DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

Myokine, a key cytokine for physical exercise to alleviate sarcopenic obesity.

Skeletal muscle has a robust endocrine function as a powerful organ and can secrete and release cytokines or polypeptides known as myokines. These myokines have significant regulatory effects on signal transduction in skeletal muscle and the metabolism of peripheral tissues and organs and exert biological effects via autocrine, paracrine, or endocrine forms. Obesity and aging cause myokine secretion dysregulation, and hastening sarcopenic obesity (SO) development. Exercise is currently an excellent intervention and prevention method for SO. Meanwhile, exercise impacts many organs and tissues. These organs and tissues will produce various myokines in response to movement and metabolism throughout the body to govern muscle differentiation, growth, and remodeling. According to accumulating data, exercise can increase the release of myokines from diverse tissues into the blood and postpone the SO onset and progression by influencing protein metabolism, inflammation, mitochondrial quality control, and other mechanisms.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.

Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

Identification of cardiomyopathy-related core genes through human metabolic networks and expression data.

BACKGROUND: Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. RESULTS: Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. CONCLUSION: The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

Network-based integration method for potential breast cancer gene identification.

Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.

Association of PD-1 polymorphisms with the risk and prognosis of lung adenocarcinoma in the northeastern Chinese Han population.

BACKGROUND: Lung cancer is a leading cause of death from cancer worldwide, especially non-small cell lung cancer (NSCLC). The marker of progression in lung adenocarcinoma, the main type of NSCLC, has been rarely studied. Programmed death 1 (PD-1) is an effective drug target for the treatment of NSCLC. Our study aimed to examine the PD-1 role in the disease process. The study of the effect of polymorphisms on the progression of lung adenocarcinoma in the Han population of Northeast China may provide a valuable reference for the research and application of these drugs. METHODS: Chi-square test, Wilcoxon rank sum test, and classification efficiency assessment were used to test SNPs of PD-1 in 287 patients and combined with clinical information. RESULTS: We successfully identified biomarkers (rs2227981, rs2227982, and rs3608432) that could distinguish between lung adenocarcinoma patients of early stages and late stages. Multiple clinical indicators showed significant differences among different SNPs and cancer stages. Furthermore, this gene was confirmed to effectively distinguish the stages of lung adenocarcinoma with RNA-seq data in TCGA. CONCLUSIONS: Out study indicated that the PD-1 gene and the SNPs on it could be used as markers for distinguishing lung adenocarcinoma staging in the Northeast Han population. Our investigation into the link between PD-1 polymorphisms and lung adenocarcinoma would help to provide guidance for the treatment and prognosis of lung adenocarcinoma.

Graphitic carbon nitride quantum dots as an "off-on" fluorescent switch for determination of mercury(II) and sulfide.

A rapid method has been developed for the determination of Hg(II) and sulfide by using graphitic carbon nitride quantum dots (g-CNQDs) as a fluorescent probe. The interaction between Hg(II) and g-CNQDs leads to the quenching of the blue g-CNQD fluorescence (with excitation/emission peaks at 390/450 nm). However, the fluorescence can be recovered after addition of sulfide such that the "turn-off" state is switched back to the "turn-on" state. The g-CNQDs were fully characterized by transmission electron microscopy, X-ray diffractometry, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, UV-vis absorption and fluorescence spectroscopy. Under the optimal experimental conditions, this probe is highly selective and sensitive to Hg(II). The linear response to Hg(II) extends from 0.20 to 21 µM with a detection limit of 3.3 nM. In addition, sulfide can be detected via the recovery of fluorescence. The linear response range for sulfide species is from 8.0 to 45 µM with a detection limit of 22 nM. The mechanism of the "turn-off-on" scheme is discussed. The methods have been applied to the analysis of spiked tap water, lake water and wastewater samples. Graphical abstract Schematic of an off-on fluorescent probe for mercury(II). The fluorescence of graphitic carbon nitride quantum dots (g-CNQDs) is quenched by Hg2+ but is recovered after reacting with S2- as it can combine with Hg2+ on the surface of g-CNQDs.

Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks.

