We define the S. cerevisiae proteome of nearly 300 proteins that specifically binds to RNAs smaller than 200 nt in length (snRBPs), identifying informative distinctions from the total RNA-binding proteome determined in parallel. Strikingly, the snRBPs include most glycolytic enzymes from yeast. With further methodological developments using silica matrices, 12 tRNAs were identified as specific binders of the glycolytic enzyme GAPDH. We show that tRNA engagement of GAPDH is carbon source–dependent and regulated by the RNA polymerase III repressor Maf1, suggesting a regulatory interaction between glycolysis and RNA polymerase III activity. We conclude that snRIC2C and other 2C-derived methods greatly facilitate the study of RBPs, revealing previously unrecognized interactions.Small noncoding RNAs fulfill key functions in cellular and organismal biology, typically working in concert with RNA-binding proteins (RBPs). While proteome-wide methodologies have enormously expanded the repertoire of known RBPs, these methods do not distinguish RBPs binding to small noncoding RNAs from the rest. To specifically identify this relevant subclass of RBPs, we developed small noncoding RNA interactome capture (snRIC RNA–protein interactions govern not only gene expression from beginning to end, but are also crucial for the function of cellular machines such as the ribosome, the spliceosome, the signal recognition particle, telomerase, and many others . Recent Decades ago, UV photo-cross-linking was shown to catalyze covalent bond formation between RNA and proteins at zero distance  without Saccharomyces cerevisiae, establishing RIC2C. Unexpected observations then motivated the development of further downstream applications of 2C, especially for the identification of the cellular RNAs that bind to an RBP of interest (CLIP2C) and the determination of those RBPs that bind to small noncoding RNAs (snRIC2C), a polyfunctional class of RNAs with multiple regulatory roles. These technical advances led us to uncover a novel connection between tRNAs, glycolytic enzymes and carbon metabolism in yeast.We  and otheSaccharomyces cerevisiae, we irradiated cultured cells with 3 J/cm2 UV light at 254 nm, using nonirradiated cells as negative controls. Lysates from three independent biological replicates were subjected to a first round of 2C purification. To minimize residual contamination with DNA-binding proteins, we treated the 2C eluates with DNase I and conducted a second round of 2C. Subsequently, RBPs in the second-round eluates were released by RNase I treatment, TMT-labeled, and analyzed by mass spectrometry , identifying 983 RBPs from yeast . Gene Ontology (GO) term enrichment analysis confirms ribosomal proteins as a highly enriched category in the cross-linked samples, reflecting that RIC2C is not restricted to mRNA-binding proteins and efficiently captures the total RNA binding proteome  . To utiltrometry A. The en2C identified numerous metabolic enzymes, including several members of the glycolytic pathway, as RBPs .Comparison with published data sets of RBPs in yeast suggests that the repertoire of RBPs in yeast may be nearing completion, with 174 additional novel RBPs D identif2C, eluates from cross-linked and non-cross-linked samples were treated with RNase I or left untreated, and subjected to immunoblotting for the RBPs Pab1 and GAPDH (Tdh3), respectively. Both proteins were only retained by RIC2C after cross-linking. Furthermore, Pab1 showed the characteristic RNA-induced smear that collapses after RNase treatment into more defined bands just exceeding the molecular mass of the native protein . In contrast, 2C extraction reduced the overall IP signal, but a shifted band became clearly visible above the size of native GAPDH in the sample not treated with RNase. This shifted band is RNase-sensitive and the signal accumulates at the expected size of GAPDH following RNA digestion . These results clearly show that 2C strongly enriches for the RNA-bound forms of GAPDH for immunoprecipitation, potentially improving the signal to noise ratio of subsequent sequencing experiments.We reasoned that enrichment of the RNA-bound fraction by 2C before immunoprecipitation could help to address this situation. To test this notion, we compared GAPDH immunoprecipitations from input lysates and 2C eluates and evaluated the efficiencies of capturing cross-linked GAPDH–RNA complexes. Input and unbound fractions, together with eluates from the IPs were analyzed by western blotting. While the IP from the input lysate showed a stronger overall signal, including more background above and below the expected size, the pattern was unaffected by RNase treatment, strongly suggesting that most of the immunoprecipitated protein was not bound to RNA . Sequencing data from the input samples were used to assess variability in gene expression between the different strains, and sequencing data from the IPs served to detect the GAPDH target RNAs. One thousand two hundred and ninety genes were identified as targets of Pab1 and as expected from a poly(A)-binding RBP, 3′UTR regions were found to be particularly enriched. Three hundred and twenty-six genes, including 299 protein coding mRNAs, were identified as targets of Tdh3. This includes several tRNAs and a subset of mRNAs encoding for proteins of the glycolytic pathway . However, the defined shift of RNA-cross-linked GAPDH by ∼25 kDa suggests that the tRNAs might be preferential targets of GAPDH . No enrichment was observed for the WT negative control sample, strongly supporting the specificity of the results. Overall, 12 different tRNAs are significantly enriched in the Tdh3 IPs, while no tRNA was enriched in the IPs of Pab1 . Thus, similarly to the human protein, yeast GAPDH also binds tRNAs in vivo.Encouraged by this result, we developed CLIPequenced A. To tesWhich other RBPs might preferentially bind to tRNAs or other small noncoding RNAs? We wondered whether 2C could be adapted to address this question, because the binding of small versus longer RNAs to silica matrices is known to be sensitive to the concentration of ethanol in the buffer . Startin2C and the excellent separation between small and long RNAs using 2C, we felt encouraged to apply 2C for the determination of the first proteome-wide data set of proteins that bind to small noncoding RNAs by snRIC2C  and analyzed by mass spectrometry. The raw TMT signals showed high reproducibility between experimental repeats and a clear enrichment of the total and small RNA-cross-linked samples over their respective non-cross-link controls .Based on the successful determination of the total RNA-binding proteome by RIC snRIC2C A. In ess2C experiment , reflecting the reproducibility of the method. snRIC2C identified 311 proteins that bind to purified small RNAs , including 52 RBPs that were not previously annotated as such in yeast  but were strongly enriched in RIC2C. We suggest that these RBPs likely bind long and small RNAs (“wide range RBPs”). Finally, 540 RBPs were detected by RIC2C but undetected in snRIC2C samples and, therefore, are considered as preferential long RNA binders , three tRNA synthetases identified as small RNA binders were examined: Cdc60, Grs1, and Gus1. As observed before with GAPDH, all four RBPs show a sharp, RNase-sensitive additional band migrating <50 kDa more slowly than the native proteins before RNase treatment F. In con2C hits . These experiments confirm that the glycolytic enzymes Tdh3, Fba1, Pgk1, Hxk2, Gpm1, Tpi1, and Eno1 all show the defined, RNase-sensitive additional band less than 50 KDa larger than the expected size of the respective proteins  and that we used as a specificity control. These results uncover a connection between tRNA binding of glycolytic enzymes, the activity of the glycolytic pathway that is influenced by the prevalent carbon source and Pol III activity.We wondered whether the binding of glycolytic enzymes to small noncoding RNAs is constitutive or subject to biological regulation. A growing body of evidence links nutrient availability with the regulation of RNA polymerase III activity by its universal repressor Maf1 . Under fSaccharomyces cerevisiae. Our unexpected biological observations drove further methodological advances, and this work hence reports both the development of enabling methods and new biological insights on the interaction of glycolytic enzymes with small noncoding RNAs in yeast.We previously showed that commercially available silica columns for the purification for total cellular RNA can also be used to select for RBPs that are covalently cross-linked to these RNAs, offering a simple method  to test the RNA-binding activity of proteins by simple immunoblotting of 2C eluates . Realizi2C methodologically and suggests that at least under standard growth conditions the number of yeast RBPs appears to approximate saturation, since we identified only a modest number of 174 RBPs that were not detected previously. RIC2C provides advantages compared to alternative methods. It does not require in vivo labeling of RNA with nucleotide analogs, like CARIC  before immunoprecipitation. The resulting RNA-loaded RBP subsequently represents an ideal substrate for library generation and sequencing, because contaminant proteins in the immunoprecipitation are reduced. Nonetheless, 2C requires protein denaturing conditions, which potentially compromises the subsequent immunoprecipitation step if the antibody used recognizes a natively folded epitope. Therefore, antigen-antibody pairs should be pre-evaluated for their compatibility with denatured/renatured proteins. We also show that proteins bearing protein-A tags can be efficiently pulled-down from 2C eluates, subsequently yielding excellent sequencing results. We used CLIP2C to demonstrate for the first time that yeast GAPDH binds tRNAs in vivo, supporting the evolutionary conservation of the GAPDH–tRNA interaction previously reported for human GAPDH  from longer RNAs. We show here that based on this principle snRICbacteria , but met2C, we identified ∼300 yeast proteins that are highly enriched for binding to small noncoding RNAs, snRBPs  columns which are included in Zymo Research RNA Extraction Midi Kits or can be purchased separately. However, the 2C method can be up- or downscaled depending on the required amount of 2C RNA and/or the available input material. We have successfully tested Zymo Research micro , mini , and maxi  columns. Alternatively, several columns can be used in parallel to process different aliquots of the same lysate simultaneously.WT yeast cells were grown, UV-cross-linked and lysed as described above. Nonirradiated cells were cultured and processed in parallel throughout the experiment as negative controls. Three biological replicates were included in the experiment. A first round of 2C extraction was performed from 1 mg of protein lysate with Zymo-Spin V-E columns, and 2C-RNA was