Lysosome-dependent Cell Death Research Articles

Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients.

Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma. We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model. A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. The high CDI group exhibited relatively lower numbers of immune-infiltrating cells and showed resistance to commonly used drugs such as docetaxel and axitinib. Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01). The CDI model, incorporating multiple PCD modes, accurately predicts the clinical prognosis and drug sensitivity of melanoma patients.

Integrative analysis of multiple cell death model for precise prognosis and drug response prediction in gastric cancer.

Gastric cancer (GC) is a common upper gastrointestinal tumor. However, the evaluation of prognosis and treatment response in patients with gastric cancer remains a challenge. Programmed cell death (PCD) is one of the important terminal paths for the cells of metazoans, and is involved in a variety of biological events that include morphogenesis, maintenance of tissue homeostasis, and elimination of harmful cells. The objective of this project is to investigate the predictive significance of cell death pathways and create prognostic signatures associated to cell death, with the purpose of forecasting prognosis and providing guidance for the treatment of gastric cancer. Gene transcription profiles and corresponding clinical data of gastric cancer patients were collected from The Cancer Genome Atlas (TCGA-STAD, n = 448) and the Gene Expression Comprehensive Database (GSE84437, n = 483). Thirteen types of cell death-related genes, including apoptosis, necroptosis, pyroptosis, ferroptosis, autophagy, cuprotosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, were analysed. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Patients were categorized into high and low-risk groups based on the median risk score for survival difference analysis. Cell death-related genes associated with prognosis were identified in the TCGA-STAD training cohort using Lasso-Cox regression to generate a risk score. Additionally, the response to immunotherapy in the high-risk and low-risk groups was calculated using the oncoPredict algorithm. Futhermore, the model genes were validated in the GEO validation set. A total of 324 differential programmed cell death (PCD)-related genes were identified, and 65 were selected through single-factor Cox analysis. Six PCD-related genes were ultimately identified by Lasso regression to construct a prognostic risk score model. The log-rank test revealed that patients in the high-risk group had inferior survival time compared with those in the low-risk group. The area under the ROC curve (AUC) for the training group at years 1, 3, and 5 were 0.684, 0.713, 0.743, respectively, while the AUC for the validation cohort at years 1, 3, and 5 were 0.695, 0.704, and 0.707, respectively. Unsupervised clustering identified potential subtypes included in the model, and a survival difference was also observed between the two subgroups. Multifactor Cox results, combined with clinical information, demonstrated that the prognostic risk score can serve as an independent prognostic factor, irrespective of other clinical features. By comprehensively analyzing multiple cell death patterns, we have established a novel model that accurately forecasts the clinical prognosis and drug sensitivity of gastric cancer. It was found that all 12 representative drugs may not be suitable for patients in high-risk groups.

Autophagy-lysosomal-associated neuronal death in neurodegenerative disease.

Autophagy, the major lysosomal pathway for degrading damaged or obsolete constituents, protects neurons by eliminating toxic organelles and peptides, restoring nutrient and energy homeostasis, and inhibiting apoptosis. These functions are especially vital in neurons, which are postmitotic and must survive for many decades while confronting mounting challenges of cell aging. Autophagy failure, especially related to the declining lysosomal ("phagy") functions, heightens the neuron's vulnerability to genetic and environmental factors underlying Alzheimer's disease (AD) and other late-age onset neurodegenerative diseases. Components of the global autophagy-lysosomal pathway and the closely integrated endolysosomal system are increasingly implicated as primary targets of these disorders. In AD, an imbalance between heightened autophagy induction and diminished lysosomal function in highly vulnerable pyramidal neuron populations yields an intracellular lysosomal build-up of undegraded substrates, including APP-βCTF, an inhibitor of lysosomal acidification, and membrane-damaging Aβ peptide. In the most compromised of these neurons, β-amyloid accumulates intraneuronally in plaque-like aggregates that become extracellular senile plaques when these neurons die, reflecting an "inside-out" origin of amyloid plaques seen in human AD brain and in mouse models of AD pathology. In this review, the author describes the importance of lysosomal-dependent neuronal cell death in AD associated with uniquely extreme autophagy pathology (PANTHOS) which is described as triggered by lysosomal membrane permeability during the earliest "intraneuronal" stage of AD. Effectors of other cell death cascades, notably calcium-activated calpains and protein kinases, contribute to lysosomal injury that induces leakage of cathepsins and activation of additional death cascades. Subsequent events in AD, such as microglial invasion and neuroinflammation, induce further cytotoxicity. In major neurodegenerative disease models, neuronal death and ensuing neuropathologies are substantially remediable by reversing underlying primary lysosomal deficits, thus implicating lysosomal failure and autophagy dysfunction as primary triggers of lysosomal-dependent cell death and AD pathogenesis and as promising therapeutic targets.

