Key lncRNAs Research Articles

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction.

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment. Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD. To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment. Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package. Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment. We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Predictive significance of glycolysis-associated lncRNA profiles in colorectal cancer progression

BackgroundThe Warburg effect is a hallmark characteristic of colorectal cancer (CRC). Despite extensive research, the role of long non-coding RNAs (lncRNAs) in influencing the Warburg effect remains incompletely understood. Our study aims to identify lncRNAs that may modulate the Warburg effect by functioning as competing endogenous RNAs (ceRNAs).MethodsUtilizing bioinformatics approaches, we extracted glycolysis-associated gene data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and identified 101 glycolysis-related lncRNAs in CRC. We employed Univariable Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and Multivariable Cox regression to develop a prognostic model comprising four glycolysis-linked lncRNAs. We then constructed a prognostic nomogram integrating this lncRNA model with other relevant clinical parameters.ResultsThe prognostic efficacy of our four-lncRNA signature and its associated nomogram was validated in both training and validation cohorts. Functional assays demonstrated significant glycolysis and hexokinase II (HK2) inhibition following the silencing of RUNDC3A − AS1, a key lncRNA in our prognostic signature, highlighting its regulatory importance in the Warburg effect.ConclusionsOur research illuminates the critical role of glycolysis-centric lncRNAs in CRC. The developed prognostic model and nomogram underscore the pivotal prognostic and regulatory significance of the lncRNA RUNDC3A − AS1 in the Warburg effect in colorectal cancer.

LINC317.5 as a novel biomarker for hypertriglyceridemia in abnormal glucose metabolism

The global rise in prediabetes and diabetes, with type 2 diabetes (T2DM) being predominant, highlights the association between T2DM and hypertriglyceridemia (HTG). Patients with both abnormal glucose levels and HTG require increased attention due to higher risks of complications and mortality. Therefore, this study aimed to find the key long non-coding RNA (lncRNA) of HTG in the abnormal glucose metabolism patients. We collected blood samples for RNA sequencing experiments and blood samples for validation in population. We have conducted RNA sequencing, weighted gene co-expression network analysis (WGCNA), quantitative real‐time polymerase chain reaction (qRT-PCR) in a 82-vs-82-sample-size population and insulin induced HepG2, RNA- Fluorescence in situ hybridization (FISH) and Cell Counting Kit-8 (CCK-8). We also explored lipid metabolism related transcription factor and the related protein expression and processed key lncRNA by both interference expression and overexpression, and the related consequences were rescued by its target mRNA. ENST00000540317.5 (LINC317.5) was lower in HTG with abnormal glucose metabolism and was found in both cytoplasm and nucleus in HepG2, inversely regulating the accumulation of TG and its target mRNA TKFC. Relative expression of peroxisome proliferator-activated receptor alpha (PPARα) and peroxisome proliferator-activated receptor gamma (PPARγ) were decreasing, and SREBP-1c (sterol regulatory element-binding protein-1c) was increasing of the interference expression of LINC317.5. Interference expression of LINC317.5 significantly decreased the protein expression of ACADM and CPT1A, whereas increased the protein expression of FAS and ACC1. TKFC partly reduced the triglyceride (TG) accumulation of LINC317.5. In conclusion, we suggested LINC317.5-TKFC as a key for TG accumulation in the HepG2-insulin resistant (IR). These might provide information of non-invasive biomarkers for the HTG with abnormal glucose.

Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer.

Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.

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.