later eluted with 300 µL of nuclease-free water. Although in our hands, the DNA contamination after a 2C extraction from a yeast lysate is below 2%, we nevertheless treated the eluates with 20 U of DNase I  at 37°C for 30 min to minimize the chances of detecting DNA binding proteins after mass spectrometry analysis. A second round of 2C was performed to eliminate the DNase I enzyme and any contaminant DNA binding protein. For this, four volumes of RNA lysis buffer were added to the DNase I–treated samples. Samples were mixed, five volumes of ethanol were added, and after mixing, the samples were added to a second Zymo-Spin V-E column. Second round 2C-RNA was eluted with 300 µL of nuclease-free water. An amount of 100 µg of 2C RNA eluates were RNase I–treated and processed for TMT labeling. Briefly, cysteines were reduced with dithiothreitol at 56°C for 30 min  and further alkylated with 2-chloroacetamide at room temperature in the dark for another 30 min . Samples were processed using the SP3 protocol  and on-bFor mass spectrometry data acquisition, an UltiMate 3000 RSLC nano LC system (Dionex) fitted with a trapping cartridge  and an analytical column  was used. Trapping was carried out with a constant flow of trapping solution (0.05% trifluoroacetic acid in water) at 30 µL/min onto the trapping column for 6 min. Subsequently, peptides were eluted via the analytical column running solvent A (0.1% formic acid in water) with a constant flow of 0.3 µL/min, with an increasing percentage of solvent B (0.1% formic acid in acetonitrile) from 2% to 4% in 4 min, then 4% to 8% in 2 min, from 8% to 28% for a further 66 min, in another 10 min from 28% to 40%, followed by an increase of B from 40%–80% for 3 min and a reequilibration back to 2% B for 5 min. The outlet of the analytical column was coupled directly to a QExactive plus Mass Spectrometer (Thermo) using the Nanospray Flex ion source in positive ion mode.Saccharomyces cerevisiae proteome database (UP000002311) containing common contaminants and reversed sequences was used. The search parameters were the following: carbamidomethyl (C) and TMT10 (K) (fixed modification), acetyl (N-term), oxidation (M), and TMT10 (N-term) (variable modifications). A mass error tolerance of 10 ppm was set for the full scan (MS1) and for MS/MS (MS2) spectra of 0.02 KDa. Trypsin was selected as protease with an allowance of a maximum of two missed cleavages. A minimum peptide length of seven amino acids and at least two unique peptides were required for individual protein identification. The false discovery rate on peptide and protein level was set to 0.01.IsobarQuant  and Masc2. RNA was fragmented by incubating the samples for 15 min at 95°C. Fragmentation was stopped by adding EDTA to a final concentration of 30 mM, quickly cooling down the samples on ice and later kept at room temperature. Samples were brought to 2 mL with buffer B , and 50 and 100 µL were saved for IP validation and input sequencing, respectively. To the remaining volume, 100 µL of prewashed Dynabeads Pan mouse IgG  were added per sample and were incubated at 4°C for 2 h with gentle rotation. Samples were washed once with buffer B, three times with wash buffer , and one time with buffer B. After the washing steps the beads were magnetically pelleted, the supernatant was discarded, and the beads were resuspended in 20 mM Tris-HCl pH 7.5. While on the beads, samples were end-repaired by the T4 PNK enzyme  following manufacturer instructions. After end-repairing, beads were resuspended in 50 µL of proteinase buffer plus 5 µL of proteinase K , and the samples were incubated for 1 h at 37°C with gentle rotation. The RNA from the IPs was purified by adding to the beads 200 µL of RNA lysis buffer. Beads were magnetically pelleted and the supernatants were transferred to new tubes. A total of 250 µL of ethanol and 25 µL of Magbeads  were added per sample. Samples were mixed and incubated for 15 min at room temperature with gentle rotation. Beads were magnetically pelleted and washed sequentially with MagBead DNA/RNA Wash1 buffer  and MagBead DNA/RNA Wash 2 buffer . After magnetically pelleted, beads were resuspended in 30 µL of H2O. Samples were incubated at 37°C for 15 min, and after magnetically pelleting the beads, the eluted RNA was finally transferred to a new tube.Pab1-TAP, Tdh3-Protein A and a WT untagged strain were grown in YPD, UV cross-linked and lysed as described above. A first round of 2C was performed from 1 mg of protein lysate, and 100 µg of the obtained 2C-RNA were DNase I–treated for 30 min at 37°C. After DNase I treatment, RNA samples were diluted with RNA fragmentation buffer to a final concentration of 20 mM Tris-HCl pH 7.5, 1% SDS and 30 mM MgCl2O. After RNA purification, 1 µg of input RNA was subjected to rRNA depletion with the Ribo-Zero Gold Yeast Kit  following manufacturer instructions.The saved 100 µL of fragmented 2C-RNA for the inputs were processed in parallel during the 2 h incubation period of the immunoprecipitation. Samples were processed for a second 2C extraction using a Zymo-Spin IC micro column  and 3 µg of the resulting RNA was end-repaired, in a final volume of 50 µL, by the T4 PNK enzyme  following manufacturer instructions. To eliminate the T4 PNK enzyme and buffers, the RNA was later purified by adding 200 µL of RNA lysis buffer, after mixing and addition of 250 µL of ethanol, 25 µL of Magbeads were added to each sample. Samples were incubated for 15 min at room temperature and gently rotated. Samples were washed with MagBead DNA/RNA wash buffer 1 and 2 as previously described, and RNA was finally eluted in 30 µL of HRNA purified from the IPs and ribodepleted RNA from the input samples were processed for library preparation with the Nextflex Small RNA Kit v3 , following manufacturer instructions. Libraries from three biological replicates of inputs and IPs were pooled together and sequenced on a NextSeq500 (Illumina) instrument on an 80 paired-end run.P-value < 0.5; log2 fold-change > 1). Transcriptome coverage plots were generated with bamCompare, computeMatrix and plotProfile functions from DeepTools (Reads were trimmed with Cutadapt (v2.3) and sequencing quality was inspected with FastQC. Novoalign (v3.07.01) was used to map to the yeast genome (sac3). Gene counts were summarized with featureCounts (v1.6.4). DESeq2  with IHWeepTools . CSAW  . Samples we mixed by gentle vortexing and later added to a Zymo-spin IIICG  column. However, and as described before, the procedure can be up- or downscaled at will. Under these conditions, the column only retains RNA molecules longer than 200 nt. The flowthrough, containing the short RNA fraction, was transferred to a new tube. Ethanol to a final concentration of 65% (vol/vol) was added; samples were mixed and added to a second silica column (mini or micro column), which will now retain the short RNAs. Both sets of columns, containing separately the long and short RNA fractions were loaded with 400 µL of RNA Prewash buffer and spun at 10,000Agilent) . In addig for 5 min and the supernatant was discarded. An amount of 4 mL of RNA prewash buffer was added and the column was centrifuged again at 3000g for 5 min. Later, the column was washed twice by centrifugation with 5 mL of RNA wash buffer and finally eluted with 2 mL of H2O.An untagged WT strain was grown in YPD, UV cross-linked and lysed as previously described. Corresponding nonirradiated cells were processed in parallel as negative controls. A first round of 2C total RNA extractions was performed as previously described, and two vials containing 550 µg of 2C RNA per sample were DNase I–treated at 37°C for 30 min in a final volume of 1 mL. One set of DNase I–treated RNA was used to purify a second round of 2C total RNA, while the second set was used to purify a fraction of 2C short RNA. For the purification of the second round of 2C total RNA, 4 mL of RNA lysis buffer were added to the DNase I–treated RNA, the samples were gently mixed by vortexing, and 5 mL of ethanol to a final concentration of 50% were added. The samples were mixed again and added to a Zymo-Spin VI maxi column  inserted on a 50 mL polypropylene centrifuge tube. Samples were centrifuged at 3000g for 5 min, and 3 mL of ethanol were added to the flowthrough, which contained the short RNA fraction. Samples were mixed and loaded on a Zymo-spin IIICG mini column and centrifuged at 10,000g for 30 sec. The flowthrough was discarded and 400 µL of RNA prewash buffer were added to the column and the samples were centrifuged at 10,000g for 30 sec. Samples were washed two times sequentially by centrifugation with 700 and 400 µL of wash buffer, respectively. Finally, 100 µL of H20 were added to the column and the 2C short RNA fraction was eluted by centrifugation at 16,000g for 1 min.For the purification of the second round of 2C short RNA, 2 mL of a 1:1 mixture of RNA lysis buffer and ethanol were added per DNase I–treated sample. The samples were mixed and loaded on a Zymo-spin VI maxi column sitting on a 50 mL Falcon tube. Samples were centrifuged at 30002C experiment with the exception that six fractions per sample were obtained after TMTsixplex labeling. Mass spectrometry data acquisition was done similarly to the RIC2C experiment described above with the exception of the gradient used. In snRIC2C, peptides were eluted via the analytical column running solvent A  with a constant flow of 0.3 µL/min, with an increasing percentage of solvent B  from 2% to 8% in 6 min, then 8% to 28% for a further 42 min, in another 5 min from 28% to 40%, followed by an increase of B from 40%–80% for 4 min and a reequilibration back to 2% B for 4 min. The outlet of the analytical column was coupled directly to an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo) using the Nanospray Flex ion source in positive ion mode.After the second round of 2C, total and short RNA fractions were quantified in Nanodrop 1000 (Thermo Fisher Scientific) and 35 µg of each fraction were RNase I  digested in 10 mM Tris-HCl pH 7.5 and 100 mM NaCl for 30 min at 37°C. The resulting RNase I–treated samples were processed for TMTsixplex labeling as described above for the RIC2 of UV light at 254 nm. After lysis, a 2C total RNA extraction was performed from 1 mg of protein lysate and the resulting 2C RNA was quantified in nanodrop. An amount of 20 µg of 2C RNA from two biological replicates was run on an SDS-PAGE, blotted to a nitrocellulose membrane and probed with antibodies against GAPDH and human ATP5b, which detects the yeast ortholog ATP1p.Precultures of WT and Maf1Δ cells were grown overnight in YPD (2% glucose), YPG (3% glycerol), or YPE (3% ethanol) media. The next day, aliquots were used to start 250 mL cultures at an O.D.600 = 0.1 and grown until mid-log phase (O.D.600 ≈ 0.8). Cells were collected and UV-cross-linked, as described before, with 3 J/cmSupplemental material is available for this article.


