AP001056.1, A Prognosis-Related Enhancer RNA in Squamous Cell Carcinoma of the Head and Neck

Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma

PURPOSE

PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learning algorithms to explore the relationships between PANoptosis-related lncRNAs (PRLs) and head and neck squamous cell carcinoma (HNSCC) and established a neural network model for prognostic prediction.

METHODS

Information about the HNSCC cohort was downloaded from the TCGA database, and the differentially expressed prognostic PRLs between tumor and normal samples were assessed in patients with different tumor subtypes via nonnegative matrix factorization (NMF) analysis. Subsequently, five kinds of machine-learning algorithms were used to select the core PRLs across the subtypes, and the interactive features were pooled into a neural network model to establish a PRL-related risk score (PLRS) system. Survival differences were compared via Kaplan‒Meier analysis, and the predictive effects were assessed with the areas under the ROCs. Moreover, functional enrichment analysis, immune infiltration, tumor mutation burden (TMB) and clinical therapeutic response were also conducted to further evaluate the novel predictive model.

RESULTS

A total of 347 PRLs were identified, 225 of which were differentially expressed between tumor and normal samples. Patients were divided into two clusters via NMF analysis, in which cluster 1 had a better prognosis and more immune cells and functional infiltrates. With the application of five machine learning algorithms, we selected 13 interactive PRLs to construct the predictive model. The AUCs for the ROCs in the entire set were 0.735, 0.740 and 0.723, respectively. Patients in the low-PLRS group exhibited a better prognosis, greater immune cell enrichment, greater immune function activation, lower TMB and greater sensitivity to immunotherapy.

CONCLUSION

In this study, we established a novel neural network prognostic model to predict survival and identify tumor subtypes in HNSCC patients. This novel assessment system is useful for prediction, providing ideas for clinical treatment.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment

Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recurrence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2-year survival (P=0.025) compared with non-diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recurrence and improved survival compared with non-diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treat diabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigate the impact of diabetes in other SCCHN subtypes.

Identification of an immune-related eRNA prognostic signature for clear cell renal cell carcinoma

BACKGROUND

Immune-related enhancer RNAs (eRNAs) have garnered significant attention in cancer metabolism research, yet their specific roles in ccRCC have remained elusive.

METHODS

We retrieved eRNA expression profiles from TCGA database and identified immune-related eRNAs (IREs) by assessing their co-expression with immune genes. Utilizing consensus clustering, we organized these IREs into two distinct clusters. The construction of an IREs signature was accomplished through the LASSO and multivariate Cox analysis. Furthermore, we performed Cell Counting Kit-8 and clonogenic assays to assess changes in the proliferative capacity of Caki-1 and 769-P cells.

RESULTS

The existence of two clusters of immune-related eRNAs in ccRCC, each with distinctive prognostic and immunological attributes. Cluster B exhibited immunosuppressive properties and displayed a positive correlation with immunosuppressive cells. Functional enrichment analysis unveiled their involvement in several tumor-promoting pathways, metabolic pathways and immune pathways. The IREs signature demonstrated its potential to accurately predict patient immune and prognostic characteristics. AC003092.1, an eRNA strongly associated with patient survival, emerged as a potential oncogene significantly linked to adverse prognosis and the presence of immunosuppressive cells and checkpoints in ccRCC patients. Notably, AC003092.1 displayed marked upregulation in ccRCC tissues and cell lines, and its knockdown substantially inhibited the proliferation of Caki-1 and 769-P cells.

CONCLUSION

We established a robust predictive model that played a vital role in determining the prognosis, clinicopathological characteristics and immune cell infiltration patterns of ccRCC patients. IRE, particularly AC003092.1, which was strongly associated with survival, hold promise as novel immunotherapeutic targets for ccRCC.

Comprehensive Investigation of m6A Regulators for Prognosis in Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) has an important impact on the clinical prognosis. N6-methyladenosine (m6A) is involved in the post-transcriptional regulation of tumorigenesis and development. In this study, the prognosis and biological functions of m6A regulator targets in HNSCC were explored. RNA-Seq expression data and clinical information from TCGA-HNSCC and GSE23036 datasets were collected. The mRNA levels of IGF2BP2 and IGF2BP3 in tumor tissues were significantly up-regulated. Differential expression and functional enrichment analysis of potential targets for IGF2BP2 and IGF2BP3 obtained from the m6A2Target database showed that they were significantly enriched in cell cycle-related pathways. The Cox regression analysis was performed to establish a three-mRNA signature including PLAU, LPIN1 and AURKA. The prognostic effect was verified in the external dataset GSE41613. Further studies revealed that the three-mRNA signature was significantly associated with survival in the clinical subgroup. The ROC curve, Harrell consistency index and decision curve comparison used to compare the predictive effect of the three-mRNA signature and the other signatures in previous studies showed that the three-mRNA signature had better predictive effect on the prognosis of HNSCC patients. The three-mRNA signature expression were verified in HNSCC cell lines with qRT-PCR and Western blot. Sequence analysis showed that m6A-modification sites existed on PLAU, LPIN1 and AURKA genes. In conclusion, the three-mRNA signature has been proved to be useful on evaluating the prognosis and contributing to the personalized treatment of HNSCC, and IGB2BP2/3 were related to the cell cycle in HNSCC.

Prognosis of colon cancer patients based on enhancer RNAs-related genes

PURPOSE

Colon cancer (CC) is a cancer of the large intestine with high prevalence and poor prognosis. enhancer RNAs. Therefore, valuable tools or biomarkers for predicting patient status, directing clinical practice, and reducing overtreatment are needed. Enhancer RNAs (eRNAs), a class of noncoding RNAs transcribed from enhancers, have been shown to function as regulators of oncogene or tumor suppressor gene expression. The aim of our study was to explore the potential roles of eRNAs and their target enhancer-related genes (ERGs) in the prognosis of CC.

METHODS

Selected CC cases (stage I-III) from The Cancer Genome Atlas database were used as a training set, and cases from the Gene Expression Omnibus were used as the validation set. ERGs associated with prognosis were screened through three steps: potential, candidate, and prognosis ERGs. Multivariate Cox proportional hazards analysis was used to identify independent prognostic factors, and a nomogram was created. Calibration curves were drawn by comparing predicted and observed survival probability. For validation, the calibration curves and ROC analysis were also applied to two external validation sets. The biological significance and clinical application of the genes obtained were investigated.

