The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the “dark matter” of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation. Carcinogenesis is a multistep process in which normal cells acquire genetic and epigenetic alterations that drive the onset of “hallmarks” of cancer finally resulting in development and progression of malignancies . DespiteThe aberrant expression of ncRNAs is an important feature of human cancer –11. The Agilent SurePrint G3 Gene Expression v3 microarray, targeting 30,606 human lncRNA transcripts, covers all of LNCipedia 2.1, the Arraystar LncRNA microarray (release Human v5.0) profiles 39,317 lncRNAs, Clariom D human array from Thermo Fisher Scientific covers more than 55,900 lncRNA NONCODE transcripts. Limitations of microarrays for ncRNA analysis are a restricted linear range of quantification, the design of probes limited to known sequences, the need for continuous updating of annotations, the relative quantification limited to compare different status . An example of microarray technology application has been reported by Zhou et al. which profiled 389 colon cancer patients identifying a signature of six lncRNAs  associated to risk of cancer recurrence [Microarray is a well-established method to profile both miRNAs and lncRNAs, although it was initially designed for protein-coding mRNAs. This technology is based on nucleic acid hybridization between labeled RNA targets and their specific and complementary probes. Advantages of microarrays are the high parallel analysis coupled with relative cost and the ability to detect low levels of molecules of RNAs without the need of PCR-enrichment steps. Various platforms for miRNA profiling include different direct miRNA labelling procedures without amplification. LncRNA microarray platforms systematically profile lncRNAs together with mRNAs. Generally, lncRNA platforms include an in vitro transcription (IVT)-based amplification step and are characterized by less technical variations respect to miRNA platforms and mainly differ for the number of lncRNAs analyzed. For example, the currence , or by LSerial Analysis of Gene Expression (SAGE) is the first high-throughput sequencing technology developed to analyze the transcriptome in term of identification and quantification of transcripts, including ncRNAs . It is bRNA sequencing (RNA-seq) allows the detection and quantification of all classes of ncRNAs through the construction of different cDNA libraries, specific for each type of ncRNA. cDNA library preparation is followed by massive parallel sequencing of transcripts of interest. The small RNA-seq is suitable for the sequencing of small ncRNAs, while total RNA-seq is recommended for lncRNA sequencing as many lncRNAs may not be polyadenilated. Compared to microarray, RNA-seq offers a more comprehensive coverage of whole transcriptomes. Importantly, RNA-seq is design-free probe allowing the detection of unknown/novel transcripts and also the detection of sequences that differ, even for a single nucleotide, such as transcripts harboring mutations or isoforms. The main limitations of RNA-seq are the complexity of data analysis and the high deep reads needed to detect low amount of the target. Using RNA-seq technology Yamada et al. identified a signature of 27 upregulated and 22 downregulated lncRNAs associated with colorectal cancer (CRC) as alternative biomarkers and/or treatment targets . Yu N etCap analysis gene expression (CAGE) is an NGS-based technology allowing the generation of a snapshot of the 5′ end of the mRNA. Similarly to SAGE, sequencing is preceded by cDNAs-tag generation, concatenation and cloning, but the main differences between the two approaches is the ability of CAGE to identify the exact location of the 5′ capped- transcript. Respect from RNA-seq, the advantage of CAGE consists in the identification of transcriptionally active promoter regions and RNA polymerase II transcription start sites (TSS). Horie et al. revealed a set of 49 coding and 10 non-coding genes upregulated in non-small cell lung cancer (NSCLC) due to promoter hypomethylation, by performing an integrative analysis of promoter level expression profiles generated through CAGE method .High-throughput ncRNAs expression profiling methods require bioinformatic contribution to analyze data generated from different platforms, including microarray and NGS technologies described above. In particular, the application of NGS is becoming predominant to explore in depth patient specific genetic background underlying intra and inter-individual variability, which acquire increasing relevance in the era of personalized medicine –30. Anali) the identification of differentially expressed genes between two classes , ii) clustering, which consists in building clusters of genes in term of expression level, iii) classification and/or, iv) analysis of pathways and interaction networks. Microarray raw data processing involves 4 phases: 1) pre-processing, which includes background adjustment, normalization and summarization, 2) annotation to enrich preprocessed data, 3) statistical and/or data mining analysis and 4) biological interpretation. Well-known algorithms of microarray data preprocessing are MAS4.0, MAS5.0, RMA, PLIER and GCRMA. Background correction is essential to remove noise in the optical detection system due to non-specific hybridization. Normalization, within and between arrays, is needed in order to remove systematic technical artifacts that could be due to different efficiency of reverse transcription, labeling or hybridization reactions, or other laboratory conditions. Summarization unifies signals generated from multiple probes, designed for the same transcripts, with multiple locations on the array. Once summarized, data can be annotated by adding information such as gene symbols or function. Data mining is the process by which groups of samples are compared in order to find differentially expressed genes on the basis of their expression values. Many of the methods for visualization and interpretation of gene expression data can be used for both microarray and RNA-seq experiment including clustering analysis, gene set enrichment analysis and pathway  or network analysis [In the case of data generated by array technology, bioinformatic data analysis includes: analysis , 32.In the case of RNA-seq, ncRNAs analysis workflow starts from raw NGS data. The first step is the filtering of low-quality reads from raw data. This process is usually performed by using tools for pre-processing of files containing short-reads encoded into FASTA-FASTQ files, which map the sequences to reference data (stored into databases). Example of such programs are the FASTX-Toolkit, Blat, SHRiMP, LastZ, MAQ and many others . Once fii) Minimal Folding Energy (MFE), ii) Hidden Markov Model (HMM) and iii) Stochastic Context Free Grammar (SCFG), whose application led to the reconstruction of the 2D structure of ncRNAs  within the target and drives sequence-based design of small molecules (SMs) targeting structured RNAs . Recent Data obtained from microarray or RNA-seq, as well as from the in silico predictions need hortogonal validation. The evaluation of ncRNAs expression level can be performed through different approaches, such as northern blot (NB), reverse transcription quantitative PCR (RT-qPCR), in situ hybridization (ISH) or fluorescence in situ hybridization (FISH).NB analysis is the earliest technique used to analyze gene expression splicing variants of a given ncRNA. The procedure involves the use of gel electrophoresis to separate RNA samples followed by RNA transfer onto a nylon membrane, RNA-probe hybridization and finally RNA-probe detection. An example of the application of this approach has been reported from Vanas et al., which used NB to measure miRNA-21 in osteosarcoma cell lines, by demonstrating that its expression is involved in cell proliferation and regulation of cisplatin activity , while LThe RT-qPCR is one of the most used method of detection and quantification of ncRNAs as easily to be incorporated in laboratory workflow. This method is often used to validate data obtained from microarray, as reported i.e. by He et al., which analyzed the differential expression profile of miRNAs in peripheral blood of lung cancer patients . RT-qPCRIn recent years, advances in probe technologies and detection methods have improved ncRNA visualization by the application of ISH and FISH methods, based on the use of fluorescent probes binding the nucleic acid sequence presenting the highest degree of complementarity. FISH and ISH provide information regarding the spatial-temporal expression of ncRNAs and their subcellular localization providing novel information on ncRNA biological function. For example, confocal microscopy for FISH demonstrated that lncRNA NKILA exerts its critical function in cellular cytoplasm preventing NF-κB activation through stabilization of NF-κB/IκB complex by playing an essential role in turning off cancer-associated inflammation ; while INon-coding RNAs exert their functions by direct interaction with other partners, which could be RNA in the case of miRNAs, and RNA, DNA and/or proteins in the case of lncRNAs.i) chromatin remodeling through histone modifications, ii) recruitment of transcription factors, iii) RNA polymerase II binding, iv) alternative splicing, v) mRNA stability, vi) recruitment of polysomes, vii) gene expression regulation in neighbor cells through extracellular vesicles and viii) miRNA interaction [i) miRNA-triggered lncRNA decay, in which lncRNA degradation is induced by miRNA binding ii) lncRNA acting as miRNA sponge/decay, in which lncRNAs sequestrate miRNAs by removing their negative control on target mRNA iii) lncRNA-miRNA competition for mRNAregulation and iv) lncRNA generating miRNAs by alternative splicing [In a previous manuscript we discussed about the integration of multi-omics data from different molecular levels in order to underline the complexity of the biological interactions. In that context we mentioned integrative analyses performed between transcriptomic data (e.g. miRNA and mRNA expression) together with genomics and epigenomics data (e.g. methylation profiling) to highlight the functional interactions between coding and non coding genes . Here, weraction . For whasplicing .Several studies focusing on non coding-coding genes interactions in human cancer, have been aimed to identify lncRNA-miRNA-mRNA axes which may promote tumor growth. For example Yu Lian et al. identified the role of the oncogenic lncRNA AFAP-AS1 to promote nasopharyngeal carcinoma metastasis by binding miR-423-5p and modulating the RAB11B and LASP1 coding genes involved in the Rho/Rac signaling pathway . Han Li Here we describe the most relevant methods developed to investigate the ncRNAs interactome, for example, dCHIRP (domain specific chromatin isolation by RNA purification) is a method for simultaneous mapping of RNA-RNA, RNA-DNA and RNA-protein interactions at single domain level . We clasThe interaction between two RNAs (inter-molecular) or between different regions of the same RNA molecule (intra-molecular) are one of mechanisms of the regulatory action of ncRNAs. Since computational methods can provide just a prediction of RNA-RNA interaction (RRI), different low and high- throughput methods have been developed to directly solve these molecular events .RRI may be directly investigated through low-throughput biophysical and biochemical methods, such as electrophoretic mobility shift assay (EMSA), surface plasmon resonance (SPR) or single molecule forster resonance energy transfer (FRET). In EMSA, RNA fragments are extracted from cells and RRI is evaluated through electrophoresis based on molecular mass (larger in the case of interaction) . In SPR,i) crosslinking, ligation and sequencing hybrid (CLASH) and ii) hybrid and individual-nucleotide resolution ultraviolet cross-linking and Immunoprecipitation (HiCLIP RNA), which are able to identify duplex of two ligated RNAs, iii) RNA interactome analysis followed by deep sequencing (RIA-seq) and iv) RNA antisense purification and sequencing (RAP-seq) that explore the interactome for a target RNA. For example, Helwak et al. mapped the human miRNA interactome by CLASH and revealed non-canonical binding sites [NGS technologies have been applied to investigate RRI at transcriptomic level. Among them, we mention ng sites .i) psoralen analysis of RNA interactions and structures (PARIS) [ii) sequencing of psoralen crosslinked, ligated, and selected hybrids (SPLASH) [iii) ligation of interacting RNA followed by high-throughput sequencing (LIGR-seq) [The last frontier for RRI detection techniques is based on sequencing-based methods at transcriptome-wide level.  (PARIS) , ii) seq(SPLASH)  and iii)IGR-seq)  are threi) Chromatin isolation by RNA purification (ChIRP) ii) RNA antisense purification (RAP) and iii) capture hybridization analysis of RNA targets (CHART) are the most common experimental approaches [i) ChIRP is a technology that enables the analysis of lncRNA-DNA complexes by the extraction of chromatin from cross-linked cultured cells, sonication, hybridization with biotinylated oligos and separation with magnetic streptavidin beads [ii) RAP differs from ChIRP for the use of longer antisense RNA probes with enhanced affinity to the target lncRNA. The products isolated with this method could undergo NGS analysis. iii) CHART similarly to ChIRP and RAP involves the purification of cross-linked RNA, DNA and proteins complexes, but differs for the use of short affinity-tagged oligonucleotides targeted to the region of predicted lncRNAs open binding sites [To investigate lncRNAs binding sites on chromatin proaches . i) ChIRin beads . The outng sites . An examng sites .lncRNAs may interact with RNA-binding proteins (RBPs) to play their regulatory roles. Immunoprecipitation and affinity-based approaches have been developed to identify proteins involved in the functional complexes.RNA Immunoprecipitation (RIP) is the most frequently used method to study lncRNA-protein interaction and is based on the immunoprecipitation of the complex by the use of an antibody directed to a target protein. After purification, lncRNAs can be analyzed through PCR, microarray (RIP-Chip) or NGS (RIP-seq) . SubsequHowever, CLIP or RIP are applicable only if an antibody against a specific protein is available. To overcome this limit, different approaches have been developed, such as RNA-tagging , or TargTo move towards the identification of RNA interactors at the proteomic level, the best approach is Biotinylated RNA-protein pull-down followed by liquid chromatography-mass spectrometry/mass spectrometry (LC–MS/MS). In this method, target RNA is synthesized, labeled with biotinylated uridines, incubated with cellular lysates, pulled-down with streptavidin beads and finally, the RNA-binding protein complexes are separated through Sodium Dodecyl Sulphate - PolyAcrylamide Gel Electrophoresis (SDS-PAGE) and analyzed through MS. Anbang Wang et al. demonstrated through this technique that the lncRNA EGFR-AS1 interact with HuR, which affects mRNA stability of EGFR by promoting cell growth and metastasis in renal cancer . Howeveri) post-transcriptional RNA degradation using small interfering RNA (siRNAs) or synthetic antisense oligonucleotides (ASOs); ii) modulation of ncRNA genes by using genome-editing techniques; iii) replacement of ncRNAs; iv) inhibition of RNA–protein interactions or preventing secondary structure formation by using small molecules. We report several references of preclinical studies which highlight the power of these techniques in functional investigation. However, despite all these approaches are promising as therapeutics interventions, many barriers, for example in delivery systems, need to be overcome in the vision of their clinical translation.The deregulation of ncRNAs in cancer cells, in term of expression profiling, interactome, as well as other intrinsic changes promoting tumor formation, offers the rational to consider them as a class of potential therapeutic targets. Given the diversity in their potential mode of action, several types of genomic and functional approaches have been developed to directly or indirectly target ncRNAs depending on whether they are oncogenes to be inhibited or tumor suppressors to be replaced. Among them we discuss about ASOs are synthetic nucleic acids sequences that binds, via Watson-Crick base pairing, to complementary RNA substrates. The two mechanisms of action of ASOs are the recruitment of RNase H to the DNA–RNA heteroduplex to degrade RNA  or the ii) the discovery of miR 195 synergic role in microtubule targeting agent response in lung cancer [ii) the identification of several miRNAs relevant in trastuzumab resistance in HER2 positive cells [iii) the discovery of miRNAs determining navitoclax susceptibility in CRC cell lines [RNA interference (RNAi) is an endogenous and well-conserved post-transcriptional modulation mechanism, which works through paring of endogenous or exogenous dsRNA with a target mRNA. Specifically, a dsRNA is firstly cleaved in a 21-RNA sequence, called siRNA, by Dicer and then is loaded in RISC (RNA induced silencing complex), which is located in cytosol. Here the passenger strand is discarded, the guide strand is paired with target mRNA and, depending on complementarity, silencing is induced through degradation or translational repression , 116. Thg cancer  ii) the ve cells  iii) thell lines . The appll lines . On the ll lines . Moreovell lines .i) LncRoR as activator of MAPK/ERK pathway [ii) LncAK023948 as positive regulator in Akt pathway [iii) LncBC200 as promoter of cell growth have been demonstrated in breast cancer [In the past 10 years, several methods for genome editing have been developed such as Zinc-finger nucleases (ZFNs), transcription activator like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease 9 (CRISPR/Cas9) . Here, w pathway , ii) Lnc pathway  and iii)t cancer . CRISPR-t cancer . Differet cancer  and of mt cancer . Furthert cancer , 134.Considering the high impact of this technology on molecular studies , CRISPR-Moreover, the last progress in this context is represented from the use of CRISPR-Cas9 pooled library, which consist in thousands of plasmids encoding for multiple barcoded gRNAs targeting different genes simultaneously, with a strong reduction of time and costs related to functional validation experiments . A repremimics. A well known example is the replacement of miR-34a, a tumor suppressor that is lost or expressed at reduced levels in a broad range of tumor types [Many relevant ncRNAs in cancer are genomically deleted or downregulated acting as tumor suppressors, whereby their reactivation may have anticancer activity. Replacement strategies are widely applied to restore the functionality of tumor suppressor miRNAs using double-stranded RNA of 22-mer oligonucleotides sharing the same sequence of mature miRNA or its precursor, and for this reason they are known as or types . The exoor types . These ror types . Anotheror types . To overor types .The reactivation of tumor suppressor ncRNAs as well as the use of nucleic acids-based methods requires the availability of efficient in vivo delivery systems to overcome biological drawbacks associated with such strategies. A first barrier is the transport across the cell membranes limited to diffusion of small and relatively hydrophobic compounds. In addition, RNA molecules show short half-life in vivo environments due to the highly risk of degradation by cellular nuclease such exonuclease or endonuclease. An important issue is also the activation of innate immune response to foreign RNAs through toll-like receptor and retinoic acid inducible gene I protein pathways. This results in the production of type 1 interferon and subsequent release of inflammation associated cytokines. Finally, it is important to avoid, or at least predict and recognize, off-target effects and to reduce the toxicity. At this aim siRNA and ASOs as well as ncRNAs can be encapsulated inside lipid-based nanoparticles in order to ensure its survival against biological agents and delivered into cancer cells, also in a target specific manner. In addition, the development of N-acetylgalactosamide  conjugated to siRNAs has enhanced hepatic uptake . To exteAlthough the RNA therapeutics research is mainly focused on oligonucleotides, the application of SMs to target specific ncRNAs have emerged as a feasible and efficient strategy and may in part overcome limitation of RNA approaches based on Watson and Crick hybridization. Computational biology can allow the identification, prediction of docking sites and design of these SMs, as we mentioned above. Moreover, the advantages of SMs are their chemical nature that make them suitable for conventional drug development. In contrast, limitations are poor specificity and complex design compared with sequence-specific methods. SMs exert their therapeutic effect on ncRNAs by specific binding to secondary or tertiary structures as miRNA hairpin precursors or structural elements of lncRNAs such as the triple-helical structure of MALAT1 and NEAT1. In this way, SMs can destabilize the transcript or allosterically interfere with the interaction between the RNA and its protein partners. An additional mechanism of action of SMs is the binding to the Dicer or Drosha nuclease processing sites, which could affect the biogenesis of miRNAs. The development of bioinformatics tools allowed to perform high-throughput screening of ncRNA libraries against datasets of small molecules identifying strong interactions. Using this strategy Li et al. tested the ability of targaprimir-96, a bleomycin A5 conjugate, to target pri-miR-96. This compound directly block pri-miR-96 maturation via Drosha leading to the upregulation of miR-96 target FOXO1 and the induction of apoptosis in breast cancer cells . SimilarAs we discussed above, numerous preclinical studies are focusing on ncRNAs characterization with the aim to clarify their role in tumorigenesis and to disclose their contribution for diagnostic, prognostic and therapeutic purposes. We reported several strategies, which emphasized the promising use of ncRNAs for cancer treatment. Now, we discuss about the bench to bedside translation of the ncRNAs therapeutics in the ongoing clinical trials. MiRNAs are the most extensively studied as both therapeutic candidates or targets , 156, foFor example, it is noteworthy that ncRNAs may play a crucial role in chemo and radio resistance, which is the major challenge of current anticancer treatments , 163.https://clinicaltrials.gov website are reported 304 studies involving miRNAs in clinical applications, of which 101 are interventional studies at different phases. Among them, we mention the phase I clinical trial NCT02369198 based on the administration of TargomiRs as 2nd or 3rd line treatment for patients with recurrent malignant pleural mesothelioma and NSCLC. This drug consists in a miR-16-based microRNA mimic, nanoparticles for delivery and an anti-EGFR bispecific antibody. Moreover 13 studies are reported concerning lncRNAs, of which 11 are observational and 3 interventional . However, in the majority of cases, miRNAs and lncRNAs are evaluated only in term of expression profiling to validate them as biomarkers, while RNA-based therapeutics or SMs are not yet clinical interventions under widespread investigation. This points the need to work hard to translate the large and promising preclinical studies in early clinical trials, This scenario strongly indicates the need of highly multidisciplinary efforts to make “dark matter” a major mean in the fight against cancer in the next future.On Here we have reviewed research strategies aimed to investigate the role of miRNAs and lncRNAs in cancer. The availability of new powerful sequencing and molecular technologies allowed the overcoming of several potential caveats, such as the low abundance of ncRNAs, the subcellular spatial localization and their instability. Improvements in wet laboratory techniques together with in silico tools significantly improved the knowledge of the “dark matter” of the genome in terms of discovery, annotation and functional validation. Apart from the most widely adopted methods that we have described, other strategies have emerged to improve the global characterization of ncRNAs in the last 10 years Table  and the