Key lncRNAs associated with the malignant progression of oral submucous fibrosis (OSF) to oral squamous cell carcinoma (OSCC) were identified.Key lncRNAs with sequential changes from normal oral mucosa (NOM) to OSF to OSCC were identified based on the GEO database. Kaplan–Meier analysis was used to screen lncRNAs related to OSCC prognosis. Cox regression analysis was used to validate the independent prognostic value. qPCR was used to confirm the expression of the candidate lncRNAs. Gene set enrichment analysis (GSEA), nucleocytoplasmic separation assay, fluorescence in situ hybridization, RNA knockdown, western blot, and cell viability assay were performed to investigate the biological functions of the candidate lncRNA. A nomogram was constructed to quantitatively predict OSCC prognosis based on TCGA.Bioinformatics methods indicated that LINC02147 was sequentially downregulated from NOM to OSF to OSCC, as confirmed by clinical tissues and cells. Meanwhile, low LINC02147 expression, as an independent prognostic factor, predicted a poor prognosis for OSCC. GSEA and in vitro studies suggested that low LINC02147 expression promoted OSF malignant progression by promoting cell proliferation and differentiation. A LINC02147 signature-based nomogram successfully quantified each indicator’s contribution to the overall survival of OSCC.Low LINC02147 expression promoted OSF malignant progression and predicted poor OSCC prognosis.The online version contains supplementary material available at 10.1186/s12903-022-02346-4. Oral submucous fibrosis (OSF) is characterized by fibrosis of the oral mucosa. As a pre-cancerous lesion of oral squamous cell carcinoma (OSCC), OSF has a malignancy rate of 1.5–15% .Epidemiologic studies have suggested that areca nut is a primary aetiologic factor responsible for OSF. Moreover, evidence has supported the role of genetic susceptibility and family history in the pathogenesis of OSF. With an increased number of areca nut chewers, OSF has shown gradual increases in incidence in recent years and has become a noticeable problem for global health . Thus, iLong noncoding RNAs (lncRNAs) exceed 200 nucleotides in length and act at the transcriptional and posttranscriptional levels to affect transcription, RNA processing, and translation. LncRNAs are essential in the pathogenesis of tumorigenesis, fibrosis, inflammation, and other diseases –9.Studies have shown that lncRNAs play a role in OSF. LncRNA GAS5-AS1 is significantly downregulated in OSF tissues, and its overexpression significantly suppresses collagen contractility in arecoline-treated buccal mucosa fibroblasts (BMFs) . LINC009To date, only two studies have investigated lncRNAs in the malignant progression of OSF to OSCC. Zhou et al. interpreted the lncRNA expression profile during the malignant evolution of normal oral mucosa (NOM)-OSF-OSCC at the genome-wide level and found 687 differentially expressed lncRNAs (DElncRNAs) during OSF progression, including 231 upregulated DElncRNAs and 456 downregulated DElncRNAs, indicating that lncRNAs were involved in different developmental stages of OSF . Based oThis study aimed to identify key lncRNAs associated with OSF progression to OSCC and construct a novel nomogram for predicting OSCC prognosis. First, differentially expressed genes (DEGs) with consistently sequential changes from NOM to OSF to OSCC were identified based on Gene Expression Omnibus (GEO). Second, we constructed lncRNA-mediated ceRNA networks related to OSF progression. Third, lncRNAs with OSCC-specific prognostic characteristics were screened based on The Cancer Genome Atlas (TCGA). Then, the expression levels of candidate lncRNAs were validated in clinical tissues and cells. Finally, using bioinformatic methods, we identified 11 lncRNAs with a sequential change from NOM to OSF to OSCC based on ceRNA networks. A receiver operating characteristic (ROC) analysis and survival analysis among the 11 lncRNAs showed that LINC02147 has excellent diagnostic and prognostic value for OSCC. Its expression was also validated to be sequentially downregulated from NOM to OSF to OSCC in clinical tissues and cells. Gene set enrichment analysis (GSEA) and in vitro studies validated the biological function of LINC02147 in OSF malignant progression. A nomogram combining the LINC02147 signature and clinicopathologic factors was constructed to quantitatively predict OSCC prognosis. The workflow of this study is shown in Fig. The raw data of the GSE125866 and GSE64216 datasets were downloaded from GEO  ≥ 1.5 or ≤ 0.67.The “ion data . “R” pacWGCNA can divide genes into different modules through the biological network, helping to find important gene modules related to sample traits. WGCNA can complement the results of DEGA, make up for the deficiency of DEGA, and narrow the screening range of key genes.WGCNA” package in “R” was used to screen gene modules [The expression spectrum data of GSE125866 and the grouping information  were used as three traits for WGCNA. The “ modules . The corThe miRcode database website predicted the interactions of lncRNA-miRNA. The interactions of miRNA-mRNA were predicted by five database websites, including miRMap, miRanda, miRDB, TargetScan, and miTarBase. According to the ceRNA theory and Cytoscape V3.7, lncRNA-mediated ceRNA networks were constructed .http://tcgadata.nci.nih.gov/) to identify lncRNAs with OSCC-specific prognostic characteristics. Among which, 326 OSCC patients with no history of malignancy or neoadjuvant therapy were included in our study. The correlation between OS of OSCC patients and 11 lncRNAs in the ceRNA networks was analyzed by K-M methods. Details are provided in the Additional file The RNA-seq and clinical data of head and neck squamous cell carcinoma were downloaded from TCGA (pROC” package was used to generate the ROC curve. The area under the ROC curve (AUC) was used as an accuracy index for evaluating the diagnostic performance of the candidate lncRNAs. The diagnostic accuracy based on the AUC value is defined as follows: 0.9–1.0, excellent; 0.8–0.9, good; 0.7–0.8, moderate; 0.6–0.7, fair; 0.5–0.6, poor. Generally, when AUC > 0.7, the candidate marker has a diagnostic value [The “ic value –21.Fresh tissue samples of NOM, OSF, and OSCC were obtained from Xiangya Stomatological Hospital, Central South University. The study conformed to the Declaration of Helsinki and was approved by the Xiangya Stomatological Hospital ethics committee. All patients consented to the protocol approved by the institutional review board . Exclusion criteria were those with any other history of oral lesions, drug treatment, or other systematic diseases. OSF was identified based on the 2005 World Health Organization classification system . Ten NOM2. The expression level of LINC02147 in cells was analyzed by qPCR.Primary hBMFs and OSF hBMFs were derived from histologically normal oral mucosa and OSF tissues, respectively . Normal hBMFs and OSF hBMFs were cultured according to reported methods , 23. OSCp-value. Detailed steps of the procedure that appeared in the GSEA have been carried out in “JAVA” and “R”. We can download GSEA packages at www.broadinstitute.org/gsea/index.jsp. The canonical pathways gene sets (c2.cp.v4.0.symbols.gmt) from the Molecular Signatures Database (MsigDB) (http://www.broad.mit.edu/gsea/msigdb/index.jsp) were used for enrichment analysis. Gene sets represented by at least 15 genes were preserved [GSEA was used to predict functions and pathways of LINC02147 in the malignant progression of OSF. Genome-wide expression profiles in GSE125866 were used to rank all genes according to their correlations with LINC02147 expression. The ranking list was then used to calculate the enrichment score (ES) and reserved , 25.The nucleocytoplasmic separation assay was performed to detect the subcellular location of LINC02147 in normal hBMFs. According to the protocol of PARIS™ Kit , total RNA can be partitioned into nuclear and cytoplasmic fractions. The isolated cytoplasm and nuclear RNA were used for subsequent qPCR. GAPDH and U6 were used as internal references for RNA from the cytoplasm and nuclear, respectively.RNA FISH assay further determined the subcellular location of LINC02147 in normal hBMFs. Cy3 fluorescein-labeled probes against U6 snRNA and LINC02147 were designed and synthesized by RIBOBIO . The FISH assay was conducted according to the manufacturer’s protocol of the Fluorescence in Situ Hybridization Kit . Nuclei were counterstained with DAPI. Fluorescence signals were scanned by using an inverted fluorescence microscope .Ribo™ lncRNA Smart Silencer for LINC02147 was designed and purchased from Guangzhou RIBOBIO . The product contains a mixture of six target sequences for LINC02147, including 5’-GTCCTCACGTGGCCTCTTT-3’, 5’-CAAGATCAAGGTGCTATCA-3’, 5’-CTGGCTTGTAGACAGCTAT-3’, 5’-CAGGGTTGGTTTCGGCTGTG-3’, 5’-CAAGATCAAGGTGCTATCAG-3’, 5’-TCCTCACGTGGCCTCTTTGT-3’. The Ribo FECT CP Transfection Kit was used to transfect LINC02147-siRNA or negative control (NC)-siRNA into normal hBMFs and SCC-9.The expression levels of LINC02147, α-SMA, COL1α1, FN1, vimentin, MCM2, MCM3, and MCM5 were examined by qPCR. Details of the qPCR assay are provided in the Additional file .The expression levels of α-SMA, COL1α1, FN1, and vimentin were examined by WB. The following antibodies were used: anti-α-SMA , anti-COL1α1 , anti-FN1 , anti-vimentin  and anti-β-actin . Details are provided in the Additional file Cell Counting Kit-8  was used to detect cell viability according to the manufacturer’s guidance. Normal hBMFs were incubated at 37 °C for 0 h, 24 h, 48 h, and 72 h. SCC-9 cells were incubated at 37 °C for 0 h, 12 h, 24 h, and 48 h. An enzyme-labeled instrument  was used to determine the cell viability by the absorbance at 490 nm or 450 nm. The experiments were repeated three times.Cox proportional hazards models were used to estimate hazard ratios (HR) to validate the prognostic value of LINC02147 further and identify independent prognostic factors. The clinicopathological characteristics of the 326 OSCC patients from TCGA are shown in Additional file rms” package in “R” (version 4.0). The nomogram was used to predict the OS rate for OSCC quantitatively. The calibration plots evaluated the consistency between actual OS and predicted OS created by the constructed nomogram. The concordance index (C-index), ranging from 0.5 to 1.0 , was used to determine the predictive accuracy of the nomogram [survConcordance” in the “survival” package was used to calculate C-index.Based on the independent prognostic factors screened out by multivariate Cox regression analysis, a nomogram was constructed using the “nomogram . The “sup < 0.05.All statistical analyses were performed using SPSS 20.0 software  or GraphPad Prism 8 . Student’s t-test and Wilcoxon test were used for analyzing two-group comparisons. One-way ANOVA was used for the comparison of multiple groups. The K-M method, log-rank test, and Cox regression analysis were performed to evaluate survival outcomes. Two-way ANOVA was