Identification of palmitoylation-related lncRNAs as prognostic biomarkers and immune modulators in liver cancer

Identification of palmitoylation-related lncRNAs as prognostic biomarkers and immune modulators in liver cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of palmitoylation-related lncRNAs as prognostic biomarkers and immune modulators in liver cancer Yongkang Zou, Xuejun Zhao, Shengpeng Yang, Yan Liu, Shuaimin Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6942496/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) has a high mortality rate. Current immunotherapy and targeted treatments have limited effectiveness. Palmitoylation, a reversible lipid modification, is increasingly recognized for its roles in tumor progression and immune regulation. However, the function of palmitoylation-related long non-coding RNAs (lncRNAs) in HCC remains unclear. Using TCGA data, we identified key palmitoylation-related lncRNAs and developed a prognostic model based on NRAV and AL031985.3. Patients were divided into high- and low-risk groups. Immune cell infiltration, immune checkpoint gene expression, tumor mutation burden (TMB), and drug sensitivity were analyzed. Furthermore, qRT-PCR was performed to validate lncRNA expression in clinical liver tissue samples from healthy organ donors (normal liver) and HCC patients (tumor tissue). The model effectively distinguished survival differences. High-risk patients showed increased Treg cells and immune checkpoint expression, indicating an immunosuppressive phenotype. Functional enrichment revealed associations with cell cycle, immune response, and inflammation pathways. Combining TMB with the risk score improved prognostic accuracy. Both NRAV and AL031985.3 were significantly upregulated in tumor tissues compared to normal liver tissues, confirming their diagnostic and prognostic potential. NRAV and AL031985.3 are promising prognostic biomarkers and immunotherapy targets in HCC. This study provides new insights into the role of palmitoylation-related lncRNAs in liver cancer immune regulation. Hepatocellular carcinoma Long non-coding RNA Palmitoylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Liver cancer is the second-deadliest cancer in the world. Hepatocellular carcinoma (HCC) is the most common type, making up about 90% of all cases 1 . HCC has a high recurrence rate, is often resistant to treatment, and lacks early diagnostic markers 2 . These factors lead to a poor outlook for patients. In recent years, research on the molecular mechanisms of HCC has made progress. However, the main factors driving its development are still not fully understood. There is an urgent need to find new treatment targets and discover which signaling pathways are abnormally regulated. Long non-coding RNAs (lncRNAs) are a new group of molecules that play important roles in regulating biological functions. In the past, they were thought to be useless, but now they are known to help maintain balance in the body, including in liver cancer. Many lncRNAs are linked to diseases, especially cancer development and spread 3 , 4 . Examples include HEPFAL, LINC01089, and HEIH 5 – 7 . In HCC, lncRNAs can affect how cancer cells grow, move, change metabolism, and interact with the immune system 8 . However, we still don’t fully understand how most lncRNAs work. At the same time, protein modifications after translation, like palmitoylation, are getting more attention in cancer research 9 . Palmitoylation adds palmitic acid to certain amino acids (like cysteine) in proteins. This affects where proteins go in the cell, how stable they are, and how they send signals 10 , 11 . Shi et al. found that deleting the lncRNA DUXAP8 enhanced sorafenib-induced ferroptosis in HCC by reducing the palmitoylation of SLC7A11. They suggested that combining sorafenib with DUXAP8 silencing could be a new way to overcome drug resistance and improve treatment outcomes in advanced HCC patients 11 . Similarly, Lin et al. showed that the lncRNA lncZBTB10 increases androgen receptor activity through S-palmitoylation, promoting prostate cancer growth and resistance to abiraterone 12 . These studies highlight the role of lncRNAs in regulating protein palmitoylation, offering new insights into the molecular mechanisms of cancer development. Although protein palmitoylation has long been recognized as a classic post-translational lipid modification, recent studies have highlighted its important role in tumor development and immune regulation 13 . Meanwhile, lncRNAs, as key regulators of gene expression, are closely associated with tumor cell proliferation, metastasis, and drug resistance. In recent years, increasing attention has been given to the potential interaction between lncRNAs and palmitoylation in post-translational regulation. Although many studies have reported the regulatory roles of lncRNAs in HCC, their specific functions in palmitoylation remain unclear. Existing evidence suggests that palmitoylation promotes tumor cell growth and migration plays a crucial role in shaping the tumor immune environment and influencing patient prognosis. Therefore, identifying lncRNAs that regulate palmitoylation may provide new insights into the pathogenesis of HCC and help discover potential diagnostic and therapeutic targets. 2. Materials and Methods 2.1 Expression Analysis of Palmitoylation-Related Genes To explore the expression patterns of palmitoylation-associated genes in liver cancer, we employed the UALCAN platform ( http://ualcan.path.uab.edu/ ), an interactive web tool based on TCGA Level 3 RNA-sequencing data 14 , 15 . This database enables comprehensive transcriptomic analysis across various cancer types, including HCC. Palmitoylation-related gene expression levels were compared between HCC tissues and normal liver samples to identify significant dysregulation potentially linked to tumorigenesis. 2.2 Download and Processing of Transcriptomic, Mutation, and Clinical Data We downloaded RNA sequencing (RNA-seq) data for liver cancer patients from the TCGA 2.0 data portal ( https://portal.gdc.cancer.gov/ ) on February 13, 2025. 371 HCC tissue samples and 50 normal liver tissue samples were included. In addition, the corresponding somatic mutation profiles and clinical data-including overall survival time, survival status, age, gender, tumor grade, clinical stage, and lymph node metastasis status, were also retrieved. The RNA-seq data were normalized and transformed into FPKM format using Strawberry Perl (version 5.30.0, https://www.perl.org ) for subsequent analyses 16 . After collating clinical information, a structured phenotype matrix was created to support integrative clinical and genomic assessments. Based on functional annotations and prior studies, we identified 30 key genes involved in protein palmitoylation, including: ZDHHC1, ZDHHC2, ZDHHC3, ZDHHC4, ZDHHC5, ZDHHC6, ZDHHC7, ZDHHC8, ZDHHC9, ZDHHC11, ZDHHC12, ZDHHC13, ZDHHC14, ZDHHC15, ZDHHC16, ZDHHC17, ZDHHC18, ZDHHC19, ZDHHC20, ZDHHC21, ZDHHC22, ZDHHC23, ZDHHC24, LYPLA1, LYPLA2, PPT1, PPT2, ABHD17A, ABHD17B, and ABHD17C 17 – 19 . The lncRNA annotation file was obtained from the GENCODE database ( https://www.gencodegenes.org/ ) and was used to identify and analyze palmitoylation-related long non-coding RNAs 20 . 2.3 Extraction and Co-Expression Analysis of Palmitoylation-Related lncRNAs To identify lncRNAs potentially associated with the palmitoylation process, expression profiles of both mRNAs and lncRNAs were extracted from the normalized RNA-seq dataset. The future. apply package in R was employed to enable parallel computing and enhance processing efficiency. A curated list of palmitoylation-related genes served as the reference for downstream analyses. Subsequently, co-expression analysis was conducted by assessing the expression correlation between these genes and lncRNAs. Pearson correlation coefficients were computed using the limma package to evaluate potential functional associations 21 . LncRNA–gene pairs with an absolute correlation coefficient (|r|) > 0.6 and a p-value < 0.05 were considered to exhibit statistically significant co-expression relationships. 2.4 Prognostic Model Construction and Validation In this study, the transcriptomic and clinical data of liver cancer patients were retrieved from the TCGA database, and the cohort was randomly divided into training and test sets at a 1:1 ratio using the caret package in R. The training set was utilized to identify palmitoylation-related lncRNAs with prognostic relevance, while the test set and the entire cohort were employed for model validation. Univariate Cox regression analysis was initially performed to screen lncRNAs significantly associated with overall survival, and their prognostic impact was visualized using forest plots. Data visualization was conducted with the assistance of R packages including limma, pheatmap, reshape2, and ggpubr 22 – 24 . For model construction, LASSO-penalized Cox regression was applied using the glmnet package in R to minimize the risk of overfitting. The optimal penalty parameter (λ) was determined via 10-fold cross-validation 25 , 26 . Subsequently, the final prognostic model was established through multivariate Cox regression, and the risk score for each patient was computed according to the following formula: RiskScore=∑ i (Coef(i)×Expr(i)), where Coef(i) represents the regression coefficient, and Expr(i) denotes the normalized expression level of the respective lncRNA 27 , 28 . To validate the model, all patients were classified into high-risk and low-risk groups based on the median risk score derived from the training set. Kaplan–Meier survival analysis, performed using the survival package in R, was employed to compare overall survival (OS) between the two groups. The predictive performance of the model was assessed by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) to evaluate its discriminative ability 29 , 30 . To further confirm the independent prognostic significance of the risk score, both univariate and multivariate Cox regression analyses were conducted. Additionally, the rms, regplot, and survival packages were used to build a nomogram incorporating the risk score and clinical variables, aimed at predicting 1-, 3-, and 5-year overall survival probabilities in liver cancer patients. A calibration curve, generated based on the Hosmer–Lemeshow goodness-of-fit test, was used to assess the consistency between predicted and actual survival outcomes, thereby validating the accuracy and reliability of the nomogram 29 . 2.5 GO and KEGG Enrichment Analysis Based on the palmitoylation-related lncRNA prognostic model, a risk score was calculated for each HCC patient, and individuals were stratified into high-risk and low-risk groups according to the median score. Subsequently, the limma package in R was used to perform differential expression analysis between the two groups to identify significantly differentially expressed genes (DEGs). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the DEGs to explore biological processes, molecular functions, and signaling pathways associated with the palmitoylation-related risk signature 31 – 33 . 2.6 Tumor immune analysis To investigate the relationship between palmitoylation-related lncRNA risk scores and tumor immune cell infiltration, the single-sample gene set enrichment analysis (ssGSEA) algorithm was employed to quantitatively assess the infiltration levels and functional states of immune cells in each HCC sample. A comprehensive immune profiling was conducted by integrating multiple computational tools, including XCELL, TIMER, QUANTISEQ, MCP-counter, EPIC, CIBERSORT-ABS, and CIBERSORT, to systematically evaluate the abundance of various immune cell subsets 34 – 36 . The correlations between immune cell infiltration scores and the palmitoylation-related risk scores were analyzed. Boxplots were generated to visualize differences in immune cell infiltration and immune function between high- and low-risk groups. Additionally, the expression patterns of immune checkpoint genes were examined to assess immune activation levels and immunosuppressive features across different risk subgroups, thereby providing further insights into alterations in the tumor immune microenvironment associated with the palmitoylation-related signature. 2.7 Calculation of tumor mutation burden scores To evaluate the association between palmitoylation-related lncRNA risk scores and tumor mutation characteristics, somatic mutation data of liver cancer patients were retrieved from the TCGA database. Perl scripts were used to extract and preprocess the Mutation Annotation Format (MAF) files, followed by mutation analysis and visualization using the maftools package in R 37 , 38 . Tumor mutation burden (TMB) for each sample was defined as the number of somatic mutations per megabase of the coding genome 39 . Based on the established palmitoylation-related risk model, patients were stratified into high-risk and low-risk groups, and TMB levels were compared between the two subgroups. Furthermore, survival analysis was conducted across different TMB levels to investigate the prognostic implications of TMB within distinct risk categories 40 , 41 . 2.8 Sample Collection, RNA Extraction, and qRT-PCR Analysis Liver cancer tissue samples were obtained from resected surgical specimens of HCC patients at Guizhou Provincial People's Hospital, while normal liver tissues were collected from organ donors. For RNA extraction, both tumor and normal tissues were processed using TRIzol reagent (Invitrogen, USA). Following lysis at room temperature for 10 minutes, chloroform was added, thoroughly mixed, and allowed to separate into phases. The samples were then centrifuged at 12,000 × g for 15 minutes at 4°C. The upper aqueous phase was carefully collected, mixed with an equal volume of isopropanol to precipitate RNA, washed with 75% ethanol, air-dried, and finally resuspended in RNase-free water. The purity and concentration of extracted RNA were assessed using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, USA). First-strand cDNA was synthesized using reverse transcription reagents (Takara, Japan). Quantitative real-time PCR (qRT-PCR) was conducted on a LightCycler 96 system (Roche, USA) using UltraSYBR Mixture (CWBio, China). All experiments were performed in triplicate to ensure reproducibility and accuracy. Additionally, qRT-PCR was used to evaluate the expression levels of palmitoylation-related lncRNAs identified through bioinformatics analysis. Primer sequences used in this study are listed in Supplementary Table 1. 2.9 Hematoxylin and Eosin (H&E) Staining Liver cancer and adjacent normal tissues were fixed in 10% neutral-buffered formalin, followed by dehydration through a graded ethanol series and clearing with xylene. The samples were then embedded in paraffin and sectioned at a thickness of 4 µm. Sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin. After staining, the slides were dehydrated, cleared, and mounted using neutral resin. Histological features were examined under a light microscope. 2.10 Ethical Approval This study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before sample collection. Participant recruitment was carried out between December 17, 2023, and December 17, 2024. Liver cancer tissue samples were collected from patients undergoing surgery at Guizhou Provincial People's Hospital. The normal liver tissues were also obtained from organ donors at Guizhou Provincial People's Hospital. No organs or tissues were obtained from incarcerated individuals. All samples were fully anonymized, and the researchers had no access to any personally identifiable information at any stage of the study. The study protocol was reviewed and approved by the Medical Ethics Committee of Guizhou Provincial People's Hospital. 2.11 Statistical Analysis Statistical analyses were performed using R software (version 4.3.1). A p-value of < 0.05 was considered statistically significant. 