Intratumoral microbiota composition in women's cancers: a systematic review and meta-analysis

Abstract

Background: The intratumoral microbiota has attracted considerable interest in carcinogenesis, progression, and treatment owing to advancements in sequencing technology. This systematic review provides a comprehensive overview of the current literature regarding the diversity and compositional characteristics of the intratumoral microbiota in women's cancers. Additionally, it also explores potential associations among intratumoral microbiota, estrogen, and anti-tumor therapies.

Methods: A comprehensive literature search was conducted using PubMed, Embase, Web of Science, and the Cochrane Library from their inception to May 1, 2024. The review protocol was pre-registered in PROSPERO (CRD 42024601213). Articles were assessed utilizing the Newcastle-Ottawa Scale (NOS). To estimate the effect size and variability in microbial diversity changes, the standardized mean difference (SMD) and 95% confidence intervals (CIs) were employed. The systematic review adhered to PRISMA reporting guidelines, and meta-analyses were performed using Review Manager version 5.4.

Results: This systematic review included 29 of 8,291 studies after a thorough screening process. Of the 22 studies investigating α-diversity in women's cancers, disease-free controls, and those with benign conditions, notable changes in diversity indices were observed. Compared to adjacent normal tissues, the Simpson index significantly decreased in breast cancer (SMD = -0.75, 95% CI: [-0.94, -0.55]) and endometrial cancer (SMD = -0.83, 95% CI: [-1.37, -0.28]). The Chao1 index was reduced in endometrial cancer tumor tissues relative to normal tissues (SMD = -2.25, 95% CI: [-3.13, -1.36]), while the Shannon index decreased in ovarian cancer tumor tissues (SMD = -0.61, 95% CI: [-1.18, -0.04]). In comparisons between tumor and benign tissues, the Chao1 index was decreased (SMD = -0.64, 95% CI: [-1.20, -0.08], I² = 0%), while the Simpson index was increased (SMD = 0.36, 95% CI: [0.01, 0.71], I² = 0%) in patients with ovarian cancer. Other microbial diversity indices showed no significant differences between tumor and non-tumor tissues. At the phylum level, Fusobacteriota were enriched in tumor tissues, while Firmicutes and Actinobacteria predominated in non-tumor tissues. At the genus level, Pseudomonas, Porphyromonas, Atopobium, Peptoniphilus, and Acinetobacter were consistently more abundant in cancerous tissues. Microbial alterations were also linked to estrogen receptor (ER) status, with Alkanindiges negatively correlated with ER status in two studies. Furthermore, one study on the effect of antineoplastic therapy indicated that neoadjuvant chemotherapy reduced microbial diversity in breast cancer patients (n = 15 vs. n = 18) (Shannon index: SMD = -0.95, 95% CI: [-1.68, -0.22]).

Conclusion: This study highlights significant differences in microbiota composition between tumor and non-tumor tissues in women's cancers, emphasizing changes in intratumoral microbiota influenced by estrogen and antineoplastic treatments. Further research is needed to explore the potential for developing targeted therapies based on estrogen-driven microbiota alterations. Investigations may yield insights into the enhancement of female reproductive health and the improvement of treatment efficacy for female cancers.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024601213, identifier CRD 42024601213.

Keywords: 16s rrna gene sequencing; breast cancer; estrogen; gynecologic cancer; intratumoral microbiota; meta-analysis.

Figure 1

Proposed conceptual framework of the microbiota-estrogen-tumor microenvironment (TME) crosstalk in cancer progression. This schematic illustrates the complex and bidirectional interactions between the intratumoral microbiota, estrogen signaling, and components of the tumor microenvironment (TME). The microbiota can influence tumor development through multiple mechanisms, including modulation of estrogen metabolism via microbial enzymes (e.g., β-glucuronidase), induction of DNA damage, activation of oncogenic signaling pathways, and shaping of the immune landscape (e.g., through STING pathway activation, antigen presentation, and tertiary lymphoid structure formation). Conversely, estrogen may regulate the composition and function of the tumor-associated microbiota by promoting or inhibiting the growth of specific taxa (e.g., Lactobacillus spp.), thereby further influencing immune responses and tumor behavior. This tripartite interaction contributes to cancer initiation, progression, and treatment response in women’s cancers such as breast, ovarian, endometrial, and cervical cancers.

Figure 2

Flow diagram of the study selection process following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.

Figure 3

Forest plots illustrating alpha diversity indices comparing intratumoral microbiota with matched normal adjacent tissues (NATs) across female cancers. (A) Chao1 index; (B) Shannon index; (C) Simpson index. Each plot presents standardized mean differences (SMDs) with 95% confidence intervals (CIs), calculated using a random-effects model. Squares indicate individual study effect sizes (with sizes proportional to study weight); horizontal lines represent 95% CIs; diamonds denote pooled estimates. Positive SMD values indicate higher diversity in tumor tissues, while negative values indicate lower diversity compared to NATs. Statistical significance was defined as p < 0.05. BC, breast cancer; EC, endometrial cancer; CC, cervical cancer.

Figure 4

Forest plots illustrating alpha diversity indices comparing intratumoral microbiota with healthy controls (HC) across female cancers. (A) Chao1 index; (B) Shannon index; (C) Simpson index. Each plot presents standardized mean differences (SMDs) with 95% confidence intervals (CIs), calculated using a random-effects model. Squares indicate individual study effect sizes (with sizes proportional to study weight); horizontal lines represent 95% CIs; diamonds denote pooled estimates. Positive SMD values indicate higher diversity in tumor tissues, while negative values indicate lower diversity compared to HCs. Statistical significance was defined as p < 0.05. BC, breast cancer; EC, endometrial cancer; OC, ovarian cancer.

Figure 5

Forest plots illustrating alpha diversity indices comparing intratumoral microbiota with benign disease tissues across female cancers. (A) Chao1 index; (B) Shannon index; (C) Simpson index. Each plot presents standardized mean differences (SMDs) with 95% confidence intervals (CIs), calculated using a random-effects model. Squares indicate individual study effect sizes (with sizes proportional to study weight); horizontal lines represent 95% CIs; diamonds denote pooled estimates. Positive SMD values indicate higher diversity in tumor tissues, while negative values indicate lower diversity compared to benign disease tissues. Statistical significance was defined as p < 0.05. BC, breast cancer; EC, endometrial cancer; OC, ovarian cancer.

Figure 6

Heatmap summarizing changes in the relative abundance of microbial taxa in tumor tissues compared to non-tumor tissues across female cancer types. This heatmap illustrates reported increases or decreases in specific microbial taxa across endometrial cancer (EC), ovarian cancer (OC), breast cancer (BC), and cervical cancer (CC). An asterisk (*) denotes consistent findings reported in two or more independent studies.