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

Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes

Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large- and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue- and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions. In this study, we comprehensively analyzed the tumor-specific ceRNA fluctuations observed in the three highest-incidence cancers, LUAD, PRAD, and BRCA, compared to healthy lung, prostate, and breast tissues, respectively. Our observations pertaining to tumor-specific competing endogenous RNA (ceRNA) interactions revealed that, in the cases of lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), and breast invasive carcinoma (BRCA), 3,204, 1,233, and 406 ceRNAs, respectively, engage in post-transcriptional intercommunication within tumor tissues, in contrast to their absence in corresponding healthy samples. We also found that 90 ceRNAs are shared by the three cancer types and that these ceRNAs participate in ceRNA interactions in tumor tissues compared to those in normal tissues. Among the 90 ceRNAs that directly interact with miRNAs, we uncovered a core network of 165 miRNAs and 63 ceRNAs that should be considered in RNA-targeted and RNA-mediated approaches in future studies and could be used in these three aggressive cancer types. More specifically, in this core interaction network, ceRNAs such as GALNT7, KLF9, and DAB2 and miRNAs like miR-106a/b-5p, miR-20a-5p, and miR-519d-3p may have potential as common targets in the three critical cancers. In contrast to conventional methods that construct ceRNA networks using differentially expressed genes compared to normal tissues, our proposed approach identifies ceRNA players by considering their context within the ceRNA:miRNA interactions. Our results have the potential to reveal distinct and common ceRNA interactions in cancer types and to pinpoint critical RNAs, thereby paving the way for RNA-based strategies in the battle against cancer.

The pivotal role of long non-coding RNAs as potential biomarkers and modulators of chemoresistance in ovarian cancer (OC)

Ovarian cancer (OC) is a fatal gynecological disease that is often diagnosed at later stages due to its asymptomatic nature and the absence of efficient early-stage biomarkers. Previous studies have identified genes with abnormal expression in OC that couldn't be explained by methylation or mutation, indicating alternative mechanisms of gene regulation. Recent advances in human transcriptome studies have led to research on non-coding RNAs (ncRNAs) as regulators of cancer gene expression. Long non-coding RNAs (lncRNAs), a class of ncRNAs with a length greater than 200 nucleotides, have been identified as crucial regulators of physiological processes and human diseases, including cancer. Dysregulated lncRNA expression has also been found to play a crucial role in ovarian carcinogenesis, indicating their potential as novel and non-invasive biomarkers for improving OC management. However, despite the discovery of several thousand lncRNAs, only one has been approved for clinical use as a biomarker in cancer, highlighting the importance of further research in this field. In addition to their potential as biomarkers, lncRNAs have been implicated in modulating chemoresistance, a major problem in OC. Several studies have identified altered lncRNA expression upon drug treatment, further emphasizing their potential to modulate chemoresistance. In this review, we highlight the characteristics of lncRNAs, their function, and their potential to serve as tumor markers in OC. We also discuss a few databases providing detailed information on lncRNAs in various cancer types. Despite the promising potential of lncRNAs, further research is necessary to fully understand their role in cancer and develop effective strategies to combat this devastating disease.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BACKGROUND

The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.

METHODS

The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.

RESULTS

The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.

CONCLUSION

Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Comprehensive analysis of candidate signatures of long non-coding RNA LINC01116 and related protein-coding genes in patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a long-term malignancy that causes high morbidities and mortalities worldwide. Notably, long non-coding RNAs (LncRNAs) have been identified as candidate targets for malignancy treatments.

METHODS

LncRNA LINC01116 and its Pearson-correlated genes (PCGs) were identified and analyzed in HCC patients. The diagnostic and prognostic value of the lncRNA was evaluated using data from The Cancer Genome Atlas (TCGA). Further, we explored the target drugs of LINC01116 for clinical application. Relationships between immune infiltration and PCGs, methylation and PCGs were explored. The diagnostic potentials were then validated by Oncomine cohorts.

RESULTS

LINC01116 and the PCG OLFML2B are differentially and highly expressed in tumor tissues (both P ≤ 0.050). We found that LINC01116, TMSB15A, PLAU, OLFML2B, and MRC2 have diagnostic potentials (all AUC ≥ 0.700, all P ≤ 0.050) while LINC01116 and TMSB15A have prognostic significance (both adjusted P ≤ 0.050). LINC01116 was enriched in the vascular endothelial growth factor (VEGF) receptor signaling pathway, mesenchyme morphogenesis, etc. After that, candidate target drugs with potential clinical significance were identified: Thiamine, Cromolyn, Rilmenidine, Chlorhexidine, Sulindac_sulfone, Chloropyrazine, and Meprylcaine. Analysis of immune infiltration revealed that MRC2, OLFML2B, PLAU, and TMSB15A are negatively associated with the purity but positively associated with the specific cell types (all P < 0.050). Analysis of promoter methylation demonstrated that MRC2, OLFML2B, and PLAU have differential and high methylation levels in primary tumors (all P < 0.050). Validation results of the differential expressions and diagnostic potential of OLFML2B (Oncomine) were consistent with those obtained in the TCGA cohort (P < 0.050, AUC > 0.700).

CONCLUSIONS

Differentially expressed LINC01116 could be a candidate diagnostic and an independent prognostic signature in HCC. Besides, its target drugs may work for HCC therapy via the VEGF receptor signaling pathway. Differentially expressed OLFML2B could be a diagnostic signature involved in HCC via immune infiltrates.

LSM2 is associated with a poor prognosis and promotes cell proliferation, migration, and invasion in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is an extremely malignant tumor that is associated with a poor prognosis. LSM2 has been found to be related to different types of tumors; however, its role in SKCM is poorly defined. We aimed to determine the value of LSM2 as a prognostic biomarker for SKCM.

