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Abstract Background Apoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes. Methods Messenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan–Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model’s predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM. Results Several apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model’s score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival. Conclusion Ourestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.

Abstract Background Cutaneous malignant melanoma is a very aggressive and metastatic form of skin cancer, typically linked with poor outcomes. Advances in genomic analysis have underscored the crucial role of T cells in tumor immunity. Immune checkpoint inhibitors have notably transformed melanoma treatment by boosting T cell activity. Studies of gene expression have found that the phosphatidylinositol-4-phosphate kinase 2A (PIP4K2A) gene is abnormally expressed in various tumors, indicating its potential role in tumor progression. Utilizing single-cell sequencing and machine learning, researchers can now explore the complex interactions between T cells and melanoma cells at a genomic level. This study aimed to investigate the role of the PIP4K2A gene in cutaneous malignant melanoma, with a focus on its influence on T cell-mediated immune responses. Methods Samples from cutaneous melanoma patients were analysed by single-cell transcriptome for differentially expressed genes and signalling pathways associated with cutaneous melanoma. Then, genes were identified and predictive models were built based on the transcriptomic data using machine learning models to assess whether the expression level of PIP4K2A could effectively predict the malignancy and prognosis of cutaneous melanoma. In addition, we also performed drug therapy predictive analysis and immunotherapy analysis.Finally, the critical role of PIP4K2A in cutaneous melanoma was further confirmed by immunohistochemistry. Results The PIP4K2A gene exhibited a significantly elevated expression level in cutaneous malignant melanoma, showing a strong correlation with the clinical stage and patient prognosis. At the therapeutic level, high PIP4K2A expression is less responsive to immunotherapy, and this gene is a risk factor for drug therapy in cutaneous malignant melanoma. Additionally, our experimental outcomes validated this observation. Conclusions The PIP4K2A gene could be a crucial prognostic marker for cutaneous malignant melanoma, as it significantly affects T cell activity within the tumor microenvironment. This study offers essential insights into melanoma pathogenesis and assists in pinpointing new early diagnostic markers and therapeutic targets. Utilizing advanced genomic tools and computational techniques, the research enhances our understanding of T cell dynamics in melanoma, facilitating the development of personalized medicine and more effective immunotherapy strategies.

BackgroundChen’s Peiyuan Tang (CSPYT) is a compound herbal formula that has shown the potential to enhance ovarian function and reduce autophagy in ovarian granulosa cells, which plays a crucial role in follicular development and maturation. The application of Chinese herbal medicine offers a promising alternative to traditional hormone replacement therapy (HRT).MethodsThis study explores CSPYT’s therapeutic mechanisms in treating POF, focusing on its modulation of autophagy through network pharmacology and transcriptomics-based analysis, predicting potential interactions and pathways. KGN cell line and rat ovarian granulosa cells were used for in vitro experiment. 4-Hydroperoxy cyclophosphamide(4-HC) stimulation was carried out for establishing the POF cell model. Q-PCR, Western Blot, Transmission electron microscopy to detect the results.ResultsAccording to the drug and disease database, the common targets of Chen’s Peiyuan Tang and premature ovarian failure were screened, combined with autophagy gene targets and transcriptome analysis, and finally 8 intersection targets were obtained, namely CDKN1B, MAPK3, PRKCD, CDKN1A, MAPK1, RAF1, BIRC5, CTSB. Enrichment analysis of 8 genes found that they were closely related to the animal autophagy pathway. Construct PPI network diagram. CytoScape 3.9.1 builds CSPYT Drug Target-POF Disease Target-Autophagy Gene Network Diagram. Based on the PPI network diagram and CytoScape 3.9.1 analysis results, it is estimated that MAPK1 and MAPK3 are the key targets of CSPYT in the treatment of POF. The eight final intersection targets were docked with the corresponding active pharmaceutical ingredients. The one that docked most closely with the MAPK family was naringenin. In cell experiment verification, it was confirmed that Chen’s Peiyuan Tang can inhibit the MAPK signaling pathway, significantly reduce the number of autophagosomes, and reduce autophagy damage in ovarian granulosa cells.DiscussionCSPYT can inhibit the MAPK signaling pathway, prevent autophagy overexpression and restore ovarian granulosa cell function, effectively alleviating the disease pressure of POF.

BackgroundBladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1’s impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors.MethodsWe retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the “Seurat” package. Differential expression analysis was then performed with the “Limma” package. The construction of scale-free gene co-expression networks was achieved using the “WGCNA” package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The “STRINGdb” package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package “mlr3verse.” Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA.ResultsOur investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1’s involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.ConclusionsOur research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.