Loss of lncRNA LINC01056 leads to sorafenib resistance in HCC

Background and aimsSorafenib is a major nonsurgical option for patients with advanced hepatocellular carcinoma (HCC); however, its clinical efficacy is largely undermined by the acquisition of resistance. The aim of this study was to identify the key lncRNA involved in the regulation of the sorafenib response in HCC.Materials and methodsA clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) single-guide RNA (sgRNA) synergistic activation mediator (SAM)-pooled lncRNA library was applied to screen for the key lncRNA regulated by sorafenib treatment. The role of the identified lncRNA in mediating the sorafenib response in HCC was examined in vitro and in vivo. The underlying mechanism was delineated by proteomic analysis. The clinical significance of the expression of the identified lncRNA was evaluated by multiplex immunostaining on a human HCC microtissue array.ResultsCRISPR/Cas9 lncRNA library screening revealed that Linc01056 was among the most downregulated lncRNAs in sorafenib-resistant HCC cells. Knockdown of Linc01056 reduced the sensitivity of HCC cells to sorafenib, suppressing apoptosis in vitro and promoting tumour growth in mice in vivo. Proteomic analysis revealed that Linc01056 knockdown in sorafenib-treated HCC cells induced genes related to fatty acid oxidation (FAO) while repressing glycolysis-associated genes, leading to a metabolic switch favouring higher intracellular energy production. FAO inhibition in HCC cells with Linc01056 knockdown significantly restored sensitivity to sorafenib. Mechanistically, we determined that PPARα is the critical molecule governing the metabolic switch upon Linc01056 knockdown in HCC cells and indeed, PPARα inhibition restored the sorafenib response in HCC cells in vitro and HCC tumours in vivo. Clinically, Linc01056 expression predicted optimal overall and progression-free survival outcomes in HCC patients and predicted a better sorafenib response. Linc01056 expression indicated a low FAO level in HCC.ConclusionOur study identified Linc01056 as a critical epigenetic regulator and potential therapeutic target in the regulation of the sorafenib response in HCC.

Whole transcriptome mapping reveals the lncRNA regulatory network of TFP5 treatment in diabetic nephropathy.

TFP5 is a Cdk5 inhibitor peptide, which could restore insulin production. However, the role of TFP5 in diabetic nephropathy (DN) is still unclear. This study aims to characterize the transcriptome profiles of mRNA and lncRNA in TFP5-treated DN mice to mine key lncRNAs associated with TFP5 efficacy. We evaluated the role of TFP5 in DN pathology and performed RNA sequencing in C57BL/6Jcontrol mice, C57BL/6J db/db model mice, and TFP5 treatment C57BL/6J db/db model mice. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were analyzed. WGCNA was used to screen hub-gene of TFP5 in treatment of DN. Our results showed that TFP5 therapy ameliorated renal tubular injury in DN mice. In addition, compared with the control group, the expression profile of lncRNAs in the model group was significantly disordered, while TFP5 alleviated the abnormal expression of lncRNAs. A total of 67 DElncRNAs shared among the three groups, 39 DElncRNAs showed a trend of increasing in the DN group and decreasing after TFP treatment, while the remaining 28 showed the opposite trend. DElncRNAs were enriched in glycosphingolipid biosynthesis signaling pathways, NF-κB signaling pathways, and complement activation signaling pathways. There were 1028 up-regulated and 1117 down-regulated DEmRNAs inthe model group compared to control group, and 123 up-regulated and 153 down-regulated DEmRNAs in theTFP5 group compared to themodel group. The DEmRNAs were involved in PPAR and MAPK signaling pathway. We confirmed that MSTRG.28304.1 is a key DElncRNA for TFP5 treatment of DN. TFP5 ameliorated DN maybe by inhibiting MSTRG.28304.1 through regulating the insulin resistance and PPAR signaling pathway. The qRT-PCR results confirmed the reliability of the sequencing data through verifying the expression of ENSMUST00000211209, MSTRG.31814.5, MSTRG.28304.1, and MSTRG.45642.14. Overall, the present study provides novel insights into molecular mechanisms of TFP5 treatment in DN.

Identification of key lncRNAs in age-related macular degeneration through integrated bioinformatics and experimental validation.