used for CCK-8 data analysis. All results were expressed as the mean value ± standard deviation (SD) for at least three separate experiments. Differences were considered statistically significant at We obtained 271 DEmRNAs (93 upregulated and 178 downregulated) and 21 DElncRNAs (8 upregulated and 13 downregulated) with sequential changes from NOM to OSF to OSCC using DEGA. Details are provided in the Additional file The “brown” module had 13,993 upregulated genes, including mRNAs and lncRNAs, based on the WGCNA. The “orangered4 + plum1” modules had 6186 downregulated genes, including mRNAs and lncRNAs. Details are provided in the Additional file The WGCNA and DEGA results were crossed to narrow the screening range of key genes. Finally, 26 upregulated mRNAs  were associated with OS in OSCC patients. OSCC patients with low LINC02147 expression and LINC01725 had significantly poorer OS than those with high expression   and RP11-108K3.1 (AUC = 0.890) were able to distinguish OSCC from normal controls Fig. . In addiSince LINC02147 and RP11-108K3.1 showed good prognostic and diagnostic values, we subsequently validated the expression of these two genes in clinical tissue samples.p < 0.05)  pathway were more active in patients with low LINC02147 expression than in patients with high LINC02147 expression, indicating that LINC02147 was negatively correlated with the MCM pathway Fig. D.The nucleocytoplasmic separation assay and FISH assay showed that LINC02147 was mainly located in the cytoplasm in hBMFs Fig. A, B.Fig.The results showed that knockdown of LINC02147 significantly elevated the expression levels of α-SMA, COL1α1, FN1, and vimentin at both the RNA and protein levels in hBMFs Fig. C–L.CCK-8 assay showed that knockdown of LINC02147 promoted the cell proliferation of hBMFs , indicating that low LINC02147 expression predicted poorer OS in OSCC patients. TNM stage , lymphovascular invasion , and perineural invasion  were risk factors for OSCC  , indicating that the nomogram had good accuracy and sensitivity.A nomogram was constructed to quantitatively predict OS based on the 3 independent prognostic factors . Points in the nomogram were assigned to represent the contribution of each factor to OS. Low LINC02147 expression accounted for 100 points, indicating that the LINC02147 signature was a vital OS predictor for OSCC  . PersistMyofibroblasts contribute to ECM production, which contains COL1α1 and FN1 . VimentiMoreover, studies have shown that α-SMA-positive fibroblasts (myofibroblasts) may identify OSF with a high risk of malignant transformation . VimentiCox regression analysis validated that LINC02147 was an independent prognostic factor for OSCC and was not affected by clinical factors Fig. . In addiOur study identified LINC02147 as a novel prognostic signature. Low LINC02147 expression promoted OSF malignant progression and predicted a poorer prognosis of OSCC. Preliminary mechanistic experiments suggested that LINC02147 may be involved in OSF malignant progression by negatively regulating cell proliferation and the MCM pathway. Although our study produced valuable insights, it still had limitations. First, due to this study’s limited clinical sample size, a more extensive study should be conducted to investigate the characteristics of LINC02147 in the future. Second, the LINC02147 signature-based nomogram can only predict the postoperative survival rate of OSCC but not the cancer risk of OSF. Based on the above considerations, clinicopathological data from more extensive multicentre OSCC patients with OSF will be collected to further confirm the prognostic value of LINC02147 and construct a nomogram model to predict the cancer risk of OSF. Rescue experiments and further mechanistic studies will also be carried out. In addition, the present study did not differentiate OSF with or without dysplasia. Whether the function of LINC02147 differs in OSF with and without dysplasia is worth studying in the future.We used bioinformatic methods, clinical tissue samples, and in vitro study to verify that LINC02147 was gradually downregulated from NOM to OSF to OSCC, with the lowest expression levels in OSCC cells and tissues. Moreover, LINC02147 acted as a potential prognostic and diagnostic biomarker for OSCC, and low LINC02147 expression predicted poor prognosis for OSCC, indicating an essential role of LINC02147 during OSF malignant progression. In our future study, the predictive value of the LINC02147 signature-based nomogram must be verified by clinical data, and the inherent mechanism of LINC02147 needs to be unveiled.Additional file 1: Table S1. Basic information of two datasets. Table S2. Primer sequence for qPCR. Table S3. Clinical Characteristics of the 326 OSCC Patients in the TCGA cohort. Table S4. Differentially Expressed lncRNAs in the ceRNA networks related to OSF malignant progression.Additional file 2: Supplementary Methods.Additional file 3: Supplementary Results.Additional file 4: Fig. S1.The relative expression of RP11-108K3.1 in NOM, OSF and OSCC clinical tissue samples. Expression differences were compared by ordinary one-way ANOVA test. .Additional file 5: Supplementary figure 2.Additional file 6: Supplementary figure 3.


Metals are common toxic environmental pollutants. Acute or chronic exposure to metal pollutants causes severe adverse health effects in animals and humans, such as developmental retardation, abnormal metabolism, and disorders of cardiovascular, neurologic, respiratory, reproductive, and urologic systems. Moreover, several metals  are classified as potent Group I carcinogens and cause various types of cancer in humans. Although the toxicity and carcinogenicity of metal pollutants are well recognized, the underlying mechanisms have not been clearly defined. The epitranscriptome includes all kinds of chemical modifications of all forms of RNA molecules inside a cell. Recent progresses in demonstrating the reversible pattern of RNA modifications and their roles in physiology and pathogenesis represent a breakthrough in the field of RNA biology and function study. The epitranscriptomic study is now an exciting emerging field in toxicology research. While few studies have been conducted so far to determine the epitranscriptomic effects of metal pollutants, they offer novel insights for understanding the mechanisms of metal toxicity and carcinogenesis. The goal of this review is to discuss recent studies on the epitranscriptomic effects of metals and propose some thoughts for future studies in the field. Toxic metals are common environmental pollutants and can be toxic to humans and animals even at low concentrations. Some toxic metals  are classified as potent Group I carcinogens by the International Agency for Research on Cancer. The natural activities and widespread applications of metals in agriculture, industries, medical fields, and consumer products lead to their wide presence in the environment, representing a significant environmental health issue. Although toxicities and carcinogenicities of metal pollutants are well recognized, the underlying mechanisms are not well understood.Previous studies have demonstrated that oxidative stress and genetic and epigenetic dysregulations could play important roles in metal toxicity and carcinogenesis, which have been discussed in recent reviews ,6,7,8,9.Analogous to DNA methylation and nuclear core histone post-translational modifications (PTMs), RNA molecules are also chemically modified. The demonstrating of critical biological functions of RNA modifications led to the birth of the terms “RNA epigenetics”, “Epitranscriptome”, and “Epitranscriptomics” ,12. The RNA chemical modifications were initially reported in the 1950s . A largeEarly RNA modification studies mainly focused on non-coding RNAs such as tRNAs (transfer ribonucleic acids) and rRNAs , as these two types of RNAs have a relatively high abundance of chemical modifications. In the 1970s, the first chemical modifications in messenger RNAs (mRNAs) were reported, and the subsequent discovery of the N6-methyladenosine (m6A) modification in mRNA changed the focus of RNA modification studies from tRNAs and rRNAs to mRNAs. It has been determined that m6A is the most abundant chemical modification in mRNAs, which occurs in around one to four m6As per every thousand adenosine nucleotides in mammalian mRNAs . The metThe levels and functional outcomes of RNA modifications are determined by three groups of proteins that make the RNA chemical modification machinery: (i) writers, the proteins that write related chemical modifications onto RNA molecules; (ii) erasers, the proteins that erase chemical modifications from RNA molecules; (iii) readers, a variety of proteins that interact with RNA molecules bearing different types of chemical modifications and determine the functional outcomes of various RNA modifications .Specifically, the level and function of eukaryotic mRNA m6A modification are modified and regulated by the following three groups of proteins: (i) The m6A writer is a multicomponent methyltransferase complex, which catalyzes the transfer of a methyl group from S-adenosylmethionine to the N-6 position of adenosine . The keyLike DNA modifications and histone core protein PTMs, RNA modifications also affect gene expression. DNA modification and histone core protein PTMs affect gene expression mostly at transcriptional levels. In contrast, RNA modifications impact gene expression at post-transcriptional levels, translational, and post-translational levels. Studies have shown that RNA modifications could affect RNA splicing, trafficking, stability, and translation . TherefoThe functional outcomes of RNA modifications are usually determined by the reader proteins that recognize and interact with various RNA modifications. The interactions between reader proteins and the modified RNAs may stabilize or destabilize RNA molecules and may increase or decrease mRNA translation efficiencies. For example, the interaction between YTH domain-containing family YTHDF2 and m6A-modified mRNAs reduces mRNA stability and increases mRNA degradation, causing down-regulations of gene expression ,27. In cConsidering the importance of RNA modifications in regulating gene expression, it is not surprising that RNA modifications are critically involved in normal development and pathological processes . StudiesAbnormal RNA modifications have been detected in a variety of diseases ,33,34. IArsenic is naturally present at high levels in the groundwater of a number of regions or countries in the world. In addition, human activities and the widespread agricultural, industrial, and medicinal applications of arsenic release a large amount of arsenic into the environment. The U.S. Environmental Protection Agency (EPA) and the Agency for Toxic Substances and Disease Registry (ATSDR) list arsenic as a number 1 hazardous substance in waste sites on the U.S. National Priority List . As a reBai et al. used the PC12 cell line and C57BL/6J mice as in vitro and in vivo models to