[PDF] Single-cell analyses implicate ascites in remodeling the ecosystems of primary and metastatic tumors in ovarian cancer | Semantic Scholar (2024)

This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research.

This review implicates the diversity of TAM phenotypes and functions, how this heterogeneity has been re-evaluated with the advent of single-cell technologies, and the impact of TME metabolic reprogramming on TAMs.

This study defines the ontological and functional heterogeneities in cellular ecosystems of primary and metastatic liver tumors, providing a foundation for future investigation of the underlying cellular mechanisms.

The impact of NETosis at almost all stages of tumor metastasis is outlined and the multifaceted interplay between NETosis and the TME components is crucial for supporting the rational development of highly effective combination immunotherapeutic strategies with anti-NETosis for patients with metastatic disease.

The findings unveil a mechanism of mitophagy in OC angiogenesis and indicate the clinical relevance of EV enriched L-kyn as a potential biomarker for tumorigenesis and progression.

A significant correlation between OR7E47P and TME modulation in LUSC is suggested and the ORPScore was a better prognostic factor and correlated positively with the immunotherapeutic response in cancer patients.

A cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), is introduced to reanalyze 178,739 single-cell reference profiles and an ensemble learning model pipeline within the framework of metabolic reprogramming is proposed and validated.

A hierarchical self‐assembly with self‐driven and energy conversion capabilities based on p‐aminophenol and diethylenetriamine building blocks is reported, and the sensitive multi‐mode detection of β‐Gal activity is realized, providing new methods for the diagnosis and treatment of ovarian cancer.

A significant increase in tumor cell state heterogeneity was identified, which was strongly correlated with patient prognosis and treatment response, and increased intra- and inter-tumoral functional clonality was associated with the characteristics of cancer-associated fibroblast (CAF).

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Recent translational findings which have used primary ascites-derived tumor cells as a tool to understand the pathogenesis of the disease are highlighted, yielding new insights and targets for therapeutic manipulation.

Single-cell transcriptomics analysis of malignant ascites samples from patients with high-grade serous ovarian cancer reveals inter- and intra-patient heterogeneity in malignant cells, cancer-associated fibroblasts and macrophages.

This review will describe the various hypotheses about the origin of CAFs, their major functions in the tumor microenvironment in ovarian cancer, and will discuss the potential of targeting CAFs as a possible therapeutic approach.

The immune evolution of metastasis is described and the black box of how tumors respond to neoadjuvant chemotherapy is uncovered, and scMetabolism, a computational pipeline for quantifying single-cell metabolism, is developed.

It is demonstrated that the presence of tumor infiltrating lymphocytes (TILs) is associated with improved clinical outcome in ovarian cancer patients, and a major direction is to develop biomarkers that would predict responsiveness to different types of immunotherapies, and allow for treatment selection based on the results.

Deep single-cell RNA sequencing of tumor-infiltrating T cells in non-small-cell lung cancer identifies features associated with functional states and clinical outcome and provides a new approach for patient stratification.

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