NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways.

SUMMARY: Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/. AVAILABILITY AND IMPLEMENTATION: NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Survival and clinicopathological significance of blood vessel invasion in operable breast cancer: a systematic review and meta-analysis.

BACKGROUND: Lymphovascular invasion, including lymphatic-vessel invasion and blood-vessel invasion, plays an important role in distant metastases. The metastatic pattern of blood-vessel invasion may differ from that of lymphatic-vessel invasion. However, its prognostic significance in breast cancer remains controversial. We evaluated the role of blood-vessel invasion in the prognosis of operable breast-cancer patients and its association with clinicopathological characteristics. METHODS: We systematically searched EMBASE, PubMed, the Cochrane Library and Web of Science for studies in English through December 2020. Disease-free survival, overall survival and cancer-specific survival were the primary outcomes. Pooled hazard ratios and 95% confidence intervals were assessed using a random-effects model. RESULTS: Twenty-seven studies involving 7954 patients were included. Blood-vessel invasion occurred in 20.4% of tumor samples. Pooled results showed significant associations of blood-vessel invasion with worse disease-free survival (hazard ratio = 1.82; 95% confidence interval = 1.43-2.31) and overall survival (hazard ratio = 1.86; 95% confidence interval = 1.16-2.99) in multivariate analyses. The results of the univariate analyses were similar. Among the clinicopathological factors, blood-vessel invasion was associated with larger tumor size, lymph-node metastasis, nonspecific invasive type, higher histological grade, estrogen receptor-negative breast cancer, human epidermal growth factor receptor 2-positive breast cancer and lymphatic-vessel invasion. In the lymph-node-negative subgroup analyses, the presence of blood-vessel invasion led to poorer disease-free survival (hazard ratio = 2.46; 95%confidence interval = 1.64-3.70) and overall survival (hazard ratio = 2.94; 95%confidence interval = 1.80-4.80). CONCLUSIONS: We concluded that blood-vessel invasion is an independent predictor of poor prognosis in operable breast cancer and is associated with aggressive clinicopathological features. Breast-cancer patients with blood-vessel invasion require more aggressive treatments after surgery.

Identification and Functional Analysis of Individual-Specific Subpathways in Lung Adenocarcinoma.

Small molecular networks within complex pathways are defined as subpathways. The identification of patient-specific subpathways can reveal the etiology of cancer and guide the development of personalized therapeutic strategies. The dysfunction of subpathways has been associated with the occurrence and development of cancer. Here, we propose a strategy to identify aberrant subpathways at the individual level by calculating the edge score and using the Gene Set Enrichment Analysis (GSEA) method. This provides a novel approach to subpathway analysis. We applied this method to the expression data of a lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas (TCGA) database. We validated the effectiveness of this method in identifying LUAD-relevant subpathways and demonstrated its reliability using an independent Gene Expression Omnibus dataset (GEO). Additionally, survival analysis was applied to illustrate the clinical application value of the genes and edges in subpathways that were associated with the prognosis of patients and cancer immunity, which could be potential biomarkers. With these analyses, we show that our method could help uncover subpathways underlying lung adenocarcinoma.

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. However, the existing experimental techniques for detecting Kcr sites are cost-ineffective, to a great need for new computational methods to address this problem. We here describe Adapt-Kcr, an advanced deep learning model that utilizes adaptive embedding and is based on a convolutional neural network together with a bidirectional long short-term memory network and attention architecture. On the independent testing set, Adapt-Kcr outperformed the current state-of-the-art Kcr prediction model, with an improvement of 3.2% in accuracy and 1.9% in the area under the receiver operating characteristic curve. Compared to other Kcr models, Adapt-Kcr additionally had a more robust ability to distinguish between crotonylation and other lysine modifications. Another model (Adapt-ST) was trained to predict phosphorylation sites in SARS-CoV-2, and outperformed the equivalent state-of-the-art phosphorylation site prediction model. These results indicate that self-adaptive embedding features perform better than handcrafted features in capturing discriminative information; when used in attention architecture, this could be an effective way of identifying protein Kcr sites. Together, our Adapt framework (including learning embedding features and attention architecture) has a strong potential for prediction of other protein posttranslational modification sites.

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization.

