Potential Role of Immune-Related lncRNAs in Prognosis of Hepatocellular Carcinoma: An Integrative Study

Potential Role of Immune-Related lncRNAs in Prognosis of Hepatocellular Carcinoma: An Integrative Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Potential Role of Immune-Related lncRNAs in Prognosis of Hepatocellular Carcinoma: An Integrative Study Peidong Miao, Chunxia Pan, Tianyu Li, Ying Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035692/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Hepatocellular carcinoma (HCC) represents a significant global health concern with persistently high incidence and mortality rates. Immune-related long non-coding RNAs (lncRNAs) may play crucial roles in the pathogenesis and progression of HCC, yet their precise mechanisms remain incompletely elucidated. Objective This study aims to explore the potential roles of immune-related lncRNAs in HCC patients through systematic biological approaches, integrating clinical data with bioinformatics analysis, and to construct a COX regression model for predicting patient survival. Methods The HCC dataset from The Cancer Genome Atlas (TCGA) was utilized as the study cohort. Immune-related mRNA and lncRNA data were extracted and screened for their association with HCC patient survival using Weighted Gene Co-expression Network Analysis (WGCNA) algorithm and COX regression method. A COX regression model was subsequently established and validated. Results Our investigation revealed that a COX regression model comprising a group of immune-related lncRNAs and mRNAs could accurately predict patient survival in HCC. Specific analyses indicated the pivotal roles of these RNAs in the occurrence and progression of HCC, particularly in immune regulation. Conclusions The findings of this study underscore the critical role of immune-related lncRNAs and mRNAs in the prognosis of HCC patients, suggesting their potential as prognostic factors. This discovery provides important insights into the immune modulation mechanisms of HCC, offering novel avenues and methods for personalized therapy and prognostic assessment of HCC. Hepatocellular carcinoma (liver cancer HCC) Long non-coding RNA (lncRNA) Immune regulation Survival prognosis Bioinformatics analysis TCGA database (LIHC) Cox regression model Immunotherapy Drug sensitivity Immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatocellular carcinoma (HCC) represents a significant global health burden, contributing to a substantial number of cancer-related deaths [1-3]. Extensive research has demonstrated the pivotal role of the tumor immune microenvironment in the progression and treatment response of HCC [4, 5]. Immune cells infiltrating the tumor microenvironment are recognized as crucial factors shaping the tumor immune landscape and influencing patient prognosis [6, 7]. In recent years, advancements in cancer immunotherapy have underscored the importance of understanding the immune-related molecular mechanisms in HCC [8, 9]. Long non-coding RNAs (lncRNAs) are among the key regulatory factors of gene expression and biological processes [10]. Dysregulated lncRNAs have been implicated in the occurrence, development, and metastasis of various cancers, including HCC [11, 12]. However, the specific roles of immune-related lncRNAs in HCC remain to be further elucidated. Therefore, exploring the interactions between lncRNAs, mRNAs, immune cells, and patient prognosis could provide valuable insights into the pathogenesis of HCC and potential therapeutic targets. The primary objective of this study is to identify immune-related lncRNAs associated with the survival of HCC patients and construct a lncRNA+mRNA survival model relevant to liver cancer immunity. This model serves as a tool for predicting the prognosis of HCC patients and guiding personalized treatment decisions. Furthermore, the correlation between the model and the tumor immune microenvironment will be investigated, along with the potential of the model as a predictive biomarker for drug sensitivity in HCC patients. It is hoped that through our research, a deeper understanding of the functions and roles of immune-related lncRNAs and mRNAs in HCC will be attained, thus providing novel therapeutic targets for HCC immunotherapy. Methods Data Acquisition The data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) LIHC dataset. This dataset comprised clinical and transcriptomic data from 377 patients with liver cancer. All data were sourced from publicly available published datasets, thus ethical approval was not required. A total of 2483 immune-related genes used in this study were obtained from the Immunology Database and Analysis Portal website (ImmPort)(RRID:SCR_012804)(Supplementary Tab.S1, https://www.immport.org/home)[13]. Analysis Platform The analyses were conducted using R language (Version: 4.3.0) within Rstudio (Version: 2023.12.1+402). The main workflow of this study is illustrated in Fig. 1. WGCNA and Univariate COX Regression for Immune-Related mRNA Selection Initially, the expression matrix of 2483 immune-related genes was extracted. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) (RRID:SCR_003302) algorithm was employed to identify mRNA modules associated with survival (including survival time and status) (P < 0.05) [14]. Further, using the survival package, the univariate Cox regression method was utilized to screen for mRNA associated with survival (p < 0.05) [15]. Screening of Immune-Related lncRNAs and Establishment of Survival Model Through correlation analysis, lncRNAs correlated with these mRNA (tool: cor.test function. Screening criteria: p 0.4) were identified. Subsequently, through the univariate Cox regression method, lncRNAs associated with survival were further screened (p < 0.05). After merging them with the immune-related mRNA identified in the previous step, variables were further screened through lasso regression, followed by the establishment of a COX regression model using the glmnet package [16]. Using the createDataPartition function from the caret package, all patients were randomly divided into training and testing sets at a 1:1 ratio[17].This immune-related mRNA+lncRNA model was employed to score all patients in the LIHC dataset, obtaining a riskscore. By incorporating riskscore and other clinical phenotypes, a nomogram predicting the survival of liver cancer patients was constructed using the regplot package [18]. Relationship between Model Scores and Other Phenotypes Using the ggpubr package, the relationship between model scores and several common clinical phenotypes was explored through boxplots. Using the estimate package, the immune microenvironment of all patients in the LIHC dataset was predicted [19]. Using the CIBERSORT package (RRID:SCR_016955), the content of 22 immune cells in all patients was predicted [20]. Via the TIDE website (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu/), we evaluated the potential of tumor immune escape from the gene expression profiles of LIHC samples [21]. Using the oncoPredict package, sensitivity to 197 drugs was predicted for all samples [22]. By analyzing the relationship between riskscore and these phenotypes, we attempted to investigate the role of inflammation-related lncRNAs in tumor immunity of liver cancer patients and provide directions for drug therapy for liver cancer patients. Additionally, we analyzed the relationship between riskscore and tumor stem cells of HCC patients (The tumor stem cell attributes of patients in the TCGA-LIHC cohort were extracted from "StemnessScores_RNAexp_20170127.2.tsv"). Differential Gene Expression Analysis between High and Low Model Score Groups Finally, through the LIMMA package (RRID:SCR_010943), we analyzed differentially expressed genes between samples with high and low riskscores (|logFC| >= 1 and fdr < 0.05) [23]. Utilizing tools such as clusterProfiler (RRID:SCR_016884), org.Hs.eg.db, enrichplot, and ggplot2 (RRID:SCR_014601), we examined the enrichment of these differential genes in which functional pathways, aiming to understand the biological roles of inflammation-related lncRNAs in liver cancer patients [24-27]. Results Selection of Immune-Related mRNA and Identification of Associated lncRNA The WGCNA algorithm identified a total of 2 mRNA modules associated with survival (Fig. 2 A), encompassing 547 mRNAs (Supplementary Tab.S2, S3). Univariate COX regression revealed that 71 mRNAs were significantly associated with survival (Supplementary Tab.S4). Subsequently, through correlation analysis, a total of 748 lncRNAs correlated with these 71 mRNAs were identified. Among them, 84 lncRNAs were found to be associated with survival (Supplementary Fig. S1 ). COX Regression Model All LIHC samples were randomly divided into training and validation sets in a 1:1 ratio. In the training set, the LASSO regression algorithm was employed to select 14 RNAs from the pool of 84 lncRNAs + 71 mRNAs, establishing the COX regression model (Fig. 2 B, C). Among these, 8 were lncRNAs: HHLA3, AC007405.3, LINC01232, AC124798.1, AC090152.1, LNCSRLR, MSC-AS1, PDXDC2P-NPIPB14P, and 6 were mRNAs: PSMC6, CSPG5, GALP, NRG4, STC2, FGF9, with their coefficients depicted in Fig. 2 D (gene names: red represents mRNA, black represents lncRNA). Based on model scores, the training set, validation set, and all samples were divided into high-risk and low-risk groups. Significant differences in the expression levels of these 14 RNAs between high and low-risk groups were observed in both the training set, validation set, and all samples (Fig. 2 E, F, G. *p < 0.05; **p < 0.01; ***p < 0.001). We predicted patient survival using our model and plotted ROC curves. We found that the AUC reached 0.827 in the training set, 0.665 in the validation set, and 0.757 in all patients (Fig. 2 H, I, J). Independent prognostic tests were conducted using child_pugh, AFP, stage, and riskScore in the validation set, validation set, and full dataset, respectively. The results showed that only the P-value of riskScore was 1, ranging from approximately 1.3 to 1.7. This