METHODS

The expression profile of LSM2 mRNA was compared between tumor and normal tissues in public databases, such as TCGA, GEO, and BioGPS. LSM2 protein expression was explored using immunohistochemistry (IHC) on a tissue microarray containing 44 SKCM tissues and 8 normal samples collected at our center. Kaplan-Meier analysis was performed to assess the prognostic value of LSM2 expression in patients with SKCM. SKCM cell lines with LSM2 knockdown were used to determine the effects of LSM2. Cell counting kit-8 (CCK8) and colony formation assays were conducted to assess cell proliferation, whereas wound healing and transwell assays were carried out to assess the migration and invasion abilities of SKCM cells.

RESULTS

LSM2 was more highly expressed at the mRNA and protein levels in SKCM than that in normal skin. Moreover, elevated expression of LSM2 was associated with shorter survival time and early recurrence in patients with SKCM. The in vitro results revealed that the silencing of LSM2 in SKCM cells significantly inhibited cell proliferation, migration, and invasion.

CONCLUSION

Overall, LSM2 contributes to malignant status and poor prognosis in patients with SKCM and may be identified as a novel prognostic biomarker and therapeutic target.

Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma

OBJECTIVE

Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood.

METHODS

In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements.

RESULTS

We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules.

CONCLUSION

It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement-The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.

MCM4 is a novel prognostic biomarker and promotes cancer cell growth in glioma

BACKGROUND

Gliomas account for 75% of all primary malignant brain tumors in adults and result in high mortality. Accumulated evidence has declared the minichromosome maintenance protein complex (MCM) gene family plays a critical role in modulating the cell cycle and DNA replication stress. However, the biological function and clinic characterization of nine MCM members in low-grade glioma are not yet clarified.

METHODS

In this study, we utilized diverse public databases, including The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, Human Protein Atlas (HPA), Linkedomics, cbioportal, Tumor and Immune System Interaction Database (TISIDB), single-sample GSEA (ssGSEA), Tumor Immune Estimation Resource (TIMER), Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal databases to explore the mRNA and protein expression profiles, gene mutation, clinical features, diagnosis, prognosis, signaling pathway, tumor mutational burden (TMB), immune subtype, immune cell infiltration, immune modulator and drug sensitivity of nine MCMs. Afterward, qRT-PCR was utilized to detect the expression of the MCM family in glioblastoma multiforme (GBM) cell lines. The one-, three-, or five-year survival rate was predicted by utilizing a nomogram established by cox proportional hazard regression.

RESULTS

In this study, we found that nine MCMs were consistently up-regulated in glioma tissues and glioma cell lines. Elevated nine MCMs expressions were significantly correlated with a higher tumor stage, isocitrate dehydrogenase (IDH) mutates, 1p/19q codeletion, histological type, and primary therapy outcome. Survival analyses showed that higher expression of MCM2-MCM8 (minichromosome maintenance protein2-8) and MCM10 (minichromosome maintenance protein 10) were linked with poor overall survival (OS) and progression-free survival (PFS) in glioma patients. On the other hand, up-regulated MCM2-MCM8 and MCM10 were significantly associated with shorter disease-specific survival (DSS) in glioma patients. Univariate and multivariate analyses revealed that MCM2 (minichromosome maintenance protein2), MCM4 (minichromosome maintenance protein 4), MCM6 (minichromosome maintenance protein 6), MCM7 (minichromosome maintenance protein 7) expression and tumor grade, 1p/19q codeletion, age, and primary therapy outcome were independent factors correlated with the clinical outcome of glioma patients. More importantly, a prognostic MCMs model constructed using the above five prognostic genes could predict the overall survival of glioma patients with medium-to-high accuracy. Furthermore, functional enrichment analysis indicated that MCMs principal participated in regulating cell cycle and DNA replication. DNA copy number variation (CNV) and DNA methylation significantly affect the expression of MCMs. Finally, we uncover that MCMs expression is highly correlated with immune cell infiltration, immune modulator, TMB, and drug sensitivity.

CONCLUSIONS

In summary, this finding confirmed that MCM4 is a potential target of precision therapy for patients with glioma.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

INTRODUCTION

Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.

MATERIALS AND METHODS

The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.

RESULTS

A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.

CONCLUSION

PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.” This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.

Next-generation sequencing identifies HOXA6 as a novel oncogenic gene in low grade glioma

Background: Low grade glioma is one of the most common lethal cancers in the human nervous system. Emerging evidence has demonstrated that homeobox A cluster (HOXA) gene family plays a critical role in the transcriptional regulation as well as cancer initiation and progression. However, the expression, biological functions and upstream regulatory mechanism of 11 HOXAs in low grade glioma are not yet clear.

Methods: In this study, we utilized various public databases and bioinformatics analyzed, including TCGA, CGGA, Rembrandt, HPA, LinkedOmics, cBioPortal, TISDIB, single-sample GSEA (ssGSEA), TIMER, LnCeVar, LASSO regression, Cox regression, Kaplan-Meier plot, and receiver operating, characteristic (ROC) analyses, GDSC and CTRP databases to analyzed the mRNA and protein expression profiles, gene mutation, clinical features, diagnosis, prognosis, signaling pathway, TMB, immune subtype, immune cell infiltration, immune modulator, ceRNA network and drug sensitivity of 11 HOXAs. Growth curve and transwell assays were utilized to study the biological characteristics of HOXA6 in LGG progression.

