A ferroptosis-related lncRNAs signature as a potential biomarker for diffuse large B-cell lymphoma patients

A ferroptosis-related lncRNAs signature as a potential biomarker for diffuse large B-cell lymphoma patients | 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 A ferroptosis-related lncRNAs signature as a potential biomarker for diffuse large B-cell lymphoma patients Haoyue Zhang, Yuanwen Wang, Xin Zhang, Yanping Shao, Minli Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5987113/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 Background: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma subtype in adult patients, with an annual incidence rate ranging from 25.0% to 40.0% worldwide. Nevertheless, the prognosis forthe disease remains poor. Objective: There is a pressing need for new, reliable biomarkers for prognosis prediction. Methods: Using 449 DLBCL samples from the Gene Expression Omnibus dataset, the relationships between ferroptosis-associated long non-coding RNAs (lncRNAs) were examined. Before applying univariate Cox analysis to exclude lncRNAs connected to prognosis, we used Pearson correlation analysis to filter a large number of lncRNAs associated with ferroptosis. Results: To predict the prognosis of DLBCL, eleven lncRNAs linked toferroptosis were subjected to selection operator Cox regression and least absolute shrinkage. Furthermore, it was demonstrated that six ferroptosis-related lncRNAs were the most effective in establishing a predictive risk model. People with DLBCL were assigned to high- and low-risk groups in terms of their median risk scores. The model built employing 11 ferroptosis-related lncRNAs demonstrated higher prognostic evaluation abilities, as demonstrated by the stratified analysis. Significant enrichment in tumor-related pathways was seen in high-risk patients. Age, World Health Organization grade, and the ferroptosis-related lncRNA prognostic factor were taken into consideration when creating a nomogram. Conclusion: In conclusion, the nomogram generated can precisely anticipate the overall survival of DLBCL patients across both cohorts. Diffuse Large B-cell lymphoma Prognostic factor RNA Ferroptosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction B-cell lymphomas encompass various types of lymphomas, including mantle cell lymphoma, diffuse large B-cell lymphoma (DLBCL), Burkitt's lymphoma (BL), and primary effusion lymphoma. ( 1 ) DLBCL has the highest incidence rate in adults of any non-Hodgkin lymphoma subtype. Between 25.0–40.0% of new DLBCL cases are reported globally each year. ( 2 ) Generic chemotherapies, such as vincristine (oncovin), doxorubicin hydrochloride, rituximab, cyclophosphamide, and prednisone, are the main treatments for patients with DLBCL, regardless of their subtype. ( 3 ) However, the prognosis has not improved with these therapies. To enhance DLBCL diagnosis and treatment, new biomarkers need to be found. Ferroptosis was first described in 2012. ( 4 ) It is distinguished by the accretion of products from severe lipid peroxidation and is referred to as planned cell death. Over time, further research has been done on ferroptosis in malignancies. Ferroptosis is dependent on iron and is influenced by the level of reactive oxygen species (ROS) within the cell, thus differing from autophagy, necrosis, or apoptosis. ( 6 ) Too much ROS causes the lipid membrane to break down and peroxide, which leads to necrosis. ( 7 ) Lipid, iron, and cysteine metabolism are associated with ferroptosis. ( 8 – 10 ) Two essential components in the control of ferroptosis are the cysteine–glutamate reverse transporter system (System XC) and glutathione-dependent peroxidase 4 (GPX4). ( 11 ) It is possible to cause ferroptosis by blocking System XC-. ( 12 ) In some cases, ferroptosis can stimulate cancer cells to proliferate more rapidly. ( 13 ) In addition, ferroptosis has been linked with the progression of various cancers, including colorectal, breast, and non-small cell lung cancers, as well as acute myeloid leukemia and hepatocellular carcinoma. ( 14 – 16 ) There is mounting evidence that ferroptosis is correlated with both the advancement of DLBCL and the response to treatment. LncRNAs ( 17 – 19 ) are functional RNAs longer than 200 nucleotides. ( 20 ) They might be implicated in the pathophysiology and normal development of several illnesses, such as the advancement of numerous human tumors ( 21 ) , and the lncRNA GABPB1 is essential for the control of oxidative stress in HepG2 hepatoma cells that have been triggered by camphorin. ( 22 ) Similarly, through the miR-128-3p/SLC7A11 signaling pathway, the lncRNA OIP5-AS1 increases resistance to ferroptosis and the progression of prostate cancer. ( 23 ) However, none of the research has looked into how DLBCL is affected by the ferroptosis-associated lncRNA signatures. Here, prognostic long non-coding RNAs (lncRNAs) were found by screening them using the Gene Expression Omnibus (GEO) database. The patients were divided into two groups, each with different prognoses, based on the level of lncRNA. Additionally, studies were conducted on the prognosis of DLBCL and the function of ferroptosis-related lncRNA in the tumor microenvironment. Several lncRNA biomarkers associated with ferroptosis were identified by doing extensive bioinformatics analysis and data statistics on the patient data of DLBCL patients. The results of this study could help enhance DLBCL treatment plans and identify relevant biomarkers for the disease's prognosis. Materials and techniques Data gathering and analysis of correlations The preliminary data, including clinical data, were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) (24). The GSE11318 and GSE10846 datasets contained 137 and 312 samples, respectively, of DLBCL patients with survival data. The gene - level patterns were normalized using the "limma" R package's scale method. Table S1 displays the primary clinical and histological features of the patient. Matrix factorization consensus clustering with non-negative values Ferroptosis-related lncRNA level was used to split patients into two subgroups using the "ConsensusClusterPlus" program (50 iterations, sample rate of 80.0%). To find the ideal number of clusters according to the clustering process, a consensus of 12 combinations of cluster analyses was employed in addition to validation techniques. A prognosis score risk model was established using distinct prognostic ferroptosis- associated lncRNAs The predictive signature of ferroptosis-associated lncRNAs in the training sample was evaluated through univariate Cox regressions with the least absolute shrinkage and selection operator (LASSO) regularization using the glmnet package in R. Risk scores were determined as coef (lncRNA1) × expr (lncRNA1) + coef (lncRNA2) × expr (lncRNA2) +... + coef (lncRNAn) × expr (lncRNAn). The low- and high-risk groupings were determined using the median risk score. Nomogram with predictive capabilities The R software's ‘ rms & amp; rsquo; package was employed to plot the prediction nomogram according to the lncRNA prognostic signature and clinically relevant parameters (version 3.5.1). The nomogram was created using the nomolog y program, and it was verified using the bootstrapped R software's rms packages. Analytical statistics R software was used to do all statistical analyses ( https://www.r-project.org/ ). The log-rank (Mantel-Cox) test was used for statistical tests, while the Kruskal-Wallis approach was employed for Kaplan-Meier survival curve analysis. The statistical significance was assessed by employing either Fisher's exact test or the Chi-square test. A P-value of less than 0.05 showed statistical significance. Results lncRNA identification (related to ferroptosis) in DLBCL patients The level matrix of 60 genes associated with ferroptosis in DLBCL