Escherichia coli (ETEC) is an important cause of post-weaning diarrhea (PWD) worldwide, resulting in huge economic losses to the swine industry worldwide. In this study, to understand the pathogenesis, the transcriptomic analysis was performed to explore the biological processes (BP) in porcine intestinal epithelial J2 cells infected with an emerging ETEC strain isolated from weaned pigs with diarrhea. Under the criteria of |fold change| (FC) ≥ 2 and P < 0.05 with false discovery rate < 0.05, a total of 131 referenced and 19 novel differentially expressed genes (DEGs) were identified after ETEC infection, including 96 upregulated DEGs and 54 downregulated DEGs. The Gene Ontology (GO) analysis of DEGs showed that ETEC evoked BP specifically involved in response to lipopolysaccharide (LPS) and negative regulation of intracellular signal transduction. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that immune response-related pathways were mainly enriched in J2 cells after ETEC infection, in which tumor necrosis factor (TNF), interleukin 17, and mitogen-activated protein kinase (MAPK) signaling pathways possessed the highest rich factor, followed by nucleotide-binding and oligomerization domain-like receptor (NLRs), C-type lectin receptor (CLR), cytokine–cytokine receptor interaction, and Toll-like receptor (TLR), and nuclear factor kappa-B (NF-κB) signaling pathways. Furthermore, 30 of 131 referenced DEGs, especially the nuclear transcription factor AP-1 and NF-κB, participate in the immune response to infection through an integral signal cascade and can be target molecules for prevention and control of enteric ETEC infection by probiotic Lactobacillus reuteri. Our data provide a comprehensive insight into the immune response of porcine intestinal epithelial cells (IECs) to ETEC infection and advance the identification of targets for prevention and control of ETEC-related PWD.Enterotoxigenic  Escherichia coli (ETEC) is one of the most common causes of diarrhea in animals and humans. Enterotoxigenic Escherichia coli is responsible for an estimated 75 million diarrheal episodes and approximately 50,000 deaths in children younger than 5 years annually  or heat-stable enterotoxin (ST) enterotoxins, leading to acute intestinal inflammation and diarrhea  are in the front line of host defense against pathogens and possess as the crucial mediators of barrier function. Enterotoxigenic diarrhea . Besidesdiarrhea . Lipopoldiarrhea . The detdiarrhea , 13. HowEscherichia coli K88 (serotype O149:K91:K88ac) challenge induces differential expression of genes encoding intestinal ion transporters and water channel aquaporins in young piglets, probably by regulation of the cyclic adenosine monophosphate–protein kinase A signaling pathway  for 24 h at 37°C under microaerophilic conditions. After overnight incubation, bacteria were inoculated 1:100 in fresh De Man, Rogosa, and Sharpe broth and grown for approximately 8 h until reaching the mid-log phase.5 cells/well) were seeded onto a six-well collagen-coated polytetrafluoroethylene transwell filter, and confluent cell monolayers were treated under two different conditions: (i) medium alone (CN); (ii) ETEC infection alone [3 × 106 colony-forming unit (CFU)] at a multiplicity of infection of 30:1 (EC). At 2 h after ETEC infection, the IPEC-J2 cells were washed three times with phosphate-buffered saline (PBS), and then, total RNA was extracted using TRIzol (Invitrogen) according to the manufacturer's instructions for subsequent library construction and sequencing.The porcine intestinal epithelial cell line (IPEC)-J2 cells were cultured in Dulbecco's modified Eagle medium/F12 medium  supplemented with 10% heat-inactivated fetal calf serum, 100 μg/ml of streptomycin, and 100 U/ml of penicillin at 37°C in an atmosphere of 5% carbon dioxide and 95% air at 95% relative humidity. Cells (1 × 106 CFU); (iii) incubation with BP325 (3 × 107 CFU) only for 2 h; or (iv) preincubation with BP325 (3 × 107 CFU) for 2 h before exposure to ETEC infection. At 2 h after preincubation with BP325, the IPEC-J2 cells were washed three times with PBS and immediately infected with ETEC (3 × 106 CFU) for 2 h. After infection, the IPEC-J2 cells were washed three times with PBS, and then, total RNA was harvested using TRIzol for quantitative real-time polymerase chain reaction (qRT-PCR) analysis, and the supernatant samples were collected for enzyme-linked immunosorbent assay (ELISA) analysis.For validating whether the genes related to differential inflammatory pathways obtained from RNA-seq were the targets in controlling ETEC infection, confluent J2 cell monolayers were treated under four different conditions: (i) medium alone; (ii) ETEC infection alone . Samples with qualified purity  were used for subsequent sequencing library preparation. Poly-(A)-containing messenger RNA (mRNA) was enriched using oligo (dT) beads and were broken into short fragments using mentation buffer. The first-strand complementary DNA was synthesized with reverse transcriptase and random hexamer primers using mRNA as templates. Then, the second-strand complementary DNA was synthesized using the buffer, DNA polymerase I, deoxynucleoside triphosphates, and RNase H, followed by end-repair and adapter ligation. Finally, PCR amplification was carried out to obtain the final libraries. The constructed library was quantified and pooled in the flow cell. After cBot clustering using a cBotCluster Generation System, the library preparations were sequenced using Illumina high-throughput sequencing platform Novaseq 6000, and paired-end reads of 150-bp length were generated.http://asia.ensembl.org/Sus_scrofa/Info/Index) using TopHat v2.1.1 (http://ccb.jhu.edu/software/tophat/index.shtml). The mapped reads were assembled into transcripts using StringTie v1.3.3b (http://ccb.jhu.edu/software/stringtie/). The genes were annotated by BLAST based on Gene Ontology (GO), Cluster of Orthologous Groups of proteins (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.The raw reads were quality filtered using Trimmomatic software. The clean reads were obtained after discarding reads containing adapter sequences, low-quality reads with Q10 <30, reads with undetermined base information >10%, and reads that were shorter than 50 bp after the adapter was removed and truncated. The clean reads were aligned to the reference genome Sscrofa11 of the pig (http://deweylab.biostat.wisc.edu/rsem/). Differentially expressed genes (DEGs) between CN and EC groups were identified using DESeq2 v1.24.0 (http://bioconductor.org/packages/stats/bioc/DESeq2/). The P-value was adjusted using Benjamini and Hochberg's approach (BH) for controlling the false discovery rate. Genes with an adjusted P < 0.05 and fold change (FC) ≥ 2 or ≤ −2 were assigned as DEGs. A heatmap of DEGs was constructed using the heatmap.2 implemented in the R package gplots (https://cran.r-project.org/web/packages/gplots).The expression of genes was calculated and normalized to fragments per kilobases per million reads (FPKMs) using RSEM v1.3.1 (https://github.com/tanghaibao/GOatools). The P-value was adjusted by BH, and GO terms with adjusted P < 0.05 were considered significantly enriched. Furthermore, KEGG enrichment analysis was carried out to assess significantly enriched metabolic or signal transduction pathways using KOBAS v2.1.1 (http://kobas.cbi.pku.edu.cn/download.php).Gene Ontology enrichment analysis was carried out to specify the potential roles of DEGs based on Fisher's exact test using Goatools v0.6.5 (https://cran.r-project.org/web/packages/Venndiagram) to obtain EC-unique genes and an overview of the DEGs among different annotation levels. Correlation analysis of gene expression was performed, and differential correlation was calculated using Fisher's transformation of Spearman rank correlation to determine the significance of a change in correlation between two conditions. The statistic P-value was corrected by the BH method. Correlations with an absolute Spearman's correlation >0.8 and adjusted P < 0.05 were transformed into links between two DEGs, and correlation networks were displayed with Cytoscape v2.8.2. Protein–protein interaction (PPI) analysis of DEGs was carried out based on the Search Tool for the Retrieval of Interacting Genes/Proteins database v11.0 (http://string-db.org/), and the minimum Search Tool for the Retrieval of Interacting Genes/Proteins score was set at 1,000. The interaction with a combined score >0.4 was considered to be significant. The protein network was visualized using Cytoscape v2.8.2.A Venn diagram was generated using the Venndiagram R package . Primer sequences are listed in The concentrations of interleukin (IL)-1β and IL-6 were determined using commercially available porcine-specific ELISA kits  according to the manufacturer's instructions.SRR13291685–SRR13291693.The raw transcriptome data have been deposited in the US National Center for Biotechnology Information Sequence Read Archive database under accession no. post-hoc test. A P-value of < 0.05 was considered statistically significant. Data were visualized using GraphPad Prism 5 software . All experiments were performed three times.Statistical analysis was performed using the IBM SPSS Statistics 21  software package. Natural logarithm transformation was performed before analysis for ELISA data of IL-1β and IL-6 to achieve a normal distribution. Differences between means were compared using one-way analysis of variance, followed by Tukey's honestly significant difference sus scrofa genome, respectively, and the GC content for CN and EC groups was more than 51.2%  were yielded across six samples. After removing low-quality reads, ambiguous and adaptor reads, retaining 344 million high-quality clean reads  were used for subsequent assembly and analysis. The average of Q20 and Q30 of clean reads across all samples was above 98.3 and 94.9%, respectively, indicating that the obtained clean reads were of high quality. Compared with the pig reference genome Sscrofa11, 93.5 and 93.2% of total reads for CN and EC groups were uniquely mapped on the an 51.2% .The clean reads of genes were normalized to FPKM values to precisely evaluate the gene expression levels. The saturation curve of sequencing showed that the FPKM values of approximately 42% of genes in CN and EC groups were below 3.5, and only a few of 7% genes were highly expressed with FPKM values higher than 60. Most of the genes with medium or above expression levels  were nearly saturated at 51% of the sequencing reads , indicating that the overall quality of saturation was high, and the sequencing quantity could cover most of the expressed genes . A SpearA total of 27,001 genes were found from all samples, including 25,880 (95.8%) referenced genes and 1,121 (4.2%) unannotated novel genes. Of 25,880 referenced genes, a total of 23,375 genes could be annotated using GO , KEGG , COG , NR , Swiss-Prot , and Pfam  databases . The COGGene Ontology analysis revealed that a total of 20,481 genes were mapped to 64 GO terms, with 17,454 genes assigned to biological process (BP), 18,2023 genes assigned to cellular component (CC), and 17,580 genes assigned to molecular function (MF). In the BP ontology, cellular processes  and single-organism processes  were the most enriched GO terms. In the CC ontology, the most enriched terms were cell , and cell part  was the most enriched GO term. In the MF