This study aimed to identify key long noncoding RNAs (lncRNAs) in age-related macular degeneration (AMD) patients and to identify relevant pathological mechanisms of AMD development. We identified 407 differentially expressed mRNAs and 429 differentially expressed lncRNAs in retinal pigment epithelium (RPE) and retina in the macular region of AMD patients versus controls (P < 0.05 and |log2FC| > 0.585) from GSE135092. A total of 14 key differentially expressed mRNAs were obtained through external data validation from GSE115828. A miRNA-mRNA and miRNA-lncRNA network containing 52 lncRNA nodes, 49 miRNA nodes, 14 mRNA nodes and 351 edges was constructed via integrated analysis of these components. Finally, the LINC00276-miR-619-5p-IFIT3 axis was identified via protein-protein network analysis. In the t-BH-induced ARPE-19 senescent cell model, LINC00276 and IFIT3 were downregulated. Overexpression of LINC00276 could accelerate cell migration in combination with IFIT3 upregulation. This compelling finding suggests that LINC00276 plays an influential role in the progression of AMD, potentially through modulating senescence processes, thereby setting a foundation for future investigative efforts to verify this relationship.

Identification of Key lncRNAs in Gout Under Copper Death and Iron Death Mechanisms: A Study Based on ceRNA Network Analysis and Random Forest Algorithm.

This study focused on identifying potential key lncRNAs associated with gout under the mechanisms of copper death and iron death through ceRNA network analysis and Random Forest (RF) algorithm, which aimed to provide new insights into the molecular mechanisms of gout, and potential molecular targets for future therapeutic strategies of gout. Initially, we conducted an in-depth bioinformatics analysis of gout microarray chips to screen the key cuproptosis-related genes (CRGs) and key ferroptosis-related genes (FRGs). Using these data, we constructed a key ceRNA network for gout. Finally, key lncRNAs associated with gout were identified through the RF algorithm combined with ROC curves, and validated using the Comparative Toxicogenomics Database (CTD). We successfully identified NLRP3, LIPT1, and DBT as key CRGs associated with gout, and G6PD, PRKAA1, LIG3, PHF21A, KLF2, PGRMC1, JUN, PANX2, and AR as key FRGs associated with gout. The key ceRNA network identified four downregulated key lncRNAs (SEPSECS-AS1, LINC01054, REV3L-IT1, and ZNF883) along with three downregulated mRNAs (DBT, AR, and PRKAA1) based on the ceRNA theory. According to CTD validation inference scores and biological functions of target mRNAs, we identified a potential gout-associated lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis. This study identified the key lncRNA ZNF883 in the context of copper death and iron death mechanisms related to gout for the first time through the application of ceRNA network analysis and the RF algorithm, thereby filling a research gap in this field and providing new insights into the molecular mechanisms of gout. We further found that lncRNA ZNF883 might function in gout patients by regulating PRKAA1, the mechanism of which was potentially related to uric acid reabsorption in the proximal renal tubules and inflammation regulation. The proposed lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis might represent a potential RNA regulatory pathway for controlling the progression of gout disease. This discovery offered new molecular targets for the treatment of gout, and had significant implications for future therapeutic strategies in managing the gout.

Construction of LncRNA-mediated CeRNA network for investigating the immune pathogenesis of myocardial infarction.

Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. However, an immune-related competitive endogenous RNA (ceRNA) network has not been reported in MI. The GSE66360, GSE19339, GSE97320, GSE61741, and GSE168281 datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) from MI patients and healthy controls were screened and an immune-related ceRNA network was constructed. Furthermore, the key long noncoding RNAs(lncRNAs) highly related to the immune mechanism of MI were identified utilizing the random walk with restart algorithm. Finally, the expression of the hub genes was further verified in the GSE66360, GSE19339, and GSE97320 datasets, and quantitativereal-time polymerase chain reaction (qRT-PCR) was performed for the MI patients and healthy controls. A total of 184 differentially expressed immune-related genes(DE-IRGs) and 432 DE-miRNAs were obtained, and an immune-related ceRNA network comprising 1421 lncRNAs, 61 DE-miRNAs, and 139 DE-IRGs was constructed. According to the order of stress, betweenness, and closeness, NEAT1, KCNQ1OT1, and XIST were identified as key lncRNAs. Moreover, random walk with restart analysis also suggested that NEAT1, KCNQ1OT1, and XIST are key lncRNAs. Subsequently, a ceRNA network of 10 hub genes and 3 lncRNAs was constructed. Finally, we found that the expression of FCER1G and TYROBP significantly differed between MI patients and control individuals in the GSE66360, GSE19339, and GSE97320 datasets. qRT-PCR revealed that the expression of NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP was significantly elevated in MI tissue samples compared to healthy control tissue samples. NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP are involved in MI and can be used as molecular biomarkers for the screening and diagnosis of MI. Furthermore, the immune system plays an essential role in the onset and progression of MI.