Background: Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose and many efforts have been made to evaluate EVs-derived RNAs as biomarkers to predict PDAC. However, lack of robust internal references largely limited their clinical application. Here we proposed an RNA ratio-based, normalizer-free algorithm to quantitate EVs-derived RNAs in PDAC.Methods: Differentially expressed RNAs in the training group were identified using “limma” package. The ratio of any two candidate RNAs in the same sample was calculated and used as a new biomarker. LASSO regression was performed to build prediction models based on those RNA ratios. RNA-seq data of 116 plasma samples and RT-qPCR data of 111 plasma samples were used for internal and external validation, separately. Three algorithms  were compared to improve the performance of this RNA signature.Results: We developed an RNA-ratio based prediction model which comprised eight EVs-derived RNAs, including FBXO7, MORF4L1, DDX17, TALDO1, AHNAK, TUBA1B, CD44, and SETD3. This model could well differentiate PDAC patients with a minimal AUC of 0.86 in internal verification using testing group. External validation using RT-qPCR data also exhibited a good classifier ability with an AUC of 0.89 when distinguishing PDAC from healthy controls.Conclusion: We've developed a qPCR-based, normalizer-free circulating EVs RNA classifier, which could well distinguish PDAC patients from noncancerous controls. Pancreatic cancer (PC) is predicted to be the second most common cause of cancer-related death around the world by 203034EVs are membrane-encapsulated heterogenous vesicles delivering various macromolecules including proteins, miRNAs, lncRNAs, mRNAs, or lipids between different microenvironments, resulting in initiation of various biological processes due to their different cargoesThere are several methods could be used to detect EVs-derived RNAs such as next generation sequencing (NGS), RNA chip, and RT-qPCR. NGS and RNA chip are relatively accurate but not suitable for large-scale clinical application because of their high-cost. Real-time quantitative PCR (RT-qPCR) is an alternative way to detect RNAs which is more cost-effective than omics tools. However, the absence of a well-established normalizer for plasma EVs derived RNA quantification largely hampered the development of qPCR-based assays, and resulted in unfavorable data consistency and reproducibility across different studiesTo solve this problem, here we proposed a ratio-based normalization for circulating EVs derived long RNA data. The ratio of any two candidate RNAs in the same sample was calculated and used as a new biomarker, and then the ratios were compared between different groups instead of a single RNA level. Considering the two RNAs in an RNA-pair are simultaneously measured under the same condition, their ratio could reflect the true difference among samples by canceling systematic biases. So instead of absolute quantification of a single RNA, we established a new scoring system based on RNA-pairs, which enables us to build a quantitative RNA classifier without an RNA internal reference.Circulating EVs-derived long RNA NGS data from GSE133684For NGS data in training group, RNAs which TPM lower than 50 were excluded because of the difficulty in qPCR verification. Differentially expressed (DE) RNA analysis was performed to training group using “limma” package in R 3.5.2RNA pair ratio was calculated from any two DE RNAs expression values in the same sample. Univariate logistic regression was subsequently performed for each RNA pair to roll out pairs not significantly associated with PDAC occurrence and constructed an RNA pair matrix of training group. Next, we used lasso regression to select variables from RNA pair matrix under 7-fold cross-validation by “glmnet” package in R. Prediction models enrolled 2, 3, 4 RNA pairs were established by a step-wise variable selection process by controlling lambda values in lasso regression. Then, logistic prediction models, support vector machine (SVM) models and Lasso regression models for each of these RNA pair signatures were constructed by “glm” function, “e1071” package and “glmnet” packageTesting group of GSE133684 was used for internal verification of these models. ROC curves of these models were drawn by using “plotROC” packageThis study isolated EVs of plasma samples of 111 PDAC patients, CP patients and normal controls. 3 mL plasma sample was collected from each subject. We performed ultracentrifugation (UC) method for EVs isolation. Firstly, we centrifugated plasma samples at 3000 × g for 15min after thawing at 37°C in order to remove cell debris. Then, we diluted supernatant of each sample by 7-fold volume of phosphate-buffered saline (PBS). Next we centrifuged the mix again at 13,000 × g for 30min and a 0.22μm filter was applied in order to filter large vesicles. Then, a P50AT2-986 rotor  was used at 150,000 × g, 4°C for 4h to pellet EVs. Pellet was resuspended in PBS and centrifuged again at 150,000 × g 4°C for 2h. Finally, the EVs enriched fraction pellet was re-suspended in 100µL PBS.Pierce BCA Protein Assay Kit  were used to quantify the protein concentration of EVs-enriched fractions following the manufacturer's protocol. 10μL of each standard and samples were pipetted into 96-Well Plates. After mixture of 200μL of the WR to each well and shaking for 30 second on the shaker, plates were covered and incubated at 37°C for 30min. Then we measured their absorbance at 562nm on the plate reader. Finally, we build a standard curve and quantified EVs-enriched fraction samples.7 /mL to 1x109 /mL. We took a 60-second video with a frame rate of 30 frames/sec, and analyzed it by using NTA software (ZetaView 8.02.28).The ZetaView PMX 110  with 405 nm laser was applied to find out size and quantity of EVs in concentrations ranged 1x10We next placed 20µL EVs enriched solution on a copper mesh for 10min in room temperature. Then, we contrasted solution with uranyloxalate solution for 1min after washed by sterile distilled water. The solution was subsequently dried for 2min under incandescent light. The copper mesh was then photographed under a TEM .Special markers for extracellular vesicles have been previously reported. In the present study, we used a combination of two positive markers (CD63 and TSG101) and one negative markers  to characterize the exosomes we extracted. Rabbit polyclonal antibody CD63 , TSG101  and calnexin  were used in Western Blot procedures. The protein bands were detected using an enhanced chemiluminescence system .TM RT Master Mix (TAKARA RR036a). The amplification of cDNA was performed in 10-μL reaction system following the SYBRGREEN life assays manufacturer's instructions. Primer sequence was shown in Total RNA isolated was extracted and purified from plasma EVs using the miRNeasy Mini kit following the manufacturer's protocol . A total RNA was reverse-transcribed and cDNA was synthesized using PrimeScripthttps://www.r-project.org/). DE RNAs were identified by “limma” package (version 3.38.3). ROC curves were graphed using “plotROC” package (version 2.2.1). Lasso regression was performed using “glmnet” package (version 2.0-18). SVM were performed using e1071 package (version 1.7-2). All statistical significance was defined as P < 0.05.All statistical analyses were calculated by R software  such as neutrophil activation and neutrophil mediated immunity (P < 0.001, Figure We paired any two of 229 candidate RNAs and conducted an RNA-pair matrix containing 26106 RNA-pairs. Among those RNA-pairs, 516 candidates achieved an AUC above 0.8 and were selected for the construction of prediction models. Lasso regression was applied in variable selection and three RNA-pair signatures were constructed by controlling lambda values in the regression Figure A. ViolinNGS data from 116 samples were used for internal validation, and we constructed Lasso regression models, logistic models, and SVM models for internal validation of these signatures. For 2 RNA pair model, AUC values of Lasso regression algorithm, logistic algorithm, and SVM algorithm were 0.83, 0.85 and 0.79, separately Figure A. For 3 In order to further evaluate the expression of EVs-derived RNAs in PDAC patients and non-cancerous individuals, we isolated EVs from plasma samples and characterized them according to the MISEV2018 guideline. We obtained images of oval or bowl-shaped microvesicles with diameters of 50-100 nm by TEM (Figure We further verified those signatures by RT-qPCR in an independent cohort of our own cohort including 111 samples (Clinical data was shown in Table Detection of PDAC at a resectable stage is challenging to gastroenterologistsHowever, there are still many problems preventing these signatures from clinical application. Testing cost is the major problem bothering physicians. Many researches were based on NGS or RNA chip data, however, these methods are not suitable for large-scale clinical application because of their high-cost. As a cost-effective tool, RT-qPCR could be widely used in the evaluation of EVs-derived RNAs, but the controversial normalization of RT-qPCR data generated from plasma EVs largely reduced the reproducibility across different studies as well as our enthusiasm in the development of liquid-biopsy based PDAC diagnostic tools.Three regular ways were often used to normalize plasma EVs-derived RNAs. Traditional reference genes were often used to normalize RT-qPCR data, such as GAPDH and β-actin, which were considered stably expressed in somatic cells. However, these genes were proved to be differential expressed in plasma EVs. Moreover, during the isolation of EVs, different expression manners between target RNAs and internal reference RNAs result in variance in their relative expression levels and bring poor reproducibility. Consequently, they are not the ideal tool for data normalization in plasma EVs-derived RNAs quantification. Some researchers chose to screen more specific reference RNAs by many algorithms such as Normfinder, Genorm and DataAssistHere we proposed an RNA-pair ratio-based algorithm to quantitate plasma EVs-derived RNAs. The ratio of any two candidate RNAs were calculated in the same sample, then ratios between two RNAs were treated as new variables to construct a ratio-based prediction system by three machine learning algorithms. Subsequently, we validated these prediction models internally and externally, and found they were capable to predict PDAC occurrence with high AUC levels no matter in NGS data or RT-qPCR data.The innovation points of this study lie on several aspects. Our major breakthrough is making the clinical application of diagnosis biomarker much easier. By using RT-qPCR method other than NGS or RNA chip, we reduced the cost of cancer screening. By using this normalizer-free algorithm, our model achieved high accuracy no matter in the NGS data or RT-qPCR data, which means we avoided the unstable factors induced by controversial reference genes and made RT-qPCR assays stable and reproducible. Cost-effective and high accuracy made our algorithm easier for clinical application. Moreover, three machine learning methods including lasso regression, SVM and logistic regression were simultaneously applied to assess the robustness of prediction model, making the result more convincing. Cross-platform property also enhanced the reproducibility of this research. Last but not least, our innovation is localized in methodology level and could be helpful to researchers intending to discover reliable biomarkers for other cancer types.Mattia Boeri etc.We identified 8 biomarkers to predict PDAC, i.e. FBXO7, MORF4L1, DDX17, TALDO1, AHNAK, TUBA1B, CD44, and SETD3. Among them, CD44 was proved to be involved in biological process as cell-cell interactions, cell adhesion and migration, which was also enriched in GO analysis of this studyIn conclusion, we firstly developed a qPCR-based, normalizer-free circulating EVs RNA classifier, which could well distinguish PDAC patients from noncancerous controls. After validation of both NGS data and RT-qPCR data of our independent cohort, we rigorously confirmed the robustness and cross-platform stability of this approach. The gene-pair focused methodology we established here would also be helpful to find more qPCR-based, normalizer-free models from the public available EVs RNA databases in the studies of other cancer types.Supplementary figures and tables.Click here for additional data file.