3. Results 3.1 Differential Expression and Survival Correlation of Palmitoylation Genes in HCC To systematically evaluate the potential role of palmitoylation-related genes in hepatocellular carcinoma (HCC), we initially analyzed their expression differences between tumor tissues and normal liver tissues using the UALCAN platform, based on RNA sequencing data from the TCGA cohort. As shown in Figure 1 , the vast majority of palmitoylation-related genes were significantly upregulated in HCC tissues, including PPT1, PPT2, LYPLA1, LYPLA2, and most members of the ZDHHC family (e.g., ZDHHC1, ZDHHC3-8, ZDHHC11-18, ZDHHC20-24) (p < 0.001). In contrast, the expression levels of ZDHHC2 and ZDHHC19 did not differ significantly between tumor and normal tissues. Building upon these expression differences, we further performed Kaplan–Meier survival analyses to explore the prognostic relevance of these genes. According to overall survival (OS) data from TCGA samples, patients were classified into high-expression and low-expression groups based on the median expression level of each gene. As shown in Supplementary Figure 1 , high expression of multiple genes (e.g., PPT1, PPT2, ZDHHC3, ZDHHC5, ZDHHC7, ZDHHC9, ZDHHC11, ZDHHC13, ZDHHC14, ZDHHC16, ZDHHC17, ZDHHC20–24, LYPLA1, and LYPLA2) was significantly associated with poor survival outcomes. Conversely, the expression of ZDHHC2 and ZDHHC19 showed no significant association with patient prognosis. 3.2 Identification of Palmitoylation-Related Prognostic lncRNAs and Construction of a Risk Model A total of 244 lncRNAs were identified to be significantly co-expressed with palmitoylation-related genes, among which 203 were differentially expressed between hepatocellular carcinoma (HCC) tissues and normal liver tissues. The distribution of these differentially expressed lncRNAs is depicted in Figure 2A , with most showing significant upregulation in tumor samples. The heatmap illustrates their expression patterns across different samples, revealing a clear clustering trend that distinguishes tumor from normal tissues ( Figure 2B ). Subsequent survival analysis identified several of these lncRNAs as significantly associated with patients’ overall survival (OS), as shown in Figure 2C . Based on this, a prognostic model was constructed, and two key lncRNAs—NRAV and AL031985.3 were ultimately selected. Their hazard ratios and confidence intervals are presented in Figure 2D . The cross-validation results demonstrated the robustness of the model parameters ( Figure 2E ), while Figure 2F displays the trajectories of regression coefficients for each lncRNA under varying penalty parameters. Two palmitoylation-related lncRNAs with the highest prognostic value, NRAV and AL031985.3, were incorporated into a multivariate Cox regression model to construct a risk score system. Patients were stratified into high-risk and low-risk groups based on the median risk score. In the entire TCGA cohort (n = 371), the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group (p < 0.001, Figure 3A ). This trend was consistently observed in both the training set (n = 186, Figure 3B ) and the test set (n = 185, Figure 3C ), demonstrating the robustness and predictive accuracy of the model. The risk score distribution plots ( Figure 3D-F ) revealed a marked increase in scores among high-risk patients. Correspondingly, the survival status plots ( Figure 3G–I ) indicated a significantly higher mortality rate in the high-risk group. Heatmap analysis ( Figure 3J–L ) further showed that NRAV and AL031985.3 were consistently upregulated in high-risk patients, suggesting their potential oncogenic roles in liver cancer progression. 3.3 Construction and Validation of the Nomogram Model To further evaluate the clinical utility of the risk score, we constructed a nomogram model integrating the risk score with clinical characteristics to predict the 1-, 3-, and 5-year overall survival rates of patients with liver cancer ( Figure 4A ). The model contained five variables: age, sex, tumor grade, clinical stage, and risk score. Each variable corresponds to a certain score, and the cumulative total score can be used to estimate the survival probability of patients at different time points. The prediction accuracy of the model was evaluated by a calibration curve. As shown in Figure 4B , the predicted survival closely matched the observed outcomes at 1, 3, and 5 years, demonstrating good calibration. The C-index of the model was 0.683 (95% CI: 0.655–0.712), indicating a reliable discriminative ability for survival prediction. In univariate Cox regression analysis ( Figure 4C ), clinical stage and risk score were significantly associated with OS. Further multivariate Cox regression analysis ( Figure 4D ) confirmed that both the risk score (HR = 1.557, 95% CI: 1.324–1.830) and clinical stage (HR = 1.575, 95% CI: 1.270–1.953) were independent prognostic factors. To evaluate the discriminative power of the model, receiver operating characteristic curves were plotted. The results showed that the predictive efficacy of the risk score variables was better than that of clinical characteristics such as gender, age, grade, and stage, with an AUC value of 0.678 ( Figure 4E ). In the prognosis prediction at different time points, the AUC values were 1 year: 0.731, 3 years: 0.678, 5 years: 0.665 ( Figure 4F ), indicating that the model had good predictive ability in the short and medium term. 3.4 Functional Enrichment Analysis of Differentially Expressed Palmitoylation-Related lncRNAs Based on the constructed risk score model, the differential expression analysis of palmitoylation-related lncRNAs between the high-risk group and the low-risk group was analyzed. The results are shown in Figure 5A , and the volcano plot shows the differentially expressed genes (DEGs), which include up-regulated genes (red) and down-regulated genes (green). Figure 5B shows the expression profiles of these DEGs in all samples, and the heat map reveals that the expression patterns between the high and low risk groups are significantly different, and gene expression shows a clear clustering trend among different samples. Additionally, Gene Ontology (GO) functional enrichment analysis was performed for these differentially expressed genes. As shown in Figure 5C , the enriched entries were mainly distributed in three categories: biological process (BP), cellular component (CC), and molecular function (MF). GO items included extracellular matrix organization, cell structure reconstruction, leukocyte migration, mitotic regulation, and plasma membrane-related structures. Further KEGG pathway enrichment analysis results are shown in Figure 5D , which shows that these genes were significantly enriched in several classical pathways, including PI3K-Akt signaling pathway, cytokine-receptor interaction pathway, cell cycle, cell adhesion molecule pathway, and a variety of viral infection and inflammation-related pathways. The number of enriched genes for each pathway is presented as bubble size, and the color reflects its significance level. 3.5 Examination of immune characteristics in high- and low risk groups Based on the constructed lncRNA risk model of palmitoylation, we further performed a comparative analysis of immune-related characteristics between the high-risk group and the low-risk group. Firstly, in this study, we used GSEA to analyze pathway enrichment based on the risk model constructed from palmitoylation-related lncRNAs. The results showed that the high-risk group was significantly enriched in several classic tumor proliferation and immune-related pathways, such as the cell cycle, cytokine-receptor interaction, ECM-receptor interaction, hematopoietic cell lineage, and neuroactive ligand-receptor interaction. These findings suggest that patients in the high-risk group may have higher tumor growth activity, along with immune imbalance and microenvironmental changes. In contrast, the low-risk group was mainly enriched in metabolism-related pathways, including β-alanine metabolism, butyrate metabolism, glycine/serine/threonine metabolism, linoleic acid metabolism, and primary bile acid biosynthesis. This indicates that these patients may have more active amino acid and lipid metabolism ( Figure 6A-B ). In addition, the abundance of tumor-infiltrating immune cell types was evaluated. As shown in Figure 6C , the high-risk group exhibited significantly higher infiltration of CD8⁺ T cells, Treg cells, macrophages, and dendritic cells (aDCs, iDCs), suggesting an immune-activated yet potentially exhausted phenotype. In contrast, the low-risk group showed higher levels of B cells and neutrophils. As shown in Figure 6D, the high-risk group exhibited significantly higher scores in inflammation-promoting, T cell co-inhibition, MHC class I, and Type II IFN Response, indicating an immunosuppressive and pro-inflammatory tumor microenvironment. In contrast, the low-risk group showed higher activity in cytolytic activity, suggesting a more effective anti-tumor immune response ( Figure 6D ). Additionally, the results of immune checkpoint gene expression analysis are shown in Figure 6E . Several key immune checkpoint molecules were significantly up-regulated in the high-risk group, including PD-1 (PDCD1), CTLA4, LAG3, TIGIT, etc., showing broad differences in the expression of immunosuppression-related molecules between different risk groups. 3.6 Tumor Mutation Landscape and Immune Infiltration Characteristics in Different Risk Groups To evaluate the somatic mutation landscape of liver cancer patients under different risk scores, we used TCGA data to perform mutation profiling on all samples. As shown in Figure 7A-B , TP53 and CTNNB1 were the genes with the highest mutation frequencies in the high and low risk groups, reaching 39% and 23%, respectively. The main mutation type was a missense mutation, which showed a similar mutation pattern in the two groups. TTN, MUC16, RYR2, and other common mutated genes were also detected. Subsequently, to systematically explore the differences in tumor immune microenvironment, we integrated seven immune estimation algorithms, including XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT, to quantify the infiltration level of immune cell subsets in each sample. And the correlation between them and the risk score was analyzed. As shown in Figure 7C , different immune cell types showed diverse risk correlation patterns. For instance, Treg cells, M1 macrophages, and CD8⁺ T cells were positively correlated with risk scores in some algorithms, while neutrophils and B-cell subsets were more correlated with low risk. In the figure, different color codes represent different algorithm sources, and the abscissa is the correlation coefficient, which intuitively reflects the relationship between the risk score and immune cell infiltration. The tumor mutation burden of each patient was calculated and statistically analyzed. The distribution of TMB values between groups is shown in Figure 7D , and the results show that there is no significant difference in TMB levels between high and low risk groups (p = 0.25). The correlation analysis between the risk score and TMB is shown in Figure 7E . The Pearson correlation coefficient is R = 0.064, p = 0.23, indicating no statistically significant association. To assess the impact of TMB on prognosis, patients were divided into high TMB and low TMB groups according to the median TMB value, and survival curves were plotted (Figure 7F ). The overall survival rate of patients in the high TMB group was significantly lower than that in the low TMB group (p = 0.031). Further grouping by combining TMB and risk score ( Figure 7G ) showed that the survival rate of the high TMB + high-risk group was the lowest, while the survival prognosis of the low TMB + low-risk group was the best, and the difference between the groups was statistically significant (p < 0.001). To explore potential treatment implications, we analyzed the predicted drug sensitivity across high- and low-risk groups. As shown in Supplementary Figure 2 , a total of 118 compounds-including AZD5363, Bortezomib, Dasatinib, Camptothecin, Cisplatin, and Cytarabine—exhibited significant differential sensitivity. 3.7 qRT-PCR Validation of Palmitoylation-Related Genes and lncRNAs in Liver Cancer To validate the predicted expression patterns of palmitoylation-related genes and lncRNAs identified by bioinformatics analysis, quantitative real-time PCR (qRT-PCR) was performed on liver tissue samples obtained from HCC patients and healthy individuals. Figure 8A presents H&E staining images showing normal histological architecture in healthy liver tissues and typical malignant features in HCC samples. As shown in Figure 8 B-C , the expression of the lncRNA NRAV was significantly upregulated in HCC tissue, while AL031985.3 exhibited an increasing trend in HCC tissues, the difference was not statistically significant. Among the 30 palmitoylation-related genes analyzed, 12 genes-including ZDHHC3, ZDHHC15, ZDHHC16, ZDHHC19, ZDHHC21, ZDHHC22, PPT1, Abhd17a, Abhd17b, Abhd17c, LYPLA1, and LYPLA2- were significantly upregulated in tumor tissues. These results indicate active involvement of the palmitoylation pathway in HCC. 4. Discussion This study systematically evaluated the prognostic significance and immunoregulatory roles of palmitoylation-related lncRNAs and their role in immune regulation in hepatocellular carcinoma (HCC). By integrating data from the TCGA database with clinical follow-up information, we identified lncRNAs that were significantly co-expressed with palmitoylation regulatory genes. Based on these candidate lncRNAs, NRAV and AL031985.3 were further screened out as key molecules closely related to the overall survival of patients, and a two-lncRNA risk score model was constructed. The model demonstrated reliable stratification capacity across the training set, test set, and the whole sample, and could effectively distinguish the survival prognosis of HCC patients. It is worth noting that this model not only has independent prognostic value, but also is significantly correlated with immune cell infiltration, immune function, and immune checkpoint molecules expression characteristics. At the same time, it shows good discrimination ability in the predictive sensitivity analysis of multiple anticancer drugs. In this study, we initially observed that the majority of key palmitoylation regulatory genes, including PPT1, PPT2, LYPLA1, LYPLA2, and several ZDHHC family members, were significantly up-regulated in HCC tissues. Moreover, their elevated expression levels were strongly associated with poorer overall survival, suggesting that these genes may contribute to HCC development and progression by modulating protein post-translational modifications. This trend was also verified by qPCR experiments, further enhancing the reliability of the results of bioinformatics analysis. We identified 244 lncRNAs that were significantly co-expressed with palmitoylation-related genes. Among them, 203 lncRNAs were differentially expressed in tumor tissues. Survival analysis revealed that NRAV and AL031985.3 had the strongest prognostic value. In recent years, lncRNAs have gained increasing attention for their roles in regulating lipid modifications. A study published in 2025 reported that lncZBTB10 promotes prostate cancer progression and drug resistance by enhancing the S-palmitoylation of the androgen receptor (AR), which increases its transcriptional activity 12 . In terms of functional studies, several reports have shown that NRAV (Negative Regulator of Antiviral Response), a long non-coding RNA involved in immune regulation, plays important roles not only in viral infections but also in tumor progression. Ouyang et al. found that NRAV can suppress the transcription of interferon-stimulated genes (ISGs), reduce antiviral factor expression, and weaken innate