was extracted using GSE10846. If the level of a lncRNA was connected to one or more of the 60 genes described above, it was thought to be related to ferroptosis. A total of 63 lncRNAs associated with ferroptosis were found [Pearson R| > 0.5; and P 0.3 with a P < 0.05]. A weighted gene co-level network analysis (WGCNA) compatible R program (v.1.63) was used for developing a co-level network (Fig. S1). Utilize consensus clustering to distinguish between groupings demonstrating distinct outcomes Based on the ferroptosis-related lncRNA level of GSE10846, the predictive value of these 60 ferroptosis-associated lncRNAs in DLBCL was examined using univariate Cox regression analysis with the coxph function in R package (Fig. 1A). Among the lncRNAs, 24 that were linked to ferroptosis had a substantial relationship with OS (Fig. S2). Through the ConsensusClusterPlus program to partition all tumor samples into k subtypes (k values range from 2 to 9), the clinical significance of ferroptosis-related lncRNA level was ascertained. A plot has been generated to display the empirical cumulative distribution function, and the most suitable value for k that ensures the most stability of the sample distribution was identified. According to the results of the consensus matrix, k = 2 is the optimal classification subtype for the GSE10846 DLBCL cohort (Fig. 1B, C). Participants in Cluster 1 had substantially reduced OS compared to Cluster 2 (Fig. 1D). Using the limma (3.40.6) tool, DEGs between distinct subtypes were subsequently determined. The GSE10846 queue contained 1377 DEGs [| log2 (FDR 1.5], of which 837 were upregulated lncRNAs and 990 were substantially downregulated (Fig. 2A). In order to produce the cluster heat map, the top 100 lncRNAs that were upregulated and downregulated were selected (Fig. 2B). Tumor occurrence and development depend on PD-L1. Consequently, the level of PD-L1 in two subgroups was examined, and it was discovered that Cluster 1 had a greater level of PD-L1 (Fig. 2C). Spearman correlations were used to evaluate the links between these prognostic genes and PD-L1 in DLBCL. It was discovered that there was a strong correlation between the level of the FAM87A, FAM87B, GAS5, LINC00092, LINC01089, NEAT1 , and THUMPD3-AS1 genes. Most of the genes demonstrated a significant correlation with PD-1 level, but SNHG19 was shown to have no relationship with PD-L1. Figure 2D displays the DLBCL correlation patterns. Gene level and prognosis affect signaling pathways and biological processes We used KEGG and GO enrichment to study prognostic genes in DLBCL signaling pathways and biological processes. The "GOplot" and "ggplot2" packages in R were employed to illustrate the results of the KEGG and GO analyses, which were carried out using clusterProfiler. Anion transmembrane transport, potassium ion transport, U2-type prespliceosome, positive control of a chromosomal organization, and primary active transmembrane transporter activity were the key enriched activities in the GO pathway (Fig. S2 A-C). RIG-I-like receptor signaling pathways, PD-L1 level, PD-1 checkpoint, MAPK signaling system, and adipocytokine signaling pathway in cancer were the key pathways where enhanced DEGs were found, according to KEGG pathway enrichment analysis. The biological consequences of lncRNAs may be better understood in light of these findings (Fig. S2 D). Cell infiltration in the microenvironment of distinct subgroups of tumors Using the ESTIMATE R program, we analyzed the characteristics of tumor microenvironment cell infiltration across multiple subtypes. The samples were evaluated by the algorithm for combined scores (ESTIMATEScore), total immune infiltration (ImmuneScore), and stromal content (StromalScore). Cluster 2 demonstrated greater stromal ( P < 0.001) and ESTIMATE ( P < 0.01) values; however, the immune scores did not differ significantly between the two clusters (Fig. 3A). The two subgroups' immune cell scores were determined using single-sample Gene Set Enrichment Analysis (ssGSEA) and MCP Counter, yielding results of 10 and 28, respectively. MpCounter data showed that Cluster 2 had a higher immune score from CD8 + T cells than Cluster 1 (Cluster 1 comprised more B lineage, neutrophil immune scores, and NK cells) (Fig. 3B). Cluster 1 had higher proportions of plasmacytoid dendritic cells, immature dendritic cells, CD56dim natural killer cells, activated dendritic cells, effector memory CD 8 + T cells, immature B cells, activated B cells, and T follicular helper cells according to the results of the ssGSEA analysis. The proportions of neutrophils, type 17 T-helper cells, natural killer T cells, gamma delta T cells, central memory CD8 + T cells, type 2 T-helper cells, effector memory CD4+ T cells, and mast cells were greater in Cluster 2 (Fig. 3C). Figure 3D shows heat maps of the immune cell score found using each of the three approaches. Prognostic risk model construction Non-univariate Cox regression was conducted on the ferroptosis-associated lncRNA levels extracted from the training set. The survival package in R was utilized for all Cox regression analyses, while the glmnet program was employed for the LASSO Cox analysis. As shown in Fig. S3, the results showed a significant (P < 0.01) connection between 11 lncRNAs and OS. Additional LASSO screening of eleven genes identified six optimal prognostic genes (DANCR, LINC01184, LOC102724532, LOC284454, SNHG19, and SNHG3) as the factors that significantly affect prognosis. The Cox model's LASSO-selected coefficients were employed to assess the patient risk scores (Fig. S4 A, B). Patients were allocated to high- and low-risk groups, each consisting of 110 samples, following the risk survival status plot of the training set. As the survival rate of participants with DLBCL decreased, the risk score increased (Fig. 4A). Utilizing the "pROC" R package, the ROC curve was analyzed. A prognostic signature for ferroptosis-associated lncRNAs was shown to be predictive of patient survival based on ROC curves (5-year OS: 0.735 and 1- and 3-year AUC: 0.651 and 0.742) (Fig. 4). Poor survival was correlated with the level characteristics of high-risk lncRNAs, according to a Kaplan–Meier curve analysis (Fig. 4C). In the testing cohort, the prognostic model's resilience was further confirmed. Each of the two risk categories, high and low, had 46 samples. An analysis was conducted to assess the risk distribution for survival, with determination of risk scores for each sample. Correlations were observed between risk scores and survival on the patients' risk survival graph; higher risk scores were indicative of a poor prognosis (Fig. S5 A). The prognostic signature of ferroptosis-associated lncRNAs was able to accurately predict patient survival, as seen by the ROC curves. The AUCs for 1-year and 3-year OS rates were 0.684 and 0.742. The overall survival rate at 5 years was 0.758, as shown in Fig. S5 B. Poor patient survival is connected with the level characteristics of high-risk lncRNAs, according to Kaplan-Meier analysis (Fig. S5 C). The risk survival status plot of the participants (156 samples) showed a correlation between the value at risk and survival, with a high-risk score indicating a bad prognosis (Fig. S6 A). According to ROC curves, patients' survival may be predicted using the ferroptosis-related lncRNA prognostic signature (5-year OS: 0.735; 1- and 3-year AUC: 0.659 and 0.736, Fig. S6 B). The Kaplan-Meier analysis revealed a strong association between low survival rates and the high-risk level characteristics of lncRNAs (Fig. S6 C). The risk survival status plot of the GSE11318 dataset subjects indicates a negative link between risk scores and OS (Fig. S7 A). The prognostic signature of ferroptosis-associated lncRNAs may accurately predict patient survival, as demonstrated by the ROC curves. The AUCs for 