FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, the upstream (epi)genetic regulatory information has not been sufficiently leveraged to predict the function of lncRNAs in various biological processes. Therefore, we present FunlncModel, a machine learning-based interpretable computational framework, which aims to screen out functional lncRNAs by integrating a large number of (epi)genetic features and functional genomic features from their upstream/downstream multi-omic regulatory networks.

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.

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.

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.

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.

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.

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.

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.

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.

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.

LncRNA625 inhibits STAT1-mediated transactivation potential in esophageal cancer cells.

Although Signal transducer and activator of transcription 1 (STAT1)-mediated transactivation potential is inhibited in cancer cells, the mechanism is poorly understood. In the present study, we implicated long non-coding RNA lncRNA625 in the inhibition of STAT1 activity. LncRNA625 knockdown up-regulated STAT1-mediated transcription and resulted in an increase of STAT1-mediated expression of IFITM2.

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.

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.

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.

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.

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.

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.

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).

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.

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.

Comprehensive bioinformation analysis of the mRNA profile of fascin knockdown in esophageal squamous cell carcinoma.

Background: Fascin, an actin-bundling protein forming actin bundles including filopodia and stress fibers, is overexpressed in multiple human epithelial cancers including esophageal squamous cell carcinoma (ESCC). Previously we conducted a microarray experiment to analyze fascin knockdown by RNAi in ESCC.

Method: In this study, the differentially expressed genes from mRNA expression profilomg of fascin knockdown were analyzed by multiple bioinformatics methods for a comprehensive understanding of the role of fascin.