ontology, binding  and catalytic activities  were the most enriched GO terms .Kyoto Encyclopedia of Genes and Genomes pathway analysis helps to better understand the biological functions of genes. A total of 3,209 genes were annotated and classified into six first KEGG categories  and 335 second KEGG categories. Signal transduction  was the most abundant KEGG pathway, followed by the sensory system  .A total of 12,227 novel transcripts were found in the 61,408 transcripts identified, including 389 transcripts with exonic overlap with reference on the opposite strand, 506 with transfer falling entirely within a reference intron, 9,450 with potentially novel isoform: at least one splice junction was shared with a reference transcript, 1,265 unknown and intergenic transcripts, and 350 with generic exonic overlap with a reference transcript .A total of 1,106 novel genes  and 11,670 novel transcripts  were successfully annotated after BLAST, with 242 and 8,280 in the GO database, 63 and 6,932 in the KEGG database, 289 and 9,906 in COG database, 757 and 10,909 in the NR database, 187 and 9,782 in the Swiss-Prot database, and 122 and 8,907 in the Pfam database, respectively . The COGFrom the Venn diagram, 134 referenced genes were uniquely present in the EC group . A totalP < 0.05 and log2FC ≥ 1 for upregulated genes or log2FC ≤ −1 for downregulated genes using the DESeq method were carried out to identify the DEGs. The list of DEGs, along with their FCs and annotations, were presented in CCL20, NR4A1, NR4A3, FOSB, NR4A2, EGR1, FOS, RCAN1, DDIT4, and CXCL2. The top 10 downregulated genes were GBA3, POU3F2, CFAP69, MASP2, ND5, USP27X, FIGNL1, CBX2, KBTBD7, and LCMT2.A rigorous comparison at adjusted P < 0.05. The top 20 ranked GO terms of DEGs are shown in Gene Ontology analysis was performed to characterize the functional terms of gene expression changes exposure to ETEC infection. Among the 131 referenced DEGs mentioned earlier, 113 were divided into three principal GO terms of level 2 : MF , IL-17 , and MAPK  signaling pathways possessed the highest rich factor , C-type lectin receptor (CLR) , cytokine–cytokine receptor interaction , TLR , and NF-κB  signaling pathways. Furthermore, the DEGs mapped to the immune response-associated KEGG pathways mentioned earlier and their expression patterns were identified  and immune system (21 genes), and the KEGG pathways enriched in the downregulated DEGs were mainly specific to environmental adaptation (four genes) and energy metabolism (four genes) . Kyoto Eh factor . Besidesentified . Of the entified , includiRNAsel gene encoding ribonuclease L was downregulated, and the other 29 genes were upregulated after ETEC infection (FOS and ENSSSCG00000031912) participated in ETEC-induced activation of TNF, IL-17, and MAPK signaling pathways. Five genes  were involved in the TLR and NF-κB signaling pathways. No gene was related to all inflammatory signaling pathways. Correlation analysis of gene expression in the 131 DEGs provided a rationale for the functional significance of “core DEGs” . The main protein interaction cluster derived from the 131 DEGs contained 40 nodes, each representing one protein and connected by 89 edges  had the highest scores for betweenness centrality, indicating they were most important for connections with other proteins.Among the 30 “core DEGs,” only the nfection . Accordinfection , some “cre DEGs” . Some ge89 edges . The IL-CCL20, CXCL2, CXCL8, IL-1β, IL-6, NFKBIA, MAP3K12, MAPK15, TNFAIP3, FOSB, FOS, and JUNB) involving the inflammatory signaling pathways were chosen for qRT-PCR to validate the RNA-seq results. The results showed that the differential expression patterns of the 12 “core DEGs” were in accord with those detected by RNA-seq analysis  contained the most abundant DEGs, whereas the response to LPS and negative regulation of intracellular signal transduction terms classified into BP class occupied the strongest enrichment degrees. Comparable studies have shown that ETEC induced typical immune-related signaling pathways in porcine IECs, but the extent of the inflammatory response is different due to the difference in ETEC strain, infection time, infection concentration, and cell lines. The cell junction organization and extracellular matrix organization terms are enriched in the DEGs of ETEC strain 11501-infected IPEC-1 cells .Escherichia coli LPS binding to TLR4 promotes activation of downstream TRIF-dependent and MyD88-dependent pathways, which in turn induces production of a cascade of activated molecules, such as NF-κB, AP-1, and inflammatory cytokines , pro-inflammatory cytokine IL-6, chemokine CXCL2, CXCL8, and CCL20 were directly related to the IL-17 pathway. In the peripheral blood leukocytes, the recombinant IL-17C could activate the NF-κB signaling and strongly promote the expression of chemokines , pro-inflammatory factors , and antibacterial peptide hepcidin  are significantly upregulated  belonging to the 30 “core DEGs” had the highest scores for betweenness centrality, indicating they were most important for connections with other proteins. The FOS gene family consists of four members: FOS, FOSB, FOSL1, and FOSL2. These genes could encode leucine zipper proteins that can dimerize with proteins of the JUN family, thereby forming the transcription factor complex AP-1. Genes for AP-1 are immediate-early genes that regulate a wide range of physiological responses such as cell death, inflammation, and proliferation. In this study, FOS, FOSB, and JUNB genes were significantly upregulated and directly related to multiple enriched pathways, such as TLR, NF-κB, IL-17, MAPK, and TNF pathways, as shown by Venn analysis. Further qRT-PCR analysis validated the RNA-seq data. Our findings indicate that immune response-related signaling pathways mediated by nuclear transcription factor AP-1 and NF-κB determine the fate of ETEC-infected J2 cells and may also be the targets for further prevention and control.Furthermore, of the 131 DEGs, a total of 30 DEGs were involved in the immune response-associated KEGG pathways discussed earlier. These 30 “core DEGs” determined the IPEC-J2 cell immune response to ETEC infection. Correlation analysis of gene expression results showed that some genes in the “core DEGs,” such as Lactobacillus is a major component of the gut microbiota and can protect the host against enteric pathogens through modulation of both local and systemic host immune responses   . Lactobadomain 1 . Our pretivation , 49. Swi pathway . Our datIn conclusion, ETEC infection elicits a strong immune response of porcine IECs, which is a result of the cooperation of multiple signaling pathways, including TNF, IL-17, MAPK, NF-κB, NLR, CLR, TLR, and cytokine–cytokine receptor interaction signaling pathways. Some key node genes, especially the nuclear transcription factor AP-1 and NF-κB, participate in the immune response to ETEC infection through an integral signal cascade and can be target molecules for prevention and control of enteric infection by probiotics.SRR13291685–SRR13291693.The raw transcriptome data have been deposited in the US National Center for Biotechnology Information Sequence Read Archive database under accession no. The animal study was reviewed and approved by the Animal Care and Use Committee of Beijing University of Agriculture.QW, D-FC, Y-HZhu, and Y-HZhang: conceived and designed the experiments and prepared the manuscript. J-XL, X-YC, PC, Y-DW, T-JS, ML, and R-YX: performed the RNA extraction, qRT-PCR, and ELISA. QW, D-FC, Y-HZhu, and Y-HZhang: performed the analysis of data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


MicroRNAs (miRNAs), a class of endogenous, non-coding RNAs, which is 20–24 nucleotide long, regulate the expression of its target genes post-transcriptionally and play critical roles in plant normal growth, development, and biotic and abiotic stresses. In cells, miRNA biogenesis and stability control are important in regulating intracellular miRNA abundance. In addition, research on these two aspects has achieved fruitful results. In this review, we focus on the recent research progress in our understanding of miRNA biogenesis and their stability control in plants. MicroRNAs (miRNAs), which are 20–24 nucleotide long, endogenous, non-coding RNAs, repress its target gene expression through sequence complementarity. miRNAs play an important role in all aspects of normal plant growth, development, and biotic and abiotic stress . Thus, tlin4 was discovered in Caenorhabditis elegans in the early 1990s (MIRNA (MIR) genes are transcribed by DNA-dependent RNA Polymerase II (Pol II), and the primary transcripts of MIRs (known as pri-miRNAs) are 5′ capped, 3′ polyadenylated, and/or spliced, similar to message RNA (mRNA) (The first miRNA ly 1990s . ResearcA (mRNA) .Pri-miRNAs, a typical hairpin-like structure RNAs, can be recognized by the processing complex, subsequently undergoing two major nuclear processing steps mediated by Dicer-like protein DCLs and two important cofactors, Serrate (SE) and Hyponastic leaves 1 (HYL1). Dicer-like 1 (DCL1) is primarily responsible for miRNA production. Notably, the production of miR839 and miR822 is dependent on DCL4 but not DCL1 . After tThe miRNA/miRNA* duplex is methylated by the Hua Enhancer 1 (HEN1), a terminal methyltransferase, which methylates miRNA/miRNA* and siRNA/siRNA* duplexes on its 2′ OH of the 3′-terminal nucleotide particularly in plants . Then, tMIR genes are transcribed by DNA-dependent RNA polymerase II (Pol II). Thus, some factors associated with Pol II may also affect MIR transcription , MEDIATOR 17 (MED17), and MEDIATOR 18 (MED18). In these mutants, the transcription level of pri-miRNAs is suppressed , a histone acetyltransferase, targets a number of MIR genes, and it is responsible for the acetylation of H3K14 of these loci , Topless Related 1 (TPR1), SMALL1 (SMA1), Short Valve 1 (STV1), Increased Level of Polyploidy1-1D (ILP1), NTC-Related protein 1 (NTR1), and HASTY (HST). NOT2a/2b, a pair of NOT2_3_5 domain-containing proteins, promotes the transcription of MIR genes via interacting with Pol II (Similar to the formation of message RNA (mRNA), the formation of pri-miRNAs follows the same processes including transcription, capping, 3′ polyadenylation, and splicing. cription . The medppressed . Tho2/Hpcription . The C-tnylation . Histoneese loci  and othelt phase . Chromatlt phase . Some trtivities . CDF2, ativities . Apart fh Pol II . ELP2/5,h Pol II . SNC1, ah Pol II . Recent h Pol II ,2018. ILh Pol II . HST, whArabidopsis DCL1 is embryo lethal , or SmD3/SmB bodies . DCL1, Ho lethal . HYL1 ano lethal . Increaso lethal .DCL1 was first identified in pri-miRNAs and pre-miRNAs processing . Severalhyl1 mutant, more pri-miRNAs but less mature miRNAs accumulate , STA1, and ILP1/NTR1. GRP7, a hnRNP-like glycine-rich RNA-binding protein, interacts with pri-miRNAs in vivo and promotes pri-miRNA splicing  into pri-miRNAs and promotes miRNA biogenesis by interacting with Pol II and TOUGH, a known regulator in miRNA processing  load into the effector protein AGO to form the RISC complex. AGO1 is the major effector protein for the vast majority of miRNAs. In recent years, relevant research in AGO1 transcriptional and translational regulation , AGO sorMaintenance of intracellular miRNA homeostasis is important for plants in the adaption of environmental and developmental changes. Except for miRNA biogenesis, miRNA