Construction and analysis of the CDKN2B-AS1 ceRNA network associated with KRAS-dependent tumorigenesis in colorectal and pancreatic cancer

Construction and analysis of the CDKN2B-AS1 ceRNA network associated with KRAS-dependent tumorigenesis in colorectal and pancreatic cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction and analysis of the CDKN2B-AS1 ceRNA network associated with KRAS-dependent tumorigenesis in colorectal and pancreatic cancer Mahsa Saliani, Ali Javadmanesh, Parisa Gonbadi, Somayeh Rahimi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4304326/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Kirsten rat sarcoma viral oncogene homolog (KRAS) exhibits the highest mutation rate in colorectal cancer (CRC) and pancreatic cancer (PC), highlighting the need for a comprehensive understanding of KRAS-dependent pathogenesis. Given the regulatory role of long noncoding RNAs (lncRNAs) in gene expression, this study focused on constructing a competing endogenous RNA (ceRNA) network of selected KRAS-related lncRNAs. By analyzing the transcriptional profiles of CRC and PC cell lines with and without KRAS mutations, differentially expressed genes (DEGs) were identified using sequencing data from the Sequencing Read Archive database. Notably, the analysis revealed 42 common upregulated DEGs (uDEGs), including differentially expressed lncRNAs and protein-coding genes, between KRAS-mutant and KRAS-wild-type cells. Among them, CDKN2B-AS1 emerged as a key KRAS-related lncRNA for the construction of the ceRNA network. Using LncTarD, miRWalk, and ToppCluster servers, a robust ceRNA network of CDKN2B-AS1 was constructed to elucidate the interactions between corresponding miRNAs and target genes. This network included 21 miRNAs and 34 genes selected from common uDEGs. Enrichment analysis of ceRNA target genes validated their involvement in critical cancer-related pathways and biological processes. Crucially, expression and survival analysis underscored the prognostic significance of selected target genes within the CDKN2B-AS1 ceRNA network. By delineating the regulatory mechanisms of the CDKN2B-AS1 ceRNA network, this study sheds light on the molecular and cellular pathways involved in KRAS-associated tumorigenesis. KRAS mutation CDKN2B-AS1 colorectal cancer pancreatic cancer ceRNA network lncRNA long noncoding RNAs differential expression analysis Background Mutations in KRAS have been identified as the most common oncogenic events in 25% of all endodermal carcinomas (1-3). The KRAS protein is a small guanosine triphosphatase (GTPase) that serves as a molecular switch by cycling between inactive guanosine diphosphate (GDP)-bound and active guanosine triphosphate (GTP)-bound states in response to extracellular signals to induce intracellular responses (4). These off/on molecular states based on GDP/GTP exchange are controlled by GTP hydrolysis reactions stimulated by GTPase-activating proteins (GAPs) and RAS-specific guanine nucleotide exchange factors (GEFs) (5, 6). While GTP-bound KRAS transduces signals to its downstream effectors, activating multiple signaling pathways, somatic mutations favor a constant active state through the impairment of GTP hydrolysis and resistance to GAP function. A high concentration of the active form leads to hyperactivation of downstream oncogenic signaling pathways, including the mitogen-activated protein kinase (MAPK) pathway, which is involved in cell growth, proliferation, development, inflammation, differentiation, survival, and apoptosis to initiate and promote malignant transformation (7). Although recent advances in the understanding of the KRAS oncoprotein structure have resulted in the clinical development of novel selective anti- KRAS inhibitors (8, 9), preclinical data, and clinical translational series have recently revealed multiple mechanisms of resistance to these inhibitors (10). Therefore, a deeper understanding of these factors, including histological features, the immune microenvironment, and the transcriptional landscape of tumor cells with KRAS mutations, is crucial. In this regard, additional studies are needed to elucidate the molecular and cellular mechanisms, including transcriptional changes and pathway-related strategies responsible for the modulation of KRAS tumorigenesis . Disturbances in lncRNAs, key regulators of gene expression, have been reported in the progression of many human cancers (11-13). Identifying the relationships between KRAS mutations and abnormal expression of some lncRNAs is expected to significantly improve our knowledge of the mechanisms of tumorigenesis controlled by mutKRAS (14). Abnormal levels of KRAS, a known mediator of many cellular signaling pathways, reciprocally cause diverse molecular alterations, such as dysregulation of lncRNA expression. Shi et al., 2021 showed that the levels of a KRAS-responsive lncRNA called KIMAT1 were positively correlated with KRAS levels both in cell lines and in lung cancer specimens (15). In addition, the role of KIMAT1 in maintaining a positive feedback loop to sustain KRAS signaling during lung cancer promotion has been reported as a strategy to improve KRAS-induced tumorigenesis. Another study indicated that Orilnc1 can be regulated by the RAS-RAF-MEK-ERK pathway, which is required for cell proliferation in RAS/BRAF-dependent human malignancies (16). The association of lncRNAs with various regulatory apparatuses, including chromatin remodeling factors, transcription factors, splicing machinery, and nuclear trafficking modulators, emphasizes the diversity and complexity of their related regulatory mechanisms (17, 18). The function of lncRNAs as competing endogenous RNAs (ceRNAs) has been suggested as one of their main gene expression regulatory approaches (19-21). Emerging evidence has indicated that many lncRNAs with oncogenic activity are upregulated in cancer tissues through the sponging of tumor suppressor microRNAs (miRNAs) (22, 23). The binding of lncRNAs (as ceRNAs) to miRNAs prevents the latter from recognizing their targets, which consequently results in mRNA upregulation. Therefore, during malignant transformation, oncogenic lncRNAs intensify cancer promotion via the downregulation of miRNAs targeting different driver oncogenes (24, 25). In this study, we investigated abnormally overexpressed lncRNAs associated with KRAS mutations by analyzing the transcriptional profiles of CRC and PC cell lines with and without KRAS mutations. Overexpressed lncRNAs, known as oncogenic KRAS -related lncRNAs, were identified, and among them, CDKN2-AS1 was selected to construct the ceRNA network. The possible function of CDKN2B-AS1 through its associated ceRNA network target genes was identified by performing functional enrichment analysis. Additionally, expression and survival analyses of target genes in the CDKN2B-AS1 ceRNA network were performed to estimate their prognostic performance as potential biomarkers in KRAS -mutant cancers. The role of ceRNAs and their associated networks in KRAS -dependent tumorigenesis is still unclear. Therefore, this study aimed to further explore the molecular and cellular mechanisms involved in the pathogenesis of KRAS -driven cancers through analysis of the lncRNA-associated ceRNA network. The results of this study improve our understanding of the potential contribution of lncRNAs to KRAS -associated pathogenesis and their application as the possible diagnostic and prognostic biomarkers for KRAS -mutant cancers. Methods Samples and Data Collection In this study, raw RNA sequencing data were extracted from the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) (26). The sequencing data of three human CRC cell lines, namely, HCT-116 (SRR1030462, SRR1030463, SRR1756569, and SRR8615282) and LoVo (SRR1756570, SRR8532655, and SRR8616185), which are the KRAS mutant (mutKRAS) samples, and SW48 (ERR208907, SRR3228439, and SRR8615504), which are the KRAS wild-type (wtKRAS) and control samples, were downloaded. In addition, transcriptomic data of PC cell lines, including Capan-2 (SRR2313117, SRR2313118, and SRR2313119) as the mutKRAS sample and BXPC3 (SRR2313123, SRR2313124, and SRR2313125) as the wtKRAS control sample, were obtained. Workflow of the study Because of the greater prevalence of KRAS mutations in pancreatic and colorectal cancers, this study used CRC and PC cell lines. Transcriptional profile analysis of PC cell lines was conducted to analyze the differential expression of genes between Capan-2 (mutKRAS) cells and BXPC3 cells, which were used as wtKRAS samples. In addition, differential expression analysis of CRC cells, including HCT-116 and LoVo (mutKRAS) vs. SW48 (wtKRAS) cells, was performed previously (27). Transcriptional profile analysis of the samples revealed DEGs between the mutKRAS and wtKRAS cells. A Venn diagram analysis (https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html) (28) revealed 42 common upregulated DEGs (uDEGs), including common differentially expressed lncRNAs (DELs) and protein-coding genes. According to the workflow of the study, recognized DELs could be assigned to KRAS -related lncRNAs; among them, CDKN2B-AS1 was selected for further analysis. Data preprocessing and differential expression analysis The RNA sequencing data were downloaded as SRA files, and fastq-dump from the SRA toolkit (v2.8.2) was used to convert the SRA to FASTQ format (26). The sequencing quality of the FASTQ files was monitored by FastQC (v0.11.5) and modified using quality control