The global landscape of immune-derived lncRNA signature in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity. However, the global landscape of immune-derived lncRNA signatures has not yet been explored in colorectal cancer. METHODS: In this study, we applied DESeq2 to identify differentially expressed lncRNAs in colon cancer. Next, we performed an integrative analysis to globally identify immune-driven lncRNA markers in CRC, including immune-associated pathways, tumor immunogenomic features, tumor-infiltrating immune cells, immune checkpoints, microsatellite instability (MSI) and tumor mutation burden (TMB). RESULTS: We also identified dysregulated lncRNAs, such as LINC01354 and LINC02257, and their clinical relevance in CRC. Our findings revealed that the differentially expressed lncRNAs were closely associated with immune pathways. In addition, we found that RP11-354P11.3 and RP11-545G3.1 had the highest association with the immunogenomic signature. As a result, these signatures could serve as markers to assess immunogenomic activity in CRC. Among the immune cells, resting mast cells and M0 macrophages had the highest association with lncRNAs in CRC. The AC006129.2 gene was significantly associated with several immune checkpoints, for example, programmed cell death protein 1 (PD-1) and B and T lymphocyte attenuator (BTLA). Therefore, the AC006129.2 gene could be targeted to regulate the condition of immune cells or immune checkpoints to enhance the efficacy of immunotherapy in CRC patients. Finally, we identified 15 immune-related lncRNA-generated open reading frames (ORFs) corresponding to 15 cancer immune epitopes. CONCLUSION: In conclusion, we provided a genome-wide immune-driven lncRNA signature for CRC that might provide new insights into clinical applications and immunotherapy.

EdeepSADPr: an extensive deep-learning architecture for prediction of the in situ crosstalks of serine phosphorylation and ADP-ribosylation.

The in situ post-translational modification (PTM) crosstalk refers to the interactions between different types of PTMs that occur on the same residue site of a protein. The crosstalk sites generally have different characteristics from those with the single PTM type. Studies targeting the latter's features have been widely conducted, while studies on the former's characteristics are rare. For example, the characteristics of serine phosphorylation (pS) and serine ADP-ribosylation (SADPr) have been investigated, whereas those of their in situ crosstalks (pSADPr) are unknown. In this study, we collected 3,250 human pSADPr, 7,520 SADPr, 151,227 pS and 80,096 unmodified serine sites and explored the features of the pSADPr sites. We found that the characteristics of pSADPr sites are more similar to those of SADPr compared to pS or unmodified serine sites. Moreover, the crosstalk sites are likely to be phosphorylated by some kinase families (e.g., AGC, CAMK, STE and TKL) rather than others (e.g., CK1 and CMGC). Additionally, we constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. We also used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. The best classifiers had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites, respectively. The lowest prediction accuracy was achieved by separating pSADPr and SADPr sites, which is consistent with the observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/. We expect our investigation will promote a comprehensive understanding of crosstalks.

Lactate regulators contribute to tumor microenvironment and predict prognosis in lung adenocarcinoma.

Background: Lactic acid, as a product of glycolysis, increases tumor cell migration and the invasion of tumor cells in the tumor microenvironment. Besides this, lactic acid promotes the expression of programmed death-1 expression (PD-1) in regulatory T cells, which could cause the failure of PD-1 blockade therapy. However, the implications of lactic acid in the tumor microenvironment of lung adenocarcinoma (LUAD) remain largely unclear. Methods: We performed unsupervised consensus clustering to identify lactic-associated subtypes using expression profile of lactate regulators in LUAD. Differentially expressed genes (DEGs) associated with lactic-associated subtypes was used to construct lactate signature (LaSig) using LASSO regression algorithm. Immune infiltration analysis was conducted by ESTIMATER and drug sensitivity was estimated by R package called "pRRophetic". The difference between two groups was calculated using Wilcox rank sum test and correlation analysis was calculated using Pearson correlation coefficient. Results: In this study, we evaluated DNA methylation and the mutation frequency of lactate regulators and found lactate regulators showed low mutation frequency in the TCGA-LUAD cohort, except TP53. At the RNA level, the expression level of lactate regulators was significantly associated with the immune cell component. In particular, expression of LDHA was positively correlated with CD4 T cell, CD8 T cell, M1 macrophages, and the enrichment score of multiple immune pathways. Two clusters were defined using the gene expression level of lactate regulators, and LDHA was significantly upregulated in cluster 1 with poor overall survival. A lactate signature (LaSig) had a robust performance in predicting the survival rate and immunotherapy response of LUAD patients. Moreover, patients in the high LaSig group may be more likely to benefit from these drugs (Cisplatin, Erlotinib, Gemcitabine, and Vinblastine) than those in the low LaSig group. Conclusion: In summary, our study explores the role of lactate regulators in guiding the clinical treatment of lung adenocarcinoma and provides additional help to supplement traditional molecular subtypes.