stability control also plays key roles in miRNA homeostasis regulation. Several aspects, including 3′-end modification (methylation and uridylation), endoribonuclease-mediated miRNA degradation, association with AGO protein, and miRNA-target interaction, can affect miRNA stability. The factors involved in these processes all affect the stability of miRNAs.Arabidopsis, HEN1 Suppressor 1 (HESO1) is the first gene identified for miRNA uridylation , a family of DEDDh 3′ to 5′ exonucleases, showed nuclease activity by removing 3′ end methylated nucleotides directly and promoted the degradation of some methylated miRNAs . SDN1 is targets .AGO protein not only serves as an effector, but also plays critical roles in maintaining the stability of miRNA . As prevArabidopsis genome encodes 10 AGO proteins, and it can be grouped into three clades: AGO1/5/10, AGO2/3/7, and AGO4/6/8/9 , a non-coding RNA, whose sequence provides a complementary motif of miR399, due to its loosely complementary around cleavage site, as a result, miR399 is sequestered from its endogenous targets by IPS1 binding. Consequently, miR399 is sequestered from its endogenous targets . Based oradation . RISC inradation . Therefo6-methyladenosine, 5-methylcytidine, N1-methyladenosine, pseudouridine, and so on) for pri-miRNAs and/or miRNAs and their biological function remain to be further explored. Further studies should identify and characterize more factors to clarify the biological function and significance of these factors in all of these processes, particularly in miRNA stability control.The transcription, stability, processing of pri-miRNA, miRNA loading, and stability control require multiple proteins and their cooperation to maintain normal function. In recent years, a large number of factors involved in miRNA biogenesis and stability control have been identified, and the molecular mechanism has been revealed. However, many questions still persist. For example, pri-miRNA processing occurs in D-bodies; however, the formation of D-bodies and how the core members of the processing machinery located at D-bodies remain unknown. DCL1 plays a critical role in processing, but little is known about the post-translational modification of DCL1. Moreover, the regulation of DCL1 activity and HEN1 remains unknown. 3′-end modification, AGOs, and targets also affect the miRNA stability, but the detailed mechanisms remain unknown. Apart from terminal modification, other modifications (NLZ and SG drafted the manuscript and revised it. YX, MS, and XJ retrieved and collected the references of miRNA biogenesis and drafted the first picture. SC and ZH retrieved and collected the other references and drafted the second picture. All authors have read and agreed to the publishment of this version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: YTHDF1 is highly expressed in multiple tumors and affects tumor progression. However, there are only a few comprehensive studies on the analysis of YTHDF1 in esophageal cancer.Methods: We analyzed YTHDF1 expression in pan-cancer by comparing both the GEPIA and TCGA cohorts, and further verified the differences in YTHDF1 expression between the ESCA and normal groups by the GEO ESCA cohort and in vitro experiments. The correlation of YTHDF1 expression and the clinical characteristics of ESCA patients was analyzed using the TCGA ESCA clinical data. The GO and KEGG enrichment analyses of the YTHDF1 coexpressed genes were completed by bioinformatics analysis, and the GGI and PPI were constructed for the YTHDF1, respectively. The relationship between YTHDF1 expression and the infiltration of ESCA immune cells was analyzed by using the TIMER database and the TCGA ESCA cohort. The relationships between YTHDF1 expression levels and glycolysis and ferroptosis-related genes were analyzed using the TCGA and GEPIA ESCA cohorts. Finally, the ceRNA network that may be involved in YTHDF1 in ESCA was predicted and constructed through a variety of databases.Results: YTHDF1 was overexpressed in various cancers, and in vitro experiments confirmed that YTHDF1 expression was higher in ESCA samples than in normal samples. The expression of YTHDF1 has some accuracy in predicting the tumor outcome. Expression of YTHDF1 was significantly associated with multiple clinical features in ESCA patients. GO and KEGG enrichment analyses indicated that YTHDF1 coexpressed genes involved multiple biological functions. There is a potential association between YTHDF1 expression and multiple immune cell infiltration, glycolysis, and ferroptosis-related genes in ESCA. YTHDF1 may be involved in multiple ceRNA regulatory networks in ESCA, including PAXIP1-AS1/hsa-miR-376c-3p/YTHDF1 axis, THUMPD3-AS1/hsa-miR-655-3p/YTHDF1 axis, and SNHG20/hsa-miR-655-3p/YTHDF1 axis, respectively.Conclusion: YTHDF1 can serve as a biomarker of ESCA, related to the immune cell infiltration of ESCA, regulation of glycolysis and ferroptosis, and the ceRNA regulatory network. The latest research shows that esophageal carcinoma (ESCA) ranks eighth in the global cancer incidence rate, and the death rate ranks sixth . AlthougOne of the most common internal modifications of mammalian mRNAs is N6-methyladenosine (m6A) RNA modification . More anTumor immune cell infiltration, inhibition of glycolysis pathway, regulation of ferroptosis, and regulation of the ceRNA network are an intense focus of research of tumor gene therapies, which are extensively applied in the research and treatment of ESCA . NeverthIn the present research, The Cancer Genome Atlas (TCGA) and gene expression synthesis (GEO) dataset cohorts were downloaded and processed. Multiple databases and online websites were analyzed and used. To study the expression difference of YTHDF1 in pan-cancer and verify the expression difference of YTHDF1 mRNA and protein between the ESCA and normal groups by cell experiment and immunohistochemical (IHC) staining experiment, the coexpression gene network of YTHDF1 in ESCA was investigated, and the possible biofunctions and signaling pathways of these genes were studied. Eventually, the relation between YTHDF1 and tumor cell immune infiltration, glycolysis, ferroptosis, and the ceRNA network was surveyed, to provide a basis for the development of new treatment strategies for ESCA. The schematic diagram of the research design is shown in http://gepia.cancer-pku.cn/index.html)     and TCGAer.gov/)  datasetsSE23400)  databaseSE23400) . All tecp < 0.05. Volcano maps and heatmaps were drawn using the ggplot2 package in the R language. The top 200 genes positively associated with YTHDF1 expression were selected for subsequent analysis. Gene ontology  term and Kyoto Encyclopedia of Genes and Genomes  pathway enrichment analyses of the above genes were performed using the clusterProfiler package in the R language  is an online website that can analyze target genes based on the hypothesis of gene function (GeneMANIA (function . GeneMANwww.string-db.org) is an online website tool that collects and integrates all available data in the public PPI sources (STRING ( sources . In the https://cistrome.shinyapps.io/timer) is a database for the systematic analysis of various types of tumor immune information  module of TIMER tool was performed to connect the genetic copy number variation (CNV) of YTHDF1 with the relative level of tumor infiltrating cells. Taking the median expression of YTHDF1 as the boundary, the TCGA ESCA cohort was divided into two groups: high and low YTHDF1 expression group. The CIBERPORT software package of R language was used to evaluate the relative proportion of 22 immune cells between the high and low YTHDF1 expression groups  (https://mirtarbase.cuhk.edu.cn/) (https://ophid.utoronto.ca/mirDIP/) (https://www.mirnet.ca/miRNet/) (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index) (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex-predicted) (The miRNAs targeting YTHDF1 were predicted using starBase (edu.cn/) , miRTarBedu.cn/) , mirDIP mirDIP/) , miRNet miRNet/) , and micS/index)  databaseedicted)  databaseedicted)  and igraedicted)  were useThe GEPIA website analysis showed that the YTHDF1 expression was higher in both DLBC (lymphoid neoplasm diffuse large B-cell lymphoma), ESCA, GBM (glioblastoma multiforme), LGG (brain lower grade glioma), PAAD (pancreatic adenocarcinoma), READ (rectum adenocarcinoma), and THYM (thymoma) than in the normal group . AnalysiBy analyzing the ESCA cohort of TCGA and GEO, the results showed that YTHDF1 expression levels were significantly elevated in the ESCA samples compared with the controls . Meanwhip < 0.05). At |cor| > 0.7 and p < 0.05, seven genes were obtained, all of which were positively correlated with YTHDF1 expression. The genes in the top three of the correlation were GMEB2 , DIDO1 , and PRPF6 , respectively. We used heat maps  and macrophages , and negatively associated with the expression level of dendritic cells . In addition, YTHDF1 CNV was found to be significantly associated with the infiltration levels of B cells, CD8+ T cells, CD4+ T cells, and macrophages (p < 0.05). CIBERPORT software package was utilized to investigate the relative proportion of immune cells between the high and low YTHDF1 groups (p = 0.027) and T-cell CD4+ memory resting (p = 0.049) increased significantly, while the relative proportion of B-cell memory (p = 0.007) and myeloid dendritic cell resting (p = 0.006) decreased significantly.The relation between the expression level of YTHDF1 in ESCA and six immune infiltrating cells was discussed by using the TIMER database . The res1 groups . The resWe analyzed the ESCA cohort of TCGA and GEPIA to explore the relation between YTHDF1 and the expression of 14 glycolysis-related genes in ESCA. The results showed that in the ESCA cohort of TCGA and GEPIA, the expression of YTHDF1 was remarkably positively associated with PDHB and SLC2A3, and negatively associated with the expression of LDHA . In addiThe difference analysis between the high and low groups showed that the expression of PDK3, PGK1, and SLC2A3 increased in the high expression group of YTHDF1 compared with the low expression group .We analyzed the ESCA cohort of TCGA and GEPIA to explore the relation between YTHDF1 and the expression of 25 iron death-related genes in ESCA. The results showed that in the ESCA cohort of TCGA and GEPIA, the expression of YTHDF1 was remarkably positively associated with HSPA5, FANCD2, SLC1A5, TFRC, NCOA4, LPCAT3, DPP4, CS, CARS1, ACSL4, and AIFM2, and negatively associated with the expression of HSPB1 . In addiThe difference analysis between the high and low expression groups indicated that compared with the low expression group, the expression of HSPA5, FANCD2, CISD1, TFRC, LPCAT3, DPP4, CS, CARS1, and AIFM2 in the YTHDF1 high expression group increased, while the expression of HSPB1 decreased .We predicted and constructed the ceRNA network of YTHDF1 in ESCA using bioinformatics database. We predicted 149, 120, 682, 155, and 104 miRNAs targeting YTHDF1 using starBase, miRTarBase, mirDIP, miRNet, and microT_CDS tools, respectively. The UpSetR diagram shows the predicted results of the five databases. A total of 10 miRNAs were predicted in all the five databases, and the 10 miRNAs were, hsa-miR-369-3p, hsa-miR-374a-5p, hsa-miR-660-5p, hsa-miR-376c-3p, hsa-miR-655-3p, hsa-miR-374c-5p, hsa-miR-374b-5p, hsa-miR-421, hsa-miR-19a-3p, and hsa-miR-19b-3p, respectively . In addiWe