Co-expression Network Analysis of Human lncRNAs and Cancer Genes

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

Identification of lncRNAs and Their Regulatory Relationships with mRNAs in Response to Cryptococcus neoformans Infection of THP-1 Cells

Aims. Cryptococcosis is an invasive fungal disease that is associated with an increasing prevalence along with a very high fatality and is primarily caused by Cryptococcus. However, its mechanism to cause pathogenicity is not yet completely understood. In this study, we aim to screen the lncRNA markers in human monocytic (THP-1) cells infected by Cryptococcus neoformans (C. neoformans) through high-throughput sequencing technology and to explore its effects on biological functions. Methods. We initially conducted an lncRNA microarray analysis of the THP-1 cells infected by C. neoformans and normal THP-1 cells. Based upon these data, RT-qPCR was used to verify the expressions of the selected lncRNAs and mRNAs. We then performed functional and pathway enrichment analyses. Lastly, target prediction was performed by using the lncRNA target tool which was based on the differentially expressed lncRNAs. Results. We determined 81 upregulated and 96 downregulated lncRNAs using microarray. In addition, the profiling data showed 42 upregulated and 57 downregulated genes and discovered that neuroactive ligand-receptor interaction, tyrosine metabolism, and phenylalanine metabolism are extremely impaired in the regulation of C. neoformans infection. GO enrichment analysis of the 99 differentially expressed mRNAs exhibited that these modules showed different signaling pathways and biological mechanisms like protein binding and metal ion binding. Moreover, lncRNAs and mRNAs were analyzed for their coexpression relations. A qRT-PCR analysis confirmed that the expression of the top 10 differently expressed mRNA and lincRNA. The expressions of the lncRNAs after C. neoformans infection in THP-1 cells were detected by RNA-sequence, suggesting that microarray analysis could reveal lncRNAs having functional significance that might be linked with the progression of patients. Conclusion. The current study analyzed the differential lncRNAs and mRNAs in C. neoformans infection and predicted the corresponding pathways and their correlations that can offer new potential insights into the mechanistic basis of this condition.

GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation

Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.

Regulatory effects of lncRNAs and miRNAs on the crosstalk between autophagy and EMT in cancer: a new era for cancer treatment

PURPOSE

Autophagy and EMT (epithelial-mesenchymal transition) are the two principal biological processes and ideal therapeutic targets during cancer development. Autophagy, a highly conserved process for degrading dysfunctional cellular components, plays a dual role in tumors depending on the tumor stage and tissue types. The EMT process is the transition differentiation from an epithelial cell to a mesenchymal-like cell and acquiring metastatic potential. There is evidence that the crosstalk between autophagy and EMT is complex in cancer. In recent years, more studies have shown that long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are involved in autophagy, EMT, and their crosstalk. Therefore, accurate understanding of the regulatory mechanisms of lncRNAs and miRNAs in autophagy, EMT and their interactions is crucial for the clinical management of cancers.

METHODS

An extensive literature search was conducted on the Google Scholar and PubMed databases. The keywords used for the search included: autophagy, EMT, crosstalk, lncRNAs, miRNAs, cancers, diagnostic biomarkers, and therapeutic targets. This search provided relevant articles published in peer-reviewed journals until 2021. Data from these various studies were extracted and used in this review.

RESULTS

The results showed that lncRNAs/miRNAs as tumor inhibitors or tumor inducers could regulate autophagy, EMT, and their interaction by regulating several molecular signaling pathways. The lncRNAs/miRNAs involved in autophagy and EMT processes could have potential uses in cancer diagnosis, prognosis, and therapy.

CONCLUSION

Such information could help find and develop lncRNAs/miRNAs based new tools for diagnosing, prognosis, and creating anti-cancer therapies.

Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19

The coronavirus disease, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global health crisis that is being endured with an increased alarm of transmission each day. Though the pandemic has activated innumerable research attention to decipher an antidote, fundamental understanding of the molecular mechanisms is necessary to halt the disease progression. The study focused on comparison of the COVID-19 infected lung tissue gene expression datasets -GSE155241 and GSE150316 with the GEO2R-limma package. The significant up- and downregulated genes were annotated. Further evaluation of the enriched pathways, transcription factors, kinases, noncoding RNAs and drug perturbations revealed the significant molecular mechanisms of the host response. The results revealed a surge in mitochondrial respiration, cytokines, neurodegenerative mechanisms and deprived oxygen, iron, copper, and glucose transport. Hijack of ubiquitination by SARS-CoV-2, hox gene differentiation, histone modification, and miRNA biogenesis were the notable molecular mechanisms inferred. Long non-coding RNAs such as C058791.1, TTTY15 and TPTEP1 were predicted to be efficient in regulating the disease mechanisms. Drugs-F-1566-0341, Digoxin, Proscillaridin and Linifanib that reverse the gene expression signatures were predicted from drug perturbations analysis. The binding efficiency and interaction of proscillaridin and digoxin as obtained from the molecular docking studies confirmed their therapeutic potential. Two overlapping upregulated genes MDH1, SGCE and one downregulated gene PFKFB3 were appraised as potential biomarkers candidates. The upregulation of PGM5, ISLR and ANK2 as measured from their expressions in normal lungs affirmed their possible prognostic biomarker competence. The study explored significant insights for better diagnosis, and therapeutic options for COVID-19.

Communicated by Ramaswamy H. Sarma

Genomic data mining for functional annotation of human long noncoding RNAs

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

Long Non-Coding RNA: Dual Effects on Breast Cancer Metastasis and Clinical Applications

As a highly heterogeneous malignancy, breast cancer (BC) has become the most significant threat to female health. Distant metastasis and therapy resistance of BC are responsible for most of the cases of mortality and recurrence. Distant metastasis relies on an array of processes, such as cell proliferation, epithelial-to-mesenchymal transition (EMT), mesenchymal-to-epithelial transition (MET), and angiogenesis. Long non-coding RNA (lncRNA) refers to a class of non-coding RNA with a length of over 200 nucleotides. Currently, a rising number of studies have managed to investigate the association between BC and lncRNA. In this study, we summarized how lncRNA has dual effects in BC metastasis by regulating invasion, migration, and distant metastasis of BC cells. We also emphasize that lncRNA has crucial regulatory effects in the stemness and angiogenesis of BC. Clinically, some lncRNAs can regulate chemotherapy sensitivity in BC patients and may function as novel biomarkers to diagnose or predict prognosis for BC patients. The exact impact on clinical relevance deserves further study. This review can be an approach to understanding the dual effects of lncRNAs in BC, thereby linking lncRNAs to quasi-personalized treatment in the future.

SFTA1P, LINC00968, GATA6-AS1, TBX5-AS1, and FEZF1-AS1 are crucial long non-coding RNAs associated with the prognosis of lung squamous cell carcinoma

Lung cancer has high incidence and mortality rates, and lung squamous cell carcinoma (LUSC) is a common form of non-small-cell lung carcinoma (NSCLC). The aim of our study was to discover long non-coding RNAs (lncRNAs) associated with LUSC prognosis. RNA-sequencing data obtained from LUSC samples were extracted from The Cancer Genome Atlas database. Using the limma package, differentially expressed genes (DEGs; including differentially expressed lncRNA genes (DELs), coding genes (DECs), and other genes (DEOs)) between LUSC and control samples were analyzed. Using Kaplan-Meier survival analysis, prognosis-associated lncRNAs were further selected. Following the calculation of Pearson's correlation coefficients between DELs and other DEGs, the DEL-DEG co-expression network was visualized using Cytoscape software. Using the clusterProfiler package, potential functions for DECs co-expressed with DELs were predicted. There were 1,305 DEGs in LUSC samples, including 153 DELs, 1,109 DECs, and 43 DEOs. Based on survival analysis, 22 prognosis-associated lncRNAs (including surfactant associated 1, pseudogene (SFTA1P), long intergenic non-protein coding RNA 968 (LINC00968), GATA6 antisense RNA 1, (GATA6-AS1) TBX5 antisense RNA 1 (TBX5-AS1) and FEZF1 antisense RNA 1 (FEZF1-AS1)) in LUSC were selected from these DELs, and the associated abnormal expression levels were also verified in LUSC clinical samples. A DEL-DEG co-expression network was constructed, which involved 93 DELs. Co-expressed DECs were enriched for only 8 prognosis-associated DELs, including LINC00968, SFTA1P, and TBX5-AS1. Specifically, mitogen-activated protein kinase (MAPK) signaling pathway-associated genes were enriched in DECs co-expressed with LINC00968, SFTA1P, GATA6-AS1, TBX5-AS1 and FEZF1-AS1, which may be prognosis-associated lncRNAs in LUSC. In addition, LINC00968 may affect the outcome of patients with LUSC via the MAPK signaling pathway.