Construction and analysis of a lysosome-dependent cell death score-based prediction model for non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is the most common type of tumor globally and the leading cause of cancer-related deaths. Although treatment strategies such as immune checkpoint inhibitors and chemotherapy have advanced, the heterogeneity among NSCLC patients results in significant variability in treatment outcomes. Studies have shown that certain patients respond poorly to immune checkpoint inhibitors, indicating that treatment response is closely related to multiple factors. Therefore, it is necessary to develop predictive models to stratify patients based on gene expression and clinical characteristics, aiming for precision therapy.ObjectiveThis study aims to construct a stratified prognostic model for NSCLC patients based on lysosome-dependent cell death (LDCD) scoring by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. By analyzing the immune-related characteristics of high-risk and low-risk groups, we further explored the impact of cell death patterns on lung cancer and identified potential therapeutic targets.MethodsThis study obtained single-cell RNA sequencing data and gene expression data of NSCLC patients and normal lung tissues from the GEO and TCGA databases. We used R packages such as Seurat and CellChat for data preprocessing and analysis, and performed dimensionality reduction and visualization through Principal Component Analysis (PCA) and UMAP algorithms. LASSO regression analysis was used to construct the predictive model, followed by cross-validation and ROC curve analysis. The model’s effectiveness was validated through survival analysis and immune microenvironment analysis.ResultsThe study showed a significant increase in the proportion of monocytes in NSCLC tissues, suggesting their important role in cancer progression. Cell communication analysis indicated that macrophages, smooth muscle cells, and myeloid cells exhibit strong intercellular communication during cancer progression. Using the constructed prognostic model based on 12 LDCD-related genes, we found significant differences in overall survival and immune microenvironment between the high-risk and low-risk groups.

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.

NIR photoactivated electron intersystem crossing to evoke calcium-mediated lysosome-dependent cell death and immunotherapy

The efficacy of photodynamic therapy (PDT) highly depends on the photosensitizer (PS). How to rationally design PS with an efficient inter-system crossing (ISC) rate to enable the capacity for reactive oxygen species (ROS) production, remains a great challenge. Herein, we have successfully provided two isomers at two positions of the BODIPY core with a decoration of cyclometalated Ir(III) fragment to avoid the disadvantages of themselves as PS for PDT. With the theoretical and experimental investigations, the intact connection with BODIPY and Ir(III) fragment has been confirmed to facilitate the ISC process to produce ROS. PS could be accumulated in the lysosome, and excited with a near-infrared wavelength light (630 nm). Due to the severe ROS production upon irradiation, the lysosome membrane permeabilization (LMP) was activated. The calcium homeostasis has been subsequently disrupted with the release of the lysosomal contents, resulting in acidosis and calcium-mediated lysosome-dependent cell death (autophagy blockage and mitochondrial apoptosis). The photodynamic immunotherapy was also evoked with the disturbed calcium homeostasis, which has been demonstrated with the bilateral tumor models in vivo. This study not only provided a superior NIR photosensitizer but also presented an effective strategy to reduce the energy gap via electron intersystem crossing to boost photodynamic immunotherapy.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and drug response. We utilized 13 PCD pathways, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis to develop predictive models based on 73 machine learning combinations from 10 algorithms. Bulk RNA-sequencing, single-cell RNA-sequencing transcriptomic data, and matched clinicopathological information were obtained from the TCGA-AML, Tyner, and GSE37642-GPL96 cohorts. These datasets were leveraged to construct and validate the models. Additionally, in vitro experiments were conducted to substantiate the bioinformatics findings. The machine learning approach established a 6-gene pan-programmed cell death-related genes index (PPCDI) signature. Validation in two external cohorts showed high PPCDI associated with worse prognosis in AML patients. Incorporating PPCDI with clinical variables, we constructed several robust prognostic nomograms that accurately predicted prognosis of AML patients. Multi-omics analysis integrating bulk and single-cell transcriptomics revealed correlations between PPCDI and immunological features, delineating the immune microenvironment landscape in AML. Patients with high PPCDI exhibited resistance to conventional chemotherapy like doxorubicin but retained sensitivity to dasatinib and methotrexate (FDA-approved drugs for other leukemias), suggesting the potential of PPCDI to guide personalized therapy selection in AML. In summary, we developed a novel PPCDI model through comprehensive analysis of diverse programmed cell death pathways. This PPCDI signature demonstrates great potential in predicting clinical prognosis and drug sensitivity phenotypes in AML patients.