AgingBank: a manually curated knowledgebase and high-throughput analysis platform that provides experimentally supported multi-omics data relevant to aging in multiple species.

Discovering the biological basis of aging is one of the greatest remaining challenges for biomedical field. Work on the biology of aging has discovered a range of interventions and pathways that control aging rate. Thus, we developed AgingBank (http://bio-bigdata.hrbmu.edu.cn/AgingBank) which was a manually curated comprehensive database and high-throughput analysis platform that provided experimentally supported multi-omics data relevant to aging in multiple species. AgingBank contained 3771 experimentally verified aging-related multi-omics entries from studies across more than 50 model organisms, including human, mice, worms, flies and yeast. The records included genome (single nucleotide polymorphism, copy number variation and somatic mutation), transcriptome [mRNA, long non-coding RNA (lncRNA), microRNA (miRNA) and circular RNA (circRNA)], epigenome (DNA methylation and histone modification), other modification and regulation elements (transcription factor, enhancer, promoter, gene silence, alternative splicing and RNA editing). In addition, AgingBank was also an online computational analysis platform containing five useful tools (Aging Landscape, Differential Expression Analyzer, Data Heat Mapper, Co-Expression Network and Functional Annotation Analyzer), nearly 112 high-throughput experiments of genes, miRNAs, lncRNAs, circRNAs and methylation sites related with aging. Cancer & Aging module was developed to explore the relationships between aging and cancer. Submit & Analysis module allows users upload and analyze their experiments data. AginBank is a valuable resource for elucidating aging-related biomarkers and relationships with other diseases.

Identification and exploration of pharmacological pyroptosis-related biomarkers of ulcerative colitis.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD). Its etiology is unclear. Much evidence suggests that the death of abnormal intestinal epithelial cells (IECs) leads to intestinal barrier disruption, and the subsequent inflammatory response plays a vital role in UC. Pyroptosis is a form of programmed inflammatory cell death, and the role of pyroptosis in UC etiology remains to be explored. This study identified 10 hub genes in pyroptosis by gene expression profiles obtained from the GSE87466 dataset. Meanwhile, the biomarkers were screened based on gene significance (GS) and module membership (MM) through the Weighted Gene Co-Expression Network Analysis (WGCNA). The following analysis indicated that hub genes were closely associated with the UC progression and therapeutic drug response. The single-cell RNA (scRNA) sequencing data from UC patients within the GSE162335 dataset indicated that macrophages were most related to pyroptosis. Finally, the expression of hub genes and response to the therapeutic drug [5-aminosalicylic acid (5-ASA)] were verified in dextran sulfate sodium (DSS)-induced colitis mice. Our study identified IL1B as the critical pyroptosis-related biomarker in UC. The crosstalk between macrophage pyroptosis and IEC pyroptosis may play an essential role in UC, deserving further exploration.

CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications.