immunity. More recently, NRAV has been linked to cancer. Wu et al. reported that NRAV is significantly overexpressed in gastric cancer and promotes tumor cell proliferation and metastasis by modulating IFN signaling and inflammatory cytokine expression. Zhang et al. found that NRAV is located at the gastric cancer susceptibility locus 12q24.31 and promotes tumor progression by reprogramming glucose metabolism. In hepatocellular carcinoma (HCC), several studies also confirmed the clinical significance of NRAV. Liu et al. identified NRAV as a potential independent prognostic biomarker for HCC. Zong et al. further demonstrated that NRAV regulates ferroptosis through the miR-375-3P/SLC7A11 axis, affecting tumor cell survival and patient prognosis. Together, these findings suggest that NRAV may promote tumor progression by regulating interferon signaling, metabolic pathways, and programmed cell death, and could serve as a potential target for liver cancer diagnosis and therapy 42-48 . In this study, AL031985.3 was identified as a key lncRNA co-expressed with palmitoylation-related genes and showed stable prognostic value in the two-lncRNA risk model. Recent studies have also highlighted its role in tumor immunity and metabolism. Luo et al. found that AL031985.3 is involved in copper metabolism and redox balance, and is associated with immune cell infiltration, suggesting a role in shaping the tumor immune microenvironment. Shen et al. reported that it is positively correlated with several immune checkpoint genes, including PD-1, LAG3, and CTLA4, and may contribute to immune escape in HCC. In addition, Chen et al. included AL031985.3 in a ferroptosis-related lncRNA signature and linked it to stress response and metabolic dysfunction in cancer cells 49-51 . Through differential expression and GO/KEGG enrichment analysis, we found that the key genes distinguishing the high- and low-risk groups were mainly enriched in extracellular matrix remodeling, cell adhesion, cell cycle control, PI3K-Akt signaling, inflammation, and viral infection pathways. In recent years, growing evidence has shown that palmitoylation reversible lipid modification-plays a crucial role in cancer development. It regulates protein localization, stability, and signaling activity, thereby affecting several cancer-related pathways. Runkle et al. showed that palmitoylation of EGFR by DHHC20 plays an important role in controlling EGFR signaling. This modification helps anchor the C-terminal tail of EGFR to the cell membrane, preventing abnormal activation. When DHHC20 is inhibited, EGFR loses its palmitoylation, leading to continuous signaling and increased cancer cell migration 52 . In addition, FASN (fatty acid synthase) has been shown to promote the stability and oncogenic activity of mutant p53 by enhancing its palmitoylation. It provides palmitic acid as a substrate, helping to stabilize mutant p53 protein and support its gain-of-function effects 53 . Based on the palmitoylation-related lncRNA prognostic model developed in this study, we further evaluated the immune microenvironment and molecular differences in HCC patients from different risk groups. GSEA analysis showed that the high-risk group was significantly enriched in several classical pathways related to tumor growth and immune response, including the cell cycle, cytokine-receptor interaction, ECM-receptor interaction, hematopoietic cell differentiation, and neurotransmitter signaling. These activated pathways suggest that high-risk patients may have stronger tumor proliferation and a more complex, dysregulated immune microenvironment. Recent studies have shown that palmitoylation of SCAP can activate cholesterol synthesis, leading to macrophage polarization and Treg cell accumulation, which promotes an immunosuppressive tumor microenvironment and enhances immune escape in HCC 54 . Palmitoylation of Foxp3 helps stabilize Treg cells and strengthen their immunosuppressive function in tumors, which supports the development of an immunosuppressive tumor microenvironment in the high-risk group 13 . In contrast, the low-risk group was mainly enriched in metabolism-related pathways, especially those involving amino acids and lipids, such as β-alanine, butyrate, glycine/serine/threonine, linoleic acid metabolism, and bile acid synthesis. This shift in metabolic and immune pathways may help explain the better prognosis of low-risk patients and suggests that palmitoylation-related lncRNAs may play a role in regulating tumor metabolism and immunity. For immune cell infiltration, the high-risk group showed higher levels of CD8⁺ T cells, Treg cells, dendritic cells (aDC/iDC), and macrophages. This suggests an “immune-activated but exhausted” state 55, 56 . Such a profile-active on the surface but functionally suppressed, is often linked to poor immunotherapy response and tumor progression. However, the infiltration of B cells and neutrophils was higher in the low-risk group, suggesting that it may maintain a stable immune surveillance state. The membrane palmitoylation protein MPP1 can significantly enhance CD8+ T cell infiltration by regulating the USP12/CCL5 axis, suggesting that the low-risk group may maintain effective immune surveillance due to the lack of such inhibitory mechanisms 57 . The immune function score also supports these findings. The high-risk group showed higher scores in inflammation-promoting, T cell co-inhibition, MHC I signaling, and type II interferon response, indicating an immunosuppressive and inflammatory microenvironment. In contrast, the low-risk group had higher cytolytic activity, suggesting a stronger anti-tumor immune response. Notably, the expression of immune checkpoint genes such as PD-1, CTLA-4, LAG3, and TIGIT was significantly higher in the high-risk group. This suggests a stronger potential for immune escape and indicates that these patients may benefit more from immune checkpoint inhibitor (ICI) therapy. Studies have also shown that palmitoylation is closely related to immune checkpoint regulation. Recent studies have shown that palmitoylation of immune checkpoint proteins such as TIM-3 promotes immune exhaustion and suppresses antitumor immunity, revealing a potential link between lipid modification and immune evasion 58 . Although there was no clear linear correlation between tumor mutation burden (TMB) and the risk score, survival analysis showed that patients with both high TMB and high risk had the worst outcomes, while those with low TMB and low risk had the best. This suggests that combining TMB with the lncRNA model provides a better prediction of immunogenicity and patient prognosis. It also shows that TMB alone is not enough to assess tumor immune status -the tumor microenvironment and regulatory factors must also be considered. 5. Conclusion Palmitoylation-related lncRNAs play an important role in the progression and immune regulation of hepatocellular carcinoma. NRAV and AL031985.3 may serve as novel prognostic biomarkers and potential targets for immunotherapy in HCC. Declarations Data availability The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. All datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Funding Statement This work was supported by Science and Technology Foundation Project of Guizhou Health Commission (grant numbers gzwkj2025-299 and gzwkj2025-616) and Talent Fund of Guizhou Provincial People's Hospital (Talent Project of the Institute [2024]-20 and [2023]-43). Ethical Approval This study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before sample collection. The study protocol was reviewed and approved by the Medical Ethics Committee of Guizhou Provincial People's Hospital. Consent to participate Informed consents (Consent to Participate) were obtained from all participants in this study. Consent for publication The authors give consent for publication of the manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Chan, Y. T.; Zhang, C.; Wu, J.; Lu, P.; Xu, L.; Yuan, H.; Feng, Y.; Chen, Z. S.; Wang, N., Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma. Mol Cancer 2024, 23 (1), 189. Lee, Y. T.; Wang, J. J.; Luu, M.; Noureddin, M.; Kosari, K.; Agopian, V. G.; Rich, N. E.; Lu, S. C.; Tseng, H. R.; Nissen, N. N.; Singal, A. G.; Yang, J. D., The Mortality and Overall Survival Trends of Primary Liver Cancer in the United States. J Natl Cancer Inst 2021, 113 (11), 1531-1541. Li, C.; Yang, J.; Liu, C.; Wang, X.; Zhang, L., Long non-coding RNAs in hepatocellular carcinoma: Ordering of the complicated lncRNA regulatory network and novel strategies for HCC clinical diagnosis and treatment. Pharmacol Res 2020, 158 , 104848. Lv, E.; Sheng, J.; Yu, C.; Rao, D.; Huang, W., LncRNA influence sequential steps of hepatocellular carcinoma metastasis. Biomed Pharmacother 2021, 136 , 111224. Zhang, B.; Bao, W.; Zhang, S.; Chen, B.; Zhou, X.; Zhao, J.; Shi, Z.; Zhang, T.; Chen, Z.; Wang, L.; Zheng, X.; Chen, G.; Wang, Y., LncRNA HEPFAL accelerates ferroptosis in hepatocellular carcinoma by regulating SLC7A11 ubiquitination. Cell Death Dis 2022, 13 (8), 734. Su, T.; Zhang, N.; Wang, T.; Zeng, J.; Li, W.; Han, L.; Yang, M., Super Enhancer-Regulated LncRNA LINC01089 Induces Alternative Splicing of DIAPH3 to Drive Hepatocellular Carcinoma Metastasis. Cancer Res 2023, 83 (24), 4080-4094. Cha, H.; Kim, M.; Ahn, N.; Jeong, S. D.; Ignatova, E.; Chi, S. W.; Kim, H. H.; Hwang, J., Role of UPF1 in lncRNA-HEIH regulation for hepatocellular carcinoma therapy. Exp Mol Med 2024, 56 (2), 344-354. Huang, Z.; Zhou, J. K.; Peng, Y.; He, W.; Huang, C., The role of long noncoding RNAs in hepatocellular carcinoma. Mol Cancer 2020, 19 (1), 77. Zhu, Z.; Feng, S.; Zeng, A.; Song, L., Advances in Palmitoylation: A key Regulator of liver cancer development and therapeutic targets. Biochem Pharmacol 2025, 234 , 116810. Zhou, B.; Hao, Q.; Liang, Y.; Kong, E., Protein palmitoylation in cancer: molecular functions and therapeutic potential. Mol Oncol 2023, 17 (1), 3-26. Busquets-Hernández, C.; Triola, G., Palmitoylation as a Key Regulator of Ras Localization and Function. Front Mol Biosci 2021, 8 , 659861. Lin, S. C.; Cheng, Y. S.; Lin, Y. S.; Nguyen, T. M. H.; Chiu, W. T.; Tsai, Y. C.; Chen, H. Y.; Lin, T. Y.; Lin, S. C., The long noncoding RNA lncZBTB10 facilitates AR function via S-palmitoylation to promote prostate cancer progression and abiraterone resistance. Br J Cancer 2025, 132 (7), 587-598. Zhou, B.; Zhang, M.; Ma, H.; Wang, Y.; Qiu, J.; Liu, Y.; Lu, L.; Li, T.; Zhang, L.; Huang, R.; Gu, Y.; Kong, E.; Liang, Y., Distinct palmitoylation of Foxp3 regulates the function of regulatory T cells via palmitoyltransferases. Cell Mol Immunol 2024, 21 (7), 787-789. Chandrashekar, D. S.; Bashel, B.; Balasubramanya, S. A. H.; Creighton, C. J.; Ponce-Rodriguez, I.; Chakravarthi, B.; Varambally, S., UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 2017, 19 (8), 649-658. Zhang, Z.; Hernandez, K.; Savage, J.; Li, S.; Miller, D.; Agrawal, S.; Ortuno, F.; Staudt, L. M.; Heath, A.; Grossman, R. L., Uniform genomic data analysis in the NCI Genomic Data Commons. Nat Commun 2021, 12 (1), 1226. Conesa, A.; Madrigal, P.; Tarazona, S.; Gomez-Cabrero, D.; Cervera, A.; McPherson, A.; Szcześniak, M. W.; Gaffney, D. J.; Elo, L. L.; Zhang, X.; Mortazavi, A., A survey of best practices for RNA-seq data analysis. Genome Biol 2016, 17 , 13. Chamberlain, L. H.; Shipston, M. J., The physiology of protein S-acylation. Physiol Rev 2015, 95 (2), 341-76. Li, M.; Zhang, L.; Chen, C. W., Diverse Roles of Protein Palmitoylation in Cancer Progression, Immunity, Stemness, and Beyond. Cells 2023, 12 (18). Chen, Y.; Li, Y.; Wu, L., Protein S-palmitoylation modification: implications in tumor and tumor immune microenvironment. Front Immunol 2024, 15 , 1337478. Mudge, J. M.; Carbonell-Sala, S.; Diekhans, M.; Martinez, J. G.; Hunt, T.; Jungreis, I.; Loveland, J. E.; Arnan, C.; Barnes, I.; Bennett, R.; Berry, A.; Bignell, A.; Cerdán-Vélez, D.; Cochran, K.; Cortés, L. T.; Davidson, C.; Donaldson, S.; Dursun, C.; Fatima, R.; Hardy, M.; Hebbar, P.; Hollis, Z.; James, B. T.; Jiang, Y.; Johnson, R.; Kaur, G.; Kay, M.; Mangan, R. J.; Maquedano, M.; Gómez, L. M.; Mathlouthi, N.; Merritt, R.; Ni, P.; Palumbo, E.; Perteghella, T.; Pozo, F.; Raj, S.; Sisu, C.; Steed, E.; Sumathipala, D.; Suner, M. M.; Uszczynska-Ratajczak, B.; Wass, E.; Yang, Y. T.; Zhang, D.; Finn, R. D.; Gerstein, M.; Guigó, R.; Hubbard, T. J. P.; Kellis, M.; Kundaje, A.; Paten, B.; Tress, M. L.; Birney, E.; Martin, F. J.; Frankish, A., GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Res 2025, 53 (D1), D966-d975. Ritchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43 (7), e47. Li, Q.; Yu, X.; Chaudhary, R.; Slebos, R. J. C.; Chung, C. H.; Wang, X., lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA. BMC Genomics 2019, 20 (1), 539. Liu, H.; Zhang, Q.; Lou, Q.; Zhang, X.; Cui, Y.; Wang, P.; Yang, F.; Wu, F.; Wang, J.; Fan, T.; Li, S., Differential Analysis of lncRNA, miRNA and mRNA Expression Profiles and the Prognostic Value of lncRNA in Esophageal Cancer. Pathol Oncol Res 2020, 26 (2), 1029-1039. Fu, Y.; Wei, X.; Han, Q.; Le, J.; Ma, Y.; Lin, X.; Xu, Y.; Liu, N.; Wang, X.; Kong, X.; Gu, J.; Tong, Y.; Wu, H., Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma. BMC Cancer 2021, 21 (1), 1165. Frost, H. R.; Amos, C. I., Gene set selection via LASSO penalized regression (SLPR). Nucleic Acids Res 2017, 45 (12), e114. Friedman, J.; Hastie, T.; Tibshirani, R., Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010, 33 (1), 1-22. Zhang, B.; Wu, Q.; Li, B.; Wang, D.; Wang, L.; Zhou, Y. L., m(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Mol Cancer 2020, 19 (1), 53. Fu, Y.; Si, A.; Wei, X.; Lin, X.; Ma, Y.; Qiu, H.; Guo, Z.; Pan, Y.; Zhang, Y.; Kong, X.; Li, S.; Shi, Y.; Wu, H., Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma. BMC Genomics 2023, 24 (1), 89. Iasonos, A.; Schrag, D.; Raj, G. V.; Panageas, K. S., How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008, 26 (8), 1364-70. Steyerberg, E. W.; Vickers, A. J.; Cook, N. R.; Gerds, T.; Gonen, M.; Obuchowski, N.; Pencina, M. J.; Kattan, M. W., Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010, 21 (1), 128-38. Ashburner, M.; Ball, C. A.; Blake, J. A.; Botstein, D.; Butler, H.; Cherry, J. M.; Davis, A. P.; Dolinski, K.; Dwight, S. S.; Eppig, J. T.; Harris, M. A.; Hill, D. P.; Issel-Tarver, L.; Kasarskis, A.; Lewis, S.; Matese, J. C.; Richardson, J. E.; Ringwald, M.; Rubin, G. M.; Sherlock, G., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25 (1), 25-9. Kanehisa, M.; Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28 (1), 27-30. Subramanian, A.; Tamayo, P.; Mootha, V. K.; Mukherjee, S.; Ebert, B. L.; Gillette, M. A.; Paulovich, A.; Pomeroy, S. L.; Golub, T. R.; Lander, E. S.; Mesirov, J. P., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102 (43), 15545-50. Aran, D.; Hu, Z.; Butte, A. J., xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017, 18 (1), 220. Racle, J.; de Jonge, K.; Baumgaertner, P.; Speiser, D. E.; Gfeller, D., Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife 2017, 6 . Newman, A. M.; Liu, C. L.; Green, M. R.; Gentles, A. J.; Feng, W.; Xu, Y.; Hoang, C. D.; Diehn, M.; Alizadeh, A. A., Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015, 12 (5), 453-7. Ellrott, K.; Bailey, M. H.; Saksena, G.; Covington, K. R.; Kandoth, C.; Stewart, C.; Hess, J.; Ma, S.; Chiotti, K. E.; McLellan, M.; Sofia, H. J.; Hutter, C.; Getz, G.; Wheeler, D.; Ding, L., Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst 2018, 6 (3), 271-281.e7. Mayakonda, A.; Lin, D. C.; Assenov, Y.; Plass, C.; Koeffler, H. P., Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018, 28 (11), 1747-1756. Chan, T. A.; Yarchoan, M.; Jaffee, E.; Swanton, C.; Quezada, S. A.; Stenzinger, A.; Peters, S., Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019, 30 (1), 