RESUMO
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.
RESUMO
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.