Complex chronic diseases are caused by the effects of genetic and environmental factors. Single nucleotide polymorphisms (SNPs), one common type of genetic variations, played vital roles in diseases. We hypothesized that disease risk functional SNPs in coding regions and protein interaction network modules were more likely to contribute to the identification of disease susceptible genes for complex chronic diseases. This could help to further reveal the pathogenesis of complex chronic diseases. Disease risk SNPs were first recognized from public SNP data for coronary heart disease (CHD), hypertension (HT) and type 2 diabetes (T2D). SNPs in coding regions that were classified into nonsense and missense by integrating several SNP functional annotation databases were treated as functional SNPs. Then, regions significantly associated with each disease were screened using random permutations for disease risk functional SNPs. Corresponding to these regions, 155, 169 and 173 potential disease susceptible genes were identified for CHD, HT and T2D, respectively. A disease-related gene product interaction network in environmental context was constructed for interacting gene products of both disease genes and potential disease susceptible genes for these diseases. After functional enrichment analysis for disease associated modules, 5 CHD susceptible genes, 7 HT susceptible genes and 3 T2D susceptible genes were finally identified, some of which had pleiotropic effects. Most of these genes were verified to be related to these diseases in literature. This was similar for disease genes identified from another method proposed by Lee et al. from a different aspect. This research could provide novel perspectives for diagnosis and treatment of complex chronic diseases and susceptible genes identification for other diseases.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

Plant and soil responses to tillage practices change arbuscular mycorrhizal fungi populations during crop growth.

Background: Tillage practices can substantially affect soil properties depending on crop stage. The interaction between tillage and crop growth on arbuscular mycorrhizal fungi (AMF) communities remains unclear. We investigated the interactions between four tillage treatments (CT: conventional tillage, RT: reduced tillage, NT: no tillage with mulch, and SS: subsoiling with mulch), maintained for 25 years, and two wheat growth stages (elongation stage and grain filling stage) on AMF diversity and community composition. Results: The AMF community composition strongly changed during wheat growth, mainly because of changes in the relative abundance of dominant genera Claroideoglomus, Funneliformi, Rhizophagu, Entrophospora, and Glomus. Co-occurrence network analysis revealed that the grain filling stage had a more complex network than the elongation stage. Redundancy analysis results showed that keystone genera respond mainly to changes in soil organic carbon during elongation stage, whereas the total nitrogen content affected the keystone genera during grain filling. Compared with CT, the treatments with mulch, i.e., NT and SS, significantly changed the AMF community composition. The change of AMF communities under different tillage practices depended on wheat biomass and soil nutrients. NT significantly increased the relative abundances of Glomus and Septoglomus, while RT significantly increased the relative abundance of Claroideoglomus. Conclusion: Our findings indicate that the relative abundance of dominant genera changed during wheat growth stages. Proper tillage practices (e.g., NT and SS) benefit the long-term sustainable development of the Loess Plateau cropping systems.

The clinical effect of minimally invasive stereotactic puncture intracranial hematoma removal in the treatment of patients with cerebral hemorrhage: a meta-analysis.

OBJECTIVE: The objective of the study was to systemically evaluate the clinical efficacy of minimally invasive stereotactic puncture for intracranial hematoma evacuation in patients with cerebral hemorrhage. MATERIALS AND METHODS: Relevant studies in PubMed, Web of Science, MEDLINE, China National Knowledge Infrastructure, Wanfang, and VIP databases were searched. A meta-analysis was performed following the inclusion and exclusion criteria screening, data extraction, and literature quality evaluation. RESULTS: Fifteen studies involving 1312 patients were included with 673 participants in the experimental group and 639 in the control group. The results of the meta-analysis showed that, compared with traditional craniotomy or treatment, minimally invasive stereotactic puncture intracranial hematoma removal had a higher clinical total effective rate in patients with cerebral hemorrhage, an outcome that could significantly shorten the hospitalization time of patients with cerebral hemorrhage. The level of post-operative activities of daily living was significantly higher, the incidence of postoperative complications was lower, and the mortality rate was lower. However, there was no significant difference in the degree of post-operative neurological deficit. CONCLUSION: Compared with traditional craniotomy or conservative treatment, minimally invasive stereotactic puncture intracranial hematoma removal has a higher clinical efficacy in the treatment of patients with cerebral hemorrhage, which can improve the post-operative daily life and abilities of patients.