44-56. Samstein, R. M.; Lee, C. H.; Shoushtari, A. N.; Hellmann, M. D.; Shen, R.; Janjigian, Y. Y.; Barron, D. A.; Zehir, A.; Jordan, E. J.; Omuro, A.; Kaley, T. J.; Kendall, S. M.; Motzer, R. J.; Hakimi, A. A.; Voss, M. H.; Russo, P.; Rosenberg, J.; Iyer, G.; Bochner, B. H.; Bajorin, D. F.; Al-Ahmadie, H. A.; Chaft, J. E.; Rudin, C. M.; Riely, G. J.; Baxi, S.; Ho, A. L.; Wong, R. J.; Pfister, D. G.; Wolchok, J. D.; Barker, C. A.; Gutin, P. H.; Brennan, C. W.; Tabar, V.; Mellinghoff, I. K.; DeAngelis, L. M.; Ariyan, C. E.; Lee, N.; Tap, W. D.; Gounder, M. M.; D'Angelo, S. P.; Saltz, L.; Stadler, Z. K.; Scher, H. I.; Baselga, J.; Razavi, P.; Klebanoff, C. A.; Yaeger, R.; Segal, N. H.; Ku, G. Y.; DeMatteo, R. P.; Ladanyi, M.; Rizvi, N. A.; Berger, M. F.; Riaz, N.; Solit, D. B.; Chan, T. A.; Morris, L. G. T., Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 2019, 51 (2), 202-206. Chalmers, Z. R.; Connelly, C. F.; Fabrizio, D.; Gay, L.; Ali, S. M.; Ennis, R.; Schrock, A.; Campbell, B.; Shlien, A.; Chmielecki, J.; Huang, F.; He, Y.; Sun, J.; Tabori, U.; Kennedy, M.; Lieber, D. S.; Roels, S.; White, J.; Otto, G. A.; Ross, J. S.; Garraway, L.; Miller, V. A.; Stephens, P. J.; Frampton, G. M., Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 2017, 9 (1), 34. Wu, Y.; Wang, D.; Zhao, J.; Guo, J.; Gao, Z.; Xu, Q.; Hu, X., NRAV promoted the malignant progression of gastric cancer. Gene 2025, 937 , 149134. Baysal, A.; Kıymaz, Y.; Şahin, N.; Bakır, M., Investigation of Long Noncoding RNA-NRAV and Long Noncoding RNA-Lethe Expression in Crimean-Congo Hemorrhagic Fever. J Med Virol 2024, 96 (12), e70142. Liu, J.; Li, W.; Lu, R.; Xu, J.; Jiang, C.; Duan, J.; Zhang, L.; Wang, G.; Chen, J., Investigation of the feasibility of NRAV as a biomarker for hepatocellular carcinoma. Oncol Res 2024, 32 (4), 717-726. Zong, K.; Lin, C.; Luo, K.; Deng, Y.; Wang, H.; Hu, J.; Chen, S.; Li, R., Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via the miR-375-3P/SLC7A11 axis. BMC Cancer 2024, 24 (1), 496. Zhang, Y.; Gao, Y.; Li, F.; Qi, Q.; Li, Q.; Gu, Y.; Zheng, Z.; Hu, B.; Wang, T.; Zhang, E.; Xu, H.; Liu, L.; Tian, T.; Jin, G.; Yan, C., Long non-coding RNA NRAV in the 12q24.31 risk locus drives gastric cancer development through glucose metabolism reprogramming. Carcinogenesis 2024, 45 (1-2), 23-34. Ouyang, J.; Zhu, X.; Chen, Y.; Wei, H.; Chen, Q.; Chi, X.; Qi, B.; Zhang, L.; Zhao, Y.; Gao, G. F.; Wang, G.; Chen, J. L., NRAV, a long noncoding RNA, modulates antiviral responses through suppression of interferon-stimulated gene transcription. Cell Host Microbe 2014, 16 (5), 616-26. Li, J.; Li, M.; Wang, X.; Sun, M.; Ma, C.; Liang, W.; Gao, X.; Wei, L., Long Noncoding RNA NRAV Promotes Respiratory Syncytial Virus Replication by Targeting the MicroRNA miR-509-3p/Rab5c Axis To Regulate Vesicle Transportation. J Virol 2020, 94 (10). Luo, R.; Huang, S.; Shi, X.; Xu, H.; Peng, J.; Lei, W.; Li, S.; Zhang, W.; Shi, L.; Peng, Y.; Tang, X., Copper metabolism-related lncRNAs predict prognosis and immune landscape in liver cancer patients. Transl Cancer Res 2024, 13 (11), 5784-5800. Shen, D.; Sha, L.; Yang, L.; Gu, X., Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1. Discov Oncol 2025, 16 (1), 544. Chen, Z. A.; Tian, H.; Yao, D. M.; Zhang, Y.; Feng, Z. J.; Yang, C. J., Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma. Front Oncol 2021, 11 , 738477. Runkle, K. B.; Kharbanda, A.; Stypulkowski, E.; Cao, X. J.; Wang, W.; Garcia, B. A.; Witze, E. S., Inhibition of DHHC20-Mediated EGFR Palmitoylation Creates a Dependence on EGFR Signaling. Mol Cell 2016, 62 (3), 385-396. Liu, J.; Shen, Y.; Liu, J.; Xu, D.; Chang, C. Y.; Wang, J.; Zhou, J.; Haffty, B. G.; Zhang, L.; Bargonetti, J.; De, S.; Hu, W.; Feng, Z., Lipogenic enzyme FASN promotes mutant p53 accumulation and gain-of-function through palmitoylation. Nat Commun 2025, 16 (1), 1762. Wu, M.; Zhou, X.; Zhou, X.; Wang, G.; Zeng, Y.; Li, J.; Prochownik, E. V.; Wang, F.; Li, Y., ZDHHC3-mediated SCAP S-acylation promotes cholesterol biosynthesis and tumor immune escape in hepatocellular carcinoma. Cell Rep 2024, 43 (11), 114962. Liu, Z.; Zhang, Y.; Ma, N.; Yang, Y.; Ma, Y.; Wang, F.; Wang, Y.; Wei, J.; Chen, H.; Tartarone, A.; Velotta, J. B.; Dayyani, F.; Gabriel, E.; Wakefield, C. J.; Kidane, B.; Carbonelli, C.; Long, L.; Liu, Z.; Su, J.; Li, Z., Progenitor-like exhausted SPRY1(+)CD8(+) T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma. Cancer Cell 2023, 41 (11), 1852-1870.e9. Lu, J. C.; Wu, L. L.; Sun, Y. N.; Huang, X. Y.; Gao, C.; Guo, X. J.; Zeng, H. Y.; Qu, X. D.; Chen, Y.; Wu, D.; Pei, Y. Z.; Meng, X. L.; Zheng, Y. M.; Liang, C.; Zhang, P. F.; Cai, J. B.; Ding, Z. B.; Yang, G. H.; Ren, N.; Huang, C.; Wang, X. Y.; Gao, Q.; Sun, Q. M.; Shi, Y. H.; Qiu, S. J.; Ke, A. W.; Shi, G. M.; Zhou, J.; Sun, Y. D.; Fan, J., Macro CD5L(+) deteriorates CD8(+)T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma. Nat Commun 2024, 15 (1), 621. Wu, Q.; Lv, J.; Li, X., Membrane palmitoylated protein MPP1 inhibits immune escape by regulating the USP12/ CCL5 axis in urothelial carcinoma. Int Immunopharmacol 2025, 146 , 113802. Zhang, Z.; Ren, C.; Xiao, R.; Ma, S.; Liu, H.; Dou, Y.; Fan, Y.; Wang, S.; Zhan, P.; Gao, C.; Yue, X.; Li, C.; Gao, L.; Liang, X.; Wu, Z.; Ma, C., Palmitoylation of TIM-3 promotes immune exhaustion and restrains antitumor immunity. Sci Immunol 2024, 9 (101), eadp7302. Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 27 Jul, 2025 Editor invited by journal 25 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 12 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6942496","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493427166,"identity":"52e3837f-f831-480f-8ea8-42d81058be93","order_by":0,"name":"Yongkang Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBADAxDB+MHgnxwbe/MB4rUwSxQcMObjOZZAvBYGng8HEudJ5CjgVSrf3mMm8XFHrTH/7PYLDBIGd9LbGHIYGH5UbMOphbHnjJnkzDPHzSTunClgKDB4ltvGcPYAUPQ2Ti3MEjlm0rxtx2wYbuQkAG1hzm1j7EtgZmzDrYUNpkUepIXHgDmdjRlI4tPCA9FSY2ZwI/0AUMvhBDY2AlokeI4VW85sO2BseCMH6EqDNMM2HraEg/j8It/evPHGx7Y6w3k30h8wfvhjIy8///HBBz8qcGsBAhYJBobDIDea/4AJHcCnHgiYPzAw1AFp9gcEFI6CUTAKRsFIBQAQfVU6MXUlngAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yongkang","middleName":"","lastName":"Zou","suffix":""},{"id":493427167,"identity":"cad02337-7d0e-4f9d-8eaf-fc4647ccf0b8","order_by":1,"name":"Xuejun Zhao","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"Zhao","suffix":""},{"id":493427168,"identity":"b352e93e-bbe6-417e-8a0b-2ed8d07123db","order_by":2,"name":"Shengpeng Yang","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shengpeng","middleName":"","lastName":"Yang","suffix":""},{"id":493427169,"identity":"465220ed-1480-4e40-965b-f27650ff28a5","order_by":3,"name":"Yan Liu","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":493427170,"identity":"fa46a5a5-f75f-4154-99df-5d6aa019bba1","order_by":4,"name":"Shuaimin Zhang","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuaimin","middleName":"","lastName":"Zhang","suffix":""},{"id":493427171,"identity":"50caaeb0-3ae5-4ef4-8cbe-1bddec0399b1","order_by":5,"name":"Xiangang Xu","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangang","middleName":"","lastName":"Xu","suffix":""},{"id":493427172,"identity":"1dfa227f-23f4-4cfe-9ca7-c72b21d01ba5","order_by":6,"name":"Gen Chen","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gen","middleName":"","lastName":"Chen","suffix":""},{"id":493427173,"identity":"26268b59-37a3-41c0-a164-57f4921173d8","order_by":7,"name":"Yi Zhang","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-21 04:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6942496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6942496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88036920,"identity":"8ed31335-d34c-4b92-a2f8-e49c76d3fd61","added_by":"auto","created_at":"2025-07-31 16:24:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of palmitoylation-related genes in liver cancer and normal tissues. \u003c/strong\u003eBoxplots illustrate the expression levels of 30 palmitoylation-related genes in hepatocellular carcinoma (HCC) tissues (n = 371) compared with normal liver tissues (n = 50), based on TCGA RNA-seq data retrieved via the UALCAN platform. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ns = not significant.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/3e99c235f86e0d91f68ee675.png"},{"id":88036911,"identity":"f3bebf96-0675-4bec-b2a1-96584fc99a1e","added_by":"auto","created_at":"2025-07-31 16:24:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":591180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and expression patterns of palmitoylation-related lncRNAs. \u003c/strong\u003e(A) Volcano plot of differentially expressed lncRNAs co-expressed with palmitoylation-related genes. (B) Heatmap of lncRNA expression in normal and tumor tissues. (C) Kaplan-Meier curves of overall survival for selected lncRNAs. (D) Forest plot of hazard ratios and confidence intervals for prognostic lncRNAs. (E) LASSO cross-validation plot. (F) LASSO coefficient profile plot.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/9bbd409be5f39cbaebea8746.png"},{"id":88036948,"identity":"b79de917-9bb9-42f5-8c0e-acfffcf68464","added_by":"auto","created_at":"2025-07-31 16:24:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":549272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of an lncRNA risk model. \u003c/strong\u003e(A-C) Kaplan-Meier survival curves for high- and low-risk groups in the full cohort, training set, and test set. (D-F) Risk score distributions. (G-I) Survival status of patients. (J–L) Heatmaps showing expression of NRAV and AL031985.3 across risk groups.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/0649f1a18b45e990977c34c0.png"},{"id":88036945,"identity":"ef20704f-563f-4fba-9ecf-3a18e8e2224a","added_by":"auto","created_at":"2025-07-31 16:24:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":342111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram model for predicting overall survival in HCC patients. \u003c/strong\u003e(A) Nomogram combining clinical variables and risk score. (B) Calibration curves for 1-, 3-, and 5-year survival predictions. (C) Univariate Cox regression analysis. (D) Multivariate Cox regression analysis. (E) ROC curves for risk score and clinical variables. (F) Time-dependent AUCs for survival prediction.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/618e7adf5fe71e3b2ba3fe9d.png"},{"id":88037729,"identity":"545b04ae-39de-4180-80d1-321f9e5bbb98","added_by":"auto","created_at":"2025-07-31 16:32:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":538473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of differentially expressed genes between risk groups. \u003c/strong\u003e(A) Volcano plot of differentially expressed genes. (B) Heatmap of gene expression across samples. (C) GO enrichment analysis in three categories: BP, CC, and MF. (D) KEGG pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/64dabc8e0c4f9f74c61c97f0.png"},{"id":88037727,"identity":"0701f57e-15bf-454d-9848-bf9e7470bed0","added_by":"auto","created_at":"2025-07-31 16:32:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":549324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune-related features of high- and low-risk groups. \u003c/strong\u003e(A–B) GSEA plots for pathway enrichment in risk groups. (C) Immune function status in different risk score groups. (D) Immune cell infiltration patterns in different risk score groups. (E) Expression of immune checkpoints in different risk score groups.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/a9d69693a204d78200c4b723.png"},{"id":88036940,"identity":"bc98968c-ee13-4825-a771-e3e58622db61","added_by":"auto","created_at":"2025-07-31 16:24:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":526498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor mutation burden (TMB) and risk score analysis in liver cancer. \u003c/strong\u003e(A-B) Mutation frequency plots for high- and low-risk groups. (C) Correlation of immune cell types with risk score across multiple algorithms. (D) Boxplot of tumor mutation burden (TMB). (E) Correlation between TMB and risk score. \u0026nbsp;(F) Kaplan–Meier curves based on TMB levels. (G) Survival curves for combined TMB and risk score groups.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/f9b69ae1cb660326c97c0822.png"},{"id":88036946,"identity":"8677a6f5-d7d9-4292-89f4-3ef55873236e","added_by":"auto","created_at":"2025-07-31 16:24:06","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":377838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistology and qRT-PCR validation of lncRNAs and palmitoylation-related genes. \u003c/strong\u003e(A) H\u0026amp;E staining of normal and HCC liver tissues. (B) qRT-PCR analysis of NRAV and AL031985.3 expression. (C) qRT-PCR analysis of palmitoylation-related gene expression in normal (n=15) and tumor tissues (n=15). *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ns = not significant. Data are presented as mean±SD.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/bf2e846aa1d7a742b8396615.jpeg"},{"id":88037742,"identity":"8658aeb6-17f4-4ba5-9034-27168252bfcd","added_by":"auto","created_at":"2025-07-31 16:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5328623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/0daa17a7-1077-401f-949a-224a73d7dc83.pdf"},{"id":88036918,"identity":"d3f37699-c668-4cba-a6c1-435fbdda269d","added_by":"auto","created_at":"2025-07-31 16:24:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15050513,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6942496/v1/82e03bae78ffcb36fad420ba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of palmitoylation-related lncRNAs as prognostic biomarkers and immune modulators in liver cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver cancer is the second-deadliest cancer in the world. Hepatocellular carcinoma (HCC) is the most common type, making up about 90% of all cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. HCC has a high recurrence rate, is often resistant to treatment, and lacks early diagnostic markers \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These factors lead to a poor outlook for patients. In recent years, research on the molecular mechanisms of HCC has made progress. However, the main factors driving its development are still not fully understood. There is an urgent need to find new treatment targets and discover which signaling pathways are abnormally regulated.\u003c/p\u003e\u003cp\u003eLong non-coding RNAs (lncRNAs) are a new group of molecules that play important roles in regulating biological functions. In the past, they were thought to be useless, but now they are known to help maintain balance in the body, including in liver cancer. Many lncRNAs are linked to diseases, especially cancer development and spread \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Examples include HEPFAL, LINC01089, and HEIH \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In HCC, lncRNAs can affect how cancer cells grow, move, change metabolism, and interact with the immune system\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, we still don\u0026rsquo;t fully understand how most lncRNAs work. At the same time, protein modifications after translation, like palmitoylation, are getting more attention in cancer research\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Palmitoylation adds palmitic acid to certain amino acids (like cysteine) in proteins. This affects where proteins go in the cell, how stable they are, and how they send signals \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eShi et al. found that deleting the lncRNA DUXAP8 enhanced sorafenib-induced ferroptosis in HCC by reducing the palmitoylation of SLC7A11. They suggested that combining sorafenib with DUXAP8 silencing could be a new way to overcome drug resistance and improve treatment outcomes in advanced HCC patients\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Similarly, Lin et al. showed that the lncRNA lncZBTB10 increases androgen receptor activity through S-palmitoylation, promoting prostate cancer growth and resistance to abiraterone\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These studies highlight the role of lncRNAs in regulating protein palmitoylation, offering new insights into the molecular mechanisms of cancer development.