scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species.

Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.

SpatialRef: a reference of spatial omics with known spot annotation.

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouse and Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and >400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

SEanalysis 2.0: a comprehensive super-enhancer regulatory network analysis tool for human and mouse.

Super-enhancers (SEs) play an essential regulatory role in various biological processes and diseases through their specific interaction with transcription factors (TFs). Here, we present the release of SEanalysis 2.0 (http://licpathway.net/SEanalysis), an updated version of the SEanalysis web server for the comprehensive analyses of transcriptional regulatory networks formed by SEs, pathways, TFs, and genes. The current version added mouse SEs and further expanded the scale of human SEs, documenting 1 167 518 human SEs from 1739 samples and 550 226 mouse SEs from 931 samples. The SE-related samples in SEanalysis 2.0 were more than five times that in version 1.0, which significantly improved the ability of original SE-related network analyses ('pathway downstream analysis', 'upstream regulatory analysis' and 'genomic region annotation') for understanding context-specific gene regulation. Furthermore, we designed two novel analysis models, 'TF regulatory analysis' and 'Sample comparative analysis' for supporting more comprehensive analyses of SE regulatory networks driven by TFs. Further, the risk SNPs were annotated to the SE regions to provide potential SE-related disease/trait information. Hence, we believe that SEanalysis 2.0 has significantly expanded the data and analytical capabilities of SEs, which helps researchers in an in-depth understanding of the regulatory mechanisms of SEs.

VARAdb: a comprehensive variation annotation database for human.

With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include 'Variation information', 'Regulatory information', 'Related genes', 'Chromatin accessibility' and 'Chromatin interaction'. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.

TP63, SOX2, and KLF5 Establish a Core Regulatory Circuitry That Controls Epigenetic and Transcription Patterns in Esophageal Squamous Cell Carcinoma Cell Lines.

BACKGROUND & AIMS: We investigated the transcriptome of esophageal squamous cell carcinoma (ESCC) cells, activity of gene regulatory (enhancer and promoter regions), and the effects of blocking epigenetic regulatory proteins. METHODS: We performed chromatin immunoprecipitation sequencing with antibodies against H3K4me1, H3K4me3, and H3K27ac and an assay for transposase-accessible chromatin to map the enhancer regions and accessible chromatin in 8 ESCC cell lines. We used the CRC_Mapper algorithm to identify core regulatory circuitry transcription factors in ESCC cell lines, and determined genome occupancy profiles for 3 of these factors. In ESCC cell lines, expression of transcription factors was knocked down with small hairpin RNAs, promoter and enhancer regions were disrupted by CRISPR/Cas9 genome editing, or bromodomains and extraterminal (BET) family proteins and histone deacetylases (HDACs) were inhibited with ARV-771 and romidepsin, respectively. ESCC cell lines were then analyzed by whole-transcriptome sequencing, immunoprecipitation, immunoblots, immunohistochemistry, and viability assays. Interactions between distal enhancers and promoters were identified and verified with circular chromosome conformation capture sequencing. NOD-SCID mice were given injections of modified ESCC cells, some mice where given injections of HDAC or BET inhibitors, and growth of xenograft tumors was measured. RESULTS: We identified super-enhancer-regulated circuits and transcription factors TP63, SOX2, and KLF5 as core regulatory factors in ESCC cells. Super-enhancer regulation of ALDH3A1 mediated by core regulatory factors was required for ESCC viability. We observed direct interactions between the promoter region of TP63 and functional enhancers, mediated by the core regulatory circuitry transcription factors. Deletion of enhancer regions from ESCC cells decreased expression of the core regulatory circuitry transcription factors and reduced cell viability; these same results were observed with knockdown of each core regulatory circuitry transcription factor. Incubation of ESCC cells with BET and HDAC disrupted the core regulatory circuitry program and the epigenetic modifications observed in these cells; mice given injections of HDAC or BET inhibitors developed smaller xenograft tumors from the ESCC cell lines. Xenograft tumors grew more slowly in mice given the combination of ARV-771 and romidepsin than mice given either agent alone. CONCLUSIONS: In epigenetic and transcriptional analyses of ESCC cell lines, we found the transcription factors TP63, SOX2, and KLF5 to be part of a core regulatory network that determines chromatin accessibility, epigenetic modifications, and gene expression patterns in these cells. A combination of epigenetic inhibitors slowed growth of xenograft tumors derived from ESCC cells in mice.

