A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data.

With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development.

Arsenic distribution and partitioning in multiple media in a typical catchment in the Qinghai-Tibetan plateau: A comparison between freshwater and saltwater lakes.

Arsenic (As) has been widely detected in surface media on the Qinghai-Tibetan Plateau (QTP); however, the differences in the As distribution and partitioning characteristics between freshwater and saltwater lakes remain poorly understood. To determine the distribution and partitioning characteristics of As, multimedia environmental samples were collected from a typical small watershed consisting of a river, wetland, and both freshwater and saltwater lakes on the QTP. Results showed that freshwater systems, represented by Hurleg Lake, were high in particulate arsenic (PAs) and low in dissolved arsenic (DAs), whereas the saltwater system represented by Tosen Lake, exhibited the reverse distribution. This discrepancy in As distribution was primarily attributed to evaporation enrichment, competitive adsorption of HCO3- and pH variations, as suggested by correlation analysis and stable isotopic composition of water. In the stratified Tosen Lake, an increasing trend of DAs in the water column was observed, potentially driven by the reductive dissolution of Fe (hydr)oxides and bacterial sulfate reduction in the anoxic bottom hypolimnion. Conversely, Hurleg Lake maintained oxic conditions with stable DAs concentrations. Notably, PAs was elevated in the bottom layer of both lakes, possibly due to uptake/adsorption by biogenic particles, as indicated by high levels of chl.α and suspended particulate matter. These findings offer insights into the potential future impact of climate change on As mobilization/redistribution in arid plateau lakes, with implications for management policies that regulate As pollution.

Effects of EFNA1 on cell phenotype and prognosis of esophageal carcinoma.

BACKGROUND: To investigate the expression and clinical significance of EFNA1 in broad-spectrum tumors, and to evaluate its relationship with prognosis and biological functions of esophageal carcinoma (ESCA). METHODS: EFNA1 expression in various cancers was analyzed according to the data in the TCGA database. The clinical data were integrated, to analyze the relationship with ESCA clinical parameters and prognosis, and EFNA1 expression in ESCA tissue samples was detected by immunohistochemistry (IHC). Based on bioinformatics, the functional background of EFNA1 overexpression was analyzed. EFNA1 knockout cell model was established by EFNA1-shRNA transfecting ESCA cells, and the effect of knocking down EFNA1 on the proliferation of ESCA cells was detected by MTT. RESULTS: Among 7563 samples from TCGA, the EFNA1 gene highly expressed in 15 samples with common cancers and endangered the prognosis of patients with tumors. Its overexpression in ESCA and its influence on the prognosis were most significant. EFNA1 expression in 80 samples with ESCA and their paired samples was tested by IHC to verify its high expression (paired t test, P < 0.001) in ESCA tissues. It was found that EFNA1 expression was related to clinical factors (TNM staging, P = 0.031; lymph node metastasis, P = 0.043; infiltration, P = 0.016). Meanwhile, EFNA1 was found to be an independent risk factor based on the COX multi-factor analysis. And to further explore the importance of EFNA1 in tumors, EC-9706 and ECA109 cells were screened from 8 ESCA-related cell lines to build EFNA1 knockdown cell models. The results showed that EFNA1 knockdown significantly inhibited the proliferation of tumor cells (P < 0.05). In terms of molecular mechanism, EFNA1 related genes were significantly enriched in the proliferative pathway according to the pathway enrichment analysis. It was found that knocking down EFNA1 did inhibit cell proliferation based on cell experiments. CONCLUSIONS: EFNA1 overexpression in ESCA tissue is related to the prognosis of patients. Knocking down EFNA1 can significantly inhibit the proliferation of ESCA cells.

[Quantifying the state of cell differentiation based on the gene networks entropy].

Studies of cellular dynamic processes have shown that cells undergo state changes during dynamic processes, controlled mainly by the expression of genes within the cell. With the development of high-throughput sequencing technologies, the availability of large amounts of gene expression data enables the acquisition of true gene expression information of cells at the single-cell level. However, most existing research methods require the use of information beyond gene expression, thus introducing additional complexity and uncertainty. In addition, the prevalence of dropout events hampers the study of cellular dynamics. To this end, we propose an approach named gene interaction network entropy (GINE) to quantify the state of cell differentiation as a means of studying cellular dynamics. Specifically, by constructing a cell-specific network based on the association between genes through the stability of the network, and defining the GINE, the unstable gene expression data is converted into a relatively stable GINE. This method has no additional complexity or uncertainty, and at the same time circumvents the effects of dropout events to a certain extent, allowing for a more reliable characterization of biological processes such as cell fate. This method was applied to study two single-cell RNA-seq datasets, head and neck squamous cell carcinoma and chronic myeloid leukaemia. The GINE method not only effectively distinguishes malignant cells from benign cells and differentiates between different periods of differentiation, but also effectively reflects the disease efficacy process, demonstrating the potential of using GINE to study cellular dynamics. The method aims to explore the dynamic information at the level of single cell disorganization and thus to study the dynamics of biological system processes. The results of this study may provide scientific recommendations for research on cell differentiation, tracking cancer development, and the process of disease response to drugs.

SOJNMF: Identifying Multidimensional Molecular Regulatory Modules by Sparse Orthogonality-Regularized Joint Non-Negative Matrix Factorization Algorithm.

