Heterogeneity of gut microbiome compositions in the third decade of life in Japanese women: insights from a comparative analysis.

The reasons for sex-associated gut microbiota differences have not been determined, and although sex hormones, diet, and other factors are considered to contribute to them, many of these factors are age related. To shed light on this complex interplay, our study aimed to investigate and compare the gut microbial compositions of males and females across a broad range of ages, aiming to identify sex-associated disparities and potential causal factors. Our study encompassed a comprehensive analysis of gut microbiota data obtained from 444 Japanese individuals, ranging from newborns to centenarians, sourced from the DNA Data Bank of Japan. We categorized the subjects into 13 distinct age groups and examined their relative microbial abundances, as well as alpha and beta diversities, in relation to sex and age. No difference was observed between gut microbiota relative abundances or alpha diversities between men and women at any age. However, the study showed that the heterogeneity of gut microbiota among women in their 20s was greater than in men. To confirm the general occurrence of this difference, we conducted additional analyses using seven datasets: three from Japan and four from other countries. Interestingly, this variance was particularly noticeable within Japanese women. We also showed a potential link between the observed heterogeneity and dietary fiber intake. It is hoped this study will provide clues that aid in the identification of factors responsible for sex-associated differences in gut microbiota compositions.

Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC. Furthermore, we investigated the potential improvement in prediction performance by incorporating clinical factors. A total of 252 LACC patients undergoing curative chemoradiotherapy are included. The patients are randomly divided into two independent groups for the training (167 patients) and test datasets (85 patients). Contrast-enhanced T1- and T2-weighted MR scans are obtained. For HCR analysis, 1890 imaging features are extracted and a support vector machine classifier with a five-fold cross-validation is trained on training dataset to predict CRT response and subsequently validated on test dataset. For DLR analysis, a 3-dimensional convolutional neural network was trained on training dataset and validated on test dataset. In conclusion, both HCR and DLR models could predict CRT responses in patients with LACC. The integration of clinical factors into radiomics prediction models tended to improve performance in HCR analysis. Our findings may contribute to the development of personalized treatment strategies for LACC patients.

Circulating-tumor DNA Assessment in Diffuse Large B-cell Lymphoma to Determine Up-front Stem Cell Transplantation: A Pilot Study.

BACKGROUND/AIM: This study evaluated the possibility of clinical use of circulating-tumor DNA (ctDNA) as a biomarker to determine up-front autologous stem cell transplantation (auto-SCT) for patients with high-risk diffuse large B-cell lymphoma (DLBCL) in practice. PATIENTS AND METHODS: To explore the dynamics of ctDNA in DLBCL, blood samples were collected sequentially before and after treatment from patients with newly diagnosed DLBCL who received rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. To conduct ctDNA genotyping and ctDNA monitoring simultaneously, targeted sequencing by cancer personalized profiling using deep sequencing was used. RESULTS: Ten patients between the ages of 50 and 60 years were enrolled. Based on the international prognostic index (IPI), seven patients were classified as high-IPI-risk group, and three patients were classified as low-IPI-risk group. The IPI risk group correlated with total metabolic tumor volume. All patients completed six cycles of R-CHOP chemotherapy, and seven patients achieved complete response. Changes in ctDNA mutation numbers did not correlate with changes in PET scan images and treatment response. In most high-risk patients, new mutations appeared in ctDNA after completion of chemotherapy that conceivably marked resistant clones. Notably, disease relapse did not occur in high-risk patients with poor prognostic mutations who underwent autologous SCT. CONCLUSION: ctDNA monitoring was meaningful in high-risk patients. Moreover, ctDNA and well-known prognostic factors should be considered in the decision making for auto-SCT. If a new genetic mutation in ctDNA with a negative prognosis would emerge during treatment, high-risk patients should consider auto-SCT.

Relationship between maternal Group B Streptococcal colonization and gestational vaginal microbiome composition: A pilot study.