1- and 3-year survival rates was 0.676 and 0.7. The OS rate at 5 years is 0.683 (Fig. S7 B). Poor survival was found to be negatively correlated with the level features of high-risk lncRNAs, according to a Kaplan–Meier curve study ( P < 0.005) (Fig. S7 C). In DLBCL, prognostic risk scores are strongly correlated with clinicopathological characteristics We also studied at the link between the risk scores and the clinicopathological traits of the participants. Those with a risk score greater than 65 showed elevated risk. Variations in risk scores among molecular subtypes were also assessed. Compared to the Cluster 1 molecular subtype, the risk score in the Cluster 1 subtype with a poor prognosis was considerably greater (Fig. S8 A-D). The risk score showed exceptional predictive capability across different clinical characteristics, effectively differentiating between different stages ( i.e. , 1, 2, 3, and 4) patients aged 65 years and younger, and male and female patients having various clinical features, classifying them into high- and low-risk groups (Fig. 5A-F). The 6-gene signature model's independent prognostic effects Univariate and multivariate Cox regression studies between lncRNA signatures in the whole dataset and clinical factors were employed to examine the independent effects of lncRNA signatures among clinical factors. Independent prognostic markers for DLBCL included age, stage, and risk score; these findings imply that the 6-gene signature model exceeds other clinical models in terms of prediction (Fig. 6A, B). Prognostic nomogram: creation and assessment The OS (for 1-, 3-, and 5-year) of the participants was evaluated using colographs. The correlation data show that there was stability between the observed and forecasted OS rates (Fig. S9 A). Risk score effects on signaling pathways and biological processes To find out how risk scores affected biological function, additional analysis was done on the samples that had high and low-risk ratings. To perform ssGSEA, the R software package GSVA was utilized. KEGG pathways that had | Pearson R| > 0.4 and a P < 0.05 were chosen (Fig. S10 A). Of these, the risk score showed a positive correlation with 11 and a negative correlation with 10 pathways, respectively. Fig. S10 B presents a heat map depicting the 21 KEGG pathways, arranged according to their enrichment scores. According to these findings, the risk score is directly linked with the enrichment scores of several pathways, including DNA replication, the spliceosome, base excision repair, glyoxylate, dicarboxylate metabolism, and the citrate (TCA) cycle. However, when the risk score increased, the enrichment scores of certain pathways declined. These pathways included the metabolism of histidine and tryptophan as well as the formation of glycan and primary bile acid. Discussion Ferroptosis is a unique kind of iron-dependent cellular death resulting from lipid peroxidation that has been linked to several clinical diseases, including cancer. Ferroptosis, in certain situations, facilitates the removal of necrotic cells and prevents cancerous cells from resisting chemotherapy. ( 25 – 28 ) Research has demonstrated that lncRNAs relevant to ferroptosis are linked to several malignancies, including DLBCL. Consequently, more research and the hunt for novel ferroptosis-related lncRNA markers will aid in our understanding of the genesis and mechanism of DLBCL development. ( 29 ) Using the GSE10846 dataset, several lncRNAs associated with ferroptosis were filtered out in this study. Twenty-four ferroptosis-associated lncRNAs were believed to be associated with a better prognosis out of these. Next, based on the gene level associated with ferroptosis, the DLBCL subtypes were examined. Based on the level of lncRNAs associated with ferroptosis, two patient subgroups were created. The level of PD-L1 is likely to be considerably impacted by the level of 24 ferroptosis-related lncRNAs. It was also connected to the cellular pathways that lead to the malignant development of DLBCL. Consequently, the MCPCounter method was employed to examine immune cell infiltration in each DLBCL patient as well as the associations between the immune cell scores and the 24 ferroptosis-associated lncRNAs. While Cluster 2 showed a higher immune score from CD8 + T cells, Cluster 1 displayed better immune scores from NK cells, B lineage, and neutrophils. As a result, in patients with DLBCL, the concentration of 24 ferroptosis-related lncRNA modulators may influence immune infiltration. Following the creation of a predictive model using six ferroptosis-associated lncRNAs, patient groups were categorized as high- or low-risk in terms of the median risk scores. Ferroptosis is linked to cancer pathophysiology. It is yet unknown, nevertheless, how ferroptosis and lncRNA levels relate to one another as DLBCL progresses. The current research discovered potential indicators and therapy targets for the ferroptosis signaling system. Through the miR-1343-3p/NFIX axis, SNHG3 facilitates the growth and spread of cancer cells, hence contributing to non-small cell lung cancer development. ( 30 ) ( 31 ) According to certain studies, SNHG19’s level is elevated in cancerous tissues. ( 32 ) In a similar vein, individuals with thyroid malignancies have markedly elevated serum levels of LOC284454 level. ( 33 ) The prognosis is not good for patients with nasopharyngeal cancer who express LOC284454 highly. ( 34 ) According to a different study, there is a clear elevation of LINC01184 level in the tissues and cells of colorectal cancer when compared to normal controls, and this upregulation is positively correlated with the disease's advancement. ( 35 ) In addition, the DANCR level is elevated in prostate cancer patients' serum and cell lines, while the miR-214-5p level is downregulated and shows a negative association with the disease's advancement. In patients with prostate cancer, a strong correlation was found between DANCR level and Gleason score, T stage, and prostate-specific antigen. ( 36 ) Nevertheless, there is a dearth of comprehensive studies on the ferroptosis-related lncRNA signature for DLBCL. Six ferroptosis-related lncRNAs—SNHG3, SNHG19, LOC284454, LOC102724532, LINC01184, and DANCR—were found to be substantially connected with the prognosis of DLBCL. These findings raise the possibility that these genes are crucial in the development of DLBCL. The findings of our study may be useful in locating new DLBCL biomarkers and offer suggestions for DLBCL treatments that work well in clinics. Conclusion Ferroptosis-related lncRNAs were thoroughly investigated, and their level levels and prognosis were examined concerning DLBCL. A prognostic risk model was created by screening six ferroptosis-related lncRNAs that might confirm DLBCL independently. This work establishes a foundation for future research on these lncRNAs and presents a novel paradigm for ferroptosis-related lncRNAs in DLBCL, offering fresh perspectives on DLBCL therapeutic approaches. Declarations Recognitions The project "To explore the mechanism of aspirin in multiple myeloma treatment and chemotherapy sensitization based on JAK/STATA3 pathway" (Project No. 23EZB13) at the Enze Medical Center in Zhejiang Province provided funding for the study. Conflicting Interest There are no potential conflicts of interest to report for any of the authors. Statement on Data Sharing This published article presents statistical summaries of the data sets created and analyzed for the current investigation. 