Convincing studies demonstrated that vaginal flora is one of the most impactful key components for the well-being of the genital tract in women. Nevertheless, the potential capability of vaginal-derived bacterial communities as biomarkers to monitor cervical carcinogenesis (CC) has yet to be studied actively compared to those of bacterial vaginosis (BV). We hypothesized that vaginal microbiota might be associated with the progression of CC. In this study, we enrolled 23 participants, including healthy controls (HC group; n = 7), patients with cervical intraepithelial neoplasia (CIN) 2 and 3 (CIN group, n = 8), and patients with invasive cervical cancer (CAN group; n = 8). Amplicon sequencing was performed using the Ion Torrent PGM to characterize the vaginal microbiota. Patients with CIN and CAN presented vaginal microbiota dysbiosis compared with HC. The alpha diversity analysis revealed that CC has a trend to be increased in terms of diversity indexes. Moreover, CC was associated with the abundance of specific microbes, of which Lactobacillus and Gardnerella were the most significantly different between HC and CIN, whereas Streptococcus was differentially abundant in CAN compared with CIN. We then evaluated their diagnostic abilities. Testing in terms of diagnostic ability using the three genera revealed considerably high performance with an area under the receiver-operating characteristic curve of 0.982, 0.953, and 0.922. The current study suggests that the presence of Gardnerella and Streptococcus may be involved in the advancment of CC.

The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon’s diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; Prevotella was significantly more abundant in the CAN group and Clostridium in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.

Although emerging evidence revealed that the gut microbiome served as a tool and as biomarkers for predicting and detecting specific cancer or illness, it is yet unknown if vaginal microbiome-derived bacterial markers can be used as a predictive model to predict the severity of CIN. In this study, we sequenced V3 region of 16S rRNA gene on vaginal swab samples from 66 participants (24 CIN 1−, 42 CIN 2+ patients) and investigated the taxonomic composition. The vaginal microbial diversity was not significantly different between the CIN 1− and CIN 2+ groups. However, we observed Lactobacillus amylovorus dominant type (16.7%), which does not belong to conventional community state type (CST). Moreover, a minimal set of 33 bacterial species was identified to maximally differentiate CIN 2+ from CIN 1− in a random forest model, which can distinguish CIN 2+ from CIN 1− (area under the curve (AUC) = 0.952). Among the 33 bacterial species, Lactobacillus iners was selected as the most impactful predictor in our model. This finding suggests that the random forest model is able to predict the severity of CIN and vaginal microbiome may play a role as biomarker.

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side effect of cancer treatment, which is more common in patients with ovarian cancer who are receiving paclitaxel and carboplatin treatment. Although prior studies have explored the association between obesity and CIPN, most relied on subjective grading systems. This study aims to evaluate the relationship between being overweight and CIPN using the Total Neuropathy Score (TNS), a more objective and comprehensive assessment tool. The purpose of this study is to investigate the relationship between being overweight and the onset and severity of CIPN in a cohort of South Korean patients with ovarian cancer who were treated with paclitaxel and carboplatin. Methods: The study was conducted retrospectively at a single tertiary hospital in South Korea from March 2013 to February 2022. Included in this study were forty-two individuals who were diagnosed with epithelial ovarian cancer and who had developed neuropathic symptoms following chemotherapy. Patient characteristics, laboratory findings, and disease-specific TNS were analyzed. Results: Overweight patients showed significantly more severe CIPN, with higher Total Neuropathy Scores (TNS; p = 0.019) and earlier symptom onset (p < 0.05) compared to normal-weight patients. Causal mediation analysis also revealed a significant direct effect of BMI on TNS (p = 0.006). However, no significant correlation was found between BMI and TNS using Kendall’s rank correlation, and other neuropathic symptoms or laboratory parameters did not show statistically significant group differences. Conclusions: The study suggests that overweight may be associated with the severity and onset of CIPN in patients with ovarian cancer who are receiving paclitaxel and carboplatin chemotherapy. While the results are preliminary, they underscore the importance of addressing this modifiable risk factor in clinical care. Further research is needed to better understand the underlying mechanisms and to inform future therapeutic strategies.