Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability.

OBJECTIVE: To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally. METHODS: Firstly, the data was cleaned and enhanced, and was divided into training and test sets according to the 7:3 ratio. Then, the metrics related to DN were filtered by difference analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and Max-relevance and Min-redundancy (MRMR) algorithms. Ten machine learning models were constructed based on the key variables. The best model was filtered by Receiver Operating Characteristic (ROC), Precision-Recall (PR), Accuracy, Matthews Correlation Coefficient (MCC), and Kappa, and was internally and externally validated. Based on the best model, an online platform had been constructed. RESULTS: 15 key variables were selected, and among the 10 machine learning models, the Random Forest model achieved the best predictive performance. In the test set, the area under the ROC curve was 0.912, and in two external validation cohorts, the area under the ROC curve was 0.828 and 0.863, indicating excellent predictive and generalization abilities. CONCLUSION: The model has a good predictive value and is expected to help in the early diagnosis and screening of clinical DN.

[Screen of key characteristic genes of nasopharyngeal carcinoma (NPC) base on machine learning and analysis of their correlation with immune cells].

Objective Machine learning was used to screen the key characteristic genes of nasopharyngeal carcinoma (NPC) and analyze their correlation with immune cells. Methods Download the NPC training datasets (GSE12452 and GSE13597) and the validation dataset (GSE53819) from the Gene Expression Omnibus (GEO). Firstly, the training data sets were merged and screened for differentially expressed genes (DEGs); Secondly, the DEGs were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Next, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms were used to identify NPC-related genes in the training datasets and examined in the validation dataset, to further identify key genes using the area under curve (AUC) of receiver operating characteristic curve (ROC); Finally, the correlation between the key genes and immune cells was analyzed. Results A total of 55 DEGs were obtained, including 43 down-regulated genes and 12 up-regulated genes. The GO functions were enriched in humoral immune response, cell differentiation, neutrophil activation and chemokine receptor binding. The KEGG were mainly enriched in the IL-17 signaling pathway. The GSEA was enriched in cell cycle, extracellular matrix receptor interactions, cancer pathways and DNA replication. Immune infiltration analysis showed that the expression of naive B cells, memory B cells, and resting memory CD4+ T cells was significantly lower in NPC, while CD8+ T cells, naive CD4+ T cells, activated memory CD4+ T cells, follicular helper T cells, M0 macrophages and M1 macrophages were highly expressed in NPC. Among the feature genes screened by LASSO and SVM, only CCDC19, LAMB1, SPAG6 and RAD51AP1 genes' AUC were greater than 0.9 in both the training and validation datasets and were closely associated with immune cell infiltration. Conclusion The key genes CCDC19, LAMB1, SPAG6 and RAD51AP1 in NPC development are screened by machine learning algorithms, and are closely associated with immune cell infiltration.

Topoisomerase â ¡α Gene as a Marker for Prognostic Prediction of Hepatocellular Carcinoma: A Bioinformatics Analysis.

Objective To investigate the expression of topoisomeraseⅡα (TOP2α) in hepatocellular carcinoma (HCC) and its role in predicting prognosis of HCC patients. Methods We used HCC-related datasets in UALCAN, HCCDB, and cBioPortal databases to analyze the expression and mutation of TOP2α and its co-expressed genes in HCC tissues. GO function and KEGG pathway enrichment of TOP2α and its co-expressed genes were identified. The TIMER database was used to analyze infiltration levels of immune cells in HCC. The impacts of TOP2α and its co-expression genes and the infiltrated immune cells on the survival of HCC patients were assayed by Kaplan-Meier plotter analysis. Results TOP2α and its co-expression genes were highly expressed in HCC (P< 0.001) and detrimental to overall survival of HCC patients (P< 0.001). TOP2α and its co-expression genes were mainly involved in cell mitosis and proliferation, and cell cycle pathway (ID: hsa04110, P = 0.001945). TOP2α and its co-expression genes were mutated in HCC and the mutations were significantly detrimental to overall survival (P = 0.0247) and disease-free survival (P = 0.0265) of HCC patients. High TOP2α expression was positively correlated with the infiltration of B cell (r = 0.459, P< 0.01), CD8+ T cell (r = 0.312, P< 0.01), CD4+ T cell (r = 0.370, P< 0.01), macrophage (r = 0.459, P< 0.01), neutrophil (r = 0.405, P< 0.01), and dendritic cell (r = 0.473, P< 0.01) in HCC. The CD8+ T cell infiltration significantly prolonged the 3- and 5-year survival of HCC patients (all P< 0.05), and CD4+ T cell infiltration significantly shortened the 3-, 5-, and 10-year survival of HCC patients (all P< 0.05). ConclusionTOP2α may be an oncogene, which was associated with poor prognosis of HCC patients and could be used as a biomarker for the prognostic prediction of HCC.

Topoisomerase Ⅱα Gene as a Marker for Prognostic Prediction of Hepatocellular Carcinoma: A Bioinformatics Analysis / 中国医学科学杂志(英文版)

Objective To investigate the expression of topoisomeraseⅡα (TOP2α) in hepatocellular carcinoma (HCC) and its role in predicting prognosis of HCC patients. Methods We used HCC-related datasets in UALCAN, HCCDB, and cBioPortal databases to analyze the expression and mutation of TOP2α and its co-expressed genes in HCC tissues. GO function and KEGG pathway enrichment of TOP2α and its co-expressed genes were identified. The TIMER database was used to analyze infiltration levels of immune cells in HCC. The impacts of TOP2α and its co-expression genes and the infiltrated immune cells on the survival of HCC patients were assayed by Kaplan-Meier plotter analysis. Results TOP2α and its co-expression genes were highly expressed in HCC (P< 0.001) and detrimental to overall survival of HCC patients (P< 0.001). TOP2α and its co-expression genes were mainly involved in cell mitosis and proliferation, and cell cycle pathway (ID: hsa04110, P = 0.001945). TOP2α and its co-expression genes were mutated in HCC and the mutations were significantly detrimental to overall survival (P = 0.0247) and disease-free survival (P = 0.0265) of HCC patients. High TOP2α expression was positively correlated with the infiltration of B cell (r = 0.459, P< 0.01), CD8+ T cell (r = 0.312, P< 0.01), CD4+ T cell (r = 0.370, P< 0.01), macrophage (r = 0.459, P< 0.01), neutrophil (r = 0.405, P< 0.01), and dendritic cell (r = 0.473, P< 0.01) in HCC. The CD8+ T cell infiltration significantly prolonged the 3- and 5-year survival of HCC patients (all P< 0.05), and CD4+ T cell infiltration significantly shortened the 3-, 5-, and 10-year survival of HCC patients (all P< 0.05). ConclusionTOP2α may be an oncogene, which was associated with poor prognosis of HCC patients and could be used as a biomarker for the prognostic prediction of HCC.