Tyrosine kinase inhibitors (TKIs) are being increasingly used to treat various malignancies. Although they were designed to target aberrant tyrosine kinases, they are also intimately linked with the mechanisms of multidrug resistance (MDR) in cancer cells. MDR-related solute carrier (SLC) and ATB-binding cassette (ABC) transporters are responsible for TKI uptake and efflux, respectively. However, the role of TKIs appears to be dual because they can act as substrates and/or inhibitors of these transporters. In addition, several TKIs have been identified to be sequestered into lysosomes either due to their physiochemical properties or via ABC transporters expressed on the lysosomal membrane. Since the development of MDR represents a great concern in anticancer treatment, it is important to elucidate the interactions of TKIs with MDR-related transporters as well as to improve the properties that would prevent TKIs from diffusing into lysosomes. These findings not only help to avoid MDR, but also help to define the possible impact of combining TKIs with other anticancer drugs, leading to more efficient therapy and fewer adverse effects in patients. Tyrosine kinase inhibitors (TKIs) are low molecular weight (<800 Da) organic compounds that are able to penetrate the cell membrane and interact with targets inside the cell. They were developed to block the ATP-binding sites of protein tyrosine kinases, thereby inhibiting or attenuating the enzymatic activity of aberrant tyrosine kinases responsible for the malignant phenotype of cells. Such targeted therapy can be aimed at either cancer cells, by inhibiting their proliferation and affecting their susceptibility to apoptosis, or the tumor microenvironment, by affecting angiogenesis and the invasion or formation of metastases.So far, a number of TKIs has been approved by FDA for clinical use , i.e., resistance to structurally and functionally different drugs . Thus, tThe ATP-binding cassette (ABC) and the solute carrier (SLC) membrane transporters are considered to be the most relevant transporters affecting the exposure to administered TKIs . Both tyABC transporters are transmembrane proteins that have been investigated in relation to their active drug efflux irrespective of the prevailing gradient, thus causing drug resistance. The most widely studied ABC transporters with respect to MDR include P-glycoprotein , multidrug resistance protein 1  and breast cancer resistance protein . Multiple mechanisms modulating the expression of ABC transporters have been proposed , includiABCG2 gene promoter enhancing its transcription [Some TKIs are able to bind to the substrate-binding pocket of an ABC transporter  7,8,9,1,19,10,11cription .In contrast, TKIs can also act as inhibitors of ABC transporters. Similarly to their interaction with protein tyrosine kinases, TKIs block the ATP-binding sites of membrane transporters, preventing the phosphorylation and inhibiting the efflux function of transporters ,15,16,17Interestingly, ponatinib treatment resulted in a decrease in ABCB1 and ABCG2 cell surface expression, and imatinib downregulated ABCG2 expression in BCR-ABL-positive cells ,19. HoweWhen inhibiting ABC transporters, substrate drugs are no longer pumped outside of cells, and the cytotoxicity of substrate drugs in resistant cells overexpressing ABC transporters is significantly increased. In vitro studies demonstrated that TKI administration increased intracellular accumulation of rhodamine 123 or doxorubicin in multidrug-resistant cells overexpressing selected ABC transporters ,21. TreaDepending on their concentration and affinity for the transporter, a number of TKIs have been reported to interact with ABC transporters as both substrates and inhibitors A 17,19,,32,33. AWhile ABC transporters harness energy from ATP hydrolysis and function as efflux transporters, SLC transporters are primarily involved in the uptake of small molecules into cells, including TKIs  Figure B. UnlikeThe activity of imatinib was linked with the expression of organic cation transporter 1 , as it was found to be a substrate for this transporter in the CEM human leukemia cell line . A positIn contrast, the cellular uptake of nilotinib seems to be independent of OCT expression. This was observed in KCL22 human leukemia cell line overexpressing OCT1  as well The uptake of drugs into nontarget (nonneoplastic) cells by SLC transporters resulting in higher drug toxicity presents another obstacle in anticancer treatment. Organic anion transporter 6  was found to regulate the entry of sorafenib into keratinocytes, contributing to sorafenib-induced skin toxicity .Lysosomes contribute to MDR via a mechanism called lysosomal trapping. Compounds can be sequestered (trapped) in lysosomes based on their physiochemical properties: (i) basic pKa, an acid dissociation constant for the conjugated acid of the weak base, which affects the extent of lysosomal trapping, and (ii) logP, the partition coefficient between octanol and water, which regulates the kinetics of passive membrane permeability . Accumul® Red [Lysosomal sequestration has been recognized as another mechanism of resistance to TKIs , and TKI® Red .Several TKIs do not harbor physiochemical properties of hydrophobic, weak base molecules but can be entrapped in the acidic milieu of lysosomes  82,83,8,883,84. Interestingly, the ABCB1-mediated resistance phenotype of leukemia cells was stronger when ABCB1 was expressed intracellularly than when it was expressed on the plasma membrane, indicating that the accumulation of drugs in lysosomes is most likely more effective than the efflux via membrane transporters . FurtherIn many cases, resistance mediated by lysosomal sequestration is reversible. Removing sunitinib from tumor cell culture for several weeks resulted in normalization of cell lysosomal capacity and recovery of drug sensitivity . SimilarThere are several mechanisms that may reverse sequestration: either preventing the accumulation of TKIs in the lysosomes by alkalizing the lysosomal milieu or disrupting the lysosomal membrane leading to efflux of TKIs. Concomitant or sequential treatment with TKIs and drugs that interfere with lysosomal function could present an effective means of overcoming the MDR mediated by lysosomal trapping B.Several alkalizing agents have been introduced to circumvent lysosomal trapping Bi. BafilInterestingly, sunitinib itself is able to reduce the activity of acid sphingomyelinase that promotes lysosomal membrane stability, leading to destabilization of lysosomes and inducing nonapoptotic lysosome-dependent cell death .Photodestruction of lysosomes with sequestered photoexcitable TKIs presents another approach for overcoming lysosomal trapping Bii. ExpoHowever, phototherapy is limited due to superficial and local treatment options, and apart from chloroquine , not manThe ability of several TKIs to modulate ABC transporters was shown in cancer cell lines as well as in xenograft models and primary cells collected from patients ,33,40,49Promising efficacy and improved clinical outcomes were described when combining paclitaxel with neratinib  or lapatABCC3, ABCC5, and ABCC10 significantly sensitized cells to gemcitabine [Similar conclusions could be drawn from the trials that combined erlotinib and gemcitabine for the treatment of advanced pancreatic cancer ,106. Gemcitabine . As erlocitabine ,54 and acitabine , these dcitabine ,106.These examples demonstrate that TKIs added to the treatment enhance the response by not only targeting aberrant tyrosine kinases in malignant cells but also by sensitizing resistant tumors to other anticancer agents. Although the clinical trials  were focNanotechnology could present a valuable strategy in combining TKIs with conventional chemotherapeutics . PolymerA more personalized approach to therapy, such as targeted therapy using TKIs, has been increasingly used in treating various types of malignancies. Emerging evidence suggests that apart from identifying specific targets of TKIs, it is also important to evaluate other characteristics of tumor cells as well as the drug itself. The expression of uptake/efflux membrane transporters and the physiochemical qualities of TKIs affect the exposure of administered TKIs.Furthermore, the dual effects of TKIs on membrane transporters allow them to not only exert anticancer effects but also act as chemosensitizers to reverse the transporter-mediated efflux of other anticancer drugs. For instance, high expression of specific membrane transporters could provide the perfect environment for the therapeutic application of the TKIs that are transported into the cancer cells by abundant SLC transporters but at the same time inhibit the drug efflux pumps. This allows for either sequential or simultaneous administration of TKIs with other cytotoxic agents, harboring great synergistic potential, improving the efficacy of therapy, avoiding or reversing drug resistance, and possibly reducing associated toxicity and adverse effects .