SEanalysis: a web tool for super-enhancer associated regulatory analysis.

Super-enhancers (SEs) have prominent roles in biological and pathological processes through their unique transcriptional regulatory capability. To date, several SE databases have been developed by us and others. However, these existing databases do not provide downstream or upstream regulatory analyses of SEs. Pathways, transcription factors (TFs), SEs, and SE-associated genes form complex regulatory networks. Therefore, we designed a novel web server, SEanalysis, which provides comprehensive SE-associated regulatory network analyses. SEanalysis characterizes SE-associated genes, TFs binding to target SEs, and their upstream pathways. The current version of SEanalysis contains more than 330 000 SEs from more than 540 types of cells/tissues, 5042 TF ChIP-seq data generated from these cells/tissues, DNA-binding sequence motifs for ∼700 human TFs and 2880 pathways from 10 databases. SEanalysis supports searching by either SEs, samples, TFs, pathways or genes. The complex regulatory networks formed by these factors can be interactively visualized. In addition, we developed a customizable genome browser containing >6000 customizable tracks for visualization. The server is freely available at http://licpathway.net/SEanalysis.

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.

Super-Enhancer-Driven Long Non-Coding RNA LINC01503, Regulated by TP63, Is Over-Expressed and Oncogenic in Squamous Cell Carcinoma.

BACKGROUND & AIMS: Long non-coding RNAs (lncRNAs) are expressed in tissue-specific pattern, but it is not clear how these are regulated. We aimed to identify squamous cell carcinoma (SCC)-specific lncRNAs and investigate mechanisms that control their expression and function. METHODS: We studied expression patterns and functions of 4 SCC-specific lncRNAs. We obtained 113 esophageal SCC (ESCC) and matched non-tumor esophageal tissues from a hospital in Shantou City, China, and performed quantitative reverse transcription polymerase chain reaction assays to measure expression levels of LINC01503. We collected clinical data from patients and compared expression levels with survival times. LINC01503 was knocked down using small interfering RNAs and oligonucleotides in TE7, TE5, and KYSE510 cell lines and overexpressed in KYSE30 cells. Cells were analyzed by chromatin immunoprecipitation sequencing, luciferase reporter assays, colony formation, migration and invasion, and mass spectrometry analyses. Cells were injected into nude mice and growth of xenograft tumors was measured. LINC01503 interaction with proteins was studied using fluorescence in situ hybridization, RNA pulldown, and RNA immunoprecipitation analyses. RESULTS: We identified a lncRNA, LINC01503, which is regulated by a super enhancer and is expressed at significantly higher levels in esophageal and head and neck SCCs than in non-tumor tissues. High levels in SCCs correlated with shorter survival times of patients. The transcription factor TP63 bound to the super enhancer at the LINC01503 locus and activated its transcription. Expression of LINC01503 in ESCC cell lines increased their proliferation, colony formation, migration, and invasion. Knockdown of LINC01503 in SCC cells reduced their proliferation, colony formation, migration, and invasion, and the growth of xenograft tumors in nude mice. Expression of LINC01503 in ESCC cell lines reduced ERK2 dephosphorylation by DUSP6, leading to activation of ERK signaling via MAPK. LINC01503 disrupted the interaction between EBP1 and the p85 subunit of PI3K, increasing AKT signaling. CONCLUSIONS: We identified an lncRNA, LINC01503, which is increased in SCC cells compared with non-tumor cells. Increased expression of LINC01503 promotes ESCC cell proliferation, migration, invasion, and growth of xenograft tumors. It might be developed as a biomarker of aggressive SCCs in patients.

