Copper Chelation Therapy Attenuates Periodontitis Inflammation through the Cuproptosis/Autophagy/Lysosome Axis.

Periodontitis development arises from the intricate interplay between bacterial biofilms and the host's immune response, where macrophages serve pivotal roles in defense and tissue homeostasis. Here, we uncover the mitigative effect of copper chelator Tetrathiomolybdate (TTM) on periodontitis through inhibiting cuproptosis, a newly identified form of cell death which is dependent on copper. Our study reveals concurrent cuproptosis and a macrophage marker within murine models. In response to lipopolysaccharide (LPS) stimulation, macrophages exhibit elevated cuproptosis-associated markers, which are mitigated by the administration of TTM. TTM treatment enhances autophagosome expression and mitophagy-related gene expression, countering the LPS-induced inhibition of autophagy flux. TTM also attenuates the LPS-induced fusion of autophagosomes and lysosomes, the degradation of lysosomal acidic environments, lysosomal membrane permeability increase, and cathepsin B secretion. In mice with periodontitis, TTM reduces cuproptosis, enhances autophagy flux, and decreases Ctsb levels. Our findings underscore the crucial role of copper-chelating agent TTM in regulating the cuproptosis/mitophagy/lysosome pathway during periodontitis inflammation, suggesting TTM as a promising approach to alleviate macrophage dysfunction. Modulating cuproptosis through TTM treatment holds potential for periodontitis intervention.

A novel cuproptosis-related signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high annual mortality. Tsvetkov et al. recently found a new form of regulated cell death, termed cuproptosis. The prognostic value of cuproptosis-related gene signature in LUAD remains uncertain. METHODS: A training cohort is identified by the TCGA-LUAD dataset, whereas validation cohorts one and two are identified by GSE72094 and GSE68465, respectively. GeneCard and GSEA were used to extract genes related to cuproptosis. Cox regression, Kaplan-Meier regression, and LASSO regression were used to construct a gene signature. The model's applicability was evaluated by Kaplan-Meier estimators, Cox models, ROC, and tAUC across two independent validation cohorts. We examined the model's connections with other forms of regulated cell death. The immunotherapy ability of the signature was demonstrated by applying TMB, immune relevant signatures, and TIDE. The GSEA and immune infiltration analysis offer a better understanding of how the signature functions and the role of immune cells in its prognostic power. RESULTS: A ten-gene signature was built and demonstrated owning prognostic power by being applied to the validation cohorts. The GSEA uncovered that the unfolded protein response, glycolysis/gluconeogenesis, and MYC were highly related to the gene signature. The ten-gene signature is closely related to related genes of apoptosis, necroptosis, pyroptosis, and ferroptosis. Our signature may have utility in predicting immunotherapy efficacy in LUADs. Mast cells were identified as key players that support the predicting capacity of the ten-gene signature through the immune infiltrating analysis. CONCLUSIONS: The novel ten-gene signature associated with apoptosis in cuproptosis that we obtained may contribute to improved LUAD management strategies and the ability to predict response to LUAD immunotherapy. It is suggested that mast cell infiltration might be related to the prognostic power of this signature.

Diagnostic value of circular free DNA for colorectal cancer detection.

BACKGROUND: Minimally invasive or noninvasive, sensitive and accurate detection of colorectal cancer (CRC) is urgently needed in clinical practice. AIM: To identify a noninvasive, sensitive and accurate circular free DNA marker detected by digital polymerase chain reaction (dPCR) for the early diagnosis of clinical CRC. METHODS: A total of 195 healthy control (HC) individuals and 101 CRC patients (38 in the early CRC group and 63 in the advanced CRC group) were enrolled to establish the diagnostic model. In addition, 100 HC individuals and 62 patients with CRC (30 early CRC and 32 advanced CRC groups) were included separately to validate the model. CAMK1D was dPCR. Binary logistic regression analysis was used to establish a diagnostic model including CAMK1D and CEA. RESULTS: To differentiate between the 195 HCs and 101 CRC patients (38 early CRC and 63 advanced CRC patients), the common biomarkers CEA and CAMK1D were used alone or in combination to evaluate their diagnostic value. The area under the curves (AUCs) of CEA and CAMK1D were 0.773 (0.711, 0.834) and 0.935 (0.907, 0.964), respectively. When CEA and CAMK1D were analyzed together, the AUC was 0.964 (0.945, 0.982). In differentiating between the HC and early CRC groups, the AUC was 0.978 (0.960, 0.995), and the sensitivity and specificity were 88.90% and 90.80%, respectively. In differentiating between the HC and advanced CRC groups, the AUC was 0.956 (0.930, 0.981), and the sensitivity and specificity were 81.30% and 95.90%, respectively. After building the diagnostic model containing CEA and CAMK1D, the AUC of the CEA and CAMK1D joint model was 0.906 (0.858, 0.954) for the validation group. In differentiating between the HC and early CRC groups, the AUC was 0.909 (0.844, 0.973), and the sensitivity and specificity were 93.00% and 83.30%, respectively. In differentiating between the HC and advanced CRC groups, the AUC was 0.904 (0.849, 0.959), and the sensitivity and specificity were 93.00% and 75.00%, respectively. CONCLUSION: We built a diagnostic model including CEA and CAMK1D for differentiating between HC individuals and CRC patients. Compared with the common biomarker CEA alone, the diagnostic model exhibited significant improvement.

A cuproptosis and copper metabolism-related gene prognostic index for head and neck squamous cell carcinoma.

Background: The purpose of this study was to identify the prognostic value of cuproptosis and copper metabolism-related genes, to clarify their molecular and immunological characteristics, and to elucidate their benefits in head and neck squamous cell carcinoma (HNSCC). Methods: The details of human cuproptosis and copper metabolism-related genes were searched and filtered from the msigdb database and the latest literature. To identify prognostic genes associated with cuproptosis and copper metabolism, we used least absolute shrinkage and selection operator regression, and this coefficient was used to set up a prognostic risk score model. HNSCC samples were divided into two groups according to the median risk. Afterwards, the function and immune characteristics of these genes in HNSCC were analyzed. Results: The 14-gene signature was constructed to classify HNSCC patients into low-risk and high-risk groups according to the risk level. In the The Cancer Genome Atlas (TCGA) cohort, the overall survival (OS) rate of the high-risk group was lower than that of the low-risk group (P < 0.0001). The area under the curve of the time-dependent Receiver Operator Characteristic (ROC) curve assessed the good performance of the genetic signature in predicting OS and showed similar performance in the external validation cohort. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment assays and Protein-Protein Interaction (PPI) protein networks have been used to explore signaling pathways and potential mechanisms that were markedly active in patients with HNSCC. Furthermore, the 14 cuproptosis and copper metabolism-related genes were significantly correlated with the immune microenvironment, suggesting that these genes may be linked with the immune regulation and development of HNSCC. Conclusions: Our results emphasize the significance of cuproptosis and copper metabolism as a predictive biomarker for HNSCC, and its expression levels seem to be correlated with immune- related features; thus, they may be a possible biomarker for HNSCC prognosis.