Integrative analysis of cuproptosis-related lncRNAs: Unveiling prognostic significance, immune microenvironment, and copper-induced mechanisms in prostate cancer

BackgroundLong non-coding ribonucleic acids (lncRNAs) regulate messenger RNA (mRNA) expression and influence cancer development and progression. Cuproptosis, a newly discovered form of cell death, plays an important role in cancer. Nonetheless, additional research investigating the association between cuproptosis-related lncRNAs and prostate cancer (PCa) prognosis is required. MethodsSequencing data and copy number variant data were obtained from 492 patients with PCa from The Cancer Genome Atlas (TCGA) Program. Prognostic models of PCa based on cuproptosis-related lncRNAs were constructed using a multi-level attention graph neural network (MLA-GNN) deep learning algorithm. Immune escape scoring was performed using Tumor Immune Dysfunction and Exclusion. Cellular experiments were conducted to explore the correlation between key lncRNAs and cuproptosis. ResultsData from 492 patients with PCa were randomized into two groups at a 1:1 ratio. Prognostic modeling was successfully established using MLA-GNN. Survival analysis suggested that patients could be divided into high- and low-risk groups according to model scores and that there was a significant difference in disease-free survival (DFS) (P < 0.01). The area under the receiver operating characteristic (ROC) curve (AUC) indicated a strong predictive performance for the model, with AUCs of 0.913, 0.847, and 0.863 for the training group and 0.815, 0.907, and 0.866 for the test group at 12, 36, and 60 months, respectively. The immune escape score and immune microenvironment analysis suggested that the high-risk group corresponded to a stronger immune escape and a poorer immune microenvironment (P < 0.05). Cellular experiments revealed that the expression of all six key lncRNAs was upregulated in the presence of copper ion carriers (P < 0.05). ConclusionsThis study identified cuproptosis-related lncRNAs that were strongly associated with PCa prognosis. Key lncRNAs could affect copper metabolism and may serve as new therapeutic targets.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis

BackgroundCuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear. MethodWe obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages. Cuproptosis-related genes were manually curated, and differentially expressed cuproptosis-related genes (DECuGs) were identified. Afterwards, we applied enrichment analysis and identified key DECuGs by performing machine learning techniques. Then, the immune cell infiltrations and correlation between DECuGs and immunocyte features were created by the CIBERSORT algorithm. Subsequently, unsupervised hierarchical clustering analysis was performed based on key DECuGs. We then constructed a ceRNA network based on key DECuGs by using multi-step computational strategies and predicted potential drugs in the DrugBank database. Finally, the role of these key genes in immune cells was validated at the single-cell RNA level between septic patients and healthy controls. ResultsOverall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples. Afterwards, we obtained six key DECuGs by performing machine learning. Then, the LIPT1-T-cell CD4 memory resting was the most positively correlated DECuG–immunocyte pair. Subsequently, two different subclusters were identified by six DECuGs. Bioinformatics analysis revealed that there were different immune characteristics between the two subclusters. Moreover, we identified the key lncRNA OIP5-AS1 within the ceRNA network and obtained 4 drugs that may represent novel drugs for sepsis. Finally, these key DECuGs were statistically significantly dysregulated in another validation set and showed a major distribution in monocytes, T cells, B cells, NK cells and platelets at the single-cell RNA level. ConclusionThese findings suggest that cuproptosis might promote the progression of sepsis by affecting the immune system and metabolic dysfunction, which provides a new direction for understanding potential pathogenic processes and therapeutic targets in sepsis.