Underlying Mechanism of Lysosomal Membrane Permeabilization in CNS Injury: A Literature Review.

Lysosomes play a crucial role in various intracellular pathways as their final destination. Various stressors, whether mild or severe, can induce lysosomal membrane permeabilization (LMP), resulting in the release of lysosomal enzymes into the cytoplasm. LMP not only plays a pivotal role in various cellular events but also significantly contributes to programmed cell death (PCD). Previous research has demonstrated the participation of LMP in central nervous system (CNS) injuries, including traumatic brain injury (TBI), spinal cord injury (SCI), subarachnoid hemorrhage (SAH), and hypoxic-ischemic encephalopathy (HIE). However, the mechanisms underlying LMP in CNS injuries are poorly understood. The occurrence of LMP leads to the activation of inflammatory pathways, increased levels of oxidative stress, and PCD. Herein, we present a comprehensive overview of the latest findings regarding LMP and highlight its functions in cellular events and PCDs (lysosome-dependent cell death, apoptosis, pyroptosis, ferroptosis, and autophagy). In addition, we consolidate the most recent insights into LMP in CNS injury by summarizing and exploring the latest advances. We also review potential therapeutic strategies that aim to preserve LMP or inhibit the release of enzymes from lysosomes to alleviate the consequences of LMP in CNS injury. A better understanding of the role that LMP plays in CNS injury may facilitate the development of strategic treatment options for CNS injury.

Phenazine Cations as Anticancer Theranostics†.

The biological properties of two water-soluble organic cations based on polypyridyl structures commonly used as ligands for photoactive transition metal complexes designed to interact with biomolecules are investigated. A cytotoxicity screen employing a small panel of cell lines reveals that both cations show cytotoxicity toward cancer cells but show reduced cytotoxicity to noncancerous HEK293 cells with the more extended system being notably more active. Although it is not a singlet oxygen sensitizer, the more active cation also displayed enhanced potency on irradiation with visible light, making it active at nanomolar concentrations. Using the intrinsic luminescence of the cations, their cellular uptake was investigated in more detail, revealing that the active compound is more readily internalized than its less lipophilic analogue. Colocalization studies with established cell probes reveal that the active cation predominantly localizes within lysosomes and that irradiation leads to the disruption of mitochondrial structure and function. Stimulated emission depletion (STED) nanoscopy and transmission electron microscopy (TEM) imaging reveal that treatment results in distinct lysosomal swelling and extensive cellular vacuolization. Further imaging-based studies confirm that treatment with the active cation induces lysosomal membrane permeabilization, which triggers lysosome-dependent cell-death due to both necrosis and caspase-dependent apoptosis. A preliminary toxicity screen in the Galleria melonella animal model was carried out on both cations and revealed no detectable toxicity up to concentrations of 80 mg/kg. Taken together, these studies indicate that this class of synthetically easy-to-access photoactive compounds offers potential as novel therapeutic leads.

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma.

Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454. Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time. A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Magnetic modulation of lysosomes for cancer therapy.

Lysosomes play a central role in biochemical signal transduction and oxidative stress in cells. Inducing lysosome membrane penetration (LMP) to cause lysosomal-dependent cell death (LCD) in tumor cells is an effective strategy for cancer therapy. Chemical drugs can destroy the stability of lysosomes by neutralizing protons within the lysosomes or enhancing the fragility of the lysosomal membranes. However, there remain several unsolved problems of traditional drugs in LMP induction due to insufficient lysosomal targeting, fast metabolism, and toxicity in normal cells. With the development of nanotechnology, magnetic nanoparticles have been demonstrated to target lysosomes naturally, providing a versatile tool for lysosomal modulation. Combined with excellent tissue penetration and spatiotemporal manipulability of magnetic fields, magnetic modulation of lysosomes progresses rapidly in inducing LMP and LCD for cancer therapy. This review comprehensively discussed the strategies of magnetic modulation of lysosomes for cancer therapy. The intrinsic mechanisms of LMP-induced LCD were first introduced. Then, the modulation of lysosomes by diverse physical outputs of magnetic fields was emphatically discussed. Looking forward, this review will shed the light on the prospect of magnetic modulation of lysosomes, inspiring future research of magnetic modulation strategy in cancer therapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

A comprehensive analysis of the prognostic value and immune microenvironment of lysosome-dependent cell death in glioma: Including glioblastoma and low-grade glioma.

