Potential value of m6A-related lncRNAs in diagnosis and therapy of pancreatic cancer

Potential value of m6A-related lncRNAs in diagnosis and therapy of 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 Potential value of m6A-related lncRNAs in diagnosis and therapy of pancreatic cancer Long Wu, Yunting Zhang, Dandan Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6975075/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Pancreatic cancer is one of the digestive tract malignancies, and five-year overall survival rate of PAAD patients remains low. m6A can modify lncRNAs that could affect cancer cell proliferation and migration, it is of great significance to explore the mechanism of m6A-related lncRNA in the development of pancreatic cancer. In our study, 130 lncRNAs with a co-expression relationship with m6A genes were obtained by TCGA dataset analysis, a new prognostic model was constructed to predict overall survival in pancreatic cancer patients. Kaplan-Meier analysis, principalcomponent analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. In addition, the relationship with the response to immunotherapy was explored. Finally, therapeutic agents targeting m6A-related lncRNA action targets were screened using the drug sensitivity database. In conclusion, our study highlighted the prognostic value of m6A-related lncRNAs in pancreatic cancer. We constructed a predictive model with high prognostic value and showed sensitivity in identifying patients with pancreatic cancer who may respond well to immunotherapy. N6-methyladenosine long noncoding RNAs pancreatic cancer nomogram immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Pancreatic cancer is one of the digestive tract malignancies. In recent years, with the change in dietary structure, lifestyle and the improvement of clinical detection rate, the incidence rate is increasing year by year while the mortality rate is almost consistent with the incidence rate [ 1 ] , which has posed serious threat to human health. Due to the insidious outset, rapid disease progression and resistance to chemotherapy of pancreatic cancer, most patients have an extremely poor prognosis, and five-year overall survival for patients is less than 10% [ 2 ] . Therefore, there is an urgent need to find potential therapeutic targets to improve the prognosis of pancreatic cancer patients, and it is of great significance to lead the diagnosis and treatment of pancreatic cancer into a new era of precision medicine and individualized treatment. While N6-methyladenosine (m6A) is the most common RNA epigenetic modification on eukaryotic mRNA, it is a dynamic and reversible regulatory modification. At present, m6A methylated protein is mainly composed of writers (methyltransferases), readers (signal transducers), and erasers (demethylases) [ 3 ] . Not only can m6A regulate coding RNAs, but also can modify long noncoding RNAs (lncRNAs) that could affect cancer cell proliferation and migration [ 4 , 5 ] . In recent years, m6A modification was found to modulate tumorigogenesis and development in pancreatic cancer. Recent study systematically analysed the m6A RNA methylation related genes using The Cancer Genome Atlas (TCGA) and ICGC database that revealed 283 candidate related genes potentially involved in pancreatic cancer development, and 4 m6A RNA methylation regulators including RBM15, METTL14, FTO and ALKBH5 [ 6 ] . Inhibition of METTL14 improves the sensitivity of gemcitabine in pancreatic cancer cells and it can be a potential target for chemotherapy resistance [ 7 ] . Another research finds that METTL3 is highly expressed in tumor tissues, reducing METTL3 expression can inhibit the proliferation, invasion and migration of pancreatic cancer cells [ 8 ] . The malignancy of tumors is associated with abnormal lncRNA expression, and it is of great significance to explore the mechanism of m6A-related lncRNA in the development of pancreatic cancer, and it is expected to find new therapeutic targets. In our study, 130 lncRNAs with a co-expression relationship with m6A genes were obtained by TCGA dataset analysis, a new prognostic model was constructed to predict overall survival in pancreatic cancer patients, and a nomogram was constructed. In addition, the relationship with the response to immunotherapy was explored. Finally, therapeutic agents targeting m6A-related lncRNA action targets were screened using the drug sensitivity database. Methods Data collection and analysis The RNA transcriptome dataset and related clinical information for pancreatic cancer patients were obtained from the TCGA database ( https://cancergenome.nih.gov/ ), and pancreatic cancer patients with missing OS values were excluded, then we divided the genomic data into m6A and lncRNA genomes based on the human genome annotation data. In this study, a total of 23 m6A genes was analyzed including writers (METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15,RBM15B), readers (YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, RBMX) and erasers ( FTO, ALKBH5). The m6A genes with differential expression were found from the gene profiling data. We also analyzed these m6A genes expression in pancreatic cancer patients tissues from GEPIA database ( http://gepia.cancer-pku.cn/ ). Based on the Pearson correlation analysis, the lncRNA of |Pearson R|>0.4 and p < 0.001 were defined as the m6A-related lncRNAs for subsequent bioinformatics analysis. Construction and validation of the m6A-associated lncRNAs model Patients were randomly divided into training dataset and testing dataset. After constructing the m6A-related lncRNAs model using the training dataset, it was verified with information from the testing dataset. Risk scores were calculated by the formula: coef(lncRNA1)×expr(lncRNA1) + coef(lncRNA2)×expr(lncRNA2) + .. + coef(lncRNAn)×expr(lncRNAn), where coef (lncRNAn) indicates the coefficient of, each lncRNA correlated with survival, and expr(lncRNAn) was each lncRNA expression. We obtained the risk score of each patient. Then patients in testing dataset were divided into high- and low-risk groups based on the median of the risk score in training dataset. Independence of the m6A-associated lncRNAs model Multivariate and univariate Cox regression analysis were used to test whether the prognostic models and other clinical features considering pancreatic cancer patients were independent variables. Establish and prove a predicted nomogram A nomogram was established to show the predictive ability of risk scores and other predictors (sex, TNM stage, age, and grade) for 1-, 3-, and 5-year OS. The correction curve is used to show the actual results with the model prediction. Principal component analysis and gene