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

Background

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.

Results

Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.

Conclusions

Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5850-7) contains supplementary material, which is available to authorized users.

Role of HDAC1 in the progression of gastric cancer and the correlation with lncRNAs

Gastric cancer (GC) is a common life-threatening cancer type worldwide, with an increasing prevalence and a high rate of mortality. Due to limitations in clinical treatment, surgery has become the most efficient strategy for the treatment of GC. It is urgent to identify novel biomarkers, which are useful for the diagnosis of GC and for improving the survival rate of patients with GC. HDACs are multi-functional proteins and are involved in regulating gene expression, cell proliferation and the epigenetic regulation. However, the precise role of HDACs in the progression of GC remains unknown. The present study demonstrated that HDAC1 is involved in the promotion of GC cell proliferation, possibly by upregulating the expression of the lncRNAs, BC01600 and AF116637, in the tissues of patients with GC. Abnormal expression profiles of lncRNAs were observed in the tissues of patients with GC. lncRNAs were analyzed in the GSE64951 and GSE19826 databases, and it was revealed that BC01600 and AF116637 were two typically upregulated lncRNAs. Furthermore, it was revealed that BC01600 and AF116637 are regulated by HDAC1, as evidenced by decreased expression of these two lncRNAs in HDAC1-knockout SC-M1 cell lines, and by reduced expression of HDAC1 in these two lncRNA-knockout SC-M1 cell lines. Silencing of HDAC1 decreased the proliferation and increased the apoptosis of SC-M1 cell lines, but had no effect on the migration of the SC-M1 cell lines. The present study provided evidence of the importance of HDAC1 in the progression of SC-M1, and the association between HDAC1 and the expression of lncRNAs. The results of the present study indicated that HDAC1 may be a promising target for the clinical treatment of GC.

Long noncoding RNA expression profile from cryptococcal meningitis patients identifies DPY19L1p1 as a new disease marker

AIMS

LncRNAs play a vital role in the pathological and physiological process. This study aimed to explore the involvement of lncRNAs in cryptococcal meningitis.

METHODS

Microarray was performed in cryptococcal meningitis patients, and then, GO and KEGG pathways were analyzed. Coexpression relationship between lncRNA and mRNA was explored. The expressions of the lncRNAs and mRNAs, and their changes after treatment were detected by PCR.

RESULTS

A total of 325 mRNAs (201 upregulated and 124 downregulated) and 497 lncRNAs (263 upregulated and 234 downregulated) were identified. The top three enriched GO terms for the mRNAs were arachidonic acid binding, activin receptor binding, and replication fork protection complex. The top three pathways in KEGG were asthma, one carbon pool by folate, and allograft rejection. A total of 305 coexpression relationships were found between 108 lncRNAs and 87 mRNAs. LncRNA-DPY19L1p1 was significantly increased in patients and decreased after treatment. ROC analysis revealed DPY19L1p1 was a potential diagnostic marker (AUC_ROC  = 0.9389). Furthermore, the target genes of DPY19L1p1 in cis or trans regulation were mainly involved in immune-related pathways like the interleukin signaling pathway.

CONCLUSIONS

This study analyzed the differential lncRNA profile in cryptococcal meningitis patients and revealed DPY19L1p1 could be used for treatment evaluation and disease diagnosis.

Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Background

Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows.

RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results

WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = − 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion

The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2553-z) contains supplementary material, which is available to authorized users.

Co-expression of long non-coding RNAs and autism risk genes in the developing human brain

Background

Autism Spectrum Disorder (ASD) is the umbrella term for a group of neurodevelopmental disorders convergent on behavioral phenotypes. While many genes have been implicated in the disorder, the predominant focus of previous research has been on protein coding genes. This leaves a vast number of long non-coding RNAs (lncRNAs) not characterized for their role in the disorder although lncRNAs have been shown to play important roles in development and are highly represented in the brain. Studies have also shown lncRNAs to be differentially expressed in ASD affected brains. However, there has yet to be an enrichment analysis of the shared ontologies and pathways of known ASD genes and lncRNAs in normal brain development.