Bioinformatics-Based Screening of Key LncRNAs for Modulating the Transcriptome Associated with Glaucoma in Human Trabecular Meshwork Cells.

The morphology and functions of the human trabecular meshwork (HTM) are dysregulated in glaucoma, and the molecular mechanisms of this dysregulation remain unknown. According to an established in vitro model, whose function was to study the regulatory networks sustaining the response of HTM cells to the increased substrate stiffness, we systematically analyzed the expression pattern of long noncoding RNAs (lncRNAs), the important regulatory RNAs in cells. Bioinformatics analysis was performed to identify the dysregulated lncRNAs in response to increased substrate stiffness using transcriptome sequencing data (RNA-seq). Then we interfered with the expression of several dysregulated lncRNAs in HTM cells to explore their molecular targets. The cross-linking immunoprecipitation and sequencing method (CLIP-seq) was used to identify enhancer of zeste homolog 2 (EZH2)-targeted RNAs in HTM cells. The chromatin IP and sequencing method (ChIP-seq) was used to identify the targets of EZH2 and histone H3 at lysine 27 (H3K27me3). The response of thousands of dysregulated lncRNAs to increased substrate stiffness was identified through RNA-seq. Functional prediction of these lncRNAs revealed that they potentially regulated key biological processes, including extracellular matrix (ECM) organization. By interfering with the expression of lncRNA SHNG8, ZFHX4-AS1, and RP11-552M11.4, the results demonstrated that those lncRNAs extensively regulated the expression levels of ECM-associated genes. Moreover, we found that EZH2 expression was significantly decreased at high substrate stiffness. Using CLIP-seq to identify EZH2-targeted RNAs in HTM cells, we found that SNHG8 was bound by EZH2. According to the CLIP-seq data of EZH2, we found that EZH2 binding sites were observed in the transcripts of SNHG8-regulated genes, but not in the ChIP-seq results of EZH2 and H3K27me3. Our results suggest that SNHG8 and EZH2 may cooperate to regulate the expression of a subset of genes by influencing their RNA abundance, explaining how they support HTM cell morphology and high density. This study contributes to the understanding of the alteration of HTM during the progression of glaucoma by identifying functional lncRNAs, especially SNHG8, and suggests novel therapeutic targets to treat glaucoma.

Analysis of the lncRNA-miRNA-mRNA network to explore the regulation mechanism in human traumatic brain injury.

Traumatic brain injury (TBI) refers to brain dysfunction with or without traumatic structural injury induced by an external force. Nevertheless, the molecular mechanism of TBI remains undefined. Differentially expressed (DE) lncRNAs, DEmRNAs and DEmiRNAs were selected between human TBI tissues and the adjacent histologically normal tissue by high-throughput sequencing. Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis of overlapping DEmRNAs between predicted mRNAs of DEmiRNAs and DEmRNAs. The competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA was established in light of the ceRNA theory. In the ceRNA network, the key lncRNAs were screened out. Then key lncRNAs related ceRNA subnetwork was constructed. After that, qRT-PCR was applied to validate the expression levels of hub genes. 114 DElncRNAs, 1807 DEmRNAs and 6 DEmiRNAs were DE in TBI. The TBI-related ceRNA network was built with 73 lncRNA nodes, 81 mRNA nodes and 6 miRNAs. According to topological analysis, two hub lncRNAs (ENST00000562897 and ENST00000640877) were selected to construct the ceRNA subnetwork. Subsequently, key lncRNA-miRNA-mRNA regulatory axes constructed by two lncRNAs including ENST00000562897 and ENST00000640877, two miRNAs including miR-6721-5p and miR-129-1-3p, two mRNAs including ketohexokinase (KHK) and cyclic nucleotide-gated channel beta1 (CNGB1), were identified. Furthermore, qRT-PCR results displayed that the expression of ENST00000562897, KHK and CNGB1 were significantly decreased in TBI, while the miR-6721-5p expression levels were markedly increased in TBI. The results of our study reveal a new insight into understanding the ceRNA regulation mechanism in TBI and select key lncRNA-miRNA-mRNA axes for prevention and treatment of TBI.