RESULTS

Based on the multiple tiers of strict screening, 11 prognostic ERGs were obtained, which were combined to obtain a prognosis signature. A compound nomogram integrating age, TNM classification, and the prognostic signature was constructed. The model was reliable in distinguishing the risk of patients with stage I-III CC, with AUCs of 0.78 and 0.70 at 5 and 7 years, respectively. There was good reproducibility in calibration curves. The prognostic model also yielded good prediction capability in the validation sets.

CONCLUSION

In this study, the usefulness and specificity of the ERGs in prognosis were described, which should be considered a key feature in the clinical guidance of CC patients with early stage. We concluded that the major implications of the eRNAs and ERGs should be valued, which would be an emerging hallmark in the prognosis of cancer.

Development of a novel prognostic signature derived from enhancer RNA-regulated genes in head neck squamous cell carcinoma

BACKGROUND

Enhancer RNAs (eRNAs) are increasingly recognized as prognostic biomarkers-across human cancers. Here, we sought to develop a novel eRNA-regulated genes (ERGs)-derived prognostic signature for head neck squamous cell carcinoma (HNSCC).

METHODS

Candidate ERGs were identified via co-expression between individual survival-related eRNAs and their putative targets by Spearman's correlation analyses. The ERG signature was developed by univariate Cox regression, Kaplan-Meier survival analysis and maximum AUC in 1000 iterations of LASSO-penalized multivariate Cox regression. An ERG nomogram incorporating ERG signature and selected clinicopathological parameters were constructed by multivariate Cox regression. Biological roles of eRNA of interest were further explored in vitro.

RESULTS

The ERG signature successfully stratified patients into subgroups with distinct survival in multiple cohorts. An ERG nomogram was developed with satisfactory performance in prognostication. Inhibition of ENSR00000165816 significantly reduced transcript level of SLC2A9 and impaired cell proliferation and invasion.

CONCLUSION

Our results establish ERG signature and nomogram as powerful prognostic predictors for HNSCC.

Transcriptomic Profiling Reveals an Enhancer RNA Signature for Recurrence Prediction in Colorectal Cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for identifying high-risk patients with the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation, and their expression is frequently dysregulated in several cancers, including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored, which is the primary aim of this study.

RESULTS

We performed a systematic analysis of eRNA expression profiles in colon cancer (CC) and rectal cancer (RC) patients from the TCGA dataset. By using rigorous biomarker discovery approaches by splitting the entire dataset into a training and testing cohort, we identified a 22-eRNA panel in CC and a 19-eRNA panel in RC for predicting tumor recurrence. The Kaplan-Meier analysis showed that biomarker panels robustly stratified low and high-risk CC (p = 7.29 × 10-5) and RC (p = 6.81 × 10-3) patients with recurrence. Multivariate and LASSO Cox regression models indicated that both biomarker panels were independent predictors of recurrence and significantly superior to TNM staging in CC (HR = 11.89, p = 9.54 × 10-4) and RC (HR = 3.91, p = 3.52 × 10-2). Notably, the ROC curves demonstrated that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.833; 95% CI: 0.74-0.93) and RC (AUC = 0.834; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature that included the eRNA panels and TNM staging achieved an even greater predictive accuracy in patients with CC (AUC = 0.85).

CONCLUSIONS

Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Further experimental validation in independent clinical cohorts, these biomarkers can potentially improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy.

Identification of tumor antigens and immune subtypes in head and neck squamous cell carcinoma for mRNA vaccine development

The mRNA vaccines have been considered effective for combating cancer. However, the core components of the mRNA vaccines against head and neck squamous cell carcinoma (HNSCC) and the effects remain unclear. Our study aims to identify effective antigens in HNSCC to develop mRNA vaccines for corresponding potential patients. Here, we analyzed alternative splicing and mutation of genes in TCGA-HNSCC samples and identified seven potential tumor antigens, including SREBF1, LUC7L3, LAMA5, PCGF3, HNRNPH1, KLC4, and OFD1, which were associated with nonsense-mediated mRNA decay factor expression, overall survival prognosis and the infiltration of antigen-presenting cells. Furthermore, to select suitable patients for vaccination, immune subtypes related to HNSCC were identified by consensus clustering analysis, and visualization of the HNSCC immune landscape was performed by graph-learning-based dimensionality reduction. To address the heterogeneity of the population that is suitable for vaccination, plot cell trajectory and WGCNA were also utilized. HNSCC patients were classified into three prognostically relevant immune subtypes (Cluster 1, Cluster 2, and Cluster 3) possessing different molecular and cellular characteristics, immune modulators, and mutation statuses. Cluster 1 had an immune-activated phenotype and was associated with better survival, while Cluster 2 and Cluster 3 were immunologically cold and linked to increased tumor mutation burden. Therefore, HNSCC patients with immune subtypes Cluster 2 and Cluster 3 are potentially suitable for mRNA vaccination. Moreover, the prognostic module hub genes screened seven genes, including IGKC, IGHV3-15, IGLV1-40, IGLV1-51, IGLC3, IGLC2, and CD79A, which could be potential biomarkers to predict prognosis and identify suitable patients for mRNA vaccines. Our findings provide a theoretical basis for further research and the development of anti-HNSCC mRNA vaccines and the selection of suitable patients for vaccination.

A prognostic signature model for unveiling tumor progression in lung adenocarcinoma

A more accurate prognosis is important for clinical treatment of lung adenocarcinoma. However, due to the limitation of sample and technical bias, most prognostic signatures lacked reproducibility, and few were applied to clinical practice. In addition, understanding the molecular driving mechanism is indispensable for developing more promising therapies for lung adenocarcinoma. Here, we built an unbiased prognostic significance model to perform an integrative analysis, including differentially expressed genes and clinical data with lung adenocarcinoma patients from TCGA. Multivariable Cox proportional hazards model with the Lasso penalty and 10-fold cross-validate were used to identify the best gene signature. We generated a 17-gene signature for prognostic risk prediction based on the overall survival time of lung adenocarcinoma patients. To further test the model's predictive ability, we have applied an independent GEO database to verify the predictive ability of prognostic signature. The model can more objectively describe several biological processes related to tumors and reveal important molecular mechanisms in tumor development by GO and KEGG analysis. Furthermore, differential expression analysis by GSEA revealed that tumor microenvironments such as ER stress, exosome, and immune microenvironment were enriched. Using single-cell RNA sequence technology, we found that risk score was positively correlated with lung adenocarcinoma marker genes and copy number variation but negatively correlated with lung epithelial marker genes. High-risk cell populations with the model had stronger cancer stemness and tumor-related pathway activation. As we expected, the risk score was in accordance with the malignancy of each cluster from tumor progression. In conclusion, the risking model established in this study is more reliable than others in evaluating the prognosis of LUAD patients.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance

PURPOSE

Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients.