Results: In the present study, we found that 11 HOXAs (HOXA1, HOXA2, HOXA3, HOXA4, HOXA5, HOXA6, HOXA7, HOXA9, HOXA10, HOXA11 and HOXA3) were consistently up-regulated in LGG tissues and GBM tissues. Up-regulated of the HOXAs expression were significantly correlated with higher tumor stage, IDH mutation status, 1p/19q co-deletion, histological type and primary therapy outcome. Survival analyses showed that higher expression of HOXA1, HOXA2, HOXA3, HOXA4, HOXA5, HOXA7, HOXA9, HOXA10, HOXA11 and HOXA13 were correlated with shorter overall survival (OS), disease-specific survival (DSS) and progression-free survival (PFS) in LGG patients. Univariate and multivariate analyses revealed that HOXA1, HOXA6 expression and tumor grade, age, primary therapy outcome and age were independent factors affecting the prognosis of LGG patients. ROC curve analysis of HOXAs showed that HOXAs had a high accuracy (AUC > 0.80) in predicting LGG. Furthermore, gene functional enrichment analysis indicated that HOXAs mainly involved in the inflammatory response and immune regulation signaling pathway. CNV and DNA methylation significantly affect the expression of HOXAs. Finally, we uncover that HOXAs expression are highly correlated with immune cells infiltrate, immune modulator and drug sensitivity. We also uncover that the HOXAs related ceRNA network in LGG. More importantly, we found that HOXA6 was highly expressed in LGG cells lines and significantly affected their proliferation and migration abilities.

Conclusions: In conclusion, our data demonstrated that HOXA was correlated with progression and immune infiltration, and could serve as a prognostic biomarker for LGG.

Long Noncoding RNA LINC02470 Sponges MicroRNA-143-3p and Enhances SMAD3-Mediated Epithelial-to-Mesenchymal Transition to Promote the Aggressive Properties of Bladder Cancer

Simple Summary

Long noncoding RNAs (lncRNAs) were proposed as novel tumor prognostic markers, including for predicting bladder cancer progression, and the competing endogenous RNA (ceRNA) hypothesis conceived an accessible entry point to discover potential lncRNA candidates. This study indicated that LINC02470 promotes bladder cancer cell viability, migration, invasion, and in vivo tumorigenicity by sponging miR-143-3p and consequently rescuing SMAD3 translation to activate the TGF-β-induced EMT process. These data demonstrate that the LINC02470–miR-143-3p–SMAD3 ceRNA axis directly regulates the major transcription factor of TGF-β signaling, SMAD3, thereby inducing the EMT process in bladder cancer and enhancing the aggressiveness of bladder cancer cells.

Abstract

Bladder cancer progression and metastasis have become major threats in clinical practice, increasing mortality and therapeutic refractoriness; recently, epigenetic dysregulation of epithelial-to-mesenchymal transition (EMT)-related signaling pathways has been explored. However, research in the fields of long noncoding RNA (lncRNA) and competing endogenous RNA (ceRNA) regulation in bladder cancer progression is just beginning. This study was designed to determine potential EMT-related ceRNA regulation in bladder cancer progression and elucidate the underlying mechanisms that provoke aggressiveness. After screening the intersection of bioinformatic pipelines, LINC02470 was identified as the most upregulated lncRNA during bladder cancer initiation and progression. Both in vitro and in vivo biological effects indicated that LINC02470 promotes bladder cancer cell viability, migration, invasion, and tumorigenicity. On a molecular level, miR-143-3p directly targets and reduces both LINC02470 and SMAD3 RNA expression. Therefore, the LINC02470–miR-143-3p–SMAD3 ceRNA axis rescues SMAD3 translation upon LINC02470 sponging miR-143-3p, and SMAD3 consequently activates the TGF-β-induced EMT process. In conclusion, this is the first study to demonstrate that LINC02470 plays a pivotally regulatory role in the promotion of TGF-β-induced EMT through the miR-143-3p/SMAD3 axis, thereby aggravating bladder cancer progression. Our study warrants further investigation of LINC02470 as an indicatively prognostic marker of bladder cancer.

Multiplex-Heterogeneous Network-Based Capturing Potential SNP "Switches" of Pathways Associating With Diverse Disease Characteristics of Asthma

Asthma is a complex heterogeneous respiratory disorder. In recent years nubbly regions of the role of genetic variants and transcriptome including mRNAs, microRNAs, and long non-coding RNAs in the pathogenesis of asthma have been separately excavated and reported. However, how to systematically integrate and decode this scattered information remains unclear. Further exploration would improve understanding of the internal communication of asthma. To excavate new insights into the pathogenesis of asthma, we ascertained three asthma characteristics according to reviews, airway inflammation, airway hyperresponsiveness, and airway remodeling. We manually created a contemporary catalog of corresponding risk transcriptome, including mRNAs, miRNAs, and lncRNAs. MIMP is a multiplex-heterogeneous networks-based approach, measuring the relevance of disease characteristics to the pathway by examining the similarity between the determined vectors of risk transcriptome and pathways in the same low-dimensional vector space. It was developed to enable a more concentrated and in-depth exploration of potential pathways. We integrated experimentally validated competing endogenous RNA regulatory information and the SNPs with significant pathways into the ceRNA-mediated SNP switching pathway network (CSSPN) to analyze ceRNA regulation of pathways and the role of SNP in these dysfunctions. We discovered 11 crucial ceRNA regulations concerning asthma disease feature pathway and propose a potential mechanism of ceRNA regulatory SNP → gene → pathway → disease feature effecting asthma pathogenesis, especially for MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL13 (rs201185816/rs1000978586/rs202101165) → Interleukin-4 and Interleukin-13 signaling → inflammation/airway remodeling and MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL17RB (rs948046241) → Interleukin-17 signaling (airway remodeling)/Cytokine-cytokine receptor interaction (inflammation). This study showed a systematic and propagable workflow for capturing the potential SNP “switch” of asthma through text and database mining and provides further information on the pathogenesis of asthma.

Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Competitive endogenous RNA network and pathway-based analysis of LncRNA single-nucleotide polymorphism in myasthenia gravis

Myasthenia gravis (MG) is a complex neurological autoimmune disease with a pathogenetic mechanism that has yet to be elucidated. Emerging evidence has revealed that genes, non-coding RNAs and genetic variants play significant roles in the pathogenesis of MG. However, the molecular mechanisms of single nucleotide polymorphisms (SNPs) located on lncRNAs could disturb lncRNA-mediated ceRNA regulatory functions still unclear in MG. In this study, we collated 276 experimentally confirmed MG risk genes and 192 MG risk miRNAs. We then constructed a lncRNA-mediated ceRNA network for MG based on multi-step computational strategies. Next, we systematically integrated risk pathways and identified candidate SNPs in lncRNAs for MG based on data acquired from public databases. In addition, we constructed a pathway-based lncRNA-SNP mediated network (LSPN) that contained 128 lncRNAs targeting 8 MG risk pathways. By analyzing network, we propose a latent mechanism for how the “lncRNA-SNP-mRNA-pathway” axis affects the pathogenesis of MG. Moreover, 25 lncRNAs and 51 SNPs on lncRNAs were extracted from the “lncRNA-SNP-mRNA-pathway” axis. Finally, functional analyses demonstrated lncRNA-SNPs mediated ceRNA regulation pairs associated with MG participated in the MAPK signaling pathway. In summary, we constructed MG-specific lncRNA-SNPs mediated ceRNA regulatory networks based on pathway in the present study, which was helpful to elucidate the roles of lncRNA-SNPs in the pathogenesis of MG and provide novel insights into mechanism of lncRNA-SNPs as potential genetic risk biomarkers of MG.

Dissecting the Pathogenesis of Diabetic Retinopathy Based on the Biological ceRNA Network and Genome Variation Disturbance

Background

Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy. It is essential to explore the gene regulatory relationship and genomic variation disturbance of biological networks in DR progression.

Methods

In this study, we constructed a comprehensive lncRNA-mRNA ceRNA network of DR procession (CLMN) and explored its topological characteristics.

Results

Modular and functional analysis indicated that the organization of CLMN performed fundamental and specific functions in diabetes and DR pathology. The differential expression of hub ceRNA nodes and positive correlation reveals the highly connected ceRNA regulation and important roles in the regulating of DR pathology. A large proportion of SNPs in the TFBS, DHS, and enhancer regions of lncRNAs will affect lncRNA transcription and further cause expression variation. Some SNPs were found to disrupt the lncRNA functional elements such as miRNA target binding sites. These results indicate the complex nature of genotypic effects in the disturbing of CLMN and further contribute to gene expression variation and different disease phenotypes.

Conclusion

The identification of individual genomic variations and analysis of biological network disturbance by these genomic variations will help provide more personalized treatment plans and promote the development of precision medicine for DR.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Regulation of Long Non-coding RNA KCNQ1OT1 Network in Colorectal Cancer Immunity

Over the past few decades, researchers have become aware of the importance of non-coding RNA, which makes up the vast majority of the transcriptome. Long non-coding RNAs (lncRNAs) in turn constitute the largest fraction of non-coding transcripts. Increasing evidence has been found for the crucial roles of lncRNAs in both tissue homeostasis and development, and for their functional contributions to and regulation of the development and progression of various human diseases such as cancers. However, so far, only few findings with regards to functional lncRNAs in cancers have been translated into clinical applications. Based on multiple factors such as binding affinity of miRNAs to their lncRNA sponges, we analyzed the competitive endogenous RNA (ceRNA) network for the colorectal cancer RNA-seq datasets from The Cancer Genome Atlas (TCGA). After performing the ceRNA network construction and survival analysis, the lncRNA KCNQ1OT1 was found to be significantly upregulated in colorectal cancer tissues and associated with the survival of patients. A KCNQ1OT1-related lncRNA-miRNA-mRNA ceRNA network was constructed. A gene set variation analysis (GSVA) indicated that the expression of the KCNQ1OT1 ceRNA network in colorectal cancer tissues and normal tissues were significantly different, not only in the TCGA-COAD dataset but also in three other GEO datasets used as validation. By predicting comprehensive immune cell subsets from gene expression data, in samples grouped by differential expression levels of the KCNQ1OT1 ceRNA network in a cohort of patients, we found that CD4⁺, CD8⁺, and cytotoxic T cells and 14 other immune cell subsets were at different levels in the high- and low-KCNQ1OT1 ceRNA network score groups. These results indicated that the KCNQ1OT1 ceRNA network could be involved in the regulation of the tumor microenvironment, which would provide the rationale to further exploit KCNQ1OT1 as a possible functional contributor to and therapeutic target for colorectal cancer.

Identification of Mutation Landscape and Immune Cell Component for Liver Hepatocellular Carcinoma Highlights Potential Therapeutic Targets and Prognostic Markers

Liver hepatocellular carcinoma (LIHC) is a primary malignancy, and there is a lack of effective treatment for advanced patients. Although numerous studies exist to reveal the carcinogenic mechanism of LIHC, few studies have integrated multi-omics data to systematically analyze pathogenesis and reveal potential therapeutic targets. Here, we integrated genomic variation data and RNA-seq profiles obtained by high-throughput sequencing to define high- and low-genomic instability samples. The mutational landscape was reported, and the advanced patients of LIHC were characterized by high-genomic instability. We found that the tumor microenvironment underwent metabolic reprograming driven by mutations accumulate to satisfy tumor proliferation and invasion. Further, the co-expression network identifies three mutant long non-coding RNAs as potential therapeutic targets, which can promote tumor progression by participating in specific carcinogenic mechanisms. Then, five potential prognostic markers (RP11-502I4.3, SPINK5, CHRM3, SLC5A12, and RP11-467L13.7) were identified by examining the association of genes and patient survival. By characterizing the immune landscape of LIHC, loss of immunogenicity was revealed as a key factor of immune checkpoint suppression. Macrophages were found to be significantly associated with patient risk scores, and high levels of macrophages accelerated patient mortality. In summary, the mutation-driven mechanism and immune landscape of LIHC revealed by this study will serve precision medicine.

Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

Evaluation of the Oncogene Function of GOLPH3 and Correlated Regulatory Network in Lung Adenocarcinoma

Background

Golgi phosphoprotein 3 (GOLPH3) is an oncoprotein localized in the Golgi apparatus. Abnormal GOLPH3 expression is potentially related to carcinogenesis. However, the potential biological regulation network of GOLPH3 in lung adenocarcinoma (LUAD) remains to be determined.

Methods

Expression of GOLPH3 was identified in LUAD via TIMER, Oncomine, Lung Cancer Explorer (LCE), Human Protein Atlas (HPA), and UALCAN database. Survival analysis was performed using the Kaplan–Meier plotter. GOLPH3 alterations were analyzed through cBioPortal. LinkedOmics was used to perform functional analysis and predict interacted targets. The protein–protein interaction network was constructed by GeneMANIA. In addition, candidate miRNAs and lncRNAs targeting GOLPH3 were generated to construct competing endogenous RNA (ceRNA) network, and survival analysis of ceRNA was performed using LnCeVar. The mRNA or protein expression of TUG1, miR-142-5p, and GOLPH3 in Beas-2B and LUAD cells was verified using qPCR or Western blotting. CCK-8 assay, wound healing assay, and transwell assay were used to detect the ability of cell proliferation, migration, and invasion.

Results

Overexpression of GOLPH3 was identified in LUAD. UALCAN analysis showed that upregulated GOLPH3 was linked to different pathological features of LUAD patients. Importantly, high GOLPH3 expression indicated a negative correlation with the first progression (FP) in LUAD patients. GOLPH3 alterations were also found. Moreover, co-expressed genes with GOLPH3 were analyzed; and they were involved in ribosome and oxidative phosphorylation pathways. Functional network analysis indicated GOLPH3 regulated T-cell receptor signaling pathway and interferon signaling pathway with kinase and transcription factor targets. Notably, TUG1/miR-142-5p/GOLPH3 affected overall survival of LUAD patients. GOLPH3 expression was decreased in the cells with overexpression of miR-142-5p and TUG1 knockdown. GOLPH3 reduction inhibited cell proliferation, migration, and invasion.

Conclusions

Upregulation of GOLPH3 has a positive correlation with clinicopathological subtypes and poor FP in LUAD. GOLPH3 promoted LUAD progression. Moreover, TUG1 may act as ceRNA to regulate GOLPH3 expression by competitive binding miR-142-5p.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Long Non-coding RNA ASNR Targeting miR-519e-5p Promotes Gastric Cancer Development by Regulating FGFR2

Gastric cancer (GC), as a common gastrointestinal tumor, is an important cause of death from cancer all around the world. Long non-coding RNAs (lncRNAs), a novel class of transcripts, have attracted great attention of researchers. However, the mechanisms of the clinical significance of most lncRNAs in human cancer are mainly undocumented. This research desires to explore the clinical significance, biological function, and mechanism of Lnc_ASNR (apoptosis suppressing-non-coding RNA) in GC. Cell proliferation, cell cycle, cell migration, and invasion abilities were respectively determined by 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT), flow cytometry, wound healing, and Transwell assay (Sigma-Aldrich, St. Louis, MO, United States). The association of Lnc_ASNR, miR-519e-5p, and fibroblast growth factor receptor 2 (FGFR2) was evaluated via luciferase reporter experiments. The tumor xenograft assay was conducted to confirm the results of cell experiments. High expressed Lnc_ASNR was detected in both GC cells and tissues using qRT-PCR. Downregulated Lnc_ASNR could reduce proliferation, migration, and invasion in GC cells, while upregulated Lnc_ASNR could promote the cell proliferation, migration, and invasion. Moreover, the effect of Lnc_ASNR on migration and invasion ability is closely related to epithelial-mesenchymal transition (EMT). The bioinformatics analysis, luciferase assay, and Western blot demonstrated that Lnc_ASNR inhibited miR-519e-5p expression but increased FGFR2 expression. Lnc_ASNR and FGFR2 were both targeted to miR-519e-5p, and they were negatively correlated with the expression of miR-519e-5p. All investigations indicated that Lnc_ASNR functioned as a ceRNA targeting miR-519e-5p and facilitated GC development by regulating the pathway of miR-519e-5p/FGFR2.

Roles of ncRNAs as ceRNAs in Gastric Cancer

Although ignored in the past, with the recent deepening of research, significant progress has been made in the field of non-coding RNAs (ncRNAs). Accumulating evidence has revealed that microRNA (miRNA) response elements regulate RNA. Long ncRNAs, circular RNAs, pseudogenes, miRNAs, and messenger RNAs (mRNAs) form a competitive endogenous RNA (ceRNA) network that plays an essential role in cancer and cardiovascular, neurodegenerative, and autoimmune diseases. Gastric cancer (GC) is one of the most common cancers, with a high degree of malignancy. Considerable progress has been made in understanding the molecular mechanism and treatment of GC, but GC’s mortality rate is still high. Studies have shown a complex ceRNA crosstalk mechanism in GC. lncRNAs, circRNAs, and pseudogenes can interact with miRNAs to affect mRNA transcription. The study of the involvement of ceRNA in GC could improve our understanding of GC and lead to the identification of potential effective therapeutic targets. The research strategy for ceRNA is mainly to screen the different miRNAs, lncRNAs, circRNAs, pseudogenes, and mRNAs in each sample through microarray or sequencing technology, predict the ceRNA regulatory network, and, finally, conduct functional research on ceRNA. In this review, we briefly discuss the proposal and development of the ceRNA hypothesis and the biological function and principle of ceRNAs in GC, and briefly introduce the role of ncRNAs in the GC’s ceRNA network.

