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