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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-5987113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413521374,"identity":"ca391ed2-abd2-4398-9c0b-2d50e36dbcf5","order_by":0,"name":"Haoyue Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACCSBmbGCQs2/vf/gAKmZAlBZjA54zzDClxGlJ3CDhwyZBlBb52c3PHn7dcZhxuwTvsYqPO+rkdNubNzD8qNiGUwvjnGPmxrJnDjNbzu5LuznzDJux2ZljBYw9Z27j1MIskWAmLdl2mI3hzgGz27xtPInbbuQYMDO24dbCJpH+DaSFh+FGglnx3zaJ+m333+DXwiORYyb5se2whMGNHDOgSoMEsxs8+LVISOSUSTO2pRtI9hxLluxtSzDcdiat4CA+v8jPSN8m+bPNur6fvfngh59tdfJmxw9vfPCjArcWcBDwMDSjihzAqx4IGH8w1BFSMwpGwSgYBSMZAABUF1xVsGeJDgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Haoyue","middleName":"","lastName":"Zhang","suffix":""},{"id":413521375,"identity":"533fd11e-3a6b-4e6f-88d3-842e0d5e9bdc","order_by":1,"name":"Yuanwen Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuanwen","middleName":"","lastName":"Wang","suffix":""},{"id":413521378,"identity":"1ff2eb6f-e6d0-410b-adba-81fbed8f0cd6","order_by":2,"name":"Xin Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":413521381,"identity":"6aaaadf6-20d7-4512-9d1d-6f7f828e0b9b","order_by":3,"name":"Yanping Shao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Shao","suffix":""},{"id":413521383,"identity":"aa50506e-2619-4ec3-99cc-78a6741cc821","order_by":4,"name":"Minli Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Minli","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-02-08 10:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5987113/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5987113/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76193858,"identity":"a119b2f9-188b-48f2-99e8-4539d1f0adbd","added_by":"auto","created_at":"2025-02-13 10:03:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1089880,"visible":true,"origin":"","legend":"\u003cp\u003eDLBCL differential OS in subgroups 1/2 of clusters. \u003cstrong\u003e(A)\u003c/strong\u003e Univariate analysis of ferroptosis lncRNAs was conducted to identify the genes that have a significant correlation with OS. \u003cstrong\u003e(B)\u003c/strong\u003e The GSE10846 DLBCL cohort was divided into two clusters, with a value of k equal to 2. \u003cstrong\u003e(C)\u003c/strong\u003e The cumulative distribution function of consensus clustering for values of k ranges from 2 to 10.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/d7bc2c202605cd290329da62.png"},{"id":76193049,"identity":"80f4e241-5a6f-4111-a96b-db04112c8dd6","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2656671,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis and differentialgene-levelanalyses between prognostic genes and PD-L1. \u003cstrong\u003e(A)\u003c/strong\u003e DEGs are delineated between clusters 1 and 2on the volcano map. \u003cstrong\u003e(B)\u003c/strong\u003e Heat maps that clusterdifferential gene expression. \u003cstrong\u003e(C)\u003c/strong\u003e A comparison of the PD-L1 concentrations of varioussubgroups. \u003cstrong\u003e(D)\u003c/strong\u003e PD and the correlation between prognostic markers-L1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/1cb95476f24b1fff1da9688d.png"},{"id":76193048,"identity":"7252e66b-4e1a-4526-b718-3e9480f7f20b","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":821415,"visible":true,"origin":"","legend":"\u003cp\u003eTranscription features and TME cell infiltration characteristics in different subgroups. \u003cstrong\u003e(A)\u003c/strong\u003e Clusters 1 and 2 had different immune, ESTIMATE, and stroma scores. \u003cstrong\u003e(B)\u003c/strong\u003e The violin plot of ten immune cell scores across multiple subgroups, as measured by the MCPCOUNTER. \u003cstrong\u003e(C)\u003c/strong\u003e The violin plot compares the 28 immune cell scores obtained from ssGSEA measurements across several subgroups. (\u003cstrong\u003eD\u003c/strong\u003e) Using three separate methodologies, heat maps representing the relative abundances of immune cells in the two categories were constructed (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/2def08bab325bc2de36433e8.png"},{"id":76193051,"identity":"bee3484f-9b77-4f0a-92e5-4aa15b1d5f97","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":589253,"visible":true,"origin":"","legend":"\u003cp\u003eThe training dataset's predictive performances of the risk prediction model. \u003cstrong\u003e(A)\u003c/strong\u003e Six lncRNAs' level levels, risk scores, and patients' survival status. \u003cstrong\u003e(B)\u003c/strong\u003e The 6-gene signature model's ROC curves for the 1-, 3-, and 5-year survival predictions. \u003cstrong\u003e(C)\u003c/strong\u003e The Kaplan-Meier curve showed that patients at higher risk had a worse prognosis than patients at lower risk.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/4f1fd146aaaf7d716d71a847.png"},{"id":76193041,"identity":"c548c17f-daed-4f4b-91a1-a7d2513d9d1b","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":384805,"visible":true,"origin":"","legend":"\u003cp\u003ePatient survival with different clinical variables in high- and low-risk groups. The following are the survival analyses: \u003cstrong\u003e(A)\u003c/strong\u003e patients over 65 who are at high and low risk; \u003cstrong\u003e(B)\u003c/strong\u003e patients under 65; \u003cstrong\u003e(C)\u003c/strong\u003e female; \u003cstrong\u003e(D)\u003c/strong\u003e male; \u003cstrong\u003e(E)\u003c/strong\u003e stage 1 and 2 patients, and \u003cstrong\u003e(F) \u003c/strong\u003estage 3 and 4\u003cstrong\u003e \u003c/strong\u003epatients.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/0444caafbd77c79e31a80ead.png"},{"id":76193042,"identity":"1cc88b99-6025-4ea2-975a-5e09350d2f4d","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":251246,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic factors for the PRCC group. \u003cstrong\u003e(A)\u003c/strong\u003e Risk scores, age, sex, and stages using the univariate method. \u003cstrong\u003e(B)\u003c/strong\u003e Risk scores, age, sex, and stagesusing multivariate method.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/ccef494e933474331b3b09fc.png"},{"id":76196347,"identity":"19f86326-559d-47bc-9856-57b72d53d43a","added_by":"auto","created_at":"2025-02-13 10:27:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8212352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/cad1d4d3-452b-4584-99a6-7dae1f980c98.pdf"},{"id":76193039,"identity":"20a3a71a-6305-42d2-b588-7539c5eaf971","added_by":"auto","created_at":"2025-02-13 09:55:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1620744,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5987113/v1/f97764af495173464da5437f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A ferroptosis-related lncRNAs signature as a potential biomarker for diffuse large B-cell lymphoma patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eB-cell lymphomas encompass various types of lymphomas, including mantle cell lymphoma, diffuse large B-cell lymphoma (DLBCL), Burkitt's lymphoma (BL), and primary effusion lymphoma. \u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e DLBCL has the highest incidence rate in adults of any non-Hodgkin lymphoma subtype. Between 25.0\u0026ndash;40.0% of new DLBCL cases are reported globally each year. \u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e Generic chemotherapies, such as vincristine (oncovin), doxorubicin hydrochloride, rituximab, cyclophosphamide, and prednisone, are the main treatments for patients with DLBCL, regardless of their subtype. \u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e However, the prognosis has not improved with these therapies. To enhance DLBCL diagnosis and treatment, new biomarkers need to be found.\u003c/p\u003e \u003cp\u003eFerroptosis was first described in 2012.