A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

Objective

Autophagy and long noncoding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.

Methods

We downloaded RNA sequencing data and clinical information of melanoma from the Cancer Genome Atlas. The coexpression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate Cox regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.

Results

According to the results of the univariate Cox analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate Cox analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p < 0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.

Conclusion

The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNA risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA-CeRNA Networks Across Eight Cancer Types

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

Long Intergenic Non-Coding RNAs in HNSCC: From "Junk DNA" to Important Prognostic Factor

Head and neck squamous cell carcinoma is one of the most common and fatal cancers worldwide. Even a multimodal approach consisting of standard chemo- and radiotherapy along with surgical resection is only effective in approximately 50% of the cases. The rest of the patients develop a relapse of the disease and acquire resistance to treatment. Especially this group of individuals needs novel, personalized, targeted therapy. The first step to discovering such solutions is to investigate the tumor microenvironment, thus understanding the role and mechanism of the function of coding and non-coding sequences of the human genome. In recent years, RNA molecules gained great interest when the complex character of their impact on our biology allowed them to come out of the shadows of the "junk DNA" label. Furthermore, long non-coding RNAs (lncRNA), specifically the intergenic subgroup (lincRNA), are one of the most aberrantly expressed in several malignancies, which makes them particularly promising future diagnostic biomarkers and therapeutic targets. This review contains characteristics of known and validated lincRNAs in HNSCC, such as XIST, MALAT, HOTAIR, HOTTIP, lincRNA-p21, LINC02487, LINC02195, LINC00668, LINC00519, LINC00511, LINC00460, LINC00312, and LINC00052, with a description of their prognostic abilities. Even though much work remains to be done, lincRNAs are important factors in cancer biology that will become valuable biomarkers of tumor stage, outcome prognosis, and contribution to personalized medicine.

Oncogenic Landscape of Somatic Mutations Perturbing Pan-Cancer lncRNA-ceRNA Regulation

Competing endogenous RNAs (ceRNA) are transcripts that communicate with and co-regulate each other by competing for the binding of shared microRNAs (miRNAs). Long non-coding RNAs (lncRNAs) as a type of ceRNA constitute a competitive regulatory network determined by miRNA response elements (MREs). Mutations in lncRNA MREs destabilize their original regulatory pathways. Study of the effects of lncRNA somatic mutations on ceRNA mechanisms can clarify tumor mechanisms and contribute to the development of precision medicine. Here, we used somatic mutation profiles collected from TCGA to characterize the role of lncRNA somatic mutations in the ceRNA regulatory network in 33 cancers. The 31,560 mutation sites identified by TargetScan and miRanda affected the balance of 70,811 ceRNA regulatory pathways. Putative mutations were categorized as high or low based on mutation frequencies. Multivariate multiple regression revealed a significant effect of 162 high-frequency mutations in six cancer types on the expression levels of target mRNAs (ceMs) through the ceRNA mechanism. Low-frequency mutations in multiple cancers perturbing 1624 ceM have been verified by Student’s t-test, indicating a significant mechanism of changes in the expression level of oncogenic genes. Oncogenic signaling pathway studies involving ceMs indicated functional heterogeneity of multiple cancers. Furthermore, we identified that lncRNA, perturbing ceMs associated with patient survival, have potential as biomarkers. Our collective findings revealed individual differences in somatic mutations perturbing ceM expression and impacting tumor heterogeneity.

Identification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes

Background: Ovarian cancer is highly malignant and has a poor prognosis in the advanced stage. Studies have shown that infiltration of tumor microenvironment cells, immune cells and stromal cells has an important impact on the prognosis of cancers. However, the relationship between tumor microenvironment genes and the prognosis of ovarian cancer has not been studied.

Methods: Gene expression profiles and SNP data of ovarian cancer were downloaded from the TCGA database. Cluster analysis, WGCNA analysis and univariate survival analysis were used to identify immune microenvironment genes as prognostic signatures for predicting the survival of ovarian cancer patients. External data were used to evaluate the signature. Moreover, the top five significantly correlated genes were evaluated by immunohistochemical staining of ovarian cancer tissues.

Results: We systematically analyzed the relationship between ovarian cancer and immune metagenes. Immune metagenes expression were associated with prognosis. In total, we identified 10 genes related to both immunity and prognosis in ovarian cancer according to the expression of immune metagenes. These data reveal that high expression of ETV7 (OS, HR = 1.540, 95% CI 1.023–2.390, p = 0.041), GBP4 (OS, HR = 1.834, 95% CI 1.242–3.055, p = 0.004), CXCL9 (OS, HR = 1.613, 95% CI 1.080 –2.471, p = 0.021), CD3E (OS, HR = 1.590, 95% CI 1.049 –2.459, p = 0.031), and TAP1 (OS, HR = 1.766, 95% CI 1.163 –2.723, p = 0.009) are associated with better prognosis in patients with ovarian cancer.

Conclusion: Our study identified 10 immune microenvironment genes related to the prognosis of ovarian cancer. The list of tumor microenvironment-related genes provides new insights into the underlying biological mechanisms driving the tumorigenesis of ovarian cancer.

