BackgroundMost patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.MethodsTwo HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).ResultsBy combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.ConclusionsThe developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

With a high prevalence of morbidity and mortality, myocardial infarction (MI) is a prevalent heart disease. The development and outcomes of post-MI heart failure (HF) continue to be a major factor in the poor post-MI prognosis despite the extensive medical treatment for MI. There are currently few indicators that can accurately predict post-MI heart failure. In this study, we re-examined single cell RNA-seq and bulk RNA-seq datasets collected from peripheral blood samples of myocardial infarction and dataset of patients who developed heart failure or heart failure did not occur after myocardial infarction. We discovered a subtype of immune-activation B cell which is associated with the discrimination of post-MI HF and nonHF patients. Such finding was further validated in independent corhorts using PCR. By incorporating the specific marker genes of the B subtype, we developed a prediction model with 13 markers that can predict the HF risk of myocardial infarction patients and provide useful clinical experience and tools.

BackgroundMyocardial infarction (MI) is a common cardiac condition with a high incidence of morbidity and mortality. Despite extensive medical treatment for MI, the development and outcomes of post-MI heart failure (HF) continue to be major factors contributing to poor post-MI prognosis. Currently, there are few predictors of post-MI heart failure.MethodsIn this study, we re-examined single-cell RNA sequencing and bulk RNA sequencing datasets derived from the peripheral blood samples of patients with myocardial infarction, including patients who developed heart failure and those who did not develop heart failure after myocardial infarction. Using marker genes of the relevant cell subtypes, a signature was generated and validated using relevant bulk datasets and human blood samples.ResultsWe identified a subtype of immune-activated B cells that distinguished post-MI HF patients from non-HF patients. Polymerase chain reaction was used to confirm these findings in independent cohorts. By combining the specific marker genes of B cell subtypes, we developed a prediction model of 13 markers that can predict the risk of HF in patients after myocardial infarction, providing new ideas and tools for clinical diagnosis and treatment.ConclusionSub-cluster B cells may play a significant role in post-MI HF. We found that the STING1, HSPB1, CCL5, ACTN1, and ITGB2 genes in patients with post-MI HF showed the same trend of increase as those without post-MI HF.

Objective and Aims: The number of circulating tumor cells (CTCs) and the presence of circulating tumor microemboli (CTM) were determined in the peripheral blood of patients with liver cancer (LC). The relationship between CTCs, CTM, clinicopathologic features, and prognosis of LC was analyzed. The objective of this study was to determine the diagnostic and prognostic value of CTCs/CTM in LC. Subjects and Methods: Patients with LC were enrolled between May 2013 and August 2017, and 67 patients were included in the study. Overall survival curves were built using the Kaplan–Meier method and the log-rank test to identify risk factors. The results were analyzed using a Cox proportional hazards model and expressed as hazard ratio and 95% confidence interval (95% CI). Results: CTCs and either CTCs or CTM were detected in 27 patients (40.3%) and 29 patients (43.3%). CTM were found in four patients. One-year, 3-year, and 5-year survival rates were 42%, 20%, and 15%, respectively. Univariate Cox regression analysis showed that alpha-fetoprotein (AFP), number of CTCs, presence of CTM, and positive CTC/CTM were associated with survival time. Multivariate Cox regression analysis showed that alpha fetoprotein (AFP), number of CTCs, and presence of CTM were independent risk factors for survival in patients with LC. Conclusion: There was no significant correlation between the number of CTCs, the presence of CTM, and clinicopathologic factors. AFP, number of CTCs, and presence of CTM were independent risk factors for survival in patients with LC.