\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e It is distinguished by the accretion of products from severe lipid peroxidation and is referred to as planned cell death. Over time, further research has been done on ferroptosis in malignancies. Ferroptosis is dependent on iron and is influenced by the level of reactive oxygen species (ROS) within the cell, thus differing from autophagy, necrosis, or apoptosis. \u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e Too much ROS causes the lipid membrane to break down and peroxide, which leads to necrosis. \u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e Lipid, iron, and cysteine metabolism are associated with ferroptosis. \u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u0026ndash; \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e Two essential components in the control of ferroptosis are the cysteine\u0026ndash;glutamate reverse transporter system (System XC) and glutathione-dependent peroxidase 4 (GPX4). \u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e It is possible to cause ferroptosis by blocking System XC-. \u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e In some cases, ferroptosis can stimulate cancer cells to proliferate more rapidly. \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e In addition, ferroptosis has been linked with the progression of various cancers, including colorectal, breast, and non-small cell lung cancers, as well as acute myeloid leukemia and hepatocellular carcinoma. \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e \u0026ndash; \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e There is mounting evidence that ferroptosis is correlated with both the advancement of DLBCL and the response to treatment. LncRNAs \u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e \u0026ndash; \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e are functional RNAs longer than 200 nucleotides. \u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e They might be implicated in the pathophysiology and normal development of several illnesses, such as the advancement of numerous human tumors \u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e, and the lncRNA GABPB1 is essential for the control of oxidative stress in HepG2 hepatoma cells that have been triggered by camphorin. \u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e Similarly, through the miR-128-3p/SLC7A11 signaling pathway, the lncRNA OIP5-AS1 increases resistance to ferroptosis and the progression of prostate cancer. \u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e However, none of the research has looked into how DLBCL is affected by the ferroptosis-associated lncRNA signatures.\u003c/p\u003e \u003cp\u003eHere, prognostic long non-coding RNAs (lncRNAs) were found by screening them using the Gene Expression Omnibus (GEO) database. The patients were divided into two groups, each with different prognoses, based on the level of lncRNA. Additionally, studies were conducted on the prognosis of DLBCL and the function of ferroptosis-related lncRNA in the tumor microenvironment. Several lncRNA biomarkers associated with ferroptosis were identified by doing extensive bioinformatics analysis and data statistics on the patient data of DLBCL patients. The results of this study could help enhance DLBCL treatment plans and identify relevant biomarkers for the disease's prognosis.\u003c/p\u003e"},{"header":"Materials and techniques","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData gathering and analysis of correlations\u003c/h2\u003e \u003cp\u003eThe preliminary data, including clinical data, were acquired from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (24). The GSE11318 and GSE10846 datasets contained 137 and 312 samples, respectively, of DLBCL patients with survival data. The gene\u003cb\u003e-\u003c/b\u003elevel patterns were normalized using the \"limma\" R package's scale method. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e displays the primary clinical and histological features of the patient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMatrix factorization consensus clustering with non-negative values\u003c/h3\u003e\n\u003cp\u003eFerroptosis-related lncRNA level was used to split patients into two subgroups using the \"ConsensusClusterPlus\" program (50 iterations, sample rate of 80.0%). To find the ideal number of clusters according to the clustering process, a consensus of 12 combinations of cluster analyses was employed in addition to validation techniques.\u003c/p\u003e \u003cp\u003eA prognosis \u003cb\u003escore risk model\u003c/b\u003e was established using distinct \u003cb\u003eprognostic ferroptosis-\u003c/b\u003eassociated \u003cb\u003elncRNAs\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe predictive signature of ferroptosis-associated lncRNAs in the training sample was evaluated through univariate Cox regressions with the least absolute shrinkage and selection operator (LASSO) regularization using the glmnet package in R. Risk scores were determined as coef (lncRNA1) \u0026times; expr (lncRNA1)\u0026thinsp;+\u0026thinsp;coef (lncRNA2) \u0026times; expr (lncRNA2) +... + coef (lncRNAn) \u0026times; expr (lncRNAn). The low- and high-risk groupings were determined using the median risk score.\u003c/p\u003e\n\u003ch3\u003eNomogram with predictive capabilities\u003c/h3\u003e\n\u003cp\u003eThe R software's \u0026amp;lsquo; rms \u0026amp; amp; rsquo; package was employed to plot the prediction nomogram according to the lncRNA prognostic signature and clinically relevant parameters (version 3.5.1). The nomogram was created using the nomolog\u003cb\u003ey\u003c/b\u003e program, and it was verified using the bootstrapped R software's rms packages.\u003c/p\u003e\n\u003ch3\u003eAnalytical statistics\u003c/h3\u003e\n\u003cp\u003eR software was used to do all statistical analyses (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The log-rank (Mantel-Cox) test was used for statistical tests, while the Kruskal-Wallis approach was employed for Kaplan-Meier survival curve analysis. The statistical significance was assessed by employing either Fisher's exact test or the Chi-square test. A P-value of less than 0.05 showed statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003elncRNA identification (related to ferroptosis) in DLBCL patients\u003c/h2\u003e\n \u003cp\u003eThe level matrix of 60 genes associated with ferroptosis in DLBCL was extracted using GSE10846. If the level of a lncRNA was connected to one or more of the 60 genes described above, it was thought to be related to ferroptosis. A total of 63 lncRNAs associated with ferroptosis were found [Pearson R| \u0026gt; 0.5; and P \u0026lt; 0.001 and absolute correlation coefficient: \u0026gt; 0.3 with a P \u0026lt; 0.05]. A weighted gene co-level network analysis (WGCNA) compatible R program (v.1.63) was used for developing a co-level network (Fig. S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eUtilize consensus clustering to distinguish between groupings demonstrating distinct outcomes\u003c/h3\u003e\n\u003cp\u003eBased on the ferroptosis-related lncRNA level of GSE10846, the predictive value of these 60 ferroptosis-associated lncRNAs in DLBCL was examined using univariate Cox regression analysis with the coxph function in R package (Fig. 1A). Among the lncRNAs, 24 that were linked to ferroptosis had a substantial relationship with OS (Fig. S2).\u003c/p\u003e\n\u003cp\u003eThrough the ConsensusClusterPlus program to partition all tumor samples into k subtypes (k values range from 2 to 9), the clinical significance of ferroptosis-related lncRNA level was ascertained. A plot has been generated to display the empirical cumulative distribution function, and the most suitable value for k that ensures the most stability of the sample distribution was identified. According to the results of the consensus matrix, k = 2 is the optimal classification subtype for the GSE10846 DLBCL cohort (Fig. 1B, C). Participants in Cluster 1 had substantially reduced OS compared to Cluster 2 (Fig. 1D).