predicted lncRNAs that may bind to two miRNAs using miRNet, starBase, and LncBase v2.0 tools, respectively. As shown in In order to further verify the possibility of ceRNA, we analyzed the association between these three lncRNAs and YTHDF1 expression. The results showed that the three lncRNAs were significantly positively correlated with the expression of YTHDF1 . The comde novo lung adenocarcinoma  . The res of ESCA . So, we The enhancement of glycolysis pathway can provide sufficient energy for the formation of tumor cells, and then enhance the proliferation and development of tumor cells . PreviouThe discovery of a new regulatory cell death process, ferroptosis, has made progress in cancer treatment. However, ferroptosis plays a dual role of promoting and inhibiting cancer in cancer progression . It has A newly discovered mechanism of mutual regulation between RNAs, the ceRNA regulatory network, is widely present and plays important roles in different tumors . In this study, we predicted and constructed a ceRNA regulatory network involving YTHDF1 in ESCA, which were PAXIP1-AS1/hsa-miR-376c-3p/YTHDF1 axis, THUMPD3-AS1/hsa-miR-655-3p/YTHDF1 axis, and SNHG20/hsa-miR-655-3p/YTHDF1 axis, respectively. It has been found that PAXIP1-AS1 , THUMPD3In conclusion, this is the first comprehensive investigation of the relation between YTHDF1 expression and ESCA tumor cell immune infiltration, glycolysis, ferroptosis, and the ceRNA regulatory network. YTHDF1 expression is correlated to multiple immune infiltrating cells, which may impact the tumor immunity of ESCA by affecting the infiltration of dendritic cell. YTHDF1 has a strong correlation with glycolysis-related gene SLC2A3, which may impact the glycolysis pathway of ESCA by affecting the expression of SLC2A3. However, YTHDF1 is potentially associated with multiple ferroptosis-related genes, which may affect the regulation of ferroptosis in tumor cells by affecting the expression of these genes. Finally, we also found that YTHDF1 may affect the progress of ESCA by participating in multiple ceRNA regulatory networks. YTHDF1 can be utilized as a possible biomarker for ESCA diagnosis and treatment. Of course, we will further verify the results of our analysis through more experiments in the future.


Chronic pancreatitis (CP) is a progressive, irreversible inflammatory disorder of the pancreas, which results from interrelations between different genetic and environmental factors. Genetic variants are the primary cause of the disease in early-onset nonalcoholic CP patients. Novel CP-associated genes are continuously emerging from genetic studies on CP cohorts, providing important clues for distinct mechanisms involved in CP development. On the basis of functional studies, the genetic alterations have been sub-grouped into CP-driving pathological pathways. This review focuses on the concept of CP as a complex disease driven by multiple genetic factors. We will discuss only well-defined genetic risk factors and distinct functional pathways involved in CP development, especially in the context of the early-onset nonalcoholic CP group. The diagnostic implications of the genetic testing will be addressed as well. Chronic pancreatitis (CP) is a long-lasting inflammatory disease starting from acute pancreatitis (AP) (sudden onset) through recurrent acute (more than one episode of acute pancreatitis) up to a progressive, irreversible inflammatory disorder. This disease is associated with periods of exacerbation and remission. It leads to irreversible pancreatic gland destruction with fibrosis. Consequently, permanent morphological changes in pancreas develop, followed by exocrine and endocrine malfunctioning ,2. Due tpancreas divisum, ansa pancreatica), biliary tract diseases , autoimmune pancreatitis, or lipid disorders  [The prevalence of CP in adults is estimated at 27.4 per 100,000 people in Western Europe, whereas the prevalence of pediatric CP is 5.8 per 100,000 . Causes obesity) ,7.Although complex interrelations between environmental and genetic factors are surely involved in adult CP development, hereditary pancreatitis (HP) occurs approximately only in 4% of all CP cases in this group. On the contrary, the most significant role in CP development in early-onset pancreatitis, especially in children, is played by gene variants ,6,8,9.13C-mixed triglyceride breath test (13C-MTBT) [Genetic variants are important risk factors for early CP and can add specificity to the likely etiology, but they are neither necessary nor sufficient to make a diagnosis. Diagnosis of CP is based on studies of pancreas structure and function as a result of multidisciplinary cooperation of geneticist, gastroenterologist, radiologist and pathomorphologist. According to INSPIRE  group recommendations, the diagnosis of CP requires 1 of 3 criteria: (1) abdominal pain consistent with pancreatic origin, 2) evidence of exocrine pancreatic insufficiency and (3) evidence of endocrine-pancreatic insufficiency, which were associated with abnormalities on imaging studies  and parenchymal tissues ). Imagi evidence3C-MTBT) . At pres3C-MTBT) ,11.PRSS1 gene (encoding cationic trypsinogen) are present or when acute pancreatitis or CP of unknown etiology occurred in 2 first-degree relatives or in 3 or more relatives of the second degree in two or more generations [HP develops mainly in childhood. According to current recommendations, the hereditary pancreatitis (HP) is recognized when pathogenic variants in erations . Howevererations .PRSS1, pathogenic variants in CFTR (encoding cystic fibrosis transmembrane conductance regulator gene) and SPINK1 (secretory trypsin inhibitor) genes were linked with increased risk of CP. Nevertheless, up to 30–40% of CP still remains idiopathic [CPA1, CTRC, TRPV6, CEL-HYB1 allele). Hence, currently, the CP is regarded as an oligogenic disease for which development depends on complex integrations between undefined number of genes and different functional pathways  in hereditary pancreatitis supported the classical theory of pancreatitis as an autodigestive disease [PRSS1 stimulate autoactivation of cationic trypsinogen within the pancreas, triggering the activation of pancreatic digestive zymogenes cascade, which leads to autodigestion of the pancreas. Therefore, most of heterozygous pathogenic variants of PRSS1 are regarded as a cause of hereditary pancreatitis with autosomal dominant inheritance [SPINK1) and chymotrypisinogen C (CTRC) [SPINK1 and CTRC genes disrupt inhibition of cationic trypsinogen activation or its degradation, respectively [The identification of activating pathogenic variants in cationic trypsinogen gene  disease . Pathogeeritance . The genC (CTRC) ,17,18. Sectively .Another pathway involved in CP genetic risk is endoplasmic reticulum (ER) stress. This mechanism is independent of the trypsin and is caused by misfolding of digestive enzymes due to genetic variants , which lPRSS1 and CPA1 genes [CEL and CELP hybrid allele (known as CEL-HYB1), may be associated with the misfolding-dependent pathway [Up to now, the best identified misfolding variants increasing CP risk or causing hereditary pancreatitis have been discovered in A1 genes ,22. More pathway .CFTR (cystic fibrosis transmembrane conductance regulator) and the TRPV6 , seem to be involved. It is speculated that the variants of these genes cause the alteration of the ductal fluid Ca2+ concentration and, hence, increase ductal Ca2+ levels that eventually would stimulate autoactivation of trypsinogen [The third pathomechanism related to pancreatitis genetic risk is a ductal pathway which is caused by genetic variants in the genes encoding proteins involved in ductal secretion. So far, variants in two genes encoding transmembrane ionic channels: psinogen ,25. PRSS1 pathogenic variants belong to the trypsin-dependent pathway and cause hereditary pancreatitis with incomplete penetrance. The presence of these variants encourages cationic trypsinogen activity by different mechanisms, i.e., direct autoactivation stimulation, increase of CTRC-mediated processing of the activation peptide or by reduction of CTRC-dependent degradation [PRSS1 variants worldwide are p.Arg122His and p.Asn29Ile, which are present in 60–80% of HP cases [PRSS1 pathogenic variants such as p.Arg112Cys, p.Ala16Val and p.Glu79Lys are less frequent and p.Asp19Ala, p.Asp21Ala, p.Asp22Gly and p.Lys23Arg are rare variants [PRSS1 variants may differ between different countries. For example, in Polish children with HP (age at onset around 8.5 years), the most common pathogenic variant is p.Arg122His (34%), which is consistent with the other data [The most frequently found radation . The mosHP cases ,29. The variants . The disher data ,31,32. Hher data ,31,32,33Functionally, the p.Arg122Cys and p.Arg122His variants prevent CTRC-dependent trypsinogen degradation, whereas the p.Asn29Ile affects trypsinogen biochemistry, strengthening the process of its autoactivation . SimilarPRSS1 variants  have an impact on the cationic trypsinogen activation peptide and hence stimulate autoactivation in a CTRC-independent way [Rare dent way ,37.PRSS1-PRSS3P2 conversion allele found in two unrelated patients in Poland and Germany [PRSS1 and PRSS3P2 contained three non-synonymous amino acid changes, p.Ser115Thr, p.Arg116Pro and p.Arg122His. The pathogenic effect of the PRSS1-PRSS3P2 conversion allele is due to increased trypsinogen activation and secretion, attributed to p.Arg122His and p.Arg116Pro variants, respectively. The distinctive mechanism based on both increased trypsinogen activation and secretion was observed in case of the  Germany . The conPRSS1 variants associated with the misfolding-dependent pathway are rare. They cause reduced secretion, intracellular retention and elevated ER stress markers. The p.Leu104Pro and p.Arg116Cys were detected in HP with incomplete penetrance, whereas p.Cys139Phe, p.Cys139Ser and p.Gly208Ala are mostly associated with sporadic CP [The radic CP . PRSS1 variants presumed to cause CP, such as p.Asp100His, p.Cys139Phe, and p.Lys92Asn, p.Ser124Phe and p.Gly208Ala, lead to diminished trypsinogen secretion in in vitro studies, indicating the misfolding-dependent pathway as a probable pathomechanism in this case [The SPINK1-related elevated risk of chronic pancreatitis. Both of them, reduced expression of mRNA and protein, cause diminished production of protective trypsin inhibitor [There are two major molecular mechanisms conferring the nhibitor .SPINK1 is p.Asn34Ser, which according to meta-analysis studies was detected in 9.7% of CP alleles (469/4842) compared to 1% of control alleles (96/9714) [SPINK1 have a frequency of 8–28% [The most frequent variant found in 96/9714) . In Poliles 469/442 comparof 8–28% . In Euroof 8–28% . Moreoveof 8–28% . The hapof 8–28% . SPINK1 p.Asn34Ser heterozygous variants contribute to CP, whereas p.Asn34Ser homozygous are causative of CP [Moreover, it has not been established yet whether heterozygous p.Asn34Ser, when present together with additional genetic or environmental risk factors, should be treated as a disease-modifying or causative factor . It shouve of CP ,48.SPINK1 variant—the intronic c.194+2T>C—is rather clear as it leads to skipping of exon 3 [SPINK1 have been reported, according to the HGMD database. Although in case of certain variants miscellaneous mechanisms were postulated to affect SPINK1 activity, the reduced expression