indicates that for liver cancer patients, riskscore is an independent high-risk factor that can effectively predict patient survival time, independent of factors such as child_pugh score, AFP value, tumor stage, etc. (Fig. 2 K, L, M, N, O, P). However, there was no significant difference in tumor mutation burden between high and low-risk groups (p > 0.05, Fig. 2 Q). The waterfall plot showing the gene mutation frequency between high and low-risk groups is presented in Fig. 2 R, S. Nomogram After numerous iterations, we established a nomogram for predicting the survival of liver cancer patients using G grade, vascular_tumor_cell_type, child_pugh classification, TNM stage, and riskscore (Fig. 3 A). Analysis revealed that the c-index of this nomogram was 0.714, indicating its relatively accurate prediction of LIHC patient survival (Fig. 3 B). Furthermore, by plotting 1-year, 3-year, and 5-year decision curve analysis (DCA) curves, we found that riskscore consistently exhibited the largest area under the curve (AUC) among all phenotypes, indicating the greatest clinical decision net benefit (Fig. 3 C, D, E). This suggests that riskscore is better at guiding clinical decisions compared to other clinical phenotypes. Relationship between Model Scores and Other Clinical Phenotypes Through boxplot analysis, we investigated the relationship between riskscore and gender, person_neoplasm_cancer_status, relative_family_cancer_history, G grade, vascular_tumor_cell_type, child_pugh, AFP, TNM stage, T stage, N stage, M stage, and riskscore among all patients (Supplementary Fig. S2). It was found that riskscore was closely associated with G grade (p < 0.05) (Fig. 3 F). Specifically, there were differences in riskscore between G1 and G2, G3, G4 patients (p 0.05). When combining samples from G2, G3, G4 stages, the difference in riskscore between these stages and G1 stage was significant (p = 0.0004) (Fig. 3 G). In stage I patients, there was a difference in survival between high and low-risk groups, with the low-risk group having longer survival than the high-risk group (p 0.05) (Fig. 3 I, J, K). In G2 and G3 stage patients, there was a difference in survival between high and low-risk groups, with the low-risk group having longer survival than the high-risk group (p 0.05) (Fig. 3 L, O). Regarding survival, significant differences were observed between high and low-risk groups in the training set, validation set, and all samples, with the low-risk group having longer survival (p < 0.05). Relationship between Model Scores and Tumor Immunity Analysis of estimate results revealed that riskscore was positively correlated with ImmuneScore (p 0.05) (Fig. 4 B, C). Analysis of CIBERSORT results showed that riskscore was negatively correlated with B cells naive and Monocytes content (p < 0.05, median values of immune cell content in low-risk samples were higher than those in the high-risk group), positively correlated with Macrophages M0 (p < 0.05, median values of immune cell content in low-risk samples were lower than those in the high-risk group), and unrelated to other immune cells (Fig. 4 D). Among them, the content of Macrophages M0 was associated with the survival of liver cancer patients in the LIHC dataset (Fig. 4 E). Furthermore, riskscore was significantly positively correlated with CD274 (R = 0.23, p = 1e − 05) (Fig. 4 F). We also found that riskscore was positively correlated with the hepatocellular carcinoma patient liver tumor stem cell index RNAss (R = 0.11, p = 0.03) (Fig. 4 G). Relationship between Model Scores and Treatment Analysis of TIDE website results revealed that the high-risk group of riskscore may potentially benefit more from immunotherapy (p = 0.017) (Fig. 4 H). In the results from oncoPredict, we found that there were differences in the efficacy of LIHC samples for 110 drugs between the high and low riskscore groups (p < 0.05) (Supplementary Tab.S5). Among them, the low-risk group showed higher sensitivity to 44 drugs, including Dasatinib, Taselisib, Pictilisib, Vinorelbine, BMS-536924, MK-8776, ABT737, Staurosporine, Ipatasertib, Afatinib, VX-11e, Gefitinib, Crizotinib, AZD7762, Osimertinib, 5-Fluorouracil, ULK1_4989, Savolitinib, Cediranib, YK-4-279, Foretinib, Alpelisib, GNE-317, BDP-00009066, BPD-00008900, AZD4547, PD173074, Erlotinib, GDC0810, Fulvestrant, Lapatinib, I-BRD9, LCL161, AMG-319, MIRA-1, Docetaxel, IWP-2, Sapitinib, XAV939, Temozolomide, CZC24832, and Paclitaxel. Conversely, the high-risk group exhibited higher sensitivity to 66 other drugs, including EPZ5676, AZD1208, Picolinici-acid, SB216763, Tamoxifen, Palbociclib, Leflunomide, NU7441, OSI-027, Olaparib, P22077, RO-3306, Niraparib, Linsitinib, IRAK4_4710, ML323, Entospletinib, PRIMA-1MET, LY2109761, Axitinib, Vorinostat, AZD1332, GSK1904529A, JAK1_8709, PF-4708671, Oxaliplatin, GSK269962A, AGI-6780, SB505124, Nelarabine, AZ6102, IGF1R_3801, AZD5991, JQ1, Sinularin, LGK974, Wnt-C59, Mirin, Sorafenib, PLX-4720, OF-1, Nutlin-3a, (-), JAK_8517, Dactolisib, PCI-34051, VSP34_8731, BMS-345541, Fludarabine, BMS-754807, TAF1_5496, Irinotecan, Cytarabine, Obatoclax, Mesylate, Elephantin, ERK_2440, BI-2536, AZD5438, KRAS (G12C), Inhibitor-12, Dihydrorotenone, Topotecan, Gemcitabine, Teniposide, Sabutoclax, Eg5_9814, Camptothecin, and Luminespib. Differentially Expressed Genes between High and Low Riskscore Groups A total of 661 genes were found to be differentially expressed between the high and low riskscore groups (filtering criteria: logFC > = 1, fdr < 0.05) (Tab.S6, Fig. 4 I). Among them, 25 genes were downregulated in the high-risk group (LOGFC 0). In the enrichment analysis, the top 10 GO terms with the smallest p.adjust were found to be myeloid leukocyte migration, neutrophil migration, granulocyte migration, granulocyte chemotaxis, neutrophil chemotaxis, cell chemotaxis, leukocyte migration, leukocyte chemotaxis, IgG binding, and leukocyte cell-cell adhesion (Supplementary Tab.S7, Fig. 4 J). The top 10 KEGG terms with the smallest p.adjust were identified as Hematopoietic cell lineage, Leishmaniasis, Osteoclast differentiation, Phagosome, Viral protein interaction with cytokine and cytokine receptor, Cytokine-cytokine receptor interaction, Rheumatoid arthritis, Tuberculosis, Staphylococcus aureus infection, and Cell adhesion molecules (Supplementary Tab.S8, Fig. 4 K). In the GSEA analysis, the top 5 enriched KEGG pathways in the high-risk group were cell adhesion molecules cams, cytokine cytokine receptor interaction, hematopoietic cell lineage, intestinal immune network for IgA production, and neuroactive ligand receptor interaction (Fig. 4 L). The top 5 enriched KEGG pathways in the low-risk group were drug metabolism cytochrome p450, fatty acid metabolism, primary bile acid biosynthesis, proximal tubule bicarbonate reclamation, and retinol metabolism (Fig. 4 M). Complete results can be found in Supplementary Tab.S9. Discussion A systems biology approach, coupled with clinical data and bioinformatics analyses, was utilized in our study to investigate the potential role of immune-related lncRNAs in hepatocellular carcinoma (LIHC) patients. Immune-related lncRNAs and mRNAs associated with the survival of LIHC patients were identified through screening processes. Subsequently, a COX regression model was established. Our research findings indicate that these lncRNAs serve not only as prognostic factors for survival but also harbor significant functional roles in immune regulation[28, 29]. COX Regression Model Comprising Immune-Related lncRNAs and mRNAs In our study, the COX regression model consisted of 8 lncRNAs and 6 mRNAs, which demonstrated robust survival predictive performance in both the validation and entire datasets. This underscores the significant roles played by these immune-related lncRNAs and mRNAs in the prognostication of hepatocellular carcinoma patients' survival. We observed a study published in 2021 that similarly utilized the TCGA LIHC dataset and employed bioinformatics analyses to identify immune-related lncRNAs and construct survival models [30]. In comparison to this study, our research incorporated immune-related mRNAs. From the obtained results, the consistency of our model was notably stronger (Fig. 5). Additionally, our study provided a more comprehensive analysis of the relationship between the model and various phenotypes of HCC patients. Leveraging the plethora of emerging bioinformatics tools in recent years, we could accurately predict information such as the immune microenvironment, immune cell infiltration, response to immune therapy, and sensitivity to various chemotherapy drugs in samples from the LIHC dataset. By analyzing the relationship between these pieces of information and model scores, we gained insights into the roles and impacts of immune-related lncRNAs and mRNAs across multiple phenotypes of HCC. These advancements lend greater significance to our study. Among the eight lncRNAs constituting our model, HHLA3 has not previously been implicated in HCC. HHLA3, namely the ANKRD13C-DT gene, has not yet been cataloged in the Genecards database with any specific associated diseases. According to data retrieved from PubMed, its primary association lies within the tumor immunology of lung adenocarcinoma, breast cancer, renal carcinoma, and oral squamous cell carcinoma [31-34]. Previous studies have experimentally validated the role of LINC01232 in HCC progression through in vitro loss-of-function assays. They observed that LINC01232 is upregulated in HCC tissues and cell lines, and its high expression correlates with poor overall survival in HCC patients [35], consistent with our findings. Additionally, experiments suggest its potential involvement in immune evasion in gliomas by downregulating surface MHC-I expression [36], and in promoting pancreatic cancer metastasis through inhibiting ubiquitin-mediated degradation of HNRNPA2B1 and activating the MAPK/ERK signaling pathway induced by A-Raf [37]. However, its specific role in hepatocellular carcinoma requires further elucidation. Two publications regarding AC124798 can be