LncRNA PRBC induces autophagy to promote breast cancer progression through modulating PABPC1-mediated mRNA stabilization.

Breast cancer is one of the major malignant tumors among women worldwide. Long noncoding RNAs (lncRNAs) have been documented as significant modulators in the development and progression of various cancers; however, the contribution of lncRNAs to breast cancer remains largely unknown. In this study, we found a novel lncRNA (NONHSAT137675) whose expression was significantly increased in the breast cancer tissues. We named the novel lncRNA as lncRNA PRBC (PABPC1-related lncRNA in breast cancer) and identified it as a key lncRNA associated with breast cancer progression and prognosis. Functional analysis displayed that lncRNA PRBC could promote autophagy and progression of breast cancer. Mechanistically, we verified that lncRNA PRBC physically interacted with PABPC1 through RIP assay, and PABPC1 overexpression could reverse the inhibiting effect of lncRNA PRBC knockdown on the malignant behaviors in breast cancer cells. Knockdown of lncRNA PRBC interfered the translocation of PABPC1 from nucleus to cytoplasm as indicated by western blot and IF assays. Significantly, the cytoplasmic location of PABPC1 was required for the interaction between PABPC1 and AGO2, which could be enhanced by lncRNA PRBC overexpression, leading to strengthened recruitment of mRNA to RNA-induced silencing complex (RISC) and thus reinforcing the inhibition efficiency of miRNAs. In general, lncRNA PRBC played a critical role in malignant progression of breast cancer by inducing the cytoplasmic translocation of PABPC1 to further regulate the function of downstream miRNAs. This study provides novel insight on the molecular mechanism of breast cancer progression, and lncRNA PRBC might be a promising therapeutic target and prognostic predictor for breast cancer.

Whole transcriptome sequencing identifies key lncRNAs,circRNAs, and mRNAs for exploring the pathogenesis and therapeutic target of mouse pneumoconiosis