and/or secretion seem to be mostly involved [Unlike in the case of p.Asn34Ser, the pathomechanism of another frequent f exon 3 ,49,50. Binvolved . CTRC generally cause loss of protein function [The pathogenic variants of function ,52. For function . In adulfunction ,18,53. Ifunction . In thisCTRC heterozygous variant found in 30–40% of CP patients, including early-onset CP patients [CTRC mRNA expression, possibly caused by altered pre-mRNA splicing [There is a frequent p.Gly60Gly patients ,54,55. ICPA1 (carboxypeptidase A1) gene are strongly associated with CP (OR ~ 25), and, in particular, with sporadic cases of CP with early onset (OR ~ 80) [CPA1 variants confer increased CP risk by misfolding affecting secretion, causing intracellular retention and inducing ER stress, rather than affecting trypsin activity. Nevertheless, it has not been analyzed if CPA1 variants are causative risk factors for the familial or hereditary disease. However, a study by Kujko et al. shed light on this issue, showing that the novel CPA1 variant p.Ser282Pro co-segregated with pancreatitis in two Polish families with autosomal dominant hereditary pancreatitis [CPA1 variant causes protein retention and degradation (indicative for misfolding), leading to ER stress. Interestingly, the same mechanism has also previously been described for the p.Leu104Pro PRSS1 variant [Digestive carboxypeptidases are pancreatic metalloproteases, which hydrolyze C-terminal peptide bonds in dietary polypeptide chains. In 2013, Witt et al. reported that variants in the OR ~ 80) . It was reatitis . The age variant . CPA1 human variant: p.Asn256Lys, and demonstrated development of spontaneous and progressive CP together with ER stress symptoms in their pancreas [Importantly, recently, a study on a mouse model for the misfolding-dependent pathway was published. The authors constructed the knock-in mice harboring the most frequent pancreas .CEL gene encodes a carboxyl ester lipase , which is a digestive enzyme produced in pancreas acinar cells and secreted into the small intestine. The CEL protein is activated by bile salts and is involved in hydrolysis and absorption of cholesterol and lipid-soluble vitamin esters [The n esters . CEL-HYB (currently referred to as CEL-HYB1), which originates from a non-allelic homologous recombination between CEL and its pseudogene CELP. The average population frequency of CEL-HYB1 recombinant was 0.5–1%, while in a group of German and French idiopathic CP, it was 5-fold overrepresented [CEL-HYB1 allele may increase CP risk through the misfolding-dependent pathway [CEL-HYB1 allele is not present in three independent CP Asian cohorts—from China, Japan and India. Hence, the authors came up with an idea that the CEL-HYB1 allele could be a European ethnicities-specific CP risk factor [CEL-HYB1 allele frequency between CP patients and controls . Moreover, the distribution of alleles in the Polish control group was higher than in German, French and Norwegian controls: 2.4% (12/500) vs. 0.7–1%, 0.7% and 0.3%, respectively [In 2015, Fjeld et al. characterized a hybrid allele resented . The cel pathway . Howeverk factor . Unexpecectively ,61,62.CEL-HYB, p.Thr488-Ile548, in CP cases but not in controls. On the contrary, the frequency of another, similar variant, thep.Thr488-Thr548, was not significantly different between patients and control groups. It was suggested that the CEL-HYB1 allele harboring the p.Thr488-Ile548 haplotype may be responsible for increased risk of CP. Surprisingly, the functional assays on p.Thr488-Ile548 and p.Thr488-Thr548 variants revealed that both variants show similar pathogenicity and cause proteotoxic protein-misfolding [CEL-HYB1 allele variants in CP development remains unclear and requires further investigation [In 2020, further studies with German and French patients revealed the presence of two amino acid substitutions in sfolding . The distigation .CFTR gene encodes the cystic fibrosis transmembrane conductance regulator, the ABC family membrane transport protein channel, localizing in the apical plasma membrane of epithelial cells [The al cells . CFTR gene are responsible for cystic fibrosis but are also risk factors of CFTR-related disorders. CF patients may clinically present with exocrine pancreatic insufficiency and, rarely, pancreatitis. Studies on cystic fibrosis indicate, however, that not all CFTR variants are correlated with pancreas insufficiency [Pathogenic variants in the ficiency ,66,67.3−) secretion [CFTR is crucial for the maintenance of proper ductal fluid levels and consistency and plays a role in regulating bicarbonate absorption [TRPV6 is a tetrameric, calcium-selective channel participating in epithelial Casorption . The TRPsorption . TRPV6 variants in Polish CP pediatric patients . These TRPV6 variants contribute to disease pathogenesis in 1.5–4% of the patients [2+ removal from the ducts, the loss-of-function variants could cause elevated ductal Ca2+ concentration, which would stimulate autoactivation of trypsinogen and increased trypsin activity. However, the exact mechanism of how TRPV6 variants alter pancreatic Ca2+ balance requires further investigation. Probably, the cumulative genetic handicap is necessary to develop CP, which is supported by the fact that TRPV6 variants often coexist with pathogenic variants in other CP susceptibility genes such as SPINK1 and CFTR, according to Masamune et al. [In nonalcoholic early-onset CP, patients ,27. Since et al. .PRSS1, CFTR genes), together with a variant protecting the pancreas from active trypsin  or chronic inflammation. Such interactions are well-documented in patients who carry both SPINK1 and CFTR variations. Pancreatitis risk increases approximately 60-fold by having the heterozygote genotype in SPINK1 and CFTR , whereas the CP risk increases up to 900-fold by having a CFTR compound heterozygote genotype (two heterogeneous variants) and the SPINK1 p.Asn34Ser variant in one allele [SPINK1 p.Asn34Ser in one allele and at least one abnormal allele in the CFTR accounts for 1.5–7.7% of the cases, depending on the population analyzed. Trans-heterozygotes of SPINK1 p.Asn34Ser and CTRC  were detected in 1% of German CP patients and in 2.3% of young French ICP patients [It is known that CP may develop due to simultaneous presence of variants in different recessive genes . The pare allele ,77. Coinpatients ,18. CFTR/SPINK1 trans-heterozygosity has a synergistic effect [SPINK1/CTRC trans-heterozygosity is rather connected with an additive or a multiplicative impact, as those genes are involved in the same pathway [trans-heterozygosity of TRPV6 and CFTR variants may have an additive effect , whereas in case of TRPV6/SPINK1, trans-heterozygosity may show a synergistic effect [While the c effect , the SPI pathway . Similarc effect . PRSS1, CPA1, SPINK1, CTRC and CFTR genes as well as testing for the CEL gene pathogenic hybrid allele is recommended [PRSS1, especially p.Arg122His, could considerably increase the risk of pancreatic adenocarcinoma (87 times more likely at age 55) [PRSS1 [According to the European guidelines, all patients with a family history or early-onset disease should be offered genetic testing for associated genes . Since aommended . Heredit age 55) . Since m age 55) . Therefo) [PRSS1 ,82. TRPV6 gene should be included for early-onset CP patients. Besides, in the light of the novel data, screening towards functionally defective CEL-HYB1 allele variants seems to be more appropriate than investigating the presence of the allele itself. Moreover, there are still several questions unanswered with the respect to the multifactorial etiology of idiopathic CP. Here, both molecular diagnostic and genetic counseling are becoming more important and require further consideration.In recent years, the knowledge about molecular aspects of chronic pancreatitis has markedly increased, and therefore, the current guidelines need further updates. In this respect, the genetic screening of the PRSS1 or CPA1 pathogenic variants the case is rather clear, it becomes much more complicated in idiopathic CP when other genes are involved. The combinations of genetic factors may be epistatic, while others are additive, therefore the genetic risk assessment should be handled on a case-by-case basis. Unfortunately, no systematic approach is available to predict the effects of most of these complex genotypes [In case of probands’ relatives, molecular testing is a basis for establishing a carrier status, which enables not only CP risk assessment, but also implementation of preventive actions or, in milder cases, even early diagnosis ,84. Whilenotypes . Since ienotypes . In 2013, Masson et al. proposed a clinically useful classification of variants and genotypes found in CP patients, which were divided into three categories: causative, contributory or neutral. The factors considered in this assessment include: allelic frequency in healthy and CP groups, functional defects, the relative importance of their associated genes in the pathogenesis of chronic pancreatitis and intergenic interactions, when applicable .SPINK1 (n = 56) and CTRC (n = 87). In the case of SPINK1, 25/56 variants were scored as established risk alleles, 8/56 as likely risk alleles, 17/56 as uncertain and 6/56 as benign. In the case of CTRC, the quantity of individual groups was: 24/87, 4/87, 22/87 and 37/87, respectively [More recently, another group proposed a scoring system, which enables the classification of variants into four categories: (1) established risk alleles, (2) likely risk alleles, (3) uncertain risk alleles and (4) benign variants. This categorization of variants is based on the results of in vitro experiments, functional and epidemiological data and in silico predictions. Importantly, the authors elaborated a very clear scoring system, which enables rather unequivocal classification. This scoring system was used to evaluate the status of all variants known at the time in ectively . These results, otherwise very important and useful in clinical settings, also illustrate the complexity of interpretational issues in CP genetics, which still involves not only establishing of individual variant status, but also assessment of intergenic relations. PRSS1, CPA1, CTRC, CFTR, SPINK1 and TRPV6, especially in early-onset cases and after the other major causes of the disease have been excluded. The diagnostic utility of the CEL-HYB1 allele and its variants requires further research. Nonalcoholic chronic pancreatitis is a complex disease which may be caused by multiple genetic factors. Genetic testing for CP patients with unknown etiology is recommended by analyzing major CP-associated genes such as Genetic counseling both before and after molecular testing is strongly recommended in all patients. Importantly, CP patients having genetic variants should be carefully followed by the clinicians since they may have a high risk of developing further medical complications such as pancreatic exocrine insufficiency, diabetes mellitus and pancreatic cancer . The progress and availability of large-scale molecular methods enable unprecedented insight into hereditary pancreatitis genetics. Hence, proof that variants in other genes contribute to early-onset nonalcoholic chronic pancreatitis will surely emerge. Clearly, the genetic guidelines for clinical use are needed to help to stratify the risk of CP associated with different genetic factors.