study the mechanism of neurological disorders caused by arsenite exposure via drinking water . It was To study the hormesis effect of arsenic exposure on human keratinocyte (HaCaT) cells, Chen et al. exposed HaCaT cells to different concentrations of sodium arsenite  for 24 h . It was Zhao et al. further determined the relationship between the levels of ROS and m6A modification in HaCaT cells exposed to different concentrations of arsenite . It was 2O3 in drinking water for 6 weeks and established the early onset of hepatic insulin resistance in a mouse model as evidenced by the impaired glucose tolerance, insulin sensitivity, and insulin signaling  has been released into the environment due to its wide applications in many manufacturing processes. Cr(VI) is listed by the U.S. EPA and the ATSDR as one of the Top 20 Hazardous Substances in waste sites on the U.S. National Priority List . Cr(VI) 2CrO4) for 24 h, indicating that the Cr(VI) may affect the A-to-I (adenosine to inosine) editing in mRNAs [To determine whether Cr(VI) could exhibit its cytotoxic effect through dysregulating the RNA modifications, Chen et al. used the LC-ESI-MS/MS analytical method to investigate the effects of Cr(VI) exposure on 14 kinds of modifications in mRNA of HEK293T cells . It was in mRNAs . Mechaniin mRNAs . Since R2CrO4, 16.2 mg/kg b.w./day, ip) for 14 consecutive days damaged the reproductive system and SSCs [2CrO4) for 1 h significantly reduced the total RNA m6A levels. It was further determined by using the m6A-IP-qPCR analysis that treatment with 10 μM Cr(VI) for 4 h reduced the m6A levels in mitochondrial fusion genes mitofusin 2 (Mfn2) and OPA1 mitochondrial dynamin-like GTPase (Opa1) and in mitophagy genes BCL2 interacting protein 3 (Bnip3) and BCL2 interacting protein 3-like (Nix) [To study the mechanism of Cr(VI) reproductive toxicity, Lv et al. determined the effect of Cr(VI) treatment on the level of RNA m6A modification in mouse spermatogonial stem cells (SSCs) . The autand SSCs . Cell cuke (Nix) . Mechanike (Nix) . Intereske (Nix) . Moreoveke (Nix) . These f2Cr2O7, 20 weeks) Cr(VI)-exposure-transformed human bronchial epithelial BEAS-2B cells and the passage-matched control cells for the m6A microarray analysis. It was found that total RNA m6A levels in Cr(VI)-transformed BEAS-2B cells were significantly higher than that in the passage-matched control BEAS-2B cells [2Cr2O7 for 40 weeks. Moreover, the total RNA m6A modification levels in the lungs of mice exposed to Cr(VI)  for 26 weeks were also significantly higher than that in the PBS-exposed control mouse lungs [We recently started exploring the epitranscriptomic mechanism of Cr(VI) carcinogenesis . We firs2B cells . This fise lungs . Then, wse lungs . CollectAlthough very limited studies have been conducted to explore the epitranscriptomic effects of exposure to metals, these pioneer studies are stimulating and offer new mechanistic insights for understanding metal toxicity and carcinogenesis. Indeed, studies revealed that the RNA m6A modification dysregulations contribute significantly to arsenic- and Cr(VI)-exposure-induced cell transformation and tumorigenesis.Although recent studies clearly showed that exposure to metals dysregulates RNA modifications in cultured cells and in animals, further studies are needed to better define the role and mechanism of abnormal RNA modifications in metal toxicity and carcinogenesis. We propose the following areas for future studies. First, current studies on the epitranscriptomic effects of metals are largely limited to exposure to arsenic, cadmium, and hexavalent chromium, and studies in this exciting emerging field need to be extended to other toxic metals such as lead, mercury, and nickel, etc. Second, current studies focus largely on the effects of metal exposure on the RNA m6A modification, and future studies need to be extended to investigate the effects of metal exposure on other important types of RNA modifications and determine their roles in metal toxicity and carcinogenicity. Third, while current studies have provided exciting evidence showing that metal exposure caused RNA modification dysregulations, the underlying mechanisms remain largely unexplored. Further studies are needed to determine how metal exposure causes abnormal RNA modifications and how dysregulated RNA modifications mediate metal toxicity and carcinogenesis. Fourth, exposure to metals causes severe health damage. Future studies need to determine whether metal exposure dysregulated RNA modifications and their modifiers could serve as biomarkers or targets for the diagnosis and treatment of diseases resulting from metal exposure.


The prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated.In this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis.P = 0.030], Cancer antigen 19–9 (CA19-9)  and MR-Distance   were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. The accuracy, sensitivity, and specificity of the model trained using the Adaboost method in the validation set are 0.786, 0.776 and 0.700, while 0.601, 0.933, 0.508 using Logistic Regression and 0.743, 0.390, 0.831 using KNeighbors Classifier.168 patients were included. Prognostic Nutritional Index (PNI) [OR =  0.998; Machine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction. Colorectal cancer (CRC) is common worldwide and associated with significant mortality . DistantAtypical metastasis diagnoses were confirmed by clinical manifestations and radiographic imaging. The clinical symptoms of atypical metastasis are determined by the specific target organ. Bone metastasis patients often suffer from tumor-induced bone pain, pathological bone fractures, and spinal cord compression ; Brain mTo date, studies related to atypical metastasis of colorectal cancer are mainly limited to case reports , 9. To oThis study was approved by the institutional review board of Sir Run Run Shaw hospital, and the requirement for patient consent waived. Atypical metastasis of colorectal cancer in this study refers to other metastases excluding local direct infiltration and distant metastases in liver metastases and lung metastases. Consecutive patients were identified that fulfilled all inclusion and none of the exclusion criteria from January 2010 to December 2018. Inclusion criteria were as follows: (1) patients with rectal cancer confirmed by surgical pathology; (2) without a history of comorbid other malignancies; and (3) complete medical record and follow-up information. Exclusion criteria were as follows: (1) patients with synchronous metastasis of rectal cancer; (2) incomplete medical history or follow-up lost; and (3) undergone radiotherapy, chemotherapy or neoadjuvant radiotherapy.α-fetoprotein (AFP), C-reactive protein (CRP), Cancer antigen 19-9 (CA19-9), albumin, aspartate transaminase (AST), alanine aminotransferase (ALT) and lymphocyte count level were measured. Prognostic Nutritional Index (PNI) was defined as 10 × serum albumin (g/L) + 0.005 × lymphocyte count (per mm3). Besides, MRI-based imaging measures, including the were also evaluated. The definition of MR-distance was tumor distance from the anal verge confirmed from preoperative magnetic resonance (MR) images.Preoperative variables including the MRI was performed with a 3.0 T MR scanner . Imaging sequences included routine high-resolution axial T2-weighted imaging (HRT2WI) and DWI. Image acquisition parameters for HRT2WI were as follows: fast recovery fast spin echo, repetition time = 3,300 ms, echo time = 130 ms, slice thickness = 3.0 mm, gap = 3.0 mm, matrix = 512 × 512, echo train length = 20, and field of view = 160 × 160 mm. The parameters for DWI was as follows: b = 800s/mm2, repetition time = 5,900 ms, echo time = 66 ms, slice thickness = 5.0 mm, matrix = 256 × 256, field of view = 290 × 290 mm, and gap = 6.0 mm.All images were reviewed on ITK-SNAP software using the picture archiving and communication system. Regions of interest were measured on the HRT2WI and DWI sequence. The image analysis was conducted by an experienced radiologist with 10 years of experience, who was blinded to the clinical information.t-test, Mann-Whitney U test or Chi-square test. Univariate and multivariate logistic regression analyses were utilized to identify factors associated with atypical metastasis of colorectal cancer. Comparison between AdaBoost , Logistic Regression  and KNeighbors classifier  based on PNI, CA19-9 and MR-Distance were assessed by AUC, as well as sensitivity, specificity and accuracy. AUC mean (and standard deviation) determined by 5-fold cross-validation. Differences were considered statistically significant at P value < 0.05.The machine learning analyses were performed using scikit-learn version 0.22.1 in Python 3.7. The R statistical package version 3.6.3 was used, with libraries “mstate”, “pROC”, “rms”, “mstate” and “dplyr”. Differences between metastasis and non-metastasis groups were analyzed using 168 patients were included in this study. Overall, 31 patients were diagnosed with atypical metastasis during follow-up, whereas the remaining 137 patients presented no metastasis at any sites. Among them, bone metastases were identified in 23 patients, while adrenal metastases in 3 patients, splenic metastases in 2 patients, and one patient each for ovarian, brain, and skeletal muscle metastases.P = 0.030], CA19-9  and MR-Distance   were independent risk factors for atypical metastasis. Furthermore, The main finding of the current study is that Prognostic Nutritional Index (PNI), Cancer antigen 19-9 (CA19-9) and MR-Distance were independent risk factors for the atypical metastasis, and the machine learning model based on AdaBoost algorithm may have good predictive performance. The results support that machine learning tools offer the promise of being more generalizable and applicable to predicting atypical metastasis in colorectal cancer after resection.In our study, PNI and serum CA19-9 have been shown to be an independent risk factor for predicting atypical metastasis in colorectal cancer. Nozoe et al. first revealed that PNI less than 40 was independently correlated with a worse prognosis of patients with colorectal cancer . SubsequArtificial intelligence and machine learning approaches are increasingly applied in medicine , 23. OurThe advantage of this study is first evaluation the machine learning approach application in predicting atypical metastasis in colorectal cancer after resection. Our study also has several limitations. First, the number of subjects in our study is limited to a single center and is relatively small, suggesting that a multicenter study with a large sample size is needed. Second, the region of interest was manually placed, which may have affected our measurement, although the radiologist was blinded to the clinical information. Third, machine learning models are relatively simple to develop. Recent advances in cutting-edge techniques may enable ensemble learning algorithms to help improve the reliability and robustness of machine learning models.