software, including FLEXBAR (v3.0) and Trimmomatic (v0.39) (29-31). The human reference genome was downloaded from the Ensemble database (http://ftp.ensembl.org/pub/release95/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz) and indexed before mapping with Bowtie2 (v2.3.4.1) (32, 33). The filtered reads were aligned with the reference genome using Bowtie2 software. The output files of the mapping in the SAM format were processed by the HTSeq-count program (v0.11.4) for simultaneous read counting and annotation using an annotated human reference genome downloaded from Ensemble (https://ftp.ensembl.org/pub/release95/gtf/homo_sapiens/Homo_sapiens.GRCh38.98.gtf.gz)(34). Normalization and differential expression analysis were conducted with the DESeq2 package (version 1.38.0) from Bioconductor in the R environment (version 3.6.1, https://www.rproject.org/)(35). Significantly upregulated DEGs were identified according to log2-fold change (log2FC) and adjusted p-value as screening criteria (|log2FC| > 3, adjusted p-value <0.01). DEGs were annotated with Ensembl Biomart (https://asia.ensembl.org/biomart/martview) and the GRCh38.p13 reference genome for division into protein-coding genes and DELs (36). All the commands and scripts used for data processing and differential expression analysis were uploaded to the GitHub platform and are publicly available via https://github.com/mahsa1985/R-scripts.git and https://github.com/mahsa1985/Linux-Commands.git. Output visualization of differential expression analysis Hierarchical clustering analysis was performed to visualize the results of differential expression analysis related to KRAS mutations based on the normalized read counts of the mutKRAS and wtKRAS samples. The heatmap plots were created using the gplots package in R, and variance stabilizing transformation (VST) was applied to the normalized count data before clustering. Linkage analysis and distance measurement were based on the complete linkage and Euclidean distance, respectively. According to the lowest adjusted p-value, the expression of 1000 genes was illustrated by heatmap plots based on expression data indicated as normalized values (Z‑scores). An MA plot was created using the plotMA function of the DESeq2 package, indicating log2FC on the y-axis and the average of normalized counts over all samples on the x-axis. Each gene is represented by a dot, and the points in blue are genes with significant differential expression and adjusted p values less than 0.01. Construction of the ceRNA network The construction of the ceRNA network was based on the ceRNA hypothesis that lncRNA and mRNAs can coregulate each other by sharing MREs (miRNA response elements). The ceRNA network of CDKN2B-AS1 was constructed based on previous studies on the ceRNA function of CDKN2B-AS1 and the findings of the present study. Table 1 shows the miRNA-CDKN2B-AS1-mRNA interactions, for which the LncTarD database (https://lnctard.bio-database.com/) was used to determine the miRNA-CDKN2B-AS1 and miRNA‒mRNA interactions based on previous publications (37). The list of the miRNAs in Table 1 was mapped into miRWalk (http://mirwalk.umm.uni-heidelberg.de/) and ToppCluster (https://toppcluster.cchmc.org/) to search for their mRNA targets (38, 39). According to the ceRNA hypothesis, the genes obtained from miRWalk and ToppCluster, which were also among the list of uDEGs, were considered the target genes of CDKN2B-AS1 to construct the ceRNA network, while considering their related miRNAs, as shown in Table 1. Finally, the CDKN2B-AS1-miRNA‒mRNA ceRNA network was constructed and visualized using the Cytoscape tool (version 3.9.1) (https://cytoscape.org/) (40). Gene ontology and pathway analysis To better understand the biological functions of the target genes in the CDKN2B-AS1 ceRNA network, gene ontology (GO) and pathway analyses were applied to underscore the potential molecular and cellular tumorigenesis of CDKN2B-AS1 as a KRAS -related lncRNA. In this study, enrichment analysis was performed using the comprehensive gene set enrichment analysis web server EnrichR (https://maayanlab.cloud/Enrichr) (36). GO analysis was based on enriched terms in the biological process, molecular function, and cellular component categories. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (41). Moreover, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) bioinformatics tool (https://david.ncifcrf.gov/) and the Gene Ontology Resource (www.geneontology.org) were used to validate the results of enrichment analysis (35,37). GO terms and KEGG pathways with a p-value < 0.05 were considered significantly enriched. The most significantly enriched GO terms and KEGG pathways were ranked based on the p-value. Eventually, the results obtained from EnrichR were visualized using http://www.bioinformatics.com.cn/srplot, an online platform for data analysis and visualization. Evaluation of the prognostic performance of ceRNA-related target genes The prognostic power of target genes in the CDKN2B-AS1 ceRNA network was evaluated through survival analysis utilizing the interactive web-based tool GEPIA (Gene Expression Profiling Interactive Analysis), which is based on the gene expression RNA-seq datasets of The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga) and Genotype-Tissue Expression (GTEx) (42, 43). Using this database to analyze gene survival, we set the group cutoff to the median and 95% confidence interval (CI) of the YES. All analyses were considered statistically significant at a log-rank p-value < 0.05. Correlation analysis between gene expression and sample type (tumor and normal samples) was performed using the UALCAN (https://ualcan.path.uab.edu/index.html) (44) online dataset based on the TCGA database. All parameters were set to default values to investigate differential expression between tumor and normal samples, considering a p-value less than 0.05 to indicate statistical significance. Results Differential expression analysis and visualization In this study, to identify DELs related to KRAS mutations, transcriptional profile analysis was conducted on the mutKRAS and wtKRAS CRC and PC cell samples (Supplementary Data 1-3). Hierarchical cluster analysis was used to visualize differential expression. The heatmap plots indicated DEGs in CRC and PC samples with and without KRAS mutations (Figure 1a). MA plots also display the log2FC of genes compared with their mean normalized counts (Figure 1b). The results of the RNA sequencing analysis revealed upregulated DEGs (|log2FC| > 3, adjusted p-value <0.01) (Supplementary Data 4-6). In this regard, for the CRC cell lines, 980 and 1525 DEGs were upregulated in HCT-116 and LoVo (mutKRAS samples) vs. SW48 (wtKRAS control sample) cells, respectively. In addition, transcriptional analysis of PC cell lines revealed a total of 894 upregulated DEGs in Capan-2 (mutKRAS) cells compared with those in BXPC3 (wtKRA) cells (Figure 2a). Identification of CDKN2B-AS1 In the present study, a multistep strategy was applied to select CDKN2B-AS1 as a lncRNA with differential expression in the context of KRAS mutation (Figure 2a). In this regard, upregulated genes with significant differential expression were identified by comparing the transcriptomes of CRC mutKRAS (HCT-116 and LoVo) and wtKRAS (SW48) cells and between PC mutKRAS (Capan-2) and wtKRAS (BXPC3) cells. Venn diagram analysis revealed 42 uDEGs, including protein-coding genes and DELs, associated with the KRAS mutation (Figure 2b). In the next step, overlapping DELs were identified according to Figure 2a, which could be assigned as KRAS -related lncRNAs. The upregulation of the overlapping DELs in the mutKRAS cell lines compared to the wtKRAS cell lines is in line with the ceRNA hypothesis. Among the overlapping and upregulated DELs, some with less annotation, such as LINC00471, LINC01842, and DNAH17-AS1, were excluded, and CDKN2B-AS1, a KRAS -related lncRNA, was selected to construct the ceRNA network for further investigation. ceRNA network of CDKN2B-AS1 Guided by the ceRNA hypothesis, CDKN2B-AS1-miRNA and miRNA‒mRNA interactions confirmed by previous studies are illustrated in Table 1. The data provided in Table 1 were extracted from LncTarD as a comprehensive resource of lncRNA‒target interactions to report experimentally supported findings. A total of 21 miRNAs were identified from Table 1 and were used as candidates for constructing the ceRNA network of CDKN2B-AS1. The results obtained from miRWalk and Toppcluster indicated that candidate miRNAs were able to target many of the 42 uDEGs in the mutKRAS vs. wtKRAS cells according to differential expression analysis. Therefore, based on the ceRNA hypothesis, the constructed network with 21 miRNAs and 34 mRNAs predicted that our upregulated DEGs could be positively correlated with upregulated CDKN2B-AS1 and negatively correlated with the miRNA expression levels involved in the ceRNA network (Figure 3). Table 1 . The list of the experimentally supported interactions between CDKN2B-AS1 and its miRNA targets and miRNA‒mRNA interactions is based on the ceRNA hypothesis extracted from LncTarD. Disease Target gene Tumorigenesis outcome miRNA target Ref Hepatocellular carcinoma NAP1L1 Cell growth (+); cell metastasis (+); PI3K/AKT/mTOR signaling pathway (+) let-7c-5p (45) Hepatocellular carcinoma PBX3 Cell viability (+); cell migration (+); cell invasion (+); apoptosis process (-); PI3K/AKT signaling pathway (+) miR-144 (46) Hepatocellular carcinoma ARHGAP18 cell metastasis (+); cell migration (+) miR-153-5p (47) Gastric cancer BMI1 Tumorigenesis (-) miR-99a (48) Cervical cancer TGFbetaI Cell proliferation (+); cell invasion (+); cell migration (+); apoptosis process (-); cell senescence (-) miR-181a-5p (49) Hepatocellular carcinoma Not reported Cell proliferation (+); cell metastasis (+); cell invasion (+) miR-122-5p (50) Malignant glioma SIRT1 Cell proliferation (+); cell migration(+);cell invasion(+);apoptosis process(-) miR-34a (51) Hepatocellular carcinoma ARL2 Mitochondrial function (+) miR-199a-5p (52) Kidney disease TXNIP Inflammatory response (+); cell pyroptosis (+) miR-497 (53) Laryngeal squamous cell carcinoma ROCK1 Cell growth (+) miR-324-5p (54) Medulloblastoma BRI3 Cell proliferation (+); cell migration (+) miR-323 (55) Osteosarcoma MAP3K3 Cell proliferation (+); cell migration (+); epithelial to mesenchymal transition (+) miR-4458 (56) Ovarian cancer SMAD3 Cancer progression (+); cell migration (+); cell invasion (+); cell growth (+) miR-143-3p (57) Ovarian cancer HMGA2 Chemosensitivity (-); apoptosis process (-); cell growth (+) let-7a (58) Renal cell carcinoma CCND1 Cell proliferation (+); cell migration (+); cell invasion (+); cell growth (-); apoptosis process (-) miR-141 (59) Renal cell carcinoma CCND2 Cell proliferation (+); cell migration (+); cell invasion (+); cell growth (-); apoptosis process (-) miR-142 (59) Thoracic aortic dissection STAT3 Cell proliferation (-); apoptosis process (+); AKT signaling pathway (+) miR-320d (60) Lung cancer NR2C2 Cell proliferation (+); invasion (+); reduced apoptosis (+) miR-378b (61) Colorectal cancer CAPRIN2 Proliferation (+); migration (+) miR-378b (62) Nasopharyngeal carcinoma E2F2 Proliferation (+); colony formation (+); invasion (+) miR-98-5p (63) Head and neck cell carcinoma FGFR1 Proliferation in vivo and in vitro (+) miR-125a-3p (64) GO and signaling pathway enrichment analysis GO analysis and pathway enrichment analysis were performed for the genes in the ceRNA network as the target genes of CDKN2B-AS1. All the genes were computationally uploaded to the DAVID, EnrichR, and Gene Ontology resources to better reveal the carcinogenicity of CDKN2-AS1 as a KRAS -related lncRNA. The results of enrichment analysis showed the involvement of the genes in the most significant and relevant enriched GO terms and KEGG pathways ranked by p-value in each category (Figure 4) (Supplementary Data 7). In the biological process group, genes were mainly enriched in terms related to the regulation of protein serine/threonine kinase activity, regulation of cellular senescence, regulation of the apoptotic process, and positive regulation of the cell cycle (Figure 4a). In the molecular function category of GO, genes were mainly enriched in cyclin-dependent protein serine/threonine kinase regulator activity, protein kinase binding, NF-kappaB binding, and protein serine/threonine kinase activity terms (Figure 4b). The results of pathway enrichment analysis indicated that genes were mainly enriched in pathways such as miRNAs in cancer, pancreatic cancer, colorectal cancer, and the p53 signaling pathway (Figure 4c). Evaluation of the prognostic performance of ceRNA-related target genes The prognostic power of target genes in the CDKN2B-AS1 ceRNA network was evaluated based on survival and expression analysis of the genes across tumor and normal samples. The significant differences between the gene expression levels of the normal and tumor groups were evaluated for the target genes of the ceRNA network. The higher expression of the CDKN2A, CCND1, HTR1D, and HMGA2 genes in the tumor samples, as determined by UALCAN, is due to the ceRNA hypothesis (Figure 5a). The GEPIA database was used for the survival analysis of target genes using RNA sequencing expression data of tumors and normal samples from the TCGA and GTEx datasets (Tang et al., 2017). Consistent with the results obtained from the expression analysis, the findings of the survival analysis showed that the CDKN2A gene in CRC patients and CCND1, HTR1D, and HMGA2 in PC patients were significantly associated with unfavorable overall survival based on Kaplan‒Meier plots (significance level at log-rank p-value < 0.05) (Figure 5b). Discussion Mutations in the KRAS oncogene with tumor-promoting activity have been identified in 25% of all cancers, whereas some cancers, such as pancreatic and colorectal cancer, have the highest mutation rates, at 90% and 45%, respectively. Despite developments in direct KRAS pharmacology, targeted therapies involving direct inhibitors are followed by rapid reactivation of KRAS signaling, leading to resistance to long-term treatment (8). Therefore , a comprehensive analysis of the different mechanisms and pathways associated with KRAS tumorigenic activity is critical for identifying potential therapeutic strategies to inhibit its oncogenic behavior. lncRNAs have been reported to have extensive ability to regulate gene expression, enabling intertwined multilayer molecular interactions in numerous pathological conditions, including cancer (67). The competing endogenous activity of lncRNAs, as one of their posttranscriptional regulatory mechanisms, is conferred by their competitive binding with shared miRNAs, freeing their targets from miRNA-induced degradation, thus significantly connecting with gene upregulation (68). During the process of malignant transformation, alterations, including chromosomal rearrangements, shortened 3′UTRs, and point mutations such as KRAS oncogenic mutations, occur in the chromosome of cancer cells. Following these alterations, transcriptional changes and, as a consequence, dysregulation of lncRNAs and their related ceRNA network are closely linked to tumorigenesis (69). Therefore, constantly updated studies on the roles of lncRNAs and their ceRNA networks as multilayered intracellular communications have led to remarkable advancements in this burgeoning hotspot to provide new insights into cancer pathogenesis. This study studied the KRAS-dependent dysregulated transcription profile in CRC and PC cells to identify upregulated DEGs and DELs to identify a ceRNA network associated with KRAS tumorigenesis. A comparison of the transcriptomes of the mutKRAS cell lines with those of their wtKRAS counterparts revealed 42 uDEGs, including protein-coding genes and DELs. Here, we identified CDKN2B-AS1 as a KRAS -related DEL. This lncRNA, also known as ANRIL, is located within the CDKN2B-CDKN2A gene cluster at chromosome 9p21, which is a significant genetic susceptibility locus for several cancers. To identify the connection between CDKN2B-AS1 and uDEGs, a ceRNA network of CDKN2B-AS1 was constructed using uDEGs as target genes. The miRNA targets of CDKN2B-AS1 were determined according to previous publications on the ceRNA function of CDKN2B-AS1. The list of candidate miRNAs was submitted to the miRWalk and ToppCluster platforms to search for potential gene targets. Interestingly, 34 genes out of 42 uDEGs were found to be targets of candidate miRNAs. Finally, the ceRNA network of CDKN2B-AS1 was constructed from 21 miRNAs and 34 uDEGs. To further understand the pathogenesis mechanism of CDKN2B-AS1 as a KRAS-related lncRNA , the top enriched functional annotations of GO and KEGG pathway analyses were identified. The target genes were enriched in GO biological process categories, such as regulation of protein serine/threonine kinase activity, regulation of the apoptotic process, and positive regulation of the cell cycle, which are closely related to tumorigenesis and cancer promotion. In addition, pathway enrichment analysis revealed several enriched pathways known as cancer-related pathways, including microRNAs involved in cancer, cell cycle regulation, pancreatic cancer, and the p53 signaling pathway. Moreover, to determine the clinical value of CDKN2B-AS1, the prognostic power of target genes of the ceRNA network was evaluated based on survival and expression analysis of tumor and normal patient samples. While the results showed a statistically significant association of CDKN2A in CRC patients and CCND1, HTR1D, and HMGA2 in PC patients with survival, their higher expression in tumor samples was also confirmed. While cyclin-dependent kinase inhibitor 2A (CDKN2A) is well known as a susceptibility gene for melanoma and pancreatic cancer, its germline variants have also been associated with a broader range of malignant transformations, including neural system tumors, breast carcinomas, head and neck squamous cell carcinomas, and sarcomas (70, 71). While the expression level of Cyclin D1 (CCND1) is strictly regulated in normal cells, its increased activity has been observed in various types of neoplasms (72). A positive correlation between CCND1 copy number in breast cancer and lymph node metastasis was observed (73). According to recent studies, 5-hydroxytryptamine receptors (HTRs), including HTR1D, are linked to several malignant tumors, such as melanoma, breast cancer, lung cancer, and colon cancer (74-76). The involvement of the HOXA10-AS/miR-340-3p/HTR1D axis in the progression of pancreatic cancer has been demonstrated (77). Moreover, the expression level of HTR1D in clinical samples of CRC adenocarcinoma suggested its role in the prognosis of patients (78). Oncogenic roles of high mobility group protein 2 (HMGA2) in different types of cancers and various strategies have revealed that HMGA2 is a candidate for cancer diagnostic, prognostic, and therapeutic purposes. To date, an increasing number