MASAN: a novel staging system for prognosis of patients with oesophageal squamous cell carcinoma.

BACKGROUND: Oesophageal squamous cell carcinoma (ESCC) is one of the most malignant cancers worldwide. Treatment of ESCC is in progress through accurate staging and risk assessment of patients. The emergence of potential molecular markers inspired us to construct novel staging systems with better accuracy by incorporating molecular markers. METHODS: We measured H scores of 23 protein markers and analysed eight clinical factors of 77 ESCC patients in a training set, from which we identified an optimal MASAN (MYC, ANO1, SLC52A3, Age and N-stage) signature. We constructed MASAN models using Cox PH models, and created MASAN-staging systems based on k-means clustering and minimum-distance classifier. MASAN was validated in a test set (n = 77) and an independent validation set (n = 150). RESULTS: MASAN possessed high predictive accuracies and stratified ESCC patients into three prognostic groups that were more accurate than the current pTNM-staging system for both overall survival and disease-free survival. To facilitate clinical utilisation, we also constructed MASAN-SI staging systems based on staining indices (SI) of protein markers, which possessed similar prognostic performance as MASAN. CONCLUSION: MASAN provides a good alternative staging system for ESCC prognosis with a high precision using a simple model.

A three-lncRNA signature predicts overall survival and disease-free survival in patients with esophageal squamous cell carcinoma.

BACKGROUND: Increasing evidence shows that dysregulated long non-coding RNAs (lncRNAs) can serve as potential biomarkers for cancer prognosis. However, lncRNA signatures, as potential prognostic biomarkers for esophageal squamous cell carcinoma (ESCC), have been seldom reported. METHODS: Based on our previous transcriptome RNA sequencing analysis from 15 paired ESCC tissues and adjacent normal tissues, we selected 10 lncRNAs with high score rank and characterized the expression of those lncRNAs, by qRT-PCR, in 138 ESCC and paired adjacent normal samples. These 138 patients were divided randomly into training (n = 77) and test (n = 59) groups. A prognostic signature of lncRNAs was identified in the training group and validated in the test group and in an independent cohort (n = 119). Multivariable Cox regression analysis evaluated the independence of the signature in overall survival (OS) and disease-free survival (DFS) prediction. GO and KEGG pathway analysis, combined with cell transwell and proliferation assays, are applied to explore the function of the three lncRNAs. RESULTS: A novel three-lncRNA signature, comprised of RP11-366H4.1.1 (ENSG00000248370), LINC00460 (ENSG00000233532) and AC093850.2 (ENSG00000230838), was identified. The signature classified patients into high-risk and low-risk groups with different overall survival (OS) and disease-free survival (DFS). For the training group, median OS: 23.1 months vs. 39.1 months, P < 0.001; median DFS: 15.2 months vs. 33.3 months, P < 0.001. For the test group, median OS: 23 months vs. 59 months, P < 0.001; median DFS: 16.4 months vs. 50.8 months, P < 0.001. For the independent cohort, median OS: 22.4 months vs. 60.4 months, P < 0.001). The signature indicates that patients in the high-risk group show poor OS and DFS, whereas patients with a low-risk group show significantly better outcome. The independence of the signature was validated by multivariable Cox regression analysis. GO and KEGG pathway analysis for 588 protein-coding genes-associated with the three lncRNAs indicated that the three lncRNAs were involved in tumorigenesis. In vitro assays further demonstrated that the three lncRNAs promoted the migration and proliferation of ESCC cells. CONCLUSIONS: The three-lncRNA signature is a novel and potential predictor of OS and DFS for patients with ESCC.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells.

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features.

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.

Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting.