found in the PubMed database [38, 39]. Research indicates that in HCC patients, AC124798 upregulates TOP2A expression by inhibiting miR-139-5p. TOP2A is closely associated with tumor-infiltrating immune cells (TIICs) and immune checkpoints [39]. Furthermore, bioinformatics analysis suggests a positive correlation between AC124798 and the survival of breast cancer patients (patients with higher AC124798 levels exhibit longer survival times) [40], contradicting our analysis results (in our study, the coefficient of AC124798 in the COX model was >0, yet higher model scores were associated with shorter survival times). Such discrepancies may stem from differences in tumor types or from variations in data and analytical methods, necessitating further investigation for clarification. AC090152 has been found to correlate with the survival of HCC patients [41]. In another study on clear cell renal cell carcinoma, AC090152 was associated with immune subtypes of renal cancer [42]. However, both studies did not delve deeply into the specific role of AC090152 in disease onset and progression, indicating the need for further research to elucidate these matters. LNCSRLR's significant role in hepatocellular carcinoma has been reported in multiple studies [43, 44]. In a study on ferroptosis in HCC patients, LNCSRLR was found to stimulate the growth, proliferation, migration, and invasion of HCC cell lines [43], with its biological mechanisms closely tied to ferroptosis [44, 45]. MSC-AS1's role in hepatocellular carcinoma has been extensively reported and will be omitted here. According to the NCBI Gene database, PDXDC2P-NPIPB14P represents two pseudogenes, which may not encode functional proteins (https://www.ncbi.nlm.nih.gov/gene/283970#summary). However, the notion that pseudogenes do not transcribe proteins is increasingly being challenged by numerous studies [46]. Furthermore, some studies indicate that pseudogenes of lncRNAs may play significant roles in the onset and progression of tumors [47]. In this study, the coefficient of PDXDC2P-NPIPB14P is -0.434, the only negative value among all model genes, suggesting a protective role in HCC patients. Moreover, it has the second-largest absolute value, exerting a significant impact on our model. Therefore, we hypothesize that PDXDC2P-NPIPB14P plays a crucial, albeit overlooked, role in the immune response of hepatocellular carcinoma, warranting further investigation. Relationship between riskscore and clinical phenotype of hepatocellular carcinoma The Cox regression model is a commonly used survival analysis method that evaluates the relationship between multiple factors and survival time to determine their prognostic significance. Our model can effectively predict the survival risk of patients with HCC and demonstrates good generalization ability. Compared to traditional clinical indicators, our riskscore exhibits independence and superiority in predicting the survival of HCC patients. In both univariate and multivariate survival analyses, riskscore shows significant prognostic effects, indicating its high potential for clinical application. Particularly noteworthy is that riskscore can predict patient survival independently and is not influenced by traditional clinical indicators such as Child-Pugh score, AFP value, and tumor stage. This suggests that riskscore may provide new reference points for personalized treatment and clinical management of HCC patients. We further analyzed the relationship between riskscore and the immune status of HCC patients. Through analysis of immune cell infiltration and expression of immune-related genes, we found a close association between riskscore and the immune status of HCC patients. Specifically, riskscore is associated with immune cell infiltration, expression of immune-related genes, and the prognosis of immunotherapy in HCC patients. This indicates that riskscore may serve as an important indicator for assessing the efficacy and prognosis of immunotherapy in HCC patients. Functional analysis of differentially expressed genes between high and low riskscore groups In this study, a higher riskscore signifies more active immune activity, which correlates negatively with the survival of HCC patients (Fig. 3P, Q, R). By identifying differentially expressed genes between high and low riskscore groups and analyzing their functions, we can understand which immune activities are associated with the survival of HCC patients. In Gene Set Enrichment Analysis (GSEA), the top five upregulated pathways are as follows: 1. Cell Adhesion Molecules (CAMs) are glycoproteins expressed on the cell surface that play crucial roles in cell adhesion and interactions between cells. They are involved in various biological processes such as hemostasis, immune responses, inflammation, embryonic development, and neural tissue development [48]. Relevance to immunity: Cell adhesion molecules play important roles in interactions between immune cells, including antigen recognition, co-stimulation, and cell adhesion [49]. CAMs facilitate the adhesion of tumor cells to surrounding tissues, vascular endothelial cells, and other cells. In HCC, CAMs influence the adhesion, invasion, and metastasis capabilities of tumor cells [50, 51]. 2. Cytokine_cytokine receptor interaction, involving the interaction between cytokines and their receptors. Cytokines are a class of protein molecules produced by various cells that regulate and transmit signals in cell communication [52]. Cytokine receptors are proteins located on the cell surface that bind to cytokines, triggering intracellular signaling pathways and inducing cellular responses. Cytokines regulate the activity, proliferation, and differentiation of immune cells by binding to receptors on the surface of immune cells [53]. For example, interleukins play roles in the interaction between T cells, B cells, and other immune cells. Interferons enhance antiviral immune responses and inhibit viral replication. Tumor necrosis factors participate in inflammation and cell apoptosis [54]. The cytokine-cytokine receptor interaction pathway is also important in the development and treatment of HCC. Some cytokines may promote tumor growth, invasion, and metastasis, while others may have inhibitory effects on tumors [55]. Researchers are exploring the regulation of cytokines and receptors to develop novel treatments for HCC [56]. 3. Hematopoietic Cell Lineage pathway, referring to the process of differentiation of various types of blood cells derived from hematopoietic stem cells (HSCs). As one of the main components of blood cells, the close relationship between immune cells and this pathway is evident. The main immune cells in the liver include Kupffer cells and intrahepatic lymphocytes. Although the liver is not a major site of immune cell production, intrahepatic immune cells are mainly responsible for clearing microorganisms, cell debris, and other foreign substances from the blood in the liver [57]. Currently, there is no direct evidence linking the Hematopoietic Cell Lineage pathway to HCC. 4. Intestinal Immune Network for IgA Production pathway.The intestine is the largest lymphoid tissue in the body. A notable feature of intestinal immunity is its ability to produce large amounts of non-inflammatory immunoglobulin A (IgA) antibodies as the first line of defense against microorganisms [58]. Various cytokines, including TGF-β, IL-10, IL-4, IL-5, and IL-6, are required to promote IgA class switching and terminal differentiation of B cells [59]. Additionally, secreted IgA traps dietary antigens and microorganisms in mucus, mediating immune exclusion and neutralizing toxins and pathogenic microorganisms [60]. IgA is involved in the development and progression of liver cancer through various mechanisms [61]. For example, IgA secreted in the intestine regulates the gut microbiota, and some components of the gut microbiota can reach the liver through the portal system, enhancing inflammation, fibrosis, and carcinogenesis [62, 63]. Liver IgA+ cells directly inhibit CTL activation, which in turn inhibits the development of liver cancer [61]. 5. Neuroactive ligand-receptor interaction pathway is a classical signaling pathway related to neural development, playing a crucial role in the early stages of neural development. It involves various neurotransmitters, neuromodulators, and their respective receptors [64]. Some neurotransmitters also play important roles in immune cells. For example, neurotransmitters such as dopamine, norepinephrine, and serotonin can affect the activity and function of immune cells [65]. At the same time, immune cells (such as macrophages, lymphocytes) also express various types of neurotransmitter receptors. These receptors play important roles in regulating the migration, activation, and function of immune cells [66]. Some studies suggest that neurotransmitters and their receptors play a role in the development and progression of liver cancer. For example, dopamine locally increases in HCC, promoting the proliferation and metastasis of HCC cells [67]. Relationship between riskscore and phenotypes predicted by some bioinformatics tools Furthermore, our study also revealed associations between riskscore and the tumor biological characteristics and treatment sensitivity of patients with HCC. The high-risk group defined by riskscore exhibited distinct features in terms of tumor mutation burden, tumor stem cell index, and drug efficacy compared to the low-risk group. These findings provide new clues for further understanding the tumor biological characteristics of HCC patients and personalized treatment. Conclusion In summary, our study elucidated the significant roles of immune-related lncRNAs and mRNAs in the development of hepatocellular carcinoma and established a reliable survival prediction model. These results uncover the underlying mechanisms of immune cell heterogeneity in HCC, providing new insights and methods for the personalized treatment and clinical management of HCC patients. Limitations of this study As a study in the field of