Cancer is not only a very aggressive but also a very diverse disease. Recent advances in high-throughput omics technologies of cancer have enabled biomedical researchers to have more opportunities for studying its multi-level biological regulatory mechanism. However, there are few methods to explore the underlying mechanism of cancer by identifying its multidimensional molecular regulatory modules from the multidimensional omics data of cancer. In this paper, we propose a sparse orthogonality-regularized joint non-negative matrix factorization (SOJNMF) algorithm which can integratively analyze multidimensional omics data. This method can not only identify multidimensional molecular regulatory modules, but reduce the overlap rate of features among the multidimensional modules while ensuring the sparsity of the coefficient matrix after decomposition. Gene expression data, miRNA expression data and gene methylation data of liver cancer are integratively analyzed based on SOJNMF algorithm. Then, we obtain 238 multidimensional molecular regulatory modules. The results of permutation test indicate that different omics features within these modules are significantly correlated in statistics. Meanwhile, the results of functional enrichment analysis show that these multidimensional modules are significantly related to the underlying mechanism of the occurrence and development of liver cancer.

FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.

Cell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and is a crucial component of single-cell clustering pipelines. However, most current single-cell feature selection methods are univariable filter methods ignoring gene dependency. Even the multivariable filter methods developed in recent years only consider "one-to-many" relationship between genes. In this paper, a novel single-cell feature selection method based on convex analysis of mixtures (FSCAM) is proposed, which takes into account "many-to-many" relationship. Compared to the previous "one-to-many" methods, FSCAM selects genes with a combination of relevancy, redundancy and completeness. Pertinent benchmarking is conducted on the real datasets to validate the superiority of FSCAM. Through plugging into the framework of partition around medoids (PAM) clustering, a single-cell clustering algorithm based on FSCAM method (SCC_FSCAM) is further developed. Comparing SCC_FSCAM with existing advanced clustering algorithms, the results show that our algorithm has advantages in both internal criteria (clustering number) and external criteria (adjusted Rand index) and has a good stability.

Carbon­sulfur coupling in a seasonally hypoxic, high-sulfate reservoir in SW China: Evidence from stable CS isotopes and sulfate-reducing bacteria.

Anthropogenic input of sulfate (SO42-) in reservoirs may enhance bacterial sulfate reduction (BSR) under seasonally hypoxic conditions in the water column. However, factors that control BSR and its coupling to organic carbon (OC) mineralization in seasonally hypoxic reservoirs remain unclear. The present study elucidates the coupling processes by analyzing the concentrations and isotopic composition of dissolved inorganic carbon (DIC) and sulfur (SO42-, sulfide) species, and the microbial community in water of the Aha reservoir, SW China, which has high SO42- concentration due to the inputs from acid mine drainage about twenty years ago. The water column at two sites in July and October revealed significant thermal stratification. In the hypoxic bottom water, the δ13C-DIC decreased while the δ34S-SO42- increased, implying organic carbon mineralization due to BSR. The magnitude of S isotope fractionation (Δ34S, obtained from δ34Ssulfate-δ34Ssulfide) during the process of BSR fell in the range of 3.4‰ to 27.0‰ in July and 21.6‰ to 31.8‰ in October, suggesting a change in the community of sulfate-reducing bacteria (SRB). The relatively low water column stability in October compared to that in July weakened the difference of water chemistry and ultimately affected the SRB diversity. The production of DIC (ΔDIC) scaled a strong positive relationship with the Δ34S in July (p < 0.01), indicating that high OC availability favored the survival of incomplete oxidizers of SRB. However, in October, Δ13C-DIC was correlated with the Δ34S in the bottom hypoxic water (p < 0.01), implying that newly degraded OC depleted in 13C could favor the dominance of complete oxidizers of SRB which caused greater S isotope fractionation. Moreover, the sulfide supplied by BSR might stimulate the reductive dissolution of Fe and Mn oxides (Fe(O)OH and MnO2). The present study helps to understand the coupling of C and S in seasonally hypoxic reservoirs characterized by high SO42- concentration.

Microbial controls on heavy metals and nutrients simultaneous release in a seasonally stratified reservoir.

The eutrophication of reservoirs can change the physicochemical parameters of water, thus affecting the migration and transformation of heavy metals. At present, there is insufficient research on the coupling mechanisms between nutrients and heavy metals, especially between heavy metals in suspended particles. In this paper, spatial and temporal distribution characteristics of nutrients dissolved heavy metals, and heavy metals in suspended particles were analyzed in a seasonally stratified reservoir. Combined with the nitrogen and phosphorus biogeochemical process, the coupling mechanisms between heavy metals and nutrients were discussed. The results showed that the Aha Reservoir had temperature and dissolved oxygen stratification in April and July. The reduction and dissolution of Fe and Mn oxide/hydroxide and the resuspension of sediments might result in a simultaneous increase in the concentrations of nutrients, dissolved heavy metals and heavy metals in suspended particles in hypolimnion in July and October. In the presence of dissimilatory iron-reducing bacteria (DRIB), the dissolution of iron-bound phosphorus in sediments and suspended particulate matter (SPM) might lead to the simultaneous release of iron and phosphorus into the water. The dissolution of metal sulfides in the sediments and SPM under the action of dissimilatory nitrate reduction to ammonium (DNRA) bacteria might lead to the simultaneous release of ammonia nitrogen and heavy metals into the water. Due to the coupling between nitrogen and phosphorus and heavy metals, seasonal stratified reservoir may face the risk of periodic simultaneous pollution of eutrophication and heavy metals in summer and autumn. This research provides theoretical support for the treatment of heavy metal and eutrophication combined pollution in karst areas.