Lysosome-dependent cell death (LCD) plays a significant role in overcoming cancer apoptosis and drug resistance. However, the relationship between LCD-associated genes (LCDGs) and glioma, including glioblastoma (GBM) and low-grade glioma (LGG), remains unclear. In this study, an LCDGs risk signature was constructed for glioma patients by utilizing 4 algorithms (Extreme Gradient Boosting, Support Vector Machine, Random Forest, and Generalized Linear Models) to identify core LCDGs. Their correlation with clinical features and the immune microenvironment was also determined in glioma, GBM, and LGG. Additionally, the role of hub LCDGs in various cancers was elucidated via pan-cancer analyses. Validation of the core gene in glioma was performed using qRT-qPCR and immunofluorescence staining analysis. The results showed that the LCDGs risk signature was strongly associated with the prognosis, cancer grades, histological types, and primary therapy outcomes of glioma patients. Furthermore, it was closely linked to the overall survival of LGG patients. Mechanistic analyses revealed a significant association between the risk signature and the immune microenvironment in glioma. Based on differential expression analysis, receiver operating characteristic analysis, and interacted model algorithms, LAPTM4A was identified as a hub LCDG in glioma. It exhibited significant upregulation in glioma, GBM, and LGG samples. Moreover, LAPTM4A expression correlated with the prognosis of glioma and LGG patients, as well as age, grades, histological types, and primary therapy outcomes in glioma. Pan-cancer analysis confirmed that LAPTM4A expression was modulated in the majority of cancers and was associated with the prognosis of various cancers. Mechanistic analyses suggested a strong relationship between LAPTM4A and immune cell infiltration, as well as several drug sensitivities. In conclusion, our findings suggest that LAPTM4A may serve as a potential oncogene associated with LCD in pan-cancer, particularly in glioma, GBM, and LGG. These findings provide important insights for individualized treatment of glioma.

Intracellular calcium links milk stasis to lysosome-dependent cell death during early mammary gland involution

Involution of the mammary gland after lactation is a dramatic example of coordinated cell death. Weaning causes distension of the alveolar structures due to the accumulation of milk, which, in turn, activates STAT3 and initiates a caspase-independent but lysosome-dependent cell death (LDCD) pathway. Although the importance of STAT3 and LDCD in early mammary involution is well established, it has not been entirely clear how milk stasis activates STAT3. In this report, we demonstrate that protein levels of the PMCA2 calcium pump are significantly downregulated within 2–4 h of experimental milk stasis. Reductions in PMCA2 expression correlate with an increase in cytoplasmic calcium in vivo as measured by multiphoton intravital imaging of GCaMP6f fluorescence. These events occur concomitant with the appearance of nuclear pSTAT3 expression but prior to significant activation of LDCD or its previously implicated mediators such as LIF, IL6, and TGFβ3, all of which appear to be upregulated by increased intracellular calcium. We further demonstrate that increased intracellular calcium activates STAT3 by inducing degradation of its negative regulator, SOCS3. We also observed that milk stasis, loss of PMCA2 expression and increased intracellular calcium levels activate TFEB, an important regulator of lysosome biogenesis through a process involving inhibition of CDK4/6 and cell cycle progression. In summary, these data suggest that intracellular calcium serves as an important proximal biochemical signal linking milk stasis to STAT3 activation, increased lysosomal biogenesis, and lysosome-mediated cell death.