\u003c/p\u003e\u003cp\u003eAlthough protein palmitoylation has long been recognized as a classic post-translational lipid modification, recent studies have highlighted its important role in tumor development and immune regulation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Meanwhile, lncRNAs, as key regulators of gene expression, are closely associated with tumor cell proliferation, metastasis, and drug resistance. In recent years, increasing attention has been given to the potential interaction between lncRNAs and palmitoylation in post-translational regulation. Although many studies have reported the regulatory roles of lncRNAs in HCC, their specific functions in palmitoylation remain unclear. Existing evidence suggests that palmitoylation promotes tumor cell growth and migration plays a crucial role in shaping the tumor immune environment and influencing patient prognosis. Therefore, identifying lncRNAs that regulate palmitoylation may provide new insights into the pathogenesis of HCC and help discover potential diagnostic and therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Expression Analysis of Palmitoylation-Related Genes\u003c/h2\u003e\u003cp\u003eTo explore the expression patterns of palmitoylation-associated genes in liver cancer, we employed the UALCAN platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an interactive web tool based on TCGA Level 3 RNA-sequencing data\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This database enables comprehensive transcriptomic analysis across various cancer types, including HCC. Palmitoylation-related gene expression levels were compared between HCC tissues and normal liver samples to identify significant dysregulation potentially linked to tumorigenesis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Download and Processing of Transcriptomic, Mutation, and Clinical Data\u003c/h2\u003e\u003cp\u003eWe downloaded RNA sequencing (RNA-seq) data for liver cancer patients from the TCGA 2.0 data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on February 13, 2025. 371 HCC tissue samples and 50 normal liver tissue samples were included. In addition, the corresponding somatic mutation profiles and clinical data-including overall survival time, survival status, age, gender, tumor grade, clinical stage, and lymph node metastasis status, were also retrieved. The RNA-seq data were normalized and transformed into FPKM format using Strawberry Perl (version 5.30.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.perl.org\u003c/span\u003e\u003cspan address=\"https://www.perl.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for subsequent analyses\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. After collating clinical information, a structured phenotype matrix was created to support integrative clinical and genomic assessments. Based on functional annotations and prior studies, we identified 30 key genes involved in protein palmitoylation, including: ZDHHC1, ZDHHC2, ZDHHC3, ZDHHC4, ZDHHC5, ZDHHC6, ZDHHC7, ZDHHC8, ZDHHC9, ZDHHC11, ZDHHC12, ZDHHC13, ZDHHC14, ZDHHC15, ZDHHC16, ZDHHC17, ZDHHC18, ZDHHC19, ZDHHC20, ZDHHC21, ZDHHC22, ZDHHC23, ZDHHC24, LYPLA1, LYPLA2, PPT1, PPT2, ABHD17A, ABHD17B, and ABHD17C\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The lncRNA annotation file was obtained from the GENCODE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gencodegenes.org/\u003c/span\u003e\u003cspan address=\"https://www.gencodegenes.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and was used to identify and analyze palmitoylation-related long non-coding RNAs\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Extraction and Co-Expression Analysis of Palmitoylation-Related lncRNAs\u003c/h2\u003e\u003cp\u003eTo identify lncRNAs potentially associated with the palmitoylation process, expression profiles of both mRNAs and lncRNAs were extracted from the normalized RNA-seq dataset. The future. apply package in R was employed to enable parallel computing and enhance processing efficiency. A curated list of palmitoylation-related genes served as the reference for downstream analyses. Subsequently, co-expression analysis was conducted by assessing the expression correlation between these genes and lncRNAs. Pearson correlation coefficients were computed using the limma package to evaluate potential functional associations\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. LncRNA\u0026ndash;gene pairs with an absolute correlation coefficient (|r|)\u0026thinsp;\u0026gt;\u0026thinsp;0.6 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to exhibit statistically significant co-expression relationships.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Prognostic Model Construction and Validation\u003c/h2\u003e\u003cp\u003eIn this study, the transcriptomic and clinical data of liver cancer patients were retrieved from the TCGA database, and the cohort was randomly divided into training and test sets at a 1:1 ratio using the caret package in R. The training set was utilized to identify palmitoylation-related lncRNAs with prognostic relevance, while the test set and the entire cohort were employed for model validation. Univariate Cox regression analysis was initially performed to screen lncRNAs significantly associated with overall survival, and their prognostic impact was visualized using forest plots. Data visualization was conducted with the assistance of R packages including limma, pheatmap, reshape2, and ggpubr\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For model construction, LASSO-penalized Cox regression was applied using the glmnet package in R to minimize the risk of overfitting. The optimal penalty parameter (λ) was determined via 10-fold cross-validation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Subsequently, the final prognostic model was established through multivariate Cox regression, and the risk score for each patient was computed according to the following formula: RiskScore=\u0026sum; i (Coef(i)\u0026times;Expr(i)), where Coef(i) represents the regression coefficient, and Expr(i) denotes the normalized expression level of the respective lncRNA\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo validate the model, all patients were classified into high-risk and low-risk groups based on the median risk score derived from the training set. Kaplan\u0026ndash;Meier survival analysis, performed using the survival package in R, was employed to compare overall survival (OS) between the two groups. The predictive performance of the model was assessed by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) to evaluate its discriminative ability\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo further confirm the independent prognostic significance of the risk score, both univariate and multivariate Cox regression analyses were conducted. Additionally, the rms, regplot, and survival packages were used to build a nomogram incorporating the risk score and clinical variables, aimed at predicting 1-, 3-, and 5-year overall survival probabilities in liver cancer patients. A calibration curve, generated based on the Hosmer\u0026ndash;Lemeshow goodness-of-fit test, was used to assess the consistency between predicted and actual survival outcomes, thereby validating the accuracy and reliability of the nomogram\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 GO and KEGG Enrichment Analysis\u003c/h2\u003e\u003cp\u003eBased on the palmitoylation-related lncRNA prognostic model, a risk score was calculated for each HCC patient, and individuals were stratified into high-risk and low-risk groups according to the median score. Subsequently, the limma package in R was used to perform differential expression analysis between the two groups to identify significantly differentially expressed genes (DEGs). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the DEGs to explore biological processes, molecular functions, and signaling pathways associated with the palmitoylation-related risk signature \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Tumor immune analysis\u003c/h2\u003e\u003cp\u003eTo investigate the relationship between palmitoylation-related lncRNA risk scores and tumor immune cell infiltration, the single-sample gene set enrichment analysis (ssGSEA) algorithm was employed to quantitatively assess the infiltration levels and functional states of immune cells in each HCC sample. A comprehensive immune profiling was conducted by integrating multiple computational tools, including XCELL, TIMER, QUANTISEQ, MCP-counter, EPIC, CIBERSORT-ABS, and CIBERSORT, to systematically evaluate the abundance of various immune cell subsets\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe correlations between immune cell infiltration scores and the palmitoylation-related risk scores were analyzed. Boxplots were generated to visualize differences in immune cell infiltration and immune function between high- and low-risk groups. Additionally, the expression patterns of immune checkpoint genes were examined to assess immune activation levels and immunosuppressive features across different risk subgroups, thereby providing further insights into alterations in the tumor immune microenvironment associated with the palmitoylation-related signature.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Calculation of tumor mutation burden scores\u003c/h2\u003e\u003cp\u003eTo evaluate the association between palmitoylation-related lncRNA risk scores and tumor mutation characteristics, somatic mutation data of liver cancer patients were retrieved from the TCGA database. Perl scripts were used to extract and preprocess the Mutation Annotation Format (MAF) files, followed by mutation analysis and visualization using the maftools package in R\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Tumor mutation burden (TMB) for each sample was defined as the number of somatic mutations per megabase of the coding genome\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Based on the established palmitoylation-related risk model, patients were stratified into high-risk and low-risk groups, and TMB levels were compared between the two subgroups. Furthermore, survival analysis was conducted across different TMB levels to investigate the prognostic implications of TMB within distinct risk categories\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Sample Collection, RNA Extraction, and qRT-PCR Analysis\u003c/h2\u003e\u003cp\u003eLiver cancer tissue samples were obtained from resected surgical specimens of HCC patients at Guizhou Provincial People's Hospital, while normal liver tissues were collected from organ donors. For RNA extraction, both tumor and normal tissues were processed using TRIzol reagent (Invitrogen, USA). Following lysis at room temperature for 10 minutes, chloroform was added, thoroughly mixed, and allowed to separate into phases. The samples were then centrifuged at 12,000 \u0026times; g for 15 minutes at 4\u0026deg;C. The upper aqueous phase was carefully collected, mixed with an equal volume of isopropanol to precipitate RNA, washed with 75% ethanol, air-dried, and finally resuspended in RNase-free water. The purity and concentration of extracted RNA were assessed using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, USA). First-strand cDNA was synthesized using reverse transcription reagents (Takara, Japan). Quantitative real-time PCR (qRT-PCR) was conducted on a LightCycler 96 system (Roche, USA) using UltraSYBR Mixture (CWBio, China). All experiments were performed in triplicate to ensure reproducibility and accuracy. Additionally, qRT-PCR was used to evaluate the expression levels of palmitoylation-related lncRNAs identified through bioinformatics analysis. Primer sequences used in this study are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Hematoxylin and Eosin (H\u0026amp;E) Staining\u003c/h2\u003e\u003cp\u003eLiver cancer and adjacent normal tissues were fixed in 10% neutral-buffered formalin, followed by dehydration through a graded ethanol series and clearing with xylene. The samples were then embedded in paraffin and sectioned at a thickness of 4 \u0026micro;m. Sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin. After staining, the slides were dehydrated, cleared, and mounted using neutral resin. Histological features were examined under a light microscope.\u003c/p\u003e\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before sample collection. Participant recruitment was carried out between December 17, 2023, and December 17, 2024. Liver cancer tissue samples were collected from patients undergoing surgery at Guizhou Provincial People\u0026apos;s Hospital. The normal liver tissues were also obtained from organ donors at\u0026nbsp;Guizhou Provincial People\u0026apos;s Hospital. No organs or tissues were obtained from incarcerated individuals. All samples were fully anonymized, and the researchers had no access to any personally identifiable information at any stage of the study. The study protocol was reviewed and approved by the Medical Ethics Committee of Guizhou Provincial People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R software (version 4.3.1). A p-value of \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Differential Expression and Survival Correlation of Palmitoylation Genes in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo systematically evaluate the potential role of palmitoylation-related genes in hepatocellular carcinoma (HCC), we initially analyzed their expression differences between tumor tissues and normal liver tissues using the UALCAN platform, based on RNA sequencing data from the TCGA cohort. As shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, the vast majority of palmitoylation-related genes were significantly upregulated in HCC tissues, including PPT1, PPT2, LYPLA1, LYPLA2, and most members of the ZDHHC family (e.g., ZDHHC1, ZDHHC3-8, ZDHHC11-18, ZDHHC20-24) (p \u0026lt; 0.001). In contrast, the expression levels of ZDHHC2 and ZDHHC19 did not differ significantly between tumor and normal tissues.\u003c/p\u003e\n\u003cp\u003eBuilding upon these expression differences, we further performed Kaplan\u0026ndash;Meier survival analyses to explore the prognostic relevance of these genes. According to overall survival (OS) data from TCGA samples, patients were classified into high-expression and low-expression groups based on the median expression level of each gene. As shown in \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 1\u003c/strong\u003e, high expression of multiple genes (e.g., PPT1, PPT2, ZDHHC3, ZDHHC5, ZDHHC7, ZDHHC9, ZDHHC11, ZDHHC13, ZDHHC14, ZDHHC16, ZDHHC17, ZDHHC20\u0026ndash;24, LYPLA1, and LYPLA2) was significantly associated with poor survival outcomes. Conversely, the expression of ZDHHC2 and ZDHHC19 showed no significant association with patient prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Identification of Palmitoylation-Related Prognostic lncRNAs and Construction of a Risk Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 244 lncRNAs were identified to be significantly co-expressed with palmitoylation-related genes, among which 203 were differentially expressed between hepatocellular carcinoma (HCC) tissues and normal liver tissues. The distribution of these differentially expressed lncRNAs is depicted in \u003cstrong\u003eFigure 2A\u003c/strong\u003e, with most showing significant upregulation in tumor samples. The heatmap illustrates their expression patterns across different samples, revealing a clear clustering trend that distinguishes tumor from normal tissues (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). Subsequent survival analysis identified several of these lncRNAs as significantly associated with patients\u0026rsquo; overall survival (OS), as shown in \u003cstrong\u003eFigure 2C\u003c/strong\u003e. Based on this, a prognostic model was constructed, and two key lncRNAs\u0026mdash;NRAV and AL031985.3 were ultimately selected. Their hazard ratios and confidence intervals are presented in \u003cstrong\u003eFigure 2D\u003c/strong\u003e. The cross-validation results demonstrated the robustness of the model parameters (\u003cstrong\u003eFigure 2E\u003c/strong\u003e), while Figure 2F displays the trajectories of regression coefficients for each lncRNA under varying penalty parameters.