Methods for the Study of Entotic Cell Death

Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell RNA sequencing: insights for targeted and personalized immunotherapy strategies

Background

High-grade serous ovarian cancer (HGSOC), the predominant subtype of epithelial ovarian cancer, is frequently diagnosed at an advanced stage due to its nonspecific early symptoms. Despite standard treatments, including cytoreductive surgery and platinum-based chemotherapy, significant improvements in survival have been limited. Understanding the molecular mechanisms, immune landscape, and drug sensitivity of HGSOC is crucial for developing more effective and personalized therapies. This study integrates insights from cancer immunology, molecular profiling, and drug sensitivity analysis to identify novel therapeutic targets and improve treatment outcomes. Utilizing single-cell RNA sequencing (scRNA-seq), the study systematically examines tumor heterogeneity and immune microenvironment, focusing on biomarkers influencing drug response and immune activity, aiming to enhance patient outcomes and quality of life.

Methods

scRNA-seq data was obtained from the GEO database in this study. Differential gene expression was analyzed using gene ontology and gene set enrichment methods. InferCNV identified malignant epithelial cells, while Monocle, Cytotrace, and Slingshot software inferred subtype differentiation trajectories. The CellChat software package predicted cellular communication between malignant cell subtypes and other cells, while pySCENIC analysis was utilized to identify transcription factor regulatory networks within malignant cell subtypes. Finally, the analysis results were validated through functional experiments, and a prognostic model was developed to assess prognosis, immune infiltration, and drug sensitivity across various risk groups.

Results

This study investigated the cellular heterogeneity of HGSOC using scRNA-seq, focusing on tumor cell subtypes and their interactions within the tumor microenvironment. We confirmed the key role of the C2 IGF2+ tumor cell subtype in HGSOC, which was significantly associated with poor prognosis and high levels of chromosomal copy number variations. This subtype was located at the terminal differentiation of the tumor, displaying a higher degree of malignancy and close association with stage IIIC tissue types. The C2 subtype was also associated with various metabolic pathways, such as glycolysis and riboflavin metabolism, as well as programmed cell death processes. The study highlighted the complex interactions between the C2 subtype and fibroblasts through the MK signaling pathway, which may be closely related to tumor-associated fibroblasts and tumor progression. Elevated expression of PRRX1 was significantly connected to the C2 subtype and may impact disease progression by modulating gene transcription. A prognostic model based on the C2 subtype demonstrated its association with adverse prognosis outcomes, emphasizing the importance of immune infiltration and drug sensitivity analysis in clinical intervention strategies.

Conclusion

This study integrates molecular oncology, immunotherapy, and drug sensitivity analysis to reveal the mechanisms driving HGSOC progression and treatment resistance. The C2 IGF2+ tumor subtype, linked to poor prognosis, offers a promising target for future therapies. Emphasizing immune infiltration and drug sensitivity, the research highlights personalized strategies to improve survival and quality of life for HGSOC patients.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.

MATERIALS AND METHODS

We conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.

RESULTS

Ten PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.

CONCLUSION

In conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.

Assessing the role of programmed cell death signatures and related gene TOP2A in progression and prognostic prediction of clear cell renal cell carcinoma

Kidney Clear Cell Carcinoma (KIRC), the predominant form of kidney cancer, exhibits a diverse therapeutic response to Immune Checkpoint Inhibitors (ICIs), highlighting the need for predictive models of ICI efficacy. Our study has constructed a prognostic model based on 13 types of Programmed Cell Death (PCD), which are intertwined with tumor progression and the immune microenvironment. Validated by analyses of comprehensive datasets, this model identifies seven key PCD genes that delineate two subtypes with distinct immune profiles and sensitivities to anti-PD-1 therapy. The high-PCD group demonstrates a more immune-suppressive environment, while the low-PCD group shows better responses to PD-1 treatment. In particular, TOP2A emerged as crucial, with its inhibition markedly reducing KIRC cell growth and mobility. These findings underscore the relevance of PCDs in predicting KIRC outcomes and immunotherapy response, with implications for enhancing clinical decision-making.

Entosis: the core mechanism and crosstalk with other cell death programs

Cell death pathways play critical roles in organism development and homeostasis as well as in the pathogenesis of various diseases. While studies over the last decade have elucidated numerous different forms of cell death that can eliminate cells in various contexts, how certain mechanisms impact physiology is still not well understood. Moreover, recent studies have shown that multiple forms cell death can occur in a cell population, with different forms of death eliminating individual cells. Here, we aim to describe the known molecular mechanisms of entosis, a non-apoptotic cell engulfment process, and discuss signaling mechanisms that control its induction as well as its possible crosstalk with other cell death mechanisms.