Results

In this study, we performed co-expression network analysis on the developing brain transcriptome to identify potential lncRNAs associated with ASD and possible annotations for functional role of lncRNAs in brain development. We found co-enrichment of lncRNA genes and ASD risk genes in two distinct groups of modules showing elevated prenatal and postnatal expression patterns, respectively. Further enrichment analysis of the module groups indicated that the early expression modules were comprised mainly of transcriptional regulators while the later expression modules were associated with synapse formation. Finally, lncRNAs were prioritized for their connectivity with the known ASD risk genes through analysis of an adjacency matrix. Collectively, the results imply early developmental repression of synaptic genes through lncRNAs and ASD transcriptional regulators.

Conclusion

Here we demonstrate the utility of mining the publically available brain gene expression data to further functionally annotate the role of lncRNAs in ASD. Our analysis indicates that lncRNAs potentially have a key role in ASD due to their convergence on shared pathways, and we identify lncRNAs of interest that may lead to further avenues of study.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0639-x) contains supplementary material, which is available to authorized users.

Non-Coding Transcriptome Maps across Twenty Tissues of the Korean Black Chicken, Yeonsan Ogye

Yeonsan Ogye is a rare Korean domestic chicken breed whose entire body, including feathers and skin, has a unique black coloring. Although some protein-coding genes related to this unique feature have been examined, non-coding elements have not been widely investigated. Thus, we evaluated coding and non-coding transcriptome expression and identified long non-coding RNAs functionally linked to protein-coding genes in Ogye. High-throughput RNA sequencing and DNA methylation sequencing were performed to profile the expression of 14,264 Ogye protein-coding and 6900 long non-coding RNA (lncRNA) genes and detect DNA methylation in 20 different tissues of an individual Ogye. Approximately 75% of Ogye lncRNAs and 45% of protein-coding genes showed tissue-specific expression. For some genes, tissue-specific expression levels were inversely correlated with DNA methylation levels in their promoters. Approximately 39% of tissue-specific lncRNAs displayed functional associations with proximal or distal protein-coding genes. Heat shock transcription factor 2-associated lncRNAs appeared to be functionally linked to protein-coding genes specifically expressed in black skin tissues, more syntenically conserved in mammals, and differentially expressed in black relative to in white tissues. Pending experimental validation, our findings increase the understanding of how the non-coding genome regulates unique phenotypes and can be used for future genomic breeding of chickens.

LncRNA ITGB2-AS1 Could Promote the Migration and Invasion of Breast Cancer Cells through Up-Regulating ITGB2

In the previous study, we screened a novel lncRNA-ITGB2-AS1, which was down-regulated by bone morphogenetic protein 9 (BMP9) in breast cancer cell. Studying ITGB2-AS1 will lay the foundation for the exploring mechanism of the BMP9 inhibitory effect on breast cancer. The expression analysis related to ITGB2-AS1 in clinical samples was conducted on online websites. The overexpression plasmid or siRNA fragment was transfected into breast cancer cells to alter its gene expression. The MTT assay and flow cytometry were used to measure cell viability and cell cycle. Additionally, cell migration and invasion were detected by wound healing and transwell assay. The results of biological function experiments showed that ITGB2-AS1 could promote the migration and invasion of breast cancer. Furthermore, ITGB2-AS1 increased the mRNA and protein expression of ITGB2. Consistent with ITGB2-AS1, ITGB2 exerted the promotion effect on the migration and invasion of breast cancer and activated integrin-related FAK signaling. The OL plasmid expressing the truncation of ITGB2-AS1, which was complementary to ITGB2, was essential for activation of FAK signaling. In conclusion, LncRNA ITGB2-AS1 could promote the migration and invasion of breast cancer cells by up-regulating ITGB2.

Transcription Factor and lncRNA Regulatory Networks Identify Key Elements in Lung Adenocarcinoma