BackgroundPneumoconiosis is a kind of lung dysfunction caused by the inhalation of mineral dust. However, the potential molecular mechanism of pneumoconiosis have not been fully elucidated. MethodsIn this study, the silica-treated pneumoconiosis mice model was constructed and the transcriptome sequencing data including lncRNA, circRNA, and mRNA were obtained. Firstly, differentially expressed lncRNA, circRNA, and mRNA (DElncRNA, DEcircRNA, DEGs) between control and pneumoconiosis/silicosis samples were screened, the target miRNAs (co-pre-miRNAs) were obtained by intersecting the miRNAs predicted by DElncRNA and DEcircRNA, respectively, and the target mRNAs (co-mRNA) were obtained by intersecting the mRNAs predicted by target miRNA and DEGs. Then, the lncRNA/circRNA-miRNA-mRNA networks were constructed by Cytoscape. Next, the key mRNAs were obtained by protein–protein interaction (PPI) analysis, and the key lncRNAs/circRNAs were selected by correlation analysis. Moreover, the expression of the key lncRNAs, circRNAs and mRNAs on chromosome were studied by the “circlize” package. Furthermore, the TFs-miRNA-mRNA network was constructed and the function of DEGs were explored by Ingenuity Pathway Analysis (IPA). To demonstrate the feasibility and value of the constructed ceRNA networks, we validated key genes and mmu-miR-682 pathway. Finally, We used the Drug-Gene Interaction database to predict potential drugs that could interfere with key genes,which may help to find promising treatment. ResultsThere were 427 DElncRNAs, 107 DEcircRNAs and 1,597 DEGs between silicosis and control groups. Totals of 77 co-pre-miRNAs and 96 co-mRNA were screened, and the lncRNA/circRNA-miRNA-mRNA networks were constructed with 27 lncRNA/25 circRNAs, 74 miRNAs and 96 mRNAs. Then, 6 key mRNAs including Igf1, Klf4, Ptgs2, Epas1, Gnao1, and Il1a were obtained by PPI, and all of these key mRNAs and 10 key lncRNAs and 8 circRNAs were significantly different between the pneumoconiosis and normal groups, in which 10 lncRNAs and 9 circRNA that have not been previously studied in pneumoconiosis/silicosis can be used as new potential therapeutic targets. Moreover, the TFs-miRNA-mRNA network were constructed with 11 TFs, 1 key miRNA (mmu-miR-682) and 3 key mRNAs (Igf1, Epas1, Ptgs2). And the validation of key genes revealing by RNA-seq through experimental approaches shows the the predictive power of this study. Finally, IPA results indicated that 41 pathways were activated and 2 pathways were suppressed in pneumoconiosis/silicosis groups, and Pathogen Induced Cytokine Storm Signaling Pathway was the most significant pathway affected by pneumoconiosis/silicosis. In addition, 93 drugs were screened out by Drug-Gene Interaction database. Among them, Hydroxychloroquine was a kind of drug which associated with Il1a and Ptgs2, may be a promising treatment. ConclusionThis study constructed the lncRNA/circRNA-miRNA-mRNA and TFs-miRNA-mRNA networks, which could deepen the potential molecular regulatory mechanism of pneumoconiosis/silicosis.

Ferroptosis‐Related LncRNA BCRP3 Promotes the Proliferation and Migration of Prostate Cancer

Background: Long noncoding RNAs (lncRNAs) are reported that play an important role in regulating tumorigenesis. This study aims to develop a ferroptosis‐related lncRNA gene signature for predicting biochemical recurrence of prostate cancer (PCa) and prove a functional lncRNA BCRP3 as a promotor toward PCa progression.Methods: The ferroptosis‐related lncRNA were identified by interoperating the databases of The Cancer Genome Atlas (TCGA) and FerrDb. A predictive model for biochemical recurrence of PCa was established based on the LASSO Cox regression. Kaplan–Meier cures were applied in the measurement of the impact on patients’ survival caused by key lncRNAs. Meanwhile, the molecular assays of CCK8, EdU, wound healing, and Transwell were conducted to evaluate the tumor‐suppressive effect of BCRP3 in vitro.Results: This study presented that 17 differentially expressed ferroptosis‐related lncRNAs were confirmed and further screened out 9 key lncRNAs for the establishment of risk model. The nomogram involved the risk model was also proved with an excellent predictive performance toward 1‐, 3‐, and 5‐year survival with the area of curves (AUC) as 0.77, 0.79, and 0.65 separately. Notably, the lncRNA BCRP3 was significantly correlated with the survival of PCa patients based on multivariate Cox regression. Additionally, downregulation of BCRP3 effectively inhibited the proliferation, migration, and invasion of PC3 cells which further proved its anti‐tumor function.Conclusion: We developed a ferroptosis‐related lncRNA risk model for predicting biochemical recurrence, which assisting in the clinical therapy of patients with PCa.

M6A Modification and Transcription Analysis of LncRNA in Cerebral Ischemia/Reperfusion Injury

LncRNA is a major factor in the occurrence and development of many diseases. However, its mechanism in cerebral ischemia/reperfusion injury (CIRI) is yet unknown. In this study, the transcriptional level and methylation modification level of LncRNAs before and after mechanical thrombectomy were compared by high‐throughput sequencing. Venn diagram, Spearman correlation analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, TargetScan, and miRanda were used to analyze the experimental data. The results showed that four key LncRNAs changed at both transcription and methylation levels. Specifically, LncRNA FAR2, LINC02431, and AL357060.1 were downregulated and hypomethylated, while LncRNA FOXD2‐AS1 was upregulated and hypomethylated. Moreover, positive regulation of angiogenesis, protein domain–specific binding, autophagy pathway, PPAR signaling pathway, and MAPK signaling pathway were co‐enriched between LncRNAs with different expression levels and different methylation levels. Finally, a LncRNA‐miRNA‐mRNA network was constructed. Therefore, this study explored the potential key LncRNAs and regulatory mechanisms of CIRI.

