lncRNA Biomarkers Research Articles

LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data.

We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing(stRNA-seq)data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including(i) 257 single-cell RNA sequencingandstRNA-seqdatasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1002988 cells and 367971 spatial spots, (iii) 15489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curationand (v) ceRNA expression profiles and follow-up clinical information of 20326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.

MIR4435-2HG: A novel biomarker for triple-negative breast cancer diagnosis and prognosis, activating cancer-associated fibroblasts and driving tumor invasion through EMT associated with JNK/c-Jun and p38 MAPK signaling pathway activation

BackgroundBreast cancer has the highest incidence rate and causes the most fatalities among all female cancers worldwide. Triple-negative breast cancer (TNBC) is known for its strong invasiveness and higher rates of recurrence. In this research, we aimed to identify MIR4435-2HG as a promising long non-coding RNA (lncRNA) biomarker and therapeutic target for TNBC. MethodsUtilizing clinicopathological information and transcriptome data from The Cancer Genome Atlas (TCGA) database, we assessed the clinical relevance of MIR4435-2HG in breast cancer through univariate and multivariate COX regression, receiver operating characteristic (ROC) analysis, as well as Kaplan-Meier survival analysis. To investigate the biological role of MIR4435-2HG in TNBC, we conducted gene set enrichment analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, we constructed and validated a nomogram to predict disease-free survival (DFS). Both the R package “pRRophetic” and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were employed to forecast the sensitivity to different therapeutics between the high- and low-MIR4435-2HG groups. We employed single-cell RNA sequencing analysis and tumor microenvironment infiltration analysis to investigate the potential involvement of MIR4435-2HG in the TNBC tumor microenvironment. Cellular biological behaviors were assessed utilizing CCK-8, transwell assays, and wound-healing assays. Furthermore, we performed RNA-seq, qRT-PCR, and western blotting analyses to elucidate and confirm the specific mechanisms underlying the role of MIR4435-2HG in TNBC. ResultsIn our study, we have identified MIR4435-2HG as a significant diagnostic and prognostic factor for TNBC. We observed that MIR4435-2HG is widely expressed and might have a significant impact on the reshaping of the TNBC tumor microenvironment. Patients with TNBC in the high-MIR4435-2HG group may show reduced sensitivity to cisplatin, doxorubicin, and gemcitabine and have an increased propensity for immune escape. Knockdown of MIR4435-2HG inhibits cancer-associated fibroblasts (CAFs) activation. Notably, MIR4435-2HG predominantly enhances the migratory and invasive capabilities of TNBC cells through the epithelial-mesenchymal transition (EMT) process. Mechanistically, we validated that MIR4435-2HG activates the JNK/c-Jun and p38 non-classical MAPK signaling pathway in TNBC cells. ConclusionsOur findings highlight the significant potential of MIR4435-2HG as a highly promising biomarker for TNBC. Targeting MIR4435-2HG could represent an appealing therapeutic approach for TNBC.

Construction of a potential long noncoding RNA prognostic model involved competitive endogenous RNA for patients with gastric cancer.

Increasing evidence has underscored the role of long noncoding RNAs (lncRNAs) make up the major proportion of the competing endogenous RNAs (ceRNAs) network and can regulate gene expression by competitively binding to miRNAs in the development and progression of tumors. Nevertheless, the role of lncRNA-mediated ceRNAs in gastric cancer (GC) and their regulatory mechanisms have not been elucidated to some extent. This study is aimed at constructing a prognostic risk model for GC based on lncRNAs. A TCGA (The Cancer Genome Atlas) dataset was analyzed using edgeR to identify differentially expressed lncRNAs (DElncRNAs) in GC tissues vs normal tissues. Subsequently, DElncRNAs that could predict GC prognosis were determined using a training set. A prognostic risk model based on the DElncRNAs was then constructed. The performance of the model was tested using a test set. The functions of these lncRNAs in GC were investigated using a lncRNA-miRNA-mRNA network. Analysis of lncRNA expression in 407 TCGA GC cases identified 3 lncRNAs that significantly correlated with prognosis. GC cases with high-risk scores showed markedly poor prognosis relative to those with low-risk scores in both the training and test sets. Univariate and multivariate Cox regression analysis of the relationship between various clinical features and prognosis found that these lncRNAs and stage significantly correlated with GC prognosis. A lncRNA-miRNA-mRNA network based on 3 lncRNAs and functional enrichment analysis of interacting mRNA indicated that these genes are enriched in various intracellular receptor signaling pathways, including regulation of muscle system process, and protein deubiquitylation. The current study provides novel insights into the lncRNA-related ceRNA network in GC and sheds lights on underlying 3 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of GC patients.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Discovery of long non-coding RNA biomarkers for prognosis in pediatric acute lymphoblastic leukemia.