bioinformatics analysis, our research inherently has limitations. Merely performing data calculations does not equate to the actual situation within organisms. Sometimes, results may be unrelated to reality or even contradictory. Therefore, further biological experiments are needed to validate our conclusions. Additionally, this study has some limitations, such as a small sample size and being a single-center study, thus necessitating further large-sample, multi-center studies in the future to validate our findings. Abbreviations TCGA The Cancer Genome Atlas LIHC Liver Hepatocellular Carcinoma WGCNA Weighted Gene Co-expression Network Analysis mRNA Messenger Ribonucleic Acid lncRNA Long Non-Coding Ribonucleic Acid DCA Decision curve analysis AFP Alpha-Fetoprotein HCC Hepatocellular Carcinoma IgA Immunoglobulin A TGF Transforming Growth Factor IL Interleukin Declarations Acknowledgement The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga Declaration of conflict of interest : The authors declare no potential conflicts of interest. Ethical approval This study is based on publicly available data, ethical approval is not required. Funding Declaration No funding was received for this study. Consent to Participate declaration Not applicable. Consent to Publish Declaration All authors have given their consent to publish the results of this study. Authors’ contributions Peidong Miao and Chunxia Pan are co-first authors of this paper. Specifically, the research was conducted based on the research framework proposed by Chunxia Pan, with Peidong Miao completing the experimental implementation and data collection. During manuscript preparation, Peidong Miao was responsible for writing the Methods and Results sections, while the Discussion section was primarily contributed by Chunxia Pan. Tianyu Li assists in writing; Ying Li reviews. All authors approved the final manuscript. References Zhou M, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58. Li J, et al. 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Supplementary Files Supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Aug, 2025 Reviews received at journal 02 Aug, 2025 Reviewers agreed at journal 02 Aug, 2025 Editor assigned by journal 02 Aug, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers invited by journal 19 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 15 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7035692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486838796,"identity":"f4633625-ea44-4125-beee-738ff14cdb61","order_by":0,"name":"Peidong Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYHACgwMfftjw8PM3EK/F8ODMnjQZyRkHiNdifJiH7bCNQUMCkep1ZzdvOMDDc57HgOEA44ePOURoMbtzrOCAhMVtHnPmBmbJmduI0XIjx+CAAc9tHsuGA2zMvERrSWA7xwMiSdBygO0AKVqAfjnY2JPMIznjYDORfrndvPnznx929vz8zQc/fCRGC4MEnMXYQIx6FC2jYBSMglEwCnAAAC6JPBcdOCY5AAAAAElFTkSuQmCC","orcid":"","institution":"Dalian NO.3 People`s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Peidong","middleName":"","lastName":"Miao","suffix":""},{"id":486838797,"identity":"35bf40fd-38d1-4a27-beb9-65f109693d84","order_by":1,"name":"Chunxia Pan","email":"","orcid":"","institution":"Dalian NO.3 People`s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Pan","suffix":""},{"id":486838798,"identity":"58af198c-6a3d-4318-adb8-d3cc0d5d3e25","order_by":2,"name":"Tianyu Li","email":"","orcid":"","institution":"Dalian NO.3 People`s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianyu","middleName":"","lastName":"Li","suffix":""},{"id":486838799,"identity":"52929acf-3544-4333-971d-946666074122","order_by":3,"name":"Ying Li","email":"","orcid":"","institution":"Dalian 7th People`s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-03 08:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035692/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87042213,"identity":"54f6ba06-3a44-4dda-b313-72091232032f","added_by":"auto","created_at":"2025-07-18 14:09:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61923,"visible":true,"origin":"","legend":"\u003cp\u003eRoadmap of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/0441ba402d838c44bea4817d.png"},{"id":87042218,"identity":"75a64878-b002-4eec-89da-6ab7bc6fdb4a","added_by":"auto","created_at":"2025-07-18 14:09:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e. Results of WGCNA calculation regarding gene modules associated with survival (survival time and survival status). Among the four gene modules, only the yellow and blue modules were significantly correlated with survival time (p \u0026lt; 0.05). \u003cstrong\u003e(B\u003c/strong\u003e, \u003cstrong\u003eC)\u003c/strong\u003e. Lasso regression was employed to screen variables from 84 lncRNAs and 71 mRNAs, followed by the construction of a COX regression model. \u003cstrong\u003e(D)\u003c/strong\u003e. Bar plot displaying the coefficients of the 14 RNAs comprising the model. On the y-axis, black font indicates lncRNAs, while red font indicates mRNAs. The x-axis represents coefficient values: yellow bars denote coefficients \u0026gt; 0, and blue bars denote coefficients \u0026lt; 0. \u003cstrong\u003e(E, F, G)\u003c/strong\u003e. Differential expression of the 14 model-constituting RNAs between high-risk and low-risk groups in the entire dataset (E), training set (F), and validation set (G). \u003cstrong\u003e(H, I, J)\u003c/strong\u003e. ROC curves predicting patient survival in the entire dataset (H), training set (I), and validation set (J). \u003cstrong\u003e(K, L, M)\u003c/strong\u003e. Univariate COX regression forest plots of child pugh classification, AFP, Stage, and riskscore in the entire dataset (K), training set (L), and validation set (M). \u003cstrong\u003e(N, O, P)\u003c/strong\u003e. Multivariate COX regression forest plots of child pugh classification, AFP, Stage, and riskscore in the entire dataset (N), training set (O), and validation set (P). \u003cstrong\u003e(Q)\u003c/strong\u003e. Differential TMB between high-risk and low-risk groups in all LIHC patients. \u003cstrong\u003e(R, S)\u003c/strong\u003e. The waterfall plot illustrates gene mutation frequency between high (R) and low-risk (S) groups. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/38f79a6da3aef868e7047e2a.png"},{"id":87042220,"identity":"21390482-a11b-4564-a2cb-55fd5d1fd220","added_by":"auto","created_at":"2025-07-18 14:09:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e A nomogram predicting the survival of liver cancer patients based on risk score, child pugh classification, G grade, TNM stage, and vascular tumor cell type. \u003cstrong\u003e(B)\u003c/strong\u003eConsistency curves of the nomogram predicting 1-year (green line), 3-year (blue line), and 5-year (red line) survival of LIHC patients. \u003cstrong\u003e(C, D, E)\u003c/strong\u003eClinical decision curves of the nomogram, risk score, child pugh classification, G grade, TNM stage, and vascular tumor cell type. \u003cstrong\u003e(F, G)\u003c/strong\u003eBoxplots illustrating the relationship between risk score and G grade. In Figure F, the risk score of G1 patients is significantly lower than that of G2, G3, and G4 patients (P \u0026lt; 0.05), while there is no significant difference in riskscore among G2, G3, and G4 patients (P \u0026gt; 0.05). Figure G re-demonstrates this result after combining G2, G3, and G4 patients. \u003cstrong\u003e(H, I, J, K)\u003c/strong\u003eSurvival differences between high and low-risk score groups in Stage I, Stage II, Stage III, and Stage IV patients, respectively. Among Stage I patients, those with low risk scores have better survival than those with high risk scores (P \u0026lt; 0.05), while there is no significant difference in the remaining three stages (P \u0026gt; 0.05). \u003cstrong\u003e(L, M, N, O)\u003c/strong\u003e Survival differences between high and low-risk score groups in G1, G2, G3, and G4 patients, respectively. Among G2 and G3 patients, those with low risk scores have better survival than those with high risk scores (P \u0026lt; 0.05), while there is no significant difference in G1 and G4 patients (P \u0026gt; 0.05). (\u003cstrong\u003eP, Q, R)\u003c/strong\u003e Significant survival differences between high and low-risk score groups are observed in the entire dataset (P), training set (Q), and validation set (R) (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/eafb1ca97120e503b3f87002.png"},{"id":87045772,"identity":"cac068aa-15d5-4b90-86ea-a14ae0abfe86","added_by":"auto","created_at":"2025-07-18 14:33:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":255544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A, B, C)\u003c/strong\u003eDifferences in tumor microenvironment between high and low-risk score groups in the LIHC dataset. Specifically, the immuneScore of the low-risk score group is significantly lower than that of the high-risk group (P \u0026lt; 0.05), while ESTIMATEScore and StromalScore show no significant differences (P \u0026gt; 0.05). \u003cstrong\u003e(D)\u003c/strong\u003eBoxplot depicting differences in the abundance of 23 immune cell types predicted by CIBERSORT between high and low-risk score groups of LIHC patients. \u003cstrong\u003e(E)\u003c/strong\u003e Kaplan-Meier curves demonstrate that patients with lower Macrophage0 cell abundance have longer survival times compared to those with higher abundance (P \u0026lt; 0.05). \u003cstrong\u003e(F)\u003c/strong\u003e Scatter plot illustrating a positive correlation between risk score and the expression of the CD274 gene in LIHC patients (P \u0026lt; 0.05, R \u0026gt; 0). \u003cstrong\u003e(G)\u003c/strong\u003e Scatter plot showing a positive correlation between risk score and the RNAss index of hepatic cancer stem cells in HCC patients (R \u0026gt; 0, p \u0026lt; 0.05). \u003cstrong\u003e(H)\u003c/strong\u003e Violin plot of TIDE scores between high and low-risk score groups of patients. Patients in the high-risk score group are more likely to benefit from immunotherapy. \u003cstrong\u003e(I)\u003c/strong\u003e Volcano plot displaying differentially expressed genes between high and low-risk score groups. \u003cstrong\u003e(J)\u003c/strong\u003e Bubble plot presenting the top 10 GO terms with the smallest adjusted p-values enriched in differentially expressed genes. \u003cstrong\u003e(K)\u003c/strong\u003e Bar plot illustrating the top 10 KEGG terms with the smallest adjusted p-values enriched in differentially expressed genes. \u003cstrong\u003e(L)\u003c/strong\u003e Enrichment plots of the top 5 KEGG pathways enriched in the high-risk group in GSEA analysis. \u003cstrong\u003e(M)\u003c/strong\u003eEnrichment plots of the top 5 KEGG pathways enriched in the low-risk group in GSEA analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/2c9ff98c37040be32d707684.png"},{"id":87042221,"identity":"c39d9ff6-c08f-4a35-a882-c748243192fa","added_by":"auto","created_at":"2025-07-18 14:09:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16438,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the C-index curves of the two models. As shown in the figure, the C-index curve of our model (red) is significantly higher than that of Xu's model (green), indicating that the consistency of our model is superior to Xu's model.