Long non-coding RNA signature for predicting gastric cancer survival based on genomic instability.

Gastric cancer is a prevalent type of tumor with a poor prognosis. Given the high occurrence of genomic instability in gastric cancer, it is essential to investigate the prognostic significance of genes associated with genomic instability in this disease. We identified genomic instability-related lncRNAs (GInLncRNAs) by analyzing somatic mutation and transcriptome profiles. We evaluated co-expression and enrichment using various analyses, including univariate COX analysis and LASSO regression. Based on these findings, we established an lncRNA signature associated with genomic instability, which we subsequently assessed for prognostic value, immune cell and checkpoint analysis, drug sensitivity, and external validation. Finally, PCR assay was used to verify the expression of key lncRNAs. Our study resulted in the establishment of a seven-lncRNA prognostic signature, including PTENP1-AS, LINC00163, RP11-169F17.1, C8ORF87, RP11-389G6.3, LINCO1210, and RP11-115H13.1. This signature exhibited independent prognostic value and was associated with specific immune cells and checkpoints in gastric cancer. Additionally, the model was correlated with somatic mutation and several chemotherapeutic drugs. We further confirmed the prognostic value of LINC00163, which was included in our model, in an independent dataset. Our model demonstrated superior performance compared to other models. PCR showed that LINC00163 was significantly up-regulated in 4 adjacent normal tissues compared with the GC tissues. Our study resulted in the establishment of a seven-lncRNA signature associated with genomic instability, which demonstrated robust prognostic value in predicting the prognosis of gastric cancer. The signature also identified potential chemotherapeutic drugs, making it a valuable tool for clinical decision-making and medication use.

Melatonin enhances cadmium tolerance in rice via long non-coding RNA-mediated modulation of cell wall and photosynthesis

In plants, melatonin (MLT) is a versatile signaling molecule involved in promoting plant development and mitigating the damage caused by heavy metal exposure. Long non-coding RNAs (lncRNAs) are essential components in the plant’s response to various abiotic stress, functioning within the gene regulatory network. Here, a hydroponic experiment was performed to explore the involvement of lncRNAs in MLT-mediated amelioration of cadmium (Cd) toxicity in rice plants. The results demonstrated that applying 250 mg L–1 MLT in a solution containing 10 μM Cd leads to an effective reduction of 30.0% in shoot Cd concentration. Remarkably, the treatment resulted in a 21.2% improvement in potassium and calcium uptake, a 164.5% enhancement in net photosynthetic rate, and a 33.2% decrease in malondialdehyde accumulation, resulting increases in plant height, root length, and biomass accumulation. Moreover, a transcriptome analysis revealed 2510 differentially expressed transcripts, including the Cd transporters (–3.82-fold downregulated) and the Cd tolerance-associated genes (1.24-fold upregulated). Notably, regulatory network prediction uncovered 6 differentially expressed lncRNAs that act as competitive endogenous RNA or in RNA complex interactions. These key lncRNAs regulate the expression of target genes that are involved in pectin and cellulose metabolism, scavenging of reactive oxygen species, salicylic acid-mediated defense response, and biosynthesis of brassinosteroids, which ultimately modify the cell wall for Cd adsorption, safeguard photosynthesis, and control hormone signaling to reduce Cd toxicity. Our results unveiled a crucial lncRNA-mediated mechanism underlying MLT’s role in Cd detoxification in rice plants, providing potential applications in agricultural practices and environmental remediation.