Natural killer (NK) cells and type 1 innate lymphoid cells (ILC1) are specific innate lymphoid cell subsets that are key for the detection and elimination of pathogens and cancer cells. In liver, while they share a number of characteristics, they differ in many features. These include their developmental pathways, tissue distribution, phenotype and functions. NK cells and ILC1 contribute to organ homeostasis through the production of key cytokines and chemokines and the elimination of potential harmful bacteria and viruses. In addition, they are equipped with a wide range of receptors, allowing them to detect “stressed cells’ such as cancer cells. Our understanding of the role of innate lymphoid cells in hepatocellular carcinoma (HCC) is growing owing to the development of mouse models, the progress in immunotherapeutic treatment and the recent use of scRNA sequencing analyses. In this review, we summarize the current understanding of NK cells and ILC1 in hepatocellular carcinoma and discuss future strategies to take advantage of these innate immune cells in anti-tumor immunity. Immunotherapies hold great promise in HCC, and a better understanding of the role and function of NK cells and ILC1 in liver cancer could pave the way for new NK cell and/or ILC1-targeted treatment. Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and accounts for ~90% of all cases . Its incTreatment options for this disease have increased considerably over the past decade . HepaticUnresectable HCC remains an area of unmet medical need, with limited treatment options after progression on atezolizumab–bevacizumab. Thus, alternative approaches must be identified to provide new therapeutic options for HCC patients. Given the promising results revealed by the use of ICB in clinical trials ,19, the Innate lymphoid cells are evolutionary conserved innate immune cells that play essential homeostatic function in tissues ,34. ThisTbx21), which drive the production of key cytokines and cytotoxic molecules. These include granzyme, perforin and interferon (IFN)-γ [+lineage−CD127+CD16− NK group 2 member A (NKG2A)−NKp44−CD117− [+CD3− population, typically defining cNK cells, an equal proportion of CD56bright and CD56dim are found in the liver, mirroring proportions found in lymph nodes  and contrasting with the proportions found in blood  [bright subset express features of tissue-residency such as CXCR6 and CD69 and thereby resembling mouse ILC1. This long-lived subset also expresses high levels of Eomes but low levels of T-bet [bright lrNK cell subset, a small population express CD49a and T-bet, but lack Eomes expression [brightCD49e− lrNK cell subset that expresses CXCR6 and Eomes, but lacks T-bet [Discovered more than 45 years ago, conventional NK (cNK) cells were first described as an effector lymphoid population with the capacity to lyse tumor cells ,45,46,47 (IFN)-γ . As earl (IFN)-γ  describe (IFN)-γ . Over th (IFN)-γ  Table 1Tbx21, wh4−CD117− . Similar4−CD117− . Howeverctively) ,59. The of T-bet ,63,64,65pression , reminispression  reportedks T-bet , a phenoks T-bet . Thus, mCD160, T cell immunoglobulin and ITIM domain (TIGIT), Killer Cell Lectin Like Receptor C1 (KLRC1), Granzyme K (GZMK), X-C Motif Chemokine Ligand 1 (XCL1) and XCL2 compared to cNK cells. Conversely, cNK cells displayed increased cytotoxic functions through augmented expression of GZMB, GZMH, Granulysin (GNLY) and enhanced FCGR3A (coding for CD16), NCR3, CD226, and KLRG1 expression compared to lrNK cells [−CD56+ subsets, as previously performed for the blood and spleen [Over the past four years, a number of single cell RNA sequencing studies have profiled the mouse and human liver, in health and disease, shedding light on the potential interactions that might occur between immune and parenchymal cell subsets at the organ level ,81,82,83NK cells ,78,84. Cd spleen , using sDespite obvious similarities, numerous preclinical studies have shown that liver NK cells and ILC1 differ in many aspects ,86. ThesDevelopmental pathways. The transfer of bone marrow into lethally irradiated recipient mice allows the reconstitution of hepatic NK cells but very few liver ILC1 [−/−Il2rg−/−Rag2 mice gives rise to liver ILC1 but not cNK cells [ver ILC1 . Howeverver ILC1 , indicatver ILC1  have revver ILC1 , who fouver ILC1 . The disNK cells ,89. ThesTranscriptional programs. The development of hepatic ILC1 and cNK cells relies on distinct transcriptional programs that involve four key transcription factors. These are Eomes, T-bet, zinc finger protein 683 (ZNF683 or Hobit) and nuclear factor, interleukin 3 regulated (Nfil3) (Eomes expression for their development [Tbx21 (encoding for T-bet) expression for their differentiation into mature hepatic ILC1 [Hobit deficient mice, hepatic ILC1 are absent, whereas the frequency of liver cNK cells are not impacted. This is due to the absence of Hobit expression in this cell type [Nfil3 is differentially required for liver ILC1 and cNK cell development. While Nfil3 deficiency in early progenitors strongly impacts the development of all ILC subsets, including cNK cells [Nfil3 strongly impaired the development of mucosal intraepithelial ILC1 [ (Nfil3) . cNK celelopment , while Itic ILC1 , furthertic ILC1 ,70  have increased expression of activating receptors NKp44, NKp46 and NKG2D and enhanced expression of TRAIL, but reduced capacity to produce IFN-γ, granzyme B and perforin compared to CD56dim or CXCR6− NK cells [dim subset [NK cells . DespiteNK cells ,70  can transdifferentiate into intermediate ILC1 (CD49a+CD49b+), sharing features from both ILC1 and NK cells, and into ILC1 (CD49a+CD49b−) [olecules ,100. In +CD49b−) ,102  ,102. In +CD49b−) ,105,106.+CD49b−) . Harmon +CD49b−)  found th+CD49b−) . When cu+CD49b−) . Similar+CD49b−) , suggest+CD49b−)  are comp+CD49b−) . However+CD49b−) , suggest+CD49b−) , may als+CD49b−) .The immune system is key to fighting against tumor cells and preventing cancer development and progression but, paradoxically, also contributes to HCC promotion as a key extrinsic factor of inflammation-driven carcinogenesis. Innate immunity is the first line of defense against viral infections and tumor development . While NSince the 1980s, seminal studies have demonstrated the critical role of NK cells in tumor immune surveillance. In mice, the absence of NK cells by using depleting antibodies or engineered mouse models deficient in NK cells or NK cell cytotoxicity resulted in increased tumor development and metastases ,123,124.Decreased NK cell frequency and numbers in HCC patients. Lower NK cell numbers, reduced NK cell activity or the absence of an NK cell signature in HCC are all associated with poor patient outcomes [bright subset compared with healthy donors. The expression of granzyme, perforin and IFN-γ were all reduced, resulting in a reduced ability to lyse target cells in vitro [bright NK cells, associated with decreased expression of CXCR6 and Eomes and an increased frequency of T-bet+ and CD49a+ CD56bright NK cells. In contrast, Sun et al. [bright cells with a corresponding increase of CD56dim NK cells compared to peri-tumoral tissues. Similar to blood NK cells, the function of intratumoral NK cells is impaired due to the reduced expression of cytotoxic molecules and pro-inflammatory cytokines [outcomes ,133,134.outcomes ,136,137.outcomes , suggestin vitro . Regardiin vitro  have repn et al.  observedytokines , indicatytokines . Togetheytokines .