HAUS2), protein phosphatase 3 catalytic subunit beta (PPP3CB), hook microtubule tethering protein 3 (HOOK3), CUB and Sushi multiple domains 1 (CSMD1), methyltransferase-like 7A (METTL7A), and kinesin light chain 2 (KLC2). Furthermore, enrichment analysis showed that fewer gene functions and KEGG pathways were enriched by edited genes in sepsis compared to controls. These results revealed alteration of A-to-I RNA editing in the human brain associated with sepsis, thus providing an important basis for understanding its role in neuropathology in SAE.Recent studies suggest that RNA editing is associated with impaired brain function and neurological and psychiatric disorders. However, the role of A-to-I RNA editing during sepsis-associated encephalopathy (SAE) remains unclear. In this study, we analyzed adenosine-to-inosine (A-to-I) RNA editing in postmortem brain tissues from septic patients and controls. A total of 3024 high-confidence A-to-I RNA editing sites were identified. In sepsis, there were fewer A-to-I RNA editing genes and editing sites than in controls. Among all A-to-I RNA editing sites, 42 genes showed significantly differential RNA editing, with 23 downregulated and 19 upregulated in sepsis compared to controls. Notably, more than 50% of these genes were highly expressed in the brain and potentially related to neurological diseases. Notably, cis-regulatory analysis showed that the level of RNA editing in six differentially edited genes was significantly correlated with the gene expression, including HAUS augmin-like complex subunit 2 ( Sepsis is a life-threatening systemic infectious disease caused by bacteria, viruses, or other factors, with high mortality worldwide . Septic ADARs) family  RNA editing is an epigenetic process of adenosine (A) to inosine (I) conversion mediated by the adenosine deaminase acting on RNA  family . It is r) family . A-to-I ) family . The pott sepsis , providit sepsis . NeverthHerein the current epitranscriptomic study analyzed A-to-I RNA editing from postmortem brain  tissues from septic patients and controls at the transcriptomic level and explored editing sites associated with sepsis and their cis-regulatory effects on the gene expression, providing new insight into the molecular mechanism involving A-to-I RNA editing in SAE.RNA sequencing raw data were obtained from NCBI’s Gene Expression Omnibus (GEO) database. The dataset contained brain tissues  from 12 patients who died from sepsis and 12 controls who died from noninfectious diseases (GSE135838) . Sepsis The obtained sequencing data were processed as previously described . In brieRNA single-nucleotide variation (SNV) was identified using VarScan (version 2.4.3) software  using a Alignment files generated by RNA STAR were analyzed using FeatureCounts to obtain counts of RNA expression , and norhttps://david.ncifcrf.gov/tools.jsp) and Enrichr (https://maayanlab.cloud/Enrichr/) with false discovery rate (FDR) < 0.05 as the significance cutoff  and p-values. Principal component analysis (PCA) was performed and visualized using R (version 3.6.3).The intergroup levels of RNA editing or gene expression were compared using the Kruskal–Wallis (KW) non-parametric test. Frequency data were analyzed using the Fisher’s exact test. Cis-regulatory effects on RNA editing on the expression of edited genes were analyzed using the Spearman correlation to calculate the correlation coefficients , kinesin light chain 2 (KLC2), proteasome 20S subunit beta 2 (PSMB2), and Matrin 3 (MATR3) were associated with amyotrophic lateral sclerosis, and PPP3CB, KLC2, and PSMB2 were associated with prion disease, Alzheimer’s disease, and pathways of neurodegeneration . Among them, the editing levels of HAUS2:chr15:42567145 (r = 0.61), PPP3CB:chr10:73450485 (r = 0.59), HOOK3:chr8:43028233 (r = 0.49), and CSMD1:chr8:4877650 (r = 0.41) were positively correlated with the gene expression level (METTL7A:chr12:50930723 (r = −0.64) and KLC2:chr11:66261078 (r = −0.41) were negatively correlated with the gene expression level (HAUS2:chr15:42567145 (p = 0.0081), PPP3CB:chr10:73450485 (p = 0.045), HOOK3:chr8:43028233 (p = 0.023), CSMD1:chr8:4877650 (p = 0.043), METTL7A:chr12:50930723 (p = 0.0049), and KLC2:chr11:66261078 (p = 0.047)  .In order to understand the biological function of A-to-I RNA editing in the human brain affected with sepsis, enrichment analysis was performed using all sites in each group. Among the top enriched GO terms, biological processes including retrograde axonal transport, regulation of microtubule depolymerization, and axon development, cellular components including trans-Golgi network, and AMPA glutamate receptor complex, and molecular functions including actin binding, sodium channel regulator activity, and sodium channel activity were unique to sepsis . In contRecent studies suggest that RNA editing is involved in brain dysfunction and neurological diseases. Our current study systematically investigated A-to-I RNA editing in human brain tissues and revealed its changes associated with sepsis on a transcriptome-wide scale.ADAR is highly expressed in macrophages and has a protective effect on sepsis -mediated signal transduction (METTL7A) encodes a methyltransferase mainly involved in DNA methylation and the innate immune system (HAUS2) interacts with the γ-tubulin ring complex and is involved in spindle assembly (HOOK3) gene is involved in protein binding and microtubule binding (CSMD1) is correlated with the development and treatment of schizophrenia (SNRPD3) and PSMB2 are also related to neurological diseases ; Ensembl Variant Effect Predictor (VEP) (https://www.ensembl.org/vep); REDIportal V2.0 database (http://srv00.recas.ba.infn.it/atlas/index.html); Enrichr (https://maayanlab.cloud/Enrichr/).URLs: Gene Expression Omnibus (GEO) database (


The application of wireless power transmission technology is becoming more and more extensive. However, in practical applications, the problem of reducing the energy transmission efficiency caused by the offset of the coupling coil needs to be solved urgently. Changing the coil structure is a widely adopted method to deal with this problem. Based on the characteristics of the existing magnetic coupling resonant wireless power transmission system and the principle of the anti-offset coil, this paper innovatively designs a new type of quadrilateral overlapping wireless power transfer coupling coil, which has a strong anti-offset capability. The new type of coil model was built in the simulation and experiment, and the relevant parameters were measured. Experimental results verify that the proposed coil structure has an excellent anti-offset capability. Wireless power transmission (WPT) is a popular technology for research and application today. Compared with traditional wired power transmission technology, wireless energy transmission technology has the advantages of flexibility, convenience, electrical isolation, strong environmental adaptability, and easy maintenance. In recent years, radio transmission technology has been gradually applied in various fields and has produced far more than the expected results. It brings many advantages in specific domains such as electric vehicles ,2,3, impAt present, the wireless power charging technology is mainly divided into three types: the microwave type, electromagnetic induction type, and magnetic coupling resonance type. This paper focuses on magnetically coupled resonant wireless power transmission (MCRWPT), which is based on two or more coils with the same resonant frequency and uses the principle of magnetic field resonance to achieve efficient energy transmission. This technology has the advantages of non-radiative energy, being not easily affected by non-magnetic obstacles, high transmission efficiency, and large output power  and has Magnetically coupled resonant wireless power charging technology has developed rapidly, but there are still many problems to be solved. The transmission efficiency of the system is determined by the relative positions of the transmission and reception coils, but coil offset is unavoidable in practical applications, especially in the equipment of single-transmit and multi-receive wireless energy transmission systems. It is difficult to share the highest point of transmission efficiency when there are multiple receivers, which leads to a decrease in the efficiency of system energy transfer. Thus, the anti-offset of the system is an important problem , and oneMagnetic coupling resonance technology was first proposed in 2006 by Professor Marin Soljacic of the Massachusetts Institute of Technology at the American Physics Industrial Physics Forum. The following year, two copper coils with the same structural parameters were used to light up the Columbia World Expo. A 60 W light bulb was tested at a distance of 2.13 m, and the system transmission efficiency was about 40%, which verified the feasibility of the magnetic coupling resonance technology .In 2008, Joshua R. Smith’s research group at Intel’s Seattle Laboratory used magnetic coupling resonance technology to achieve wireless charging of notebooks, PDAs, and other appliances with 60 W output power and 75% transmission efficiency when the transmission distance was 1m. In 2008, Professor Sun Mingus from the University of Pittsburgh first applied magnetic resonance technology to the power supply of implanted electronic devices in animals. The operating frequency is about 7 MHz When the axial distance between the two coils is 20 cm, it can output watt-level power, and the transmission efficiency reaches above 50. In 2013, the team of Professor John T. Boys of Auckland University improved the topology of the coupler, which can improve the coupling coefficient of the coupler ,25.China started a little later, researching magnetic coupling resonance technology, mainly on the research and verification of transmission mechanism and characteristics, and related research is also mainly concentrated in university research institutes. The team of Professor Zhu Chunbo of the Harbin Institute of Technology produced an LC series resonator with a radius of 250 mm. When the transmission distance is 0.7 m and the transmission power is 23 W ,28,29,30It can be seen that the magnetic coupling resonant wireless energy transfer technology is still popular, and increasing the performance of the system energy transmission efficiency is also a research highlight, including improving the coil structure to reduce the impact of offset on the wireless charging system.The basic schematic diagram of wireless charging technology is shown in M is the mutual inductance coefficient, I be current, and the coil-induced electromotive force is shown in (1).Power electronic converters are used to convert 50/60 Hz alternating current into high-frequency alternating currents to enhance the effect of electromagnetic induction. The compensation network is used to improve the quality of the alternating current signal, transmission efficiency, and system stability. Let The existence of the coil-induced electromotive force means the induced current is generated in the receiving end loop. The current is adjusted to provide electrical energy to the receiving end impedance by the rectifier circuit. To ensure the high transmission efficiency of the system, it is necessary to set the capacitance parameters so that making the transmitting and receiving circuits (composed of coils and capacitors) are in a resonant state.In practical applications, the relative positions of the receiving coil and the transmitting coil of the wireless energy transmission system are likely to change within a certain range, resulting in a change in the coupling coefficient. The offset has a large impact on the transmission efficiency and leads to system instability.The magnetic coupling resonant circuit model of the wireless power transfer system is shown in AC is the AC power supply. R1 is the sum of the internal resistance of the AC power supply, the radiation resistance of the transmitting coil, and the ohmic loss resistance. C1 and C2 are the compensation capacitors of the transmitter and receiver, respectively. L1 and L2 are the transmitters. LR is the load resistance of the receiving end. I1 and I2 are the total currents of the two loops. According to Kirchhoff’s voltage (KVL) law, the equilibrium Equation (2) is obtained.I1 and 2I, as shown in Equation (3).When the two coils resonate, and there is In summary , the sysη has a positive correlation with the mutual inductance M. For a fixed system, the component parameters and loads of the system are unchanged. When the coil is offset, the change of mutual inductance causes the system’s wireless transmission efficiency to change. A schematic diagram of the coil offset is shown in It can be seen from the above power formula that In mula (5) .