METHODS

Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p<0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments.

CONCLUSION

The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Emerging Role of Enhancer RNAs as Potential Diagnostic and Prognostic Biomarkers in Cancer

Enhancers are distal cis-acting elements that are commonly recognized to regulate gene expression via cooperation with promoters. Along with regulating gene expression, enhancers can be transcribed and generate a class of non-coding RNAs called enhancer RNAs (eRNAs). The current discovery of abundant tissue-specific transcription of enhancers in various diseases such as cancers raises questions about the potential role of eRNAs in disease diagnosis and therapy. This review aimed to demonstrate the current understanding of eRNAs in cancer research with a focus on the potential roles of eRNAs as prognostic and diagnostic biomarkers in cancers.

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies.

METHODS

We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score model was created and validated using a combination of univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Combining risk scores and clinical characteristics, a nomogram was established and assessed. Subsequently, the function, gene mutation, immune difference, and chemotherapeutic drug sensitivity of the groups with high- and low-risk scores were analyzed. Consequently, the mode's validity was evaluated comprehensively by combining single gene analysis.

RESULTS

The FAM risk score model for predicting HNSCC prognosis had certain validity. Patients in the high- and low-risk groups had genetic mutations, and the prognosis was the poorest for the high-risk groups with high genetic mutations. The patients with low-risk scores were suitable for immunotherapy since they had increased infiltration of immune cells. In contrast, the patients in the other groups were more suitable for chemotherapy.

CONCLUSION

The results of this study demonstrated that the FAM risk score model may predict the prognosis of HSNCC and has a certain therapeutic guidance value.

Comprehensive transcriptomic analysis of immune-related eRNAs associated with prognosis and immune microenvironment in melanoma

Background

Recent evidence suggests that enhancer RNAs (eRNAs) play key roles in cancers. Identification of immune-related eRNAs (ireRNAs) in melanoma can provide novel insights into the mechanisms underlying its genesis and progression, along with potential therapeutic targets.

Aim

To establish an ireRNA-related prognostic signature for melanoma and identify potential drug candidates.

Methods

The ireRNAs associated with the overall survival (OS-ireRNAs) of melanoma patients were screened using data from The Cancer Genome Atlas (TCGA) via WGCNA and univariate Cox analysis. A prognostic signature based on these OS-ireRNAs was then constructed by performing the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The immune landscape associated with the prognostic model was evaluated by the ESTIMATE algorithm and CIBERSORT method. Finally, the potential drug candidates for melanoma were screened through the cMap database.

Results

A total of 24 OS-ireRNAs were obtained, of which 7 ireRNAs were used to construct a prognostic signature. The ireRNAs-related signature performed well in predicting the overall survival (OS) of melanoma patients. The risk score of the established signature was further verified as an independent risk factor, and was associated with the unique tumor microenvironment in melanoma. We also identified several potential anti-cancer drugs for melanoma, of which corticosterone ranked first.

Conclusions

The ireRNA-related signature is an effective prognostic predictor and provides reliable information to better understand the mechanism of ireRNAs in the progression of melanoma.

Systematic Construction and Validation of a Novel Ferroptosis-Related Gene Model for Predicting Prognosis in Cervical Cancer

Background and Purpose. Ferroptosis, a mechanism of cell death that is iron-dependent, participates in various pathologies of cancer (CC). Nevertheless, the specific function that ferroptosis plays in the onset and progression of cervical cancer (CC) is yet uncertain. This research sought to examine the value of ferroptosis-related genes (FRGs) in the progression and prognosis of CC. Methods. Datasets containing RNA sequencing and corresponding clinical data of cervical cancer patients were obtained from searching publicly accessible databases. The “NMF” R package was conducted to calculate the matrix of the screened prognosis gene expression. Ferroptosis-related differential genes in cervical cancer were detected using the “limma” R function and WGCNA. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were conducted to develop a novel prognostic signature. The prediction model was verified by the nomogram integrating clinical characteristics; the GSE44001 dataset was used as an external verification. Then, the immune status and tumor mutation load were explored. Finally, immunohistochemistry as well as quantitative polymerase chain reaction (RT-qPCR) was utilized to ascertain the expression of FRGs. Results. Two molecular subgroups (cluster 1 and cluster 2) with different FRG expression patterns were recognized. A ferroptosis-related model based on 4 genes (VEGFA, CA9, DERL3, and RNF130) was developed through TCGA database to identify the unfavorable prognosis cases. Patients in cluster 1 showed significantly decreased overall survival in contrast with those in cluster 2 ( $P<0.05$ ). The LASSO technique and Cox regression analysis were both utilized to establish the independence of the prognostic model. The validity of nomogram prognostic predictions has been well demonstrated for 3- and 5-year survival in both internal and external data validation cohorts. These two subgroups showed striking differences in tumor-infiltrating leukocytes and tumor mutation burden. The low-risk subgroup showed a longer overall survival time with a higher immune cell score and higher tumor mutation rate. Gene functional enrichment analyses revealed predominant enrichment in various tumor-associated signaling pathways. Finally, the expression of each gene was confirmed by immunohistochemistry and RT-qPCR. Conclusion. A novel and comprehensive ferroptosis-related gene model was proposed for cervical cancer which was capable of distinguishing the patients independently with high risk for poor survival, and targeting ferroptosis may represent a promising approach for the treatment of CC.

Identification of Enhancer RNA CDK6-AS1 as a Potential Novel Prognostic Biomarker in Gastric Cancer

Background: This study aimed to confirm the role of enhancer RNAs (eRNAs) in gastric cancer and their clinical utility.

Methods: We used Cox survival and relevance analysis to identify the candidate eRNAs in gastric cancer and performed Gene Ontology and Reactome pathway enrichment to determine the potential functions of eRNAs. Correlation between eRNA, tumor-infiltrating immune cells, and drug sensitivity was then analyzed.

Results:CDK6-AS1, a long non-coding RNA cyclin-dependent kinase 6, may serve as a poor potential prognostic biomarker candidate in gastric cancer with a positive correlation with its target gene CDK6. The low CDK6-AS1 expression group showed more frequent mutated driver genes than the high expression group. Moreover, CDK6-AS1 is involved in a key oncogenic pathway of the cell cycle and RNA transcription. CDK6-AS1 also shows dysregulations and associations with prognosis at the pan-cancer level. This eRNA may also be associated with immune cell infiltration and drug sensitivity.

Conclusion:CDK6-AS1 may be a potential prognostic biomarker and chemotherapeutic drug sensitivity predictor in gastric cancer.