enrichment analysis The grouping power of the model was assessed by PCA to group and visualized the data from the entire gene expression profile, the 23 m6A gene, the 4 m6A associated lncrna and the risk model. Kaplan-Meier survival analyses could be used to assess the OS diversity between the high- and low-risk groups. We employed a gene enrichment analysis approach to explore the potential KEGG pathways involved in the prognostic features of lncRNA. Based on the R language package tool, we examined the tumor mutation load based on the tumor-specific mutant genes. Small-molecule drug prediction Based on small molecule drug database, p < 0.05 indicated that the drug varies in treatment between high and low risk groups. Statistical Analysis All statistical analyses were performed using R software version 4.2.3. Independent-sample t-tests were conducted for normally distributed continuous variables, while Wilcoxon rank-sum tests were used for non-normally distributed variables. Pearson and Spearman coefficients were used to analyze correlations between continuous variables, depending on their distribution. Chi-square tests or Fisher’s exact tests were used for categorical variables. A p value < 0.05 was considered statistically significant. Results Identification and Characterization of m6A-related lncRNAs in pancreatic cancer Correlations of 23 m6A-related genes and 14,056 lncRNAs were extracted from the TCGA database, and 11 m6A genes were high expressed in pancreatic cancer (Fig. 1 ). The lncRNAs significantly associated with m6A were defined as the m6A-associated lncRNA (|Pearson R|>0.4 and p < 0.001). The Sankey plot exhibited co-expression relationship between 130 lncRNAs and 17 m6A genes (Fig. 2 ). We screened 39 m6A-related lncRNAs significantly correlated with clinical prognosis from TCGA database that was revealed by univariate Cox regression analysis (Fig. 3 A). Lasso regression is a common multiple regression analysis method effectively avoiding the occurrence of overfitting which can achieve variable selection and regularization while improving the prediction accuracy of the statistical model. The values corresponding to the dashed line obtained from cross validation are the number of significant lncRNAs from the Lasso regression. Therefore, we finally obtained 10 m6A-related lncRNAs from 39 lncRNAs (Fig. 3 B and 3 C), further optimized the results by multivariate regression analysis. The heatmap showed 4 m6A-related lncRNAs for subsequent prognostic model construction (Fig. 3 D), so as to evaluate the prognostic risk of pancreatic cancer patients. Construction and evaluation of prognostic models The pancreatic cancer patients were divided into high-risk and low-risk groups based on the median value prognostic risk scores, the Kaplan-Meier curve analysis in both training dataset and testing dataset showed that survival time was much higher in the low-risk group than in the high-risk group (Fig. 4 ). To test the prognostic power of the established model, we calculated risk scores for each patient in the training and testing dataset and ranked patients by risk grade (Fig. 5 A- 5 B), with significantly higher survival time and survival status in the low-risk group than in the high-risk group (Fig. 5 C- 5 D). As the patient risk score increased, the relative expression of the 4 m6A-related lncRNAs gradually decreased, the result showed that all lncRNAs were low-risk lncRNAs (Fig. 5 E- 5 F). Meanwhile, the univariate and multivariate analysis of independent prognostic factors found that risk score was associated with patient prognosis (Fig. 6 A) and acted as a prognostic factor independent of other interference factors (Fig. 6 B). Concordance index was an indicator used to evaluate the predictive power of the model and demonstrated that the accuracy of risk score was much higher than other factors (Fig. 6 C). The larger of ROC area indicated that the higher the accuracy of patient survival prediction through the model. The results showed that the area under the risk score curve was significantly greater than the other factors (Fig. 6 D), and the greater the accuracy of the model prediction with the survival time (Fig. 6 E). Construction and evaluation of prognostic nomograms By constructing a nomogram containing risk grade and clinical risk characteristics, we were able to clearly see the scores corresponding to different clinical traits according on the score scale, thus predicting the patients’ 1-, 2-, and 3-year survival based on the value of the total score (Fig. 7 A). The calibration plot indicated an agreement between overall survival and prediction rates at 1-, 2-, and 3-year (Fig. 7 B). Analyze the grouping power of the model Principal component analysis (PCA) showed differences between the low- and high-risk groups, respectively, based on the whole gene expression profile, 23 m6A gene, 4 m6A-related lncRNA and 4 m6A-related lncRNA classified by the risk model (Fig. 8 A- 8 D). The PCA further validated that the m6A-related lncRNAs model had a high grouping power and a relatively dispersed distribution of the high- and low-risk groups. These results suggest that prognostic features can distinguish between low-risk and high-risk populations. Evaluation of the tumor immune microenvironment and cancer immunotherapy response Immune-related functions and pathways in pancreatic cancer patients were further analyzed according to the m6A-related lncRNAs model. The heatmap results suggested that the expression of immune function varied significantly between low- and high-risk groups, with high expression in the high-risk group (Fig. 9 A). In order to initially explore the pathways involved in m6A-related lncRNAs in the regulation of PAAD, we conducted gene enrichment analysis and divided the pathways into BP, CC and MF. We could see clear differences in the number of samples between the different pathways, and ultimately found that m6A-related lncRNAs was involved in many immune-related biological processes (Fig. 9 B).Tumor mutation burden (TMB) was a biomarker that could be used to predict the effect of immunotherapy in tumor patients. Consistent with expectations, pancreatic cancer patients in the high-risk group were more sensitive to respond to immunotherapy than in the low-risk group (Fig. 9 C). Meantime, people with high tumor mutation burden in the high-risk group had a poor prognosis (Fig. 9 D). Drug sensitivity analysis between the high-risk group and low-risk group The circulating drug database found that nine drugs varied in treatment between the high- and low-risk groups, obviously, there were two drugs that low-risk group’s pancreatic cancer patients were more sensitive and the remaining high-risk groups