Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.
Background: The current study demonstrates that necroptosis is an important mechanism of carcinogenesis. However, the predictive value of necroptosis-associated long non-coding RNAs (LncRNAs) in hepatocellular carcinoma has not been demonstrated. The purpose of this study is to apply necroptosis-related lncRNAs to construct a predictive signature to predict the prognosis of hepatocellular carcinoma patients.Methods: The clinical and RNA-seq data were downloaded using The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox analysis was used to screen out suitable necroptosis-related lncRNAs, and then predictive signature was constructed. The TCGA data were randomly divided into high- and low-risk groups, and the Kaplan-Meier method was used to analyze the overall survival (OS) of the two groups to verify the predictive signature. Finally, a prognostic correlation model for predicting disease-freesurvival (DFS)in hepatocellular carcinoma(HCC) was constructed and validated.Results: We had screened out 9 necroptosis-related lncRNAs (BACE1-AS, LINC01188, LUCAT1, PI3KCD-AS2, Z83851.1, AC009283.1, AC012360.2, AC015908.3, AC103760.1), which were used to construct a predictive signature, and draw the receiver operating characteristic (ROC) curve by the high- and low-risk group. It was found that the area under the curve (AUC) of risk score was 0.874, and the AUCs values of 1-,3-,and 5-years were 0.8, 0.759, 0.787. The TCGA data were randomly divided into two cohorts. In two cohorts, The OS of the high-risk groups were significantly lower than that of the low-risk groups, and the AUC of the ROC curves of the two cohorts were 0.851, 0.804, 0.802 and 0.735, 0.716, 0.76 at 1-, 3-, and 5-years. After quantifying immune cell subsets and related functions, it was found that the infiltration of active dendritic cells (aDCs), macrophages, mast cells, natural killer cells (NK), T regulatory cells (Tregs) were significantly different, and the expressions of immune checkpoints CD86, LAIR1, CTLA4, VTCN1, TNFRSF18, CD80, CD276, HHLA2, TNFSF4, TNFRSF8, TNFRSF4, TNFRSF9, LGALS9, HAVCR2 and TNFSF15 were also significantly different.Conclusion: This predictive signature can accurately predict the prognosis of hepatocellular carcinoma patients and provide guidance for the clinical treatment of hepatocellular carcinoma patients.
Background Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4–7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis. Methods We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the “monocle” software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The “scissor” software was used to associate high-risk and low-risk patients with individual cells. The “cellchat” software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The “CBNplot” software based on Bayesian network inference was used to construct EVL regulatory networks. Results We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation. Conclusions Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.
Background: The current study demonstrates that necroptosis is an important mechanism of carcinogenesis. However, the predictive value of necroptosis-associated long non-coding RNAs (LncRNAs) in hepatocellular carcinoma has not been demonstrated. The purpose of this study is to apply necroptosis-related lncRNAs to construct a predictive signature to predict the prognosis of hepatocellular carcinoma patients.Methods: The clinical and RNA-seq data were downloaded using The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox analysis was used to screen out suitable necroptosis-related lncRNAs, and then predictive signature was constructed. The TCGA data were randomly divided into high- and low-risk groups, and the Kaplan-Meier method was used to analyze the overall survival (OS) of the two groups to verify the predictive signature. Finally, a prognostic correlation model for predicting disease-freesurvival (DFS)in hepatocellular carcinoma(HCC) was constructed and validated.Results: We had screened out 9 necroptosis-related lncRNAs (BACE1-AS, LINC01188, LUCAT1, PI3KCD-AS2, Z83851.1, AC009283.1, AC012360.2, AC015908.3, AC103760.1), which were used to construct a predictive signature, and draw the receiver operating characteristic (ROC) curve by the high- and low-risk group. It was found that the area under the curve (AUC) of risk score was 0.874, and the AUCs values of 1-,3-,and 5-years were 0.8, 0.759, 0.787. The TCGA data were randomly divided into two cohorts. In two cohorts, The OS of the high-risk groups were significantly lower than that of the low-risk groups, and the AUC of the ROC curves of the two cohorts were 0.851, 0.804, 0.802 and 0.735, 0.716, 0.76 at 1-, 3-, and 5-years. After quantifying immune cell subsets and related functions, it was found that the infiltration of active dendritic cells (aDCs), macrophages, mast cells, natural killer cells (NK), T regulatory cells (Tregs) were significantly different, and the expressions of immune checkpoints CD86, LAIR1, CTLA4, VTCN1, TNFRSF18, CD80, CD276, HHLA2, TNFSF4, TNFRSF8, TNFRSF4, TNFRSF9, LGALS9, HAVCR2 and TNFSF15 were also significantly different.Conclusion: This predictive signature can accurately predict the prognosis of hepatocellular carcinoma patients and provide guidance for the clinical treatment of hepatocellular carcinoma patients.
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