(5)M=NaChanges in coil position and offset affect the mutual inductance of the coils, resulting in changes in transmission efficiency. The change in mutual inductance becomes more pronounced with further distance from the ideal alignment state.During the charging process, an offset inevitably occurs between the two coils in the system coupling structure, thereby affecting the mutual inductance between the two coils and reducing the transmission efficiency of the system. This has become a key factor restricting the wide application of wireless charging technology . The couOptimizing the design of the coil structure can improve the anti-offset characteristics of the coupled coil  and the The efficiency of the magnetic coupling mechanism mainly depends on several factors, such as resonant frequency, the size and design of the coupling structure of the transmitting coil and the receiving coil, and the distance between the magnetic coupling structures . The ANSIn ANSYS HFSS, the influence of the position change (offset) of the simplest circular-wound coil on the mutual inductance coefficient is analyzed and the law of coil offset is summarized. It can be seen from The curve of angular migration is similar to that of radial migration. When the offset amplitude is small, the change of mutual inductance is small, and the subsequent mutual inductance coefficient decays faster. In practice, the offset of the coil is often in the order of millimeters, and the axial offset, radial offset, and large angular offset can be easily avoided. Thus, anti-offset research focuses more on reducing the energy caused by small radial offsets. The anti-migration capability mentioned later also refers to the resistance to the influence of radial offset.The magnetic field of an anti-offset cross helical coil structure is uniformly distributed along a diagonal line of the square magnetic core, and the magnitude of the diagonal magnetic field is larger than that of other regions, which have better anti-offset ability . Use ANSFrom the above The magnetic field of the cross spiral coil is uniformly distributed along a diagonal line of the square magnetic core and the magnetic field strength is larger than in other areas based on the above characteristics. A new coupling structure in which four cross coils are superimposed on a plane is designed, as shown in The magnetic field distribution of this coil is deduced by Maxwell’s equations and Bio–Savart Equation  37,38,3,40,41,42The first and second formulas of Maxwell’s Equation (7) describe the relationship between electric and magnetic fields, which can be converted into each other. The third formula states that the magnetic flux through any closed surface is equal to zero, and the magnetic field is passive. The fourth formula describes the relationship between the electric flux through any closed surface and the charge within that closed surface. In the process of mutual conversion between the electric field and the magnetic field, the direction of the magnetic field generated by the electric field can be determined by Ampere’s law, and the direction of the induced electromotive force can be determined by Lenz’s law.Combined with Bio–Savart’s Law, Maxwell’s equations, and Lenz’s law, the direction of the current in each coil is adjusted so that the magnetic field generated by each coil is directed from the coil to the center. The superposition results in a dense, uniform, and centrosymmetric magnetic field. The schematic diagram of the derivation process is shown in The cross helical coil structure is a mutually superimposed enhancement area, while the magnetic field intensity on the other diagonal is relatively small. The uniformity and symmetry of the magnetic field generated by the overall coil are not enough. The new coil moves the coil originally located in the center to the outside of the magnetic core, retains the diagonally enhanced area of the original cross coil, removes the area with the weak magnetic field, and obtains a larger magnetic field enhancement range. Four identical coils and four corners are placed symmetrically about the center to make the magnetic field center-symmetrical. Finally, the quadrilateral overlapping coupled coil structure is obtained, and the current flow direction, plane magnetic field distribution, and coupling mode on each coil section are shown in The simulation results are consistent with the plane magnetic field distribution theoretically analyzed by the quadrangular overlapping coupled coil structure. At the same time, the upper and lower coils of the space structure are symmetrical about the center of the z-axis, and the magnetic field generated by the coils is also symmetrical about the center of the z-axis. Compared with the previous coil structure, the magnetic field distribution in the central area of the quadrilateral overlapping coupling coil structure is more uniform and concentrated. In theory, when the coil is offset, the change rate of the mutual inductance is relatively smaller.The coupling structure parameters of the simulation model are shown in Define the change rate of the mutual inductance coefficient by dividing the mutual inductance coefficient after an offset by the mutual inductance coefficient when there is no offset. When each structure is offset by 80 mm, the mutual inductance of the new structure can be maintained at 75.06% of the starting value, while the cross coil and circular coil are 71.63% and 31.86%, respectively. It is not difficult to see that when the coil is offset, the change rate of the mutual inductance of the new structure is significantly lower than that of the circular coil, and is more stable than that of the crossed coil. When the offset is small, the change of the mutual inductance of the new structure is very small, and the offset can be kept within 95% within 30 mm. When the offset is large, the mutual inductance can remain stable. In summary, the abovementioned new coils have strong radial offset resistance and have the potential to ensure that the energy transmission efficiency is always stable and efficient during the energy transmission process.The thickness of the iron core and the presence or absence of the core can affect the deflection resistance of the structure. The changes in mutual inductance when the new coil is facing and offset under different core thicknesses are shown in As shown in As shown in To test the characteristics of the anti-offset capability of the new quadrilateral overlapping wireless energy transmission coupling coil structure, a wireless energy transmission system platform, as shown in The platform consists of a simple topology and novel coils. Select the core type according to the simulation environment: core material ferrite core. The experimental platform mainly includes a transmitting coil power supply device, a new type of magnetic coupling coil mechanism  offset is tested by comparing it with circular coils of similar size and the same number of turns. Keep the power supply voltage of 20.98 V, the load resistance of 15 Ω, and the coil interval of 10 cm unchanged; the initial position of the coil is positive, and the receiving end is moved radially by 1 cm each time to test and record the transmission power of the new coil and the circular coil, respectively. The specific values of transmission power when coils face each other are shown in It can be seen that when the coil model and specifications are tested strictly according to the simulation parameters, the experimental test results are in good agreement with the simulation theory. When each offset is 90 mm, the transmission efficiency of the circular coil is only 30.03%, while the transmission efficiency of the new structure is 67.42%. Compared with the circular coil, the anti-deflection ability is significantly improved. The new coil structure has good anti-offset performance.Then, the stability of the energy transfer efficiency and the anti-offset characteristics of the new coil under different loads and different distances  were tested. The coil spacing remains 10 cm when the load changes and the load resistance remains 10 Ω when the spacing changes. The energy transfer efficiency is the ratio of the transmission power to the power supply. The experimental results are shown in It can be seen from the above experimental results that when the load resistance changes, the transmission efficiency of the wireless energy transfer system using the new coil is still relatively considerable and stable, the effect of resisting offset is also good, and the difference between the curved edges under different loads is small; when the coil spacing changes, the transmission efficiency changes greatly, but the trends of different curves are less different. It is not difficult to see that the new structure has good anti-migration performance under different loads and different spacings.This paper starts from the problem that the offset of the coupling coil affects the transmission efficiency in the wireless power transmission system, aiming at the anti-offset method of improving the coil structure to homogenize the magnetic field. The existing anti-offset coils were studied and analyzed, and a new anti-offset coil was innovatively designed. The quadrilateral overlapping wireless power transmission coupling coil structure was simulated and experimentally verified for its energy transfer characteristics and anti-offset ability. It was further verified by simulation that it has excellent performance since the energy mutual inductance coefficient does not change much when migrating within a certain range. A magnetic coupling resonant wireless power transmission test platform was built, and tests for the comparison experiment of the anti-excursion ability of the new coil and the circular coil, the anti-excursion ability experiment under different loads or spacings, and the anti-excursion ability of the small circular coil as the receiving end were carried out. In the previous simulation and experiment, by comparing other coil structures, the corresponding experimental parameters were measured. It was verified that the new coil has excellent anti-offset characteristics. The quadrilateral overlapped wireless power transmission coupling coil has certain application prospects in scenarios such as the wireless charging stations of electric vehicles and drones.


Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide that bears an enormous healthcare burden and aging is a major contributing factor to CVDs. Functional gene expression network during aging is regulated by mRNAs transcriptionally and by non-coding RNAs epi-transcriptionally. RNA modifications alter the stability and function of both mRNAs and non-coding RNAs and are involved in differentiation, development, and diseases. Here we review major chemical RNA modifications on mRNAs and non-coding RNAs, including N6-adenosine methylation, N1-adenosine methylation, 5-methylcytidine, pseudouridylation, 2′ -O-ribose-methylation, and N7-methylguanosine, in the aging process with an emphasis on cardiovascular aging. We also summarize the currently available methods to detect RNA modifications and the bioinformatic tools to study RNA modifications. More importantly, we discussed the specific implication of the RNA modifications on mRNAs and non-coding RNAs in the pathogenesis of aging-associated CVDs, including atherosclerosis, hypertension, coronary heart diseases, congestive heart failure, atrial fibrillation, peripheral artery disease, venous insufficiency, and stroke. The incidence of cardiovascular disease (CVD) is increasing rapidly around the world, taking nearly 17.9 million lives every year, and this number will increase to 23.6 million by 2030 , 5. ThesMore than a hundred RNA modifications have been identified that could alter the chemical and topological properties of the ribonucleotide molecules to perform specific biological functions during their post-transcriptional regulation. Initially, RNA modifications were only studied in tRNA, rRNA, and small nuclear RNAs (snRNA). Eventually, through multiple advanced tools together with subsequent next-generation sequencing , 7, evenRNA is one of the key molecules, that can perform most types of functions in a cell, and dynamic modifications of RNA have been identified in the transcriptome which are largely conserved across species evolutionarily . TherefoRNAs other than protein coding mRNAs are literally categorized as ncRNAs. For the ease of understanding, ncRNAs are further classified into two categories based on their function. The first class is “infrastructural RNAs” that involve in basic housekeeping functions, such as protein-coding related RNAs including RNAs designated for the protein translation, namely rRNA and tRNA, as well as RNAs responsible for their maturation and even splicing, including snRNAs and snoRNAs . SnRNAs The second class of ncRNAs includes regulatory RNAs that include all other cellular ncRNAs not included as infrastructural RNAs. For instance, short ncRNAs include miRNAs, which can alter gene expression by degrading mRNA molecules after transcription, while the piwi-interacting RNAs (piRNAs) silence retrotransposons in the germ cells . A lesseWhile techniques such as chromatography or mass spectrometry could recognize many extensive modifications, more rare RNA modifications require advanced sequencing techniques to identify the nucleosides that have undergone modifications and differentiate these from unmodified ones , 21. IdeIt is the most direct method for the high-throughput and sequence-specific analysis of the transcriptome stems from