were more sensitive (Fig. 10 ). Discussion Pancreatic cancer is a highly malignant digestive tract tumor, and despite the great progress in traditional treatment methods, the prognosis of pancreatic cancer patients is still poor and prone to distant metastasis and invasion [ 9 ] . Exploring new targeted therapies for pancreatic cancer is an urgent problem to be solved. The current study has found that m6A-related lncRNAs is involved in the development of gastric cancer, breast cancer and lung cancer [ 10 – 12 ] , so exploring the role of lncRNA in the prognosis or diagnosis of pancreatic cancer will help to understand the molecular mechanisms of it. A growing number of studies are devoted to identify characteristics of lncRNA to predict survival and immunotherapy response in tumor patients, however, the role of m6A-related lncRNAs in the prognosis and treatment of pancreatic cancer remains unclear. In this study, we systematically investigated the role of m6A-related lncRNAs in pancreatic cancer patients. It is found that m6A modified "writers", "erasers" and "readers" can be involved in regulating biological processes such as RNA metabolism, stem cell self-renewal, and immune response, especially play an important role in the development of tumors. Long non-coding RNA refers to non-coding RNA with over 200nt that does not have protein coding function and can be involved in various physiopathological activities of human body by regulating gene expression [ 13 ] . Several studies have shown that m6A methylation modification plays a regulatory role by altering lncRNA structure. For example, methyltransferase METTL16 can bind to the triple helix of lncRNA MALAT1 to affect its structural stability and functional expression, thus participating in tumor development [ 14 ] . Therefore, both m6A and lncRNA are important factors regulating the development of pancreatic cancer [ 15 , 16 ] , however, studies on the biological mechanism of m6A-related lncRNAs in regulating pancreatic cancer and its effects on immunotherapy are still poorly reported. In this study, by mining the information from the TCGA database, we found that "writers" (METTL14, METTL16, ZC3H13, RBM15), "readers" (YTHDF1, YTHDF2, YTHDF3, HNRNPC, HNRNPA2B1), and "erasers" (FTO) were highly expressed in pancreatic cancer patients. Sanki diagram is a specific type of flow chart that can visualize and analyze our data and clearly show the correlation between 23 m6A genes and 14,056 lncRNAs. According to the formula, there are 130 m6A-related lncRNAs. The final four m6A-related lncRNAs (AC002091.1, AC005089.1, LINC01091, PANS-AS1) provide a new idea for predicting the prognosis of pancreatic cancer patients. The analysis showed that these four types of lncRNA were less expressed in the high-risk group and were all low-risk lncRNA. ROC analysis showed that the model was superior to conventional clinical features in survival prediction of pancreatic cancer, and the nomogram showed agreement between overall survival at 1-, 3-, or 5-year and prediction rates. Finally, the observed rates of OS prediction with 1-, 3-, and 5-year showed excellent agreement. This prediction model can identify new biomarkers for subsequent studies. However, our study still has limitations in analyzing the biological mechanisms of m6A-related lncRNAs, which can only preliminarily explain the immunotherapy and immune response processes involved, and the specific mechanisms need to be expanded in clinical samples and external experimental verification. The higher the tumor mutation burden (TMB), the more types and number of new antigens produced by tumor cells, the higher the probability of being recognized by the immune system, and the greater the probability of killing these tumor cells after immune checkpoint inhibitors activate the anti-tumor immune response [ 17 ] . There has been extensive literature reporting that the level of TMB is associated with the efficacy of PD1/PD-L1 antibodies, that tumor cells with higher TMB are more susceptible to recognition by the immune system and have a higher probability of immunotherapy being effective in this patient [ 18 ] . Calculating the TMB score value of the tumor patients can better guide the clinical treatment. The results of this study suggested that patients in the high-risk group had significantly higher TMB scores than the low-risk group, indicating that patients in the high-risk group were more sensitive to immunotherapy. In conclusion, our study analyzed the expression level of m6A-related lncRNAs and its prognostic value in pancreatic cancer patients through a large amount of data. We constructed a predictive model with high prognostic value and showed sensitivity in identifying patients with pancreatic cancer who may respond well to immunotherapy. Declarations Funding This research was supported by Open Project of Hubei Key Laboratories,China (Funding number: 2021KFY022) (Dandan Wu) Human Ethics and Consent to Participate declarations Not applicable Competing interests The authors declare no competing interests Data availability All data generated or analysed during this study are included in this published article. References SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49. SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020 [J]. CA Cancer J Clin, 2020, 70(1): 7-30. HE L, LI H, WU A, et al. Functions of N6-methyladenosine and its role in cancer [J]. Mol Cancer, 2019, 18(1): 176. ZHOU K I, PARISIEN M, DAI Q, et al. N(6)-Methyladenosine Modification in a Long Noncoding RNA Hairpin Predisposes Its Conformation to Protein Binding [J]. J Mol Biol, 2016, 428(5 Pt A): 822-33. HUANG H, WENG H, CHEN J. m6A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer [J]. Cancer Cell, 2020, 37(3): 270-88. GENG Y, GUAN R, HONG W, et al. Identification of m6A-related genes and m6A RNA methylation regulators in pancreatic cancer and their association with survival [J]. Ann Transl Med, 2020, 8(6): 387. ZHANG C, OU S, ZHOU Y, et al. m(6)A Methyltransferase METTL14-Mediated Upregulation of Cytidine Deaminase Promoting Gemcitabine Resistance in Pancreatic Cancer [J]. Front Oncol, 2021, 11(696371. XIA T, WU X, CAO M, et al. The RNA m6A methyltransferase METTL3 promotes pancreatic cancer cell proliferation and invasion [J]. Pathol Res Pract, 2019, 215(11): 152666. NIELSEN S R, QUARANTA V, LINFORD A, et al. Macrophage-secreted granulin supports pancreatic cancer metastasis by inducing liver fibrosis [J]. Nat Cell Biol, 2016, 18(5): 549-60. XU F, HUANG X, LI Y, et al. m(6)A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD [J]. Mol Ther Nucleic Acids, 2021, 24(780-91. WANG H, MENG Q, MA B. Characterization of the Prognostic m6A-Related lncRNA