Key lncRNAs Research Articles

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.

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.

Microarray analysis of lncRNAs and mRNAs in spinal cord contusion rats with iPSC-derived A2B5+ oligodendrocyte precursor cells transplantation

Spinal cord injury (SCI) is a severe complication of spinal trauma with high disability and mortality rates. Effective therapeutic methods to alleviate neurobehavioural deficits in patients with SCI are still lacking. In this study, we established a spinal cord contusion (SCC) model in adult Sprague Dawley rats. Induced pluripotent stem cell-derived A2B5+ oligodendrocyte precursor cells (iP-A2B5+OPCs) were obtained from mouse embryonic fibroblasts and injected into the lesion sites of SCC rats. Serological testing and magnetic resonance imaging were employed to determine the effect of iP-A2B5+OPCs cell therapy. The Basso-Beattie-Bresnahan score and inclined plane test were performed on days 1, 3, 7, and 14 after cell transplantation, respectively. Differentially expressed long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) were detected by microarray analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to analyse the biological functions of these lncRNAs and mRNAs. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify variations in the expression of crucial target genes. The results demonstrated that induced pluripotent stem cells exhibited embryonic stem cell-like morphology and could differentiate into diverse neural cells dominated by oligodendrocytes. The neurobehavioural performance of rats treated with iP-A2B5+OPCs transplantation was better than that of rats with SCC without cell transplantation. Notably, we found that 22 lncRNAs and 42 mRNAs were concurrently altered after cell transplantation, and the key lncRNA (NR_037671) and target gene (Cntnap5a) were identified in the iP-A2B5+OPCs group. Moreover, RT-qPCR revealed that iP-A2B5+OPCs transplantation reversed the downregulation of NR_037671 induced by SCC. Our findings indicated that iP-A2B5+OPCs transplantation effectively improves neurological function recovery after SCC, and the mechanism might be related to alterations in the expression of lncRNAs and mRNAs, such as NR_037671 and Cntnap5a.

Key Non-coding Variants in Three Neuroapoptosis and Neuroinflammation-Related LncRNAs Are Protectively Associated with Susceptibility to Parkinson's Disease and Some of Its Clinical Features.

Research findings show that genetic susceptibility to sporadic Parkinson's disease (PD), a common neurodegenerative disorder, is determined through gene variation of loci involved in its development and pathogenesis. A growing body of strong evidence has revealed that dysfunction of long non-coding RNAs (lncRNAs) plays key roles in the pathogenesis and progression of PD through impairing neuronal signaling pathways, but little is known about the relationship between their variants and PD susceptibility. In this research, we intended to study the relationship between functional SNPs rs12826786C>T, rs3200401C>T, and rs6931097G>A in the key lncRNAs stimulating neuroapoptosis and neuroinflammation in PD, including HOTAIR, MALAT1, and lincRNA-P21, respectively, with susceptibility to PD as well as its clinical symptoms.The population of this study consisted of 240 individuals, including 120 controls and 120 cases, and the sample taken from them was peripheral blood. Genotyping of the target SNPs was done using PCR-RFLP. We found that the healthy individuals carry more T allele of MALAT1-rs3200401C>T compared to the patients (P= 0.019). Furthermore, it was observed that in the dominant genetic model, subjects with genotypes carrying the T allele have a lower risk of PD (OR= 0.530; CI= 0.296-0.950; P= 0.033). Regarding the lincRNA-P21-rs6931097G>A, we observed a significant protective relationship between its GA (OR= 0.144; CI= 0.030-0.680; P= 0.014) and AA (OR= 0.195; CI= 00.047-0.799; P= 0.023) genotypes with the manifestation of tremor and bradykinesia symptoms, respectively. Furthermore, the findings indicated that the minor TT genotype of HOTAIR-rs12826786C>T was significantly associated with a reduced risk of bradykinesia symptoms (OR= 0.147; CI= 0.039-0.555; P= 0.005). Collectively, these findings suggest that MALAT1-rs3200401C>T may be an important lncRNA SNP against the development of PD, while the other two SNPs show protective effects on the clinical manifestations of PD in a way that lincRNA-P21-rs6931097G>A has a protective effect against the occurrence of tremor and bradykinesia symptoms in PD patients, and HOTAIR -rs12826786C>T indicates a protective effect against the display of bradykinesia feature. Therefore, they can have valuable potential as biomarkers for clinical evaluations of this disease.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study

BackgroundSeveral studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.MethodsIn this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.ResultsThe RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.ConclusionsThis study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.