The unique chemical reactivity of cysteine residues results in various posttranslational modifications (PTMs), which are implicated in regulating a range of fundamental biological processes. With the advent of chemical proteomics technology, thousands of cysteine PTM (CysPTM) sites have been identified from multiple species. A few CysPTM-based databases have been developed, but they mainly focus on data collection rather than various annotations and analytical integration. Here, we present a platform-dubbed CysModDB, integrated with the comprehensive CysPTM resources and analysis tools. CysModDB contains five parts: (1) 70 536 experimentally verified CysPTM sites with annotations of sample origin and enrichment techniques, (2) 21 654 modified proteins annotated with functional regions and structure information, (3) cross-references to external databases such as the protein-protein interactions database, (4) online computational tools for predicting CysPTM sites and (5) integrated analysis tools such as gene enrichment and investigation of sequence features. These parts are integrated using a customized graphic browser and a Basket. The browser uses graphs to represent the distribution of modified sites with different CysPTM types on protein sequences and mapping these sites to the protein structures and functional regions, which assists in exploring cross-talks between the modified sites and their potential effect on protein functions. The Basket connects proteins and CysPTM sites to the analysis tools. In summary, CysModDB is an integrated platform to facilitate the CysPTM research, freely accessible via https://cysmoddb.bioinfogo.org/.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma.

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer.

Long non-coding RNAs (lncRNAs) that emanate from enhancer regions (defined as enhancer-associated lncRNAs, or elncRNAs) are emerging as critical regulators in disease progression. However, their biological characteristics and clinical relevance have not been fully portrayed. Here, based on the traditional expression quantitative loci (eQTL) and our optimized residual eQTL method, we comprehensively described the genetic effect on elncRNA expression in more than 300 lymphoblastoid cell lines. Meanwhile, a chromatin atlas of elncRNAs relative to the genetic regulation state was depicted. By applying the maximum likelihood estimate method, we successfully identified causal elncRNAs for protein-coding gene expression reprogramming and showed their associated single nucleotide polymorphisms (SNPs) favor binding of transcription factors. Further epigenome analysis revealed two immune-associated elncRNAs AL662844.4 and LINC01215 possess high levels of H3K27ac and H3K4me1 in human cancer. Besides, pan-cancer analysis of 3D genome, transcriptome, and regulatome data showed they potentially regulate tumor-immune cell interaction through affecting MHC class I genes and CD47, respectively. Moreover, our study showed there exist associations between elncRNA and patient survival. Finally, we made a user-friendly web interface available for exploring the regulatory relationship of SNP-elncRNA-protein-coding gene triplets (http://bio-bigdata.hrbmu.edu.cn/elncVarReg). Our study provides critical mechanistic insights for elncRNA function and illustrates their implications in human cancer.

Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer.

BACKGROUND: Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. METHODS: In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. RESULTS: We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. CONCLUSIONS: Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

DNA methylation dynamics of long noncoding RNA during human fetal development.

Aim: To determine whether the promoters of long noncoding RNAs (lncRNAs) undergo dynamic changes in DNA methylation during fetal development. Methods: ANOVA and the tissue specificity index were used to identify and validate tissue-specific methylation sites. Age-associated DNA methylation signatures were identified by applying the elastic net method. Results: The lncRNA methylome landscape was characterized in four types of fetal tissue and at three gestational time points, and specific characteristics relative to the tissue of origin and developmental age were identified. Higher levels of lncRNA methylation might be involved in tissue differentiation. LncRNAs harboring age-associated methylation signatures may participate in the fetal developmental process. Conclusion: This study provides novel insights into the role of lncRNA methylomes in fetal tissue specification and development.

Lay abstract The addition of a methyl group to DNA is known as DNA methylation and has a large impact on early embryonic development. The role of specific DNA methylation sites located in the promoter region of long noncoding RNA (lncRNA) during human fetal development is not fully understood. The DNA methylation of lncRNA promoters was investigated in four types of tissue and at three gestational time points. They display tissue-specific characteristics that change over time. Tissue-specific methylation sites have higher levels of methylation, suggesting they might play a role in tissue differentiation. The lncRNAs that show loss or gain of methylation over time may participate in the fetal developmental process. This study describes the DNA methylation status of lncRNA promoters and elucidates their potential role in fetal development.

MeImmS: Predict Clinical Benefit of Anti-PD-1/PD-L1 Treatments Based on DNA Methylation in Non-small Cell Lung Cancer.