\u003c/p\u003e\n\u003cp\u003eUsing the limma (3.40.6) tool, DEGs between distinct subtypes were subsequently determined. The GSE10846 queue contained 1377 DEGs [| log2 (FDR \u0026lt; 0.05 and a fold change) | \u0026gt; 1.5], of which 837 were upregulated lncRNAs and 990 were substantially downregulated (Fig. 2A). In order to produce the cluster heat map, the top 100 lncRNAs that were upregulated and downregulated were selected (Fig. 2B). Tumor occurrence and development depend on PD-L1. Consequently, the level of PD-L1 in two subgroups was examined, and it was discovered that Cluster 1 had a greater level of PD-L1 (Fig. 2C). Spearman correlations were used to evaluate the links between these prognostic genes and PD-L1 in DLBCL. It was discovered that there was a strong correlation between the level of the \u003cem\u003eFAM87A, FAM87B, GAS5, LINC00092, LINC01089, NEAT1\u003c/em\u003e, and \u003cem\u003eTHUMPD3-AS1\u003c/em\u003e genes. Most of the genes demonstrated a significant correlation with PD-1 level, but SNHG19 was shown to have no relationship with PD-L1. Figure 2D displays the DLBCL correlation patterns.\u003c/p\u003e\n\u003ch3\u003eGene level and prognosis affect signaling pathways and biological processes\u003c/h3\u003e\n\u003cp\u003eWe used KEGG and GO enrichment to study prognostic genes in DLBCL signaling pathways and biological processes. The \"GOplot\" and \"ggplot2\" packages in R were employed to illustrate the results of the KEGG and GO analyses, which were carried out using clusterProfiler. Anion transmembrane transport, potassium ion transport, U2-type prespliceosome, positive control of a chromosomal organization, and primary active transmembrane transporter activity were the key enriched activities in the GO pathway (Fig. S2 A-C). RIG-I-like receptor signaling pathways, PD-L1 level, PD-1 checkpoint, MAPK signaling system, and adipocytokine signaling pathway in cancer were the key pathways where enhanced DEGs were found, according to KEGG pathway enrichment analysis. The biological consequences of lncRNAs may be better understood in light of these findings (Fig. S2 D).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eCell infiltration in the microenvironment of distinct subgroups of tumors\u003c/h2\u003e\n \u003cp\u003eUsing the ESTIMATE R program, we analyzed the characteristics of tumor microenvironment cell infiltration across multiple subtypes. The samples were evaluated by the algorithm for combined scores (ESTIMATEScore), total immune infiltration (ImmuneScore), and stromal content (StromalScore). Cluster 2 demonstrated greater stromal (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and ESTIMATE (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) values; however, the immune scores did not differ significantly between the two clusters (Fig. 3A). The two subgroups' immune cell scores were determined using single-sample Gene Set Enrichment Analysis (ssGSEA) and MCP Counter, yielding results of 10 and 28, respectively. MpCounter data showed that Cluster 2 had a higher immune score from CD8 + T cells than Cluster 1 (Cluster 1 comprised more B lineage, neutrophil immune scores, and NK cells) (Fig. 3B). Cluster 1 had higher proportions of plasmacytoid dendritic cells, immature dendritic cells, CD56dim natural killer cells, activated dendritic cells, effector memory CD 8 + T cells, immature B cells, activated B cells, and T follicular helper cells according to the results of the ssGSEA analysis. The proportions of neutrophils, type 17 T-helper cells, natural killer T cells, gamma delta T cells, central memory CD8 + T cells, type 2 T-helper cells, effector memory CD4+ T cells, and mast cells were greater in Cluster 2 (Fig. 3C). Figure 3D shows heat maps of the immune cell score found using each of the three approaches.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePrognostic risk model construction\u003c/h2\u003e\n \u003cp\u003eNon-univariate Cox regression was conducted on the ferroptosis-associated lncRNA levels extracted from the training set. The survival package in R was utilized for all Cox regression analyses, while the glmnet program was employed for the LASSO Cox analysis. As shown in Fig. S3, the results showed a significant (P \u0026lt; 0.01) connection between 11 lncRNAs and OS. Additional LASSO screening of eleven genes identified six optimal prognostic genes (DANCR, LINC01184, LOC102724532, LOC284454, SNHG19, and SNHG3) as the factors that significantly affect prognosis.\u003c/p\u003e\n \u003cp\u003eThe Cox model's LASSO-selected coefficients were employed to assess the patient risk scores (Fig. S4 A, B). Patients were allocated to high- and low-risk groups, each consisting of 110 samples, following the risk survival status plot of the training set. As the survival rate of participants with DLBCL decreased, the risk score increased (Fig. 4A). Utilizing the \"pROC\" R package, the ROC curve was analyzed. A prognostic signature for ferroptosis-associated lncRNAs was shown to be predictive of patient survival based on ROC curves (5-year OS: 0.735 and 1- and 3-year AUC: 0.651 and 0.742) (Fig. 4). Poor survival was correlated with the level characteristics of high-risk lncRNAs, according to a Kaplan–Meier curve analysis (Fig. 4C).\u003c/p\u003e\n \u003cp\u003eIn the testing cohort, the prognostic model's resilience was further confirmed. Each of the two risk categories, high and low, had 46 samples. An analysis was conducted to assess the risk distribution for survival, with determination of risk scores for each sample. Correlations were observed between risk scores and survival on the patients' risk survival graph; higher risk scores were indicative of a poor prognosis (Fig. S5 A). The prognostic signature of ferroptosis-associated lncRNAs was able to accurately predict patient survival, as seen by the ROC curves. The AUCs for 1-year and 3-year OS rates were 0.684 and 0.742. The overall survival rate at 5 years was 0.758, as shown in Fig. S5 B. Poor patient survival is connected with the level characteristics of high-risk lncRNAs, according to Kaplan-Meier analysis (Fig. S5 C).\u003c/p\u003e\n \u003cp\u003eThe risk survival status plot of the participants (156 samples) showed a correlation between the value at risk and survival, with a high-risk score indicating a bad prognosis (Fig. S6 A). According to ROC curves, patients' survival may be predicted using the ferroptosis-related lncRNA prognostic signature (5-year OS: 0.735; 1- and 3-year AUC: 0.659 and 0.736, Fig. S6 B). The Kaplan-Meier analysis revealed \u003cstrong\u003ea strong association\u003c/strong\u003e between low survival rates and the high-risk level characteristics of lncRNAs (Fig. S6 C).\u003c/p\u003e\n \u003cp\u003eThe risk survival status plot of the GSE11318 dataset subjects indicates a negative link between risk scores and OS (Fig. S7 A). The prognostic signature of ferroptosis-associated lncRNAs may accurately predict patient survival, as demonstrated by the ROC curves. The AUCs for 1- and 3-year survival rates was 0.676 and 0.7. The OS rate at 5 years is 0.683 (Fig. S7 B). Poor survival was found to be negatively correlated with the level features of high-risk lncRNAs, according to a Kaplan–Meier curve study (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005) (Fig. S7 C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eIn DLBCL, prognostic risk scores are strongly correlated with clinicopathological characteristics\u003c/h2\u003e\n \u003cp\u003eWe also studied at the link between the risk scores and the clinicopathological traits of the participants. Those with a risk score greater than 65 showed elevated risk. Variations in risk scores among molecular subtypes were also assessed. Compared to the Cluster 1 molecular subtype, the risk score in the Cluster 1 subtype with a poor prognosis was considerably greater (Fig. S8 A-D). The risk score showed exceptional predictive capability across different clinical characteristics, effectively differentiating between different stages (\u003cem\u003ei.e.