Vaginal microbiome dysbiosis as a novel noninvasive biomarker for detection of chronic endometritis in infertile women.

OBJECTIVE: To identify the vaginal microbial signature in women with chronic endometritis (CE) and investigate the potential of vaginal microbiome characterization as a novel diagnostic tools for CE. METHODS: A cross-sectional study was conducted to compare the characteristics of the vaginal microbiome in 98 women who underwent endometrial biopsy for routine clinical inspection of infertility (49 women diagnosed with CE and 49 with non-CE). The vaginal microbiome was analyzed using 16S ribosomal RNA gene amplicon sequencing. The study included an analysis of diversity, bacterial abundance, and microbial function. In addition, microbial markers were identified, and a CE classifier was developed. RESULTS: The relative abundances of genera, including Bifidobacterium, Prevotella and Gardnerella, were found to be different between the two groups. Analysis of the Kyoto Encyclopedia of Genes and Genomes pathways reported differential expression in metabolism-related pathways in the two groups. We identified four microbial markers of CE (Enterobacter, Prevotella, Faecalibacterium, and Phascolarctobacterium) and developed a predictive classifier for diagnosing CE, achieving an area under the curve of 83.26%. CONCLUSION: The results of the current study revealed that, compared with the non-CE controls, patients with CE have a different vaginal microbiota, highlighting the diagnostic significance of the vaginal microbiome as a promising noninvasive biomarker in detecting CE.

Comprehensive single-cell and bulk RNA-seq analyses reveal a novel CD8+ T cell-associated prognostic signature in ovarian cancer.

CD8+ T cells play pivotal roles in combating intracellular pathogens and eliminating malignant cells in cancer. However, the prognostic role of CD8+ T cells in ovarian carcinoma is insufficiently exploited. Herein, through univariate Cox regression along with least absolute shrinkage and selection operator (LASSO) regression analyses, we developed a novel prognostic model based on CD8+ T cell markers identified by single-cell sequencing (scRNA-seq) analyses. Patient grouping by the median risk score reveals an excellent prognostic efficacy of this model in both training and validation cohorts. Of note, patients classified as low-risk group exhibit a dramatically improved prognosis. In addition, higher enrichment level of immune-related pathways and increased infiltration level of multiple immune cells are found in patients with lower risk score. Importantly, low-risk patients also exhibited higher response rate to immunotherapies. Summarily, this developed CD8+ T cell-associated prognostic model serves as an excellent predictor for clinical outcomes and aids in guiding therapeutic strategy choices for ovarian cancer patients.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. METHODS: We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. RESULTS: A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. CONCLUSION: Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.