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/1bf4b0476e2ddac6f01d3c2c.png"},{"id":87045774,"identity":"73f10f8e-3db1-4e3b-8f14-cc0f81dfc060","added_by":"auto","created_at":"2025-07-18 14:33:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/2c825b18-27e4-447e-af80-36422be49ec7.pdf"},{"id":87043563,"identity":"24ba509b-7c7f-411f-a8bd-3f719d066517","added_by":"auto","created_at":"2025-07-18 14:17:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2026117,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035692/v1/4437f48c85889fc1337ca9e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential Role of Immune-Related lncRNAs in Prognosis of Hepatocellular Carcinoma: An Integrative Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) represents a significant global health burden, contributing to a substantial number of cancer-related deaths [1-3]. Extensive research has demonstrated the pivotal role of the tumor immune microenvironment in the progression and treatment response of HCC [4, 5]. Immune cells infiltrating the tumor microenvironment are recognized as crucial factors shaping the tumor immune landscape and influencing patient prognosis [6, 7]. In recent years, advancements in cancer immunotherapy have underscored the importance of understanding the immune-related molecular mechanisms in HCC [8, 9].\u003c/p\u003e\n\u003cp\u003eLong non-coding RNAs (lncRNAs) are among the key regulatory factors of gene expression and biological processes [10]. Dysregulated lncRNAs have been implicated in the occurrence, development, and metastasis of various cancers, including HCC [11, 12]. However, the specific roles of immune-related lncRNAs in HCC remain to be further elucidated. Therefore, exploring the interactions between lncRNAs, mRNAs, immune cells, and patient prognosis could provide valuable insights into the pathogenesis of HCC and potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003eThe primary objective of this study is to identify immune-related lncRNAs associated with the survival of HCC patients and construct a lncRNA+mRNA survival model relevant to liver cancer immunity. This model serves as a tool for predicting the prognosis of HCC patients and guiding personalized treatment decisions. Furthermore, the correlation between the model and the tumor immune microenvironment will be investigated, along with the potential of the model as a predictive biomarker for drug sensitivity in HCC patients. It is hoped that through our research, a deeper understanding of the functions and roles of immune-related lncRNAs and mRNAs in HCC will be attained, thus providing novel therapeutic targets for HCC immunotherapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eData Acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) LIHC dataset. This dataset comprised clinical and transcriptomic data from 377 patients with liver cancer. All data were sourced from publicly available published datasets, thus ethical approval was not required. A total of 2483 immune-related genes used in this study were obtained from the Immunology Database and Analysis Portal website (ImmPort)(RRID:SCR_012804)(Supplementary Tab.S1, https://www.immport.org/home)[13].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis Platform\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe analyses were conducted using R language (Version: 4.3.0) within Rstudio (Version: 2023.12.1+402). The main workflow of this study is illustrated in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWGCNA and Univariate COX Regression for Immune-Related mRNA Selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInitially, the expression matrix of 2483 immune-related genes was extracted. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) (RRID:SCR_003302) algorithm was employed to identify mRNA modules associated with survival (including survival time and status) (P \u0026lt; 0.05)\u0026nbsp;[14]. Further, using the survival package, the univariate Cox regression method was utilized to screen for mRNA associated with survival (p \u0026lt; 0.05)\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreening of Immune-Related lncRNAs and Establishment of Survival Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThrough correlation analysis, lncRNAs correlated with these mRNA (tool: cor.test function. Screening criteria: p \u0026lt; 0.001 and absolute value of correlation coefficient \u0026gt; 0.4) were identified. Subsequently, through the univariate Cox regression method, lncRNAs associated with survival were further screened (p \u0026lt; 0.05). After merging them with the immune-related mRNA identified in the previous step, variables were further screened through lasso regression, followed by the establishment of a COX regression model using the glmnet package\u0026nbsp;[16]. Using the createDataPartition function from the caret package, all patients were randomly divided into training and testing sets at a 1:1 ratio[17].This immune-related mRNA+lncRNA model was employed to score all patients in the LIHC dataset, obtaining a riskscore. By incorporating riskscore and other clinical phenotypes, a nomogram predicting the survival of liver cancer patients was constructed using the regplot package\u0026nbsp;[18].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelationship between Model Scores and Other Phenotypes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the ggpubr package, the relationship between model scores and several common clinical phenotypes was explored through boxplots. Using the estimate package, the immune microenvironment of all patients in the LIHC dataset was predicted\u0026nbsp;[19]. Using the CIBERSORT package (RRID:SCR_016955), the content of 22 immune cells in all patients was predicted\u0026nbsp;[20]. Via the TIDE website (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu/), we evaluated the potential of tumor immune escape from the gene expression profiles of LIHC samples\u0026nbsp;[21]. Using the oncoPredict package, sensitivity to 197 drugs was predicted for all samples [22]. By analyzing the relationship between riskscore and these phenotypes, we attempted to investigate the role of inflammation-related lncRNAs in tumor immunity of liver cancer patients and provide directions for drug therapy for liver cancer patients. Additionally, we analyzed the relationship between riskscore and tumor stem cells of HCC patients (The tumor stem cell attributes of patients in the TCGA-LIHC cohort were extracted from \u0026quot;StemnessScores_RNAexp_20170127.2.tsv\u0026quot;).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferential Gene Expression Analysis between High and Low Model Score Groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinally, through the LIMMA package (RRID:SCR_010943), we analyzed differentially expressed genes between samples with high and low riskscores (|logFC| \u0026gt;= 1 and fdr \u0026lt; 0.05) [23]. Utilizing tools such as clusterProfiler (RRID:SCR_016884), org.Hs.eg.db, enrichplot, and ggplot2 (RRID:SCR_014601), we examined the enrichment of these differential genes in which functional pathways, aiming to understand the biological roles of inflammation-related lncRNAs in liver cancer patients [24-27].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSelection of Immune-Related mRNA and Identification of Associated lncRNA\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe WGCNA algorithm identified a total of 2 mRNA modules associated with survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), encompassing 547 mRNAs (Supplementary Tab.S2, S3). Univariate COX regression revealed that 71 mRNAs were significantly associated with survival (Supplementary Tab.S4). Subsequently, through correlation analysis, a total of 748 lncRNAs correlated with these 71 mRNAs were identified. Among them, 84 lncRNAs were found to be associated with survival (Supplementary Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCOX Regression Model\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll LIHC samples were randomly divided into training and validation sets in a 1:1 ratio. In the training set, the LASSO regression algorithm was employed to select 14 RNAs from the pool of 84 lncRNAs\u0026thinsp;+\u0026thinsp;71 mRNAs, establishing the COX regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). Among these, 8 were lncRNAs: HHLA3, AC007405.3, LINC01232, AC124798.1, AC090152.1, LNCSRLR, MSC-AS1, PDXDC2P-NPIPB14P, and 6 were mRNAs: PSMC6, CSPG5, GALP, NRG4, STC2, FGF9, with their coefficients depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD (gene names: red represents mRNA, black represents lncRNA). Based on model scores, the training set, validation set, and all samples were divided into high-risk and low-risk groups. Significant differences in the expression levels of these 14 RNAs between high and low-risk groups were observed in both the training set, validation set, and all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F, G. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eWe predicted patient survival using our model and plotted ROC curves. We found that the AUC reached 0.827 in the training set, 0.665 in the validation set, and 0.757 in all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, I, J).\u003c/p\u003e\u003cp\u003eIndependent prognostic tests were conducted using child_pugh, AFP, stage, and riskScore in the validation set, validation set, and full dataset, respectively. The results showed that only the P-value of riskScore was \u0026lt;\u0026thinsp;0.05 in all datasets, whether in univariate or multivariate independent prognostic tests. Moreover, the Hazard ratio was \u0026gt;\u0026thinsp;1, ranging from approximately 1.3 to 1.7. This indicates that for liver cancer patients, riskscore is an independent high-risk factor that can effectively predict patient survival time, independent of factors such as child_pugh score, AFP value, tumor stage, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK, L, M, N, O, P).\u003c/p\u003e\u003cp\u003eHowever, there was no significant difference in tumor mutation burden between high and low-risk groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eQ). The waterfall plot showing the gene mutation frequency between high and low-risk groups is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eR, S.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNomogram\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAfter numerous iterations, we established a nomogram for predicting the survival of liver cancer patients using G grade, vascular_tumor_cell_type, child_pugh classification, TNM stage, and riskscore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Analysis revealed that the c-index of this nomogram was 0.714, indicating its relatively accurate prediction of LIHC patient survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, by plotting 1-year, 3-year, and 5-year decision curve analysis (DCA) curves, we found that riskscore consistently exhibited the largest area under the curve (AUC) among all phenotypes, indicating the greatest clinical decision net benefit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D, E). This suggests that riskscore is better at guiding clinical decisions compared to other clinical phenotypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRelationship between Model Scores and Other Clinical Phenotypes\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThrough boxplot analysis, we investigated the relationship between riskscore and gender, person_neoplasm_cancer_status, relative_family_cancer_history, G grade, vascular_tumor_cell_type, child_pugh, AFP, TNM stage, T stage, N stage, M stage, and riskscore among all patients (Supplementary Fig. S2). It was found that riskscore was closely associated with G grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Specifically, there were differences in riskscore between G1 and G2, G3, G4 patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there were no differences in riskscore among G2, G3, G4 patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). When combining samples from G2, G3, G4 stages, the difference in riskscore between these stages and G1 stage was significant (p\u0026thinsp;=\u0026thinsp;0.0004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). In stage I patients, there was a difference in survival between high and low-risk groups, with the low-risk group having longer survival than the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). This phenomenon was not observed in other stages (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, J, K). In G2 and G3 stage patients, there was a difference in survival between high and low-risk groups, with the low-risk group having longer survival than the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eM, N). However, this difference was not observed in other G grade patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL, O).\u003c/p\u003e\u003cp\u003eRegarding survival, significant differences were observed between high and low-risk groups in the training set, validation set, and all samples, with the low-risk group having longer survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cem\u003eRelationship between Model Scores and Tumor Immunity\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAnalysis of estimate results revealed that riskscore was positively correlated with ImmuneScore (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), but not correlated with ESTIMATEScore and StromalScore (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of CIBERSORT results showed that riskscore was negatively correlated with B cells naive and Monocytes content (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, median values of immune cell content in low-risk samples were higher than those in the high-risk group), positively correlated with Macrophages M0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, median values of immune cell content in low-risk samples were lower than those in the high-risk group), and unrelated to other immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Among them, the content of Macrophages M0 was associated with the survival of liver cancer patients in the LIHC dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eFurthermore, riskscore was significantly positively correlated with CD274 (R\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eWe also found that riskscore was positively correlated with the hepatocellular carcinoma patient liver tumor stem cell index RNAss (R\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003cem\u003eRelationship between Model Scores and Treatment\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAnalysis of TIDE website results revealed that the high-risk group of riskscore may potentially benefit more from immunotherapy (p\u0026thinsp;=\u0026thinsp;0.017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eIn the results from oncoPredict, we found that there were differences in the efficacy of LIHC samples for 110 drugs between the high and low riskscore groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Tab.S5). Among them, the low-risk group showed higher sensitivity to 44 drugs, including Dasatinib, Taselisib, Pictilisib, Vinorelbine, BMS-536924, MK-8776, ABT737, Staurosporine, Ipatasertib, Afatinib, VX-11e, Gefitinib, Crizotinib, AZD7762, Osimertinib, 5-Fluorouracil, ULK1_4989, Savolitinib, Cediranib, YK-4-279, Foretinib, Alpelisib, GNE-317, BDP-00009066, BPD-00008900, AZD4547, PD173074, Erlotinib, GDC0810, Fulvestrant, Lapatinib, I-BRD9, LCL161, AMG-319, MIRA-1, Docetaxel, IWP-2, Sapitinib, XAV939, Temozolomide, CZC24832, and Paclitaxel. Conversely, the high-risk group exhibited higher sensitivity to 66 other drugs, including EPZ5676, AZD1208, Picolinici-acid, SB216763, Tamoxifen, Palbociclib, Leflunomide, NU7441, OSI-027, Olaparib, P22077, RO-3306, Niraparib, Linsitinib, IRAK4_4710, ML323, Entospletinib, PRIMA-1MET, LY2109761, Axitinib, Vorinostat, AZD1332, GSK1904529A, JAK1_8709, PF-4708671, Oxaliplatin, GSK269962A, AGI-6780, SB505124, Nelarabine, AZ6102, IGF1R_3801, AZD5991, JQ1, Sinularin, LGK974, Wnt-C59, Mirin, Sorafenib, PLX-4720, OF-1, Nutlin-3a, (-), JAK_8517, Dactolisib, PCI-34051, VSP34_8731, BMS-345541, Fludarabine, BMS-754807, TAF1_5496, Irinotecan, Cytarabine, Obatoclax, Mesylate, Elephantin, ERK_2440, BI-2536, AZD5438, KRAS (G12C), Inhibitor-12, Dihydrorotenone, Topotecan, Gemcitabine, Teniposide, Sabutoclax, Eg5_9814, Camptothecin, and Luminespib.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDifferentially Expressed Genes between High and Low Riskscore Groups\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA total of 661 genes were found to be differentially expressed between the high and low riskscore groups (filtering criteria: logFC\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1, fdr\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Tab.S6, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Among them, 25 genes were downregulated in the high-risk group (LOGFC\u0026thinsp;\u0026lt;\u0026thinsp;0), while the remaining 636 genes were upregulated in the high-risk group (LOGFC\u0026thinsp;\u0026gt;\u0026thinsp;0).\u003c/p\u003e\u003cp\u003eIn the enrichment analysis, the top 10 GO terms with the smallest p.adjust were found to be myeloid leukocyte migration, neutrophil migration, granulocyte migration, granulocyte chemotaxis, neutrophil chemotaxis, cell chemotaxis, leukocyte migration, leukocyte chemotaxis, IgG binding, and leukocyte cell-cell adhesion (Supplementary Tab.S7, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). The top 10 KEGG terms with the smallest p.adjust were identified as Hematopoietic cell lineage, Leishmaniasis, Osteoclast differentiation, Phagosome, Viral protein interaction with cytokine and cytokine receptor, Cytokine-cytokine receptor interaction, Rheumatoid arthritis, Tuberculosis, Staphylococcus aureus infection, and Cell adhesion molecules (Supplementary Tab.S8, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK).\u003c/p\u003e\u003cp\u003eIn the GSEA analysis, the top 5 enriched KEGG pathways in the high-risk group were cell adhesion molecules cams, cytokine cytokine receptor interaction, hematopoietic cell lineage, intestinal immune network for IgA production, and neuroactive ligand receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL). The top 5 enriched KEGG pathways in the low-risk group were drug metabolism cytochrome p450, fatty acid metabolism, primary bile acid biosynthesis, proximal tubule bicarbonate reclamation, and retinol metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eM). Complete results can be found in Supplementary Tab.S9.