\u003c/em\u003e, 1, 2, 3, and 4) patients aged 65 years and younger, and male and female patients having various clinical features, classifying them into high- and low-risk groups (Fig. 5A-F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eThe 6-gene signature model's independent prognostic effects\u003c/h2\u003e\n \u003cp\u003eUnivariate and multivariate Cox regression studies between lncRNA signatures in the whole dataset and clinical factors were employed to examine the independent effects of lncRNA signatures among clinical factors. Independent prognostic markers for DLBCL included age, stage, and risk score; these findings imply that the 6-gene signature model exceeds other clinical models in terms of prediction (Fig. 6A, B).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003ePrognostic nomogram: creation and assessment\u003c/h2\u003e\n \u003cp\u003eThe OS (for 1-, 3-, and 5-year) of the participants was evaluated using colographs. The correlation data show that there was stability between the observed and forecasted OS rates (Fig. S9 A).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eRisk score effects on signaling pathways and biological processes\u003c/h2\u003e\n \u003cp\u003eTo find out how risk scores affected biological function, additional analysis was done on the samples that had high and low-risk ratings. To perform ssGSEA, the R software package GSVA was utilized. KEGG pathways that had | Pearson R| \u0026gt; 0.4 and a P \u0026lt; 0.05 were chosen (Fig. S10 A). Of these, the risk score showed a positive correlation with 11 and a negative correlation with 10 pathways, respectively. Fig. S10 B presents a heat map depicting the 21 KEGG pathways, arranged according to their enrichment scores. According to these findings, the risk score is directly linked with the enrichment scores of several pathways, including DNA replication, the spliceosome, base excision repair, glyoxylate, dicarboxylate metabolism, and the citrate (TCA) cycle. However, when the risk score increased, the enrichment scores of certain pathways declined. These pathways included the metabolism of histidine and tryptophan as well as the formation of glycan and primary bile acid.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFerroptosis is a unique kind of iron-dependent cellular death resulting from lipid peroxidation that has been linked to several clinical diseases, including cancer. Ferroptosis, in certain situations, facilitates the removal of necrotic cells and prevents cancerous cells from resisting chemotherapy. \u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e \u0026ndash; \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e Research has demonstrated that lncRNAs relevant to ferroptosis are linked to several malignancies, including DLBCL. Consequently, more research and the hunt for novel ferroptosis-related lncRNA markers will aid in our understanding of the genesis and mechanism of DLBCL development. \u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUsing the GSE10846 dataset, several lncRNAs associated with ferroptosis were filtered out in this study. Twenty-four ferroptosis-associated lncRNAs were believed to be associated with a better prognosis out of these. Next, based on the gene level associated with ferroptosis, the DLBCL subtypes were examined. Based on the level of lncRNAs associated with ferroptosis, two patient subgroups were created. The level of PD-L1 is likely to be considerably impacted by the level of 24 ferroptosis-related lncRNAs. It was also connected to the cellular pathways that lead to the malignant development of DLBCL. Consequently, the MCPCounter method was employed to examine immune cell infiltration in each DLBCL patient as well as the associations between the immune cell scores and the 24 ferroptosis-associated lncRNAs. While Cluster 2 showed a higher immune score from CD8\u0026thinsp;+\u0026thinsp;T cells, Cluster 1 displayed better immune scores from NK cells, B lineage, and neutrophils. As a result, in patients with DLBCL, the concentration of 24 ferroptosis-related lncRNA modulators may influence immune infiltration. Following the creation of a predictive model using six ferroptosis-associated lncRNAs, patient groups were categorized as high- or low-risk in terms of the median risk scores.\u003c/p\u003e \u003cp\u003eFerroptosis is linked to cancer pathophysiology. It is yet unknown, nevertheless, how ferroptosis and lncRNA levels relate to one another as DLBCL progresses. The current research discovered potential indicators and therapy targets for the ferroptosis signaling system. Through the miR-1343-3p/NFIX axis, SNHG3 facilitates the growth and spread of cancer cells, hence contributing to non-small cell lung cancer development. \u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003eAccording to certain studies, SNHG19\u0026rsquo;s level is elevated in cancerous tissues. \u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e In a similar vein, individuals with thyroid malignancies have markedly elevated serum levels of LOC284454 level. \u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e The prognosis is not good for patients with nasopharyngeal cancer who express LOC284454 highly. \u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e According to a different study, there is a clear elevation of LINC01184 level in the tissues and cells of colorectal cancer when compared to normal controls, and this upregulation is positively correlated with the disease's advancement. \u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e In addition, the DANCR level is elevated in prostate cancer patients' serum and cell lines, while the miR-214-5p level is downregulated and shows a negative association with the disease's advancement. In patients with prostate cancer, a strong correlation was found between DANCR level and Gleason score, T stage, and prostate-specific antigen. \u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e Nevertheless, there is a dearth of comprehensive studies on the ferroptosis-related lncRNA signature for DLBCL. Six ferroptosis-related lncRNAs\u0026mdash;SNHG3, SNHG19, LOC284454, LOC102724532, LINC01184, and DANCR\u0026mdash;were found to be substantially connected with the prognosis of DLBCL. These findings raise the possibility that these genes are crucial in the development of DLBCL. The findings of our study may be useful in locating new DLBCL biomarkers and offer suggestions for DLBCL treatments that work well in clinics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFerroptosis-related lncRNAs were thoroughly investigated, and their level levels and prognosis were examined concerning DLBCL. A prognostic risk model was created by screening six ferroptosis-related lncRNAs that might confirm DLBCL independently. This work establishes a foundation for future research on these lncRNAs and presents a novel paradigm for ferroptosis-related lncRNAs in DLBCL, offering fresh perspectives on DLBCL therapeutic approaches.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eRecognitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project \u0026quot;To explore the mechanism of aspirin in multiple myeloma treatment and chemotherapy sensitization based on JAK/STATA3 pathway\u0026quot; (Project No. 23EZB13) at the Enze Medical Center in Zhejiang Province provided funding for the study.\u003c/p\u003e\n\n\u003cp\u003eConflicting Interest\u003c/p\u003e\n\u003cp\u003eThere are no potential conflicts of interest to report for any of the authors.\u003c/p\u003e\n\n\u003cp\u003eStatement on Data Sharing\u003c/p\u003e\n\u003cp\u003eThis published article presents statistical summaries of the data sets created and analyzed for the current investigation. Data can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBasso K, Dalla-Favera R. Germinal centres and B cell lymphomagenesis. Nat Rev Immunol 2015;15:172-84.