OBJETIVO: Evaluación sistemática de la eficacia clínica de la punción estereotáctica mínimamente invasiva para la evacuación de hematomas intracraneales en pacientes con hemorragia cerebral. MATERIAL Y MÉTODOS: Se realizaron búsquedas en estudios relevantes en PubMed, Web of Science, MEDLINE, Infraestructura Nacional de Conocimiento de China, base de datos Wanfang y base de datos VIP. El metanálisis se realizó después de la selección de criterios de inclusión y exclusión, la extracción de datos y la evaluación de la calidad de la literatura. RESULTADOS: Se incluyeron 15 estudios en los que participaron 1.312 sujetos, 673 en el grupo experimental y 639 en el grupo control. En comparación con la Craneotomía tradicional o el tratamiento, el aclaramiento estereotáctico mínimamente invasivo de hematomas intracraneales tiene una alta eficiencia clínica total en pacientes con hemorragia intracerebral y puede acortar significativamente el tiempo de hospitalización de los pacientes con hemorragia intracerebral. El nivel de actividad de la vida diaria postoperatoria (ADL) aumentó significativamente, la incidencia de complicaciones postoperatorias disminuyó y la mortalidad disminuyó. Sin embargo, no hubo diferencia significativa en el grado de déficit neurológico postoperatorio. CONCLUSIÓN: En comparación con la Craneotomía tradicional o el tratamiento conservador, la Craneotomía estereotáctica mínimamente invasiva tiene un mayor efecto clínico en el tratamiento de la hemorragia cerebral y puede mejorar la capacidad de la vida diaria de los pacientes después de la operación.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer.

Background: Esophageal cancer (EC) had the sixth-highest mortality rate of all cancers due to its poor prognosis. Immune cells and mutation genes influenced the prognosis of EC, but their combined effect on predicting EC prognosis was unknown. In this study, we comprehensively analyzed the immune cell infiltration (ICI) and mutation genes and their combined effects for predicting prognosis in EC. Methods: The CIBERSORT and ESTIMATE algorithms were used to analyse the ICI scape based on the TCGA and GEO databases. EC tissues and pathologic sections from Huai'an, China, were used to verify the key immune cells and mutation genes and their interactions. Results: Stromal/immune score patterns and ICI/gene had no statistical significance in overall survival (OS) (p > 0.05). The combination of ICI and tumor mutation burden (TMB) showed that the high TMB and high ICI score group had the shortest OS (p = 0.004). We recognized that the key mutation gene NRF2 was significantly different in the high/low ICI score subgroups (p = 0.002) and positivity with mast cells (MCs) (p < 0.05). Through experimental validation, we found that the MCs and activated mast cells (AC-MCs) were more infiltration in stage II/III (p = 0.032; p = 0.013) of EC patients and that NRF2 expression was upregulated in EC (p = 0.045). AC-MCs combined with NRF2 had a poor prognosis, according to survival analysis (p = 0.056) and interactive analysis (p = 0.032). Conclusions: We presume that NRF2 combined with AC-MCs could be a marker to predict prognosis and could influence immunotherapy through regulating PD-L1 in the EC.

Extracellular shuttling miR-21 contributes to esophageal cancers and human umbilical vein endothelial cell communication in the tumor microenvironment and promotes tumor angiogenesis by targeting phosphatase and tensinhomolog.

BACKGROUND: Cell-cell communication by carcinoma-derived exosomes can influence the tumor microenvironment (TME) and regulate cancer progression. Based on the overexpression of microRNA-21-5p (miR-21) in plasma from patients diagnosed with esophageal squamous cell carcinoma (ESCC) and exosomes from ESCC cell lines identified earlier, this study aimed to explore the influence of exosomal miR-21 within the TME. METHOD: ScRNA-Seq and Bulk RNA-Seq were integrated to elucidate the communication between cancer and endothelial cells. The functionality and mechanisms by which exo-miR-21 derived from carcinoma regulate endothelial cell-mediated angiogenesis were assessed using a cocultivation model of EC9706 cells and recipient human umbilical vein endothelial cells (HUVECs), through blood vessel formation experiments, luciferase reporter assays, RT-qPCR, and western blot analysis. RESULT: A total of 3842 endothelial cells were extracted from the scRNA-seq data of ESCC samples and reclustered into five cell subtype. Cell-cell communication analysis revealed cancer cells presented a strong interaction with angiogenesis-like endothelial cells in secreted signaling. MiR-21 was unregulated in ESCC and the carcinoma-derived exo-miR-21 was significantly raised in HUVECs. The exo-miR-21 promoted the proliferation and migration of HUVECs while also enhancing, closed mesh count, and junction number in HUVECs. Mechanistically, dual-luciferase reporter assay revealed that PTEN was the target of miR-21. Meanwhile, p-Akt was significantly increased and suppressed by inhibition of miR-21 and PI3K inhibitor LY294002. CONCLUSION: Exo-miR-21-mediated communication between endothelial and cancer cells plays a pivotal role in promoting the angiogenesis of ESCC. Therefore, controlling exo-miR-21 could serve as a novel therapeutic strategy for ESCC by targeting angiogenesis.