of dysregulated lncRNAs, key regulators of gene expression with vital roles in human neoplasms, such as CRC and PC, have been identified (79). Based on the ceRNA phenomenon, the sequestration of tumor suppressor miRNAs from their mRNA target is one of the oncogenic mechanisms for gene expression regulation by lncRNAs (15, 80, 81). In the present study, dysregulated lncRNAs between the mutKRAS and wtKRAS CRC and PC cell lines were identified by using RNA-seq datasets from the SRA. Among the DELs considered KRAS -related lncRNAs, some with less annotation were excluded, and finally, CDKN2B-AS1 was selected for further analysis. In addition, because of the indispensable role of CDKN2B-AS1 in multiple diseases, particularly cancer, we selected this lncRNA (82, 83). Therefore, the ceRNA network of CDKN2B-AS1 was constructed from upregulated differentially expressed DEGs-CDKN2B-AS1. The results of previous studies on the sponging effect of CDKN2B-AS1 were used to identify miRNAs that mediate the ceRNA function of lncRNAs to identify all the elements needed for the construction of the ceRNA network. The results of the GO and pathway analyses of the target genes included in the ceRNA network of CDKN2B-AS1 indicated their role in cancer-related pathways and biological processes. Furthermore, survival and expression analysis of the corresponding ceRNA genes revealed the prognostic power of CDKN2A, CCND1, HTR1D, and HMGA2. This study has several limitations that should be considered for a more precise interpretation of the results. The findings were based on the transcriptional profile analysis of cancer cell lines, which should be validated by patient sample data. Although it is fully agreed that lncRNAs are worthy of investigation and that too much remains in this class of biomolecules, the mechanism of action of lncRNAs is often very complex, and there is always uncertainty about their biological impact. Therefore, our findings are closer to predictions than to certainty, and more computational methods and molecular biology experiments should be applied to increase the credibility of our findings. Conclusion In conclusion, we analyzed the KRAS -dependent dysregulated transcription profiles of CRC and PC cells to identify DELs. As a result, CDKN2B-AS1 was identified as a KRAS -related lncRNA, and its ceRNA activity was further investigated as one of the main gene expression regulatory mechanisms of lncRNAs. The ultimate purpose of this study was to underscore the great significance of the ceRNA network of CDKN2B-AS1 underlying the tumorigenesis mediated by KRAS mutation . Abbreviations KRAS : Kirsten rat sarcoma viral oncogene homolog, CRC : colorectal cancer, PC : pancreatic cancer, lncRNAs : long noncoding RNAs, ceRNA : competing endogenous RNA, SRA : sequencing read archive, DEGs: differentially expressed genes, uDEGs : overlapping upregulated DEGs, DELs : differentially expressed lncRNAs (DELs), mutKRAS : KRAS mutant, wtKRAS : wild-type KRAS, GTPase : small guanosine triphosphatase, GDP : guanosine diphosphate, GTP : guanosine triphosphate, GAPs : GTPase activating proteins, GEFs : guanine nucleotide exchange factors, MAPK : mitogen-activated protein kinase, miRNAs : microRNAs, log2FC : log2-fold change, CDKN2A : cyclin-dependent kinase inhibitor 2A, HTRs : 5-hydroxytryptamine receptors, HMGA2 : high mobility group protein 2, CCND1 : cyclin D1. Declarations Availability of data and materials The datasets generated and/or analyzed during the current study are available in the Sequence Read Archive (SRA) repository (SRR1030462, SRR1030463, SRR1756569, SRR8615282, SRR1756570, SRR8532655, SRR8616185, ERR208907, SRR3228439, SRR8615504, SRR2313117, SRR2313118, SRR2313119, SRR2313123, SRR2313124, SRR2313125). Competing interests The authors declare that they have no competing interests. Funding This study was supported and funded by the Foundation for Aging Research of Heinrich Heine University (grant number 701.810.845) and the German Research Foundation (DFG; grant number: AH 92/8-1). Author contributions M.S. conceptualized and designed the study, and did analysis and prepared the final draft; A.J. substantively revised the manuscript; P.G. interpreted data and improved introduction, S.R. conducted gene ontology and pathway analysis; F.D., H.M., F.G., and N.G. prepared the figures and table and did literature search; M.A. proofread and substantively revised the final draft. All authors reviewed and approved the manuscript. Competing interests The authors declare that they have no competing interests. References Simanshu DK, Nissley DV, McCormick F. RAS proteins and their regulators in human disease. Cell. 2017;170(1):17-33. Saito Y, Koya J, Araki M, Kogure Y, Shingaki S, Tabata M, et al. Landscape and function of multiple mutations within individual oncogenes. Nature. 2020;582(7810):95-9. Del Re M, Rofi E, Restante G, Crucitta S, Arrigoni E, Fogli S, et al. Implications of KRAS mutations in acquired resistance to treatment in NSCLC. Oncotarget. 2018;9(5):6630. 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Int J Cancer. 2023;153(2):373-9. Hjazi A, Ghaffar E, Asghar W, Alauldeen Khalaf H, Ikram Ullah M, Mireya Romero-Parra R, et al. CDKN2B-AS1 as a novel therapeutic target in cancer: Mechanism and clinical perspective. Biochem Pharmacol. 2023;213:115627. Additional Declarations No competing interests reported. Supplementary Files Salianietal.SupplementaryDatalegends.docx SupplementaryData1.xlsx SupplementaryData2.xlsx SupplementaryData3.xlsx SupplementaryData4.xlsx SupplementaryData5.xlsx SupplementaryData6.xlsx SupplementaryData7.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The KRAS protein is a small guanosine triphosphatase (GTPase) that serves as a molecular switch by cycling between inactive guanosine diphosphate (GDP)-bound and active guanosine triphosphate (GTP)-bound states in response to extracellular signals to induce intracellular responses (4). These off/on molecular states based on GDP/GTP exchange are controlled by GTP hydrolysis reactions stimulated by GTPase-activating proteins (GAPs) and RAS-specific guanine nucleotide exchange factors (GEFs) (5, 6).\u003c/p\u003e\n\u003cp\u003eWhile GTP-bound\u0026nbsp;KRAS\u003cem\u003e\u0026nbsp;\u003c/em\u003etransduces signals to its downstream effectors, activating multiple signaling pathways, somatic mutations favor a constant active state through the impairment of GTP hydrolysis and resistance to GAP function. A high concentration of the active form leads to hyperactivation of downstream oncogenic signaling pathways, including the mitogen-activated protein kinase (MAPK) pathway, which is involved in cell growth, proliferation, development, inflammation, differentiation, survival, and apoptosis to initiate and promote malignant transformation (7).\u003c/p\u003e\n\u003cp\u003eAlthough recent advances in the understanding of the KRAS oncoprotein structure have resulted in the clinical development of novel selective anti-\u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003einhibitors (8, 9), preclinical data, and clinical translational series have recently revealed multiple mechanisms of resistance to these inhibitors (10). Therefore, a deeper understanding of these factors, including histological features, the immune microenvironment, and the transcriptional landscape of tumor cells with \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003emutations, is crucial. In this regard, additional studies are needed to elucidate the molecular and cellular mechanisms, including transcriptional changes and pathway-related strategies responsible for the modulation of \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003etumorigenesis\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDisturbances in lncRNAs,\u0026nbsp;key regulators of gene expression, have been reported in the progression of many human\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ecancers (11-13). Identifying the relationships between \u003cem\u003eKRAS\u003c/em\u003e mutations and abnormal expression of some lncRNAs is expected to significantly improve our knowledge of the mechanisms of tumorigenesis controlled by mutKRAS (14). Abnormal levels of KRAS, a known mediator of many cellular signaling pathways, reciprocally cause diverse molecular alterations, such as dysregulation of lncRNA expression. Shi et al., 2021 showed that the levels of a KRAS-responsive lncRNA called KIMAT1 were positively correlated with KRAS levels both in cell lines and in lung cancer specimens (15). In addition, the role of KIMAT1 in maintaining a positive feedback loop to sustain KRAS signaling during lung cancer promotion has been reported as a strategy to improve KRAS-induced tumorigenesis. Another study indicated that Orilnc1 can be regulated by the RAS-RAF-MEK-ERK pathway, which is required for cell proliferation in RAS/BRAF-dependent human malignancies (16).\u003c/p\u003e\n\u003cp\u003eThe association of lncRNAs with various regulatory apparatuses, including chromatin remodeling factors, transcription factors, splicing machinery, and nuclear trafficking modulators, emphasizes the diversity and complexity of their related regulatory mechanisms (17, 18). The function of lncRNAs as competing endogenous RNAs (ceRNAs) has been suggested as one of their main gene expression regulatory approaches (19-21). Emerging evidence has indicated that many lncRNAs with oncogenic activity are upregulated in cancer tissues through the sponging of tumor suppressor microRNAs (miRNAs) (22, 23). The binding of lncRNAs (as ceRNAs) to miRNAs prevents the latter from recognizing their targets, which consequently results in mRNA upregulation. Therefore, during malignant transformation, oncogenic lncRNAs intensify cancer promotion via the downregulation of miRNAs targeting different driver oncogenes (24, 25).