an adaptive mean widely used in proteomics . The eluIn this technique, nuclease protection was achieved by forming double-stranded bodies with complementary DNA oligonucleotides, such that the unhybridized RNA was then degraded by the nucleases and the remaining double-stranded bodies were analyzed by the LC-MS for nucleoside analysis , 27. OthThese are very useful tools known for their very high affinity and the potential to specifically recognize molecular structures . NucleicAnother dimension of information can be accessed using high-throughput sequencing data after processing RNA templates with reagents that can react specifically with the modifications to alter their RT characteristics in the terms of RT stasis or misincorporation. For example, the inosine specific cyanoethylation (ICE-SEQ) of acrylonitrile produces a strong RT termination that can distinguish true A to I conversion sites from simple sequencing errors . CurrentRecent adaptation for the detection of m5C in bisulfite based on RNA. In the process of this technology, the technology was based on the mature detection of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) in the DNA, and the modification of cytosine to m5C or 5hmC can protect these sites from the impacts of bisulfite treatment , 38.In the MeRIP protocol or ribose methylation sequencing (RiboMethSeq) method, the precise definition of the fragment is located in the 5ʹ and 3ʹ linker connection protocol . The ribThe latest and most complex development is the SCARLET variation. In the SCARLET method, the target RNA was first cleaved with RNase H at the desired site and the cleavage was guided by the chimeric oligonucleotides containing DNA and 2ʹ -O-Me-RNA , 41. In In some cases, RT primer extension can also be useful as a verification method for high-throughput mapping candidates of the RT arrest signals . One exaSeveral epitranscriptome-wide landscapes of RNA modifications have been performed using high-throughput sequencing and these have led to the development of extensive databases for such modifications. They are important tools in furthering this field and have been discussed as shown below:The following are some common RNA modification databases: MODOMICS-RNA modification pathway database , RNA modThe MODOMICS database is a reference database for RNA modification because it provides the most complete information on the chemical structure of the modified ribosides, reaction abstracts, functional characteristic enzymes involved in the modification, and the biosynthetic pathways of RNA modifications .As a reference for updating the RNA modification results, the RNAMDB portal provides many useful tools for the mass spectrometry identification of natural or modified RNAs. Beginning with RNA sequences, molecular weight, electrospray series, CID fragment, internal fragment, base loss, and fragment digestion can be calculated .The MeT-DB is a synthetical database focused on the m6A mammalian methyl transcriptome. It includes approximately 300,000 m6A methylation sites, which have been detected in the samples from humans, mice, and cells under various experimental conditions . Data weThe RMBase is also a comprehensive database. It integrates epigenome sequencing data to explore post-transcriptional modification of RNAs and their relationships with the miRNAs, the disease-associated the RNA-binding proteins (RBPs) and single nucleotide polymorphisms (SNPs) . MeanwhiThe computer methods development based on the support vector machines, which can accurately predict post-transcriptional modification sites from sequence information, is very helpful for the scientific community to further understand epigenetic modification , 52. As Aging is viewed as a multifactorial process that derives from the interaction of genetic and environmental factors. It can be divided into pathological aging and physiological aging. Physiological aging is a complex biological process characterized by the decline of tissue and organ function, structural degradation, and decline in adaptability and resistance, all of which led to an increase in the morbidity and mortality of many chronic diseases , 69. TheCardiovascular aging can be roughly categorized into four diverse groups of disorders based on their pathogenesis , 77:Changes in cardiac structure due to aging – Aging leads to molecular and cellular changes, ultimately leading to compromised functional changes such as decrease in adaptive capacity of cardiac cells and cardiomyocyte loss .Aging promoted epigenetic cardiovascular changes – Epigenetic changes such as DNA methylation changes, post-translational histone modifications, and chromatin remodeling changes have been observed to be important factors in development of CVD .Autonomous Nervous System (ANS) – The effect of aging can irreversibly affect the ANS, which is known to have an important role in cardiovascular homeostasis and can in turn have an adverse impact on the cardiovascular system .Development of certain CVDs – Aging itself is a known risk factor for several CVDs that develop in an aging body such as cardiac fibrosis, hypertrophy, diabetes, hypertension, atherosclerosis, ischemic injury, MI, stroke, etc. , 80.The study of RNA provides new molecular insights into cardiovascular aging and can serve as a short-term repository for the genetic information in the form of mRNA, or as a long-term repository in form of viral genomes . siRNA, To date, more than 140 RNA chemical modifications have been reported. Among them, the methylation of mRNA at position N-6 of adenosine residue (m6A) has been the most extensively studied . In manyin vitro and in vivo fibrosis models with TGF-β1 activation and in MI mouse models respectively  lead to multiple clinical diseases; the main unifying feature is altered energy homeostasis due to reduced ATP production.ψ, also called PU, is the most extensive distributed RNA modification, found among tRNA, rRNA, snRNA, and mRNA , 160. ThThis modification has been reported to be involved in mRNA stability as see in yeast and certain parasites . In factPseuduridylation is observed in several types of ncRNA including snoRNAs, telomerase RNA (with a known impact in aging), other low abundant RNAs like rRNAs, snoRNA, intronic RNAs, lncRNAs, RNAse MRP, Steroid Receptor activator, 7SK RNA where it usually contributes to transcriptional stability. However, its exact role in ncRNA should be further explored. With respect to tRNA, this is one of the most observed modifications and has an important role in its stability and function.The 16S rRNA residues pseuduridylation in mitochondria is required for mitochondrial protein synthesis and for overall cell viability and survival. Designed to determine the association of new metabolites with LV remodeling, this study examined 1,052 Bogalusa Heart Study participants and performed untargeted metabolomics analysis of fasting serum samples . In the This is the process of adding non-template uridine to RNA end for regulation of certain cellular processes . PolyU pin vivo [Previous studies have shown that uridine is incorporated in mammalian heart cells, and this incorporation leads to an increased level of free uridine in Langendorff-perfused hearts and in vivo . Isolatein vivo . CreatinAdenylate-uridylate (AU) rich elements in 3'UTR of many transiently expressed genes modulated mRNA instability and translation. CUGBP and ETR 3-like factor (CELF) proteins are the family of RBPs that play a dominant role in the RNA metabolism . Using tAdenosine-to-inosine (A-to-I) RNA editing is a usual post-transcriptional modification, which plays an important role in preventing endogenous double-stranded RNA from erroneously activating innate immunity, and is related to various regulatory processes and diseases, such as autoimmune, cardiovascular diseases and cancer . In addiA-to-I RNA editing of adenosine deaminases act on RNA (ADAR) modified the mRNA and redirected it to translate a new protein and editing of mRNA encoding actin crosslinking protein filaggrin A (FLNA) mediated Q-to-R conversion in interactive C-terminal region . One stuLncRNA has become a key regulatory factor in atherosclerosis, but the post-transcriptional regulatory mechanism involved in disease related lncRNA expression is not completely understood. These studies showed a more than two-fold increase in nuclear paraspeckle assembled transcription 1 (NEAT1) lncRNA expression in PBMC of CAD patients . The NEAA-to-I editing in the microRNA seed sequences can alter the miRNAs target set, thereby altering their function . Using t4C) was detected as a highly conserved RNA modification of cytidine in the non-coding RNA such as tRNA and near the translation initiation codon of mRNA [in vivo [N4-acetylcytidine  is defined as methylation of ribose at the 2-OH group and is one of the most usual RNA modifications. Because it can modify any nucleotide base to produce 2'-O-methyladenosine, 2'-O-methylcytidine, 2'-O-methylguanosine and 2'-O-methyluridine, collectively known as Nm. The 2'O-Me/Nm had been widely researched in the tRNA, rRNA and snoRNA, and latterly been reported in mRNAs as well.Nm is one of the most plentiful RNA modifications quantized after PU. Though found more commonly in mRNA cap, it has also been surveyed in internal coding sequences (CDS). The mRNA 2'O-Me is actively involved in the regulation of gene expression and may adjust these protein levels. Cap methylation is usually carried out by two enzymes - CMTR1 and CMTR2 that 2′-O-methylates at the first and second transcribed nucleotide of the mRNA respectively. There have been reports of increased expression of CMTR1 in mice models of the neurodegenerative Alzheimer’s disease (AD). This mRNA modification is also thought to play a role in splicing. Therefore, Nm can adjust a variety of biological and pathophysiological processes including CVDs , 192.rRNAs are the ncRNAs that undergo most of Nm modification while this modification has also been observed in snoRNA and tRNAs. It has been observed that with respect to adult heart tissue, developing tissues undergo lesser methylation in specific regions as specific patterns of methylation occurs during development . These cth position wobble Uridine (U34) on tRNA and this modification occurs post-transcriptionally to ensure proper functionality of the tRNA. Such aberrant modifications have been observed in mt-tRNA leading to the development of hypertrophic cardiomyopathies and lactic acidosis. This is commonly known as mitochondrial dysfunctions usually associated with aging disorders, wherein mitochondria-nuclear signaling pathways are activated. In this study, the mutant mitochondrial (mt)-tRNAsLeu(UUR) was observed to lack the taurine containing modification of wobble uridine (U34) normally present in the wild-type UUR [This is a modification of the 34type UUR . This datype UUR . In additype UUR . U34 modtype UUR .Dynamic RNA modification is quite abundant in most RNA species such as mRNA, rRNA, and tRNA and have been proved to be critical in cellular functions. This review attempted to consolidate different aspects of RNA modifications that are crucial in regulating aging-associated CVDs. We first briefly summarized the different types of RNA modifications commonly abundant in cells as well the different ncRNA types. We then enumerated the different methods of identifying the RNA modifications. As sequencing techniques were one of the most superior methods of identifying RNA modifications, we also summarize the different bioinformatic tools and databases that help in their study. We then summarized the role of different modifications in various aging-driven CVDs. These observations have augmented our current understanding of the effect of epigenetics on cardiovascular aging and disease that has tremendously increased during the last few years. Therefore, epigenetic modifications like chromatin remodeling, DNA occupation, and changes in ncRNA expression, which are driven by modifications in RNA bases, contribute to the progression of cardiovascular aging and disease. However, one of the major challenges in the field is the difficulties in detecting these modifications by sequencing methods. Despite improvements in the field, there is still an urgent unmet need in developing advanced tools/techniques to detect and study these RNA modifications, especially with respect to precise profiling of these modifications. Unfortunately, a non-uniform detection of these modifications also poses a disadvantage of not accurately representing the profile of RNA modifications. It is certain that more work needs to be done to understand the reason behind such dynamic modifications and pathogenesis, ultimately moving further towards pattern recognition in aging-driven diseases. There is no doubt that unraveling such crosstalk between RNA modifications and other DNA or RNA modification methods, will further deepen our understanding of these mechanisms and potential therapeutic strategies for CVDs. In the future, further research enhancing the understanding of RNA epigenetic mechanisms, facilitating the development and use of epigenetic modification therapies to improve clinical outcomes for heart disease and other age-related diseases is needed.