Association Between Diverse Cell Death Patterns Related Gene Signature and Prognosis, Drug Sensitivity, and Immune Microenvironment in Glioblastoma

Glioblastoma (GBM) is the most invasive type of glioma and is difficult to treat. Diverse programmed cell death (PCD) patterns have a significant association with tumor initiation and progression. A novel prognostic model based on PCD genes may serve as an effective tool to predict the prognosis of GBM. The study incorporated 11 PCD patterns, namely apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis, to develop the model. To construct and validate the model, both bulk and single-cell transcriptome data, along with corresponding clinical data from GBM cases, were obtained from the TCGA-GBM, REMBRANDT, CGGA, and GSE162631 datasets. A cell death-related signature containing 14 genes was constructed with the TCGA-GBM cohort and validated in the REMBRANDT and CGGA datasets. GBM patients with a higher cell death index (CDI) were significantly associated with poorer survival outcomes. Two separate clusters associated with clinical outcomes emerged from unsupervised analysis. A multivariate Cox regression analysis was conducted to examine the association of CDI with clinical characteristics, and a prognostic nomogram was developed. Drug sensitivity analysis revealed high-CDI GBM patients might be resistant to carmustine while sensitive to 5-fluorouracil. Less abundance of natural killer cells was found in GBM cases with high CDI and bulk transcriptome data. A cell death-related prognostic model that could predict the prognosis of GBM patients with good performance was established, which could discriminate between the prognosis and drug sensitivity of GBM.

Constructing a Prognostic Model of Uterine Corpus Endometrial Carcinoma and Predicting Drug-Sensitivity Responses Using Programmed Cell Death-Related Pathways.

Background: Uterine Corpus Endometrial Carcinoma (UCEC) is the most common type of cancer that develops in the uterus, specifically originating from the endometrium, the inner lining of the uterus. Programmed cell death (PCD) is a highly regulated process that eliminates damaged, aged, or unwanted cells in the body. Dysregulation of PCD pathways can contribute to the formation and progression of various cancers, including UCEC. Methods: Fourteen PCD pathways (autophagy-dependent cell death, alkaliptosis, apoptosis, cuproptosis, entotic cell death, ferroptosis, immunogenic cell death, lysosome-dependent cell death, MPT-driven necrosis, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were involved in building a prognostic signature. The model was trained and tested using data from the TCGA-UCEC and validated with the GSE119041 dataset. Results: A 12-gene PCD signature (DRAM1, ELAPOR1, MAPT, TRIM58, UCHL1, CDKN2A, CYFIP2, AKT2, LINC00618, TTPA, TRIM46, and NOS2) was established and validated in an independent dataset. UCEC patients with a high PCD score (PCDS) exhibited worse prognosis. Furthermore, PCDS was found to be associated with immune related cells and key tumor microenvironment components through multiple methods. It was observed that UCEC patients with a high PCD score may not benefit from immunotherapy, but some chemo drugs like Bortezomib may be useful. Conclusion: In conclusion, a novel PCD model was established by comprehensively analyzing diverse cell death patterns. This model accurately predicts the clinical prognosis and drug sensitivity of UCEC. The findings suggest that the PCD signature can serve as a valuable tool in assessing prognosis and guiding treatment decisions for UCEC patients.

A monofunctional Pt(II) complex combats triple negative breast cancer by triggering lysosome-dependent cell death.

Monofunctional Pt(II) complexes with potent efficacy to overcome the drawbacks of current platinum drugs represent a promising therapeutic approach for triple negative breast cancer (TNBC). A heterocyclic-ligated monofunctional Pt(II) complex PtL with a unique action of mode was designed and investigated. PtL induced DNA single-strand breaks and caused genomic instability in TNBC cells. Mechanism studies demonstrated that PtL disrupted lysosomal acidity and function, which in turn triggered lysosome-dependent cell death. Furthermore, PtL showed convincing suppression in the tube forming and cell migratory abilities against the metastatic potential of TNBC cells. The synthesis and investigation of PtL revealed its potential value as an anti-TNBC drug and extended the family of monofunctional Pt(II) complexes.

Hexamethylene amiloride synergizes with venetoclax to induce lysosome-dependent cell death in acute myeloid leukemia

Tumors maintain an alkaline intracellular environment to enable rapid growth. The proton exporter NHE1 participates in maintenance of this pH gradient. However, whether targeting NHE1 could inhibit the growth of tumor cells remains unknown. Here, we report that the NHE1 inhibitor Hexamethylene amiloride (HA) efficiently suppresses the growth of AML cell lines. Moreover, HA combined with venetoclax synergized to efficiently inhibit the growth of AML cells. Interestingly, lysosomes are the main contributors to the synergism of HA and venetoclax in inhibiting AML cells. Most importantly, the combination of HA and venetoclax also had prominent anti-leukemia effects in both xenograft models and bone marrow samples from AML patients. In summary, our results provide evidence that the NHE1 inhibitor HA or its combination with venetoclax efficiently inhibits the growth of AML invitro and invivo.

