Exploration and validation of a combined Hypoxia and m6A/m5C/m1A regulated gene signature for prognosis prediction of liver cancer.

BACKGROUND: It is widely acknowledged that hypoxia and m6A/m5C/m1A RNA modifications promote the occurrence and development of tumors by regulating the tumor microenvironment. This study aimed to establish a novel liver cancer risk signature based on hypoxia and m6A/m5C/m1A modifications. METHODS: We collected data from The Cancer Genome Atlas (TCGA-LIHC), the National Omics Data Encyclopedia (NODE-HCC), the International Cancer Genome Consortium (ICGC), and the Gene Expression Omnibus (GEO) databases for our study (GSE59729, GSE41666). Using Cox regression and least absolute shrinkage and selection operator (LASSO) method, we developed a risk signature for liver cancer based on differentially expressed genes related to hypoxia and genes regulated by m6A/m5C/m1A modifications. We stratified patients into high- and low-risk groups and assessed differences between these groups in terms of gene mutations, copy number variations, pathway enrichment, stemness scores, immune infiltration, and predictive capabilities of the model for immunotherapy and chemotherapy efficacy. RESULTS: Our analysis revealed a significantly correlated between hypoxia and methylation as well as m6A/m5C/m1A RNA methylation. The three-gene prognosis signature (CEP55, DPH2, SMS) combining hypoxia and m6A/m5C/m1A regulated genes exhibited strong predictive performance in TCGA-LIHC, NODE-HCC, and ICGC-LIHC-JP cohorts. The low-risk group demonstrated a significantly better overall survival compared to the high-risk group (p < 0.0001 in TCGA, p = 0.0043 in NODE, p = 0.0015 in ICGC). The area under the curve (AUC) values for survival at 1, 2, and 3 years are all greater than 0.65 in the three cohorts. Univariate and Multivariate Cox regression analyses of the three datasets indicated that the signature could serve as an independent prognostic predictor (p < 0.001 in the three cohorts). The high-risk group exhibited more genome changes and higher homologous recombination deficiency scores and stemness scores. Analysis of immune infiltration and immune activation confirmed that the signature was associated with various immune microenvironment characteristics. Finally, patients in the high-risk group experienced a more favorable response to immunotherapy, and various common chemotherapy drugs. CONCLUSION: Our prognostic signature which integrates hypoxia and m6A/m5C/m1A-regulated genes, provides valuable insights for clinical prediction and treatment guidance for liver cancer patients.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia.

BACKGROUND: Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients. METHODS: Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated. RESULTS: From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group. CONCLUSION: The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Mixing of plant litters strengthens their remediation effects on crude oil-contaminated soil.

To investigate the effects of the mixing of litters on their remediation efficiency in petroleum-contaminated soil, litters from two common plants in the petroleum-contaminated region of Northern Shaanxi, China, Bothriochloa ischaemum (L.) Keng and Sophora davidii Kom. ex Pavol., and their mixture were mixed with 45 g/kg petroleum-contaminated soil. Based on these, a 150-day simulated remediation experiment was conducted at 25 °C and consistent moisture conditions. The effects on the degradation of petroleum components and the restoration of nutrient contents, pH, and enzymatic activity in the disturbed soil were detected. The effects of the litter treatments on the community structure and carbon source utilization characteristics of soil microorganisms were also studied. The results indicated that all litter treatments significantly accelerated the degradation of petroleum components, while the mixing of litter exhibited significant synergistic effects, leading to significantly higher degradation rates of saturated hydrocarbons, aromatic hydrocarbons, and nonhydrocarbon substances than the observed rates in the single-litter treatments and the predicted rates based on the single-litter treatments. Litter treatment significantly increased the N and P contents and enzymatic activity of contaminated soil. The effects of mixed litter on soil chemical and biological properties fell between the effects of the 2 types of single-litter treatments. However, the mixing of litters exhibited significant synergistic effects in supplementing available P and increasing sucrase, dehydrogenase, lignin peroxidase, and laccase activity, while it exhibited significant antagonistic effects in supplementing nitrate nitrogen and increasing urease, phosphatase, polyphenol oxidase, and manganese peroxidase activity. Litter treatment significantly altered the community structure of soil microorganisms. The relative abundances of some petroleum-degrading microbial phyla or genera in mixed litter-treated soil were significantly different from those in single litter-treated soils, which might contribute to the strengthened remediation effects of mixed litter treatment. The results might provide a theoretical basis for the more effect utilization of biomass resources in the remediation of petroleum-contaminated soil.

Context-Aware Time Series Imputation for Multi-Analyte Clinical Data.

Clinical time series imputation is recognized as an essential task in clinical data analytics. Most models rely either on strong assumptions regarding the underlying data-generation process or on preservation of only local properties without effective consideration of global dependencies. To advance the state of the art in clinical time series imputation, we participated in the 2019 ICHI Data Analytics Challenge on Missing Data Imputation (DACMI). In this paper, we present our proposed model: Context-Aware Time Series Imputation (CATSI), a novel framework based on a bidirectional LSTM in which patients' health states are explicitly captured by learning a "global context vector" from the entire clinical time series. The imputations are then produced with reference to the global context vector. We also incorporate a cross-feature imputation component to explore the complex feature correlations. Empirical evaluations demonstrate that CATSI obtains a normalized root mean square deviation (nRMSD) of 0.1998, which is 10.6% better than that of state-of-the-art models. Further experiments on consecutive missing datasets also illustrate the effectiveness of incorporating the global context in the generation of accurate imputations.