Esophageal cancer (EC) is a type of aggressive cancer without clinically relevant molecular subtypes, hindering the development of effective strategies for treatment. To define molecular subtypes of EC, we perform mass spectrometry-based proteomic and phosphoproteomics profiling of EC tumors and adjacent non-tumor tissues, revealing a catalog of proteins and phosphosites that are dysregulated in ECs. The EC cohort is stratified into two molecular subtypes-S1 and S2-based on proteomic analysis, with the S2 subtype characterized by the upregulation of spliceosomal and ribosomal proteins, and being more aggressive. Moreover, we identify a subtype signature composed of ELOA and SCAF4, and construct a subtype diagnostic and prognostic model. Potential drugs are predicted for treating patients of S2 subtype, and three candidate drugs are validated to inhibit EC. Taken together, our proteomic analysis define molecular subtypes of EC, thus providing a potential therapeutic outlook for improving disease outcomes in patients with EC.

Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs.

Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23-52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.

Characterization of super-enhancer-associated functional lncRNAs acting as ceRNAs in ESCC.

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well-characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA-mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce-lncRNAs). We found that most of them significantly regulated the expression of cancer-related hallmark genes. These ce-lncRNAs were significantly regulated by enhancers, especially super-enhancers (SEs). Landscape analyses for lncRNAs further identified SE-associated functional ce-lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE-associated ce-lncRNAs. We further demonstrate that a SE-associated ce-lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE-associated ce-lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.

Co-activation of super-enhancer-driven CCAT1 by TP63 and SOX2 promotes squamous cancer progression.

Squamous cell carcinomas (SCCs) are aggressive malignancies. Previous report demonstrated that master transcription factors (TFs) TP63 and SOX2 exhibited overlapping genomic occupancy in SCCs. However, functional consequence of their frequent co-localization at super-enhancers remains incompletely understood. Here, epigenomic profilings of different types of SCCs reveal that TP63 and SOX2 cooperatively and lineage-specifically regulate long non-coding RNA (lncRNA) CCAT1 expression, through activation of its super-enhancers and promoter. Silencing of CCAT1 substantially reduces cellular growth both in vitro and in vivo, phenotyping the effect of inhibiting either TP63 or SOX2. ChIRP analysis shows that CCAT1 forms a complex with TP63 and SOX2, which regulates EGFR expression by binding to the super-enhancers of EGFR, thereby activating both MEK/ERK1/2 and PI3K/AKT signaling pathways. These results together identify a SCC-specific DNA/RNA/protein complex which activates TP63/SOX2-CCAT1-EGFR cascade and promotes SCC tumorigenesis, advancing our understanding of transcription dysregulation in cancer biology mediated by master TFs and super-enhancers.

A three-gene signature from protein-protein interaction network of LOXL2- and actin-related proteins for esophageal squamous cell carcinoma prognosis.

Current staging is inadequate for predicting clinical outcome of esophageal squamous cell carcinoma (ESCC). Aberrant expression of LOXL2 and actin-related proteins plays important roles in ESCC. Here, we aimed to develop a novel molecular signature that exceeds the power of the current staging system in predicting ESCC prognosis. We found that LOXL2 colocalized with filamentous actin in ESCC cells, and gene set enrichment analysis (GSEA) showed that LOXL2 is related to the actin cytoskeleton. An ESCC-specific protein-protein interaction (PPI) network involving LOXL2 and actin-related proteins was generated based on genome-wide RNA-seq in 15 paired ESCC samples, and the prognostic significance of 14 core genes was analyzed. Using risk score calculation, a three-gene signature comprising LOXL2, CDH1, and FN1 was derived from transcriptome data of patients with ESCC. The high-risk three-gene signature strongly correlated with poor prognosis in a training cohort of 60 patients (P = 0.003). In mRNA and protein levels, the prognostic values of this signature were further validated in 243 patients from a testing cohort (P = 0.001) and two validation cohorts (P = 0.021, P = 0.007). Furthermore, Cox regression analysis revealed that the signature was an independent prognostic factor. Compared with using the signature or TNM stage alone, the combined model significantly enhanced the accuracy in evaluating ESCC prognosis. In conclusion, our data reveal that the tumor-promoting role of LOXL2 in ESCC is mediated by perturbing the architecture of actin cytoskeleton through its PPIs. We generated a novel three-gene signature (PPI interfaces) that robustly predicts poor clinical outcome in ESCC patients.