Inflammatory Immune-Associated eRNA: Mechanisms, Functions and Therapeutic Prospects

The rapid development of multiple high-throughput sequencing technologies has made it possible to explore the critical roles and mechanisms of functional enhancers and enhancer RNAs (eRNAs). The inflammatory immune response, as a fundamental pathological process in infectious diseases, cancers and immune disorders, coordinates the balance between the internal and external environment of the organism. It has been shown that both active enhancers and intranuclear eRNAs are preferentially expressed over inflammation-related genes in response to inflammatory stimuli, suggesting that enhancer transcription events and their products influence the expression and function of inflammatory genes. Therefore, in this review, we summarize and discuss the relevant inflammatory roles and regulatory mechanisms of eRNAs in inflammatory immune cells, non-inflammatory immune cells, inflammatory immune diseases and tumors, and explore the potential therapeutic effects of enhancer inhibitors affecting eRNA production for diseases with inflammatory immune responses.

The role of long non-coding RNAs in the pathogenesis of head and neck squamous cell carcinoma

Head and neck cancers are a heterogeneous collection of malignancies of the upper aerodigestive tract, salivary glands, and thyroid. However, the molecular mechanisms underlying the carcinogenesis of head and neck squamous cell carcinomas (HNSCCs) remain poorly understood. Over the past decades, overwhelming evidence has demonstrated the regulatory roles of long non-coding RNAs (lncRNAs) in tumorigenesis, including HNSCC. Notably, these lncRNAs have vital roles in gene regulation and affect various aspects of cellular homeostasis, including proliferation, survival, and metastasis. They exert regulating functions by interacting with nucleic acids or proteins and affecting cancer cell signaling. LncRNAs represent a burgeoning field of cancer research, and we are only beginning to understand the importance and complicity of lncRNAs in HNSCC. In this review, we summarize the deregulation and function of lncRNAs in human HNSCC. We also review the working mechanism of lncRNAs in HNSCC pathogenesis and discuss the potential application of lncRNAs as diagnostic/prognostic tools and therapeutic targets in human HNSCC.

CYP1B1-AS1 Is a Novel Biomarker in Glioblastoma by Comprehensive Analysis

Objective

Growing evidence shows that enhancer RNAs (eRNAs) are pivotal for tumor progression. In this research, our team aimed to identify the survival-related eRNAs and further explore their potential function in glioblastoma (GBM).

Methods

RNA-sequencing data in 31 tumor types were acquired from TCGA datasets. The survival-related eRNAs were identified by the use of Kaplan-Meier survival analyses and Spearman's correlation analyses. KEGG pathway enrichment analysis was completed to investigate the underlying signal paths of the critical eRNA. Pancancer assays were applied to explore the association between CYP1B1-AS1 and CYP1B1.

Results

We identified 74 survival-related eRNAs and focused on CYP1B1-AS1 which displayed the greatest cor value. CYP1B1 was identified as a regulatory target of CYP1B1-AS1. KEGG analyses suggested that CYP1B1-AS1 might play an essential role through CK-CKR mutual effect, complement and coagulation cascades, TNF signal path, and JAK-STAT signal path. The pancancer verification outcomes revealed that CYP1B1-AS1 was related to survival in 4 cancers, i.e., LIHC, KIRP, KICH, and KIRC. Association was discovered between CYP1B1-AS1 and the targeted gene, CYP1B1, in 29 cancer types.

Conclusion

The outcomes herein provided the first evidence that overexpression of CYP1B1-AS1 might be a potential molecular biomarker for predicting the prognosis of patients with GBM.

Gender specific eRNA TBX5-AS1 as the immunological biomarker for male patients with lung squamous cell carcinoma in pan-cancer screening

As an innate feature of human beings, gender differences have an influence on various biological phenotypes, yet it does not attract enough attention in genomics studies. The prognosis of multiple carcinomas usually exhibits a favorable ending for female patients, but the neglect of gender differences can cause serious bias in survival analysis. Enhancer RNAs (eRNAs) are mostly downstream of androgens or estrogen. The present study was aimed to screen eRNAs in patients with non-small-cell lung cancer. The findings revealed that eRNA TBX5-AS1 was expressed differently between female and male patients. Meanwhile, its prognostic significance appeared only in male patients with squamous cell carcinoma (SCC) type. The Gene Set Enrichment Analysis proved that the expression level of TBX5-AS1 increased following the activation of the androgen signaling pathway. In pan-cancer analysis, the prognostic prediction based on gender grouping obtained more meaningful results, and the synergy between TBX5-AS1 and its homologous target was more consistent. Furthermore, immunity variations between sexes prompted us to explore the role that TBX5-AS1 played in tumor microenvironment and immunotherapy. The robust evidence proved that male patients with high expression of TBX5-AS1 possessed a malignant immune microenvironment and urgently needed immune checkpoint inhibitor treatment. In conclusion, TBX5-AS1 may be one of the strongest candidates to predict prognosis for male patients with SCC and provide a reference for immunotherapy.

DCP1A is an unfavorable prognostic-related enhancer RNA in hepatocellular carcinoma

Long non-coding RNAs (lncRNAs) are associated with occurrence and development of tumors. Enhancer RNA (eRNA) is a special type of lncRNAs produced from transcription of enhancer elements. The function of eRNAs in tumors have elicited significant attention recently. However, the clinical significance and role of eRNAs in hepatocellular carcinoma (HCC) has not been fully explored. The current study sought to explore the expression level and prognostic value of key eRNAs in HCC. Bioinformatics analyses were used to explore expression levels of key prognostic eRNAs in HCC and their correlation with target genes. A total of 1580 enhancer RNAs (eRNAs) and 1791 target genes were initially retrieved from TCGA-LIHC gene expression database. Further analysis through survival and correlation analysis led to identification of 12 eRNAs and 13 target genes. The findings showed that DCP1A was the most prognosis-related eRNA. This eRNA showed the highest correlation with the target gene, PRKCD. Analysis showed that poor overall survival (OS) in HCC patients was correlated with high expression level of DCP1A (eRNA) and PRKCD (target gene). The up-regulation of DCP1A was associated with advanced tumor stage, larger tumor size and higher histological grade. The results of pan-cancer analysis showed that the expression of DCP1A was differentially expressed in 13 other types of tumor tissues and their corresponding normal tissues. This eRNA was highly expressed in digestive system tumors. Functional analysis showed that high expression level of DCP1A was implicated in multiple tumor-related signaling pathways. The findings of the current study indicated DCP1A is a novel biomarker that can be used as a potential therapeutic target for HCC patients.