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA systems biology approach, coupled with clinical data and bioinformatics analyses, was utilized in our study to investigate the potential role of immune-related lncRNAs in hepatocellular carcinoma (LIHC) patients. Immune-related lncRNAs and mRNAs associated with the survival of LIHC patients were identified through screening processes. Subsequently, a COX regression model was established. Our research findings indicate that these lncRNAs serve not only as prognostic factors for survival but also harbor significant functional roles in immune regulation[28, 29].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOX Regression Model Comprising Immune-Related lncRNAs and mRNAs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, the COX regression model consisted of 8 lncRNAs and 6 mRNAs, which demonstrated robust survival predictive performance in both the validation and entire datasets. This underscores the significant roles played by these immune-related lncRNAs and mRNAs in the prognostication of hepatocellular carcinoma patients' survival.\u003c/p\u003e\n\u003cp\u003eWe observed a study published in 2021 that similarly utilized the TCGA LIHC dataset and employed bioinformatics analyses to identify immune-related lncRNAs and construct survival models [30]. In comparison to this study, our research incorporated immune-related mRNAs. From the obtained results, the consistency of our model was notably stronger (Fig. 5). Additionally, our study provided a more comprehensive analysis of the relationship between the model and various phenotypes of HCC patients. Leveraging the plethora of emerging bioinformatics tools in recent years, we could accurately predict information such as the immune microenvironment, immune cell infiltration, response to immune therapy, and sensitivity to various chemotherapy drugs in samples from the LIHC dataset. By analyzing the relationship between these pieces of information and model scores, we gained insights into the roles and impacts of immune-related lncRNAs and mRNAs across multiple phenotypes of HCC. These advancements lend greater significance to our study.\u003c/p\u003e\n\u003cp\u003eAmong the eight lncRNAs constituting our model, HHLA3 has not previously been implicated in HCC. HHLA3, namely the ANKRD13C-DT gene, has not yet been cataloged in the Genecards database with any specific associated diseases. According to data retrieved from PubMed, its primary association lies within the tumor immunology of lung adenocarcinoma, breast cancer, renal carcinoma, and oral squamous cell carcinoma [31-34].\u003c/p\u003e\n\u003cp\u003ePrevious studies have experimentally validated the role of LINC01232 in HCC progression through in vitro loss-of-function assays. They observed that LINC01232 is upregulated in HCC tissues and cell lines, and its high expression correlates with poor overall survival in HCC patients [35], consistent with our findings. Additionally, experiments suggest its potential involvement in immune evasion in gliomas by downregulating surface MHC-I expression [36], and in promoting pancreatic cancer metastasis through inhibiting ubiquitin-mediated degradation of HNRNPA2B1 and activating the MAPK/ERK signaling pathway induced by A-Raf [37]. However, its specific role in hepatocellular carcinoma requires further elucidation.\u003c/p\u003e\n\u003cp\u003eTwo publications regarding AC124798 can be found in the PubMed database [38, 39]. Research indicates that in HCC patients, AC124798 upregulates TOP2A expression by inhibiting miR-139-5p. TOP2A is closely associated with tumor-infiltrating immune cells (TIICs) and immune checkpoints [39]. Furthermore, bioinformatics analysis suggests a positive correlation between AC124798 and the survival of breast cancer patients (patients with higher AC124798 levels exhibit longer survival times) [40], contradicting our analysis results (in our study, the coefficient of AC124798 in the COX model was \u0026gt;0, yet higher model scores were associated with shorter survival times). Such discrepancies may stem from differences in tumor types or from variations in data and analytical methods, necessitating further investigation for clarification.\u003c/p\u003e\n\u003cp\u003eAC090152 has been found to correlate with the survival of HCC patients [41]. In another study on clear cell renal cell carcinoma, AC090152 was associated with immune subtypes of renal cancer [42]. However, both studies did not delve deeply into the specific role of AC090152 in disease onset and progression, indicating the need for further research to elucidate these matters.\u003c/p\u003e\n\u003cp\u003eLNCSRLR's significant role in hepatocellular carcinoma has been reported in multiple studies [43, 44]. In a study on ferroptosis in HCC patients, LNCSRLR was found to stimulate the growth, proliferation, migration, and invasion of HCC cell lines [43], with its biological mechanisms closely tied to ferroptosis [44, 45].\u003c/p\u003e\n\u003cp\u003eMSC-AS1's role in hepatocellular carcinoma has been extensively reported and will be omitted here.\u003c/p\u003e\n\u003cp\u003eAccording to the NCBI Gene database, PDXDC2P-NPIPB14P represents two pseudogenes, which may not encode functional proteins (https://www.ncbi.nlm.nih.gov/gene/283970#summary). However, the notion that pseudogenes do not transcribe proteins is increasingly being challenged by numerous studies [46]. Furthermore, some studies indicate that pseudogenes of lncRNAs may play significant roles in the onset and progression of tumors [47]. In this study, the coefficient of PDXDC2P-NPIPB14P is -0.434, the only negative value among all model genes, suggesting a protective role in HCC patients. Moreover, it has the second-largest absolute value, exerting a significant impact on our model. Therefore, we hypothesize that PDXDC2P-NPIPB14P plays a crucial, albeit overlooked, role in the immune response of hepatocellular carcinoma, warranting further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelationship between riskscore and clinical phenotype of hepatocellular carcinoma\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Cox regression model is a commonly used survival analysis method that evaluates the relationship between multiple factors and survival time to determine their prognostic significance. Our model can effectively predict the survival risk of patients with HCC and demonstrates good generalization ability. Compared to traditional clinical indicators, our riskscore exhibits independence and superiority in predicting the survival of HCC patients. In both univariate and multivariate survival analyses, riskscore shows significant prognostic effects, indicating its high potential for clinical application. Particularly noteworthy is that riskscore can predict patient survival independently and is not influenced by traditional clinical indicators such as Child-Pugh score, AFP value, and tumor stage. This suggests that riskscore may provide new reference points for personalized treatment and clinical management of HCC patients.\u003c/p\u003e\n\u003cp\u003eWe further analyzed the relationship between riskscore and the immune status of HCC patients. Through analysis of immune cell infiltration and expression of immune-related genes, we found a close association between riskscore and the immune status of HCC patients. Specifically, riskscore is associated with immune cell infiltration, expression of immune-related genes, and the prognosis of immunotherapy in HCC patients. This indicates that riskscore may serve as an important indicator for assessing the efficacy and prognosis of immunotherapy in HCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional analysis of differentially expressed genes between high and low riskscore groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a higher riskscore signifies more active immune activity, which correlates negatively with the survival of HCC patients (Fig. 3P, Q, R). By identifying differentially expressed genes between high and low riskscore groups and analyzing their functions, we can understand which immune activities are associated with the survival of HCC patients. In Gene Set Enrichment Analysis (GSEA), the top five upregulated pathways are as follows:\u003c/p\u003e\n\u003cp\u003e1. Cell Adhesion Molecules (CAMs) are glycoproteins expressed on the cell surface that play crucial roles in cell adhesion and interactions between cells. They are involved in various biological processes such as hemostasis, immune responses, inflammation, embryonic development, and neural tissue development [48].\u003c/p\u003e\n\u003cp\u003eRelevance to immunity: Cell adhesion molecules play important roles in interactions between immune cells, including antigen recognition, co-stimulation, and cell adhesion [49]. CAMs facilitate the adhesion of tumor cells to surrounding tissues, vascular endothelial cells, and other cells. In HCC, CAMs influence the adhesion, invasion, and metastasis capabilities of tumor cells [50, 51].\u003c/p\u003e\n\u003cp\u003e2. Cytokine_cytokine receptor interaction, involving the interaction between cytokines and their receptors. Cytokines are a class of protein molecules produced by various cells that regulate and transmit signals in cell communication \u0026nbsp;[52]. Cytokine receptors are proteins located on the cell surface that bind to cytokines, triggering intracellular signaling pathways and inducing cellular responses.\u003c/p\u003e\n\u003cp\u003eCytokines regulate the activity, proliferation, and differentiation of immune cells by binding to receptors on the surface of immune cells [53]. For example, interleukins play roles in the interaction between T cells, B cells, and other immune cells. Interferons enhance antiviral immune responses and inhibit viral replication. Tumor necrosis factors participate in inflammation and cell apoptosis [54].\u003c/p\u003e\n\u003cp\u003eThe cytokine-cytokine receptor interaction pathway is also important in the development and treatment of HCC. Some cytokines may promote tumor growth, invasion, and metastasis, while others may have inhibitory effects on tumors [55]. Researchers are exploring the regulation of cytokines and receptors to develop novel treatments for HCC [56].