Subcellular localization of microRNAs (miRNAs) is an important reflection of their biological functions. Considering the spatio-temporal specificity of miRNA subcellular localization, experimental detection techniques are expensive and time-consuming, which strongly motivates an efficient and economical computational method to predict miRNA subcellular localization. In this paper, we describe a computational framework, MiRLoc, to predict the subcellular localization of miRNAs. In contrast to existing methods, MiRLoc uses the functional similarity between miRNAs instead of sequence features and incorporates information about the subcellular localization of the corresponding target mRNAs. The results show that miRNA functional similarity data can be effectively used to predict miRNA subcellular localization, and that inclusion of subcellular localization information of target mRNAs greatly improves prediction performance.

SPMLMI: predicting lncRNA-miRNA interactions in humans using a structural perturbation method.

Long non-coding RNA (lncRNA)-microRNA (miRNA) interactions are quickly emerging as important mechanisms underlying the functions of non-coding RNAs. Accordingly, predicting lncRNA-miRNA interactions provides an important basis for understanding the mechanisms of action of ncRNAs. However, the accuracy of the established prediction methods is still limited. In this study, we used structural consistency to measure the predictability of interactive links based on a bilayer network by integrating information for known lncRNA-miRNA interactions, an lncRNA similarity network, and an miRNA similarity network. In particular, by using the structural perturbation method, we proposed a framework called SPMLMI to predict potential lncRNA-miRNA interactions based on the bilayer network. We found that the structural consistency of the bilayer network was higher than that of any single network, supporting the utility of bilayer network construction for the prediction of lncRNA-miRNA interactions. Applying SPMLMI to three real datasets, we obtained areas under the curves of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021 based on 5-fold cross-validation, suggesting good model performance. In addition, the generalizability of SPMLMI was better than that of the previously established methods. Case studies of two lncRNAs (i.e., SNHG14 and MALAT1) further demonstrated the feasibility and effectiveness of the method. Therefore, SPMLMI is a feasible approach to identify novel lncRNA-miRNA interactions underlying complex biological processes.

Identifying RNA N6-Methyladenine Sites in Three Species Based on a Markov Model.

N6-methyladenosine (m6A), the most common posttranscriptional modification in eukaryotic mRNAs, plays an important role in mRNA splicing, editing, stability, degradation, etc. Since the methylation state is dynamic, methylation sequencing needs to be carried out over different time periods, which brings some difficulties to identify the RNA methyladenine sites. Thus, it is necessary to develop a fast and accurate method to identify the RNA N6-methyladenosine sites in the transcriptome. In this study, we use first-order and second-order Markov models to identify RNA N6-methyladenine sites in three species (Saccharomyces cerevisiae, mouse, and Homo sapiens). These two methods can fully consider the correlation between adjacent nucleotides. The results show that the performance of our method is better than that of other existing methods. Furthermore, the codons encoded by three nucleotides have biases in mRNA, and a second-order Markov model can capture this kind of information exactly. This may be the main reason why the performance of the second-order Markov model is better than that of the first-order Markov model in the m6A prediction problem. In addition, we provide a corresponding web tool called MM-m6APred.

EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.

In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, "small sample size problem", diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer's disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer's disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases.

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

Mining influential genes based on deep learning.

BACKGROUND: Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. RESULTS: Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. CONCLUSIONS: We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data.

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.

Identification of cancer-related miRNA-lncRNA biomarkers using a basic miRNA-lncRNA network.

LncRNAs are regulatory noncoding RNAs that play crucial roles in many biological processes. The dysregulation of lncRNA is thought to be involved in many complex diseases; lncRNAs are often the targets of miRNAs in the indirect regulation of gene expression. Numerous studies have indicated that miRNA-lncRNA interactions are closely related to the occurrence and development of cancers. Thus, it is important to develop an effective method for the identification of cancer-related miRNA-lncRNA interactions. In this study, we compiled 155653 experimentally validated and predicted miRNA-lncRNA associations, which we defined as basic interactions. We next constructed an individual-specific miRNA-lncRNA network (ISMLN) for each cancer sample and a basic miRNA-lncRNA network (BMLN) for each type of cancer by examining the expression profiles of miRNAs and lncRNAs in the TCGA (The Cancer Genome Atlas) database. We then selected potential miRNA-lncRNA biomarkers based on the BLMN. Using this method, we identified cancer-related miRNA-lncRNA biomarkers and modules specific to a certain cancer. This method of profiling will contribute to the diagnosis and treatment of cancers at the level of gene regulatory networks.

V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature.

Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .

LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature.

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features.

MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. Therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). These features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). The results show that the specificity ([Formula: see text])and sensitivity ([Formula: see text]) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.

Does metal pollution matter with C retention by rice soil?

Soil respiration, resulting in decomposition of soil organic carbon (SOC), emits CO2 to the atmosphere and increases under climate warming. However, the impact of heavy metal pollution on soil respiration in croplands is not well understood. Here we show significantly increased soil respiration and efflux of both CO2 and CH4 with a concomitant reduction in SOC storage from a metal polluted rice soil in China. This change is linked to a decline in soil aggregation, in microbial abundance and in fungal dominance. The carbon release is presumably driven by changes in carbon cycling occurring in the stressed soil microbial community with heavy metal pollution in the soil. The pollution-induced increase in soil respiration and loss of SOC storage will likely counteract efforts to increase SOC sequestration in rice paddies for climate change mitigation.

Promoter recognition based on the maximum entropy hidden Markov model.

Since the fast development of genome sequencing has produced large scale data, the current work uses the bioinformatics methods to recognize different gene regions, such as exon, intron and promoter, which play an important role in gene regulations. In this paper, we introduce a new method based on the maximum entropy Markov model (MEMM) to recognize the promoter, which utilizes the biological features of the promoter for the condition. However, it leads to a high false positive rate (FPR). In order to reduce the FPR, we provide another new method based on the maximum entropy hidden Markov model (ME-HMM) without the independence assumption, which could also accommodate the biological features effectively. To demonstrate the precision, the new methods are implemented by R language and the hidden Markov model (HMM) is introduced for comparison. The experimental results show that the new methods may not only overcome the shortcomings of HMM, but also have their own advantages. The results indicate that, MEMM is excellent for identifying the conserved signals, and ME-HMM can demonstrably improve the true positive rate.

Correlation analysis of prognostic and pathological features of patients with chronic sinusitis and nasal polyps following endoscopic surgery.

The aim of this study was to evaluate the therapeutic value of pathological indicators to predict the efficacy of endoscopic sinus surgery (ESS) in patients with chronic rhinosinusitis (CRS) with nasal polyps. A total of 53 patients with CRS with nasal polyps, who had undergone endoscopic surgery at least one year before, were surveyed for their clinical symptoms. Surgical specimen biopsies were consulted and related pathological indicators were measured. The association between the main symptoms of CRS with nasal polyps following ESS and pathological indicators were statistically analyzed. The main symptoms of patients with CRS with nasal polyps following ESS were nasal congestion, thick nasal discharge, rhinorrhea or sneezing. Goblet cells are associated with the symptoms of sneezing and thick nasal discharge, pathological gland formation is associated with dizziness, and the degree of tissue edema is associated with post-nasal discharge (P<0.05). Pathological indicators aid the prediction of the efficacy of nasal ESS in patients with CRS with nasal polyps.

[Variation of Cd, Zn and Se contents of polished rice and the potential health risk for subsistence-diet farmers from typical areas of South China].

In this study, a total of 70 polished rice samples were randomly collected at agro-product markets from some typical regions of South China. Their contents of Cd, Zn and Se were determined by atomic adsorption spectrophotometers (AAS) and atomic fluorescence spectrometry (AES) respectively. The variation of the contents with rice areas was described in terms of soil conditions and the potential health risk by food exposure to these rice rains for subsistence-diet fanners is discussed. Over 70% of the total samples have Cd contents exceeding the State Food Security Standards (0.2 mg x kg(-1)) with Cd/Zn ratios exceeding the suggested critical threshold of 0.015. Widest variation was found for Cd and smallest for Zn, showing rice Cd prone to environmental stress. The extent to which the contents of the analyzed elements varied with rice areas was rater for Cd and Se than for Zn, though the contents followed in the same order: polluted area > acid paddy area and neutral paddy area. This further evidenced a determinacy of chemical availability in rice Cd uptake. Taking the reference dose values by WHO and USEPA, the health risk by Cd exposure to the rice diet from different areas was estimated. The consumption of rice from polluted area and acid paddy area may impose serious health risks for subsistence diet farmers though those from neutral paddy area may be still safe under the WHO guideline. It is demanded that the problem of the rice rains high in Cd and low in Zn and Se, and serious potential risk should be taken into account while developing high-yielding rice in acid and polluted rice ares of South China. Technology for depressing Cd uptake and low-Cd cultivar breeding should he pursued in rice production sector in the future.