Signature in Gastric Cancer [J]. Front Oncol, 2021, 11(630260. LV W, WANG Y, ZHAO C, et al. Identification and Validation of m6A-Related lncRNA Signature as Potential Predictive Biomarkers in Breast Cancer [J]. Front Oncol, 2021, 11(745719. GUTTMAN M, AMIT I, GARBER M, et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals [J]. Nature, 2009, 458(7235): 223-7. RUSZKOWSKA A, RUSZKOWSKI M, DAUTER Z, et al. Structural insights into the RNA methyltransferase domain of METTL16 [J]. Sci Rep, 2018, 8(1): 5311. TANG X, ZHANG M, SUN L, et al. The Biological Function Delineated Across Pan-Cancer Levels Through lncRNA-Based Prognostic Risk Assessment Factors for Pancreatic Cancer [J]. Front Cell Dev Biol, 2021, 9(694652. ZENG J, ZHANG H, TAN Y, et al. Genetic alterations and functional networks of m6A RNA methylation regulators in pancreatic cancer based on data mining [J]. J Transl Med, 2021, 19(1): 323. ADDEO A, FRIEDLAENDER A, BANNA G L, et al. TMB or not TMB as a biomarker: That is the question [J]. Crit Rev Oncol Hematol, 2021, 163(103374. ELVIN, JULIA A, GOLDBERG, et al. Profiling of Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), and PD1/PD-L1 Immunohistochemistry (IHC) in Gynecological Tumors [J]. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Editor invited by journal 14 Jul, 2025 Submission checks completed at journal 04 Jul, 2025 First submitted to journal 04 Jul, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6975075","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497823492,"identity":"7b33c1bc-25d6-4f4b-909b-4ebf8f5eb405","order_by":0,"name":"Long Wu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Wu","suffix":""},{"id":497823493,"identity":"59d08342-0047-41b3-9065-83ee03254254","order_by":1,"name":"Yunting Zhang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yunting","middleName":"","lastName":"Zhang","suffix":""},{"id":497823494,"identity":"d579af48-da07-44cc-a836-0c9f8e04fada","order_by":2,"name":"Dandan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCAwwMEiCan4EHRDGToEWygWQtBgeI1cJ3/OzBGz93HE7cfCP3mARDhXViA/vZA3i1SJ7JS7bsPXM4cduNvDQJhjPpiQ08eQl4tRgcyDGT4G27nbjtNpDB2HY4sUGCxwC/lvNvzCT/ArVsng3S8o8YLTdyzKRBtmyQBmlpIEKL5I03xtaybf+NZ9x/Y2yRcCzduI0nB78WvvM5hjfftqXJ9vecMbzxocZatp/9DH4tqCABiNlIUD8KRsEoGAWjAAcAAFIdSM49vQ9eAAAAAElFTkSuQmCC","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-06-25 13:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6975075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6975075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88891017,"identity":"3d5ae214-8a9e-4242-9216-49a6949a85e5","added_by":"auto","created_at":"2025-08-12 12:48:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232063,"visible":true,"origin":"","legend":"\u003cp\u003eHigh expression of 11 m6A genes in PAAD\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/630111670f02437e4510d207.png"},{"id":88893475,"identity":"36866cd2-405d-4b11-a6d0-a90652fd9f45","added_by":"auto","created_at":"2025-08-12 12:56:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":421925,"visible":true,"origin":"","legend":"\u003cp\u003eSankey relational diagram for m6A genes and m6A-related lncRNAs\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/bd648c61c68f3f3901fd2bbb.png"},{"id":88895774,"identity":"0b3a3892-4b4a-4849-9a80-b29e48938da1","added_by":"auto","created_at":"2025-08-12 13:04:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388174,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of m6A-related lncRNAs signature.\u003cstrong\u003e \u003c/strong\u003e(A) Univariate Cox regression analysis revealed that 39 lncRNAs significantly correlated with clinical prognosis; (B) 39 OS-related lncRNAs were shown by LASSO regression analysis ; (C) 10 OS-related lncRNAs were selected by cross validation; (D) Heatmap for the correlations between 23 m6A genes and the 4 prognostic m6A-related lncRNAs.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/8ed64e493f4cc45d01854709.png"},{"id":88893478,"identity":"93ff716f-b879-4ecb-81a3-b853114b74f5","added_by":"auto","created_at":"2025-08-12 12:56:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122107,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve analysis between the high-risk group and low-risk group.(A) Training dataset; (B) Testing dataset.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/d28eaa84b125e8a013c567ab.png"},{"id":88893484,"identity":"ef2602f7-6cbc-4e5c-97bc-7d2e6f8b673e","added_by":"auto","created_at":"2025-08-12 12:56:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189338,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of the risk patterns of the 4 m6A-related lncRNAs. (A, B) Distribution of m6A-related lncRNA model-based risk score; (C, D) Different patterns of survival status and survival time between the high- and low-risk groups; (E,F) Clustering analysis heatmap shows the expression standards of the 4 prognostic lncRNAs for each patient.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/31d794a1804f356bd944abb7.png"},{"id":88895806,"identity":"c44db6c8-7953-427f-80ee-f1b4b899df95","added_by":"auto","created_at":"2025-08-12 13:04:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188253,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of the prognostic risk model of the m6A-related lncRNAs and clinical features in PAAD in the TCGA entire set. (A) Univariate and multivariate analyses of the clinical characteristics and risk score with the OS; (B) Concordance indexes of the risk score and clinical characteristics; (C) ROC curves of the clinical characteristics and risk score; (D) ROC curves of the survival time.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/7cd6bd521d4364a3cad030a1.png"},{"id":88891027,"identity":"907328e1-9018-490d-9b0e-0f9fa2b08e34","added_by":"auto","created_at":"2025-08-12 12:48:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":152855,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and evaluation of a prognostic nomogram. The nomogram predicts the probability of the 1-, 2-, and 3-year OS; (B) The calibration plot of the nomogram predicts the probability of the 1-, 2-, and 3-year OS.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/b4c8d6ddfa88e670ea17b66b.png"},{"id":88895808,"identity":"5fec89b1-d7d2-401a-960e-9f0e9c176379","added_by":"auto","created_at":"2025-08-12 13:04:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":237198,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis. (A) risk model based on the representation profiles of the 4 m6A-related lncRNAs in the TCGA entire set; (B) 4 m6A-related lncRNAs; (C) 23 m6A genes; (D) entire gene expression profiles.