PURPOSE: Group B Streptococcus (GBS) colonization and vaginal microbiome (VMB) dysbiosis are associated with adverse perinatal outcomes. However, the role of GBS colonization in maternal VMB remains unclear. Herein, we aimed to investigate this relationship and identify additional pathogens associated with GBS colonization for potential implications in understanding their clinical significance. MATERIALS AND METHODS: Vaginal swab samples were obtained before delivery from nine women with normal pregnancies for GBS detection and 16S rRNA gene sequencing. The diversity analysis and community state types clustering were used to compare the GBS-positive vs. GBS-negative groups. ANCOM-BC was implemented to identify differentially abundant microbes (DAMs) associated with GBS colonization. The correlation and receiver operating characteristic analysis were used to evaluate the relationship between DMAs and clinical parameters. RESULTS: There were 6/9 (66,7%) GBS-negative pregnant women. The α-diversity index (p â€‹= â€‹0.71 for observed operational taxonomic units and p â€‹= â€‹0.90 for Shannon diversity), ß-diversity index (p â€‹= â€‹0.583), and community state types clustering (p â€‹= â€‹0.23) were not significantly different between the GBS-positive and -negative groups. Four DAMs, namely, Actinomyces, Shigella, Fenollaria, and Gemella, were significantly associated with GBS colonization, reflecting the dynamicity of the gestational VMB. Their abundances were negatively correlated with birth weight and had acceptable discriminating ability in premature membrane rupture (area under the curve, 0.9). CONCLUSIONS: Despite the absence of significant effects on overall VMB composition, our preliminary results investigated that maternal GBS colonization related to high abundance of four pathogens with potential clinical utility as microbial signatures.

Relationship between Human Papillomavirus Status and the Cervicovaginal Microbiome in Cervical Cancer.

Uterine cervical cancer (CC) is a complex, multistep disease primarily linked to persistent infection with high-risk human papillomavirus (HR-HPV). However, it is widely acknowledged that HR-HPV infection alone cannot account for the formation and progression of CC. Emerging evidence suggests that the cervicovaginal microbiome (CVM) also plays a significant role in HPV-related CC. Certain bacteria, such as Fusobacterium spp., Porphyromonas, Prevotella, and Campylobacter, are currently being considered as potential microbiomarkers for HPV-positive CC. However, the composition of the CVM in CC is inconsistent; thus, further studies are needed. This review comprehensively discusses the complex interplay between HPV and the CVM in cervical carcinogenesis. It is postulated that the dynamic interaction between HPV and the CVM creates an imbalanced cervicovaginal microenvironment that triggers dysbiosis, enhances HPV persistence, and promotes cervical carcinogenesis. Moreover, this review aims to provide updated evidence on the potential role of bacteriotherapy, particularly probiotics, in the treatment of CC.

Transcriptomic Network Analysis Using Exfoliative Cervical Cells Could Discriminate a Potential Risk of Progression to Cancer in HPV-related Cervical Lesions: A Pilot Study.

BACKGROUND/AIM: Cervical cancer is the fourth most common type of cancer in women worldwide and it is a major cause of cancer-related deaths in developing countries. Despite the marked reduction observed in the rates of the disease as a result of screening programs, it is necessary to develop robust biomarkers that can detect the neoplastic progression early in HPV-related cervical lesions. MATERIALS AND METHODS: We performed comparative mRNA sequencing from exfoliative cervical cytology samples from nine Korean women using the Illumina NovaSeq6000 platform. Each pathological tissue was matched to the corresponding cytological sample. The pathologic diagnosis was scrutinized with ancillary immunohistochemistry and was considered a confirmative (endpoint) diagnosis. The pathological diagnoses consisted of three cases of chronic cervicitis, 2 high-grade squamous intraepithelial lesions (HSILs), 2 squamous cell carcinomas in situ (CIS), and 2 invasive squamous cell carcinomas (SQCCs), respectively. Using bioinformatic analyses, differentially expressed genes (DEGs; fold change ≥1.5; p<0.05) were applied for Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) networks. RESULTS: From a total of 55,882 genes, 438 DEGs were pinpointed; 282 genes were up-regulated and 156 genes down-regulated. These transcriptomic profiles were clearly divided into neoplastic (HSIL, CIS, and SQCC; ≥HSILs) and non-neoplastic lesions. The up-regulated DEGs were HIF-1a, EDN1, PIK3R3, PPP1CA and AKR1C1. GO, GSEA, and PPI network analyses showed marked associations with metabolism, proteolysis, or proteoglycan process pathways in cervical carcinogenesis. CONCLUSION: The transcriptomic analysis using exfoliative cervical cells was more likely representative of its corresponding histopathological diagnosis, thus emphasizing its potential utility in clinical practice. This study provides comprehensive transcriptomic network analyses for robust biomarkers that might present a high potential risk of progression to cancer in the exfoliative cervical cytology; our findings support their clinical utility for improved cervical cancer screening.

Axonal Regeneration: Underlying Molecular Mechanisms and Potential Therapeutic Targets.