Lung cancer is the second most commonly diagnosed carcinoma and is the leading cause of cancer death. Although significant progress has been made towards its understanding and treatment, unraveling the complexities of lung cancer is still hampered by a lack of comprehensive knowledge on the mechanisms underlying the disease. High-throughput and multidimensional genomic data have shed new light on cancer biology. In this study, we developed a network-based approach integrating somatic mutations, the transcriptome, DNA methylation, and protein-DNA interactions to reveal the key regulators in lung adenocarcinoma (LUAD). By combining Bayesian network analysis with tissue-specific transcription factor (TF) and targeted gene interactions, we inferred 15 disease-related core regulatory networks in co-expression gene modules associated with LUAD. Through target gene set enrichment analysis, we identified a set of key TFs, including known cancer genes that potentially regulate the disease networks. These TFs were significantly enriched in multiple cancer-related pathways. Specifically, our results suggest that hepatitis viruses may contribute to lung carcinogenesis, highlighting the need for further investigations into the roles that viruses play in treating lung cancer. Additionally, 13 putative regulatory long non-coding RNAs (lncRNAs), including three that are known to be associated with lung cancer, and nine novel lncRNAs were revealed by our study. These lncRNAs and their target genes exhibited high interaction potentials and demonstrated significant expression correlations between normal lung and LUAD tissues. We further extended our study to include 16 solid-tissue tumor types and determined that the majority of these lncRNAs have putative regulatory roles in multiple cancers, with a few showing lung-cancer specific regulations. Our study provides a comprehensive investigation of transcription factor and lncRNA regulation in the context of LUAD regulatory networks and yields new insights into the regulatory mechanisms underlying LUAD. The novel key regulatory elements discovered by our research offer new targets for rational drug design and accompanying therapeutic strategies.

Exploring functions of long noncoding RNAs across multiple cancers through co-expression network

In contrast to protein-coding genes, long-noncoding RNAs (lncRNAs) are much less well understood, despite increasing evidence indicating a wide range of their biological functions, and possible roles in various cancers. Based on public RNA-seq datasets of four solid cancer types, we here utilize Weighted Correlation Network Analysis (WGCNA) to propose a strategy for exploring the functions of lncRNAs altered in more than two cancer types, which we call onco-lncRNAs. Results indicate that cancer-expressed lncRNAs show high tissue specificity and are weakly expressed, more so than protein-coding genes. Most of the 236 onco-lncRNAs we identified have not been reported to have associations with cancers before. Our analysis exploits co-expression network to reveal that onco-lncRNAs likely play key roles in the multistep development of human cancers, covering a wide range of functions in genome stability maintenance, signaling, cell adhesion and motility, morphogenesis, cell cycle, immune and inflammatory response. These observations contribute to a more comprehensive understanding of cancer-associated lncRNAs, while demonstrating a novel and efficient strategy for subsequent functional studies of lncRNAs.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

LncRNAs and cancer

Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs composed of >200 nucleotides. Recent studies have revealed that lncRNAs exert an important role in the development and progression of cancer. In this review, the involvement of the most extensively investigated lncRNAs in cancers of the digestive, respiratory, reproductive, urinary and central nervous systems are discussed. LncRNAs function via molecular and biochemical mechanisms that include cis- and trans-regulation of gene expression, epigenetic modulation in the nucleus and post-transcriptional control in the cytoplasm. Although the detailed biological functions and molecular mechanisms of the majority of lncRNAs remain to be elucidated, this review aims to provide a novel insight into the diagnosis and treatment of cancer using lncRNAs.

Potential role of lncRNA cyp2c91-protein interactions on diseases of the immune system

With unprecedented increase in next generation sequencing technologies, there has been a persistent interest on transcript profiles of long non-coding RNAs (lncRNAs) and protein-coding genes forming an interaction network. Apart from protein–protein interaction (PPI), gene network models such as Weighted Gene Co-expression Network Analysis (WGCNA) are used to functionally annotate lncRNAs in identifying their potential disease associations. To address this, studies have led to characterizing transcript structures and understanding expression profiles mediating regulatory roles. In the current exploratory analysis, we show how a lncRNA – cyp2c91 contributes to the transcriptional regulation localized to cytoplasm thereby making refractory environment for transcription. By applying network methods and pathway analyses on genes related to a disease such as obesity and systemic lupus erythematosus, we show that we can gain deeper insight in biological processes such as the perturbances in immune system, and get a better understanding of the systems biology of diseases.

Advances in long noncoding RNAs: identification, structure prediction and function annotation

Long noncoding RNAs (lncRNAs), generally longer than 200 nucleotides and with poor protein coding potential, are usually considered collectively as a heterogeneous class of RNAs. Recently, an increasing number of studies have shown that lncRNAs can involve in various critical biological processes and a number of complex human diseases. Not only the primary sequences of many lncRNAs are directly interrelated to a specific functional role, strong evidence suggests that their secondary structures are even more interrelated to their known functions. As functional molecules, lncRNAs have become more and more relevant to many researchers. Here, we review recent, state-of-the-art advances in the three levels (the primary sequence, the secondary structure and the function annotation) of the lncRNA research, as well as computational methods for lncRNA data analysis.