Immunotherapy has become an effective therapy for cancer treatment. However, the development of biomarkers to predict immunotherapy response still remains a challenge. We have developed the DNA Methylation Immune Score, named "MeImmS," which can predict clinical benefits of non-small cell lung cancer (NSCLC) patients based on DNA methylation of 8 CpG sites. The 8 CpG sites regulate the expression of immune-related genes and MeImmS was related to immune-associated pathways, exhausted T cell markers and immune cells. Copy-number loss in 1p36.33 may affect the response of cancer patients to immunotherapy. In addition, SAA1, CXCL10, CCR5, CCL19, CXCL11, CXCL13, and CCL5 were found to be key immune regulatory genes in immunotherapy. Together, MeImmS discovered the heterogeneous of NSCLC patients and guided the immunotherapy of cancer patients in the future.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements.

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data.

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.

LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution.

Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the 'One Cell, One World' theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation.

LnCeVar (http://www.bio-bigdata.net/LnCeVar/) is a comprehensive database that aims to provide genomic variations that disturb lncRNA-associated competing endogenous RNA (ceRNA) network regulation curated from the published literature and high-throughput data sets. LnCeVar curated 119 501 variation-ceRNA events from thousands of samples and cell lines, including: (i) more than 2000 experimentally supported circulating, drug-resistant and prognosis-related lncRNA biomarkers; (ii) 11 418 somatic mutation-ceRNA events from TCGA and COSMIC; (iii) 112 674 CNV-ceRNA events from TCGA; (iv) 67 066 SNP-ceRNA events from the 1000 Genomes Project. LnCeVar provides a user-friendly searching and browsing interface. In addition, as an important supplement of the database, several flexible tools have been developed to aid retrieval and analysis of the data. The LnCeVar-BLAST interface is a convenient way for users to search ceRNAs by interesting sequences. LnCeVar-Function is a tool for performing functional enrichment analysis. LnCeVar-Hallmark identifies dysregulated cancer hallmarks of variation-ceRNA events. LnCeVar-Survival performs COX regression analyses and produces survival curves for variation-ceRNA events. LnCeVar-Network identifies and creates a visualization of dysregulated variation-ceRNA networks. Collectively, LnCeVar will serve as an important resource for investigating the functions and mechanisms of personalized genomic variations that disturb ceRNA network regulation in human diseases.

CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer.

Many biological indicators related to chronological age have been proposed. Recent studies found that epigenetic clock or DNA methylation age is highly correlated with chronological age. In particular, a significant difference between DNA methylation age (m-age) and chronological age was observed in cancers. However, the prediction and characterization of m-age in pan-cancer remains an explored area. In this study, 1,631 age-related methylation sites in normal tissues were discovered and analyzed. A comprehensive computational model named CancerClock was constructed to predict the m-age for normal samples based on methylation levels of the extracted methylation sites. LASSO linear regression model was used to screen and train the CancerClock model in normal tissues. The accuracy of CancerClock has proved to be 81%, and the correlation value between chronological age and m-age was 0.939 (P < 0.01). Next, CancerClock was used to evaluate the difference between m-age and chronological age for 33 cancer types from TCGA. There were significant differences between predicted m-age and chronological age in large number of cancer samples. These cancer samples were defined as "age-related cancer samples" and they have some differential methylation sites. The differences between predicted m-age and chronological age may contribute to cancer development. Some of these differential methylation sites were associated with cancer survival. CancerClock provided assistance in estimating the m-age in normal and cancer samples. The changes between m-age and chronological age may improve the diagnosis and prognosis of cancers.

The MS-lincRNA landscape reveals a novel lincRNA BCLIN25 that contributes to tumorigenesis by upregulating ERBB2 expression via epigenetic modification and RNA-RNA interactions in breast cancer.