\u003c/p\u003e\n\u003cp\u003eTwo palmitoylation-related lncRNAs with the highest prognostic value, NRAV and AL031985.3, were incorporated into a multivariate Cox regression model to construct a risk score system. Patients were stratified into high-risk and low-risk groups based on the median risk score. In the entire TCGA cohort (n = 371), the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group (p \u0026lt; 0.001, \u003cstrong\u003eFigure 3A\u003c/strong\u003e). This trend was consistently observed in both the training set (n = 186, \u003cstrong\u003eFigure 3B\u003c/strong\u003e) and the test set (n = 185, \u003cstrong\u003eFigure 3C\u003c/strong\u003e), demonstrating the robustness and predictive accuracy of the model. The risk score distribution plots (\u003cstrong\u003eFigure 3D-F\u003c/strong\u003e) revealed a marked increase in scores among high-risk patients. Correspondingly, the survival status plots (\u003cstrong\u003eFigure 3G\u0026ndash;I\u003c/strong\u003e) indicated a significantly higher mortality rate in the high-risk group. Heatmap analysis (\u003cstrong\u003eFigure 3J\u0026ndash;L\u003c/strong\u003e) further showed that NRAV and AL031985.3 were consistently upregulated in high-risk patients, suggesting their potential oncogenic roles in liver cancer progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Construction and Validation of the Nomogram Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the clinical utility of the risk score, we constructed a nomogram model integrating the risk score with clinical characteristics to predict the 1-, 3-, and 5-year overall survival rates of patients with liver cancer (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). The model contained five variables: age, sex, tumor grade, clinical stage, and risk score. Each variable corresponds to a certain score, and the cumulative total score can be used to estimate the survival probability of patients at different time points. The prediction accuracy of the model was evaluated by a calibration curve. As shown in \u003cstrong\u003eFigure 4B\u003c/strong\u003e, the predicted survival closely matched the observed outcomes at 1, 3, and 5 years, demonstrating good calibration. The C-index of the model was 0.683 (95% CI: 0.655\u0026ndash;0.712), indicating a reliable discriminative ability for survival prediction. In univariate Cox regression analysis (\u003cstrong\u003eFigure 4C\u003c/strong\u003e), clinical stage and risk score were significantly associated with OS. Further multivariate Cox regression analysis (\u003cstrong\u003eFigure 4D\u003c/strong\u003e) confirmed that both the risk score (HR = 1.557, 95% CI: 1.324\u0026ndash;1.830) and clinical stage (HR = 1.575, 95% CI: 1.270\u0026ndash;1.953) were independent prognostic factors. To evaluate the discriminative power of the model, receiver operating characteristic curves were plotted. The results showed that the predictive efficacy of the risk score variables was better than that of clinical characteristics such as gender, age, grade, and stage, with an AUC value of 0.678 (\u003cstrong\u003eFigure 4E\u003c/strong\u003e). In the prognosis prediction at different time points, the AUC values were 1 year: 0.731, 3 years: 0.678, 5 years: 0.665 (\u003cstrong\u003eFigure 4F\u003c/strong\u003e), indicating that the model had good predictive ability in the short and medium term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Functional Enrichment Analysis of Differentially Expressed Palmitoylation-Related lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the constructed risk score model, the differential expression analysis of palmitoylation-related lncRNAs between the high-risk group and the low-risk group was analyzed. The results are shown in \u003cstrong\u003eFigure 5A\u003c/strong\u003e, and the volcano plot shows the differentially expressed genes (DEGs), which include up-regulated genes (red) and down-regulated genes (green). \u003cstrong\u003eFigure 5B\u003c/strong\u003e shows the expression profiles of these DEGs in all samples, and the heat map reveals that the expression patterns between the high and low risk groups are significantly different, and gene expression shows a clear clustering trend among different samples. Additionally, Gene Ontology (GO) functional enrichment analysis was performed for these differentially expressed genes. As shown in \u003cstrong\u003eFigure 5C\u003c/strong\u003e, the enriched entries were mainly distributed in three categories: biological process (BP), cellular component (CC), and molecular function (MF). GO items included extracellular matrix organization, cell structure reconstruction, leukocyte migration, mitotic regulation, and plasma membrane-related structures. Further KEGG pathway enrichment analysis results are shown in \u003cstrong\u003eFigure 5D\u003c/strong\u003e, which shows that these genes were significantly enriched in several classical pathways, including PI3K-Akt signaling pathway, cytokine-receptor interaction pathway, cell cycle, cell adhesion molecule pathway, and a variety of viral infection and inflammation-related pathways. The number of enriched genes for each pathway is presented as bubble size, and the color reflects its significance level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Examination of immune characteristics in high- and low risk groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the constructed lncRNA risk model of palmitoylation, we further performed a comparative analysis of immune-related characteristics between the high-risk group and the low-risk group. Firstly, in this study, we used GSEA to analyze pathway enrichment based on the risk model constructed from palmitoylation-related lncRNAs. The results showed that the high-risk group was significantly enriched in several classic tumor proliferation and immune-related pathways, such as the cell cycle, cytokine-receptor interaction, ECM-receptor interaction, hematopoietic cell lineage, and neuroactive ligand-receptor interaction. These findings suggest that patients in the high-risk group may have higher tumor growth activity, along with immune imbalance and microenvironmental changes. In contrast, the low-risk group was mainly enriched in metabolism-related pathways, including \u0026beta;-alanine metabolism, butyrate metabolism, glycine/serine/threonine metabolism, linoleic acid metabolism, and primary bile acid biosynthesis. This indicates that these patients may have more active amino acid and lipid metabolism (\u003cstrong\u003eFigure 6A-B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition, the abundance of tumor-infiltrating immune cell types was evaluated. As shown in \u003cstrong\u003eFigure 6C\u003c/strong\u003e, the high-risk group exhibited significantly higher infiltration of CD8⁺ T cells, Treg cells, macrophages, and dendritic cells (aDCs, iDCs), suggesting an immune-activated yet potentially exhausted phenotype. In contrast, the low-risk group showed higher levels of B cells and neutrophils. As shown in Figure 6D, the high-risk group exhibited significantly higher scores in inflammation-promoting, T cell co-inhibition, MHC class I, and Type II IFN Response, indicating an immunosuppressive and pro-inflammatory tumor microenvironment. In contrast, the low-risk group showed higher activity in cytolytic activity, suggesting a more effective anti-tumor immune response (\u003cstrong\u003eFigure 6D\u003c/strong\u003e). Additionally, the results of immune checkpoint gene expression analysis are shown in \u003cstrong\u003eFigure 6E\u003c/strong\u003e. Several key immune checkpoint molecules were significantly up-regulated in the high-risk group, including PD-1 (PDCD1), CTLA4, LAG3, TIGIT, etc., showing broad differences in the expression of immunosuppression-related molecules between different risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Tumor Mutation Landscape and Immune Infiltration Characteristics in Different Risk Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the somatic mutation landscape of liver cancer patients under different risk scores, we used TCGA data to perform mutation profiling on all samples. As shown in \u003cstrong\u003eFigure 7A-B\u003c/strong\u003e, TP53 and CTNNB1 were the genes with the highest mutation frequencies in the high and low risk groups, reaching 39% and 23%, respectively. The main mutation type was a missense mutation, which showed a similar mutation pattern in the two groups. TTN, MUC16, RYR2, and other common mutated genes were also detected.\u003c/p\u003e\n\u003cp\u003eSubsequently, to systematically explore the differences in tumor immune microenvironment, we integrated seven immune estimation algorithms, including XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT, to quantify the infiltration level of immune cell subsets in each sample. And the correlation between them and the risk score was analyzed. As shown in \u003cstrong\u003eFigure 7C\u003c/strong\u003e, different immune cell types showed diverse risk correlation patterns. For instance, Treg cells, M1 macrophages, and CD8⁺ T cells were positively correlated with risk scores in some algorithms, while neutrophils and B-cell subsets were more correlated with low risk. In the figure, different color codes represent different algorithm sources, and the abscissa is the correlation coefficient, which intuitively reflects the relationship between the risk score and immune cell infiltration.\u003c/p\u003e\n\u003cp\u003eThe tumor mutation burden of each patient was calculated and statistically analyzed. The distribution of TMB values between groups is shown in \u003cstrong\u003eFigure 7D\u003c/strong\u003e, and the results show that there is no significant difference in TMB levels between high and low risk groups (p = 0.25). The correlation analysis between the risk score and TMB is shown in \u003cstrong\u003eFigure 7E\u003c/strong\u003e. The Pearson correlation coefficient is R = 0.064, p = 0.23, indicating no statistically significant association. To assess the impact of TMB on prognosis, patients were divided into high TMB and low TMB groups according to the median TMB value, and survival curves were plotted \u003cstrong\u003e(Figure 7F\u003c/strong\u003e). The overall survival rate of patients in the high TMB group was significantly lower than that in the low TMB group (p = 0.031). Further grouping by combining TMB and risk score (\u003cstrong\u003eFigure 7G\u003c/strong\u003e) showed that the survival rate of the high TMB + high-risk group was the lowest, while the survival prognosis of the low TMB + low-risk group was the best, and the difference between the groups was statistically significant (p \u0026lt; 0.001). To explore potential treatment implications, we analyzed the predicted drug sensitivity across high- and low-risk groups. As shown in \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e, a total of 118 compounds-including AZD5363, Bortezomib, Dasatinib, Camptothecin, Cisplatin, and Cytarabine\u0026mdash;exhibited significant differential sensitivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 qRT-PCR Validation of Palmitoylation-Related Genes and lncRNAs in Liver Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the predicted expression patterns of palmitoylation-related genes and lncRNAs identified by bioinformatics analysis, quantitative real-time PCR (qRT-PCR) was performed on liver tissue samples obtained from HCC patients and healthy individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 8A\u003c/strong\u003e presents H\u0026amp;E staining images showing normal histological architecture in healthy liver tissues and typical malignant features in HCC samples. As shown in \u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003eB-C\u003c/strong\u003e, the expression of the lncRNA NRAV was significantly upregulated in HCC tissue, while AL031985.3 exhibited an increasing trend in HCC tissues, the difference was not statistically significant. Among the 30 palmitoylation-related genes analyzed, 12 genes-including ZDHHC3, ZDHHC15, ZDHHC16, ZDHHC19, ZDHHC21, ZDHHC22, PPT1, Abhd17a, Abhd17b, Abhd17c, LYPLA1, and LYPLA2- were significantly upregulated in tumor tissues. These results indicate active involvement of the palmitoylation pathway in HCC.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically evaluated the prognostic significance and immunoregulatory roles of palmitoylation-related lncRNAs and their role in immune regulation in hepatocellular carcinoma (HCC). By integrating data from the TCGA database with clinical follow-up information, we identified lncRNAs that were significantly co-expressed with palmitoylation regulatory genes. Based on these candidate lncRNAs, NRAV and AL031985.3 were further screened out as key molecules closely related to the overall survival of patients, and a two-lncRNA risk score model was constructed. The model demonstrated reliable stratification capacity across the training set, test set, and the whole sample, and could effectively distinguish the survival prognosis of HCC patients. It is worth noting that this model not only has independent prognostic value, but also is significantly correlated with immune cell infiltration, immune function, and immune checkpoint molecules expression characteristics. At the same time, it shows good discrimination ability in the predictive sensitivity analysis of multiple anticancer drugs.\u003c/p\u003e\n\u003cp\u003eIn this study, we initially observed that the majority of key palmitoylation regulatory genes, including PPT1, PPT2, LYPLA1, LYPLA2, and several ZDHHC family members, were significantly up-regulated in HCC tissues. Moreover, their elevated expression levels were strongly associated with poorer overall survival, suggesting that these genes may contribute to HCC development and progression by modulating protein post-translational modifications. This trend was also verified by qPCR experiments, further enhancing the reliability of the results of bioinformatics analysis. We identified 244 lncRNAs that were significantly co-expressed with palmitoylation-related genes. Among them, 203 lncRNAs were differentially expressed in tumor tissues. Survival analysis revealed that NRAV and AL031985.3 had the strongest prognostic value.