Identification of enhancer RNAs for the prognosis of head and neck squamous cell carcinoma

BACKGROUND

Enhancer RNAs (eRNAs) play an important role in carcinogenesis. The landscape of eRNAs in head and neck squamous cell carcinoma (HNSCC) remains largely unknown.

METHODS

The eRNA expression matrix was obtained from the enhancer RNA in the cancer database. Functional enrichment analyses were performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). Prognostic eRNAs were identified using Cox regression analysis, and a prognostic prediction model was constructed based on coefficients.

RESULTS

KEGG analysis showed that eRNA-related transcription factors were mainly enriched in herpes simplex virus 1 (HSV1) infection. The zinc finger (ZNF) family may play an essential role in HNSCC. ENSR00000188847, ENSR00000250663, ENSR00000313345, ENSR00000317887, and ENSR00000336429 were identified. The prediction model was robust.

CONCLUSIONS

We constructed a robust 5-eRNA prognostic prediction model, and these eRNAs are potential biomarkers for HNSCC prognosis.

Gene Model Related to m6A Predicts the Prognostic Effect of Immune Infiltration on Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive solid tumor. Because most studies have focused on the intrinsic carcinogenic pathways of tumors, we focused on the relationship between N6-methyladenosine (m6A) and the prognosis of HNSCC in the tumor immune microenvironment. We downloaded RNA-seq data from the TCGA dataset and used univariate Cox regression to screen m6A-related lncRNAs. The expression value of LASSO-screened genes was the sum of LASSO regression coefficients. We then evaluated relationships between the risk score and cellular components or cellular immune response. Differences in immune response under various algorithms were visualized with heat maps. The GSVA package in R was used to analyze GO, BP, KEGG, and hallmark gene sets of immune checkpoint clusters and immune checkpoint scores. The GSEA analysis was performed with the cluster profile package, yielding 21 m6A genes. Related lncRNAs were screened with Pearson's correlations, and the resulting 442 lncRNAs were screened using single-factor analysis. Eight lncRNAs closely related to prognosis were identified through survival random forest. Survival analysis showed that patients with a high risk score had a poor prognosis. Low- and high-risk-score groups differed significantly in m6A gene expression. Prognostic scores from different algorithms were significantly correlated with B cells, T cells, and memory cells in the immune microenvironment. Expression of immune checkpoints and signal pathways differed significantly across risk-score groups, suggesting that m6A could mediate lncRNA-induced immune system dysfunction and affect HNSCC development. A comprehensive study of tumor-cell immune characteristics should provide more insight into the complex immune microenvironment, thus contributing to the development of new immunotherapeutic agents.

Tobacco Smoke and Electronic Cigarette Vapor Alter Enhancer RNA Expression That Can Regulate the Pathogenesis of Lung Squamous Cell Carcinoma

Simple Summary

It is well established that tobacco smoke is the key player in lung squamous cell carcinoma (LUSC) pathogenesis, and there is growing evidence that electronic cigarette (e-cigarette) vapor may also cause LUSC. Recently, several studies have associated tobacco smoke with differential enhancer RNA (eRNA) expression. However, the effects of tobacco smoke and e-cigarette vapor on eRNA expression in correlation to LUSC outcomes have not been fully elucidated. This study demonstrates that tobacco smoke and e-cigarette vapor may decrease DNA methylation and increase chromosomal alterations at key sites, which ultimately upregulate the expression of oncogenic eRNAs and downregulate the expression of tumor-suppressing eRNAs. Subsequently, we demonstrate that these eRNAs may have altered interactions with immune cells to promote LUSC pathogenesis and reduced patient survival. We hope our results can be validated in future studies, and the key eRNAs we identified may be used as effective targets for more specialized treatments for smoking-mediated LUSC.

Abstract

Tobacco is the primary etiologic agent in worsened lung squamous cell carcinoma (LUSC) outcomes. Meanwhile, it has been shown that etiologic agents alter enhancer RNAs (eRNAs) expression. Therefore, we aimed to identify the effects of tobacco and electronic cigarette (e-cigarette) use on eRNA expression in relation to LUSC outcomes. We extracted eRNA counts from RNA-sequencing data of tumor/adjacent normal tissue and before/after e-cigarette tissue from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), respectively. Tobacco-mediated LUSC eRNAs were correlated to patient survival, clinical variables, and immune-associated elements. eRNA expression was also correlated to mutation rates through the Repeated Evaluation of Variables Conditional Entropy and Redundance (REVEALER) algorithm and methylated sites through methylationArrayAnalysis. Differential expression analysis was then completed for the e-cigarette data to compare with key tobacco-mediated eRNAs. We identified 684 downregulated eRNAs and 819 upregulated eRNAs associated with tobacco-mediated LUSC, specifically, with the cancer pathological stage. We also observed a decrease in immune cell abundance in tobacco-mediated LUSC. Yet, we found an increased association of eRNA expression with immune cell abundance in tobacco-mediated LUSC. We identified 16 key eRNAs with significant correlations to 8 clinical variables, implicating these eRNAs in LUSC malignancy. Furthermore, we observed that these 16 eRNAs were highly associated with chromosomal alterations and reduced CpG site methylation. Finally, we observed large eRNA expression upregulation with e-cigarette use, which corresponded to the upregulation of the 16 key eRNAs. Our findings provide a novel mechanism by which tobacco and e-cigarette smoke influences eRNA interactions to promote LUSC pathogenesis and provide insight regarding disease progression at a molecular level.

Enhancer RNA: biogenesis, function, and regulation

Enhancers are noncoding DNA elements that are present upstream or downstream of a gene to control its spatial and temporal expression. Specific histone modifications, such as monomethylation on histone H3 lysine 4 (H3K4me1) and H3K27ac, have been widely used to assign enhancer regions in mammalian genomes. In recent years, emerging evidence suggests that active enhancers are bidirectionally transcribed to produce enhancer RNAs (eRNAs). This finding not only adds a new reliable feature to define enhancers but also raises a fundamental question of how eRNAs function to activate transcription. Although some believe that eRNAs are merely transcriptional byproducts, many studies have demonstrated that eRNAs execute crucial tasks in regulating chromatin conformation and transcription activation. In this review, we summarize the current understanding of eRNAs from their biogenesis, functions, and regulation to their pathological significance. Additionally, we discuss the challenges and possible mechanisms of eRNAs in regulated transcription.

