA sequencing of the V4 region of the 16S rDNA gene Decreased bacterial diversity in ovarian cancer Miao et al. 2020
Changes to the serum microbiome
166 ovarian cancer vs. 76 patients with benign ovarian tumors Sequencing V3-V4 hypervariable regions 16S rDNA The genus Acinetobacter showed high relative abundances in ovarian cancer
No difference in α and β diversity
Genus-level microbiome biomarkers in combination with clinical biomarkers (CA-125) can be used for diagnostic purposes
Kim et al. 2020
Changes to the gut microbiome
A subset of 10 Lynch syndrome patients with confirmed DNA mismatch repair pathogenic mutations developing ovarian cancer (Shih Ie and Kurman 2004) vs. 8 healthy females without a family history of cancer V4 region of the 16S rDNA was sequenced by Illumina sequencing In the gynecological cancer group, Bacteroides abundance decreased and Firmicutes, Actinobacteria, and Proteobacteria increased. At the family level, Lachinospiraceae, Bacteroideacea, and Rikenelacea decreased and Bifidobacteriacea and Ruminococcacea increased Mori et al. 2019
  1. IHC immunohistochemistry