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, lncRNAs have gained increasing attention for their roles in regulating lipid modifications. A study published in 2025 reported that lncZBTB10 promotes prostate cancer progression and drug resistance by enhancing the S-palmitoylation of the androgen receptor (AR), which increases its transcriptional activity\u003csup\u003e12\u003c/sup\u003e. In terms of functional studies, several reports have shown that NRAV (Negative Regulator of Antiviral Response), a long non-coding RNA involved in immune regulation, plays important roles not only in viral infections but also in tumor progression. Ouyang et al. found that NRAV can suppress the transcription of interferon-stimulated genes (ISGs), reduce antiviral factor expression, and weaken innate immunity. More recently, NRAV has been linked to cancer. Wu et al. reported that NRAV is significantly overexpressed in gastric cancer and promotes tumor cell proliferation and metastasis by modulating IFN signaling and inflammatory cytokine expression. Zhang et al. found that NRAV is located at the gastric cancer susceptibility locus 12q24.31 and promotes tumor progression by reprogramming glucose metabolism. In hepatocellular carcinoma (HCC), several studies also confirmed the clinical significance of NRAV. Liu et al. identified NRAV as a potential independent prognostic biomarker for HCC. Zong et al. further demonstrated that NRAV regulates ferroptosis through the miR-375-3P/SLC7A11 axis, affecting tumor cell survival and patient prognosis. Together, these findings suggest that NRAV may promote tumor progression by regulating interferon signaling, metabolic pathways, and programmed cell death, and could serve as a potential target for liver cancer diagnosis and therapy\u0026nbsp;\u003cstrong\u003e\u003csup\u003e42-48\u003c/sup\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, AL031985.3 was identified as a key lncRNA co-expressed with palmitoylation-related genes and showed stable prognostic value in the two-lncRNA risk model. Recent studies have also highlighted its role in tumor immunity and metabolism. Luo et al. found that AL031985.3 is involved in copper metabolism and redox balance, and is associated with immune cell infiltration, suggesting a role in shaping the tumor immune microenvironment. Shen et al. reported that it is positively correlated with several immune checkpoint genes, including PD-1, LAG3, and CTLA4, and may contribute to immune escape in HCC. In addition, Chen et al. included AL031985.3 in a ferroptosis-related lncRNA signature and linked it to stress response and metabolic dysfunction in cancer cells \u003csup\u003e49-51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThrough differential expression and GO/KEGG enrichment analysis, we found that the key genes distinguishing the high- and low-risk groups were mainly enriched in extracellular matrix remodeling, cell adhesion, cell cycle control, PI3K-Akt signaling, inflammation, and viral infection pathways. In recent years, growing evidence has shown that palmitoylation reversible lipid modification-plays a crucial role in cancer development. It regulates protein localization, stability, and signaling activity, thereby affecting several cancer-related pathways. Runkle et al. showed that palmitoylation of EGFR by DHHC20 plays an important role in controlling EGFR signaling. This modification helps anchor the C-terminal tail of EGFR to the cell membrane, preventing abnormal activation. When DHHC20 is inhibited, EGFR loses its palmitoylation, leading to continuous signaling and increased cancer cell migration\u003csup\u003e52\u003c/sup\u003e. In addition, FASN (fatty acid synthase) has been shown to promote the stability and oncogenic activity of mutant p53 by enhancing its palmitoylation. It provides palmitic acid as a substrate, helping to stabilize mutant p53 protein and support its gain-of-function effects\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the palmitoylation-related lncRNA prognostic model developed in this study, we further evaluated the immune microenvironment and molecular differences in HCC patients from different risk groups. GSEA analysis showed that the high-risk group was significantly enriched in several classical pathways related to tumor growth and immune response, including the cell cycle, cytokine-receptor interaction, ECM-receptor interaction, hematopoietic cell differentiation, and neurotransmitter signaling. These activated pathways suggest that high-risk patients may have stronger tumor proliferation and a more complex, dysregulated immune microenvironment. Recent studies have shown that palmitoylation of SCAP can activate cholesterol synthesis, leading to macrophage polarization and Treg cell accumulation, which promotes an immunosuppressive tumor microenvironment and enhances immune escape in HCC\u003csup\u003e54\u003c/sup\u003e. Palmitoylation of Foxp3 helps stabilize Treg cells and strengthen their immunosuppressive function in tumors, which supports the development of an immunosuppressive tumor microenvironment in the high-risk group \u003csup\u003e13\u003c/sup\u003e. In contrast, the low-risk group was mainly enriched in metabolism-related pathways, especially those involving amino acids and lipids, such as \u0026beta;-alanine, butyrate, glycine/serine/threonine, linoleic acid metabolism, and bile acid synthesis. This shift in metabolic and immune pathways may help explain the better prognosis of low-risk patients and suggests that palmitoylation-related lncRNAs may play a role in regulating tumor metabolism and immunity.\u003c/p\u003e\n\u003cp\u003eFor immune cell infiltration, the high-risk group showed higher levels of CD8⁺ T cells, Treg cells, dendritic cells (aDC/iDC), and macrophages. This suggests an \u0026ldquo;immune-activated but exhausted\u0026rdquo; state\u003csup\u003e55, 56\u003c/sup\u003e. Such a profile-active on the surface but functionally suppressed, is often linked to poor immunotherapy response and tumor progression. However, the infiltration of B cells and neutrophils was higher in the low-risk group, suggesting that it may maintain a stable immune surveillance state. The membrane palmitoylation protein MPP1 can significantly enhance CD8+ T cell infiltration by regulating the USP12/CCL5 axis, suggesting that the low-risk group may maintain effective immune surveillance due to the lack of such inhibitory mechanisms\u003csup\u003e57\u003c/sup\u003e. The immune function score also supports these findings. The high-risk group showed higher scores in inflammation-promoting, T cell co-inhibition, MHC I signaling, and type II interferon response, indicating an immunosuppressive and inflammatory microenvironment. In contrast, the low-risk group had higher cytolytic activity, suggesting a stronger anti-tumor immune response.\u003c/p\u003e\n\u003cp\u003eNotably, the expression of immune checkpoint genes such as PD-1, CTLA-4, LAG3, and TIGIT was significantly higher in the high-risk group. This suggests a stronger potential for immune escape and indicates that these patients may benefit more from immune checkpoint inhibitor (ICI) therapy. Studies have also shown that palmitoylation is closely related to immune checkpoint regulation. Recent studies have shown that palmitoylation of immune checkpoint proteins such as TIM-3 promotes immune exhaustion and suppresses antitumor immunity, revealing a potential link between lipid modification and immune evasion\u003csup\u003e58\u003c/sup\u003e. Although there was no clear linear correlation between tumor mutation burden (TMB) and the risk score, survival analysis showed that patients with both high TMB and high risk had the worst outcomes, while those with low TMB and low risk had the best. This suggests that combining TMB with the lncRNA model provides a better prediction of immunogenicity and patient prognosis. It also shows that TMB alone is not enough to assess tumor immune status -the tumor microenvironment and regulatory factors must also be considered.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003ePalmitoylation-related lncRNAs play an important role in the progression and immune regulation of hepatocellular carcinoma. NRAV and AL031985.3 may serve as novel prognostic biomarkers and potential targets for immunotherapy in HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. All datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Foundation Project of Guizhou Health Commission (grant numbers gzwkj2025-299 and gzwkj2025-616) and Talent Fund of Guizhou Provincial People\u0026apos;s Hospital (Talent Project of the Institute [2024]-20 and [2023]-43).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before sample collection. The study protocol was reviewed and approved by the Medical Ethics Committee of Guizhou Provincial People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consents (Consent to Participate) were obtained from all participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors give consent for publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChan, Y. T.; Zhang, C.; Wu, J.; Lu, P.; Xu, L.; Yuan, H.; Feng, Y.; Chen, Z. S.; Wang, N., Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma. \u003cem\u003eMol Cancer \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e23\u003c/em\u003e (1), 189.\u003c/li\u003e\n\u003cli\u003eLee, Y. T.; Wang, J. J.; Luu, M.; Noureddin, M.; Kosari, K.; Agopian, V. G.; Rich, N. E.; Lu, S. C.; Tseng, H. R.; Nissen, N. N.; Singal, A. G.; Yang, J. D., The Mortality and Overall Survival Trends of Primary Liver Cancer in the United States. \u003cem\u003eJ Natl Cancer Inst \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e113\u003c/em\u003e (11), 1531-1541.\u003c/li\u003e\n\u003cli\u003eLi, C.; Yang, J.; Liu, C.; Wang, X.; Zhang, L., Long non-coding RNAs in hepatocellular carcinoma: Ordering of the complicated lncRNA regulatory network and novel strategies for HCC clinical diagnosis and treatment. \u003cem\u003ePharmacol Res \u003c/em\u003e\u003cstrong\u003e2020,\u003c/strong\u003e \u003cem\u003e158\u003c/em\u003e, 104848.\u003c/li\u003e\n\u003cli\u003eLv, E.; Sheng, J.; Yu, C.; Rao, D.; Huang, W., LncRNA influence sequential steps of hepatocellular carcinoma metastasis. \u003cem\u003eBiomed Pharmacother \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e136\u003c/em\u003e, 111224.\u003c/li\u003e\n\u003cli\u003eZhang, B.; Bao, W.; Zhang, S.; Chen, B.; Zhou, X.; Zhao, J.; Shi, Z.; Zhang, T.; Chen, Z.; Wang, L.; Zheng, X.; Chen, G.; Wang, Y., LncRNA HEPFAL accelerates ferroptosis in hepatocellular carcinoma by regulating SLC7A11 ubiquitination. \u003cem\u003eCell Death Dis \u003c/em\u003e\u003cstrong\u003e2022,\u003c/strong\u003e \u003cem\u003e13\u003c/em\u003e (8), 734.\u003c/li\u003e\n\u003cli\u003eSu, T.; Zhang, N.; Wang, T.; Zeng, J.; Li, W.; Han, L.; Yang, M., Super Enhancer-Regulated LncRNA LINC01089 Induces Alternative Splicing of DIAPH3 to Drive Hepatocellular Carcinoma Metastasis. \u003cem\u003eCancer Res \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e83\u003c/em\u003e (24), 4080-4094.\u003c/li\u003e\n\u003cli\u003eCha, H.; Kim, M.; Ahn, N.; Jeong, S. D.; Ignatova, E.; Chi, S. W.; Kim, H. H.; Hwang, J., Role of UPF1 in lncRNA-HEIH regulation for hepatocellular carcinoma therapy. \u003cem\u003eExp Mol Med \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e56\u003c/em\u003e (2), 344-354.\u003c/li\u003e\n\u003cli\u003eHuang, Z.; Zhou, J. K.; Peng, Y.; He, W.; Huang, C., The role of long noncoding RNAs in hepatocellular carcinoma. \u003cem\u003eMol Cancer \u003c/em\u003e\u003cstrong\u003e2020,\u003c/strong\u003e \u003cem\u003e19\u003c/em\u003e (1), 77.\u003c/li\u003e\n\u003cli\u003eZhu, Z.; Feng, S.; Zeng, A.; Song, L., Advances in Palmitoylation: A key Regulator of liver cancer development and therapeutic targets. \u003cem\u003eBiochem Pharmacol \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e234\u003c/em\u003e, 116810.\u003c/li\u003e\n\u003cli\u003eZhou, B.; Hao, Q.; Liang, Y.; Kong, E., Protein palmitoylation in cancer: molecular functions and therapeutic potential. \u003cem\u003eMol Oncol \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e17\u003c/em\u003e (1), 3-26.\u003c/li\u003e\n\u003cli\u003eBusquets-Hern\u0026aacute;ndez, C.; Triola, G., Palmitoylation as a Key Regulator of Ras Localization and Function. \u003cem\u003eFront Mol Biosci \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e8\u003c/em\u003e, 659861.\u003c/li\u003e\n\u003cli\u003eLin, S. C.; Cheng, Y. S.; Lin, Y. S.; Nguyen, T. M. H.; Chiu, W. T.; Tsai, Y. C.; Chen, H. Y.; Lin, T. Y.; Lin, S. C., The long noncoding RNA lncZBTB10 facilitates AR function via S-palmitoylation to promote prostate cancer progression and abiraterone resistance. \u003cem\u003eBr J Cancer \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e132\u003c/em\u003e (7), 587-598.\u003c/li\u003e\n\u003cli\u003eZhou, B.; Zhang, M.; Ma, H.; Wang, Y.; Qiu, J.; Liu, Y.; Lu, L.; Li, T.; Zhang, L.; Huang, R.; Gu, Y.; Kong, E.; Liang, Y., Distinct palmitoylation of Foxp3 regulates the function of regulatory T cells via palmitoyltransferases. \u003cem\u003eCell Mol Immunol \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e21\u003c/em\u003e (7), 787-789.\u003c/li\u003e\n\u003cli\u003eChandrashekar, D. S.; Bashel, B.; Balasubramanya, S. A. H.; Creighton, C. J.; Ponce-Rodriguez, I.; Chakravarthi, B.; Varambally, S., UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. \u003cem\u003eNeoplasia \u003c/em\u003e\u003cstrong\u003e2017,\u003c/strong\u003e \u003cem\u003e19\u003c/em\u003e (8), 649-658.\u003c/li\u003e\n\u003cli\u003eZhang, Z.; Hernandez, K.; Savage, J.; Li, S.; Miller, D.; Agrawal, S.; Ortuno, F.; Staudt, L. M.; Heath, A.; Grossman, R. L., Uniform genomic data analysis in the NCI Genomic Data Commons. \u003cem\u003eNat Commun \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e12\u003c/em\u003e (1), 1226.\u003c/li\u003e\n\u003cli\u003eConesa, A.; Madrigal, P.; Tarazona, S.; Gomez-Cabrero, D.; Cervera, A.; McPherson, A.; Szcześniak, M. W.; Gaffney, D. J.; Elo, L. L.; Zhang, X.; Mortazavi, A., A survey of best practices for RNA-seq data analysis. \u003cem\u003eGenome Biol \u003c/em\u003e\u003cstrong\u003e2016,\u003c/strong\u003e \u003cem\u003e17\u003c/em\u003e, 13.\u003c/li\u003e\n\u003cli\u003eChamberlain, L. H.; Shipston, M. J., The physiology of protein S-acylation. \u003cem\u003ePhysiol Rev \u003c/em\u003e\u003cstrong\u003e2015,\u003c/strong\u003e \u003cem\u003e95\u003c/em\u003e (2), 341-76.\u003c/li\u003e\n\u003cli\u003eLi, M.; Zhang, L.; Chen, C. W., Diverse Roles of Protein Palmitoylation in Cancer Progression, Immunity, Stemness, and Beyond. \u003cem\u003eCells \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e12\u003c/em\u003e (18).\u003c/li\u003e\n\u003cli\u003eChen, Y.; Li, Y.; Wu, L., Protein S-palmitoylation modification: implications in tumor and tumor immune microenvironment. \u003cem\u003eFront Immunol \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e15\u003c/em\u003e, 1337478.\u003c/li\u003e\n\u003cli\u003eMudge, J. M.; Carbonell-Sala, S.; Diekhans, M.; Martinez, J. G.; Hunt, T.; Jungreis, I.; Loveland, J. E.; Arnan, C.; Barnes, I.; Bennett, R.; Berry, A.; Bignell, A.; Cerd\u0026aacute;n-V\u0026eacute;lez, D.; Cochran, K.; Cort\u0026eacute;s, L. T.; Davidson, C.; Donaldson, S.; Dursun, C.; Fatima, R.; Hardy, M.; Hebbar, P.; Hollis, Z.; James, B. T.; Jiang, Y.; Johnson, R.; Kaur, G.; Kay, M.; Mangan, R. J.; Maquedano, M.; G\u0026oacute;mez, L. M.; Mathlouthi, N.; Merritt, R.; Ni, P.; Palumbo, E.; Perteghella, T.; Pozo, F.; Raj, S.; Sisu, C.; Steed, E.; Sumathipala, D.; Suner, M. M.; Uszczynska-Ratajczak, B.; Wass, E.; Yang, Y. T.; Zhang, D.; Finn, R. D.; Gerstein, M.; Guig\u0026oacute;, R.; Hubbard, T. J. P.; Kellis, M.; Kundaje, A.; Paten, B.; Tress, M. L.; Birney, E.; Martin, F. J.; Frankish, A., GENCODE 2025: reference gene annotation for human and mouse. \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e53\u003c/em\u003e (D1), D966-d975.\u003c/li\u003e\n\u003cli\u003eRitchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K., limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2015,\u003c/strong\u003e \u003cem\u003e43\u003c/em\u003e (7), e47.\u003c/li\u003e\n\u003cli\u003eLi, Q.; Yu, X.; Chaudhary, R.; Slebos, R. J. C.; Chung, C. H.; Wang, X., lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA. \u003cem\u003eBMC Genomics \u003c/em\u003e\u003cstrong\u003e2019,\u003c/strong\u003e \u003cem\u003e20\u003c/em\u003e (1), 539.\u003c/li\u003e\n\u003cli\u003eLiu, H.; Zhang, Q.; Lou, Q.; Zhang, X.; Cui, Y.; Wang, P.; Yang, F.; Wu, F.; Wang, J.; Fan, T.; Li, S., Differential Analysis of lncRNA, miRNA and mRNA Expression Profiles and the Prognostic Value of lncRNA in Esophageal Cancer. \u003cem\u003ePathol Oncol Res \u003c/em\u003e\u003cstrong\u003e2020,\u003c/strong\u003e \u003cem\u003e26\u003c/em\u003e (2), 1029-1039.