Long Noncoding RNA KCNQ1OT1 is a Prognostic Biomarker and mediates CD8+ T cell exhaustion by regulating CD155 Expression in Colorectal Cancer

Background: Long noncoding RNA KCNQ1 opposite strand/antisense transcript 1 (lncRNA KCNQ1OT1) is abnormally expressed in various solid tumors. The purpose of this study was to explore the prognostic value and potential functional role of lncRNA KCNQ1OT1 across cancers. Methods: We performed a meta-analysis of published literature to evaluate the prognostic value of lncRNA KCNQ1OT1 across cancers. Verification, functional analysis, and genomic variation analysis were performed using the GEPIA, TIMER, and LnCeVar databases. According to the immune cell infiltration level, we established a prognostic model of lncRNA KCNQ1OT1 expression using public datasets of TIMER. We used quantitative real-time polymerase chain reaction (RT-qPCR) and western blot to detect the expression levels of lncRNA KCNQ1OT1 and the CD155 protein in colorectal cancer (CRC) tissues and cell lines. Then, a lncRNA KCNQ1OT1-knockdown cell line was cocultured to explore the role of lncRNA KCNQ1OT1 and CD155 in the T cell response by flow cytometric analysis. Results: Our results showed that the high expression of lncRNA KCNQ1OT1 was significantly related to poor overall survival across cancers, especially CRC. Interestingly, we found that COAD patients with high lncRNA KCNQ1OT1 expression and high CD8⁺ T cell infiltration levels had a worse prognosis than those with low lncRNA KCNQ1OT1 expression and high CD8⁺ T cell infiltration levels. Moreover, lncRNA KCNQ1OT1 and CD155 showed significantly higher expression in CRC tissue than in normal tissue, and lncRNA KCNQ1OT1 expression was positively correlated with CD155 expression in CRC. Finally, knockdown of lncRNA KCNQ1OT1 reduced CD155 expression in HCT116 and SW620 cells and enhanced the immune response in coculture with CD8⁺ T cells. Conclusions: High lncRNA KCNQ1OT1 expression is significantly correlated with poor prognosis of CRC patients and mediates the CD8⁺ T cell response in CRC. These findings indicate that lncRNA KCNQ1OT1 is a prognostic biomarker and potential immune therapeutic target for enhancing the CD8⁺ T cell response in CRC.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

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.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m⁶A, m⁵C, m¹A, m⁵U, Ψ, m⁶Am, m⁷G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

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.

SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions

microRNAs (miRNAs) are post-transcriptional regulators involved in many biological processes and human diseases, including cancer. The majority of transcripts compete over a limited pool of miRNAs, giving rise to a complex network of competing endogenous RNA (ceRNA) interactions. Currently, gene-regulatory networks focus mostly on transcription factor-mediated regulation, and dedicated efforts for charting ceRNA regulatory networks are scarce. Recently, it became possible to infer ceRNA interactions genome-wide from matched gene and miRNA expression data. Here, we inferred ceRNA regulatory networks for 22 cancer types and a pan-cancer ceRNA network based on data from The Cancer Genome Atlas. To make these networks accessible to the biomedical community, we present SPONGEdb, a database offering a user-friendly web interface to browse and visualize ceRNA interactions and an application programming interface accessible by accompanying R and Python packages. SPONGEdb allows researchers to identify potent ceRNA regulators via network centrality measures and to assess their potential as cancer biomarkers through survival, cancer hallmark and gene set enrichment analysis. In summary, SPONGEdb is a feature-rich web resource supporting the community in studying ceRNA regulation within and across cancer types.

LINC00467, Driven by Copy Number Amplification and DNA Demethylation, Is Associated with Oxidative Lipid Metabolism and Immune Infiltration in Breast Cancer

Breast cancer (BRCA) is a malignant tumor with a high incidence and poor prognosis in females. However, its pathogenesis remains unclear. In this study, based on bioinformatic analysis, we found that LINC00467 was highly expressed in BRCA and was associated with tumor metastasis and poor prognosis. The genomic and epigenetic analysis showed that LINC00467 may also be regulated by copy number amplification (CNA), chromatin openness, and DNA methylation. In vitro experiments showed that it could promote the proliferation, migration, and invasion of BRCA cells. Competitive endogenous RNA (ceRNA) regulatory network analysis and weighted gene coexpression network analysis (WGCNA) suggested that LINC00467 may play a role in signaling pathways of peroxisomal lipid metabolism, immunity, and others through microRNAs (miRNAs) targeting transforming growth factor beta 2 (TGFB2). In addition, copy number amplification and high expression of LINC00467 were associated with the low infiltration of CD8+ and CD4+ T cells. In conclusion, we found that LINC00467, driven by copy number amplification and DNA demethylation, may be a potential biomarker for the diagnosis and prognosis of BRCA and a tumor promoter acting as a potential therapeutic target for BRCA as well.