TBX5-AS1, an enhancer RNA, is a potential novel prognostic biomarker for lung adenocarcinoma

BACKGROUND

Enhancer RNAs (eRNAs) are demonstrated to be closely associated with tumourigenesis and cancer progression. However, the role of eRNAs in lung adenocarcinoma (LUAD) remains largely unclear. Thus, a comprehensive analysis was constructed to identify the key eRNAs, and to explore the clinical utility of the identified eRNAs in LUAD.

METHODS

First, LUAD expression profile data from the Cancer Genome Atlas (TCGA) dataset and eRNA-relevant information were integrated for Kaplan-Meier survival analysis and Spearman's correlation analysis to filtered the key candidate eRNAs that was associated with survival rate and their target genes in LUAD. Then, the key eRNA was selected for subsequent clinical correlation analysis. KEGG pathway enrichment analyses were undertaken to explore the potential signaling pathways of the key eRNA. Data from the human protein atlas (HPA) database were used to validate the outcomes and the quantitative real time-polymerase chain reaction (qRT-PCR) analysis was conducted to measure eRNA expression levels in tumor tissues and paired normal adjacent tissues from LUAD patients. Finally, the eRNAs were validated in pan-cancer.

RESULTS

As a result, TBX5-AS1 was identified as the key eRNA, which has T-box transcription factor 5 (TBX5) as its regulatory target. KEGG analysis indicated that TBX5-AS1 may exert a vital role via the PI3K/AKT pathway, Ras signaling pathway, etc. Additionally, the qRT-PCR results and the HPA database indicated that TBX5-AS1 and TBX5 were significantly downregulated in tumour samples compared to matched-adjacent pairs. The pan-cancer validation results showed that TBX5-AS1 was associated with survival in four tumors, namely, adrenocortical carcinoma (ACC), LUAD, lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). Correlations were found between TBX5-AS1 and its target gene, TBX5, in 26 tumor types.

CONCLUSION

Collectively, our results indicated that TBX5-AS1 may be a potential prognostic biomarker for LUAD patients and promote the targeted therapy of LUAD.

Prognostic Value of Eight-Gene Signature in Head and Neck Squamous Carcinoma

Head and neck cancer (HNC) is the fifth most common cancer worldwide. In this study, we performed an integrative analysis of the discovery set and established an eight-gene signature for the prediction of prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Univariate Cox analysis was used to identify prognosis-related genes (with P < 0.05) in the GSE41613, GSE65858, and TCGA-HNSC RNA-Seq datasets after data collection. We performed LASSO Cox regression analysis and identified eight genes (CBX3, GNA12, P4HA1, PLAU, PPL, RAB25, EPHX3, and HLF) with non-zero regression coefficients in TCGA-HNSC datasets. Survival analysis revealed that the overall survival (OS) of GSE41613 and GSE65858 datasets and the progression-free survival(DFS)of GSE27020 and GSE42743 datasets in the low-risk group exhibited better survival outcomes compared with the high-risk group. To verify that the eight-mRNA prognostic model was independent of other clinical features, KM survival analysis of the specific subtypes with different clinical characteristics was performed. Univariate and multivariate Cox regression analyses were used to identify three independent prognostic factors to construct a prognostic nomogram. Finally, the GSVA algorithm identified six pathways that were activated in the intersection of the TCGA-HNSC, GSE65858, and GSE41613 datasets, including early estrogen response, cholesterol homeostasis, oxidative phosphorylation, fatty acid metabolism, bile acid metabolism, and Kras signaling. However, the epithelial–mesenchymal transition pathway was inhibited at the intersection of the three datasets. In conclusion, the eight-gene prognostic signature proved to be a useful tool in the prognostic evaluation and facilitate personalized treatment of HNSCC patients.

Development and validation of a novel ferroptosis-related gene signature for predicting prognosis and immune microenvironment in head and neck squamous cell carcinoma

Ferroptosis plays an important role across variable cancer types. However, few studies have focused on the prognostic patterns of ferroptosis-related genes in HNSCC. Cohorts with mRNA expression profiles, as well as corresponding clinical data of HNSCC patients from published studies, were collected and consolidated from public databases. We performed random survival forest analysis, Kaplan-Meier (KM) analysis of best combinations, and Cox regression analysis on 231 ferroptosis-related genes to construct a gene signature in the discovery cohort (TCGA), and later validated it in the validation cohort (GEO). The 7-gene signature was constructed to stratify patients into two groups according to their level of risk. Poorer overall survival (OS) was detected in the high risk (HRisk) group than in the low risk (LRisk) group in both the TCGA cohort (P < 0.0001, HR = 1.71, 95%CI:1.41-2.07) and the GEO cohort (P < 0.001, HR = 1.68, 95%CI:1.32-2.13). The risk score was identified as an independent predictive factor of OS in multivariate Cox regression analyses (HR > 1, P < 0.0001) in both cohorts. The signature's predictive capacity was proven by the time-dependent receiver operating characteristic (ROC) curve analysis and nomogram analysis. Functional enrichment analysis revealed that immunosuppressive pathways such as matrix extracellular space, and (transforming growth factor-β)TGF-β were enriched. The HRisk group was strongly associated with upregulation of both cancer-related pathways and stromal scores, while higher proportions of anti-tumor immune cells and immune signatures were enriched in the LRisk group. In conclusion, the signature based on 7 ferroptosis-related genes could be applicable for predicting the prognosis of HNSCC, indicating that ferroptosis may be a potential therapeutic target for HNSCC.

Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Background

Enhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.

Methods

Gene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.

Results

A total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8⁺ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.

Conclusion

Our results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

Current Advances on the Important Roles of Enhancer RNAs in Molecular Pathways of Cancer

Enhancers are critical genomic elements that can cooperate with promoters to regulate gene transcription in both normal and cancer cells. Recent studies reveal that enhancer regions are transcribed to produce a class of noncoding RNAs referred to as enhancer RNAs (eRNAs). Emerging evidence shows that eRNAs play important roles in enhancer activation and enhancer-driven gene regulation, and the expression of eRNAs may be a critical factor in tumorigenesis. The important roles of eRNAs in cancer signaling pathways are also gradually unveiled, providing a new insight into cancer therapy. Here, we review the roles of eRNAs in regulating cancer signaling pathways and discuss the potential of eRNA-targeted therapy for human cancers.