The algebraic characterizations for a formal power series over complete strong bimonoids.

On the basis of run semantics and breadth-first algebraic semantics, the algebraic characterizations for a classes of formal power series over complete strong bimonoids are investigated in this paper. As recognizers, weighted pushdown automata with final states (WPDAs for short) and empty stack (WPDAs[Formula: see text]) are shown to be equivalent based on run semantics. Moreover, it is demonstrated that for every WPDA there is an equivalent crisp-simple weighted pushdown automaton with final states by run semantics if the underlying complete strong bimonoid satisfies multiplicatively local finiteness condition. As another type of generators, weighted context-free grammars over complete strong bimonoids are introduced, which are proven to be equivalent to WPDAs[Formula: see text] based on each one of both run semantics and breadth-first algebraic semantics. Finally examples are presented to illuminate the proposed methods and results.

Protein-coding genes combined with long non-coding RNAs predict prognosis in esophageal squamous cell carcinoma patients as a novel clinical multi-dimensional signature.

Esophageal carcinoma is one of the most malignant gastrointestinal cancers worldwide, and has a high mortality rate. Both protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) have been shown to play an important role in the development of malignant tumors. However, the clinical significance of PCGs combined lncRNAs is yet to be investigated in esophageal squamous cell carcinoma (ESCC). Using probe re-annotation, univariable Cox regression and the random survival forest algorithm to identify PCG-lncRNA combinations predictive of the overall survival, we found a signature comprised of three PCGs (ANGPTL7, OBP2A, SLC27A5) and two lncRNAs (RP11-702B10.1, RP11-523H24.3) to have the highest accurate prediction, with an area under ROC curve (AUC) of 0.85 in the training group and 0.63 in the test group, and it was significantly associated with the survival of ESCC patients in the training group (median survival: 32.2 months > 60 months, P < 0.001). The application of the signature to the test group showed similar prognostic values (median survival: 39.3 months vs. >60 months, P = 0.03). The chi-square test and multivariable Cox regression analysis showed that the three-PCG, two-lncRNA signature was an independent prognostic factor for patients with ESCC. Stratified analysis suggested that the PCG-lncRNA signature combined with the TNM stage could more accurately categorize ESCC patients. Our study suggests that the three-PCG, two-lncRNA signature has clinical significance for the prognosis of patients with ESCC. This signature can serve as a potential auxiliary biomarker of the TNM stage to subdivide ESCC patients more precisely.

Construction and analysis of cardiac hypertrophy-associated lncRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in cardiac hypertrophy.

Cardiac hypertrophy (CH) could increase cardiac after-load and lead to heart failure. Recent studies have suggested that long non-coding RNA (lncRNA) played a crucial role in the process of the cardiac hypertrophy, such as Mhrt, TERMINATOR. Some studies have further found a new interacting mechanism, competitive endogenous RNA (ceRNA), of which lncRNA could interact with micro-RNAs (miRNA) and indirectly interact with mRNAs through competing interactions. However, the mechanism of ceRNA regulated by lncRNA in the CH remained unclear. In our study, we generated a global triple network containing mRNA, miRNA and lncRNA, and extracted a CH related lncRNA-mRNA network (CHLMN) through integrating the data from starbase, miRanda database and gene expression profile. Based on the ceRNA mechanism, we analyzed the characters of CHLMN and found that 3 lncRNAs (SLC26A4-AS1, RP11-344E13.3 and MAGI1-IT1) were high related to CH. We further performed cluster module analysis and random walk with restart for the CHLMN, finally 14 lncRNAs had been discovered as the potential CH related disease genes. Our results showed that lncRNA played an important role in the CH and could shed new light to the understanding underlying mechanisms of the CH.