Axons in the peripheral nervous system have the ability to repair themselves after damage, whereas axons in the central nervous system are unable to do so. A common and important characteristic of damage to the spinal cord, brain, and peripheral nerves is the disruption of axonal regrowth. Interestingly, intrinsic growth factors play a significant role in the axonal regeneration of injured nerves. Various factors such as proteomic profile, microtubule stability, ribosomal location, and signalling pathways mark a line between the central and peripheral axons' capacity for self-renewal. Unfortunately, glial scar development, myelin-associated inhibitor molecules, lack of neurotrophic factors, and inflammatory reactions are among the factors that restrict axonal regeneration. Molecular pathways such as cAMP, MAPK, JAK/STAT, ATF3/CREB, BMP/SMAD, AKT/mTORC1/p70S6K, PI3K/AKT, GSK-3ß/CLASP, BDNF/Trk, Ras/ERK, integrin/FAK, RhoA/ROCK/LIMK, and POSTN/integrin are activated after nerve injury and are considered significant players in axonal regeneration. In addition to the aforementioned pathways, growth factors, microRNAs, and astrocytes are also commendable participants in regeneration. In this review, we discuss the detailed mechanism of each pathway along with key players that can be potentially valuable targets to help achieve quick axonal healing. We also identify the prospective targets that could help close knowledge gaps in the molecular pathways underlying regeneration and shed light on the creation of more powerful strategies to encourage axonal regeneration after nervous system injury.

Updates on Group B Streptococcus Infection in the Field of Obstetrics and Gynecology.

Group B Streptococcus (GBS, Streptococcus agalactiae) is a Gram-positive bacterium that is commonly found in the gastrointestinal and urogenital tracts. However, its colonization during pregnancy is an important cause of maternal and neonatal morbidity and mortality worldwide. Herein, we specifically looked at GBS in relation to the field of Obstetrics (OB) along with the field of Gynecology (GY). In this review, based on the clinical significance of GBS in the field of OBGY, topics of how GBS is being detected, treated, and should be prevented are addressed.

Immune Pathway and Gene Database (IMPAGT) Revealed the Immune Dysregulation Dynamics and Overactivation of the PI3K/Akt Pathway in Tumor Buddings of Cervical Cancer.

Tumor budding (TB) is a small cluster of malignant cells at the invasive front of a tumor. Despite being an adverse prognosis marker, little research has been conducted on the tumor immune microenvironment of tumor buddings, especially in cervical cancer. Therefore, RNA sequencing was performed using 21 formalin-fixed, paraffin-embedded slides of cervical tissues, and differentially expressed genes (DEGs) were analyzed. Immune Pathway and Gene Database (IMPAGT) was generated for immune profiling. "Pathway in Cancer" was identified as the most enriched pathway for both up- and downregulated DEGs. Kyoto Encyclopedia of Genes and Genomes Mapper and Gene Ontology further revealed the activation of the PI3K/Akt signaling pathway. An IMPAGT analysis revealed immune dysregulation even at the tumor budding stage, especially in the PI3K/Akt/mTOR axis, with a high efficiency and integrity. These findings emphasized the clinical significance of tumor buddings and the necessity of blocking the overactivation of the PI3K/Akt/mTOR pathway to improve targeted therapy in cervical cancer.

Simple Electric Device to Isolate Nucleic Acids from Whole Blood Optimized for Point of Care Testing of Brain Damage.

BACKGROUND: Detection or monitoring of brain damage is a clinically crucial issue. Nucleic acids in the whole blood can be used as biomarkers for brain injury. Polymerase chain reaction (PCR) which is one of the most commonly used molecular diagnostic assays requires isolated nucleic acids to initiate amplification. Currently used nucleic acid isolation procedures are complicated and require laboratory equipments. OBJECTIVE: In this study, we tried to develop a simple and convenient method to isolate nucleic acids from the whole blood sample using a tiny battery-powered electric device. The quality of the isolated nucleic acids should be suitable for PCR assay without extra preparation. METHODS: A plastic device with separation chamber was designed and printed with a 3D printer. Two platinum electrodes were placed on both sides and a battery was used to supply the electricity. To choose the optimal nucleic acid isolation condition, diverse lysis buffers and separation buffers were evaluated, and the duration and voltage of the electricity were tested. Western blot analysis and PCR assay were used to determine the quality of the separated nucleic acids. RESULTS: 2ul of whole blood was applied to the cathode side of the separation chamber containing 78 ul of normal saline. When the electricity at 5 V was applied for 5 min, nucleic acids were separated from segment 1 to 3 of the separation chamber. The concentration of nucleic acids peaked around 7~8 mm from cathode side. PCR assay using the separation buffer as the template was performed successfully both in conventional and realtime PCR methods. The hemoglobin in the whole blood did not show the inhibitory effect in our separation system and it may be due to structural modification of hemoglobin during electric separation. CONCLUSION: Our simple electric device can separate nucleic acids from the whole blood sample by applying electricity at 5 V for 5 min. The separation buffer solution taken from the device can be used for PCR assay successfully.