3055 Background: Acute lymphoblastic leukemia (ALL) ranks among the most prevalent pediatric cancers. Molecular testing, including cytogenetics, has been widely used for prognosticating pediatric ALL. However, cytogenetic testing in pediatric ALL is incapable of detecting submicroscopic genetic changes and suffers from variable sensitivity, which may lead to heterogeneous outcomes within cytogenetic subgroups. In addition to conventional prognostic biomarkers, long non-coding RNAs (lncRNAs) are expressed in both a disease- and tissue-specific manner, emerging as promising biomarkers. However, well-established lncRNA prognostic biomarkers for pediatric ALL have yet to be identified. Therefore, in this study, we discovered three singular lncRNA prognostic biomarkers and a prognostic panel composed of multi-lncRNAs for pediatric ALL using our newly developed BAMBI method. Methods: This study utilized RNA-seq data from 561 distinct individuals, comprising 647 blood or bone marrow biospecimens obtained from pediatric patients with ALL from the dbGaP TARGET dataset. Among these individuals with RNA-seq and survival data, 25.8% deceased during follow-up. The average age at enrollment was 8.1 years. Of this cohort, 36.0% were female, and 61.5% were Caucasian white. Initially, our lncRNA identification tool, Flnc (Li Z., et al., Noncoding RNA. 2022), was employed to identify and quantify both novel and known lncRNAs expressed in pediatric ALL. Subsequently, our machine-learning based method, BAMBI, was utilized to discover lncRNAs as new prognostic biomarkers in pediatric ALL. Results: Three lncRNAs (ZBTB47 and NKTR antisense RNA, AC097359.2’s new isoform, and a novel lncRNA near STXBP5) were identified as individual putative biomarkers for prognosis. Each of these three lncRNA biomarkers exhibited predictive power for survival, with accuracy ranging from 68.2% to 72.8% and AUROC scores ranging from 67.7% to 75%. Furthermore, we established three lncRNA biomarker panels composed of these three lncRNAs along with a few additional lncRNAs, which showed improved prediction power with accuracy ranging from 78.3% to 79.6%, and AUROC scores ranging from 82.1% to 85.2%. Conclusions: We discovered both singular lncRNA and panels of multi-lncRNAs as prognostic biomarkers of pediatric ALL patients. These putative lncRNA biomarkers could be further validated through independent cohorts or prospective clinical study designs.

Sperm long non-coding RNAs as markers for ram fertility.

It is critical in sheep farming to accurately estimate ram fertility for maintaining reproductive effectiveness and for production profitability. However, there is currently a lack of reliable biomarkers to estimate semen quality and ram fertility, which is hindering advances in animal science and technology. The objective of this study was to uncover long non-coding RNAs (lncRNAs) in sperm from rams with distinct fertility phenotypes. Mature rams were allocated into two groups: high and low fertility (HF; n = 31; 94.5 ± 2.8%, LF; n = 25; 83.1 ± 5.73%; P = 0.028) according to the pregnancy rates sired by the rams (average pregnancy rate; 89.4 ± 7.2%). Total RNAs were isolated from sperm of the highest- and lowest-fertility rams (n = 4, pregnancy rate; 99.2 ± 1.6%, and 73.6 ± 4.4%, respectively) followed by next-generation sequencing of the transcripts. We uncovered 11,209 lncRNAs from the sperm of rams with HF and LF. In comparison to each other, there were 93 differentially expressed (DE) lncRNAs in sperm from the two distinct fertility phenotypes. Of these, 141 mRNAs were upregulated and 134 were downregulated between HF and LF, respectively. Genes commonly enriched for 9 + 2 motile cilium and sperm flagellum were ABHD2, AK1, CABS1, ROPN1, SEPTIN2, SLIRP, and TEKT3. Moreover, CABS1, CCDC39, CFAP97D1, ROPN1, SLIRP, TEKT3, and TTC12 were commonly enriched in flagellated sperm motility and sperm motility. Differentially expressed mRNAs were enriched in the top 16 KEGG pathways. Targets of the differentially expressed lncRNAs elucidate functions in cis and trans manner using the genetic context of the lncRNA locus, and lncRNA sequences revealed 471 mRNAs targets of 10 lncRNAs. This study illustrates the existence of potential lncRNA biomarkers that can be implemented in analyzing the quality of ram sperm and determining the sperm fertility and is used in breeding soundness exams for precision livestock farming to ensure food security on a global scale.

