ObjectiveTo construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on disulfidptosis-associated genes (DAGs) and ferroptosis-associated genes (FAGs) using data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and explore the immune characteristics and antitumor drug sensitivity of HCC patients with high- and low-risk score. MethodsThe transcriptomic and clinical data of HCC were downloaded from the TCGA and ICGC databases. The expression levels of DAGs and FAGs were extracted. Subsequently, the differentially expressed and prognostically relevant DAGs and FAGs (DFAGs) were screened through differential expression and prognostic analyses. A prognostic prediction model for HCC was constructed by LASSO regression analysis. The prognostic value of risk factors was evaluated using univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed stochastic neighbor embedding. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to further elucidate the mechanisms of genes associated with HCC prognosis. The impact of risk factors on immune cells and immune cells functions was analyzed using single-sample gene set enrichment analysis. Based on the Genomics of Drug Sensitivity in Cancer database, the oncoPredict package was used to predict responses to antitumor drugs in for different risk groups. ResultsFour DFAGs (SLC7A11, SLC1A5, G6PD, and LRPPRC) with respective risk coefficients of 0.0350, 0.0442, 0.1597, and 0.0132 were selected to construct the prognostic prediction model. The risk score of prognostic prediction model was calculated as: Risk score =(0.0350×SLC7A11 expression level) + (0.0442×SLC1A5 expression level) + (0.159 7×G6PD expression level) + (0.013 2×LRPPRC expression level). The multivariate Cox regression analysis indicated that a high-risk score was an independent risk factor for HCC patient survival [HR (95%CI) = 5.414 (1.918, 15.279), P<0.001]. Both TCGA and ICGC datasets demonstrated that the high-risk patients had significantly worse survival than low-risk patients (P<0.001 and P=0.003, respectively). Enrichment analysis revealed that the risk-associated genes influenced HCC progression through multiple pathways, such as immune response, cell cycle, glycolysis, gluconeogenesis. Immune analysis showed that the high-risk patients exhibited increased infiltration of immunosuppressive cells, such as activated dendritic cells, macrophages, and regulatory T cells, while natural killer cell infiltration was significantly reduced. The drug sensitivity analysis suggested that the high-risk HCC patients might respond better to 5-fluorouracil, afatinib, cyclophosphamide, and lapatinib, whereas the low-risk patients might benefit more from oxaliplatin and sorafenib. ConclusionsHCC prognosis prediction model based on DFAGs in this study suggests a certain predictive value for the survival of HCC patients in the data from both TCGA and ICGC datasets. There are significant differences in pionts of immune cells infiltration and immune cells functions between high-risk and low-risk HCC patients. Additionally, significant differences exist in sensitivity to targeted drugs and chemotherapeutic drugs. This model can provide some references for immunotherapy, personalized treatment, and prognosis evaluation of HCC patients.
Objective To review the latest research progress of heme oxygenase 1 (HO-1), to thoroughly understand different functions of HO-1 and its influence on osteogenesis and angiogenesis of stem cells, and to analyze HO-1 application in bone tissue engineering. Methods Domestic and international literature on HO-1 in recent years was extensively reviewed and analyzed. Results The activity of HO-1 and its enzymatic products not only have the properties of anti-inflammatory, anti-apoptosis, and cytoprotection, but also can promote angiogenesis combined with other growth factors and protect the vessel which already exist. Moreover, HO-1 has an effect on the proliferation, paracrine signaling, osteoblastic differentiation, and anti-apoptosis of stem cells. Conclusion HO-1 can be used as a multi-function growth factor in bone tissue engineering, but more investigation should emphasis on synergistic effect of each function so as to improve bone repair.
ObjectiveTo construct a model for predicting prognosis risk in patients with pancreatic malignancy (PM).MethodsThe clinicopathological data of 8 763 patients with PM undergone resection between 2010 and 2015 were collected and analyzed by SEER*Stat (v8.3.5) and R software, respectively. The univariate and multivariate Cox proportional hazard regression analysis were used to analyze the factors for predicting prognosis outcome risk and constructed the nomograms of patients with PM, respectively. Kaplan-Meier method was used to evaluate the survival of patients according to relevant factors and the high risk group and low risk group of patients with PM. The discriminative ability and calibration of the nomograms to predict overall survival were tested by using C-index, area under ROC curve (AUC) and calibration plots.ResultsThe multivariate Cox proportional hazard regression analysis showed that age, T staging, N staging, M staging, histological type, the differentiation, number of regional lymph node dissection, chemotherapy, and radiotherapy were independent factors for predicting the prognosis of patients with PM (P<0.05). Based on regression analysis of patients with PM, a nomograms model for predicting the risk of patients with PM was established, including age, T staging, N staging, M staging, histological type, the differentiation, tumor location, type of surgery, number of regional lymph node dissection, chemotherapy, and radiotherapy. The discriminative ability and calibration of the nomograms revealed good predictive ability as indicated by the C-index (0.747 for modeling group and 0.734 for verification group). The 3- and 5-year survival AUC values of the modeling group were 0.766 and 0.781, and the validation group were 0.758 and 0.783, respectively. The calibration plots showed that predictive value of the 3- and 5-year survival were close to the actual values in both modeling group and the verification group. ConclusionsIndependent predictors of survival risk after curative-intent surgery for PM were selected to create nomograms for predicting overall survival. The nomograms provide a basis for judging the prognosis of PM patients.