DExD (DDX)- and DExH (DHX)-box RNA helicases, named after their Asp-Glu-x-Asp/His motifs, are integral to almost all RNA metabolic processes in eukaryotic cells. They play myriad roles in processes ranging from transcription and mRNA-protein complex remodeling, to RNA decay and translation. This last facet, translation, is an intricate process that involves DDX/DHX helicases and presents a regulatory node that is highly targetable. Studies aimed at better understanding this family of conserved proteins have revealed insights into their structures, catalytic mechanisms, and biological roles. They have also led to the development of chemical modulators that seek to exploit their essential roles in diseases. Herein, we review the most recent insights on several general and target-specific DDX/DHX helicases in eukaryotic translation initiation. RNA helicases play a role in all facets of RNA metabolism, ranging from transcription and translation, to processing and decay. These highly conserved enzymes use ATP to bind, unwind, and disrupt RNA structures and RNA-protein complexes . Found iiMet•GTP•eukaryotic initiation factor (eIF) 2 ternary complex availability or the flow of ribosome recruitment to mRNA templates by eIF4F . The. The2]. DDX helicases act as molecular switches that normally adopt an open conformation in the absence of RNA or ATP and close upon cooperative binding of the two molecules, which occurs via a two-step process ,25. ThisBeyond the helicase core, most DDX/DHX helicases also contain N- and C-terminal extensions. EIF4A is an exception in that it has very short extensions. For the other DDX/DHX helicases, however, these extensions impart functional diversity to the family. Among some of the activities gained are modified RNA/DNA/protein-binding, oligomerization, and nuclease capabilities . It is important to note that DHX proteins, despite being overall quite similar to DDX family members, exhibit significant differences in key residues within their conserved motifs . DDX famEIF4A was one of the first DDX proteins discovered and represents the quintessential DEAD-box helicase due to its minimal structure that contains all the activities characteristic of the family but not much more  [4]. EIFcat) of 0.01–0.05 per second [Similar to all other DDX helicases, eIF4A does not exhibit directional or processive helicase activity alone . It unwir second . The RNAr second , and altr second ,45. In cr second ,44. In tr second . While tr second . Furtherr second .While it has been demonstrated that dependence on the eIF4A unwinding activity is directly proportional to the amount of secondary structure within the 5′ leader, whether all structural barriers play equivalent roles in determining eIF4A-dependency is not clear. Certain studies, such as that of Wolfe et al.  suggest EIF4A2 has been implicated in translational repression and microRNA (miRNA) regulation through its interactions with the Ccr4-Not complex ,52. ThisEIF4A does not function alone in translation initiation. As indicated above, eIF4B and eIF4H act as eIF4A cofactors to stimulate protein synthesis . EIF4B s682-1105 , eIF4H, or eIF4B alone enhance the ability of eIF4A to unwind by just under 2-fold [682-1105 and eIF4H, or eIF4G682-1105 and eIF4B, stimulated activity synergistically, with a greater increase being observed for the 4A/4G/4B combination (~100-fold) [Only ~5% of eIF4A is thought to reside within the eIF4F complex at one time, while the majority exists as the free form in cells . Howeverr 2-fold . However00-fold) . The res00-fold) . Similar00-fold) , real-ti00-fold) , RNA pul00-fold) , and sinThe mechanism by which the ATP turnover rate increased was found to be through a joint stimulation of eIF4A by eIF4G and eIF4B of the open-closed conformational cycle of the helicase . Both eIBeyond its interactions with other initiation factors, the eIF4A availability itself is dictated by factors such as the tumor suppressor programmed cell death 4 (PDCD4) protein. Structural studies have revealed that PDCD4 binds eIF4A and prevents the formation of the closed conformation, thus blocking RNA binding . Under nThe eIF4A activity is also regulated by the long non-coding (lnc) RNA, human brain cytoplasmic RNA 1 (aka BC200), a 200-nt, predominantly cytoplasmic RNA linked to neurodegeneration and cancer initiation and progression . BC200 iWith regards to modified or additional roles of eIF4A, a recent study by Sokabe and Fraser  suggestsThe functional diversity of eIF4A was recently expanded by Tauber et al. , who desDDX3, or Ded1p in yeast, is ubiquitously expressed and is involved in a plethora of biological processes. DDX3 can be considered the next-most-investigated DDX family member, after eIF4A . Ded1p iIn translation, conflicting reports have found that DDX3X in HeLa and Huh7 reporter cell assays stimulate cap-dependent translation ,96 but cDHX29 binds to 40S ribosomes through its N-terminal domain . Its NTPDepleting DHX29 by RNAi disrupts polysomes, suppresses translation, and inhibits cancer cell proliferation . WhetherA number of less well-studied DDX/DHX-box helicases have also been implicated in translation. Although the evidence for these playing a role in the initiation process is mounting, biochemical and mechanistic insights are required to consolidate their role in this process and to properly situate where they stand in the initiation pathway . Here, win vitro and in vivo and is capable of resolving such structures [DHX36 (aka RHAU or G4R1) binds DNA and RNA G4 structures with a high affinity ructures ,115,116.ructures ,118,119.ructures . Depletiructures . A speciructures . The conructures . Among tructures . Co-crystallization of DHX36 with a DNA G4 structure has revealed that DHX36 interacts with both the top face of the G4 structure and the adjacent single-stranded nucleic acid segment . DHX36 dDHX36 can also associate with the BC200 lncRNA . This inDHX9 has been implicated in a number of cellular processes ranging from DNA replication, transcription, translation, RNA processing and transport, miRNA processing, and maintenance of genomic stability . A role DHX9 facilitates the translation of α1 and α2 type I collagen, but in this case, via a different mechanism. The 5′ leader regions of the α1(I) and α2(I) mRNAs both harbor a stem-loop to which the La ribonucleoprotein (LARP) 6 family member binds and recruits DHX9, stimulating ribosome loading and translation initiation . KnockdoDHX9 has also been implicated in IRES-mediated translation—specifically, of an IRES in the 5′ leader region of the p53 mRNA that is responsive to DNA damage. DHX9 and translational control protein (TCP) 80 cooperatively bind to the p53 IRES in response to exposure of cells to DNA-damaging agents ,135. TheIn vitro, DHX9 has also been reported to bind and resolve RNA G4 structures [ructures . Similarructures . Depletiructures . Individructures . Taken t35S-Met metabolic labelling and luciferase reporter assays [In vitro, DHX33 failed to stimulate translation of a firefly luciferase reporter mRNA, indicating it may not be limiting in reticulocyte lysates, or that its role in translation in cellula is indirect [DHX33 is not well-studied and was initially implicated in rRNA biogenesis . Howeverr assays . Sub-celr assays ,139. DHXr assays ,139. DHXhttps://oncologynibr.shinyapps.io/drive/). Overexpression of DHX33 conferred resistance to the mTOR inhibitors, rapamycin, and Torin1 [DHX33 is highly expressed in several cancer types, including glioblastoma . Knockdod Torin1 .35S-Met into cellular proteins without impacting global mRNA levels. Whether and how hCLE specifically and directly recognizes cap structures awaits structural resolution. In pull-down experiments, 144 mRNAs were found to interact with hCLE in HEK293 cells [DDX1 was found to be an insulin mRNA-binding protein in RNA pull-down assays . Prolong93 cells . Whetherin vitro synthesized 3′ leader region of p21 (cyclin-dependent kinase inhibitor) mRNA [DDX41 was postulated to have a role in translation following its identification in RNA pull-down assays using the or) mRNA . Suppresor) mRNA . The helor) mRNA . What isDDX6 has been implicated in mRNA storage, decay, and the repression of translation . It has in vitro, DDX6 levels decline during hypoxia, and DDX6 depletion stimulated DDX6 expression under hypoxia [DDX6 has also been implicated in the stimulation of translation of specific mRNAs. In this case, ectopic expression of DDX6 resulted in elevated levels of c-Myc protein in COS-7 and SW480 human colorectal cells . Transla hypoxia . In thisin vitro and in vivo [Protein synthesis is the most energetically expensive process in cells, and consequently, its regulation or dysregulation will have profound implications on cellular physiology. Ranging from cancer biology to viral replication, perturbation of translation is found in many diseases. In particular, cancer  has been tied to altered translation initiation, the rate-limiting step of the entire process . EI in vivo ,158,159. in vivo ,161,162. in vivo ,156. For in vivo . It has  in vivo . By targ in vivo ,156. Thein trans on initiation [To date, several small molecules have been identified that selectively target DDX/DHX-box helicase family members, and these have been extensively reviewed in  summari. In brieitiation . Furtheritiation . Interfaitiation , is confitiation . On the other hand, hippuristanol and 1,4-diacylpiperazine derivatives, respectively, target eIF4A1/2 and eIF4A3 by binding to the CTD and inhibiting RNA binding ,169. It Two inhibitors of DDX3X have recently been reported ,172. RK-Twenty-six compounds emerged from a high-throughput screen undertaken by Takeda for DDX41 ATPase inhibitors . Their cThere is emerging interest in the study of RNA helicases, given their fundamental role in a number of biological processes. Even with the best-studied DDX helicase, eIF4A, there is much that remains to be uncovered with respect to its role in translation alone. Why is only ~5% of eIF4A present in the eIF4F complex? What role, if any, does the remaining 95% free eIF4A play in or outside of translation? It is becoming evident that one key to understanding helicase specificity of action is defining their interactomes, as their associated factors often impact function and modulate activity in drastic manners. Helicases also exhibit diversity in activity—from resolving RNA secondary structure, to affecting the processivity of the scanning ribosome, to remodeling protein complexes. How these activities are regulated is another aspect that remains to be explored. Lastly, the characterization of small molecules that can selectively interfere with helicase activity has opened up the possibility of targeting these for drug development and provided a roadmap for effectively inhibiting downstream gene expression. Due to their central role in many disease processes, RNA helicases represent a biological target with significant, unexplored potential. Whether it be targeting host helicases in cancer or viral helicases in infection, small molecules and therapeutics can only be further developed if we continue to expand on our knowledge.


Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the “novel insertions” observed in the spike protein are naturally evolved from bat coronaviruses.As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji  Rhinolophus affinis may serve as the natural reservoir of 2019-nCoV1, it is still unclear which animal serves as an intermediate host that brought the bat coronavirus to human hosts. During the 2019-nCoV’s infection of host cells, a critical virion protein is the Spike surface glycoprotein, also known as the S protein. Spike proteins constitute the outermost component in a coronavirus virion particle and are responsible for viral recognition of Angiotensin Converting Enzyme 2 (ACE2), a transmembrane receptor on mammalian hosts that facilitate viral entrance into host cells2. Therefore, the spike protein largely determines host specificity and infectivity of a coronavirus. In this report, we re-analyzed two recent studies4, which have spurred numerous interests and discussions in the community and the society, regarding the sequence and structure of spike protein in the 2019-nCoV and the identification of its intermediate hosts.The 2019 novel conronavirus, or 2019-nCoV, recently caused a major pneumonia outbreak in Wuhan, China and has spread to at least 20 countries in the world. As of February 3, 2020, there are more than 20,000 patients diagnosed with 2019-nCoV infection in China alone with more than 400 deaths. Understanding the viral infection mechanisms and animal hosts are of high urgency for the control and treatment of the 2019-nCoV virus. While it is now commonly recognized that bats such as 3, Pradhan et al. presented a discovery of four novel insertions unique to 2019-nCoV spike protein 7 by DEMO8 to form a spike-ACE2 complex. As shown in et al. which stated that the insertions are located on the interface with ACE2.Since the structure of the spike protein in 2019-nCoV is not yet available, we used C-I-TASSER9 is to emulate the original report by Pradhan et al. which mainly aimed to identify near identical sequences. The top 5 sequence homologs (including the query itself) identified for each insertion are listed in 9, are all greater than 4, except for a bat coronavirus hit for IS2. These high E-values suggest that the majority of these similarities are likely to be coincidental.To investigate viral homologs of the four insertions, we further performed a BLAST sequence search of these four insertions against the non-redundant (NR) sequence database, restricting the search results to viruses (taxid:10239), but leaving other search parameters at default values. The choice of BLAST instead of the more sensitive PSI-BLAST algorithm14 , where many of them, including Severe Acute Respiratory Syndrome-related Coronavirus (SARS-CoV) and Middle East Respiratory Syndrome-related Coronavirus (MERS-CoV), were shown to be transmitted from bat14 . To furtet al. in the spike protein are not unique to 2019-nCoV and HIV-1. In fact, they are shared with many other viruses including the bat coronavirus. Yes, the similarities in the sequence-based alignments built on these very short fragments are statistically insignificant, as assessed by the BLAST E-values. Structurally, these “insertions” are far away from the binding interface of the spike protein with the ACE2 receptor, as shown in et al.Putting these together, we believe that there is a close evolutionary relation between 2019-nCoV and bat coronavirus RaTG13. The four insertions highlighted by Pradhan et al.4. In this study, the authors analyzed the Relative Synonymous Codon Usage (RSCU) of 2019-nCoV and 8 vertebrates, including two species of snakes (Bungarus multicinctus and Naja atra), hedgehog (Erinaceus europaeus), bat (Rhinolophus sinicus), marmot (Marmota), pangolin (manis javanica), chicken , and human (Homo sapiens). Among these vertebrates, snakes have the smallest codon usage difference (squared Euclidiean distance of RSCU) from 2019-nCoV and were therefore proposed by Ji et al. as the intermediate hosts of 2019-nCoV. This conclusion is controversial among virologists, mainly due to the lack of prior biological evidence that coronavirus can infect animals other than mammals and birds15. In this report, we further scrutinize the bioinformatics approach and the underlying biological assumptions by a large-scale replication of the RSCU analysis.Another early study attempting to understanding the infection of 2019-nCoV was performed by Ji et al. study has several limitations. First, there are only approximately 300 protein coding sequences (CDSs) in the NCBI GenBank for the snake species they chose from Bungarus multicinctus and Naja atra. These CDSs represent <2% of all protein coding genes in a typical snake genome; the genome of King cobra (Naja hannah), for example, encodes 18,387 proteins according to UniProt (https://www.uniprot.org/proteomes/UP000018936). The limited numbers of known CDSs in Bungarus multicinctus and Naja atra mean that the RSCU statistics may not reflect the actual RSCU distribution in the whole genome. Second, the Codon Usage Database used in the analysis of Ji et al. has not been updated since 2007; a re-analysis using more recent codon usage database such as CoCoPUTs is needed. Third, only 8 vertebrates were analyzed in their study, while there are >100,000 vertebrates with at least one CDS in the NCBI GenBank database. Finally, there is no established evidence that viruses evolve their codon usage to resemble that of their animal hosts16; this calls for a careful benchmark of RSCU analysis in terms of its ability to re-discover known hosts of characterized viruses.The bioinformatics analysis performed in Ji et al. to analyze the codon usage in the 2019-nCoV genome (NCBI accession MN908947.3) and those of all 102,367 vertebrate species in the CoCoPUTS database. To test whether this kind of analysis can recover known hosts of well-studied coronavirus, severe acute respiratory syndrome-related coronavirus  and Middle East respiratory syndrome-related coronavirus  are also included. Codon usage frequency are converted to squared Euclidean distance of RSCU in two separate analyses: one based on all vertebrates  have the smallest RSCU distances among all vertebrates, while another two kinds of frogs  have the smallest RSCU distances among all vertebrates with sufficient sequences. In fact, the RSCU distances between coronaviruses and frogs are consistently smaller than that between coronaviruses and the two snakes (Bungarus multicinctus and Naja atra) analyzed in Ji et al., as shown in As shown in Rhinolophus sinicus, Paguma larvata, and Homo sapiens) of SARS-CoV or the hosts (Camelus dromedarius and Homo sapiens) of MERS-CoV, as the RSCU distances of these vertebrates are all larger than the cold blooded vertebrates (frogs and snakes). In fact, SARS-CoV and MERS-CoV, the two coronaviruses known to utilize different intermediate hosts (Paguma larvata and Camelus dromedarius), have almost no difference in RSCU. These data suggest that the RSCU analysis proposed by Ji et al. cannot provide conclusive evidence for the natural, intermediate or eventual hosts of coronavirus. Based on our current knowledge of existing zoonotic coronaviruses15, we conceive that, if 2019-nCoV requires an intermediate host, this host is more likely to be a warm-blooded vertebrate , than snakes.Due to the scarcity of experimental and clinical data, as well as the urgency to understand the infectivity of the deadly coronaviruses, we have been increasingly relying on computational analyses to study the 2019-nCoV virus in terms of protein structures, functions, phylogeny, and interactions at both molecular and organismal levels. Indeed, within less than a month of the publication of the 2019-nCoV genome in January 2020, multiple bioinformatics analyses regarding 2019-nCoV have been either published or posted as preprint. While such expeditious analyses provide much needed insights into the biology of the 2019-nCoV virus, there is a caution to avoid over-interpretation of the data at the absence of comprehensive benchmarks or follow-up experimental validations. In this report, we have investigated two recently published computational analyses regarding intermediate host identification and the analysis of spike protein insertions. In both cases, we found that the conclusions proposed by the original studies do not hold in the face of more comprehensive replications of these analyses. We hope our analysis presented herein can clarify some of the misinterpretations resulted from previous bioinformatics analyses for the 2019-nCoV virus.17 and visualized by SeaView18.Global protein sequence alignment of the full-length coronavirus spike proteins was performed by MUSCLE5 to create structural models of the full-length spike protein. Here, C-I-TASSER is an extended pipeline of I-TASSER19 and utilizes the deep convolutional neural-network based contact-maps20 to guide the Monte Carlo fragment assembly simulations. Since the RBD domain of spike exhibits different conformations relative to the remaining portion of the protein, the DEMO pipeline8 was then used to re-assembly the domains and to construct a complex structure consisting of spike trimer and the extracellular domain of human ACE2, using the ACE2-bound conformation 2 of SARS-CoV spike glycoprotein (PDB ID: 6ACJ) as a template. Our complex modeling did not use the template originally used in the Pradhan et al. study (PDB ID: 6ACD) because it did not include the ACE2 receptor.We use C-I-TASSER4, the RSCU for codon j in a species is calculated askj is the number of codons synonymous to codon j (including j itself), pj is the probability of the respective amino acid being encoded by codon j among all kj synonymous codons in the CDSs of the whole genome. The difference of codon usage in two different species (a virus versus a vertebrate in our case) is defined by squared Euclidean distance of RSCU, i.e.,N=61 is the number of codons that encodes amino acids, thereby excluding the 3 stop codons. Xj and Xj’ are the RSCU for codon j in the virus and in the vertebrate, respectively. In our report, the codon usages of all vertebrates are taken from the CoCoPUTS21 database, which was last updated in January 2020. This database was therefore much more recent than the Codon Usage Database22 last updated in 2007 that was used in the previous research4. To obtain the codon usage of coronaviruses, we imported the GenBank annotations of the three coronavirus genomes to SnapGene  in the coronavirus genome.As per the previous studySupplement 1Supplement 2Supplement 3Supplement 4