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/dba17344e9f308520485f839.png"},{"id":88897541,"identity":"6c9941e8-2ca0-45c9-b6d9-4849cc88eca1","added_by":"auto","created_at":"2025-08-12 13:12:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":395501,"visible":true,"origin":"","legend":"\u003cp\u003eEstimation of the tumor immune microenvironment and cancer immunotherapy response using the m6A-related lncRNA model. (A) Immune function analysis for each patient; (B) GO enrichment analysis; (C) TMB difference in the high- and low-risk patients; (D) Kaplan-Meier curve analysis of OS is shown for patients classified according to the TMB status and m6A-related lncRNA model.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/f3f5582510ea65cd1005c0db.png"},{"id":88891023,"identity":"1e642656-7956-47ba-994a-0589c522e17a","added_by":"auto","created_at":"2025-08-12 12:48:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":210336,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis between the high-risk group and low-risk group\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/96a23dde3eb2628accfc8a98.png"},{"id":89063781,"identity":"3403c73b-3cdc-4eb7-b3cd-edb1d7c33645","added_by":"auto","created_at":"2025-08-14 10:06:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3161383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6975075/v1/af9c1814-6658-46e2-b32d-138a6f3b905e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential value of m6A-related lncRNAs in diagnosis and therapy of pancreatic cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is one of the digestive tract malignancies. In recent years, with the change in dietary structure, lifestyle and the improvement of clinical detection rate, the incidence rate is increasing year by year while the mortality rate is almost consistent with the incidence rate\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, which has posed serious threat to human health. Due to the insidious outset, rapid disease progression and resistance to chemotherapy of pancreatic cancer, most patients have an extremely poor prognosis, and five-year overall survival for patients is less than 10%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Therefore, there is an urgent need to find potential therapeutic targets to improve the prognosis of pancreatic cancer patients, and it is of great significance to lead the diagnosis and treatment of pancreatic cancer into a new era of precision medicine and individualized treatment.\u003c/p\u003e\u003cp\u003eWhile N6-methyladenosine (m6A) is the most common RNA epigenetic modification on eukaryotic mRNA, it is a dynamic and reversible regulatory modification. At present, m6A methylated protein is mainly composed of writers (methyltransferases), readers (signal transducers), and erasers (demethylases)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Not only can m6A regulate coding RNAs, but also can modify long noncoding RNAs (lncRNAs) that could affect cancer cell proliferation and migration\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In recent years, m6A modification was found to modulate tumorigogenesis and development in pancreatic cancer.\u003c/p\u003e\u003cp\u003eRecent study systematically analysed the m6A RNA methylation related genes using The Cancer Genome Atlas (TCGA) and ICGC database that revealed 283 candidate related genes potentially involved in pancreatic cancer development, and 4 m6A RNA methylation regulators including RBM15, METTL14, FTO and ALKBH5\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Inhibition of METTL14 improves the sensitivity of gemcitabine in pancreatic cancer cells and it can be a potential target for chemotherapy resistance\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Another research finds that METTL3 is highly expressed in tumor tissues, reducing METTL3 expression can inhibit the proliferation, invasion and migration of pancreatic cancer cells\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The malignancy of tumors is associated with abnormal lncRNA expression, and it is of great significance to explore the mechanism of m6A-related lncRNA in the development of pancreatic cancer, and it is expected to find new therapeutic targets.\u003c/p\u003e\u003cp\u003e In our study, 130 lncRNAs with a co-expression relationship with m6A genes were obtained by TCGA dataset analysis, a new prognostic model was constructed to predict overall survival in pancreatic cancer patients, and a nomogram was constructed. In addition, the relationship with the response to immunotherapy was explored. Finally, therapeutic agents targeting m6A-related lncRNA action targets were screened using the drug sensitivity database.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData collection and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe RNA transcriptome dataset and related clinical information for pancreatic cancer patients were obtained from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and pancreatic cancer patients with missing OS values were excluded, then we divided the genomic data into m6A and lncRNA genomes based on the human genome annotation data. In this study, a total of 23 m6A genes was analyzed including writers (METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15,RBM15B), readers (YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, RBMX) and erasers ( FTO, ALKBH5). The m6A genes with differential expression were found from the gene profiling data. We also analyzed these m6A genes expression in pancreatic cancer patients tissues from GEPIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on the Pearson correlation analysis, the lncRNA of |Pearson R|\u0026gt;0.4 and p \u0026lt; 0.001 were defined as the m6A-related lncRNAs for subsequent bioinformatics analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction and validation of the m6A-associated lncRNAs model\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were randomly divided into training dataset and testing dataset. After constructing the m6A-related lncRNAs model using the training dataset, it was verified with information from the testing dataset. Risk scores were calculated by the formula: coef(lncRNA1)×expr(lncRNA1) + coef(lncRNA2)×expr(lncRNA2) + .. + coef(lncRNAn)×expr(lncRNAn), where coef (lncRNAn) indicates the coefficient of, each lncRNA correlated with survival, and expr(lncRNAn) was each lncRNA expression. We obtained the risk score of each patient. Then patients in testing dataset were divided into high- and low-risk groups based on the median of the risk score in training dataset.