Identification and analysis of diverse cell death patterns in diabetic kidney disease using microarray-based transcriptome profiling and single-nucleus RNA sequencing

BackgroundDiabetic kidney disease (DKD) is the most lethal complication of diabetes. Diverse programmed cell death (PCD) has emerged as a crucial disease phenotype that has the potential to serve as an indicator of renal function decline and can be used as a target for researching drugs for DKD. MethodsMicroarray-based transcriptome profiling and single-nucleus transcriptome sequencing (snRNA-seq) related to DKD were retrieved from the Gene Expression Omnibus (GEO) database. 13 PCD-related genes (including alkaliptosis, apoptosis, autophagy-dependent cell death, cuproptosis, disulfidptosis, entotic cell death, ferroptosis, lysosome-dependent cell death, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were obtained from various public databases and reviews. The gene set variation analysis (GSVA) analysis was used to explore the pathway activity of these 13 PCDs in DKD, and the pathway activity of these PCDs in different renal cells was studied based on DKD-related snRNA-seq data. To identify the core PCDs that play a significant role in DKD, we analyzed the relationships between different types of PCD and immune infiltration, fibrosis-related gene expression levels, glomerular filtration rate (GFR), and diagnostic efficiency in DKD. Using the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we screened for core death genes among the core PCDs and constructed a cell death-related signature (CDS) risk score based on the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, we validated the predictive performance of the CDS risk score in an independent validation set. ResultsWe identified 4 core PCD pathways, namely entotic cell death, apoptosis, necroptosis, and pyroptosis in DKD, and further applied the WGCNA algorithm to screen 4 core death genes (CASP1, CYBB, PLA2G4A, and CTSS) and constructed a CDS risk score based on these genes. The CDS risk score demonstrated high diagnostic efficiency for DKD patients, and those with higher scores had higher levels of immune cell infiltration and poorer GFR. ConclusionOur study sheds light on the fact that multiple PCDs contribute to the progression of DKD, highlighting potential therapeutic targets for treating this disease.

Engineering Crystallinity Gradients for Tailored CaO2 Nanostructures: Enabling Alkalinity-Reinforced Anticancer Activity with Minimized Ca2+/H2O2 Production.

CaO2 nanoparticles (CNPs) can produce toxic Ca2+ and H2O2 under acidic pH, which accounts for their intrinsic anticancer activity but at the same time raises safety concerns upon systemic exposure. Simultaneously realizing minimized Ca2+/H2O2 production and enhanced anticancer activity poses a dilemma. Herein, we introduce a "crystallinity gradient-based selective etching" (CGSE) strategy, which is realized by creating a crystallinity gradient in a CNP formed by self-assembled nanocrystals. The nanocrystals distributed in the outer layer have a higher crystallinity and thus are chemically more robust than those distributed in the inner layer, which can be selectively etched. CGSE not only leads to CNPs with tailored single- and double-shell hollow structures and metal-doped compositions but more surprisingly enables significantly enhanced anticancer activity as well as tumor growth inhibition under limited Ca2+/H2O2 production, which is attributed to an alkalinity-reinforced lysosome-dependent cell death pathway.

Ultrasound-Induced Gold Nanoparticle United with Acoustic Reprogramming of Macrophages for Enhanced Cancer Therapy.

Sonodynamic therapy (SDT) has considerable potential in cancer treatment and exhibits high tissue penetration with minimal damage to healthy tissues. The efficiency of SDT is constrained by the complex immunological environment and tumor treatment resistance. Herein, a specific acoustic-actuated tumor-targeted nanomachine is proposed to generate mechanical damage to lysosomes for cancer SDT. The hybrid nanomachine was assembled with gold nanoparticles (GNPs) as the core and encapsulated with macrophage exosomes modified by AS1411 aptamers (GNP@EXO-APs) to optimize the pharmacokinetics and tumor aggregation. GNP@EXO-APs could be specifically transferred to the lysosomes of tumor cells. After induction with ultrasound, GNP@EXO-APs generated strong mechanical stress to produce lysosomal-dependent cell death in cancer cells. Notably, tumor-associated macrophages were reprogrammed in the ultrasound environment to an antitumor phenotype. Enhanced mechanical destruction via GNP@EXO-APs and immunotherapy of cancer cells were verified both in vitro and in vivo under SDT. This study provides a new direction for inside-out killing effects on tumor cells for cancer treatment.