FOXP4-AS1 is a favorable prognostic-related enhancer RNA in ovarian cancer

Ovarian cancer (OV) is the main cause of deaths worldwide in female reproductive system malignancies. Enhancer RNAs (eRNAs) are derived from the transcription of enhancers and has attracted increasing attention in cancers recently. However, the biological functions and clinical significance of eRNAs in OV have not been well described presently. We used an integrated data analysis to identify prognostic-related eRNAs in OV. Tissue-specific enhancer-derived RNAs and their regulating genes were considered as putative eRNA–target pairs using the computational pipeline PreSTIGE. Gene expression profiles and clinical data of OV and 32 other cancer types were obtained from the UCSC Xena platform. Altogether, 71 eRNAs candidates showed significant correlation with overall survival (OS) of OV samples (Kaplan–Meier log-rank test, P<0.05). Among which, 23 were determined to be correlated with their potential target genes (Spearman’s r > 0.3, P<0.001). It was found that among the 23 prognostic-related eRNAs, the expression of forkhead box P4 antisense RNA 1 (FOXP4-AS1) had the highest positive correlation with its predicted target gene FOXP4 (Spearman’s r = 0.61). Moreover, the results were further validated by RT-qPCR analysis in an independent OV cohort. Our results suggested the eRNA FOXP4-AS1 expression index may be a favorable independent prognostic biomarker candidate in OV.

LINC02257, an Enhancer RNA of Prognostic Value in Colon Adenocarcinoma, Correlates With Multi-Omics Immunotherapy-Related Analysis in 33 Cancers

Accumulated evidence supports that long non-coding RNAs (lncRNAs) are involved significantly in the development of human cancers. Enhancer RNAs (eRNAs), a subtype of lncRNAs, have recently attracted much attention about their roles in carcinogenesis. Colon adenocarcinoma is one of the most commonly diagnosed tumors with unfavorable prognosis. It highlights the great significance of screening and identifying novel biomarkers. More importantly, it remains to be elucidated with respect to the function of eRNAs in colon adenocarcinoma, as is in pan-cancers. The expression of LINC02257 was determined based on the data obtained from The Cancer Genome Atlas (TCGA). Further evaluation was performed on the basis of the following analyses: clinicopathology and survival analysis, gene ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as multi-omics immunotherapy-related analysis and co-expression analysis. The statistical analysis was conducted in R software, and immune cell infiltration of LINC02257 expression in cancers was investigated by using the CIBERSORT algorithm. By large-scale data mining, our study highlighted that a total of 39 eRNA genes were associated with colon adenocarcinoma prognosis, among which 25 eRNAs showed significant associations with their predicted target genes. LINC02257 was identified as the most significant survival-associated eRNA, with DUSP10 as its target gene. Besides, the high expression of LINC02257 in colon adenocarcinoma was more vulnerable to unfavorable prognosis and correlated with various clinical characteristics. GO and KEGG analyses revealed that LINC02257 was closely correlated with extracellular matrix organization via the PI3K-Akt signaling pathway. Besides, LINC02257 expression correlated with a multi-omics analysis of 33 cancer types, such as survival analysis [overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI)] and immunotherapy-related analysis [tumor microenvironment (TME), tumor mutational burden (TMB), and microsatellite instability (MSI)]. Finally, we investigated the co-expression genes of LINC02257 and its potential signaling pathways across different cancer types. LINC02257 is screened and can function as an independent prognostic biomarker through the PI3K-Akt signaling pathway for colon adenocarcinoma. Simultaneously, LINC02257 may be a multifaceted and significant immunotherapy-related eRNA in different cancers.

Characterization of transcripts emanating from enhancer Eβ of the murine TCRβ locus

Enhancers are well established as critical regulators of gene expression, but the mechanisms underlying the molecular basis of their specificity and activity are only partly understood. One of the most exciting recent observations is the discovery of enhancer RNA (eRNA), a class of noncoding RNAs derived from enhancer regions. Transcription of developmentally regulated enhancers has been observed to be associated with their active state. The nature of transcripts (eRNA) and their functional attributes are diverse and context dependent. The majority of eRNA are nonpolyadenylated and present in low abundance owing to their low stability, and may represent transcriptional noise. However, some eRNAs have been reported to be reasonably long and stable, are enriched in nuclei, exhibit tissue-specific expression and may contribute to enhancer function. Transcription of enhancers has been postulated to mediate enhancer function through either the act of transcription or via the transcribed RNA per se and is a useful feature to be analysed to understand mechanisms underlying enhancer activity. Enhancer Eβ at the murine TCRβ locus has been reported to exhibit enhanced occupancy of RNA polymerase II in developing thymocytes. Here, we investigated the transcriptional potential of Eβ in developing thymocytes and detected overlapping bidirectional transcripts at Eβ ranging between 0.7 and 1.7 kb. These noncoding transcripts are capped, polyadenylated, nuclear and expressed specifically in thymocytes. Delineation of these characteristics is important to further investigate their functional roles in mediating enhancer activity.

Enhancer RNAs: transcriptional regulators and workmates of NamiRNAs in myogenesis

miRNAs are well known to be gene repressors. A newly identified class of miRNAs termed nuclear activating miRNAs (NamiRNAs), transcribed from miRNA loci that exhibit enhancer features, promote gene expression via binding to the promoter and enhancer marker regions of the target genes. Meanwhile, activated enhancers produce endogenous non-coding RNAs (named enhancer RNAs, eRNAs) to activate gene expression. During chromatin looping, transcribed eRNAs interact with NamiRNAs through enhancer-promoter interaction to perform similar functions. Here, we review the functional differences and similarities between eRNAs and NamiRNAs in myogenesis and disease. We also propose models demonstrating their mutual mechanism and function. We conclude that eRNAs are active molecules, transcriptional regulators, and partners of NamiRNAs, rather than mere RNAs produced during enhancer activation.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

An integrated analysis of enhancer RNAs in glioma and a validation of their prognostic values

Glioma, a highly aggressive neuroepithelial malignant brain tumor, is associated with high disability and recurrence rates. Enhancer RNA (eRNA) plays a significant role in tumor proliferation and metastasis; however, their functions in gliomas need further evaluation. We used the computational pipeline, PreSTIGE, to predict tissue-specific enhancer-derived RNAs and the underlying regulatory genes. Using data retrieved from the TCGA and CGGA databases, a LASSO regression analysis and multiCox proportional hazards regression analyses were performed to determine the hub eRNAs associated with glioma prognosis. Quantitative reverse transcription PCR was performed on the glioma samples to evaluate the expression characteristics of the identified hub eRNAs. To construct a risk signature, we selected three eRNAs, including CRNDE, MRPS31P5, and LINC00844, for their significant prognostic values. The predictive value of the risk signature was validated using the CGGA and Rembrandt cohorts. Apart from the risk signature, the nomogram performed well at predicting OS in glioma patients. An eRNA-target gene regulatory network was established, which we evaluated using a target gene enrichment analysis. Pathway and gene ontology (GO) analyses demonstrated that the risk signature is associated with mRNA processing and spliceosome in glioma. Furthermore, we found that hub eRNAs potentially regulate the expressions of numerous splicing factors, such as MOV10 and SEC31B, and are correlated with prognosis-associated alteration splicing (AS). In conclusion, we established a risk signature that comprises three eRNAs, which can accurately be utilized as targets to predict prognosis in glioma patients.