\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated abnormally overexpressed lncRNAs associated with \u003cem\u003eKRAS\u003c/em\u003e mutations by analyzing the transcriptional profiles of CRC and PC cell lines with and without \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003emutations. Overexpressed lncRNAs, known as oncogenic \u003cem\u003eKRAS\u003c/em\u003e-related lncRNAs,\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere identified, and among them, CDKN2-AS1 was selected to construct the ceRNA network. The possible function of CDKN2B-AS1 through its associated ceRNA network target genes was identified by performing functional enrichment analysis. Additionally, expression and survival analyses of target genes in the CDKN2B-AS1 ceRNA network were performed to estimate their prognostic performance as potential biomarkers in \u003cem\u003eKRAS\u003c/em\u003e-mutant cancers. The role of ceRNAs and their associated networks in \u003cem\u003eKRAS\u003c/em\u003e-dependent tumorigenesis is still unclear. Therefore, this study aimed to further explore the molecular and cellular mechanisms involved in the pathogenesis of \u003cem\u003eKRAS\u003c/em\u003e-driven cancers through analysis of the lncRNA-associated ceRNA network. The results of this study improve our understanding of the potential contribution of lncRNAs to \u003cem\u003eKRAS\u003c/em\u003e-associated pathogenesis and their application as the possible diagnostic and prognostic biomarkers for \u003cem\u003eKRAS\u003c/em\u003e-mutant cancers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSamples and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, raw RNA sequencing data were extracted from the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) (26). The sequencing data of three human CRC cell lines, namely, HCT-116 (SRR1030462, SRR1030463, SRR1756569, and SRR8615282) and LoVo (SRR1756570, SRR8532655, and SRR8616185), which are the \u003cem\u003eKRAS\u003c/em\u003e mutant (mutKRAS) samples, and SW48 (ERR208907, SRR3228439, and SRR8615504), which are the \u003cem\u003eKRAS\u003c/em\u003e wild-type (wtKRAS) and control samples, were downloaded. In addition, transcriptomic data of PC cell lines, including Capan-2 (SRR2313117, SRR2313118, and SRR2313119) as the mutKRAS sample and BXPC3 (SRR2313123, SRR2313124, and SRR2313125) as the wtKRAS control sample, were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWorkflow of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause of the greater prevalence of \u003cem\u003eKRAS \u003c/em\u003emutations in pancreatic and colorectal cancers, this study used CRC and PC cell lines. Transcriptional profile analysis of PC cell lines was conducted to analyze the differential expression of genes between Capan-2 (mutKRAS) cells and BXPC3 cells, which were used as wtKRAS samples. In addition, differential expression analysis of CRC cells, including HCT-116 and LoVo (mutKRAS) vs. SW48 (wtKRAS) cells, was performed previously (27). Transcriptional profile analysis of the samples revealed DEGs between the mutKRAS and wtKRAS cells. A Venn diagram analysis (https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html) (28) revealed 42 common upregulated DEGs (uDEGs), including common differentially expressed lncRNAs (DELs) and protein-coding genes. According to the workflow of the study, recognized DELs could be assigned to \u003cem\u003eKRAS\u003c/em\u003e-related lncRNAs; among them, CDKN2B-AS1 was selected for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData preprocessing and differential expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA sequencing data were downloaded as SRA files, and fastq-dump from the SRA toolkit (v2.8.2) was used to convert the SRA to FASTQ format (26). The sequencing quality of the FASTQ files was monitored by FastQC (v0.11.5) and modified using quality control software, including FLEXBAR (v3.0) and Trimmomatic (v0.39) (29-31). The human reference genome was downloaded from the Ensemble database (http://ftp.ensembl.org/pub/release95/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz) and indexed before mapping with Bowtie2 (v2.3.4.1) (32, 33). The filtered reads were aligned with the reference genome using Bowtie2 software. The output files of the mapping in the SAM format were processed by the HTSeq-count program (v0.11.4) for simultaneous read counting and annotation using an annotated human reference genome downloaded from Ensemble (https://ftp.ensembl.org/pub/release95/gtf/homo_sapiens/Homo_sapiens.GRCh38.98.gtf.gz)(34).\u003c/p\u003e\n\u003cp\u003eNormalization and differential expression analysis were conducted with the DESeq2 package (version 1.38.0) from Bioconductor in the R environment (version 3.6.1, https://www.rproject.org/)(35). Significantly upregulated DEGs were identified according to log2-fold change (log2FC) and adjusted p-value as screening criteria (|log2FC| \u0026gt; 3, adjusted p-value \u0026lt;0.01). DEGs were annotated with Ensembl Biomart (https://asia.ensembl.org/biomart/martview) and the GRCh38.p13 reference genome for division into protein-coding genes and DELs (36). All the commands and scripts used for data processing and differential expression analysis were uploaded to the GitHub platform and are publicly available via https://github.com/mahsa1985/R-scripts.git and https://github.com/mahsa1985/Linux-Commands.git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutput visualization of differential expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHierarchical clustering analysis was performed to visualize the results of differential expression analysis related to \u003cem\u003eKRAS\u003c/em\u003e mutations based on the normalized read counts of the mutKRAS and wtKRAS samples. The heatmap plots were created using the gplots package in R, and variance stabilizing transformation (VST) was applied to the normalized count data before clustering. Linkage analysis and distance measurement were based on the complete linkage and Euclidean distance, respectively. According to the lowest adjusted p-value, the expression of 1000 genes was illustrated by heatmap plots based on expression data indicated as normalized values (Z‑scores). An MA plot was created using the plotMA function of the DESeq2 package, indicating log2FC on the y-axis and the average of normalized counts over all samples on the x-axis. Each gene is represented by a dot, and the points in blue are genes with significant differential expression and adjusted p values less than 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the ceRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe construction of the ceRNA network was based on the ceRNA hypothesis that lncRNA and mRNAs can coregulate each other by sharing MREs (miRNA response elements). The ceRNA network of CDKN2B-AS1 was constructed based on previous studies on the ceRNA function of CDKN2B-AS1 and the findings of the present study. Table 1 shows the miRNA-CDKN2B-AS1-mRNA interactions, for which the LncTarD database (https://lnctard.bio-database.com/) was used to determine the miRNA-CDKN2B-AS1 and miRNA‒mRNA interactions based on previous publications (37). The list of the miRNAs in Table 1 was mapped into miRWalk (http://mirwalk.umm.uni-heidelberg.de/) and ToppCluster (https://toppcluster.cchmc.org/) to search for their mRNA targets (38, 39). According to the ceRNA hypothesis, the genes obtained from miRWalk and ToppCluster, which were also among the list of uDEGs, were considered the target genes of CDKN2B-AS1 to construct the ceRNA network, while considering their related miRNAs, as shown in Table 1. Finally, the CDKN2B-AS1-miRNA‒mRNA ceRNA network was constructed and visualized using the Cytoscape tool (version 3.9.1) (https://cytoscape.org/) (40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology and pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the biological functions of the target genes in the CDKN2B-AS1 ceRNA network, gene ontology (GO) and pathway analyses were applied to underscore the potential molecular and cellular tumorigenesis of CDKN2B-AS1 as a \u003cem\u003eKRAS\u003c/em\u003e-related lncRNA. In this study, enrichment analysis was performed using the comprehensive gene set enrichment analysis web server EnrichR (https://maayanlab.cloud/Enrichr) (36). GO analysis was based on enriched terms in the biological process, molecular function, and cellular component categories. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (41). Moreover, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) bioinformatics tool (https://david.ncifcrf.gov/) and the Gene Ontology Resource (www.geneontology.org) were used to validate the results of enrichment analysis (35,37). GO terms and KEGG pathways with a p-value \u0026lt; 0.05 were considered significantly enriched. The most significantly enriched GO terms and KEGG pathways were ranked based on the p-value. Eventually, the results obtained from EnrichR were visualized using http://www.bioinformatics.com.cn/srplot, an online platform for data analysis and visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of the prognostic performance of ceRNA-related target genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic power of target genes in the CDKN2B-AS1 ceRNA network was evaluated through survival analysis utilizing the interactive web-based tool GEPIA (Gene Expression Profiling Interactive Analysis), which is based on the gene expression RNA-seq datasets of The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga) and Genotype-Tissue Expression (GTEx) (42, 43). Using this database to analyze gene survival, we set the group cutoff to the median and 95% confidence interval (CI) of the YES. All analyses were considered statistically significant at a log-rank p-value \u0026lt; 0.05. Correlation analysis between gene expression and sample type (tumor and normal samples) was performed using the UALCAN (https://ualcan.path.uab.edu/index.html) (44) online dataset based on the TCGA database. All parameters were set to default values to investigate differential expression between tumor and normal samples, considering a p-value less than 0.05 to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis and visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, to identify DELs related to KRAS mutations, transcriptional profile analysis was conducted on the mutKRAS and wtKRAS CRC and PC cell samples (Supplementary Data 1-3). Hierarchical cluster analysis was used to visualize differential expression. The heatmap plots indicated DEGs in CRC and PC samples with and without KRAS mutations (Figure 1a). MA plots also display the log2FC of genes compared with their mean normalized counts (Figure 1b). The results of the RNA sequencing analysis revealed upregulated DEGs (|log2FC| \u0026gt; 3, adjusted p-value \u0026lt;0.01) (Supplementary Data 4-6). In this regard, for the CRC cell lines, 980 and 1525 DEGs were upregulated in HCT-116 and LoVo (mutKRAS samples) vs. SW48 (wtKRAS control sample) cells, respectively. In addition, transcriptional analysis of PC cell lines revealed a total of 894 upregulated DEGs in Capan-2 (mutKRAS) cells compared with those in BXPC3 (wtKRA) cells (Figure 2a).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of CDKN2B-AS1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, a multistep strategy was applied to select CDKN2B-AS1\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eas a lncRNA with differential expression in the context of \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003emutation (Figure 2a). In this regard, upregulated genes with significant differential expression were identified by comparing the transcriptomes of CRC mutKRAS (HCT-116 and LoVo) and wtKRAS (SW48) cells and between PC mutKRAS (Capan-2) and wtKRAS (BXPC3) cells. Venn diagram analysis revealed 42 uDEGs, including protein-coding genes and DELs, associated with the \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003emutation (Figure 2b). In the next step, overlapping DELs were identified according to Figure 2a, which could be assigned as \u003cem\u003eKRAS\u003c/em\u003e-related lncRNAs. The upregulation of the overlapping DELs in the mutKRAS cell lines compared to the wtKRAS cell lines is in line with the ceRNA hypothesis. Among the overlapping and upregulated DELs, some with less annotation, such as LINC00471, LINC01842, and DNAH17-AS1, were excluded, and CDKN2B-AS1,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ea \u003cem\u003eKRAS\u003c/em\u003e-related lncRNA, was selected to construct the ceRNA network for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eceRNA network of CDKN2B-AS1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuided by the ceRNA hypothesis, CDKN2B-AS1-miRNA and miRNA‒mRNA interactions confirmed by previous studies are illustrated in Table 1. The data provided in Table 1 were extracted from LncTarD as a comprehensive resource of lncRNA‒target interactions to report experimentally supported findings. A total of 21 miRNAs were identified from Table 1 and were used as candidates for constructing the ceRNA network of CDKN2B-AS1. The results obtained from miRWalk and Toppcluster indicated that candidate miRNAs were able to target many of the 42 uDEGs in the mutKRAS vs. wtKRAS cells according to differential expression analysis. Therefore, based on the ceRNA hypothesis, the constructed network with 21 miRNAs and 34 mRNAs predicted that our upregulated DEGs could be positively correlated with upregulated CDKN2B-AS1 and negatively correlated with the miRNA expression levels involved in the ceRNA network (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. The list of the experimentally supported interactions between CDKN2B-AS1 and its miRNA targets and miRNA‒mRNA interactions is based on the ceRNA hypothesis extracted from LncTarD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumorigenesis outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003emiRNA target\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eNAP1L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell growth (+); cell metastasis (+); PI3K/AKT/mTOR signaling pathway (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003elet-7c-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePBX3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell viability (+); cell migration (+); cell invasion (+); apoptosis process (-); PI3K/AKT signaling pathway (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;miR-144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eARHGAP18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ecell metastasis (+); cell migration (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;miR-153-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eBMI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eTumorigenesis (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;miR-99a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCervical cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eTGFbetaI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell invasion (+); cell migration (+); apoptosis process (-); cell senescence (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;miR-181a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell metastasis (+); cell invasion (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-122-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMalignant glioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSIRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell migration(+);cell invasion(+);apoptosis process(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-34a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eARL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMitochondrial function (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-199a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eKidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eTXNIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eInflammatory response (+); cell pyroptosis (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eLaryngeal squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eROCK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell growth (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-324-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMedulloblastoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eBRI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell migration (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOsteosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMAP3K3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell migration (+); epithelial to mesenchymal transition (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-4458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSMAD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCancer progression (+); cell migration (+); cell invasion (+); cell growth (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-143-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHMGA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eChemosensitivity (-); apoptosis process (-); cell growth (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003elet-7a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eRenal cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCCND1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell migration (+); cell invasion (+); cell growth (-); apoptosis process (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eRenal cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCCND2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); cell migration (+); cell invasion (+); cell growth (-); apoptosis process (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eThoracic aortic dissection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (-); apoptosis process (+); AKT signaling pathway (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-320d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eNR2C2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCell proliferation (+); invasion (+); reduced apoptosis (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-378b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCAPRIN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eProliferation (+); migration (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-378b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eNasopharyngeal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eE2F2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eProliferation (+); colony formation (+); invasion (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-98-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e(63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.52577319587629%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHead and neck cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eFGFR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.45360824742268%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eProliferation \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003emiR-125a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e(64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"30\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGO and signaling pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO analysis and pathway enrichment analysis were performed for the genes in the ceRNA network as the target genes of CDKN2B-AS1. All the genes were computationally uploaded to the DAVID, EnrichR, and Gene Ontology resources to better reveal the carcinogenicity of CDKN2-AS1 as a \u003cem\u003eKRAS\u003c/em\u003e-related lncRNA. The results of enrichment analysis showed the involvement of the genes in the most significant and relevant enriched GO terms and KEGG pathways ranked by p-value in each category (Figure 4) (Supplementary Data 7). In the biological process group, genes were mainly enriched in terms related to the regulation of protein serine/threonine kinase activity, regulation of cellular senescence, regulation of the apoptotic process, and positive regulation of the cell cycle (Figure 4a). In the molecular function category of GO, genes were mainly enriched in cyclin-dependent protein serine/threonine kinase regulator activity, protein kinase binding, NF-kappaB binding, and protein serine/threonine kinase activity terms (Figure 4b). The results of pathway enrichment analysis indicated that genes were mainly enriched in pathways such as miRNAs in cancer, pancreatic cancer, colorectal cancer, and the p53 signaling pathway (Figure 4c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of the prognostic performance of ceRNA-related target genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic power of target genes in the CDKN2B-AS1 ceRNA network was evaluated based on survival and expression analysis of the genes across tumor and normal samples. The significant differences between the gene expression levels of the normal and tumor groups were evaluated for the target genes of the ceRNA network. The higher expression of the CDKN2A, CCND1, HTR1D, and HMGA2 genes in the tumor samples, as determined by UALCAN, is due to the ceRNA hypothesis (Figure 5a). The GEPIA database was used for the survival analysis of target genes using RNA sequencing expression data of tumors and normal samples from the TCGA and GTEx datasets (Tang et al., 2017). Consistent with the results obtained from the expression analysis, the findings of the survival analysis showed that the CDKN2A gene in CRC patients and CCND1, HTR1D, and HMGA2 in PC patients were significantly associated with unfavorable overall survival based on Kaplan‒Meier plots (significance level at log-rank p-value \u0026lt; 0.05) (Figure 5b).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMutations in the \u003cem\u003eKRAS\u003c/em\u003e oncogene with tumor-promoting activity have been identified in 25% of all cancers, whereas some cancers, such as pancreatic and colorectal cancer, have the highest mutation rates, at 90% and 45%, respectively. Despite developments in direct \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003epharmacology, targeted therapies involving direct inhibitors are followed by rapid reactivation of \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003esignaling,\u0026nbsp;leading to resistance\u0026nbsp;to\u0026nbsp;long-term treatment\u0026nbsp;(8). Therefore\u003cem\u003e,\u0026nbsp;\u003c/em\u003ea comprehensive analysis of the different mechanisms and pathways associated with \u003cem\u003eKRAS\u003c/em\u003e tumorigenic activity is critical for identifying potential therapeutic strategies to inhibit its oncogenic behavior.\u003c/p\u003e\n\u003cp\u003elncRNAs have been reported to have extensive ability to regulate gene expression, enabling intertwined multilayer molecular interactions in numerous pathological conditions, including cancer (67). The competing endogenous activity of lncRNAs, as one of their posttranscriptional regulatory mechanisms, is conferred by their competitive binding with shared miRNAs, freeing their targets from miRNA-induced degradation, thus significantly connecting with gene upregulation (68).\u003c/p\u003e\n\u003cp\u003eDuring the process of malignant transformation, alterations, including chromosomal rearrangements, shortened 3\u0026prime;UTRs, and point mutations such as KRAS oncogenic mutations, occur in the chromosome of cancer cells. Following these alterations, transcriptional changes and, as a consequence, dysregulation of lncRNAs and their related ceRNA network are closely linked to tumorigenesis (69). Therefore, constantly updated studies on the roles of lncRNAs and their ceRNA networks as multilayered intracellular communications have led to remarkable advancements in this burgeoning hotspot to provide new insights into cancer pathogenesis.\u003c/p\u003e\n\u003cp\u003eThis study studied the KRAS-dependent dysregulated transcription profile in CRC and PC cells to identify upregulated DEGs and DELs to identify a ceRNA network associated with \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003etumorigenesis. A comparison of the transcriptomes of the mutKRAS cell lines with those of their wtKRAS counterparts revealed 42 uDEGs, including protein-coding genes and DELs. Here, we identified CDKN2B-AS1 as a \u003cem\u003eKRAS\u003c/em\u003e-related DEL. This lncRNA, also known as ANRIL, is located within the CDKN2B-CDKN2A gene cluster at chromosome 9p21, which is a significant genetic susceptibility locus for several cancers. To identify the connection between CDKN2B-AS1 and uDEGs, a ceRNA network of CDKN2B-AS1 was constructed using uDEGs as target genes. The miRNA targets of CDKN2B-AS1 were determined according to previous publications on the ceRNA function of CDKN2B-AS1. The list of candidate miRNAs was submitted to the miRWalk and ToppCluster platforms to search for potential gene targets. Interestingly, 34 genes out of 42 uDEGs were found to be targets of candidate miRNAs. Finally, the ceRNA network of CDKN2B-AS1 was constructed from 21 miRNAs and 34 uDEGs.\u003c/p\u003e\n\u003cp\u003eTo further understand the pathogenesis mechanism of CDKN2B-AS1 as a \u003cem\u003eKRAS-related\u0026nbsp;\u003c/em\u003elncRNA\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003etop enriched functional annotations of GO and KEGG pathway analyses were identified. The target genes were enriched in GO biological process categories, such as regulation of protein serine/threonine kinase activity, regulation of the apoptotic process, and positive regulation of the cell cycle, which are closely related to tumorigenesis and cancer promotion. In addition, pathway enrichment analysis revealed several enriched pathways known as cancer-related pathways, including microRNAs involved in cancer, cell cycle regulation, pancreatic cancer, and the p53 signaling pathway.\u003c/p\u003e\n\u003cp\u003eMoreover, to determine the clinical value of CDKN2B-AS1, the prognostic power of target genes of the ceRNA network was evaluated based on survival and expression analysis of tumor and normal patient samples. While the results showed a statistically significant association of CDKN2A in CRC patients and CCND1, HTR1D, and HMGA2 in PC patients with survival, their higher expression in tumor samples was also confirmed. While cyclin-dependent kinase inhibitor 2A (CDKN2A) is well known as a susceptibility gene for melanoma and pancreatic cancer, its germline variants have also been associated with a broader range of malignant transformations, including neural system tumors, breast carcinomas, head and neck squamous cell carcinomas, and sarcomas (70, 71). While the expression level of Cyclin D1 (CCND1) is strictly regulated in normal cells, its increased activity has been observed in various types of neoplasms (72). A positive correlation between \u003cem\u003eCCND1\u003c/em\u003e copy number in breast cancer and lymph node metastasis was observed (73). According to recent studies, 5-hydroxytryptamine receptors (HTRs), including HTR1D, are linked to several malignant tumors, such as melanoma, breast cancer, lung cancer, and colon cancer (74-76). The involvement of the HOXA10-AS/miR-340-3p/HTR1D axis in the progression of pancreatic cancer has been demonstrated (77). Moreover, the expression level of HTR1D in clinical samples of CRC adenocarcinoma suggested its role in the prognosis of patients (78). Oncogenic roles of high mobility group protein 2 (HMGA2) in different types of cancers and various strategies have revealed that HMGA2 is a candidate for cancer diagnostic, prognostic, and therapeutic purposes.