LncRNA NDUFA6-DT: A Comprehensive Analysis of a Potential LncRNA Biomarker and Its Regulatory Mechanisms in Gliomas.

Gliomas are the most prevalent primary malignant tumors affecting the brain, with high recurrence and mortality rates. Accurate diagnoses and effective treatment challenges persist, emphasizing the need for identifying new biomarkers to guide clinical decisions. Long noncoding RNAs (lncRNAs) hold potential as diagnostic and therapeutic biomarkers in cancer. However, only a limited subset of lncRNAs in gliomas have been explored. Therefore, this study aims to identify lncRNA signatures applicable to patients with gliomas across all grades and explore their clinical significance and potential biological mechanisms. Data used in this study were obtained from TCGA, CGGA, and GEO datasets to identify key lncRNA signatures in gliomas through differential and survival analyses and machine learning algorithms. We examined their associations with the clinical characteristics, gene mutations, diagnosis, and prognosis of gliomas. Functional enrichment analysis was employed to elucidate the potential biological mechanisms associated with these significant lncRNA signatures. We explored competing endogenous RNA (ceRNA) regulatory networks. We found that NDUFA6-DT emerged as a significant lncRNA signature in gliomas, with reduced NDUFA6-DT expression associated with a worse prognosis in gliomas. Nomogram analysis incorporating NDUFA6-DT expression levels exhibited excellent prognostic and predictive capabilities. Functional annotation suggested that NDUFA6-DT might influence immunological responses and synaptic transmission, potentially modifying glioma initiation and progression. The associated ceRNA network revealed the possible presence of the NDUFA6-DT-miR-455-3p-YWHAH/YWHAG axis in low-grade glioma (LGG) and glioblastoma multiforme (GBM), regulating the PI3K-AKT signaling pathway and influencing glioma cell survival and apoptosis. We believe that NDUFA6-DT is a novel lncRNA linked to glioma diagnosis and prognosis, potentially becoming a pivotal biomarker for glioma.

Novel potential lncRNA biomarker in B cells indicates essential pathogenic pathway activation in patients with SLE

ObjectivesSystemic lupus erythematosus (SLE) is a highly heterogeneous disease, and B cell abnormalities play a central role in the pathogenesis of SLE. Long non-coding RNAs (lncRNAs) have also been implicated...

Retracted: Identification of lncRNA Biomarkers and LINC01198 Promotes Progression of Chronic Rhinosinusitis with Nasal Polyps through Sponge miR-6776-5p.

[This retracts the article DOI: 10.1155/2022/9469207.].

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Diagnostic lncRNA high expression for liver patients prognosis and medication guidance: a systematic review and meta-analysis.

The study of long non-coding RNAs (lncRNAs) has gained significant attention due to their roles in regulating gene expression and their potential as diagnostic biomarkers. This systematic review and meta-analysis aimed to evaluate the diagnostic value of high-expression lncRNAs in liver disease patients, including those with hepatitis, cirrhosis, and hepatocellular carcinoma (HCC). A comprehensive literature search was conducted across multiple electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library, up to July 2024. Studies were included if they investigated the expression of lncRNAs in liver disease patients and evaluated their diagnostic performance. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included studies. Pooled sensitivity, specificity, diagnostic odds ratios (DOR), and summary receiver operating characteristic (SROC) curves were calculated using a bivariate random-effects model. Nine studies involving 888 samples were included in the meta-analysis. The pooled hazard ratio (HR) for overall survival (OS) was 2.01 (95% CI: 1.71-2.36), indicating a significant association between high lncRNA expression and poor liver disease outcomes. Subgroup analyses revealed a pooled odds ratio (OR) of 1.99 (95% CI: 1.53-2.60) for tissue samples and 8.62 (95% CI: 1.16-63.71) for blood samples, suggesting a stronger diagnostic value for blood-based lncRNAs. The funnel plots indicated minimal publication bias, and sensitivity analyses confirmed the robustness of the findings. High-expression lncRNAs show significant potential as diagnostic biomarkers for liver diseases, offering non-invasive, accurate, and timely diagnostic information. Despite the promising results, further research is needed to standardize detection methods, elucidate the biological functions of lncRNAs, and validate their clinical utility in diverse patient populations. Integrating lncRNA biomarkers with traditional diagnostic approaches could enhance diagnostic accuracy and improve patient management and outcomes in liver disease.