Downregulation of enhancer RNA EMX2OS is associated with poor prognosis in kidney renal clear cell carcinoma

Enhancer RNAs are a subclass of long non-coding RNAs transcribed from enhancer regions that play an important role in the transcriptional regulation of genes. However, their role in kidney renal clear cell carcinoma (KIRC) is largely unknown. Herein, we identified the key enhancer RNAs in KIRC via an integrated data analysis method. Gene expression profiles and clinical data of KIRC and 32 other cancer types were acquired using the University of California Santa Cruz Xena platform. Reported enhancer RNAs and genes regulated by them were selected as putative enhancer RNA-target pairs. Kaplan-Meier survival and correlation analyses were performed to identify the key enhancer RNAs. Finally, EMX2OS was identified as the enhancer RNA most associated with survival, with EMX2 as its target. EMX2OS downregulation was significantly associated with higher histological grade, advanced stage, and poorer prognosis. The results were validated in pan-cancer data from The Cancer Genome Atlas and RT-qPCR analysis of 12 pairs of KIRC and normal real-world samples. Functional enrichment analysis indicated that several metabolism-associated signaling pathways were enriched. This study demonstrated that EMX2OS is a key metabolism-associated enhancer RNA in KIRC with a favorable impact on survival and may be a novel therapeutic target in KIRC.

Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma

Background

Immune-related genes is closely related to the occurrence and prognosis of head and neck squamous cell carcinoma (HNSCC). At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. The prognosis of HNSCC is still poor currently, and it may be effective to predict the clinical outcome of HNSCC by immunogenic analysis.

Methods

RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), the MINiML format GSE65858 chip expression data was downloaded from NCBI, and immune-related genes was downloaded from the InnateDB database. Immune-related genes in 519 HNSC patients were integrated from TCGA dataset. By using multivariate COX analysis and Lasso regression, robust immune-related gene pairs (IRGPs) that predict clinical outcomes of HNSCC were identified. Finally, a risk prognostic model related to immune gene pair was established and verified by clinical features, test sets and GEO external validation set.

Results

A total of 699 IRGPs were significantly correlated with the prognosis of HNSCC patients. Fourteen robust IRGPs were finally obtained by Lasso regression and a prognostic risk prediction model was constructed. Risk score of each sample were calculated based on Risk models and divided into the high-risk group (Risk-H) and low Risk group (Risk-L). Risk models were able to stratify the risk in patients with TNM Stage, Age, gender, and smoking history, and the AUC > 0.65 in training set and test set, shows that 14-IRGPs signature in patients with HNSCC has excellent classification performance. In addition, 14-IRGPs had the highest average C index compared with the prognostic characteristics and T, N, and Age of the 3 previously reported HNSCC.

Conclusion

This study constructed 14-IRGPs as a novel prognostic marker for predicting survival in HNSCC patients.

Expression and prognostic value of Chromobox family members in gastric cancer

Background

The Chromobox (CBX) protein family, which is a crucial part of the epigenetic regulatory complex, plays an important role in the occurrence and development of cancer; however, the function and prognostic value of CBX family members in gastric cancer is not clear.

Methods

we investigated the relationship between CBX members and gastric cancer using a range of tools and databases: Oncomine, Kaplan-Meier plotter, cBioPortal, ULCAN, Metascape, and GEPIA.

Results

The results showed that, relative to normal gastric tissue, mRNA expression levels of CBX1-6 were significantly higher in gastric cancer tissue, whereas the level of CBX7 was significantly lower. Furthermore, overexpression of CBX3-6 and underexpression of CBX7 mRNAs was significantly related to the poor prognosis and survival of gastric cancer patients, making these CBX family members useful biomarkers. Finally, overexpression of CBX1 mRNA was significantly related to the poor prognosis of gastric cancer patients treated with adjuvant 5-fluorouracil-based chemotherapy.

Conclusions

The members of the CBX family can be used as prognosis and survival biomarkers for gastric cancer and CBX1 may be a biomarker for choosing the chemotherapy regimen of gastric cancer patients.

Enhancer RNAs in cancer: regulation, mechanisms and therapeutic potential

Enhancers are distal genomic elements critical for gene regulation and cell identify control during development and diseases. Many human cancers were found to associate with enhancer malfunction, due to genetic and epigenetic alterations, which in some cases directly drive tumour growth. Conventionally, enhancers are known to provide DNA binding motifs to recruit transcription factors (TFs) and to control target genes. However, recent progress found that most, if not all, active enhancers pervasively transcribe noncoding RNAs that are referred to as enhancer RNAs (eRNAs). Increasing evidence points to functional roles of at least a subset of eRNAs in gene regulation in both normal and cancer cells, adding new insights into the action mechanisms of enhancers. eRNA expression was observed to be widespread but also specific to tumour types and individual patients, serving as opportunities to exploit them as potential diagnosis markers or therapeutic targets. In this review, we discuss the brief history of eRNA research, their functional mechanisms and importance in cancer gene regulation, as well as their therapeutic and diagnostic values in cancer. We propose that further studies of eRNAs in cancer will offer a promising 'eRNA targeted therapy' for human cancer intervention.

Diversity and Emerging Roles of Enhancer RNA in Regulation of Gene Expression and Cell Fate

Enhancers are cis-regulatory elements in the genome that cooperate with promoters to control target gene transcription. Unlike promoters, enhancers are not necessarily adjacent to target genes and can exert their functions regardless of enhancer orientations, positions and spatial segregations from target genes. Thus, for a long time, the question as to how enhancers act in a temporal and spatial manner attracted considerable attention. The recent discovery that enhancers are also abundantly transcribed raises interesting questions about the exact roles of enhancer RNA (eRNA) in gene regulation. In this review, we highlight the process of enhancer transcription and the diverse features of eRNA. We review eRNA functions, which include enhancer-promoter looping, chromatin modifying, and transcription regulating. As eRNA are transcribed from active enhancers, they exhibit tissue and lineage specificity, and serve as markers of cell state and function. Finally, we discuss the unique relationship between eRNA and super enhancers in phase separation wherein eRNA may contribute significantly to cell fate decisions.