Emerging roles of long noncoding RNAs in cholangiocarcinoma: Advances and challenges

Cholangiocarcinoma (CCA), a cancer with a relatively low incidence rate, is usually associated with poor prognosis. Current modalities for the diagnosis and treatment of CCA patients are still far from satisfactory. In recent years, numerous long noncoding RNAs (lncRNAs) have been identified as crucial players in the development of various cancers, including CCA. Abnormally expressed lncRNAs in CCA, regulated by some upstream molecules, significantly influence the biological behavior of tumor cells and are involved in tumor development through various mechanisms, including interactions with functional proteins, participation in competing for endogenous RNA (ceRNA) regulatory networks, activation of cancer‐related signaling pathways and epigenetic modification of gene expression. Furthermore, several lncRNAs are closely associated with the clinicopathological features of CCA patients, and are promising biomarkers for diagnosing and prognostication of CCA. Some of these lncRNAs play an important role in chemotherapy drug resistance. In addition, lncRNAs have also been shown to be involved in the inflammation microenvironment of CCA and malignant outcome of CCA risk factors, such as cholestatic liver diseases. In view of the difficulty of diagnosing CCA, more attention should be paid to detectable lncRNAs in the serum or bile. This review summarizes the recent knowledge on lncRNAs in CCA and provides a new outlook on the molecular mechanisms of CCA development from the perspective of lncRNAs. Moreover, we also discussed the limitations of the current studies and differential expression of lncRNAs in different types of CCA.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

Long Intergenic Non-protein Coding RNA 511 in Cancers

Background: Long intergenic non-protein coding RNA 511 (LINC00511) is upregulated in diverse cancers and involved in prognosis. This study aimed to evaluate the prognostic profile of LINC00511 in cancer patients.

Methods: Published studies evaluating the prognosis of LINC00511 in patients with different cancers were identified from Medline, Embase, and Web of Science. Analysis of the association between LINC00511 and clinicopathological characteristics was conducted. GEPIA was used to validation and functional analysis and LnCeVar was used to get genomic variations.

Results: We eventually included 9 studies, and the combined results showed LINC00511 was significantly associated with decreased OS (HR = 3.18, 95% CI = 2.29 ~ 4.42, P < 0.001) albeit with mild heterogeneity (I² = 58.1%, P_h = 0.014), similarly in cancer type subgroups: breast cancer, digestive system cancer, and cervical cancer (all P < 0.001). There is no publication bias and meta-regression indicated follow-up time maybe heterogeneity of the results (P = 0.008). Additionally, LINC00511 appeared to be correlated with age, clinical stage, tumor size, and lymph node metastasis. Those findings were confirmed in GEPIA. Through LnCeVars, gene ontology and functional pathways were enriched, and dysregulated hallmarks and related ceRNA network of LINC00511 were disturbed.

Conclusions: LINC00511 could be predictive of poor OS and lymph node metastasis in multiple cancers, in another word, LINC00511 serves as an unfavorable prognostic factor, and its mechanism is related to ceRNA.

Identification of prognostic biomarkers and drug target prediction for colon cancer according to a competitive endogenous RNA network

Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that long‑chain non‑coding RNAs (lncRNAs) and protein‑coding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNA‑mediated competing endogenous (ce)RNA networks in colon cancer have yet to be elucidated. The expression profiles of mRNAs, lncRNAs and miRNAs from 471 colon cancer and 41 paracancerous tissue samples were downloaded from The Cancer Genome Atlas database. A lncRNA‑miRNA‑mRNA ceRNA network in colon cancer was constructed and comprised 17 hub lncRNAs, 87 hub miRNA and 144 hub mRNAs. The topological properties of the network were analyzed, and the random walk algorithm was used to identify the nodes significantly associated with colon cancer. Survival analysis using the UALCAN database indicated that 2/17 lncRNAs identified [metastasis‑associated lung adenocarcinoma transcript (MALAT1) and maternally expressed gene 3 (MEG3)] and 5/144 mRNAs [FES upstream region (FURIN), nuclear factor of activated T‑cells 5 (NFAT5), RNA Binding Motif Protein 12B (RBM12B), Ras related GTP binding A (RRAGA) and WD repeat domain phosphoinositide‑interacting protein 2 (WIPI2)] were significantly associated with the overall survival of patients with colon cancer, and may therefore be used as potential prognostic biomarkers of colon cancer. According to extracted lncRNA‑miRNA‑mRNA interaction pairs, the GSE26334 dataset was used to confirm that the lncRNA MALAT1/miR‑129‑5p/NFAT5 axis may represent a novel regulatory mechanism concerning the progression of colon cancer. The clusterProfiler package was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in colon cancer. Finally, drugs that significantly interact with the core genes identified in colon cancer were predicted using a hypergeometric test. Of these, fostamatinib was identified to be a targeted drug for colon cancer therapy. The present findings provide a novel perspective for improved understanding of the lncRNA‑associated ceRNA network and may facilitate the development of novel targeted therapeutics in colon cancer.

Integrative Clustering Reveals a Novel Subtype of Soft Tissue Sarcoma With Poor Prognosis

Background

Soft tissue sarcomas (STSs) are heterogeneous at the clinical and molecular level and need to be further sub-clustered for treatment and prognosis.

Materials And Methods

STSs were sub-clustered based on RNAseq and miRNAseq data extracted from The Cancer Genome Atlas (TCGA) through the combined process of similarity network fusion (SNF) and consensus clustering (CC). The expression and clinical characteristics of each sub-cluster were analyzed. The genes differentially expressed (lncRNAs, miRNAs, and mRNAs) between the poor prognosis and good prognosis clusters were used to construct a competing endogenous RNA (ceRNA) network. Functional enrichment analysis was conducted and a hub network was extracted from the constructed ceRNA network.

Results

A total of 247 STSs were classified into three optimal sub-clusters, and patients in cluster 2 (C2) had a significantly lower rate of survival. A ceRNA network with 91 nodes and 167 edges was constructed according to the hypothesis of ceRNA. Functional enrichment analysis revealed that the network was mainly associated with organism development functions. Moreover, LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1)-competing endogenous gene pairs were identified as hub networks of the ceRNA network, in which each component showed survival significance.

Conclusion

Integrative clustering analysis revealed that the STSs could be clustered into three sub-clusters. The ceRNA network, especially the subnetwork LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1) was a promising therapeutic target for the STS sub-cluster associated with a poor prognosis.