The landscape of molecular subtype-specific long intergenic noncoding RNAs (MS-lincRNAs) in breast cancer has not been elucidated. No study has investigated the biological function of BCLIN25, serving as a novel HER2 subtype-specific lincRNA, in human disease, especially in malignancy. Moreover, the mechanism of BCLIN25 in the regulation of ERBB2 expression remains unknown. Our present study aimed to investigate the role and underlying mechanism of BCLIN25 in the regulation of ERBB2 expression. The transcriptional landscape across five subtypes of breast cancer was investigated using RNA sequencing. Integrative transcriptomic analysis was performed to identify the landscape of novel lincRNAs. Next, WEKA was used to identify lincRNA-based subtype classification and MS-lincRNAs for breast cancer. The MS-lincRNAs were validated in 250 breast cancer samples in our cohort and datasets from The Cancer Genome Atlas and Gene Expression Omnibus. Furthermore, BCLIN25 was selected, and its role in tumorigenesis was examined in vitro and in vivo. Finally, the mechanism by which BCLIN25 regulates ERBB2 expression was investigated in detail. A total of 715 novel lincRNAs were differentially expressed across five breast cancer subtypes. Next, lincRNA-based subtype classifications and MS-lincRNAs were identified and validated using our breast cancer samples and public datasets. BCLIN25 was found to contribute to tumorigenesis in vitro and in vivo. Mechanistically, BCLIN25 was shown to increase the expression of ERBB2 by enhancing promoter CpG methylation of miR-125b, leading to miR-125b downregulation. In turn, ERBB2 mRNA degradation was found to be abolished due to decreased binding of miR-125b to the 3'-untranslated region (UTR) of ERBB2. These findings reveal the role of novel lincRNAs in breast cancer and provide a comprehensive landscape of breast cancer MS-lincRNAs, which may complement the current molecular classification system in breast cancer.

Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer.

Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments.

Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers.

Lnc2Cancer 2.0 (http://www.bio-bigdata.net/lnc2cancer) is an updated database that provides comprehensive experimentally supported associations between lncRNAs and human cancers. In Lnc2Cancer 2.0, we have updated the database with more data and several new features, including (i) exceeding a 4-fold increase over the previous version, recruiting 4989 lncRNA-cancer associations between 1614 lncRNAs and 165 cancer subtypes. (ii) newly adding about 800 experimentally supported circulating, drug-resistant and prognostic-related lncRNAs in various cancers. (iii) appending the regulatory mechanism of lncRNA in cancer, including microRNA (miRNA), transcription factor (TF), variant and methylation regulation. (iv) increasing more than 70 high-throughput experiments (microarray and next-generation sequencing) of lncRNAs in cancers. (v) Scoring the associations between lncRNA and cancer to evaluate the correlations. (vi) updating the annotation information of lncRNAs (version 28) and containing more detailed descriptions for lncRNAs and cancers. Moreover, a newly designed, user-friendly interface was also developed to provide a convenient platform for users. In particular, the functions of browsing data by cancer primary organ, biomarker type and regulatory mechanism, advanced search following several features and filtering the data by LncRNA-Cancer score were enhanced. Lnc2Cancer 2.0 will be a useful resource platform for further understanding the associations between lncRNA and human cancer.

Integrated analysis of the prognostic role of the lymph node ratio in node-positive gastric cancer: A meta-analysis.

BACKGROUND: The lymph node ratio (LNR) as a prognostic parameter for gastric cancer has yet to be fully validated in the current tumor node metastasis staging system. We assessed the prognostic role of LNR in lymph node-positive gastric cancer through a meta-analysis. MATERIALS AND METHODS: PubMed and EMBASE were searched for relevant studies up until December 2016. The effect measure for meta-analysis of primary outcomes was the hazard ratio (HR) for overall survival. Pooled HRs and 95% confidence intervals were calculated using random effects models. The I2 statistic was used to measure heterogeneity. Subgroup analysis and meta-regression were chosen to illustrate the potential heterogeneity of the risk factors of outcomes. Publication bias was assessed using Egger's test and Begg's funnel plots. Sensitivity analysis was applied to evaluate the origin of the heterogeneity. RESULTS: We included 27 studies in this meta-analysis. Higher LNRs were significantly associated with a shorter overall survival (OS). High heterogeneity among the studies was identified (I2 = 85.6), and the publication bias was moderate. Subgroup analysis showed similar results, and elevated LNR was associated with late-stage gastric cancer and indicative of a worse prognosis. Univariate meta-regression analysis of OS indicated that both treatment type and ethnicity may be causes of heterogeneity in patients with gastric cancer (p values were 0.005 and 0.008, respectively). CONCLUSION: LNR was associated with a significantly poorer OS and LNR was an independent predictor of survival in patients with gastric cancer. LNR should be added as one of the parameters to be used in future tumor staging classification systems.