Background: Camptothecin (CPT) has recently attracted increasing attention as a promising anticancer agent for a variety of tumors. But the clinical application is largely hampered by its extreme water insolubility and unpredictable side effect. It is essential to establish an efficient and safe protocol for the administration of CPT versus melanoma. Methods: Camptothecin was encapsulated with N-trimethyl chitosan (CPT-TMC) through microprecipitation and sonication. Its inhibition effect on B16-F10 cell proliferation and induction of apoptosis was evaluated by MTT assay and flow cytometric analysis in vitro. The anti-tumor activity of CPT-TMC was evaluated in C57BL/6 mice bearing B16-F10 melanoma. Tumor volume, tumor weight and survival time were recorded. Assessment of apoptotic cells within tumor tissue was performed by TUNEL assay. Antiangiogenesis and antiproliferation effects of CPT-TMC in vivo were conducted via CD31 and PCNA immunohistochemistry, respectively. Results: CPT-TMC efficiently inhibited B16-F10 cells proliferation and increased apoptosis in vitro. Experiment group showed significant inhibition compared with free CPT-treated group (81.3% vs. 56.9%) in the growth of B16-F10 melanoma xenografts and prolonged the survival time of the treated mice (P < 0.05). Decreased cell proliferation, increased tumor apoptosis as well as a reduction in angiogenesis were observed. Conclusions: Our data suggest that N-trimethyl chitosan-encapsulated camptothecin is superior to free CPT by overcoming its insolubility and finally raises the potential of its application in melanoma therapy.

Background: Cervical cancer is the fourth most frequent gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer.Methods: Raw data and clinical information of cervical cancer samples were downloaded from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through the ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis, and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway.Results: There were eight immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing Programmed cell death ligand-1 (PD-L1) expression and PD-1 checkpoint pathway differences between high-risk and low-risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment.Conclusion: The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

Background: The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).Methods: Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.Results: A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).Conclusions: We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.

Background: Endometrial cancer (EC) is a common and increasingly prevalent gynecological malignancy. Pyroptosis, a pro-inflammatory form of programmed cell death, plays dual roles in cancer but remains poorly understood in the context of EC and its immune microenvironment.Methods: We identified pyroptosis-associated genes (PAGs) and applied a 101-combination machine learning framework to construct and validate a robust prognostic model using TCGA bulk RNA-seq and single-cell transcriptomic data. Immune infiltration was assessed using CIBERSORT and Tumor Immune Dysfunction and Exclusion (TIDE), while CellChat was employed to investigate pyroptosis-related cell–cell communication. Drug sensitivity was predicted with OncoPredict.Results: A seven-gene prognostic model demonstrated robust predictive performance with concordance index (C-index) values exceeding 0.70 in both training and validation cohorts. The model stratified EC patients into high- and low-risk groups with distinct immune infiltration profiles and differential responses to programmed cell death protein 1 (PD-1) blockade. Drug sensitivity analysis revealed several therapeutic agents with potential efficacy in high-risk and low-risk subgroups.Conclusion: This study highlights the clinical and immunological relevance of pyroptosis in EC and introduces a PAG-based model with strong predictive and therapeutic potential. These findings provide a foundation for developing pyroptosis-guided precision immunotherapy strategies in EC.