\u003c/p\u003e\n\u003cp\u003e3. Hematopoietic Cell Lineage pathway, referring to the process of differentiation of various types of blood cells derived from hematopoietic stem cells (HSCs). As one of the main components of blood cells, the close relationship between immune cells and this pathway is evident.\u003c/p\u003e\n\u003cp\u003eThe main immune cells in the liver include Kupffer cells and intrahepatic lymphocytes. Although the liver is not a major site of immune cell production, intrahepatic immune cells are mainly responsible for clearing microorganisms, cell debris, and other foreign substances from the blood in the liver [57]. Currently, there is no direct evidence linking the Hematopoietic Cell Lineage pathway to HCC.\u003c/p\u003e\n\u003cp\u003e4. Intestinal Immune Network for IgA Production pathway.The intestine is the largest lymphoid tissue in the body. A notable feature of intestinal immunity is its ability to produce large amounts of non-inflammatory immunoglobulin A (IgA) antibodies as the first line of defense against microorganisms [58]. Various cytokines, including TGF-β, IL-10, IL-4, IL-5, and IL-6, are required to promote IgA class switching and terminal differentiation of B cells\u0026nbsp;[59]. Additionally, secreted IgA traps dietary antigens and microorganisms in mucus, mediating immune exclusion and neutralizing toxins and pathogenic microorganisms\u0026nbsp;[60].\u003c/p\u003e\n\u003cp\u003eIgA is involved in the development and progression of liver cancer through various mechanisms [61]. For example, IgA secreted in the intestine regulates the gut microbiota, and some components of the gut microbiota can reach the liver through the portal system, enhancing inflammation, fibrosis, and carcinogenesis [62, 63]. Liver IgA+ cells directly inhibit CTL activation, which in turn inhibits the development of liver cancer [61].\u003c/p\u003e\n\u003cp\u003e5. Neuroactive ligand-receptor interaction pathway is a classical signaling pathway related to neural development, playing a crucial role in the early stages of neural development. It involves various neurotransmitters, neuromodulators, and their respective receptors [64]. Some neurotransmitters also play important roles in immune cells. For example, neurotransmitters such as dopamine, norepinephrine, and serotonin can affect the activity and function of immune cells [65]. At the same time, immune cells (such as macrophages, lymphocytes) also express various types of neurotransmitter receptors. These receptors play important roles in regulating the migration, activation, and function of immune cells [66].\u003c/p\u003e\n\u003cp\u003eSome studies suggest that neurotransmitters and their receptors play a role in the development and progression of liver cancer. For example, dopamine locally increases in HCC, promoting the proliferation and metastasis of HCC cells [67].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelationship between riskscore and phenotypes predicted by some bioinformatics tools\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, our study also revealed associations between riskscore and the tumor biological characteristics and treatment sensitivity of patients with HCC. The high-risk group defined by riskscore exhibited distinct features in terms of tumor mutation burden, tumor stem cell index, and drug efficacy compared to the low-risk group. These findings provide new clues for further understanding the tumor biological characteristics of HCC patients and personalized treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study elucidated the significant roles of immune-related lncRNAs and mRNAs in the development of hepatocellular carcinoma and established a reliable survival prediction model. These results uncover the underlying mechanisms of immune cell heterogeneity in HCC, providing new insights and methods for the personalized treatment and clinical management of HCC patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations of this study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs a study in the field of bioinformatics analysis, our research inherently has limitations. Merely performing data calculations does not equate to the actual situation within organisms. Sometimes, results may be unrelated to reality or even contradictory. Therefore, further biological experiments are needed to validate our conclusions. Additionally, this study has some limitations, such as a small sample size and being a single-center study, thus necessitating further large-sample, multi-center studies in the future to validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLiver Hepatocellular Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeighted Gene Co-expression Network Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMessenger Ribonucleic Acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLong Non-Coding Ribonucleic Acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAFP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlpha-Fetoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHepatocellular Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIgA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunoglobulin A\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTransforming Growth Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflict of interest\u003c/strong\u003e: The authors declare no potential conflicts of interest.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThis study is based on publicly available data, ethical approval is not required.\u003c/p\u003e\n\u003cp\u003eFunding Declaration\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Publish Declaration\u003c/p\u003e\n\u003cp\u003eAll authors have given their consent to publish the results of this study.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeidong Miao and Chunxia Pan are co-first authors of this paper. Specifically, the research was conducted based on the research framework proposed by Chunxia Pan, with Peidong Miao completing the experimental implementation and data collection. During manuscript preparation, Peidong Miao was responsible for writing the Methods and Results sections, while the Discussion section was primarily contributed by Chunxia Pan. Tianyu Li assists in writing; Ying Li reviews. All authors approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou M, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, et al. Tumor-associated lymphatic vessel density is a postoperative prognostic biomarker of hepatobiliary cancers: a systematic review and meta-analysis. 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Cancer Commun (Lond). 2020;40(12):694\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma (liver cancer, HCC), Long non-coding RNA (lncRNA), Immune regulation, Survival prognosis, Bioinformatics analysis, TCGA database (LIHC), Cox regression model, Immunotherapy, Drug sensitivity, Immune microenvironment ","lastPublishedDoi":"10.21203/rs.3.rs-7035692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHepatocellular carcinoma (HCC) represents a significant global health concern with persistently high incidence and mortality rates. Immune-related long non-coding RNAs (lncRNAs) may play crucial roles in the pathogenesis and progression of HCC, yet their precise mechanisms remain incompletely elucidated.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eObjective\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to explore the potential roles of immune-related lncRNAs in HCC patients through systematic biological approaches, integrating clinical data with bioinformatics analysis, and to construct a COX regression model for predicting patient survival.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe HCC dataset from The Cancer Genome Atlas (TCGA) was utilized as the study cohort. Immune-related mRNA and lncRNA data were extracted and screened for their association with HCC patient survival using Weighted Gene Co-expression Network Analysis (WGCNA) algorithm and COX regression method. A COX regression model was subsequently established and validated.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur investigation revealed that a COX regression model comprising a group of immune-related lncRNAs and mRNAs could accurately predict patient survival in HCC. Specific analyses indicated the pivotal roles of these RNAs in the occurrence and progression of HCC, particularly in immune regulation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study underscore the critical role of immune-related lncRNAs and mRNAs in the prognosis of HCC patients, suggesting their potential as prognostic factors. This discovery provides important insights into the immune modulation mechanisms of HCC, offering novel avenues and methods for personalized therapy and prognostic assessment of HCC.\u003c/p\u003e","manuscriptTitle":"Potential Role of Immune-Related lncRNAs in Prognosis of Hepatocellular Carcinoma: An Integrative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:09:34","doi":"10.21203/rs.3.rs-7035692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-16T07:03:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-02T07:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325714330035138166150027414150871401732","date":"2025-08-02T07:24:10+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-02T07:14:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T15:54:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208318253790367177296260062253432056733","date":"2025-07-21T15:17:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-19T12:14:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T10:39:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-15T11:07:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"928369d2-ba15-4ed1-9aa7-d6dc3b19ef8f","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T12:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:09:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7035692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035692","identity":"rs-7035692","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}