Transcriptomic Immune Profiles Can Represent the Tumor Immune Microenvironment Related to the Tumor Budding Histology in Uterine Cervical Cancer.

Tumor budding (TB) histology has become a critical biomarker for several solid cancers. Despite the accumulating evidence for the association of TB histology with poor prognosis, the biological characteristics of TB are little known about in the context related to the tumor immune microenvironment (TIME) in uterine cervical cancer (CC). Therefore, this study aimed to identify the transcriptomic immune profiles related to TB status and further provide robust medical evidence for clinical application. In our study, total RNA was extracted and sequenced from 21 CC tissue specimens. As such, 1494 differentially expressed genes (DEGs) between the high- and low-TB groups were identified by DESeq2. After intersecting the list of DEGs and public immune genes, we selected 106 immune-related DEGs. Then, hub genes were obtained using Least Absolute Shrinkage and Selection Operator regression. Finally, the correlation between the hub genes and immune cell types was analyzed and four candidate genes were identified (one upregulated (FCGR3B) and three downregulated (ROBO2, OPRL1, and NR4A2) genes). These gene expression levels were highly accurate in predicting TB status (area under the curve >80%). Interestingly, FCGR3B is a hub gene of several innate immune pathways; its expression significantly differed in the overall survival analysis (p = 0.0016). In conclusion, FCGR3B, ROBO2, OPRL1, and NR4A2 expression can strongly interfere with TB growth and replace TB to stratify CC patients.

Diagnostic Accuracy of Loop-Mediated Isothermal Amplification Assay for Group B Streptococcus Detection in Recto-Vaginal Swab: Comparison with Polymerase Chain Reaction Test and Conventional Culture.

A rapid method for obtaining group B streptococcus (GBS) screening results has been required in the obstetric field. We aimed to determine the diagnostic performance of the Loop-Mediated Isothermal Amplification (LAMP) assay is acceptable compared to the existing polymerase chain reaction (PCR) assay. The study involved 527 pregnant women aged 19 to 44 years. Rectovaginal swabs were collected between 35 and 37 weeks of gestation or prior to impending preterm births or term labor without GBS screening. We presented the diagnostic performance of the LAMP assay with a 95% confidence interval (CI) compared to the PCR and microbiological culture. In total, 115 (21.8%), 115 (21.8%) and 23 (4.4%) patients showed positive results using the LAMP, PCR assay and microbiological culture method, respectively. The LAMP assay showed 100% sensitivity (95% CI, 96.8-100.0), 100% specificity (95% CI, 99.1-100.0) and 100% diagnostic accuracy (95% CI, 99.3-100.0) with the reference being the PCR assay. Meanwhile, the LAMP assay showed 87.0% sensitivity (95% CI, 71.0-100.0), 81.2% specificity (95% CI, 77.6-84.7), and 81.4% diagnostic accuracy (95% CI, 78.0-84.8) with the microbiological culture as a reference. This study presented the LAMP assay as an acceptable method for GBS screening with a similar performance to the existing PCR method.

Point of Care Test Technology Suitable for Early Detection and Monitoring of Ischemic Stroke.

BACKGROUND: Stroke is one of the leading causes of death and disability in adulthood worldwide. A simple and convenient diagnostic method is needed for monitoring high-risk patients for stroke. Few POCTs are available for stroke diagnosis. Soluble blood P-selectin is known as a biomarker for platelet aggregation. Increased expression of P-selectin is observed in coronary artery disease, acute myocardial infarction, stroke and peripheral arterial disease. OBJECTIVE: A simple method that can measure the increased expression of P-selectin in stroke patients is intended to be used for diagnosis or early detection and hospital monitoring of ischemic stroke. METHODS: Plasma proteins in blood were separated using a three-layered filter system. Quantum dot and antibody were conjugated to detect biomarkers present in plasma and then measured with a fluorescence spectrophotometer. RESULTS: The detection limit of soluble P-selectin confirmed by immunoassay was 1 ng/ul. In order to increase the sensitivity and simplify the reaction, the detection limit was measured to evaluate the sensitivity of the quantum dot labeled anti P-selectin antibody. As a result, P-selectin of 5 ng/ul or more showed saturation signal intensity, indicating the upper limit of detection, and 10 pg/ul was the lower limit of detection. CONCLUSION: In this study, we proposed a three-layer filter membrane system that can separate biomarker- rich fractions from whole blood, simplifying the analysis process and improving sensitivity by using quantum dot-labeled antibodies to detect biomarkers. We hope that our system complements the advantages of POCT and can be applied to real clinical applications.

Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics.

BACKGROUND: Our previous study demonstrated that tumor budding (TB) status was associated with inferior overall survival in cervical cancer. The purpose of this study is to evaluate whether radiomic features can predict TB status in cervical cancer patients. METHODS: Seventy-four patients with cervical cancer who underwent preoperative MRI and radical hysterectomy from 2011 to 2015 at our institution were enrolled. The patients were randomly allocated to the training dataset (n = 48) and test dataset (n = 26). Tumors were segmented on axial gadolinium-enhanced T1- and T2-weighted images. A total of 2074 radiomic features were extracted. Four machine learning classifiers, including logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (NN), were used. The trained models were validated on the test dataset. RESULTS: Twenty radiomic features were selected; all were features from filtered-images and 85% were texture-related features. The area under the curve values and accuracy of the models by LR, RF, SVM and NN were 0.742 and 0.769, 0.782 and 0.731, 0.849 and 0.885, and 0.891 and 0.731, respectively, in the test dataset. CONCLUSION: MRI-based radiomic features could predict TB status in patients with cervical cancer.

Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer.

OBJECTIVE: To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic feature of 18F-FDG PET/CT in patients with cervical cancer. MATERIALS AND METHODS: Seventy-six patients with cervical cancer who underwent radical hysterectomy and preoperative 18F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and fifty-nine features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomic findings for TB status. The prediction model for TB status was built using 3 machine learning classifiers (random forest, support vector machine, and neural network). RESULTS: Univariate analysis led to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to intratumoral budding (ITB) status. Among these parameters, following multivariate analysis for the ITB status, only compacity remained significant (odds ratio, 5.0047; 95% confidence interval, 1.1636-21.5253; p = 0.0305). Two conventional metabolic parameters and 25 radiomic features were selected by the Lasso regularization, and the prediction model for the ITB status had a mean area under the curve of 0.762 in the test dataset. CONCLUSION: Radiomic features of 18F-FDG PET/CT were associated with the ITB status. The prediction model using radiomic features successfully predicted the TB status in patients with cervical cancer. The prediction models for the ITB status may contribute to personalized medicine in the management of patients with cervical cancer.

Intratumoral budding: A novel prognostic biomarker for tumor recurrence and a potential predictor of nodal metastasis in uterine cervical cancer.

OBJECTIVE: To evaluate the prognostic and predictive significance of lymphovascular invasion (LVI) and lymph node metastasis of intratumoral budding (ITB) and its correlation with clinicopathological parameters in patients with cervical cancer. METHODS: Total 151 patients with cervical cancer who underwent radical hysterectomy with pelvic and/or paraaortic lymphadenectomy were included. We assessed the status of ITB and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Univariate and multivariate analyses were performed for ITB, PTB, and other clincopathological parameters as predictors of recurrence. RESULTS: ITBhigh (≥3TB/HPF) was significantly associated with large tumor size, deep stromal invasion, LVI, parametrial invasion, and lymph node metastasis. The numbers of ITBs and PTBs were positively correlated (r2 = 0.754, p < 0.0001). ITBhigh was more frequently observed in squamous cell carcinoma compared with adenocarcinoma and adenosquamous cell carcinoma (p = 0.010). ITBhigh was found to be an independent prognostic factor for tumor recurrence by multivariate analysis (hazard ratio, 1.92; 95% confidence interval [CI], 1.37-9.90; p = 0.026). Multiple logistic regression showed association of LVI (odds ratio [OR], 1.85; 95% CI, 1.11-3.06; p = 0.017) and lymph node metastasis (OR, 1.96; 95% CI, 1.26-4.66; p = 0.019). CONCLUSION: ITBhigh is an independent prognostic factor for tumor recurrence. ITB is a surrogate marker for predicting LVI in cervical cancers. The evaluation of ITB may be readily applied in the clinical setting for improved prognosis and to guide the clinical management of patients with cervical cancer.

Potential Association between Vaginal Microbiota and Cervical Carcinogenesis in Korean Women: A Cohort Study.

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.

Vaginal Microbiome-Based Bacterial Signatures for Predicting the Severity of Cervical Intraepithelial Neoplasia.

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.

Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis.

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.