Sus scrofa), little is known about the characteristics and dynamic regulation of RNA editing in the pineal gland (PG), a small neuroendocrine gland that synthesizes and secretes melatonin, which is primarily responsible to modulate sleep patterns.RNA editing is a widespread post-transcriptional modification mechanism in mammalian genomes. Although many editing sites have been identified in domestic pigs  were of the canonical A-to-I editing type, followed by C-to-T editing. Analysis of its characteristics showed that the A-to-I editing sites mostly localized in SINE retrotransposons PRE-1/Pre0_SS. Moreover, a strong deficiency and preference for guanine nucleotides at positions of one base upstream or downstream were found, respectively. The overall editing level at the puberty stage was higher than at both infancy and adulthood stages. Additionally, genome-wide RNA editing was found to exhibit a dynamic stage-specific fashion . Genes that underwent developmental changes in RNA editing were associated with catabolic processes as well as protein localization and transport functions, implying that RNA editing might be responsible for the molecular machineries of the postnatal developing PG. Remarkably, RNA editing in 3′-UTRs might regulate gene expression by influencing miRNA binding during PG development.This study analyzed the expression of adenosine-to-inosine (A-to-I) editing regulators and profiled the first dynamic A-to-I RNA editome during postnatal PG development. The results identified This study profiles the first comprehensive developmental RNA editome in the pig PG, which contributes to the understanding of the importance of post-transcriptionally mediated regulation during mammalian postnatal PG development. Moreover, this study widely extends RNA editome resources in mammals. RNA editing is a widespread post-transcription modification mechanism in mammalian genomes that alters the nucleotide composition at the RNA level, while no affecting the corresponding DNA sequence . Among dWith the rapid adoption of high-throughput sequencing technologies, the RNA editome has been profiled in human and other mammals across many tissues and developmental stages , 9–11. TThe mammalian pineal gland (PG) is a neuroendocrine transducer whose main, and most conserved, function is the conversion of photoperiodic information into the synthesis and secretion of the nocturnal hormonal signal melatonin . MelatonThis study systematically identified and characterized the RNA editome of developing porcine PGs by combining strand-specific total RNA sequencing and whole-genome re-sequencing data. Most of the RNA editing sites exhibited developmental-dependent changes during postnatal PG development. The differentially edited sites (DESs) in the 3′-UTR were found to regulated gene expression by affecting miRNA binding. The present study profiled the dynamic editome in the pig PG, which provides a rich resource for epitranscriptome studies in pigs and an exceptional opportunity to study PG development in mammals.Sus scrofa reference genome using TopHat2 (v2.1.0) [S. scrofa reference genome sequence (Sscrofa 11.1) and the gene annotation GTF file were downloaded from the Ensembl database . Information of long non-coding RNAs (lncRNAs) was obtained from our previous study [The transcriptome data, which were generated from the PG of Yorkshire (Y) pigs at postnatal days 30, 180, and 300 , were obtained from our previous study (SRA accession number: SRP172576) . The tra(v2.1.0)  with kno(v2.1.0) . The S. us study . The expGenomic DNA of an individual Yorkshire pig  at days 300, which was one of the pigs adopted for RNA-seq analysis, was isolated from the skeletal muscle tissue. The whole-genome sequencing (WGS) library was prepared and sequenced on the Illumina HiSeq X Ten platform  according to the manufacturer’s instructions. A total of 909.92 million paired-end reads (150 bp × 2) were generated from this whole-genome sequencing library, representing a genome-wide coverage depth of ~ 35×. Clean reads were aligned to the reference genome by BWA (v0.7.17). Paired reads were mapped separately using the commands “bwa aln” and “bwa sampe”, allowing a maximum of four mismatches.q > 10), mapped to the reference genome were retained by samtools (v1.6) [For the aligned bam files of WGS and RNA-Seq, duplicate reads were removed by the MarkDuplicates tool in the Picard package (v2.17.0). Only unique reads (s (v1.6) . The Hapftp://ftp.ensembl.org/pub/release-91/variation/vcf/sus_scrofa/); (2) intronic sites that occurred within 4 bp of splice junctions were removed; (3) variants in homopolymer runs were discarded; (4) variants that located within 6 bp of both ends of a read were filtered to avoid the sequencing errors; (5) Finally, BLAT alignment was used to identify and discard sites in regions with highly similarity to other regions of the genome were discarded. The remaining variants were considered as candidate RNA editing sites.The RNA editing sites in PG were identified using a previously published pipeline . This piq > 25) and at least three reads were required to support the editing form.Only A-to-I editing sites were retained for the following analysis. The overall editing rate of each PG sample was quantified as the ratio of the total number of G reads with at all A-to-I editing positions to all A and G reads covering the editing positions. To obtain the total amount of RNA editing in each PG sample, we took all editing sites we identified into account and did not set any sequencing coverage criteria . The edit-test using cutoffs of FDR ≤ 0.05 and absolute editing differences ≥0.1. Genes that contained at least one DESs were considered as differentially edited genes and were subjected to Gene Ontology (GO) enrichment analysis via the DAVID website  [Significantly differentially edited sites (DESs) between different developmental stages were identified by Student’s rf.gov/) .S. scrofa were extracted from miRBase (release 22) [For the prediction of miRNA binding, two kinds of sequences with regions (50 bp upstream and downstream) flanking the 3′-UTR DESs were prepared first: the reference sequences and A-to-I editing sequences. Then, the miRNA binding sites on the two kinds of sequences were predicted by Miranda software (v3.3a) using default parameters . Mature ease 22) .ADAR  was higher than that of ADARB1  and ADARB2  in postnatal PG and was down-regulated during development. ADAR2 expression was first down-regulated in Y180 and then up-regulated in Y300, while ADARB2 was expressed at very low levels . As expected, A-to-G (I) editing was the most dominant type of RNA editing , which was followed by C-to-T editing , followed by intergenic (20.8%) and 3′-UTR (5.2%) regions  contained DESs in CDS regions that might lead to changes in encoded amino acids, the expressions of these genes did not change significantly during postnatal PG development. For instance, the recoded RNA editing (chr14:114304916) transcription factor TATA-Box binding protein associated factor 5 (TAF5) led to an Asn → Ser amino acid substitution. TAF5 plays a major role in the formation of scaffold that is critical for transcription initiation factor TFIID complex formation [To explore the dynamic regulation of RNA editing during postnatal PG development, DESs between different developmental stages was identified with significance thresholds of |editing differences| ≥ 0.1 and FDR ≤ 0.05. A total of 3,709 DESs were identified across these three comparisons, including 1,578 Y180-Y30 , 1,056 Y300-Y30 (335 up-regulated and 721 down-regulated), and 2,135 Y300-Y180 . Interestingly, no DESs were shared by all the comparisons. A heatmap based on the editing level of all DESs showed that these particular DESs exhibited a stage-specific editing pattern across postnatal PG development . More than 90% of these editing sites were of the A-to-I type, which is indicative of a canonical ADAR-catalyzed RNA editing event. Consistent with observations in other mammals , 12, 35,RNA editing can be dynamically regulated by ADAR protein activities. The dynamic expression of RNA editing regulators indicated that A-to-I RNA editing was likely functional during the PG development. Dynamic changes in genome-wide RNA editing during postnatal PG development were demonstrated to be stage-specific. The overall editing level showed a tendency to be higher at Y180 compared with both Y30 and Y300, whereas no positive correlation was observed between the expression of ADARs and the overall editing rate. This suggests a complex regulatory mechanism of A-to-I RNA editing during postnatal PG development.PSEN1 and JAK2. PSEN1 mutation has been associated with Alzheimer’s disease [Differentially edited genes were significantly enriched in vesicle-mediated transport and phosphorylation functions, and similar results were also found in our previous gene expression analysis . Additio disease . Melaton disease . These rCACNB2, CACNA1A, and CACNA1D) encode voltage-dependent calcium channels, which are involved in a variety of calcium-dependent processes, including cell motility, cell division, and release of hormones or neurotransmitters [Additionally, genes that underwent simultaneous changes in expression and editing during postnatal PG development were associated with synaptic transmission and ion transport, both of which are essential for the process of communication between neurons. Interestingly, three of these genes (smitters . These rVAMP5 is involved in neurodegeneration and regulation of mitochondrial processes. Therefore, it can be speculated that the differential RNA editing sites might regulate both the expression and function of gene related to PG development by affecting miRNA binding.Previous studies suggested that A-to-I RNA editing might disturb the existing miRNA binding or generate novel miRNA binding , 13, 39.Overall, this study provides the first comprehensive developmental RNA editome in pig postnatal PG. This new resource is expected to contribute to the understanding of the importance of post-transcriptionally mediated regulation in mammalian postnatal PG development. Although both the function and mechanism of RNA editing in PG remain unknown, these are may be promising targets for further experimental studies of PG development.Additional file 1 Summary of the identified A-to-I editing sites in the Sus scrofa pineal gland.


Despite the effectiveness of carbon ion radiotherapy (CIRT) for locally advanced unresectable pancreatic cancer (URPC), isolated local recurrence after CIRT is a therapeutic challenge. Herein, we aimed to evaluate the feasibility and safety of a second course of CIRT for locally recurrent URPC in 10 patients. One patient developed grade 3 diarrhea immediately after the second CIRT; no other grade 3 or higher adverse events were induced by CIRT. The estimated 1-year overall survival rate and local control rate after the second CIRT were 48% and 67%, respectively. Repeated CIRT is feasible with acceptable toxicity for selected patients with locally advanced URPC after CIRT.Purpose: The feasibility and safety of re-irradiation with carbon ion beams for locally recurrent unresectable pancreatic cancer (URPC) after carbon ion radiotherapy (CIRT) was evaluated. Methods: Medical records from patients with re-irradiated URPC who were treated with CIRT between November 2017 and February 2019 were reviewed. Inclusion criteria were (1) isolated local recurrence after CIRT, (2) URPC, and (3) tumor located at least 3 mm from the gastrointestinal tract. The first and second CIRT irradiation doses were 55.2 Gy  in 12 fractions. Results: Ten patients met the inclusion criteria. The median follow-up period was 25.5 months  after the first CIRT and 8.9 months  after the second CIRT. The median interval between the initial CIRT and the local recurrence was 15.8 months . One patient developed grade 3 diarrhea immediately after the second CIRT; no other grade 3 or higher adverse events were attributed to CIRT. The estimated 1-year overall survival, local control, and progression-free survival rates after the second CIRT were 48%, 67%, and 34%, respectively. Conclusion: Repeated CIRT is feasible with acceptable toxicity for selected patients with locally advanced URPC after CIRT. Chemotherapy or chemoradiotherapy (CRT) is the standard of care for locally advanced unresectable pancreatic cancer (URPC). However, because the pancreas is surrounded by radiosensitive organs such as the stomach, duodenum, liver, kidneys, and intestines, delivering high dose X-ray irradiation is usually limited. Consequently, long-term local tumor control after irradiation for locally advanced URPC is insufficient. A meta-analysis comparing chemoradiotherapy with radiotherapy or chemotherapy alone from 15 randomized controlled trials (1128 patients) .Conversely, carbon ion radiotherapy (CIRT) has emerged as a promising therapy over the last two decades. The carbon ion (C-ion) beam has two advantages. First, because of the Bragg peak, the CIRT beam can be stopped at the end of the tumor, resulting in a decreased irradiated volume in healthy organs. Second, the relative biological effectiveness (RBE) of the CIRT high linear energy transfer is approximately three times higher than that of X-rays ,5,6.Several studies have focused on the efficacy and safety of CIRT for locally advanced pancreatic cancer ,8. In a From November 2017 to December 2019, 10 patients met the inclusion criteria and underwent re-irradiation with CIRT. The patient characteristics are summarized in A typical case (patient #8) is shown in The median follow-up periods after the first and second CIRTs were 25.5  months and 8.9  months, respectively. The 1-year OS, LC, and progression-free survival (PFS) rates after the second CIRT were 48%, 67%, and 34%, respectively . The medAdverse events that occurred after the second CIRT are summarized in Dose volume histragam (DVH) parameters between the first and second CIRTs are summarized in The management of isolated local recurrences of URPC after definitive radiation therapy is challenging. Surgical resection is the most promising approach; however, it is often not possible in unresectable tumors or when prior radiotherapy causes fibrosis development in the area. Of note, pancreatic cancer is a systemic disease. However, when the recurrent tumor is truly localized and potentially curable but difficult to resect, there are currently no curative treatment alternatives. Our findings showed that re-irradiation with carbon ion beams is feasible with acceptable toxicities for selected patients with locally advanced URPC.When considering re-irradiation using carbon ions, the development of severe adverse events is the most important concern. In this study, no grade 3 or higher late adverse events developed. Conversely, one grade 3 acute adverse event (diarrhea) developed in this study and the concurrent chemotherapy may have contributed or caused this adverse event. According to previous studies, the incidence of grade 3 or higher non-hematological toxicity caused by CIRT was approximately 4–10% ,8. Thus,The 1-year LC rate in the current study was 67%, which is slightly lower than the reported 84% LC rate for the first treatment [Another possible reason for the decreased LC rate after the second CIRT is the acquisition of radioresistance. At present, there are very few basic studies on the acquisition of radioresistance to carbon ion beams after CIRT. One report demonstrated that repeated carbon ion irradiation induced moderate radioresistance to carbon ions in a mouse squamous cell carcinoma cell line . HoweverRecently, several reports described re-irradiation for pancreatic cancer using C-ion radiotherapy , stereotFor recurrence after radiation therapy, chemotherapy should be considered a standard therapy. We routinely recommend adjuvant chemotherapy after CIRT. However, chemotherapy regimens currently available for pancreatic cancer are limited, and there is often no effective chemotherapy for recurrence. Furthermore, CIRT can be used concurrently with chemotherapy to add a local effect to the benefits of systemic chemotherapy.In recent years, re-irradiation with CIRT for head and neck tumors  and lungOur study has some limitations. First, the study included a small number of patients. Second, the follow-up periods were relatively short. Conversely, this analysis suggests that adverse events following re-irradiation are acceptable under appropriate patient selection; hence, we will accumulate more cases and follow-up for a longer period.We reviewed the medical records of prospectively registered re-irradiated pancreatic cancer patients treated with C-ion radiation therapy at Gunma University Heavy Ion Medical Center (GHMC) between November 2017 and February 2019. The eligibility criteria for the re-irradiation were as follows: (1) history of CIRT for locally advanced URPC with curative intent; (2) unresectable disease at the time of recurrence; (3) no distant metastasis except for para-aortic lymph nodes that could be included in the irradiation target volume; (4) tumor location at least 3 mm from the external wall of the gastrointestinal tract; (5) Eastern Cooperative Oncology Group performance status of 0 to 2; and (6) well-informed and consented to join this treatment. Patients were excluded if they had (1) active ulcer of the stomach and/or duodenum; (2) active infection site in the upper abdomen; and (3) metallic stent inserted to treat obstructive jaundice. The treatment protocol of the current study was reviewed and approved by the Gunma University Ethics Committee of Human Clinical Research, and all patients signed an informed consent form before the initiation of therapy.The heavy ion accelerator at GHMC generated C-ion beams and 290 MeV/u, 380 MeV/u, and 400 MeV/u beam energy levels were selected according to the depth of the tumor. We used the XiO-N system  for treatment planning. This system incorporates a dosing engine for ion beam RT (K2dose). MIM Maestro  was used to evaluate and sum up the treatment plans. We calculated the clinical radiation dose, which was expressed as Gy (RBE), based on the physical dose multiplied by the RBE of the C-ion beams . Before 3. The maximum irradiation dose to the spinal cord was restricted to <30 Gy (RBE). The dose constraints were generally based on the same criteria for the first and second treatments, but more restrictive dose constraints were used for the second treatment for patients in whom the gastrointestinal tract was close, based on the decision of the radiation oncologist. Based on the anatomy of the pancreas, CT for treatment planning for the first and second CIRTs is rigidly matched on the radiation treatment planning system to confirm the dose distribution for the surrounding risk organs. Then, the dose distribution of the second CIRT was optimized such that the 70% isodose lines of the first and second treatments did not overlap on the structures of the gastrointestinal tract. Prescribed doses were 55.2 Gy (RBE) in 12 fractions for standard cases. If the dose constraints for the OAR did not meet the criteria, the prescribed dose was reduced to 52.8 Gy (RBE) in 12 fractions for the second CIRT.The treatment planning CT images were merged with the dynamic enhanced CT and magnetic resonance imaging and FDG-PET CT images to delineate the GTV. The CTV was defined as the GTV, including at least 5 mm margins in all directions; for initial CIRT, 1 cm margins in the long axis of the pancreas were added. The prophylactic lymph node and neuroplexus regions were included in the initial CIRT but not the second CIRT. The planning organ at risk volume (PRV) was obtained by adding a 2 mm margin to the gastrointestinal tract. Areas that overlapped with the PRV of gastrointestinal tract were excluded from the CTV. The planning target volume (PTV) included the CTV with a 3 mm margin for possible positioning errors. When the PTV overlapped with an organ at risk, the margin was accordingly reduced. The maximal irradiated doses of the stomach and duodenum were restricted to <45 Gy (RBE). The stomach and duodenum volumes that were irradiated with >30 Gy (RBE) were kept under 10 cmAcute and late toxicities were graded according to the National Cancer Institute Common Toxicity Criteria (version 4.0). The highest grade within 3 months and after 3 months was evaluated as acute and late toxicity, respectively. Tumor response was evaluated by comparing pre- and post-treatment CT scans and FDG-PET scans and the RECIST was used for evaluation. When increased FDG accumulation was detected in the tumor evaluated using RECIST, the tumor was determined to be a progressive disease. Residual FDG accumulation 6 months after treatment was considered a local recurrence. Follow-ups were performed at least every 6 months. CT and/or FDG-PET scans, tumor markers, physical examination, and interviews were performed at each visit.p-value of <0.05 was considered statistically significant. All statistical analyses were conducted using IBM SPSS Statistics for Mac, version 26.0 .The LC, PFS, and OS rates were calculated using the Kaplan–Meier method. Local recurrence was defined in terms of lesions occurring in the PTV according to CT or FDG-PET scans. LC was defined by the absence of local recurrence, as indicated by no evidence of an increase in tumor size by >20% on CT and no accumulation by FDG-PET at >6 months after treatment. Univariate analysis was performed, and the log-rank test was used to compare subgroups. A Although the observation period and the number of cases are not yet sufficient, carbon ion re-irradiation for local recurrence after CIRT for locally advanced pancreatic cancer had acceptable adverse events and a favorable LC rate. It is suggested that re-irradiation with carbon ion is an effective treatment option for patients with local recurrence and no obvious distant metastases.