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The precise delivery of drugs to tumor sites and the thermoresistance of tumors remain major challenges in photothermal therapy (PTT). Somatostatin receptor 2 (SSTR2) is proposed as an ideal target for the precise treatment of SCLC. We developed a targeting nano-drug delivery system comprising anti-SSTR2 monoclonal antibody (MAb) surface-modified nanoparticles co-encapsulating Cypate and gambogic acid (GA). The formed SGCPNs demonstrated excellent monodispersity, physiological stability, preferable biocompatibility, and resultant efficient photothermal conversion efficacy. SGCPNs were quickly internalized by SSTR2-overexpressing SCLC cells, triggering the release of GA under acidic and near-infrared (NIR) laser irradiation environments, leading to their escape from lysosomes to the cytosol and then diffusion into the nucleus. SGCPNs can not only decrease the cell survival rate but also inhibit the activity of heat shock protein 90 (HSP90). SGCPNs can be precisely delivered to xenograft tumors of SSTR2-positive SCLC in vivo. Upon NIR laser irradiation, therapy of SGCPNs showed significant tumor regression. In conclusion, SGCPNs provide a new chemo-photothermal synergistic treatment strategy for targeting SCLC.
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BACKGROUND: Immunogenic cell death (ICD) is closely related to anti-tumor therapy and regulates the tumor microenvironment (TME). This study aims to explore the molecular characteristics of ICD in acute myeloid leukemia (AML) and to analyze the value of ICD-related biomarkers in TME indication, prognosis prediction, and treatment response evaluation in AML. METHODS: Single-sample gene set enrichment analysis was used to calculate the ICD score. LASSO regression was used to construct a prognostic risk score model. We also analyzed differences in clinical characteristics, immune landscape, immunotherapy response, and chemotherapy sensitivity between high-risk and low-risk patients. RESULTS: This study identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune landscape between different ICD subtypes. Subsequently, a novel ICD-related prognostic risk score model was developed, which accurately predicted the prognosis of AML patients and was validated in nine AML cohorts. Moreover, there were significant correlations between risk scores and clinicopathological factors, somatic mutations, TME characteristics, immune cell infiltration, immunotherapy response, and chemosensitivity. We further validated the model gene expression in a clinically real-world cohort. CONCLUSIONS: The novel ICD-related signatures identified and validated by us can serve as promising biomarkers for predicting clinical outcomes, chemotherapy sensitivity, and immunotherapy response in AML patients, guiding the establishment of personalized and accurate treatment strategies for AML.
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ABSTRACT: Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers (ATRX, TSC1, CD40, BACH2, BCL2, and LEF1) with survival and diagnostic significance through Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) analyses. Afterwards, the ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. Moreover, we examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. The molecular regulatory network displayed that hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified Paclitaxel, Docetaxel, and Rasagiline as potential BCL2-targeting sepsis treatments. Finally, Real-Time Quantitative PCR confirmed ATRX, TSC1, and LEF1 down-regulation in sepsis samples, contrasting CD40's increased expression in CTL samples. In conclusion, ATRX, TSC1, CD40, BACH2, BCL2, and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.
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Background: The relationship between ferroptosis and the progression and treatment of hematological tumors has been extensively studied, although its precise association with chronic myeloid leukemia (CML) remains uncertain. Methods: Multi-transcriptome sequencing data were utilized to analyze the ferroptosis level of CML samples and its correlation with the tumor microenvironment, disease progression, and treatment response. Machine learning algorithms were employed to identify diagnostic ferroptosis-related genes (FRGs). The consensus clustering algorithm was applied to identify ferroptosis-related molecular subtypes. Clinical samples were collected for sequencing to validate the results obtained from bioinformatics analysis. Cell experiments were conducted to investigate the therapeutic efficacy of induced ferroptosis in drug-resistant CML. Results: Ferroptosis scores were significantly lower in samples from patients with CML compared to normal samples, and these scores further decreased with disease progression and non-response to treatment. Most FRGs were downregulated in CML samples. A high ferroptosis score was also associated with greater immunosuppression and increased activity of metabolic pathways. Through support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage selection operator (LASSO), and random forest (RF) algorithms, we identified five FRGs (ACSL6, SLC11A2, HMOX1, SLC38A1, AKR1C3) that have high diagnostic value. The clinical diagnostic value of these five FRGs and their effectiveness in differentiating CML from other hematological malignancies were validated using additional validation cohorts and our real-world cohort. There are significant differences in immune landscape, chemosensitivity, and immunotherapy responsiveness between the two ferroptosis-related molecular subtypes. By conducting cellular experiments, we confirmed that CML-resistant cells are more sensitive to induction of ferroptosis and can enhance the sensitivity of imatinib treatment. Conclusion: Our study unveils the molecular signature of ferroptosis in samples from patients with CML. FRG identified by a variety of machine learning algorithms has reliable clinical diagnostic value. Furthermore, the characterization of different ferroptosis-related molecular subtypes provides valuable insights into individual patient characteristics and can guide clinical treatment strategies. Targeting and inducing ferroptosis holds great promise as a therapeutic approach for drug-resistant CML.