\u003c/li\u003e\n\u003cli\u003eFu, Y.; Wei, X.; Han, Q.; Le, J.; Ma, Y.; Lin, X.; Xu, Y.; Liu, N.; Wang, X.; Kong, X.; Gu, J.; Tong, Y.; Wu, H., Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma. \u003cem\u003eBMC Cancer \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e21\u003c/em\u003e (1), 1165.\u003c/li\u003e\n\u003cli\u003eFrost, H. R.; Amos, C. I., Gene set selection via LASSO penalized regression (SLPR). \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2017,\u003c/strong\u003e \u003cem\u003e45\u003c/em\u003e (12), e114.\u003c/li\u003e\n\u003cli\u003eFriedman, J.; Hastie, T.; Tibshirani, R., Regularization Paths for Generalized Linear Models via Coordinate Descent. \u003cem\u003eJ Stat Softw \u003c/em\u003e\u003cstrong\u003e2010,\u003c/strong\u003e \u003cem\u003e33\u003c/em\u003e (1), 1-22.\u003c/li\u003e\n\u003cli\u003eZhang, B.; Wu, Q.; Li, B.; Wang, D.; Wang, L.; Zhou, Y. L., m(6)A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. \u003cem\u003eMol Cancer \u003c/em\u003e\u003cstrong\u003e2020,\u003c/strong\u003e \u003cem\u003e19\u003c/em\u003e (1), 53.\u003c/li\u003e\n\u003cli\u003eFu, Y.; Si, A.; Wei, X.; Lin, X.; Ma, Y.; Qiu, H.; Guo, Z.; Pan, Y.; Zhang, Y.; Kong, X.; Li, S.; Shi, Y.; Wu, H., Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma. \u003cem\u003eBMC Genomics \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e24\u003c/em\u003e (1), 89.\u003c/li\u003e\n\u003cli\u003eIasonos, A.; Schrag, D.; Raj, G. V.; Panageas, K. S., How to build and interpret a nomogram for cancer prognosis. \u003cem\u003eJ Clin Oncol \u003c/em\u003e\u003cstrong\u003e2008,\u003c/strong\u003e \u003cem\u003e26\u003c/em\u003e (8), 1364-70.\u003c/li\u003e\n\u003cli\u003eSteyerberg, E. W.; Vickers, A. J.; Cook, N. R.; Gerds, T.; Gonen, M.; Obuchowski, N.; Pencina, M. J.; Kattan, M. W., Assessing the performance of prediction models: a framework for traditional and novel measures. \u003cem\u003eEpidemiology \u003c/em\u003e\u003cstrong\u003e2010,\u003c/strong\u003e \u003cem\u003e21\u003c/em\u003e (1), 128-38.\u003c/li\u003e\n\u003cli\u003eAshburner, M.; Ball, C. A.; Blake, J. A.; Botstein, D.; Butler, H.; Cherry, J. M.; Davis, A. P.; Dolinski, K.; Dwight, S. S.; Eppig, J. T.; Harris, M. A.; Hill, D. P.; Issel-Tarver, L.; Kasarskis, A.; Lewis, S.; Matese, J. C.; Richardson, J. E.; Ringwald, M.; Rubin, G. M.; Sherlock, G., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. \u003cem\u003eNat Genet \u003c/em\u003e\u003cstrong\u003e2000,\u003c/strong\u003e \u003cem\u003e25\u003c/em\u003e (1), 25-9.\u003c/li\u003e\n\u003cli\u003eKanehisa, M.; Goto, S., KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2000,\u003c/strong\u003e \u003cem\u003e28\u003c/em\u003e (1), 27-30.\u003c/li\u003e\n\u003cli\u003eSubramanian, A.; Tamayo, P.; Mootha, V. K.; Mukherjee, S.; Ebert, B. L.; Gillette, M. A.; Paulovich, A.; Pomeroy, S. L.; Golub, T. R.; Lander, E. S.; Mesirov, J. P., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc Natl Acad Sci U S A \u003c/em\u003e\u003cstrong\u003e2005,\u003c/strong\u003e \u003cem\u003e102\u003c/em\u003e (43), 15545-50.\u003c/li\u003e\n\u003cli\u003eAran, D.; Hu, Z.; Butte, A. J., xCell: digitally portraying the tissue cellular heterogeneity landscape. \u003cem\u003eGenome Biol \u003c/em\u003e\u003cstrong\u003e2017,\u003c/strong\u003e \u003cem\u003e18\u003c/em\u003e (1), 220.\u003c/li\u003e\n\u003cli\u003eRacle, J.; de Jonge, K.; Baumgaertner, P.; Speiser, D. E.; Gfeller, D., Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. \u003cem\u003eElife \u003c/em\u003e\u003cstrong\u003e2017,\u003c/strong\u003e \u003cem\u003e6\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eNewman, A. M.; Liu, C. L.; Green, M. R.; Gentles, A. J.; Feng, W.; Xu, Y.; Hoang, C. D.; Diehn, M.; Alizadeh, A. A., Robust enumeration of cell subsets from tissue expression profiles. \u003cem\u003eNat Methods \u003c/em\u003e\u003cstrong\u003e2015,\u003c/strong\u003e \u003cem\u003e12\u003c/em\u003e (5), 453-7.\u003c/li\u003e\n\u003cli\u003eEllrott, K.; Bailey, M. H.; Saksena, G.; Covington, K. R.; Kandoth, C.; Stewart, C.; Hess, J.; Ma, S.; Chiotti, K. E.; McLellan, M.; Sofia, H. J.; Hutter, C.; Getz, G.; Wheeler, D.; Ding, L., Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. \u003cem\u003eCell Syst \u003c/em\u003e\u003cstrong\u003e2018,\u003c/strong\u003e \u003cem\u003e6\u003c/em\u003e (3), 271-281.e7.\u003c/li\u003e\n\u003cli\u003eMayakonda, A.; Lin, D. C.; Assenov, Y.; Plass, C.; Koeffler, H. P., Maftools: efficient and comprehensive analysis of somatic variants in cancer. \u003cem\u003eGenome Res \u003c/em\u003e\u003cstrong\u003e2018,\u003c/strong\u003e \u003cem\u003e28\u003c/em\u003e (11), 1747-1756.\u003c/li\u003e\n\u003cli\u003eChan, T. A.; Yarchoan, M.; Jaffee, E.; Swanton, C.; Quezada, S. A.; Stenzinger, A.; Peters, S., Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. \u003cem\u003eAnn Oncol \u003c/em\u003e\u003cstrong\u003e2019,\u003c/strong\u003e \u003cem\u003e30\u003c/em\u003e (1), 44-56.\u003c/li\u003e\n\u003cli\u003eSamstein, R. M.; Lee, C. H.; Shoushtari, A. N.; Hellmann, M. D.; Shen, R.; Janjigian, Y. Y.; Barron, D. A.; Zehir, A.; Jordan, E. J.; Omuro, A.; Kaley, T. J.; Kendall, S. M.; Motzer, R. J.; Hakimi, A. A.; Voss, M. H.; Russo, P.; Rosenberg, J.; Iyer, G.; Bochner, B. H.; Bajorin, D. F.; Al-Ahmadie, H. A.; Chaft, J. E.; Rudin, C. M.; Riely, G. J.; Baxi, S.; Ho, A. L.; Wong, R. J.; Pfister, D. G.; Wolchok, J. D.; Barker, C. A.; Gutin, P. H.; Brennan, C. W.; Tabar, V.; Mellinghoff, I. K.; DeAngelis, L. M.; Ariyan, C. E.; Lee, N.; Tap, W. D.; Gounder, M. M.; D\u0026apos;Angelo, S. P.; Saltz, L.; Stadler, Z. K.; Scher, H. I.; Baselga, J.; Razavi, P.; Klebanoff, C. A.; Yaeger, R.; Segal, N. H.; Ku, G. Y.; DeMatteo, R. P.; Ladanyi, M.; Rizvi, N. A.; Berger, M. F.; Riaz, N.; Solit, D. B.; Chan, T. A.; Morris, L. G. T., Tumor mutational load predicts survival after immunotherapy across multiple cancer types. \u003cem\u003eNat Genet \u003c/em\u003e\u003cstrong\u003e2019,\u003c/strong\u003e \u003cem\u003e51\u003c/em\u003e (2), 202-206.\u003c/li\u003e\n\u003cli\u003eChalmers, Z. R.; Connelly, C. F.; Fabrizio, D.; Gay, L.; Ali, S. M.; Ennis, R.; Schrock, A.; Campbell, B.; Shlien, A.; Chmielecki, J.; Huang, F.; He, Y.; Sun, J.; Tabori, U.; Kennedy, M.; Lieber, D. S.; Roels, S.; White, J.; Otto, G. A.; Ross, J. S.; Garraway, L.; Miller, V. A.; Stephens, P. J.; Frampton, G. M., Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. \u003cem\u003eGenome Med \u003c/em\u003e\u003cstrong\u003e2017,\u003c/strong\u003e \u003cem\u003e9\u003c/em\u003e (1), 34.\u003c/li\u003e\n\u003cli\u003eWu, Y.; Wang, D.; Zhao, J.; Guo, J.; Gao, Z.; Xu, Q.; Hu, X., NRAV promoted the malignant progression of gastric cancer. \u003cem\u003eGene \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e937\u003c/em\u003e, 149134.\u003c/li\u003e\n\u003cli\u003eBaysal, A.; Kıymaz, Y.; Şahin, N.; Bakır, M., Investigation of Long Noncoding RNA-NRAV and Long Noncoding RNA-Lethe Expression in Crimean-Congo Hemorrhagic Fever. \u003cem\u003eJ Med Virol \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e96\u003c/em\u003e (12), e70142.\u003c/li\u003e\n\u003cli\u003eLiu, J.; Li, W.; Lu, R.; Xu, J.; Jiang, C.; Duan, J.; Zhang, L.; Wang, G.; Chen, J., Investigation of the feasibility of NRAV as a biomarker for hepatocellular carcinoma. \u003cem\u003eOncol Res \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e32\u003c/em\u003e (4), 717-726.\u003c/li\u003e\n\u003cli\u003eZong, K.; Lin, C.; Luo, K.; Deng, Y.; Wang, H.; Hu, J.; Chen, S.; Li, R., Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via the miR-375-3P/SLC7A11 axis. \u003cem\u003eBMC Cancer \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e24\u003c/em\u003e (1), 496.\u003c/li\u003e\n\u003cli\u003eZhang, Y.; Gao, Y.; Li, F.; Qi, Q.; Li, Q.; Gu, Y.; Zheng, Z.; Hu, B.; Wang, T.; Zhang, E.; Xu, H.; Liu, L.; Tian, T.; Jin, G.; Yan, C., Long non-coding RNA NRAV in the 12q24.31 risk locus drives gastric cancer development through glucose metabolism reprogramming. \u003cem\u003eCarcinogenesis \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e45\u003c/em\u003e (1-2), 23-34.\u003c/li\u003e\n\u003cli\u003eOuyang, J.; Zhu, X.; Chen, Y.; Wei, H.; Chen, Q.; Chi, X.; Qi, B.; Zhang, L.; Zhao, Y.; Gao, G. F.; Wang, G.; Chen, J. L., NRAV, a long noncoding RNA, modulates antiviral responses through suppression of interferon-stimulated gene transcription. \u003cem\u003eCell Host Microbe \u003c/em\u003e\u003cstrong\u003e2014,\u003c/strong\u003e \u003cem\u003e16\u003c/em\u003e (5), 616-26.\u003c/li\u003e\n\u003cli\u003eLi, J.; Li, M.; Wang, X.; Sun, M.; Ma, C.; Liang, W.; Gao, X.; Wei, L., Long Noncoding RNA NRAV Promotes Respiratory Syncytial Virus Replication by Targeting the MicroRNA miR-509-3p/Rab5c Axis To Regulate Vesicle Transportation. \u003cem\u003eJ Virol \u003c/em\u003e\u003cstrong\u003e2020,\u003c/strong\u003e \u003cem\u003e94\u003c/em\u003e (10).\u003c/li\u003e\n\u003cli\u003eLuo, R.; Huang, S.; Shi, X.; Xu, H.; Peng, J.; Lei, W.; Li, S.; Zhang, W.; Shi, L.; Peng, Y.; Tang, X., Copper metabolism-related lncRNAs predict prognosis and immune landscape in liver cancer patients. \u003cem\u003eTransl Cancer Res \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e13\u003c/em\u003e (11), 5784-5800.\u003c/li\u003e\n\u003cli\u003eShen, D.; Sha, L.; Yang, L.; Gu, X., Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1. \u003cem\u003eDiscov Oncol \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e16\u003c/em\u003e (1), 544.\u003c/li\u003e\n\u003cli\u003eChen, Z. A.; Tian, H.; Yao, D. M.; Zhang, Y.; Feng, Z. J.; Yang, C. J., Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma. \u003cem\u003eFront Oncol \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e \u003cem\u003e11\u003c/em\u003e, 738477.\u003c/li\u003e\n\u003cli\u003eRunkle, K. B.; Kharbanda, A.; Stypulkowski, E.; Cao, X. J.; Wang, W.; Garcia, B. A.; Witze, E. S., Inhibition of DHHC20-Mediated EGFR Palmitoylation Creates a Dependence on EGFR Signaling. \u003cem\u003eMol Cell \u003c/em\u003e\u003cstrong\u003e2016,\u003c/strong\u003e \u003cem\u003e62\u003c/em\u003e (3), 385-396.\u003c/li\u003e\n\u003cli\u003eLiu, J.; Shen, Y.; Liu, J.; Xu, D.; Chang, C. Y.; Wang, J.; Zhou, J.; Haffty, B. G.; Zhang, L.; Bargonetti, J.; De, S.; Hu, W.; Feng, Z., Lipogenic enzyme FASN promotes mutant p53 accumulation and gain-of-function through palmitoylation. \u003cem\u003eNat Commun \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e16\u003c/em\u003e (1), 1762.\u003c/li\u003e\n\u003cli\u003eWu, M.; Zhou, X.; Zhou, X.; Wang, G.; Zeng, Y.; Li, J.; Prochownik, E. V.; Wang, F.; Li, Y., ZDHHC3-mediated SCAP S-acylation promotes cholesterol biosynthesis and tumor immune escape in hepatocellular carcinoma. \u003cem\u003eCell Rep \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e43\u003c/em\u003e (11), 114962.\u003c/li\u003e\n\u003cli\u003eLiu, Z.; Zhang, Y.; Ma, N.; Yang, Y.; Ma, Y.; Wang, F.; Wang, Y.; Wei, J.; Chen, H.; Tartarone, A.; Velotta, J. B.; Dayyani, F.; Gabriel, E.; Wakefield, C. J.; Kidane, B.; Carbonelli, C.; Long, L.; Liu, Z.; Su, J.; Li, Z., Progenitor-like exhausted SPRY1(+)CD8(+) T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma. \u003cem\u003eCancer Cell \u003c/em\u003e\u003cstrong\u003e2023,\u003c/strong\u003e \u003cem\u003e41\u003c/em\u003e (11), 1852-1870.e9.\u003c/li\u003e\n\u003cli\u003eLu, J. C.; Wu, L. L.; Sun, Y. N.; Huang, X. Y.; Gao, C.; Guo, X. J.; Zeng, H. Y.; Qu, X. D.; Chen, Y.; Wu, D.; Pei, Y. Z.; Meng, X. L.; Zheng, Y. M.; Liang, C.; Zhang, P. F.; Cai, J. B.; Ding, Z. B.; Yang, G. H.; Ren, N.; Huang, C.; Wang, X. Y.; Gao, Q.; Sun, Q. M.; Shi, Y. H.; Qiu, S. J.; Ke, A. W.; Shi, G. M.; Zhou, J.; Sun, Y. D.; Fan, J., Macro CD5L(+) deteriorates CD8(+)T cells exhaustion and impairs combination of Gemcitabine-Oxaliplatin-Lenvatinib-anti-PD1 therapy in intrahepatic cholangiocarcinoma. \u003cem\u003eNat Commun \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e15\u003c/em\u003e (1), 621.\u003c/li\u003e\n\u003cli\u003eWu, Q.; Lv, J.; Li, X., Membrane palmitoylated protein MPP1 inhibits immune escape by regulating the USP12/ CCL5 axis in urothelial carcinoma. \u003cem\u003eInt Immunopharmacol \u003c/em\u003e\u003cstrong\u003e2025,\u003c/strong\u003e \u003cem\u003e146\u003c/em\u003e, 113802.\u003c/li\u003e\n\u003cli\u003eZhang, Z.; Ren, C.; Xiao, R.; Ma, S.; Liu, H.; Dou, Y.; Fan, Y.; Wang, S.; Zhan, P.; Gao, C.; Yue, X.; Li, C.; Gao, L.; Liang, X.; Wu, Z.; Ma, C., Palmitoylation of TIM-3 promotes immune exhaustion and restrains antitumor immunity. \u003cem\u003eSci Immunol \u003c/em\u003e\u003cstrong\u003e2024,\u003c/strong\u003e \u003cem\u003e9\u003c/em\u003e (101), eadp7302.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Long non-coding RNA, Palmitoylation","lastPublishedDoi":"10.21203/rs.3.rs-6942496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6942496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) has a high mortality rate. Current immunotherapy and targeted treatments have limited effectiveness. Palmitoylation, a reversible lipid modification, is increasingly recognized for its roles in tumor progression and immune regulation. However, the function of palmitoylation-related long non-coding RNAs (lncRNAs) in HCC remains unclear. Using TCGA data, we identified key palmitoylation-related lncRNAs and developed a prognostic model based on NRAV and AL031985.3. Patients were divided into high- and low-risk groups. Immune cell infiltration, immune checkpoint gene expression, tumor mutation burden (TMB), and drug sensitivity were analyzed. Furthermore, qRT-PCR was performed to validate lncRNA expression in clinical liver tissue samples from healthy organ donors (normal liver) and HCC patients (tumor tissue). The model effectively distinguished survival differences. High-risk patients showed increased Treg cells and immune checkpoint expression, indicating an immunosuppressive phenotype. Functional enrichment revealed associations with cell cycle, immune response, and inflammation pathways. Combining TMB with the risk score improved prognostic accuracy. Both NRAV and AL031985.3 were significantly upregulated in tumor tissues compared to normal liver tissues, confirming their diagnostic and prognostic potential. NRAV and AL031985.3 are promising prognostic biomarkers and immunotherapy targets in HCC. This study provides new insights into the role of palmitoylation-related lncRNAs in liver cancer immune regulation.\u003c/p\u003e","manuscriptTitle":"Identification of palmitoylation-related lncRNAs as prognostic biomarkers and immune modulators in liver cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 16:23:43","doi":"10.21203/rs.3.rs-6942496/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T09:53:16+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"325645134097238593414109737859544270999","date":"2025-08-03T22:03:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T09:37:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T01:50:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2306634022645211626877745030789932138","date":"2025-07-30T01:14:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215344452724226506766253825559193663367","date":"2025-07-29T11:54:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T08:58:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T13:54:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-25T09:35:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-13T03:14:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-13T03:10:37+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":"9d2d0fd7-4084-46c5-b4d9-345478aa135b","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-21T07:53:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 16:23:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6942496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6942496","identity":"rs-6942496","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}