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIndependence of the m6A-associated lncRNAs model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultivariate and univariate Cox regression analysis were used to test whether the prognostic models and other clinical features considering pancreatic cancer patients were independent variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstablish and prove a predicted nomogram\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA nomogram was established to show the predictive ability of risk scores and other predictors (sex, TNM stage, age, and grade) for 1-, 3-, and 5-year OS. The correction curve is used to show the actual results with the model prediction.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrincipal component analysis and gene enrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe grouping power of the model was assessed by PCA to group and visualized the data from the entire gene expression profile, the 23 m6A gene, the 4 m6A associated lncrna and the risk model. Kaplan-Meier survival analyses could be used to assess the OS diversity between the high- and low-risk groups. We employed a gene enrichment analysis approach to explore the potential KEGG pathways involved in the prognostic features of lncRNA. Based on the R language package tool, we examined the tumor mutation load based on the tumor-specific mutant genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSmall-molecule drug prediction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on small molecule drug database, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 indicated that the drug varies in treatment between high and low risk groups.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software version 4.2.3. Independent-sample t-tests were conducted for normally distributed continuous variables, while Wilcoxon rank-sum tests were used for non-normally distributed variables. Pearson and Spearman coefficients were used to analyze correlations between continuous variables, depending on their distribution. Chi-square tests or Fisher’s exact tests were used for categorical variables. A \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIdentification and Characterization of m6A-related lncRNAs in pancreatic cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCorrelations of 23 m6A-related genes and 14,056 lncRNAs were extracted from the TCGA database, and 11 m6A genes were high expressed in pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The lncRNAs significantly associated with m6A were defined as the m6A-associated lncRNA (|Pearson R|\u0026gt;0.4 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Sankey plot exhibited co-expression relationship between 130 lncRNAs and 17 m6A genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe screened 39 m6A-related lncRNAs significantly correlated with clinical prognosis from TCGA database that was revealed by univariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Lasso regression is a common multiple regression analysis method effectively avoiding the occurrence of overfitting which can achieve variable selection and regularization while improving the prediction accuracy of the statistical model. The values corresponding to the dashed line obtained from cross validation are the number of significant lncRNAs from the Lasso regression. Therefore, we finally obtained 10 m6A-related lncRNAs from 39 lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), further optimized the results by multivariate regression analysis. The heatmap showed 4 m6A-related lncRNAs for subsequent prognostic model construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), so as to evaluate the prognostic risk of pancreatic cancer patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction and evaluation of prognostic models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe pancreatic cancer patients were divided into high-risk and low-risk groups based on the median value prognostic risk scores, the Kaplan-Meier curve analysis in both training dataset and testing dataset showed that survival time was much higher in the low-risk group than in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To test the prognostic power of the established model, we calculated risk scores for each patient in the training and testing dataset and ranked patients by risk grade (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), with significantly higher survival time and survival status in the low-risk group than in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). As the patient risk score increased, the relative expression of the 4 m6A-related lncRNAs gradually decreased, the result showed that all lncRNAs were low-risk lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMeanwhile, the univariate and multivariate analysis of independent prognostic factors found that risk score was associated with patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and acted as a prognostic factor independent of other interference factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Concordance index was an indicator used to evaluate the predictive power of the model and demonstrated that the accuracy of risk score was much higher than other factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The larger of ROC area indicated that the higher the accuracy of patient survival prediction through the model. The results showed that the area under the risk score curve was significantly greater than the other factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and the greater the accuracy of the model prediction with the survival time (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction and evaluation of prognostic nomograms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy constructing a nomogram containing risk grade and clinical risk characteristics, we were able to clearly see the scores corresponding to different clinical traits according on the score scale, thus predicting the patients\u0026rsquo; 1-, 2-, and 3-year survival based on the value of the total score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The calibration plot indicated an agreement between overall survival and prediction rates at 1-, 2-, and 3-year (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalyze the grouping power of the model\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) showed differences between the low- and high-risk groups, respectively, based on the whole gene expression profile, 23 m6A gene, 4 m6A-related lncRNA and 4 m6A-related lncRNA classified by the risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). The PCA further validated that the m6A-related lncRNAs model had a high grouping power and a relatively dispersed distribution of the high- and low-risk groups. These