Identification of three lncRNA-related prognostic signatures in gastric cancer by integrated multi-omics analysis.

Aims: The systematic identification of molecular features correlated with the clinical status of gastric cancer (GC) inpatients is significant, although such investigation remains insufficient. Methods: GC subtyping based on RNA sequencing, copy number variation and DNA methylation data were derived from The Cancer Genome Atlasprogram. Prognostics lncRNA biomarkers for GC were identified by univariate Cox, LASSO and SVM-RFE analysis. Results: Three molecular subtypes with significant survival discrepancies, and their specific DEmRNAs and DElncRNAs were identified. Three reliable prognostic-associated lncRNA, including LINC00670, LINC00452 and LINC00160, were selected for GC. Conclusion: Our findings expanded the understanding on the regulatory network of lncRNAs in GC, providing potential targets for prognosis and treatment of GC patients.

BioKA: a curated and integrated biomarker knowledgebase for animals.

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkersand 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

Identifying immune checkpoint-related lncRNA biomarkers for immunotherapy response and prognosis in cancers

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) in tumor-immune. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here, a multiple-step pipeline was developed to identify cancer- and immune-context ICP and lncRNA cooperative regulation pairs (ICPaLncCRPs) across cancers. Immune-related ICPs and lncRNAs were extracted follow immune cell lines and immunologic constant of rejection groups. ICPaLncCRP networks were constructed, which likely to modulate tumor-immune by specific patterns. Common and specific hub ICPaLncs such as MIR155HG, TRG-AS1 and PCED1B-AS1 maybe play central roles in prognosis and circulating. Moreover, these hub ICPaLncs were significantly correlated with immune cell infiltration based on bulk and single-cell RNA sequencing data. Some ICPaLncCRPs such as IDO1-MIR155HG could predict three- and five-year prognosis of melanoma in two independent datasets. We also validated that some ICPaLncCRPs could effectively predict ICI-response follow six independent datasets. Collectively, this study will enhance our understanding of lncRNA functions and accelerate discovery of lncRNA-based biomarkers in ICI treatment.

Development of Screen Printed Electrode biosensor for the selective and sensitive sensing of the lncRNA biomarker MALAT1 in adenocarcinoma lung cancer by non-noble metal electrocatalyst

Development of Screen Printed Electrode biosensor for the selective and sensitive sensing of the lncRNA biomarker MALAT1 in adenocarcinoma lung cancer by non-noble metal electrocatalyst

Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM.

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross-validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.

Construction of ceRNA network based on RNA-seq for identifying prognostic lncRNA biomarkers in Perthes disease.

Introduction: Legg-Calvé-Perthes disease or Perthes disease is a condition that occurs in children aged 2 to 15years, and is characterized by osteonecrosis of the femoral head, which results in physical limitations. Despite ongoing research, the pathogenesis and molecular mechanisms underlying the development of Perthes disease remain unclear. In order to obtain further insights, the expression patterns of long non-coding RNAs (lncRNAs), miRNAs, and mRNAs in a rabbit model of Perthes disease were analyzed in this study by transcriptome sequencing. Methods and results: The results of RNA-seq analyses revealed that 77 lncRNAs, 239 miRNAs, and 1027 mRNAs were differentially expressed in the rabbit model. This finding suggested that multiple genetic pathways are involved in the development of Perthes disease. A weighted gene co-expression network analysis (WGCNA) network was subsequently constructed using the differentially expressed mRNAs (DEmRNAs), and network analysis revealed that the genes associated with angiogenesis and platelet activation were downregulated, which was consistent with the findings of Perthes disease. A competing endogenous RNA (ceRNA) network was additionally constructed using 29 differentially expressed lncRNAs (including HIF3A and LOC103350994), 28 differentially expressed miRNAs (including ocu-miR-574-5p and ocu-miR-324-3p), and 76 DEmRNAs (including ALOX12 and PTGER2). Disscusion: The results obtained herein provide novel perspectives regarding the pathogenesis and molecular mechanisms underlying the development of Perthes disease. The findings of this study can pave the way for the development of effective therapeutic strategies for Perthes disease in future.

Development of a Novel Pyroptosis-Associated lncRNA Biomarker Signature in Lung Adenocarcinoma.