+ lrNK cells in human HCC correlated with poor prognosis. These cells express higher levels of immune checkpoint molecules compared to CD49a− NK cells and include TIGIT, CD96 and PD-1 [+ subset expresses lower levels of NCR NKp30 and NKp46 as well as other activating receptors compared to CD49a− NK cells. The accumulation of lrNK cells was associated with increased tumor diameter, reduced disease free and overall survival of HCC patients [More recently, Sun et al.  have obsand PD-1 . In addipatients , potentiNK cells are one of the main actors that efficiently detect and kill tumor cells. Once activated, owing to the expression of activating receptors, NK cells kill target cells via the release of effector molecules forming pores. In contrast, to protect healthy cells from our body, NK cells are also equipped with multiple inhibitory receptors to avoid non-specific targeting and auto-immune disorders.NK cell activating receptors. NK cell activity is determined by a fine balance between activating and inhibitory receptors, recognizing ligands expressed by immune and non-immune cells, which, together, dictate NK cell responsiveness [siveness ,141,142 siveness , NKG2C:Csiveness ,145, NKGsiveness ,147, NCRsiveness ,149, NCRsiveness , NCR3  and CD160 expression, which was associated with the impairment of NK cell function and poor prognosis [+ NK cells has revealed an enrichment in the expression of the inhibitory receptors PDCD1 and LAG3 , as well as in the expression of the anti-inflammatory cytokine IL-10. This immunoregulatory phenotype was associated with a diminution of the expression of the cytotoxic molecules PRF1 and GZMB and the activating receptors NCR2, NCR3, CD226, CD244 and NKG7. This exhausted NK cell phenotype was associated with reduced disease-free and overall survival [+ NK cells displayed increased levels of expression of NCRs, NKG2D and CD244, as well as TIGIT and increased IFN-γ production [+ NK cells showed signs of exhaustion. They exhibited reduced IFN-γ production and increased inhibitory receptor expression compared with their CD160+ peri-tumoral counterpart. Despite this functional impairment, high infiltration of HCC tumors with CD160+ NK cell was associated with both increased disease-free and overall survivals [+ NK cells have increased NKp30 and NKp46 expression associated with augmented TNF-α expression [rognosis ,184,185.rognosis  reportedsurvival . Similarsurvival . In factsurvival , indicatsurvival . Peripheoduction . Intratuurvivals . Similarpression . Its genpression , providipression  have obspression . While Npression ,193,194.pression . Furtherpression . In addipression ,195. Parpression , which, NK cell extrinsic mechanisms. The tumor microenvironment modulates NK cell function. The expression of ligands for inhibitory receptors or the secretion of soluble mediators by immune and non-immune cells inhibit NK cell function. It is well demonstrated that cancer-associated fibroblasts (CAF), myeloid cells and regulatory T cells (Tregs), in most cases, promote tumor growth and metastasis through diverse mechanisms [+ NK cells [chanisms ,201,202.chanisms . These ichanisms . These rchanisms ,134,204.chanisms . The adoNK cells , indicatNK cells , which mNK cells ,208. The+ T cells infiltrating MIC+ tumors [The stress-inducible NKG2D ligands are expressed at the membrane of tumor cells across many cancer types  and stim+ tumors . Similar+ tumors . Mechani+ tumors ,219. In + tumors  and the + tumors . Similar+ tumors . These fNK cells represent a major target to improve cancer immunotherapies. Four major axis are currently widely studied to take advantage of NK cell function in cancer treatment ,225,226.Adoptive NK cell therapies. The liver is home to a large proportion of NK cells and, thus, infusion of ex vivo activated/expanded or engineered autologous NK cells may represent a valuable strategy to treat HCC [+CD3−), ranging from 71.6% to 96.1%. Patients who have undergone cryoablation treatment associated with the injection of allogeneic NK cells showed increased progression free survival compared to patients who received cryoablation treatment alone [+ HCC cell lines in vitro, and significantly reduced the tumor growth of GPC3+ HHC in vivo [+ tumors [reat HCC . Despitereat HCC , the effreat HCC . Additioreat HCC ,230. Furreat HCC ,232,233,reat HCC . Howeverreat HCC , while treat HCC ,240,241.reat HCC  in NK cereat HCC ,206,208.reat HCC . Other sreat HCC ,245. Linreat HCC  found thnt alone . In addint alone , indicatnt alone . Glypicant alone ,248. Thent alone  transduc in vivo . Similar in vivo ,252,253. in vivo  ingeniou in vivo . In addi in vivo . Importa+ tumors , offerinTargeting NK cell receptors. Many strategies to engage activating receptors or to block the inhibitory receptors-ligands are currently being evaluated in order to increase NK cell function in cancer [n cancer ,256. Then cancer  or an NKn cancer , engineen cancer ,262,263,n cancer , Siglec-n cancer  or LAG3 n cancer ,267, repn cancer . Particun cancer ,270,271,n cancer ,273. TogNK cell stimulation with cytokines. Intratumoral hepatic NK cells are dysfunctional. However, in vitro cultured tumor-infiltrating NK cells with IL-15 overcome resistances and restore NK cell function and anti-tumor activity in HCC [y in HCC . These ry in HCC . Althougy in HCC . Considey in HCC  without y in HCC ,279. They in HCC ,282,283.y in HCC . Other ay in HCC ,287,288 y in HCC . Given tTargeting the tumor microenvironment. TGF-β induces CD96 and reduces CD226 expression on NK cells [NK cells , a phenoNK cells , indicatNK cells ,291,292.NK cells . MechaniNK cells . ConsequNK cells . The theNK cells ,295. In NK cells . As a coNK cells . FurtherNK cells . Arai etNK cells  performeNK cells . Given tNK cells . Such stThe targeting of the immune system represents a therapeutic revolution that has fundamentally changed the clinical management and prognosis of cancer patients. However, the success of these treatments, thought to be mainly based on the reactivation of T cell anti-tumor functions, may not be relevant for every tumor type. In HCC, the overall response rates remain modest, and only a minority of patients exhibit durable responses to the combination atezolimumb–bevacizumab. Thus, alternative treatments are needed to improve the clinical prognosis of this disease. It is clear that innate cells, particularly ILCs, offer a number of advantages in cancer treatment. They provide early critical mediators to mount potent anti-tumor responses  and exert critical cytotoxic function to eliminate cancer cells. However, these cells are often dysregulated in the tumor microenvironment, displaying an immunoregulatory and exhausted phenotype. Novel approaches are currently being investigated to overcome ILC dysfunction and to exploit their potential. Although the targeting of ILCs holds great promise, a better understanding of their function, prognostic value and therapeutic potentials are still needed.Many important questions remain to be answered. In humans, what is the role and function of tissue resident hepatic ILC1, as identified by Forkel et al. ? Given tNcr1 (encoding NK-46) [Eomes in NKp46 expressing cells abolished NK cell development and maturation, but not ILC1 [fl/flTbx21 mice with Cre/+NKp46 mice, aiming to delete ILC1 [Cre/+Ifngfl/flNKp46 mice harbor a specific deletion of liver ILC1. However, when investigating the function of the cells in HCC development, the impairment of IFN-γ production by other NKp46 expressing cells, and notably cNK cells, will certainly influence disease outcomes, confounding the effect of ILC1 on cancer development and progression. Thus, so far, there are no specific mouse models to precisely investigate the role of liver ILC1 in HCC development and progression. Recent investigations using preclinical models of liver metastasis suggest that ILC1 are essential to control metastatic seeding, whereas NK cells restrain tumor outgrowth [Important differences were observed in liver group 1 ILC composition and function between mice and humans. Thus, we can ask whether preclinical mouse models are suitable to study the role and function of NK cells and ILC1 in HCC development and treatment efficacies. In addition, despite substantial progress in conditional gene deletion, targeting ILC1 has not yet been achieved with the current models . Given tg NK-46) ,299,300,not ILC1 ,91. Thusete ILC1 ,70,87. Iete ILC1 . More reutgrowth .Finally, the safety and clinical efficacy of newly developed NK cell-directed therapies such as multispecific NK cell engagers, remains to be fully evaluated ,303,304.