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis.

Introduction: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. Methods: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. Results: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction. Discussion: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.

Introduction: Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. Methods: This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Results: Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. Conclusion: To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Taurodeoxycholic acid-YAP1 upregulates OTX1 in promoting gallbladder cancer malignancy through IFITM3-dependent AKT activation.

Orthodenticle homeobox (OTX1) is reported to be involved in numerous cancers, but the expression level and molecular function of OTX1 in gallbladder cancer (GBC) remain unknown. Here, we found the elevated level of OTX1 associated with poor prognosis in human gallbladder cancer. In vitro and in vivo studies of human gallbladder cancer cell lines demonstrated that overexpression of OTX1 promoted cell proliferation, whereas the downregulation inhibited it. Additionally, we found a tight correlation between the serum level of taurodeoxycholic acid (TDCA) and OTX1 expression. TDCA-induced activation of YAP1 by phosphorylation inhibition contributed to the transcriptional activation of OTX1. Mechanistically, we identified that OTX1 activated AKT signaling pathway by transactivating the expression of IFITM3 and thus promoted the proliferation of GBC cells. Taken together, our results showed that TDCA-YAP1-dependent expression of OTX1 regulated IFITM3 and affected GBC proliferation via the AKT signaling pathway. Our experiments also suggested that OTX1 is a novel therapeutic target for GBC.

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein-protein interaction networks. A total of 69, 43, and 64 leader genes were identified for stomach, liver, and colon cancers, respectively. Furthermore, literature reviews and experimental data including protein expression levels and independent datasets from other databases all verified their association with corresponding cancer types. These final leader genes were expected to be used as diagnostic biomarkers and targets for new treatment strategies. The procedure for identifying cancer leader genes could be expanded to open up a window into the mechanisms, early diagnosis, and treatment of other cancer types.

Integrating Expression Data-Based Deep Neural Network Models with Biological Networks to Identify Regulatory Modules for Lung Adenocarcinoma.

Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis.

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1.

BACKGROUND: RNA-binding proteins (RBPs) play diverse roles in eukaryotic RNA processing. Despite their pervasive functions in coding and noncoding RNA biogenesis and regulation, elucidating the sequence specificities that define protein-RNA interactions remains a major challenge. Recently, CLIP-seq (Cross-linking immunoprecipitation followed by high-throughput sequencing) has been successfully implemented to study the transcriptome-wide binding patterns of SRSF1, PTBP1, NOVA and fox2 proteins. These studies either adopted traditional methods like Multiple EM for Motif Elicitation (MEME) to discover the sequence consensus of RBP's binding sites or used Z-score statistics to search for the overrepresented nucleotides of a certain size. We argue that most of these methods are not well-suited for RNA motif identification, as they are unable to incorporate the RNA structural context of protein-RNA interactions, which may affect to binding specificity. Here, we describe a novel model-based approach--RNAMotifModeler to identify the consensus of protein-RNA binding regions by integrating sequence features and RNA secondary structures. RESULTS: As an example, we implemented RNAMotifModeler on SRSF1 (SF2/ASF) CLIP-seq data. The sequence-structural consensus we identified is a purine-rich octamer 'AGAAGAAG' in a highly single-stranded RNA context. The unpaired probabilities, the probabilities of not forming pairs, are significantly higher than negative controls and the flanking sequence surrounding the binding site, indicating that SRSF1 proteins tend to bind on single-stranded RNA. Further statistical evaluations revealed that the second and fifth bases of SRSF1octamer motif have much stronger sequence specificities, but weaker single-strandedness, while the third, fourth, sixth and seventh bases are far more likely to be single-stranded, but have more degenerate sequence specificities. Therefore, we hypothesize that nucleotide specificity and secondary structure play complementary roles during binding site recognition by SRSF1. CONCLUSION: In this study, we presented a computational model to predict the sequence consensus and optimal RNA secondary structure for protein-RNA binding regions. The successful implementation on SRSF1 CLIP-seq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins.