Prognostic signature constructed of seven ferroptosis related lncRNAs predicts the prognosis of HBV related HCC

Prognostic signature constructed of seven ferroptosis related lncRNAs predicts the prognosis of HBV related HCC | 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 Prognostic signature constructed of seven ferroptosis related lncRNAs predicts the prognosis of HBV related HCC Wenwen Wang, Lifen Wang, Chunxia Song, Tong Mu, Jinhua Hu, Hua Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2974952/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2023 Read the published version in Journal of Gastrointestinal Cancer → Version 1 posted You are reading this latest preprint version Abstract Background Ferroptosis play crucial roles in cancer. Many lncRNAs were expressed aberrantly and regulated the tumorigenesis and progression of HCC. But the roles of ferroptosis related lncRNAs (FRLncs) in HBV-related HCC (HBV-HCC) remain ambiguous. Methods The Cancer Genome Atlas (TCGA) database was applied to achieve gene expression profile and clinical data. The risk signature was constructed by FRLncs based on the univariate and multivariable cox regression analysis. The survival curve, cox regression analysis and time-dependent receiver operating characteristic (ROC) curve was adopted to verify the independence and reliability of the signature. A nomogram was established. Immune infiltrating cells, immune functions and check points were also analyzed. Results A risk signature composed of 7 FRLncs (LINC00942, AC131009.1, POLH-AS1, AC090772.3, MKLN1-AS, AC009403.1, AL031985.3) was constructed. The signature divided HBV-HCC patients into high- and low-risk groups. Patients in high-risk group showed a poor prognosis. Even though compared to the stage (areas under curves (AUC) = 0.715), the risk score (AUC = 0.703) seemed not perfect enough, its long-term prediction effect is satisfactory. And the AUC of the risk score for 1-, 3-, 5-year was 0.703, 0.839 and 0.767, respectively. A nomogram composed of gender, stage, age, grade, and risk signature was established. The risk signature and nomogram displayed appreciable independence and reliability in HBV-HCC patients. The endothelial cell, stroma score, hematopoietic stem cell and microenvironment score were expressed differently significantly in the high- and low-risk group according to the XCELL algorithm. PDCD1 and CTL4 were expressed higher in the high-risk group of HCC patients, but no common target was found in the HBV-HCC group. Conclusion A 7-lncRNA signature was identified as a potential prognostic predictor for HBV-HCC patients. The current study may help provide new ideas for individualized and precise treatment of HCC, especially HBV-HCC. HBV-related HCC ferroptosis related LncRNAs TCGA Prognostic signature nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Primary liver cancer is the third leading cause of cancer-related deaths worldwide, and ranks the sixth most commonly diagnosed cancer in 2022( 1 , 2 ). Primary liver cancer includes hepatocellular carcinoma (HCC) (comprising 75%-85% of cases) and intrahepatic cholangiocarcinoma (10%-15%), as well as other rare types. Despite the rapid development has achieved in terms of diagnosis and therapy, the overall 5-year survival of HCC is still worrisome. Therefore, an effective prognostic signature that could identify patients with a high risk of poor prognosis would guide clinical management and help make the personalized treatment strategy. Generally known, TCGA is a database based on gene sequencing. And accumulating evidence has authenticated that the prognostic signatures founded on TCGA had great potential in predicting HCC prognosis. As HBV related HCC accounts for a large proportion of HCC in China, it is also of great importance to explore the prognostic signatures with TCGA gene sequence in HBV-HCC patients. Ferroptosis was first coined in 2012 to represent the form of regulated cell death (RCD) marked by iron-dependent lipid peroxidation. Up to now, ferroptosis has been implicated in many diseases, including degenerative diseases (such as Huntington’s, Alzheimer’s, and Parkinson’s diseases), ischemia-reperfusion injury, and cardiovascular diseases( 3 ). More importantly, a growing number of studies have proven the great potential of ferroptosis in cancer initiation, progression, and suppression. In breast cancer cells, the lysosome disrupting agent (siramesine) and the tyrosine kinase inhibitor (lapatinib) could induce ferroptosis, conversely, the cell death could be reversed by the ferroptosis inhibitor, ferroportin-1( 4 ). The inducer of ferroptosis, RSL3 and erastin, caused obvious decrease in cell growth and migration of prostate cancer in vitro and dramatically postponed the tumor growth of treatment-resistant prostate cancer in vivo, without measurable side effects( 5 ). Notably, increasing researches have found that ferroptosis was one of the important factors of sorafenib in the treatment of HCC. It showed that loss of Lifr promoted liver tumorigenesis and conferred resistance to drug-induced ferroptosis through upregulating the iron-sequestering cytokine LCN2 and neutralizing LCN2 enhanced the ferroptosis-induction and anticancer effects of sorafenib( 6 ). The transcription factors YAP/TAZ drove sorafenib resistance in HCC through repressing sorafenib-induced ferroptosis by inducing the expression of SLC7A11( 7 ). Thus, therapy targeting ferroptosis may be a promising way to improve the treatment of liver cancer. Quite a few of genes, which are named ferroptosis related genes (FRGs) are related with the process, such as GPX4, PTGS2, ACSL4 and others and a number of noncoding RNAs including lncRNAs, miroRNAs (miRNAs) are also involved in the process of ferroptosis( 3 , 8 ). Among them, lncRNAs have attracted a great deal of attention in the last decade due to their wide range of action and mostly unexplored functions. LncRNAs are RNA transcripts longer than 200 nucleotides and they do not code for proteins ( 9 ). LncRNAs were engaged in a wide range of cellular mechanisms, from almost all aspects of gene expression to protein translation and stability, and played crucial roles in pathological conditions, for example, cancer and cardiovascular disease ( 10 ). An increasing body of evidence suggested that lncRNAs were expressed aberrantly and acted as key players in the tumorigenesis and progression of HCC. They could bind with DNA, RNA or proteins, or encoding small peptides in HCC and participate in cancerous phenotypes, such as persistent proliferation, evading apoptosis, accelerated vessel formation and gain of invasive capability( 11 ). So far, emerging evidence has proved the abnormal expression of ferroptosis related lncRNAs (FRLncs) had the potential to influence the development of HCC, but the prognostic effect of FRLncs in HCC, especially HBV-related HCC needs further investigation. In the present study, ferroptosis related lncRNAs were screened to construct a prognostic signature to stratify HBV-HCC patients into low- and high-risk groups. A nomogram was constructed with the risk signature and other clinical parameters. Signaling pathways enrichment, Kaplan-Meier survival, relevance between prognostic signature and clinicopathological parameters, immune cells infiltration, immune function, immune checkpoints were also assessed. The data will help physicians predict survival and formulate individualized and efficacious treatments for HCC, especially HBV-HCC patients. Materials and Methods Data resource The RNA sequencing data with clinical information were acquired from The Cancer Genome Atlas (TCGA) ( https://portal.gdc.cancer.gov/ ). There are 374 tumor tissues and 50 normal tissues in this cohort. The HCC patients with clinical data characteristics were enrolled in the further study. The perl language was adopted to distinguish the lncRNAs from the RNA sequencing data of HCC patients. There were 484 ferroptosis related genes (FRGs) acquired in total from the FerrDb V2 ( http://www.zhounan.org/ferrdb/current/ ). The Infiltration Estimation for all TCGA tumors was downloaded from TIMER2.0 ( http://timer.cistrome.org/ ). TCGA data are freely accessible and all above data acquired were fully complied with the access principles of the database. Identification of the differentially expressed ferroptosis related lncRNAs (DEFRLncs) in HCC. On the basis of the 484 FRGs, 440 FRGs and the corresponding expression were extracted from the HCC TCGA database. The ferroptosis related lncRNAs (FRLncs) were acquired through the co-expression analysis with threshold value setting as corFilter = 0.4 and p = 0.001. Further, the differentially expressed ferroptosis related genes (DEFRGs) and lncRNAs (DEFRLncs) of HCC database between the normal tissue and the tumor tissue were achieved by the R package. The threshold value was set as |log fold change (FC)| =1 and fdr = 0.001. And the DEFRGs were in the progress of gene ontology (GO) and KEGG analysis. Establishment of the prognostic ferroptosis related lncRNAs signature Univariate cox regression analysis was performed based on the DEFRLncs to determine the FRLncs with significant prognostic value in HCC (pFilte = 0.0001). Among them, there were 23 FRLncs screened out for the multivariable cox regression analysis and the prognostic signature was established with 7 FRLncs to stratify HCC patients into low- and high-risk groups finally. The risk score was calculated as follows: ƸCoef FRLncs × Exp FRLncs (Exp FRLncs means the expression of FRLncs). The Kaplan–Meier survival curve was used to compare the survival difference of the HCC patients in the high- and low-risk group. The univariate and multivariate cox regression analysis were performed to verify the independence of the signature to predict the prognosis of the HCC patients in different groups. The ROC curve was plotted to assess the predictive value of the prognostic gene signature for 1-, 2-, 3-yeare survival. Chi square test was employed to analyze the relationship between the relevant clinicopathological characteristics and risk signature in HCC. Based on the HCC database, the HBV-HCC data was extracted and the signature constructed on the foundation of the HCC was verified satisfactory in the HBV-HCC group which divided the HBV-HCC into high- and low-risk group. Gene Set Enrichment Analysis (GSEA) and co-expression network The risk signature divided HCC patients into low- and high-risk groups. GSEA 4.2.3 software was used to perform a GSEA analysis in order to explore the different signaling pathways. The co-expression network of genes and FRLncs was constructed through the Cytoscape. Construction of the Nomogram A nomogram consist of HBV status, gender, stage, grade, age and risk score was constructed and revealed the capability to predict overall survival (OS) at 1- year, 3-year and 5-year. Furthermore, the corresponding calibration curve of the nomogram to evaluate the predictive ability of the nomogram was performed. Results Discrimination of differentially expressed ferroptosis related lncRNAs in HCC. A total of 19895 mRNA and 16773 lncRNAs were extracted from the HCC TCGA database. On the foundation of the 484 FRGs downloaded from the FerrDb V2 database (Supplementary Table 1), there were 1317 FRLncs identified with threshold value setting as corFilter = 0.4 and p = 0.001(Supplementary Table 2). Based on the differential expression analysis, 141 differently expressed ferroptosis related genes (DEFRGs) (Supplementary Table 3) and 784 differentially expressed ferroptosis related lncRNAs (DEFRLncs) (Supplementary Table 4) were extract by comparing normal liver tissues and HCC with threshold value setting as (|logFC|) = 1 and P = 0.001. The procedure was showcased in Figure 1. Establishment of the ferroptosis related lncRNAs prognostic signature According to the 784 DEFRLncs of HCC, there were 23 FRLncs with prognostic value collected by the univariate cox regression analysis with pFilter = 0.0001(Figure 2A). On this base, the multivariable cox regression analysis was carried out and 7 FRLncs were recognized ultimately (Table 1). The 7 FRLncs were picked and the signature was constructed as follows: Risk score = LINC00942* 0.0116+ AC131009.1* 0.2838+ `POLH-AS1`* 0.7316+ AC090772.3* 0.2422+ `MKLN1-AS`* 0.7662+ AC009403.1* 0.4396 +AL031985.3 * 0.3085. The HCC patients were divided into high-risk (n=185) and low-risk (n=185) group in accordance with the cutoff value of the risk score. As shown in the Kaplan–Meier curves, patients in the low-risk group exhibited a better overall survival (OS) compared with the patients in the high-risk group (Figure 2B). The expression levels of the 7 FRLncs visualized in the heatmap were consistent with the risk coefficient in the prognostic signature (Figure 2C). The distributions of risk scores and survival status were exhibited in Figure 2D and E. In order to confirm the reliability of the risk signature to forecast the prognosis, the AUC of each ROC curve was calculated. The AUC of the risk score was superior obviously compared with stage, grade, gender and age (Figure 2F). Similarly, the AUC of the risk score for 1-, 2-, 3-year was 0.780, 0.744 and 0.723, respectively (Figure 2G) and this result also suggest the good reliability of the risk signature to estimate the prognosis of HCC patients. Table 1. Construction of prognostic lncRNAs signature based on 7 FRLncs. LncRNA coefficient HR HR.95Cl(lower) HR.95Cl(upper) LINC00942 0.01156 1.01163 1.00095 1.02242 AC131009.1 0.28379 1.32816 0.98413 1.79246 POLH-AS1 0.73160 2.07840 1.25538 3.44098 AC090772.3 0.24224 1.27410 1.04791 1.54911 MKLN1-AS 0.76621 2.15160 1.14054 4.05894 AC009403.1 0.43958 1.55205 1.16676 2.06459 AL031985.3 0.30853 1.36143 1.07637 1.72198 Table 1. Construction of prognostic lncRNAs signature based on 7 FRLncs. 7 FRLncs were filtered to construct a prognostic lncRNAs signature on the basis of multivariable Cox regression analysis. Verification of the risk signature as the independent prognostic factor and the construction of the nomogram Based on the univariate (Figure 3A) and multivariate cox regression analysis (Figure 3B), the stage, HBV status and the risk score were selected to be the independent prognostic factors. The relationship between clinical variables and risk score was also evaluated which suggested that the risk scores showed significant differences in different stage and grade groups (Figure 3C, D). Based on the median value of each FRLncs in the risk signature calculated by the SPSS (Supplementary Table 5), the patients were correspondingly divided into high-expression and low-expression group. On the foundation of grouping, the Kaplan–Meier curves was plotted to display the difference between the high- and low-expression group. And the relevance between the selected seven FRLncs and grade, stage, survival state and risk were analyzed as well. According to the K–M curves, each FRLnc was able to predict the prognosis of HCC patients and the prognosis was poor in the high-expression group (p<0.05). In addition to the LINC00942, AC090772.3 and AC009403.1 with stage and AC090772.3 with grade, others exhibited obvious correlation with grade, stage, survival state and risk (Figure 3E-K). A nomogram consisted of age, stage, gender, grade and risk score was established (Figure 4A). It could intuitively display the survival probability of 1-, 3-, and 5-year. To further verify reliability of the nomogram, the AUC of each ROC for 1-, 3-, and 5-year was computed and the calculations was 0.755, 0.753 and 0.736, respectively (Figure 4B) in HCC patients. The calibration curve was also depicted and presented good accuracy of nomogram for prediction (Figure 4C). Gene Set Enrichment Analysis (GSEA), Enrichment Analysis of DEGs and Co-Expression Network Based on the GSEA, the biological functions between the high- and low-risk groups showed obvious difference. The base excision repair, oocyte meiosis, RNA degradation, spliceosome and ubiquitin mediated proteolysis were enriched in the high-risk group (Figure 5A-E). Simultaneously, the complement and coagulation cascades, fatty acid metabolism, primary bile acid biosynthesis, tryptophan metabolism, and valine leucine and isoleucine degradation were enriched in the low-risk group (Figure 5F-J). According to the difference analysis, there were 141 genes screened differently expressed between the normal and tumor tissue. Afterwards, the GO and KEGG analysis were performed. Speaking of biological process, the DEGs were mainly concentrated in the cellular response to chemical stress, response to oxidative stress, response to nutrient levels, cellular response to oxidative stress, fatty acid metabolic process, response to metal ion, response to oxygen levels and others. As to cellular component, the DEGs were enriched in the lipid droplet, focal adhesion and so on. For molecular function, the DEGs were grouped in the oxidoreductase activity, acting on NAD(P)H, dioxygenase activity as well as iron ion binding (Figure 5K). In the KEGG enrichment analysis, the DEGs were significantly enriched in the thermogenesis, AGE-RAGE signaling pathway in diabetic complications, ferroptosis, etc (Figure 5L). As for the co-expression network, the Cytoscape was adopted to visualize the outcome which could display the connections and mechanisms linking prognosis-related FRLnc and related mRNAs obviously (Figure 5M). Confirmation of the reliability of the risk signature for predicting the prognosis of HBV-HCC patients. The data of HBV-HCC patients was extracted from HCC TAGA database. The risk signature and nomogram were verified in the HBV-HCC patients. As to the HBV-HCC patients, patients in the low-risk group displayed a better overall survival (OS) compared with the patients in the high-risk group (Figure 6A). And in the group of age<55, G1-G3group, M0-M1 group, N0 group, stage I-II group, and T1-2 group, patients in the low-risk group also showed a better OS in comparison with that in the high-risk group (Figure 6B-G). Even though compared to the stage (AUC=0.715), the risk score (AUC=0.703) seemed not perfect enough, its long-term prediction effect is satisfactory (Figure 6H). And the AUC of the risk score for 1-, 3-, 5-year was 0.703, 0.839 and 0.767, respectively and this data prompt the good reliability of the risk score to evaluate the prognosis of HBV-HCC patients (Figure 6I). The univariate and multivariate cox regression analysis hinted that in HBV-HCC patients, the stage and risk score were the independent prognostic factors (Figure 6J, K). Importantly, the nomogram showed a good practicability in predicting the prognosis of HBV-HCC patients. The AUC of each ROC for 1-, 3-, and 5-year was 0.808, 0.777 and 0.730 (Figure 6L) and the calibration curve also revealed good accuracy of nomogram for prediction (Figure 6M). Immune Microenvironment, Immune Function and Checkpoints in HCC patients. The stroma score, microenvironment score and relative infiltration abundance of immune cells was calculated through 7 algorithms, for instance, TIMER, CIBERSORT, CIBERSORE-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC. Data analysis showed that there was a significant difference of the immune cells in the low- and high-risk group. For example, the stroma score, endothelial cell, T cell CD4+ Th2 and Hematopoietic stem cell were expressed differently significantly in the high- and low risk group according to the XCELL algorithm in HCC patients(Figure 7A). For HBV-HCC group, endothelial cell, stroma score, Hematopoietic stem cell and microenvironment score displayed marked difference in the high- and low risk group according to the XCELL algorithm (Figure 7B). As to immune function, cytolytic activity, Type-I IFN response and Type-II IFN response were weakened in the high-risk group and others showed no statistical significance in HCC patients (Figure 7C). With respect to the HBV-HCC patients, in addition to the aforementioned immune function, APC (antigen presenting cell) co-inhibition, APC co-stimulation, CCR (chemokine C-C-Motif receptor), check point, HLA (Human Leukocyfe Antigen), inflammation promoting, parainflammation, T cell co-inhibition and T cell co-stimulation were attenuated in the high-risk group (Figure 7D). Given the importance of immune checkpoints in cancer treatment, the expression of checkpoint genes was compared between the two risk groups. We found PDCD1(programmed cell death 1, PD-1) and CTL4 (cytotoxicTlymphocyte-associatedantigen-4) were expressed higher in the high-risk group which meant that immunotherapy may be a promising choice for HCC (Figure 7E). While for the HBV-HCC patients, the common immune checkpoints indicated no obvious differential expression (Figure 7F). Discussion HCC is one of the leading cause of cancer related mortality in the world with a complex etiology and its limited treatment options( 12 ), thus precise treatment may occupy the considerable position in the treatment of HCC. Therefore, in order to help patients receive individualized and precise treatment, constructing a risk signature which could help to predict the prognosis of HCC seems necessary. The growing knowledge of ferroptosis has suggested the role and therapeutic potential of ferroptosis in cancer, but has not been translated into effective therapy( 6 ). Available researches have proved that lncRNAs perform a critical regulatory role in ferroptosis- related biological processes in various cancer types( 13 ). Relevant studies also have indicated that ferroptosis could be induced by sorafenib. However, a prognostic tool basing on ferroptosis- related lncRNAs for HCC patients is still lacking. In the current study, seven ferroptosis related lncRNAs, including LINC00942, AC131009.1, `POLH-AS1`, AC090772.3, MKLN1-AS, AC009403.1, and AL031985.3 were picked out to develop a prognostic signature to predict the OS of HCC. LINC00942 was found to be up-regulated in chemoresistant gastric cancer (GC) cells, and its high expression was positively correlated with the poor prognosis of patients with GC. And functional studies indicated that LINC00942 confers chemoresistance to GC cells by impairing apoptosis and inducing stemness( 14 ). Previous researches have proven that AC131009.1 and `POLH-AS1` were associated with necroptosis, suggesting that they play roles in different ways of programmed cell death( 15 ). AC090772.3 was found to be highly expressed in the HCC as a ceRNA related to hypoxia( 16 ) and more in-depth research about AC090772.3 is needed in the future. MKLN1-AS was proven up‑regulated in HCC, and mediates the effects of SOX9 on promoting the cell viability, proliferation, invasion and EMT of HCC( 17 ). AL031985.3 was highly expressed in the tumor tissue rather than the non-tumor tissue and it was interrelated with pyroptosis( 18 ). Together, these results indicate that the screened lncRNAs participate in the tumor behavior of HCC through multiple pathways. In addition, no study on the biological functions associated with AC009403.1 has been reported, and its molecular mechanism of action in HCC deserves further exploration. It is also worth mentioning that TIMER database was employed to proclaim connections between the risk signature and immune infiltration levels in HCC and HBV-HCC. The results showed different types of immune cells in different risk groups through several algorithms. When it comes to immune checkpoints, a currently hot issue, we found several common genes, such as PD-1 and CTL4 was expressed higher in the high-risk group but no common target was found in the HBV-HCC group. Meanwhile, a certain number of studies have confirmed that anti-PD-1 therapy for HBV-HCC patients is plausible( 19 ), so may be more detailed grouping and research needed for us. Anyway, immunotherapy may be a viable choice for HCC patients. Conclusions In summary, our study constructed an FRLncs-based prognostic signature. The signature stratified the HBV-HCC patients into high- and low-risk group and could predict the prognosis of HBV-HCC excellently. The immune checkpoints such as PD-1 and CTL4 tended to be highly expressed in the high-risk group of HCC patients, which suggested that immunotherapy may be a promising therapy for these patients. A nomogram was constructed based on risk signature and clinical characteristics and displayed good accuracy. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data Availability The RNA sequencing data with clinical information were acquired from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). The ferroptosis related genes were acquired from the FerrDb V2 (http://www.zhounan.org/ferrdb/current/). The Infiltration Estimation for all TCGA tumors was downloaded from TIMER2.0 (http://timer.cistrome.org/). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Foundation of Shandong Province [No. ZR2020QH035 and No. ZR2021QH195]; Medical Health Science and Technology Development Plan Project of Shandong Province [No. 202103030765]. Author Contributions Wenwen Wang and Hua Feng contributed to the conception and design of the study. Lifen Wang and Jinhua Hu organized the database and statistical analysis for the manuscript. Wenwen Wang wrote the draft of the manuscript and Chunxia Song revised the manuscript. Tong mu performed visualizations. Hua Feng acted as corresponding author. All the authors have read and approved the final manuscript. Acknowledgements The authors thank The Cancer Genome Atlas (TCGA), FerrDb V2 and TIMER2.0 database for providing valuable data. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: a cancer journal for clinicians. 2022;72(1):7-33. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Supplementary Files SupplementaryMaterialslegend.docx SupplementaryTable1.ferroptosisrelatedgenes.xlsx SupplementaryTable2.ferroptosisrelatedlncRNAs.xlsx SupplementaryTable3.differentgenesbetweennormalandtumortissue.xlsx SupplementaryTable4.differentlncRNAsbetweennormalandtumortissue.xlsx SupplementaryTable5.lncRNAmedian.xlsx SupplementaryTable6.HBVHCCclinicaldata.xlsx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2023 Read the published version in Journal of Gastrointestinal Cancer → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-2974952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":205525793,"identity":"e7eb5849-b8e6-4d05-ae96-0589d3d8a367","order_by":0,"name":"Wenwen Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Wang","suffix":""},{"id":205525794,"identity":"35b3673b-df4b-445b-a35f-19337184b6d7","order_by":1,"name":"Lifen Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifen","middleName":"","lastName":"Wang","suffix":""},{"id":205525795,"identity":"27aaabe5-29e0-4c72-b4cc-09b4f161eeb3","order_by":2,"name":"Chunxia Song","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Song","suffix":""},{"id":205525796,"identity":"480879f7-53cb-4610-977b-22cc1df6f689","order_by":3,"name":"Tong Mu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Mu","suffix":""},{"id":205525797,"identity":"c49290d2-86fe-4789-8ed9-39c67b77b215","order_by":4,"name":"Jinhua Hu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Hu","suffix":""},{"id":205525798,"identity":"1d2e3bf3-a07e-40af-95bb-231d3a978537","order_by":5,"name":"Hua Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBAC9gYGBmYgzcPP3thwQKJCQk6ekBaeAxAtMpI9hxsfWJyxMDZsIFKLjcGN9GaDyraKRIYDhLSw9x5+Xdhmx2Nw5mCbxM15EgmMDcwPH93Ap4XnXJr1zLZkHsnjjW2SM7dJ5LEzsBkb5+DRYi+RY2bM28bMwwe0RVpym0QxYwMPmzQ+LTzyb0Ba6nkYbiS2Sf+dI5HYcICQFgke48e8bYd5BG4kNhtINhCjhSfHjJnn3HEeyZ6DjQ8kjkkYGzYT8AsP+xnjzzxl1fb87O0PDkjU1MnJszc/fIxPCxCwSaDymfErByv5QFjNKBgFo2AUjGgAAOBZSp5fvbsBAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hua","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2023-05-24 07:59:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2974952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2974952/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12029-023-00977-6","type":"published","date":"2023-11-25T08:38:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":37916786,"identity":"523c7728-4633-4199-b163-5e8d1cd28504","added_by":"auto","created_at":"2023-06-02 14:52:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152960,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/560e57aaff4ccbbd11e205dd.png"},{"id":37916787,"identity":"870f7239-24b8-46e5-9522-1a0244de0496","added_by":"auto","created_at":"2023-06-02 14:52:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":636820,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of prognostic signature. (\u003cstrong\u003eA\u003c/strong\u003e) 23 FRLncs with prognostic value were screened through the univariate cox regression analysis. (\u003cstrong\u003eB\u003c/strong\u003e) The patients in the high-risk group displayed a relatively poor prognosis through the Kaplan–Meier (K-M) analysis compared with the patients in the low-risk group (p\u0026lt;0.001). (\u003cstrong\u003eC\u003c/strong\u003e) Different expression levels of the 7 FRLncs between the high- and low-risk groups were visualized in the heatmapping. (\u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eE\u003c/strong\u003e) The distributions of risk scores and survival status of patients showed that with the increase of risk score, the number of death increased. (\u003cstrong\u003eF\u003c/strong\u003e) The AUC of the risk score was superior evidently compared with stage, grade, gender and age. (\u003cstrong\u003eG\u003c/strong\u003e) Confirmation of the prognostic value of the FRLncs signature by ROC analysis for 1-, 2-, 3-year.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/b90c3129c7b74bb7fc119311.png"},{"id":37916105,"identity":"8fe55981-78c2-473b-a63a-0dece630db6c","added_by":"auto","created_at":"2023-06-02 14:44:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1041188,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the independence of the risk signature in HCC patients. (\u003cstrong\u003eA\u003c/strong\u003e) On the basis of the univariate cox regression analysis, stage, HBV status and risk score retained the prognostic value (p\u0026lt;0.05). (\u003cstrong\u003eB\u003c/strong\u003e) The stage, HBV status and the risk score were identified as the independent prognostic factors based on the multivariate cox regression analysis. (\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e) In the Grade I/II, Grade III/IV group and Stage I/II, Stage III/IV group, the risk score showed marked difference (p\u0026lt;0.001). (\u003cstrong\u003eE-K\u003c/strong\u003e) In addition to the LINC00942, AC090772.3 and AC009403.1 with stage and AC090772.3 with grade, others exhibited obvious correlation with grade, stage, survival state and risk.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/5f491e7b31dca759dd2fe75b.png"},{"id":37916096,"identity":"115394e3-6cb0-47f2-9ecf-34837a754e40","added_by":"auto","created_at":"2023-06-02 14:44:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143362,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram. (\u003cstrong\u003eA\u003c/strong\u003e) A nomogram consisted of age, stage, gender, grade and risk score was established. (\u003cstrong\u003eB\u003c/strong\u003e) The AUC of each ROC was calculated for 1-, 3-, and 5-year to validate the reliability of the nomogram in HCC patients. (\u003cstrong\u003eC\u003c/strong\u003e) Calibration curve for the predictive accuracy of the nomogram.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/4d89c9d15020a80cbee39a34.png"},{"id":37916101,"identity":"1e1261dd-239f-40bc-93ae-2b62b4f9727f","added_by":"auto","created_at":"2023-06-02 14:44:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":861050,"visible":true,"origin":"","legend":"\u003cp\u003eGene Set Enrichment Analysis (GSEA), Enrichment Analysis of DEGs and Co-Expression Network. (\u003cstrong\u003eA-E\u003c/strong\u003e) Pathways enriched in the high-risk group. (\u003cstrong\u003eF-J\u003c/strong\u003e) Pathways enriched in the low-risk group. (\u003cstrong\u003eK\u003c/strong\u003e, \u003cstrong\u003eL\u003c/strong\u003e) Go and KEGG enrichment Analysis of DEGs. (\u003cstrong\u003eM\u003c/strong\u003e) Co-expression network of ARlncs.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/0de9dbb22276d012df601676.png"},{"id":37916084,"identity":"3e598e45-7a31-48b6-ad11-95dac27a5a52","added_by":"auto","created_at":"2023-06-02 14:44:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":365884,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmation of the reliability of the risk signature for predicting the prognosis of HBV-HCC patients. (\u003cstrong\u003eA\u003c/strong\u003e) For HBV-HCC patients, patients in the low-risk group displayed a better OS compared with the patients in the high-risk group (p\u0026lt;0.05). (\u003cstrong\u003eB-G\u003c/strong\u003e) In the group of age\u0026lt;55, G1-G3group, M0-M1 group, N0 group, stage I-II group, and T1-2 group, HBV-HCC patients in the low-risk group also showed a better OS in comparison with that in the high-risk group (p\u0026lt;0.05). (\u003cstrong\u003eH\u003c/strong\u003e) The AUC of the risk score seemed satisfactory compared to other clinical factors besides stage in HBV-HCC patients. (\u003cstrong\u003eI\u003c/strong\u003e) The AUC of the risk score for 1-, 3-, 5-year was 0.703, 0.839 and 0.767, respectively in HBV-HCC patients. (\u003cstrong\u003eJ\u003c/strong\u003e) On the foundation of the univariate cox regression analysis, the stage and risk score retained the prognostic value (p\u0026lt;0.05) in HBV-HCC patients. (\u003cstrong\u003eK\u003c/strong\u003e) The stage and the risk score were identified as the independent prognostic factors through the multivariate cox regression analysis in HBV-HCC patients. (\u003cstrong\u003eL\u003c/strong\u003e) The AUC of each ROC for 1-, 3-, and 5-year to verify the reliability of the nomogram in HBV-HCC patients. (\u003cstrong\u003eM\u003c/strong\u003e) The calibration curve revealed good accuracy of nomogram for prediction in HBV-HCC patients.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/882d8aea42d08de936f2ba80.png"},{"id":37916100,"identity":"1f8c5014-88d3-41b6-9e59-0b94187f2ccb","added_by":"auto","created_at":"2023-06-02 14:44:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":620459,"visible":true,"origin":"","legend":"\u003cp\u003eImmune microenvironment, immune functions and checkpoint genes in HBV-HCC patients. (\u003cstrong\u003eA\u003c/strong\u003e) The stroma score, endothelial cell, T cell CD4+ Th2 and Hematopoietic stem cell were expressed differently significantly in the high- and low risk group based on the XCELL algorithm in HCC patients. (\u003cstrong\u003eB\u003c/strong\u003e) For HBV-HCC patients, endothelial cell, stroma score, Hematopoietic stem cell and microenvironment score displayed marked difference in the high- and low risk group according to the XCELL algorithm. (\u003cstrong\u003eC\u003c/strong\u003e) Cytolytic activity, Type-I IFN response and Type-II IFN response were weakened in the high-risk group and others showed no statistical significance in HCC patients. (\u003cstrong\u003eD\u003c/strong\u003e) Besides the MHC Class I, immune functions exhibited evident difference between the two risk groups in HBV-HCC patients. (\u003cstrong\u003eE\u003c/strong\u003e) PDCD1(PD-1) and CTL4 were expressed higher in the high-risk group in HCC patients. (\u003cstrong\u003eF\u003c/strong\u003e) No common check points showed differently expressed between the two risk groups in HBV-HCC patients.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/52210e3b84289c0d8d784bf9.png"},{"id":56520097,"identity":"c342f958-4def-4dea-b90d-61ce455791c2","added_by":"auto","created_at":"2024-05-15 08:38:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4059228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/a6079dea-0d12-46c3-bd6a-11fca9bd4f6a.pdf"},{"id":37916085,"identity":"12bcb96d-958e-43f2-aec0-b73bb57f6a3b","added_by":"auto","created_at":"2023-06-02 14:44:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16435,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialslegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/d21371ee8de661dd370d1a2b.docx"},{"id":37916090,"identity":"c93cd0cf-c881-4394-9a6a-abf1badfe39e","added_by":"auto","created_at":"2023-06-02 14:44:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.ferroptosisrelatedgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/e50ef4457d190eb451f09663.xlsx"},{"id":37916088,"identity":"0aed7979-6876-428e-9597-3f68112ca8d3","added_by":"auto","created_at":"2023-06-02 14:44:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.ferroptosisrelatedlncRNAs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/e2cccc079378a237f0e5429a.xlsx"},{"id":37916097,"identity":"5d81bcd5-6394-4da7-bbf5-a0237dba5339","added_by":"auto","created_at":"2023-06-02 14:44:29","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22013,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.differentgenesbetweennormalandtumortissue.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/d5beb64df6a1d8de8e502cc0.xlsx"},{"id":37916785,"identity":"96e7a77e-09c2-4799-84e4-0464056b02cf","added_by":"auto","created_at":"2023-06-02 14:52:28","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":77986,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.differentlncRNAsbetweennormalandtumortissue.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/f38586eda7dbbb0e3d41dd5b.xlsx"},{"id":37916099,"identity":"0269ce0f-f80e-4da8-864a-1f751391b108","added_by":"auto","created_at":"2023-06-02 14:44:29","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9819,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.lncRNAmedian.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/1fe243e20492685a17b46ac6.xlsx"},{"id":37916104,"identity":"634cfefb-0968-40a9-959a-272d30682de5","added_by":"auto","created_at":"2023-06-02 14:44:30","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21771,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.HBVHCCclinicaldata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2974952/v1/d90e1b2608d8f7d24ab84542.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic signature constructed of seven ferroptosis related lncRNAs predicts the prognosis of HBV related HCC","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary liver cancer is the third leading cause of cancer-related deaths worldwide, and ranks the sixth most commonly diagnosed cancer in 2022(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Primary liver cancer includes hepatocellular carcinoma (HCC) (comprising 75%-85% of cases) and intrahepatic cholangiocarcinoma (10%-15%), as well as other rare types. Despite the rapid development has achieved in terms of diagnosis and therapy, the overall 5-year survival of HCC is still worrisome. Therefore, an effective prognostic signature that could identify patients with a high risk of poor prognosis would guide clinical management and help make the personalized treatment strategy. Generally known, TCGA is a database based on gene sequencing. And accumulating evidence has authenticated that the prognostic signatures founded on TCGA had great potential in predicting HCC prognosis. As HBV related HCC accounts for a large proportion of HCC in China, it is also of great importance to explore the prognostic signatures with TCGA gene sequence in HBV-HCC patients.\u003c/p\u003e \u003cp\u003eFerroptosis was first coined in 2012 to represent the form of regulated cell death (RCD) marked by iron-dependent lipid peroxidation. Up to now, ferroptosis has been implicated in many diseases, including degenerative diseases (such as Huntington\u0026rsquo;s, Alzheimer\u0026rsquo;s, and Parkinson\u0026rsquo;s diseases), ischemia-reperfusion injury, and cardiovascular diseases(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). More importantly, a growing number of studies have proven the great potential of ferroptosis in cancer initiation, progression, and suppression. In breast cancer cells, the lysosome disrupting agent (siramesine) and the tyrosine kinase inhibitor (lapatinib) could induce ferroptosis, conversely, the cell death could be reversed by the ferroptosis inhibitor, ferroportin-1(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The inducer of ferroptosis, RSL3 and erastin, caused obvious decrease in cell growth and migration of prostate cancer in vitro and dramatically postponed the tumor growth of treatment-resistant prostate cancer in vivo, without measurable side effects(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Notably, increasing researches have found that ferroptosis was one of the important factors of sorafenib in the treatment of HCC. It showed that loss of Lifr promoted liver tumorigenesis and conferred resistance to drug-induced ferroptosis through upregulating the iron-sequestering cytokine LCN2 and neutralizing LCN2 enhanced the ferroptosis-induction and anticancer effects of sorafenib(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The transcription factors YAP/TAZ drove sorafenib resistance in HCC through repressing sorafenib-induced ferroptosis by inducing the expression of SLC7A11(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Thus, therapy targeting ferroptosis may be a promising way to improve the treatment of liver cancer. Quite a few of genes, which are named ferroptosis related genes (FRGs) are related with the process, such as GPX4, PTGS2, ACSL4 and others and a number of noncoding RNAs including lncRNAs, miroRNAs (miRNAs) are also involved in the process of ferroptosis(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among them, lncRNAs have attracted a great deal of attention in the last decade due to their wide range of action and mostly unexplored functions. LncRNAs are RNA transcripts longer than 200 nucleotides and they do not code for proteins (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). LncRNAs were engaged in a wide range of cellular mechanisms, from almost all aspects of gene expression to protein translation and stability, and played crucial roles in pathological conditions, for example, cancer and cardiovascular disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). An increasing body of evidence suggested that lncRNAs were expressed aberrantly and acted as key players in the tumorigenesis and progression of HCC. They could bind with DNA, RNA or proteins, or encoding small peptides in HCC and participate in cancerous phenotypes, such as persistent proliferation, evading apoptosis, accelerated vessel formation and gain of invasive capability(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). So far, emerging evidence has proved the abnormal expression of ferroptosis related lncRNAs (FRLncs) had the potential to influence the development of HCC, but the prognostic effect of FRLncs in HCC, especially HBV-related HCC needs further investigation.\u003c/p\u003e \u003cp\u003eIn the present study, ferroptosis related lncRNAs were screened to construct a prognostic signature to stratify HBV-HCC patients into low- and high-risk groups. A nomogram was constructed with the risk signature and other clinical parameters. Signaling pathways enrichment, Kaplan-Meier survival, relevance between prognostic signature and clinicopathological parameters, immune cells infiltration, immune function, immune checkpoints were also assessed. The data will help physicians predict survival and formulate individualized and efficacious treatments for HCC, especially HBV-HCC patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData resource\u003c/h2\u003e \u003cp\u003eThe RNA sequencing data with clinical information were acquired from The Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). There are 374 tumor tissues and 50 normal tissues in this cohort. The HCC patients with clinical data characteristics were enrolled in the further study. The perl language was adopted to distinguish the lncRNAs from the RNA sequencing data of HCC patients. There were 484 ferroptosis related genes (FRGs) acquired in total from the FerrDb V2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/ferrdb/current/\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/ferrdb/current/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Infiltration Estimation for all TCGA tumors was downloaded from TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). TCGA data are freely accessible and all above data acquired were fully complied with the access principles of the database.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of the differentially expressed ferroptosis related lncRNAs (DEFRLncs) in HCC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOn the basis of the 484 FRGs, 440 FRGs and the corresponding expression were extracted from the HCC TCGA database. The ferroptosis related lncRNAs (FRLncs) were acquired through the co-expression analysis with threshold value setting as corFilter\u0026thinsp;=\u0026thinsp;0.4 and p\u0026thinsp;=\u0026thinsp;0.001. Further, the differentially expressed ferroptosis related genes (DEFRGs) and lncRNAs (DEFRLncs) of HCC database between the normal tissue and the tumor tissue were achieved by the R package. The threshold value was set as |log fold change (FC)| =1 and fdr\u0026thinsp;=\u0026thinsp;0.001. And the DEFRGs were in the progress of gene ontology (GO) and KEGG analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of the prognostic ferroptosis related lncRNAs signature\u003c/h2\u003e \u003cp\u003eUnivariate cox regression analysis was performed based on the DEFRLncs to determine the FRLncs with significant prognostic value in HCC (pFilte\u0026thinsp;=\u0026thinsp;0.0001). Among them, there were 23 FRLncs screened out for the multivariable cox regression analysis and the prognostic signature was established with 7 FRLncs to stratify HCC patients into low- and high-risk groups finally. The risk score was calculated as follows: ƸCoef FRLncs \u0026times; Exp FRLncs (Exp FRLncs means the expression of FRLncs). The Kaplan\u0026ndash;Meier survival curve was used to compare the survival difference of the HCC patients in the high- and low-risk group. The univariate and multivariate cox regression analysis were performed to verify the independence of the signature to predict the prognosis of the HCC patients in different groups. The ROC curve was plotted to assess the predictive value of the prognostic gene signature for 1-, 2-, 3-yeare survival. Chi square test was employed to analyze the relationship between the relevant clinicopathological characteristics and risk signature in HCC. Based on the HCC database, the HBV-HCC data was extracted and the signature constructed on the foundation of the HCC was verified satisfactory in the HBV-HCC group which divided the HBV-HCC into high- and low-risk group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA) and co-expression network\u003c/h2\u003e \u003cp\u003eThe risk signature divided HCC patients into low- and high-risk groups. GSEA 4.2.3 software was used to perform a GSEA analysis in order to explore the different signaling pathways. The co-expression network of genes and FRLncs was constructed through the Cytoscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the Nomogram\u003c/h2\u003e \u003cp\u003eA nomogram consist of HBV status, gender, stage, grade, age and risk score was constructed and revealed the capability to predict overall survival (OS) at 1- year, 3-year and 5-year. Furthermore, the corresponding calibration curve of the nomogram to evaluate the predictive ability of the nomogram was performed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDiscrimination of differentially expressed ferroptosis related lncRNAs in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 19895 mRNA and 16773 lncRNAs were extracted from the HCC TCGA database. On the foundation of the 484 FRGs downloaded from the FerrDb V2 database (Supplementary Table 1), there were 1317 FRLncs identified with threshold value setting as corFilter = 0.4 and p = 0.001(Supplementary Table 2). Based on the differential expression analysis, 141 differently expressed ferroptosis related genes (DEFRGs) (Supplementary Table 3) and 784 differentially expressed ferroptosis related lncRNAs (DEFRLncs) (Supplementary Table 4) were extract by comparing normal liver tissues and HCC with threshold value setting as (|logFC|) = 1 and P = 0.001.\u0026nbsp;The procedure was showcased in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment of the ferroptosis related lncRNAs prognostic signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the 784 DEFRLncs of HCC, there were 23 FRLncs with prognostic value collected by the univariate cox regression analysis with pFilter = 0.0001(Figure 2A). On this base, the multivariable cox regression analysis was carried out and 7 FRLncs were recognized ultimately (Table 1). The 7 FRLncs were picked and the signature was constructed as follows: Risk score = LINC00942* 0.0116+ AC131009.1* 0.2838+ `POLH-AS1`*\u0026nbsp;0.7316+\u0026nbsp;AC090772.3*\u0026nbsp;0.2422+\u0026nbsp;`MKLN1-AS`*\u0026nbsp;0.7662+\u0026nbsp;AC009403.1*\u0026nbsp;0.4396 +AL031985.3 *\u0026nbsp;0.3085. The HCC patients were divided into high-risk (n=185) and low-risk (n=185) group in accordance with the cutoff value of the risk score. As shown in the Kaplan\u0026ndash;Meier curves, patients in the low-risk group exhibited a better overall survival (OS) compared with the patients in the high-risk group (Figure 2B). The expression levels of the 7 FRLncs visualized in the heatmap were consistent with the risk coefficient in the prognostic signature (Figure 2C). The distributions of risk scores and survival status were exhibited in Figure 2D and E. In order to confirm the reliability of the risk signature to forecast the prognosis, the AUC of each ROC curve was calculated. The AUC of the risk score was superior obviously compared with stage, grade, gender and age (Figure 2F). Similarly, the AUC of the risk score for 1-, 2-, 3-year was 0.780, 0.744 and 0.723, respectively (Figure 2G) and this result also suggest the good reliability of the risk signature to estimate the prognosis of HCC patients.\u003c/p\u003e\n\u003cp\u003eTable 1. Construction of prognostic lncRNAs signature based on 7 FRLncs.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eLncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003ecoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003eHR.95Cl(lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003eHR.95Cl(upper)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eLINC00942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.01156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e1.01163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.00095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e1.02242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eAC131009.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.28379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e1.32816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e0.98413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e1.79246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003ePOLH-AS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.73160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e2.07840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.25538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e3.44098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eAC090772.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.24224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e1.27410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.04791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e1.54911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eMKLN1-AS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.76621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e2.15160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.14054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e4.05894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eAC009403.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.43958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e1.55205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.16676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e2.06459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eAL031985.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003e0.30853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.90235081374322%\" valign=\"top\"\u003e\n \u003cp\u003e1.36143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.423146473779386%\" valign=\"top\"\u003e\n \u003cp\u003e1.07637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.965641952983724%\" valign=\"top\"\u003e\n \u003cp\u003e1.72198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Construction of prognostic lncRNAs signature based on 7 FRLncs. 7 FRLncs were filtered to construct a prognostic lncRNAs signature on the basis of multivariable Cox regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of the risk signature as the independent prognostic factor and the construction of the nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the univariate (Figure 3A) and multivariate cox regression analysis (Figure 3B), the stage, HBV status and the risk score were selected to be the independent prognostic factors. The relationship between clinical variables and risk score was also evaluated which suggested that the risk scores showed significant differences in different stage and grade groups (Figure 3C, D). Based on the median value of each FRLncs in the risk signature calculated by the SPSS (Supplementary Table 5), the patients were correspondingly divided into high-expression and low-expression group. On the foundation of grouping, the Kaplan\u0026ndash;Meier curves was plotted to display the difference between the high- and low-expression group. And the relevance between the selected seven FRLncs and grade, stage, survival state and risk were analyzed as well. According to the K\u0026ndash;M curves, each FRLnc was able to predict the prognosis of HCC patients and the prognosis was poor in the high-expression group (p\u0026lt;0.05). In addition to the LINC00942, AC090772.3 and AC009403.1 with stage and AC090772.3 with grade, others exhibited obvious correlation with grade, stage, survival state and risk (Figure 3E-K). A nomogram consisted of age, stage, gender, grade and risk score was established (Figure 4A). It could intuitively display the survival probability of 1-, 3-, and 5-year. To further verify reliability of the nomogram, the AUC of each ROC for 1-, 3-, and 5-year was computed and the calculations was 0.755, 0.753 and 0.736, respectively (Figure 4B) in HCC patients. The calibration curve was also depicted and presented good accuracy of nomogram for prediction (Figure 4C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA), Enrichment Analysis of DEGs and Co-Expression Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the GSEA, the biological functions between the high- and low-risk groups showed obvious difference. The base excision repair, oocyte meiosis, RNA degradation, spliceosome and ubiquitin mediated proteolysis were enriched in the high-risk group (Figure 5A-E). Simultaneously, the complement and coagulation cascades, fatty acid metabolism, primary bile acid biosynthesis, tryptophan metabolism, and valine leucine and isoleucine degradation were enriched in the low-risk group (Figure 5F-J). According to the difference analysis, there were 141 genes screened differently expressed between the normal and tumor tissue. Afterwards, the GO and KEGG analysis were performed. Speaking of biological process, the DEGs were mainly concentrated in the cellular response to chemical stress, response to oxidative stress, response to nutrient levels, cellular response to oxidative stress,\u0026nbsp;fatty acid metabolic process, response to metal ion,\u0026nbsp;response to oxygen levels and others. As to cellular component, the DEGs were enriched in the lipid droplet, focal adhesion and so on. For molecular function, the DEGs were grouped in the oxidoreductase activity, acting on NAD(P)H, dioxygenase activity as well as iron ion binding (Figure 5K). In the KEGG enrichment analysis, the DEGs were significantly enriched in the thermogenesis, AGE-RAGE signaling pathway in diabetic complications, ferroptosis, etc (Figure 5L). As for the co-expression network, the Cytoscape was adopted to visualize the outcome which could display the connections and mechanisms linking prognosis-related FRLnc and related mRNAs obviously (Figure 5M).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmation of the reliability of the risk signature for predicting the prognosis of HBV-HCC patients.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of HBV-HCC patients was extracted from HCC TAGA database. The risk signature and nomogram were verified in the HBV-HCC patients. As to the HBV-HCC patients, patients in the low-risk group displayed a better overall survival (OS) compared with the patients in the high-risk group (Figure 6A). And in the group of age\u0026lt;55, G1-G3group, M0-M1 group, N0 group, stage I-II group, and T1-2 group, patients in the low-risk group also showed a better OS in comparison with that in the high-risk group (Figure 6B-G). Even though compared to the stage (AUC=0.715), the risk score (AUC=0.703) seemed not perfect enough, its long-term prediction effect is satisfactory (Figure 6H). And the AUC of the risk score for 1-, 3-, 5-year was 0.703, 0.839 and 0.767, respectively and this data prompt the good reliability of the risk score to evaluate the prognosis of HBV-HCC patients (Figure 6I). The univariate and multivariate cox regression analysis hinted that in HBV-HCC patients, the stage and risk score were the independent prognostic factors (Figure 6J, K). Importantly, the nomogram showed a good practicability in predicting the prognosis of HBV-HCC patients. The AUC of each ROC for 1-, 3-, and 5-year was\u0026nbsp;0.808, 0.777 and 0.730 (Figure 6L) and the calibration curve also revealed good accuracy of nomogram for prediction (Figure 6M).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Microenvironment, Immune Function and Checkpoints in HCC patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stroma score, microenvironment score and relative infiltration abundance of immune cells was calculated through 7 algorithms, for instance, TIMER, CIBERSORT, CIBERSORE-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC. Data analysis showed that there was a significant difference of the immune cells in the low- and high-risk group. For example, the stroma score, endothelial cell, T cell CD4+ Th2 and Hematopoietic stem cell were expressed differently significantly in the high- and low risk group according to the XCELL algorithm in HCC patients(Figure 7A). For HBV-HCC group, endothelial cell, stroma score, Hematopoietic stem cell and microenvironment score displayed marked difference in the high- and low risk group according to the XCELL algorithm (Figure 7B). As to immune function, cytolytic activity, Type-I IFN response and Type-II IFN response were weakened in the high-risk group and others showed no statistical significance in HCC patients (Figure 7C). With respect to the HBV-HCC patients, in addition to the aforementioned immune function, APC (antigen presenting cell) co-inhibition, APC co-stimulation, CCR (chemokine C-C-Motif receptor), check point, HLA (Human Leukocyfe Antigen), inflammation promoting, parainflammation, T cell co-inhibition and T cell co-stimulation were attenuated in the high-risk group (Figure 7D). Given the importance of immune checkpoints in cancer treatment, the expression of checkpoint genes was compared between the two risk groups. We found PDCD1(programmed cell death 1, PD-1) and CTL4 (cytotoxicTlymphocyte-associatedantigen-4) were expressed higher in the high-risk group which meant that immunotherapy may be a promising choice for HCC (Figure 7E). While for the HBV-HCC patients, the common immune checkpoints indicated no obvious differential expression (Figure 7F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCC is one of the leading cause of cancer related mortality in the world with a complex etiology and its limited treatment options(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), thus precise treatment may occupy the considerable position in the treatment of HCC. Therefore, in order to help patients receive individualized and precise treatment, constructing a risk signature which could help to predict the prognosis of HCC seems necessary.\u003c/p\u003e \u003cp\u003eThe growing knowledge of ferroptosis has suggested the role and therapeutic potential of ferroptosis in cancer, but has not been translated into effective therapy(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Available researches have proved that lncRNAs perform a critical regulatory role in ferroptosis- related biological processes in various cancer types(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Relevant studies also have indicated that ferroptosis could be induced by sorafenib. However, a prognostic tool basing on ferroptosis- related lncRNAs for HCC patients is still lacking.\u003c/p\u003e \u003cp\u003eIn the current study, seven ferroptosis related lncRNAs, including LINC00942, AC131009.1, `POLH-AS1`, AC090772.3, MKLN1-AS, AC009403.1, and AL031985.3 were picked out to develop a prognostic signature to predict the OS of HCC. LINC00942 was found to be up-regulated in chemoresistant gastric cancer (GC) cells, and its high expression was positively correlated with the poor prognosis of patients with GC. And functional studies indicated that LINC00942 confers chemoresistance to GC cells by impairing apoptosis and inducing stemness(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Previous researches have proven that AC131009.1 and `POLH-AS1` were associated with necroptosis, suggesting that they play roles in different ways of programmed cell death(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). AC090772.3 was found to be highly expressed in the HCC as a ceRNA related to hypoxia(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and more in-depth research about AC090772.3 is needed in the future. MKLN1-AS was proven up‑regulated in HCC, and mediates the effects of SOX9 on promoting the cell viability, proliferation, invasion and EMT of HCC(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). AL031985.3 was highly expressed in the tumor tissue rather than the non-tumor tissue and it was interrelated with pyroptosis(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Together, these results indicate that the screened lncRNAs participate in the tumor behavior of HCC through multiple pathways. In addition, no study on the biological functions associated with AC009403.1 has been reported, and its molecular mechanism of action in HCC deserves further exploration.\u003c/p\u003e \u003cp\u003eIt is also worth mentioning that TIMER database was employed to proclaim connections between the risk signature and immune infiltration levels in HCC and HBV-HCC. The results showed different types of immune cells in different risk groups through several algorithms. When it comes to immune checkpoints, a currently hot issue, we found several common genes, such as PD-1 and CTL4 was expressed higher in the high-risk group but no common target was found in the HBV-HCC group. Meanwhile, a certain number of studies have confirmed that anti-PD-1 therapy for HBV-HCC patients is plausible(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), so may be more detailed grouping and research needed for us. Anyway, immunotherapy may be a viable choice for HCC patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study constructed an FRLncs-based prognostic signature. The signature stratified the HBV-HCC patients into high- and low-risk group and could predict the prognosis of HBV-HCC excellently. The immune checkpoints such as PD-1 and CTL4 tended to be highly expressed in the high-risk group of HCC patients, which suggested that immunotherapy may be a promising therapy for these patients. A nomogram was constructed based on risk signature and clinical characteristics and displayed good accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA sequencing data with clinical information were acquired from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). The ferroptosis related genes were acquired from the FerrDb V2 (http://www.zhounan.org/ferrdb/current/). The Infiltration Estimation for all TCGA tumors was downloaded from TIMER2.0 (http://timer.cistrome.org/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Foundation of Shandong Province [No. ZR2020QH035 and No. ZR2021QH195]; Medical Health Science and Technology Development Plan Project of Shandong Province [No. 202103030765].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWenwen Wang and Hua Feng contributed to the conception and design of the study. Lifen Wang and Jinhua Hu organized the database and statistical analysis for the manuscript. Wenwen Wang wrote the draft of the manuscript and Chunxia Song revised the manuscript. Tong mu performed visualizations. Hua Feng acted as corresponding author. All the authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank The Cancer Genome Atlas (TCGA), FerrDb V2 and TIMER2.0 database for providing valuable data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: a cancer journal for clinicians. 2022;72(1):7-33.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3):209-49.\u003c/li\u003e\n\u003cli\u003eWei X, Yi X, Zhu XH, Jiang DS. Posttranslational Modifications in Ferroptosis. Oxidative medicine and cellular longevity. 2020;2020:8832043.\u003c/li\u003e\n\u003cli\u003eMa S, Henson ES, Chen Y, Gibson SB. Ferroptosis is induced following siramesine and lapatinib treatment of breast cancer cells. Cell death \u0026amp; disease. 2016;7:e2307.\u003c/li\u003e\n\u003cli\u003eGhoochani A, Hsu EC, Aslan M, Rice MA, Nguyen HM, Brooks JD, et al. Ferroptosis Inducers Are a Novel Therapeutic Approach for Advanced Prostate Cancer. Cancer research. 2021;81(6):1583-94.\u003c/li\u003e\n\u003cli\u003eYao F, Deng Y, Zhao Y, Mei Y, Zhang Y, Liu X, et al. A targetable LIFR-NF-kappaB-LCN2 axis controls liver tumorigenesis and vulnerability to ferroptosis. Nature communications. 2021;12(1):7333.\u003c/li\u003e\n\u003cli\u003eGao R, Kalathur RKR, Coto-Llerena M, Ercan C, Buechel D, Shuang S, et al. YAP/TAZ and ATF4 drive resistance to Sorafenib in hepatocellular carcinoma by preventing ferroptosis. EMBO molecular medicine. 2021;13(12):e14351.\u003c/li\u003e\n\u003cli\u003eZhang H, Deng T, Liu R, Ning T, Yang H, Liu D, et al. CAF secreted miR-522 suppresses ferroptosis and promotes acquired chemo-resistance in gastric cancer. Molecular cancer. 2020;19(1):43.\u003c/li\u003e\n\u003cli\u003eTsagakis I, Douka K, Birds I, Aspden JL. Long non-coding RNAs in development and disease: conservation to mechanisms. The Journal of pathology. 2020;250(5):480-95.\u003c/li\u003e\n\u003cli\u003eSchmitz SU, Grote P, Herrmann BG. Mechanisms of long noncoding RNA function in development and disease. Cellular and molecular life sciences : CMLS. 2016;73(13):2491-509.\u003c/li\u003e\n\u003cli\u003eHuang Z, Zhou JK, Peng Y, He W, Huang C. The role of long noncoding RNAs in hepatocellular carcinoma. Molecular cancer. 2020;19(1):77.\u003c/li\u003e\n\u003cli\u003eNia A, Dhanasekaran R. Genomic Landscape of HCC. Current hepatology reports. 2020;19(4):448-61.\u003c/li\u003e\n\u003cli\u003eWoo Y, Lee HJ, Jung YM, Jung YJ. Regulated Necrotic Cell Death in Alternative Tumor Therapeutic Strategies. Cells. 2020;9(12).\u003c/li\u003e\n\u003cli\u003eZhu Y, Zhou B, Hu X, Ying S, Zhou Q, Xu W, et al. LncRNA LINC00942 promotes chemoresistance in gastric cancer by suppressing MSI2 degradation to enhance c-Myc mRNA stability. Clinical and translational medicine. 2022;12(1):e703.\u003c/li\u003e\n\u003cli\u003eWang W, Ye Y, Zhang X, Ye X, Liu C, Bao L. Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma. Frontiers in molecular biosciences. 2022;9:937979.\u003c/li\u003e\n\u003cli\u003eTang Y, Zhang H, Chen L, Zhang T, Xu N, Huang Z. Identification of Hypoxia-Related Prognostic Signature and Competing Endogenous RNA Regulatory Axes in Hepatocellular Carcinoma. International journal of molecular sciences. 2022;23(21).\u003c/li\u003e\n\u003cli\u003eGuo C, Zhou S, Yi W, Yang P, Li O, Liu J, et al. SOX9/MKLN1-AS Axis Induces Hepatocellular Carcinoma Proliferation and Epithelial-Mesenchymal Transition. Biochemical genetics. 2022;60(6):1914-33.\u003c/li\u003e\n\u003cli\u003eWu ZH, Li ZW, Yang DL, Liu J. Development and Validation of a Pyroptosis-Related Long Non-coding RNA Signature for Hepatocellular Carcinoma. Frontiers in cell and developmental biology. 2021;9:713925.\u003c/li\u003e\n\u003cli\u003eZhou ZY, Liu SR, Xu LB, Liu C, Zhang R. Clinicopathological and Prognostic Value of Programmed Cell Death 1 Expression in Hepatitis B Virus-related Hepatocellular Carcinoma: A Meta-analysis. Journal of clinical and translational hepatology. 2021;9(6):889-97. \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":"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":"HBV-related HCC, ferroptosis related LncRNAs, TCGA, Prognostic signature, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-2974952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2974952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFerroptosis play crucial roles in cancer. Many lncRNAs were expressed aberrantly and regulated the tumorigenesis and progression of HCC. But the roles of ferroptosis related lncRNAs (FRLncs) in HBV-related HCC (HBV-HCC) remain ambiguous.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) database was applied to achieve gene expression profile and clinical data. The risk signature was constructed by FRLncs based on the univariate and multivariable cox regression analysis. The survival curve, cox regression analysis and time-dependent receiver operating characteristic (ROC) curve was adopted to verify the independence and reliability of the signature. A nomogram was established. Immune infiltrating cells, immune functions and check points were also analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA risk signature composed of 7 FRLncs (LINC00942, AC131009.1, POLH-AS1, AC090772.3, MKLN1-AS, AC009403.1, AL031985.3) was constructed. The signature divided HBV-HCC patients into high- and low-risk groups. Patients in high-risk group showed a poor prognosis. Even though compared to the stage (areas under curves (AUC)\u0026thinsp;=\u0026thinsp;0.715), the risk score (AUC\u0026thinsp;=\u0026thinsp;0.703) seemed not perfect enough, its long-term prediction effect is satisfactory. And the AUC of the risk score for 1-, 3-, 5-year was 0.703, 0.839 and 0.767, respectively. A nomogram composed of gender, stage, age, grade, and risk signature was established. The risk signature and nomogram displayed appreciable independence and reliability in HBV-HCC patients. The endothelial cell, stroma score, hematopoietic stem cell and microenvironment score were expressed differently significantly in the high- and low-risk group according to the XCELL algorithm. PDCD1 and CTL4 were expressed higher in the high-risk group of HCC patients, but no common target was found in the HBV-HCC group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA 7-lncRNA signature was identified as a potential prognostic predictor for HBV-HCC patients. The current study may help provide new ideas for individualized and precise treatment of HCC, especially HBV-HCC.\u003c/p\u003e","manuscriptTitle":"Prognostic signature constructed of seven ferroptosis related lncRNAs predicts the prognosis of HBV related HCC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-02 14:44:22","doi":"10.21203/rs.3.rs-2974952/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":"b268c757-7cfd-4092-a0bf-5a31b4d2682c","owner":[],"postedDate":"June 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-15T08:38:51+00:00","versionOfRecord":{"articleIdentity":"rs-2974952","link":"https://doi.org/10.1007/s12029-023-00977-6","journal":{"identity":"journal-of-gastrointestinal-cancer","isVorOnly":false,"title":"Journal of Gastrointestinal Cancer"},"publishedOn":"2023-11-25 08:38:50","publishedOnDateReadable":"November 25th, 2023"},"versionCreatedAt":"2023-06-02 14:44:22","video":"","vorDoi":"10.1007/s12029-023-00977-6","vorDoiUrl":"https://doi.org/10.1007/s12029-023-00977-6","workflowStages":[]},"version":"v1","identity":"rs-2974952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2974952","identity":"rs-2974952","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}