Brain tumors are associated with adverse outcomes despite improvements in radiation therapy, chemotherapy, and photodynamic therapy. However, treatment approaches are evolving, and new biological phenomena are being explored to identify the appropriate treatment of brain tumors. Long non-coding RNAs (lncRNAs), a type of non-coding RNA longer than 200 nucleotides, regulate gene expression at the transcriptional, post-transcriptional, and epigenetic levels and are involved in a variety of biological functions. Recent studies on lncRNAs have revealed their aberrant expression in various cancers, with distinct expression patterns associated with their instrumental roles in cancer. Abnormal expression of lncRNAs has also been identified in brain tumors. Here, we review the potential roles of lncRNAs and their biological functions in the context of brain tumors. We also summarize the current understanding of the molecular mechanisms and signaling pathways related to lncRNAs that may guide clinical trials for brain tumor therapy. The occurrence of brain tumors is increasing worldwide and is more common among men than women  Fig. 1)1). GliomPreviously, non-coding regions of the genome, which do not translate into proteins, were regarded as junk DNA. However, high-throughput technologies have accelerated and improved our understanding of the non-coding genome. Studies have revealed that long non-coding RNAs (lncRNAs), whose transcripts are longer than 200 nucleotides, are involved in a wide range of biological processes  Recent studies have shown that aberrantly expressed lncRNAs are closely involved with the pathology and prognosis of gliomas. These studies also identified the molecular mechanisms underlying lncRNA activity, which eventually modulate biological processes in brain tumors . In addiThe roles of lncRNAs and their associated pathways in gliomas have been identified, and their interactions with miRNAs have been studied , 22. ThiThe PI3K/Akt/mTOR pathway is often deregulated in cancer cells, leading to enhanced cell growth, proliferation, and survival of these cells. This pathway also plays a crucial role in apoptosis and autophagy in glioma , 24.The cancer susceptibility candidate 2 (CASC2) lncRNA, which is downregulated gene in various cancers including gliomas, is an important modulator of this pathway. CASC2 has been identified as a tumor suppressor that is located on chromosome 10q26 , 26. CASThe differentiation antagonizing non-protein coding RNA (DANCR) lncRNA, a tumor-associated lncRNA located on chromosome 14, is upregulated in several types of human cancers . AberranThe highly upregulated in liver cancer (HULC) lncRNA is located on chromosome 6p24.3 and highly expressed in glioma as an oncogene that contributes to tumor progression . HULC siThe small nucleolar RNA host gene 16 (SNHG16) lncRNA is highly expressed in gliomas and is correlated with short survival and poor clinicopathologic features. Knockdown of SNHG16 suppresses the viability of glioma cells and promotes apoptosis by regulating the expression of B-cell lymphoma 2 family (Bcl-2) proteins and activating the PI3K/Akt signaling pathway. Moreover, SNHG16 promotes glioma tumorigenesis by acting as the ceRNA that upregulates the expression of the miR-4518 targeted gene, protein arginine methyltransferase 5 (PRMT5) by directly sponging miR-4518 . AdditioThe colorectal neoplasia differentially expressed (CRNDE) lncRNA, which is specifically expressed in certain regions of the human brain, is also upregulated in gliomas . This lnThe HOX transcript antisense RNA (HOTAIR) lncRNA is a ~ 2.2 kb lncRNA belonging to the homeobox superfamily that is transcribed from the HOXC locus at chromosome 12q13. HOTAIR acts as a negative prognostic factor for various cancers, including glioma, and its abundant expression is closely related to tumor progression , 44. KnoThe X-inactive-specific transcript (XIST) lncRNA, produced by the XIST gene, is a critical regulator of cancer, levels of which correlate with cell differentiation, proliferation, and genome maintenance. Abnormal expression of XIST has been seen in various human cancers, where it plays a pathological function , 47. MorThe cancer susceptibility candidate 2 (CCAT2) lncRNA is an oncogenic 1,752 bp lncRNA located on chromosome 8q24.21 that is upregulated in various human cancers, including glioma . CCAT2 eMitogen-activated protein kinases (MAPKs) are a family of signaling molecules, which are activated by growth factors, cytokines, and environmental stress. They control crucial cellular processes, including cell growth, proliferation, migration, and apoptosis .The metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA, also called nuclear-enriched transcript 2, is an 8.7 kb lncRNA located on chromosome 11q13. MALAT1 is known as a prognostic indicator in non-small cell lung cancer, but is abnormally expressed in various human cancers; it is upregulated in gliomas, where its expression is associated with the progression of glioma and poor prognosis –54. AddiThe downregulation of MATN1 antisense RNA 1 (MATN-AS1) lncRNA is associated with short survival duration and poor prognosis of glioblastoma patients. Upregulation of MATN1-AS2 suppressed the expression of RELA, a group of the Rel proteins family and MAPK pathway, thereby inhibiting cell proliferation and invasion through apoptosis of glioblastoma cells .The transmembrane phosphatase with tensin homology pseudogene 1 (TPTEP1) lncRNA is one of the most downregulated lncRNAs in non-small cell lung cancer, where it inhibits the cell proliferation by competitively sponging miR-328-5p . In glioWnt/β-catenin signaling is essential in embryonic development and tissue homeostasis, and thus controls cell proliferation and cell fate. Hence, activation and mutation of the Wnt/ β-catenin pathway is associated with various human cancers and other diseases , 61.Overexpression of CASC2 significantly suppresses migration, invasion, and proliferation in glioma through the inhibition of Wnt/β-catenin signaling pathway .DANCR, which is upregulated in glioma, that activates the Wnt/β-catenin signaling pathway, leading to increased migration and proliferation of glioma cells. However, this tendency is reduced by si-DANCR .The H19 lncRNA is located downstream of insulin growth factor 2 (IGF2) on chromosome 11p15 in humans and chromosome 7 in mice. Its expression is increased in various cancers including gliomas, where it correlates with cell growth and invasion and it plays an oncogenic role –67. FurtThe nuclear-enriched abundant transcript 1 (NEAT1) lncRNA, a 3.7 kb lncRNA found on chromosome 11q13.1, localizes to the nucleus and is an essential core component of the paraspeckle substructure , 72. ThiWnt inhibitory factor 1 (WIF1) attenuates non-canonical Wnt signaling through downregulation of MALAT1, thereby suppressing migration, but not proliferation, of glioblastoma cells .Similarly, knockdown of CCAT2 suppresses cell migration, proliferation, cell cycle progression, and tumorigenesis in glioma by downregulating the Wnt/β-catenin signaling pathway .In addition to these pathways, several other pathways, including the miRNA axis, bone morphogenetic protein (BMP), Notch, nuclear factor kappa-light-chain-enhancer of activation B cells (NF-κB) pathways are involved in cancer. miRNAs are small non-coding RNAs that are 21–25 nucleotides in length, which mainly regulate expression of gene at the post-transcriptional level, and the expression of protein via binding to their target mRNAs . BMP proThe overexpression of plasmacytoma variant translocation 1 (PVT1) lncRNA located on chromosome 8q24, has been reported to play an important role in tumor progression and biological processes, such as apoptosis, invasion, mobility, and proliferation in various types of cancer –85. It aAs mentioned previously, CASC2, which is downregulated in glioma, modulates miRNAs. Overexpression of CASC2 inhibits glioma malignancy, migration, invasion, and proliferation and induces cell apoptosis via negative modulation of miR-21 .The ZNFX1 antisense RNA 1 (ZFAS1) lncRNA located on chromosome 20q13 is another lncRNA involved in progression and metastasis of various cancers . ZFAS1 iSimilarly, DANCR promotes glioma progression by acting as a sponge of miR-634, thus modulating RAB1A, which has an important role in tumor progression . FurtherThe ceRNA role of SNHG16, which is overexpressed in glioma, also promotes the malignancy through the enhancement of endogenous target gene, E2F1 via sponging of miR-20a-5p .Knockdown of CRNDE promotes the miR-348 expression, leading to inhibition of piwi-like RNA-mediated gene silencing 4 (PIWIL4), which has an oncogenic role in glioma, thus inhibiting the malignant progression of glioma . CRNDE aGlioma malignancy is also promoted by HOTAIR via its interactions with the polycomb repressive complex 2 (PRC2) and certain miRNAs . Also, HThe H19 lncRNA increases proliferation and invasion via downregulating miR-152 , and miR6-methylguanine-DNA methyltransferase (MGMT) and specificity protein 1 (SP1) [Similarly, XIST regulates miR-429, thus promoting tumorigenicity and angiogenesis in gliomas . XIST is 1 (SP1) . XIST al 1 (SP1) . Knockdo 1 (SP1) . It also 1 (SP1) .NEAT1 directly targets miR-449b-5p as a molecular sponge and promotes upregulation of c-Met, a direct target of miR-449b-5p, to increase pathogenesis in glioma . MeanwhiMALAT1 increases the level of zinc fingers and homeoboxes 1 (ZHX1), which plays a role in the carcinogenesis of several cancers including glioblastoma by acting as a transcription repressor via molecular sponging of miR-199a and as a ceRNA, thereby promoting proliferation and progression of glioblastoma . MALAT1 In glioma, MATN1-AS1 enhances the carcinogenesis by sponging the miR-200b/c/429 as the ceRNA to regulate chromodomain helicase DNA-binding protein 1 (CHD1), which has an important role in the determination of cell fate .CCAT2 also acts as a molecular sponge to downregulate miR-424 and upregulates Chk1, thereby enhancing chemotolerance in glioma cells .The immune system shows responses to pathogens that may be categorized as part of innate or adaptive immunity. Pathogen-infected cells synthesize inflammatory mediators and cytokines via transcriptional and post-transcriptional regulation of genes as part of the immune response. Such immune responses start with discriminative recognition of pathogens by certain receptors, such as toll-like receptors . Toll-liIn anaplastic glioma patients, nine immune-related lncRNAs  have been identified. Among these, PGM5-AS1, ST20-AS1, AGAP2-AS1 and MIR155HG have been identified as risk-related genes, whereas SNHG8, LINC00937, TUGI, MAPKAPK5-AS1 and HCG18 are considered protective genes. The expression of these genes reveals differences in immune status among low- and high-risk groups. These immune-related lncRNAs directly or indirectly regulate several prognostic immune-related mRNAs . SimilarExtracellular vesicles (EVs) are nano-sized membranous and lipid bilayer vesicles. They can be divided into exosomes, ectosomes, microvesicles, and apoptotic bodies based on their biogenesis and size . These vThe POU class 3 homeobox 3 (POU3F3) lncRNA is an oncogene seen in various human cancers, such as cervical cancer, nasopharyngeal carcinoma, and breast cancer . In glioSimilarly, the SBF2 antisense RNA 1 (SBF2-AS1) lncRNA, located on the 11p15.1 locus, has been identified as a critical regulation factor in cancer progression , 167. InCCAT2-enriched exosomes are also released by glioma cells and may also be involved in human umbilical vein endothelial cells. In these endothelial cells, upregulation of CCAT2 enhances angiogenesis via increasing vascular endothelial growth factor A and transforming growth factor β expression and inhibits apoptosis via increasing Bcl-2 and decreasing Bcl-2-associated X (Bax) and caspase-3 expression. Therefore, the release of exosomes containing the lncRNA CCAT2 by glioma cells promotes angiogenesis and inhibits endothelial cell apoptosis .LncRNA activated by transforming growth factor (TGF)-β (lncRNA-ATB), is abnormally expressed and functions as a progression factor in various cancers, such as hepatocellular carcinoma, gastric cancer, and breast cancer . This exIn 1955, Georges Palade, the cell biologist, identified the first ncRNA, which was linked to the ribonucleotide protein complex. This was followed by discoveries of regulatory ncRNAs, such as miRNAs, ribosomal RNAs, transfer RNAs, small nuclear RNAs, short interfering RNAs, ceRNAs, enhancer RNAs, piwi-interacting RNAs, and lncRNAs in the both prokaryotic and eukaryotic organisms . HoweverMolecular insights into lncRNAs-mediated intracellular pathways and transcriptional networks have provided answers for several unanswered questions in transcriptional and translational regulation. Recent observations have also revealed the roles of lncRNAs in diverse diseases and responses to various stresses. Besides, abnormal transcription and mutation of lncRNAs have been observed in many sporadic tumors . Indeed,cis- or trans-acting transcriptional regulation and they are involved in modulation of targeted RNAs or proteins [In this review, we have summarized the current understanding of the roles of lncRNAs in brain tumors Table . Understproteins . Since tproteins . Therefoproteins . Using tproteins . The div