\u003c/li\u003e\n\u003cli\u003eSabattini E, Bacci F, Sagramoso C, Pileri SA. WHO classification of tumours of haematopoietic and lymphoid tissues in 2008: an overview. Pathologica 2010;102: 83-7.\u003c/li\u003e\n\u003cli\u003eRosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002;346:1937-47.\u003c/li\u003e\n\u003cli\u003eDixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. 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Randomized clinical trial of weekly vs. triweekly cisplatin-based chemotherapy concurrent with radiotherapy in the treatment of locally advanced cervical cancer. Int J Radiat Oncol Biol Phys 2011;81:e577-e81.\u003c/li\u003e\n\u003cli\u003eLouandre C, Ezzoukhry Z, Godin C, Barbare JC, Mazi\u0026egrave;re JC, Chauffert B, et al. Iron-dependent cell death of hepatocellular carcinoma cells exposed to sorafenib. Int J Cancer 2013;133:1732-42.\u003c/li\u003e\n\u003cli\u003eGuo J, Xu B, Han Q, Zhou H, Xia Y, Gong C, et al. Ferroptosis: A novel anti-tumor action for cisplatin. Cancer Res Treat 2018;50:445-60.\u003c/li\u003e\n\u003cli\u003eSchmitt A, Xu W, Bucher P, Grimm M, Konantz M, Horn H, et al. Dimethyl fumarate induces ferroptosis and impairs NF-\u0026kappa;B/STAT3 signaling in DLBCL. Blood 2021.\u003c/li\u003e\n\u003cli\u003eChen H, He Y, Pan T, Zeng R, Li Y, Chen S, et al. Ferroptosis-Related Gene Signature: A New Method for Personalized Risk Assessment in Patients with Diffuse Large B-Cell Lymphoma. Pharmgenomics Pers Med 2021;14:609-19.\u003c/li\u003e\n\u003cli\u003eKinowaki Y, Kurata M, Ishibashi S, Ikeda M, Tatsuzawa A, Yamamoto M, et al. Glutathione peroxidase 4 overlevel inhibits ROS-induced cell death in diffuse large B-cell lymphoma. Lab Invest 2018;98:609-19.\u003c/li\u003e\n\u003cli\u003eZhang T, Li K, Zhang ZL, Gao K, Lv CL. LncRNA Airsci increases the inflammatory response after spinal cord injury in rats through the nuclear factor kappa B signaling pathway. Neural Regen Res 2021;16:772-7.\u003c/li\u003e\n\u003cli\u003eGupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 2010;464(7291):1071-6.\u003c/li\u003e\n\u003cli\u003eLu J, Xu F, Lu H. LncRNA PVT1 regulates ferroptosis through miR-214-mediated TFR1 and p53. Life Sci 2020;260:118305.\u003c/li\u003e\n\u003cli\u003eZhang Y, Guo S, Wang S, Li X, Hou D, Li H, et al. LncRNA OIP5-AS1 inhibits ferroptosis in prostate cancer with long-term cadmium exposure through miR-128-3p/SLC7A11 signaling. Ecotoxicol Environ Saf 2021;220:112376.\u003c/li\u003e\n\u003cli\u003eClough E, Barrett T. The Gene Level Omnibus Database. Methods Mol Biol 2016;1418:93-110.\u003c/li\u003e\n\u003cli\u003eConrad M, Angeli JP, Vandenabeele P, Stockwell BR. Regulated necrosis: disease relevance and therapeutic opportunities. Nat Rev Drug Discov 2016;15:348-66.\u003c/li\u003e\n\u003cli\u003eStockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M, Dixon SJ, et al. Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell 2017;171:273-85.\u003c/li\u003e\n\u003cli\u003eTonnus W, Linkermann A. The in vivo evidence for regulated necrosis. Immunol Rev 2017;277:128-49.\u003c/li\u003e\n\u003cli\u003eToyokuni S, Ito F, Yamashita K, Okazaki Y, Akatsuka S. Iron and thiol redox signaling in cancer: An exquisite balance to escape ferroptosis. Free Radic Biol Med 2017;108:610-26.\u003c/li\u003e\n\u003cli\u003eChandra Gupta S, Nandan Tripathi Y. Potential of long non-coding RNAs in cancer patients: From biomarkers to therapeutic targets. Int J Cancer 2017;140:1955-67.\u003c/li\u003e\n\u003cli\u003eLi Y, Gao L, Zhang C, Meng J. LncRNA SNHG3 promotes proliferation and metastasis of non-small-cell lung cancer cells through miR-515-5p/SUMO2 axis. Technol Cancer Res Treat 2021;20:15330338211019376.\u003c/li\u003e\n\u003cli\u003eZhao L, Song X, Guo Y, Ding N, Wang T, Huang L. Long non‑coding RNA SNHG3 promotes the development of non‑small cell lung cancer via the miR‑1343‑3p/NFIX pathway. Int J Mol Med 2021;48:147.\u003c/li\u003e\n\u003cli\u003eZhao GY, Ning ZF, Wang R. lncrna snhg19 promotes the development of non-small cell lung cancer via mediating miR-137/E2F7 axis. Front Oncol 2021;11:630241.\u003c/li\u003e\n\u003cli\u003eFan C, Wang J, Tang Y, Zhang S, Xiong F, Guo C, et al. Upregulation of long non-coding RNA LOC284454 may serve as a new serum diagnostic biomarker for head and neck cancers. BMC Cancer 2020;20:917.\u003c/li\u003e\n\u003cli\u003eFan C, Tang Y, Wang J, Wang Y, Xiong F, Zhang S, et al. Long non-coding RNA LOC284454 promotes migration and invasion of nasopharyngeal carcinoma via modulating the Rho/Rac signaling pathway. Carcinogenesis 2019;40:380-91.\u003c/li\u003e\n\u003cli\u003eSui YX, Zhao DL, Yu Y, Wang LC. The role, function, and mechanism of long intergenic noncoding RNA1184 (linc01184) in colorectal cancer. Dis Markers 2021;2021:8897906.\u003c/li\u003e\n\u003cli\u003eDeng H, Zhu B, Dong Z, Jiang H, Zhao X, Wu S. miR-214-5p targeted by LncRNA DANCR mediates TGF-\u0026beta; signaling pathway to accelerate proliferation, migration and inhibit apoptosis of prostate cancer cells. Am J Transl Res 2021;13:2224-40.\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":"Diffuse Large B-cell lymphoma, Prognostic factor, RNA, Ferroptosis","lastPublishedDoi":"10.21203/rs.3.rs-5987113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5987113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eDiffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma subtype in adult patients, with an annual incidence rate ranging from 25.0% to 40.0% worldwide. Nevertheless, the prognosis forthe disease remains poor.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003c/em\u003e There is a pressing need for new, reliable biomarkers for prognosis prediction.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u003c/em\u003eUsing 449 DLBCL samples from the Gene Expression Omnibus dataset, the relationships between ferroptosis-associated long non-coding RNAs (lncRNAs) were examined. Before applying univariate Cox analysis to exclude lncRNAs connected to prognosis, we used Pearson correlation analysis to filter a large number of lncRNAs associated with ferroptosis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e To predict the prognosis of DLBCL, eleven lncRNAs linked toferroptosis were subjected to selection operator Cox regression and least absolute shrinkage. Furthermore, it was demonstrated that six ferroptosis-related lncRNAs were the most effective in establishing a predictive risk model. People with DLBCL were assigned to high- and low-risk groups in terms of their median risk scores. The model built employing 11 ferroptosis-related lncRNAs demonstrated higher prognostic evaluation abilities, as demonstrated by the stratified analysis. Significant enrichment in tumor-related pathways was seen in high-risk patients. Age, World Health Organization grade, and the ferroptosis-related lncRNA prognostic factor were taken into consideration when creating a nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003e\u003c/em\u003eIn conclusion, the nomogram generated can precisely anticipate the overall survival of DLBCL patients across both cohorts.\u003c/p\u003e","manuscriptTitle":"A ferroptosis-related lncRNAs signature as a potential biomarker for diffuse large B-cell lymphoma patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 09:55:17","doi":"10.21203/rs.3.rs-5987113/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":"c4a98f4b-74e8-4bb0-845d-f0b4a1229945","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T21:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 09:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5987113","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5987113","identity":"rs-5987113","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}