results suggest that prognostic features can distinguish between low-risk and high-risk populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation of the tumor immune microenvironment and cancer immunotherapy response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImmune-related functions and pathways in pancreatic cancer patients were further analyzed according to the m6A-related lncRNAs model. The heatmap results suggested that the expression of immune function varied significantly between low- and high-risk groups, with high expression in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In order to initially explore the pathways involved in m6A-related lncRNAs in the regulation of PAAD, we conducted gene enrichment analysis and divided the pathways into BP, CC and MF. We could see clear differences in the number of samples between the different pathways, and ultimately found that m6A-related lncRNAs was involved in many immune-related biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).Tumor mutation burden (TMB) was a biomarker that could be used to predict the effect of immunotherapy in tumor patients. Consistent with expectations, pancreatic cancer patients in the high-risk group were more sensitive to respond to immunotherapy than in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Meantime, people with high tumor mutation burden in the high-risk group had a poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrug sensitivity analysis between the high-risk group and low-risk group\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe circulating drug database found that nine drugs varied in treatment between the high- and low-risk groups, obviously, there were two drugs that low-risk group\u0026rsquo;s pancreatic cancer patients were more sensitive and the remaining high-risk groups were more sensitive (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePancreatic cancer is a highly malignant digestive tract tumor, and despite the great progress in traditional treatment methods, the prognosis of pancreatic cancer patients is still poor and prone to distant metastasis and invasion\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Exploring new targeted therapies for pancreatic cancer is an urgent problem to be solved. The current study has found that m6A-related lncRNAs is involved in the development of gastric cancer, breast cancer and lung cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, so exploring the role of lncRNA in the prognosis or diagnosis of pancreatic cancer will help to understand the molecular mechanisms of it. A growing number of studies are devoted to identify characteristics of lncRNA to predict survival and immunotherapy response in tumor patients, however, the role of m6A-related lncRNAs in the prognosis and treatment of pancreatic cancer remains unclear. In this study, we systematically investigated the role of m6A-related lncRNAs in pancreatic cancer patients.\u003c/p\u003e\u003cp\u003eIt is found that m6A modified \"writers\", \"erasers\" and \"readers\" can be involved in regulating biological processes such as RNA metabolism, stem cell self-renewal, and immune response, especially play an important role in the development of tumors. Long non-coding RNA refers to non-coding RNA with over 200nt that does not have protein coding function and can be involved in various physiopathological activities of human body by regulating gene expression\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Several studies have shown that m6A methylation modification plays a regulatory role by altering lncRNA structure. For example, methyltransferase METTL16 can bind to the triple helix of lncRNA MALAT1 to affect its structural stability and functional expression, thus participating in tumor development\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Therefore, both m6A and lncRNA are important factors regulating the development of pancreatic cancer \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, however, studies on the biological mechanism of m6A-related lncRNAs in regulating pancreatic cancer and its effects on immunotherapy are still poorly reported.\u003c/p\u003e\u003cp\u003eIn this study, by mining the information from the TCGA database, we found that \"writers\" (METTL14, METTL16, ZC3H13, RBM15), \"readers\" (YTHDF1, YTHDF2, YTHDF3, HNRNPC, HNRNPA2B1), and \"erasers\" (FTO) were highly expressed in pancreatic cancer patients. Sanki diagram is a specific type of flow chart that can visualize and analyze our data and clearly show the correlation between 23 m6A genes and 14,056 lncRNAs. According to the formula, there are 130 m6A-related lncRNAs. The final four m6A-related lncRNAs (AC002091.1, AC005089.1, LINC01091, PANS-AS1) provide a new idea for predicting the prognosis of pancreatic cancer patients. The analysis showed that these four types of lncRNA were less expressed in the high-risk group and were all low-risk lncRNA. ROC analysis showed that the model was superior to conventional clinical features in survival prediction of pancreatic cancer, and the nomogram showed agreement between overall survival at 1-, 3-, or 5-year and prediction rates. Finally, the observed rates of OS prediction with 1-, 3-, and 5-year showed excellent agreement. This prediction model can identify new biomarkers for subsequent studies. However, our study still has limitations in analyzing the biological mechanisms of m6A-related lncRNAs, which can only preliminarily explain the immunotherapy and immune response processes involved, and the specific mechanisms need to be expanded in clinical samples and external experimental verification.\u003c/p\u003e\u003cp\u003eThe higher the tumor mutation burden (TMB), the more types and number of new antigens produced by tumor cells, the higher the probability of being recognized by the immune system, and the greater the probability of killing these tumor cells after immune checkpoint inhibitors activate the anti-tumor immune response\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. There has been extensive literature reporting that the level of TMB is associated with the efficacy of PD1/PD-L1 antibodies, that tumor cells with higher TMB are more susceptible to recognition by the immune system and have a higher probability of immunotherapy being effective in this patient\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Calculating the TMB score value of the tumor patients can better guide the clinical treatment. The results of this study suggested that patients in the high-risk group had significantly higher TMB scores than the low-risk group, indicating that patients in the high-risk group were more sensitive to immunotherapy.\u003c/p\u003e\u003cp\u003eIn conclusion, our study analyzed the expression level of m6A-related lncRNAs and its prognostic value in pancreatic cancer patients through a large amount of data. We constructed a predictive model with high prognostic value and showed sensitivity in identifying patients with pancreatic cancer who may respond well to immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by\u0026nbsp;Open Project of Hubei Key Laboratories,China (Funding number: 2021KFY022)\u0026nbsp;(Dandan Wu)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49.\u003c/li\u003e\n\u003cli\u003eSIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020 [J]. CA Cancer J Clin, 2020, 70(1): 7-30.\u003c/li\u003e\n\u003cli\u003eHE L, LI H, WU A, et al. Functions of N6-methyladenosine and its role in cancer [J]. Mol Cancer, 2019, 18(1): 176.\u003c/li\u003e\n\u003cli\u003eZHOU K I, PARISIEN M, DAI Q, et al. N(6)-Methyladenosine Modification in a Long Noncoding RNA Hairpin Predisposes Its Conformation to Protein Binding [J]. J Mol Biol, 2016, 428(5 Pt A): 822-33.\u003c/li\u003e\n\u003cli\u003eHUANG H, WENG H, CHEN J. m6A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer [J]. Cancer Cell, 2020, 37(3): 270-88.\u003c/li\u003e\n\u003cli\u003eGENG Y, GUAN R, HONG W, et al. Identification of m6A-related genes and m6A RNA methylation regulators in pancreatic cancer and their association with survival [J]. Ann Transl Med, 2020, 8(6): 387.\u003c/li\u003e\n\u003cli\u003eZHANG C, OU S, ZHOU Y, et al. m(6)A Methyltransferase METTL14-Mediated Upregulation of Cytidine Deaminase Promoting Gemcitabine Resistance in Pancreatic Cancer [J]. Front Oncol, 2021, 11(696371.\u003c/li\u003e\n\u003cli\u003eXIA T, WU X, CAO M, et al. The RNA m6A methyltransferase METTL3 promotes pancreatic cancer cell proliferation and invasion [J]. Pathol Res Pract, 2019, 215(11): 152666.\u003c/li\u003e\n\u003cli\u003eNIELSEN S R, QUARANTA V, LINFORD A, et al. Macrophage-secreted granulin supports pancreatic cancer metastasis by inducing liver fibrosis [J]. Nat Cell Biol, 2016, 18(5): 549-60.\u003c/li\u003e\n\u003cli\u003eXU F, HUANG X, LI Y, et al. m(6)A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD [J]. Mol Ther Nucleic Acids, 2021, 24(780-91.\u003c/li\u003e\n\u003cli\u003eWANG H, MENG Q, MA B. Characterization of the Prognostic m6A-Related lncRNA Signature in Gastric Cancer [J]. Front Oncol, 2021, 11(630260.\u003c/li\u003e\n\u003cli\u003eLV W, WANG Y, ZHAO C, et al. Identification and Validation of m6A-Related lncRNA Signature as Potential Predictive Biomarkers in Breast Cancer [J]. Front Oncol, 2021, 11(745719.\u003c/li\u003e\n\u003cli\u003eGUTTMAN M, AMIT I, GARBER M, et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals [J]. Nature, 2009, 458(7235): 223-7.\u003c/li\u003e\n\u003cli\u003eRUSZKOWSKA A, RUSZKOWSKI M, DAUTER Z, et al. Structural insights into the RNA methyltransferase domain of METTL16 [J]. Sci Rep, 2018, 8(1): 5311.\u003c/li\u003e\n\u003cli\u003eTANG X, ZHANG M, SUN L, et al. The Biological Function Delineated Across Pan-Cancer Levels Through lncRNA-Based Prognostic Risk Assessment Factors for Pancreatic Cancer [J]. Front Cell Dev Biol, 2021, 9(694652.\u003c/li\u003e\n\u003cli\u003eZENG J, ZHANG H, TAN Y, et al. Genetic alterations and functional networks of m6A RNA methylation regulators in pancreatic cancer based on data mining [J]. J Transl Med, 2021, 19(1): 323.\u003c/li\u003e\n\u003cli\u003eADDEO A, FRIEDLAENDER A, BANNA G L, et al. TMB or not TMB as a biomarker: That is the question [J]. Crit Rev Oncol Hematol, 2021, 163(103374.\u003c/li\u003e\n\u003cli\u003eELVIN, JULIA A, GOLDBERG, et al. Profiling of Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), and PD1/PD-L1 Immunohistochemistry (IHC) in Gynecological Tumors [J]. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"N6-methyladenosine, long noncoding RNAs, pancreatic cancer, nomogram, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-6975075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6975075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic cancer is one of the digestive tract malignancies, and five-year overall survival rate of PAAD patients remains low. m6A can modify lncRNAs that could affect cancer cell proliferation and migration, it is of great significance to explore the mechanism of m6A-related lncRNA in the development of pancreatic cancer. In our study, 130 lncRNAs with a co-expression relationship with m6A genes were obtained by TCGA dataset analysis, a new prognostic model was constructed to predict overall survival in pancreatic cancer patients. Kaplan-Meier analysis, principalcomponent analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. In addition, the relationship with the response to immunotherapy was explored. Finally, therapeutic agents targeting m6A-related lncRNA action targets were screened using the drug sensitivity database. In conclusion, our study highlighted the prognostic value of m6A-related lncRNAs in pancreatic cancer. We constructed a predictive model with high prognostic value and showed sensitivity in identifying patients with pancreatic cancer who may respond well to immunotherapy.\u003c/p\u003e","manuscriptTitle":"Potential value of m6A-related lncRNAs in diagnosis and therapy of pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 12:48:33","doi":"10.21203/rs.3.rs-6975075/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T17:34:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T08:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205708663984828148501516344547397089036","date":"2025-08-31T07:48:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91801739524190445325294734407740980086","date":"2025-08-29T17:44:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105698328558861560353975076463642119569","date":"2025-08-29T08:34:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T12:35:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132538391693300886717672246325271642267","date":"2025-08-06T16:51:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T16:44:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T16:41:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T16:36:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-04T16:12:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-04T16:09:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8cd49cf-9247-41c6-9e31-84229681cb4f","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T18:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 12:48:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6975075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6975075","identity":"rs-6975075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}