Mechanisms and significance of entosis for tumour growth and progression

To date, numerous mechanisms have been identified in which one cell engulfs another, resulting in the creation of 'cell-in-cell' (CIC) structures, which subsequently cause cell death. One of the mechanisms of formation of these structures is entosis, which is presumably associated with possible carcinogenesis and tumour progression. The peculiarity of the process is that entotic cells themselves actively invade the host cell, and afterwards have several possible variants of fate. Entotic formations are structures where one cell is engulfed by another cell, creating a cell-in-cell structure. The nucleus of the outer cell has a crescent shape, while the inner cell is surrounded by a large entotic vacuole. These characteristics differentiate entosis from cell cannibalism. It's worth noting that entotic formations are not necessarily harmful and may even be beneficial in some cases. In this article we will consider the mechanism of entosis and variants of entotic cell death, and also put forward hypothesis about possible variants of participation of this process on the formation and progression of cancer. This article also presents our proposed classification of functional forms of entosis.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.

Heterotypic cell-in-cell structures between cancer and NK cells is associated with enhanced anti-cancer drug resistance

The heterotypic CIC structures formed of cancer and immune cells have been observed in tumor tissues. We aimed to assess the feasibility of using heterotypic CICs as a functional biomarker to predict NK susceptibility and drug resistance. The heterotypic CIC-forming cancer cells showed a lower response to NK cytotoxicity and higher proliferative ability than non-CIC cancer cells. After treatment with anticancer drugs, cancer cells that formed heterotypic CICs showed a higher resistance to anticancer drugs than non-CIC cancer cells. We also observed the formation of more CIC structures in cancer cells treated with anticancer drugs than in the non-treated group. Our results confirm the association between heterotypic CIC structures and anticancer drug resistance in CICs formed from NK and cancer cells. These results suggest a mechanism underlying immune evasion in heterotypic CIC cancer cells and provide insights into the anticancer drug resistance of cancer cells.

•

Conformation of heterotypic CIC structures formed between cancer and NK cells

•

Heterotypic CICs exhibit a higher proliferative ability than non-CIC cells

•

Heterotypic CICs are associated with NK susceptibility

•

Heterotypic CICs are involved in anticancer drug resistance

Cell Death and the p53 Enigma During Mammalian Embryonic Development

Twelve forms of programmed cell death (PCD) have been described in mammalian cells, but which of them occurs during embryonic development and the role played by the p53 transcription factor and tumor suppressor remains enigmatic. Although p53 is not required for mouse embryonic development, some studies conclude that PCD in pluripotent embryonic stem cells from mice (mESCs) or humans (hESCs) is p53-dependent whereas others conclude that it is not. Given the importance of pluripotent stem cells as models of embryonic development and their applications in regenerative medicine, resolving this enigma is essential. This review reconciles contradictory results based on the facts that p53 cannot induce lethality in mice until gastrulation and that experimental conditions could account for differences in results with ESCs. Consequently, activation of the G2-checkpoint in mouse ESCs is p53-independent and generally, if not always, results in noncanonical apoptosis. Once initiated, PCD occurs at equivalent rates and to equivalent extents regardless of the presence or absence of p53. However, depending on experimental conditions, p53 can accelerate initiation of PCD in ESCs and late-stage blastocysts. In contrast, DNA damage following differentiation of ESCs in vitro or formation of embryonic fibroblasts in vivo induces p53-dependent cell cycle arrest and senescence.

Classification of Cell-in-Cell Structures: Different Phenomena with Similar Appearance

A phenomenon known for over 100 years named “cell-in-cell” (CIC) is now undergoing its renaissance, mostly due to modern cell visualization techniques. It is no longer an esoteric process studied by a few cell biologists, as there is increasing evidence that CICs may have prognostic and diagnostic value for cancer patients. There are many unresolved questions stemming from the difficulties in studying CICs and the limitations of current molecular techniques. CIC formation involves a dynamic interaction between an outer or engulfing cell and an inner or engulfed cell, which can be of the same (homotypic) or different kind (heterotypic). Either one of those cells appears to be able to initiate this process, which involves signaling through cell–cell adhesion, followed by cytoskeleton activation, leading to the deformation of the cellular membrane and movements of both cells that subsequently result in CICs. This review focuses on the distinction of five known forms of CIC (cell cannibalism, phagoptosis, enclysis, entosis, and emperipolesis), their unique features, characteristics, and underlying molecular mechanisms.