Pyroptosis is a novel type of cell death observed in various diseases. Our study aimed to investigate the relationship between pyroptosis-associated-long non-coding RNAs (lncRNAs), immune infiltration, and expression of immune checkpoints in the setting of lung adenocarcinoma and the prognostic value of pyroptosis-related lncRNAs. RNA-seq transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) were downloaded, and consensus clustering analysis was used to separate the samples into two groups. Least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk signature. The association between pyroptosis-associated lncRNAs, immune infiltration, and expression of immune checkpoints were analysed. The cBioPortal tool was used to discover genomic alterations. Gene set enrichment analysis (GSEA) was utilized to investigate downstream pathways of the two clusters. Drug sensitivity was also examined. A total of 43 DEGs and 3643 differentially expressed lncRNAs were identified between 497 lung adenocarcinoma tissues and 54 normal samples. A signature consisting of 11 pyroptosis-related lncRNAs was established as prognostic for overall survival. Patients in the low-risk group have a significant overall survival advantage over those in the high-risk group in the training group. Immune checkpoints were expressed differently between the two risk groups. Risk scores were validated to develop an independent prognostic model based on multivariate Cox regression analysis. The area under time-dependent receiver operating characteristic curve (AUC of the ROC) at 1-, 3-, and 5-years measured0.778, 0.757, and 0.735, respectively. The high-risk group was more sensitive to chemotherapeutic drugs than the low-risk group. This study demonstrates the association between pyroptosis-associated lncRNAs and prognosis in lung adenocarcinoma and enables a robust predictive signature of 11 lncRNAs to inform overall survival.

Prediction of disease-related miRNAs by voting with multiple classifiers

There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs.

Abstract 5703: The immune cell state atlas analysis predicts therapeutic benefits with immune checkpoint inhibitors

Abstract Introduction: In this study, we investigated the prognostic role of the immune cell state atlas in predicting therapeutic benefits of patients treated with immune checkpoint inhibitors (ICI) within the ORIEN network of 18 collaborating cancer centers under the Total Cancer Care protocol. Methods: We utilized RNA-seq data of 926 samples generated from 875 individuals. Gene expression data were deconvoluted for immune cell states using the Carcinoma EcoTyper software. We then conducted a series of survival analyses to test the association between survival outcomes and predicted cell types and states in five malignant tumors: Genitourinary (GU), Gastrointestinal (GI), Thoracic (THO), Cutaneous (CUT), Head & Neck (H&N). The regularized Cox regression model in R package ‘glmnet’ was then applied to select the complementary pathway signatures (including gene ontology and KEGG pathways) to the immune cell states in predicting survival outcomes. We also explored the immune-related long non-coding RNAs (lncRNA) as potential biomarkers for cell states and patient outcomes. Results: EcoTyper analysis revealed that 692 (~80%) of patients were assigned to the 10 pre-identified Carcinoma Ecotypes (CE1 to CE10) or cell state atlas group. Overall, two immune deficiency ecotype patient groups (CE1 and CE2) pre-identified based on the independent training data were linked to worse survival, while two proinflammatory ecotype groups (CE9 and CE10) were associated with favorable surxvival. Those ecotype groups showed strong prognostic significance in predicting OS in melanoma and H&N. Meanwhile, CE6, a non-neoplastic tissue enriched cell subtype, was also found to be highly associated with longer OS in H&N and GU. CE7, an age-related mutation patient subgroup, contributed to shorter survival in both melanoma and GI. We also found that a subset of activated B cell state and the exhausted/effector CD4 T cell state were significantly associated with patient survival in melanoma and GU, respectively. The penalized Cox regression model revealed that β-catenin signaling pathway, P53 pathway and heme metabolism in the MSigDB Hallmark gene sets are the most complementary pathways to the ecotype scores in multiple cancer types. In additional, multiple pathways in KEGG such as endocytosis were found to jointly contribute to the ecotype-pathway composite prognostic model. In anazlying immune-related lncRNA biomarkers, we highlighted the prognostic role of NKILA in our dataset, which has been studied to promote tumor immune evasion. Conclusion: Our analysis has successfully established the utility of immune cell state atlas in predicting therapeutic benefits with ICIs. We expect that the discovered complementary signatures in the cancer-cell compartment will also lead to a novel spectrum of tumor-based biomarkers to ICI. Citation Format: Tingyi Li, Vineeth Sukrithan, Aakrosh Ratan, Martin McCarter, John Carpten, Howard Colman, Alexandra P. Ikeguchi, Igor Puzanov, Susanne Arnold, Michelle Churchman, Patrick Hwu, Paulo C. Rodriguez, William S. Dalton, George J. Weiner, Ahmad Tarhini, Xuefeng Wang. The immune cell state atlas analysis predicts therapeutic benefits with immune checkpoint inhibitors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5703.