Overexpression of NOP58 Facilitates Proliferation, Migration, Invasion, and Stemness of Non-small Cell Lung Cancer by Stabilizing hsa_circ_0001550.

BACKGROUND: NOP58 ribonucleoprotein (NOP58) is associated with the recurrence of lung adenocarcinoma. AIMS: Few investigations concentrate on the role of NOP58 in non-small cell lung cancer (NSCLC), which is the focus of our current study. METHODS: Following transfection, the proliferation, migration, and invasion of NSCLC cells were assessed by 5- ethynyl-2'-deoxyuridine (EdU), wound healing, and transwell assays. The percentage of CD9+ cells was evaluated by flow cytometry assay. Based on target genes and binding sites predicted through bioinformatics analysis, a dual-luciferase reporter assay was performed to verify the targeting relationship between hsa_circ_0001550 and NOP58. The effect of NOP58 overexpression on hsa_circ_0001550 stability was gauged using Actinomycin D. The hsa_circ_0001550 and NOP58 expression levels, as well as protein expressions of CD44, CD133, OCT4, and SOX2 in NSCLC cells were determined by quantitative real-time PCR and Western blot, respectively. RESULTS: Hsa_circ_0001550 was remarkably up-regulated in NSCLC cell lines A549 and PC9, silencing of which weakened cell abilities to proliferate, migrate and invade, decreased CD9+ cell ratio, and diminished protein expressions of CD44, CD133, OCT4, and SOX2. NOP58 could bind to hsa_circ_0001550 and stabilize its expression, and NOP58 overexpression partially abrogated hsa_circ_0001550 knockdown-inhibited NSCLC cell proliferation, migration, invasion and stemness. CONCLUSION: Overexpression of NOP58 facilitates proliferation, migration, invasion, and stemness of NSCLC cells by stabilizing hsa_circ_0001550, hinting that NOP58 is a novel molecular target for NSCLC therapy.

Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion.

Background: Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity. Methods: Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET. Results: The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells. Conclusion: To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.

PTPRC promoted CD8+ T cell mediated tumor immunity and drug sensitivity in breast cancer: based on pan-cancer analysis and artificial intelligence modeling of immunogenic cell death-based drug sensitivity stratification.

Background: Immunogenic cell death (ICD) is a result of immune cell infiltration (ICI)-mediated cell death, which is also a novel acknowledgment to regulate cellular stressor-mediated cell death, including drug therapy and radiotherapy. Methods: In this study, TCGA and GEO data cohorts were put into artificial intelligence (AI) to identify ICD subtypes, and in vitro experiments were performed. Results: Gene expression, prognosis, tumor immunity, and drug sensitivity showed significance among ICD subgroups, Besides, a 14-gene-based AI model was able to represent the genome-based drug sensitivity prediction, which was further verified in clinical trials. Network analysis revealed that PTPRC was the pivotal gene in regulating drug sensitivity by regulating CD8+ T cell infiltration. Through in vitro experiments, intracellular down-regulation of PTPRC enhanced paclitaxel tolerance in triple breast cancer (TNBC) cell lines. Meanwhile, the expression level of PTPRC was positively correlated with CD8+ T cell infiltration. Furthermore, the down-regulation of PTPRC increased the level of TNBC-derived PD-L1 and IL2. Discussion: ICD-based subtype clustering of pan-cancer was helpful to evaluate chemotherapy sensitivity and immune cell infiltration, and PTPRC was a potential target to against drug resistance of breast cancer.