\u003c/p\u003e\n\u003cp\u003eTo date, an increasing number of dysregulated lncRNAs, key regulators of gene expression with vital roles in human neoplasms, such as CRC and PC, have been identified (79). Based on the ceRNA phenomenon, the sequestration of tumor suppressor miRNAs from their mRNA target is one of the oncogenic mechanisms for gene expression regulation by lncRNAs (15, 80, 81). In the present study, dysregulated lncRNAs between the mutKRAS and wtKRAS CRC and PC cell lines were identified by using RNA-seq datasets from the SRA. Among the DELs considered \u003cem\u003eKRAS\u003c/em\u003e-related lncRNAs, some with less annotation were excluded, and finally, CDKN2B-AS1 was selected for further analysis. In addition, because of the indispensable role of CDKN2B-AS1 in multiple diseases, particularly cancer, we selected this lncRNA (82, 83). Therefore, the ceRNA network of CDKN2B-AS1 was constructed from upregulated differentially expressed DEGs-CDKN2B-AS1. The results of previous studies on the sponging effect of CDKN2B-AS1 were used to identify miRNAs that mediate the ceRNA function of lncRNAs to identify all the elements needed for the construction of the ceRNA network. The results of the GO and pathway analyses of the target genes included in the ceRNA network of CDKN2B-AS1 indicated their role in cancer-related pathways and biological processes. Furthermore, survival and expression analysis of the corresponding ceRNA genes revealed the prognostic power of CDKN2A, CCND1, HTR1D, and HMGA2.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be considered for a more precise interpretation of the results. The findings were based on the transcriptional profile analysis of cancer cell lines, which should be validated by patient sample data. Although it is fully agreed that lncRNAs are worthy of investigation and that too much remains in this class of biomolecules, the mechanism of action of lncRNAs is often very complex, and there is always uncertainty about their biological impact. Therefore, our findings are closer to predictions than to certainty, and more computational methods and molecular biology experiments should be applied to increase the credibility of our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we analyzed the \u003cem\u003eKRAS\u003c/em\u003e-dependent dysregulated transcription profiles of CRC and PC cells to identify DELs. As a result, CDKN2B-AS1 was identified as a \u003cem\u003eKRAS\u003c/em\u003e-related lncRNA, and its ceRNA activity was further investigated as one of the main gene expression regulatory mechanisms of lncRNAs. The ultimate purpose of this study was to underscore the great significance of the ceRNA network of CDKN2B-AS1 underlying the tumorigenesis mediated by \u003cem\u003eKRAS\u0026nbsp;\u003c/em\u003emutation\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKRAS\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u003c/em\u003e Kirsten rat sarcoma viral oncogene homolog, \u003cstrong\u003eCRC\u003c/strong\u003e: colorectal cancer, \u003cstrong\u003e\u003cem\u003ePC\u003c/em\u003e\u003c/strong\u003e: pancreatic cancer, \u003cstrong\u003e\u003cem\u003elncRNAs\u003c/em\u003e\u003c/strong\u003e: long noncoding RNAs, \u003cstrong\u003e\u003cem\u003eceRNA\u003c/em\u003e\u003c/strong\u003e: competing endogenous RNA, \u003cstrong\u003e\u003cem\u003eSRA\u003c/em\u003e\u003c/strong\u003e: sequencing read archive, DEGs: differentially expressed genes, \u003cstrong\u003e\u003cem\u003euDEGs\u003c/em\u003e\u003c/strong\u003e: overlapping upregulated DEGs, \u003cstrong\u003e\u003cem\u003eDELs\u003c/em\u003e\u003c/strong\u003e: differentially expressed lncRNAs (DELs), \u003cstrong\u003e\u003cem\u003emutKRAS\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003eKRAS mutant, \u003cstrong\u003e\u003cem\u003ewtKRAS\u003c/em\u003e\u003c/strong\u003e: wild-type KRAS, \u003cstrong\u003e\u003cem\u003eGTPase\u003c/em\u003e\u003c/strong\u003e: small guanosine triphosphatase, \u003cstrong\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/strong\u003e: guanosine diphosphate, \u003cstrong\u003e\u003cem\u003eGTP\u003c/em\u003e\u003c/strong\u003e: guanosine triphosphate, \u003cstrong\u003e\u003cem\u003eGAPs\u003c/em\u003e\u003c/strong\u003e: GTPase activating proteins, \u003cstrong\u003e\u003cem\u003eGEFs\u003c/em\u003e\u003c/strong\u003e: guanine nucleotide exchange factors, \u003cstrong\u003e\u003cem\u003eMAPK\u003c/em\u003e\u003c/strong\u003e: mitogen-activated protein kinase, \u003cstrong\u003e\u003cem\u003emiRNAs\u003c/em\u003e\u003c/strong\u003e: microRNAs, \u003cstrong\u003elog2FC\u003c/strong\u003e: log2-fold change, \u003cstrong\u003e\u003cem\u003eCDKN2A\u003c/em\u003e\u003c/strong\u003e: cyclin-dependent kinase inhibitor 2A, \u003cstrong\u003e\u003cem\u003eHTRs\u003c/em\u003e\u003c/strong\u003e: 5-hydroxytryptamine receptors, \u003cstrong\u003e\u003cem\u003eHMGA2\u003c/em\u003e\u003c/strong\u003e: high mobility group protein 2, \u003cstrong\u003e\u003cem\u003eCCND1\u003c/em\u003e\u003c/strong\u003e: cyclin D1.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the Sequence Read Archive (SRA) repository (SRR1030462, SRR1030463, SRR1756569, SRR8615282, SRR1756570, SRR8532655, SRR8616185, ERR208907, SRR3228439, SRR8615504, SRR2313117, SRR2313118, SRR2313119, SRR2313123, SRR2313124, SRR2313125).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported and funded by the Foundation for Aging Research of Heinrich Heine University (grant number 701.810.845) and the German Research Foundation (DFG; grant number: AH 92/8-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.S. conceptualized and designed the study, and did analysis and prepared the final draft; A.J. substantively revised the manuscript; P.G. interpreted data and improved introduction, S.R. conducted gene ontology and pathway analysis; F.D., H.M., F.G., and N.G. prepared the figures and table and did literature search; M.A. proofread and substantively revised the final draft. All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSimanshu DK, Nissley DV, McCormick F. RAS proteins and their regulators in human disease. Cell. 2017;170(1):17-33.\u003c/li\u003e\n\u003cli\u003eSaito Y, Koya J, Araki M, Kogure Y, Shingaki S, Tabata M, et al. Landscape and function of multiple mutations within individual oncogenes. Nature. 2020;582(7810):95-9.\u003c/li\u003e\n\u003cli\u003eDel Re M, Rofi E, Restante G, Crucitta S, Arrigoni E, Fogli S, et al. Implications of KRAS mutations in acquired resistance to treatment in NSCLC. Oncotarget. 2018;9(5):6630.\u003c/li\u003e\n\u003cli\u003eMullard A. Cracking KRAS. Nature reviews Drug discovery. 2019;18(12):887-92.\u003c/li\u003e\n\u003cli\u003eScheffzek K, Ahmadian MR, Wittinghofer A. 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Cell. 2011;146(3):353-8.\u003c/li\u003e\n\u003cli\u003eGiaccherini M, Farinella R, Gentiluomo M, Mohelnikova‐Duchonova B, Kauffmann EF, Palmeri M, et al. Association between a polymorphic variant in the CDKN2B‐AS1/ANRIL gene and pancreatic cancer risk. Int J Cancer. 2023;153(2):373-9.\u003c/li\u003e\n\u003cli\u003eHjazi A, Ghaffar E, Asghar W, Alauldeen Khalaf H, Ikram Ullah M, Mireya Romero-Parra R, et al. CDKN2B-AS1 as a novel therapeutic target in cancer: Mechanism and clinical perspective. Biochem Pharmacol. 2023;213:115627.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"KRAS mutation, CDKN2B-AS1, colorectal cancer, pancreatic cancer, ceRNA network, lncRNA, long noncoding RNAs, differential expression analysis","lastPublishedDoi":"10.21203/rs.3.rs-4304326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4304326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Kirsten rat sarcoma viral oncogene homolog (KRAS) exhibits the highest mutation rate in colorectal cancer (CRC) and pancreatic cancer (PC), highlighting the need for a comprehensive understanding of KRAS-dependent pathogenesis. Given the regulatory role of long noncoding RNAs (lncRNAs) in gene expression, this study focused on constructing a competing endogenous RNA (ceRNA) network of selected KRAS-related lncRNAs. By analyzing the transcriptional profiles of CRC and PC cell lines with and without KRAS mutations, differentially expressed genes (DEGs) were identified using sequencing data from the Sequencing Read Archive database. Notably, the analysis revealed 42 common upregulated DEGs (uDEGs), including differentially expressed lncRNAs and protein-coding genes, between KRAS-mutant and KRAS-wild-type cells. Among them, CDKN2B-AS1 emerged as a key KRAS-related lncRNA for the construction of the ceRNA network. Using LncTarD, miRWalk, and ToppCluster servers, a robust ceRNA network of CDKN2B-AS1 was constructed to elucidate the interactions between corresponding miRNAs and target genes. This network included 21 miRNAs and 34 genes selected from common uDEGs. Enrichment analysis of ceRNA target genes validated their involvement in critical cancer-related pathways and biological processes. Crucially, expression and survival analysis underscored the prognostic significance of selected target genes within the CDKN2B-AS1 ceRNA network. By delineating the regulatory mechanisms of the CDKN2B-AS1 ceRNA network, this study sheds light on the molecular and cellular pathways involved in KRAS-associated tumorigenesis.","manuscriptTitle":"Construction and analysis of the CDKN2B-AS1 ceRNA network associated with KRAS-dependent tumorigenesis in colorectal and pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 17:24:33","doi":"10.21203/rs.3.rs-4304326/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60ce5ea7-ec64-4598-988b-b382ae216925","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-08T06:53:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 17:24:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4304326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4304326","identity":"rs-4304326","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}