Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via miR-375-3P/SLC7A11 axis

doi:10.21203/rs.3.rs-1749498/v3

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Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via miR-375-3P/SLC7A11 axis

https://doi.org/10.21203/rs.3.rs-1749498/v3

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published 18 Apr, 2024

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Ferroptosis has important value in cancer treatment. It is significant to explore the new ferroptosis-related lncRNAs(FRLs) prediction model in Hepatocellular carcinoma (HCC) and the potential molecular mechanism of ferroptosis-related lncRNAs. We constructed a prognostic multi-lncRNA signature based on ferroptosis-related differentially expressed lncRNAs in HCC. qRT-PCR was applied to determine the expression of lncRNA in HCC cells. The biological roles of NRAV in vitro and in vivo were determined by performing a series of functional experiments. Further, dual-luciferase reporter and RNA immunoprecipitation (RIP) assays were used to confirm the interaction of NRAV with miR-375-3P. We identified 6 differently expressed lncRNAs associated with the prognosis of HCC. Kaplan-Meier analyses revealed the high-risk lncRNAs signature associated with poor prognosis of HCC. Moreover, the AUC of the lncRNAs signature showed the utility in predicting HCC prognosis. Further functional experiments show that the high expression of NRAV can strengthen the viciousness of HCC. Interestingly, we found that NRAV can enhance iron export and ferroptosis resistance. Further research showed NRAV via competitively binds to miR-375-3P and attenuates the inhibitory effect of miR-375-3P on SLC7A11, affecting the prognosis of patients with HCC. In conclusion, We developed a novel FRLs prognostic model with important predictive value for the prognosis of hepatocellular carcinoma. NRAV plays an important role in ferroptosis induction through the miR-375-3P/SLC7A11 axis.

lncRNA

ferroptosis

predictive models

NRAV

miR-375-3P/SLC7A11

Hepatocellular carcinoma (hepatocellular carcinoma, HCC) is the liver's most common primary malignant tumor. The incidence of HCC is on the rise worldwide, causing about 690000 deaths each year, ranking third among the causes of cancer death [1]. Treatments such as hepatectomy, liver transplantation, microwave ablation, and systemic therapy have progressed. However, due to the limited means of early detection, the diagnosis and curative effect of HCC are still not satisfactory, resulting in a 5-year survival rate of only 7%[2]. It is exciting that the success of immunotherapy in recent years has ushered in a new era of cancer treatment. Immunotherapy has also achieved a remarkable curative effect in hepatocellular carcinoma, including the application of PD-1 monoclonal antibody and CTLA-4 monoclonal antibody. Therefore, continuing research around immunity has positive implications for patients

Regulatory cell death (RCD) is a mode of cell death controlled by specific signal transduction pathways, the most widely studied RCD types include apoptosis, pyroptosis, necrosis, autophagy, and ferroptosis, each of which has its unique molecular mechanism. Ferroptosis is a new type of iron-dependent regulatory cell death. Regarding mechanism, ferroptosis is an iron-dependent high expression of unsaturated fatty acids on the cell membrane, resulting in lipid peroxidation and induced cell death. Exogenous or endogenous pathways can induce Ferroptosis, the exogenous pathway is initiated by inhibiting cell membrane transporters, and the endogenous pathway is initiated by blocking the activation of intracellular antioxidant enzymes[3]. The effect of ferroptosis on the efficacy of chemotherapy, radiotherapy, and immunotherapy has been determined[3, 4]. In the process of tumorigenesis, ferroptosis plays a dual role in promoting and inhibiting tumors, which depends on the release of damage-associated molecular patterns (DAMPs) can mediate immunogenic cell death that could stimulate antitumor immunity, so the combined use of drugs for ferroptosis signals can improve the effectiveness of these therapies [5]. It is an indisputable fact that ferroptosis can enhance the therapeutic effect of immunotherapy [6]. However, many underlying molecular mechanisms remain unclear. Therefore, it is still necessary to strengthen the research on ferroptosis.Strengthening the study of ferroptosis is necessary to improve clinical prognosis.

LncRNA is defined as a noncoding RNA of more than 200bp. Current studies have shown that dysfunctional lncRNA plays an important role in tumorigenesis through many cancer-related biological processes, including apoptosis[7], cell cycle, metastasis, and DNA damage response[8, 9]. In recent years, increasing evidence has shown that lncRNAs are closely related to ferroptosis. The lncRNA LINC00336 was found to inhibit ferroptosis in lung cancer by acting as a ceRNA by Wang et al. [10]. LINC00336 overexpression significantly reduced intracellular Fe²⁺, lipid ROS. Mao et al. found that the p53-related lncRNA P53RRA can directly interact with the functional domain of signaling proteins in the cytoplasm by activating the p53 pathway, promoting ferroptosis, and playing a tumor suppressor role[11]. Therefore, the study of lncRNA related to ferroptosis and HCC is very important for understanding the mechanism of tumor development.

Our study first constructed a prognostic multi-lncRNA signature of differentially expressed FRLs based on the Cancer Genome Atlas (TCGA) data and International Cancer Genome Consortium (ICGC) data, further verifying the model's potential role in immunotherapy to combine ferroptosis and immunotherapy and increasing the possibility of clinical transformation. Furthermore, in combination with clinicopathological features, a nomogram based on our gene signature presented improved predictive power and risk stratification for HCC. Then we verified the key genes in the prognosis model and verified the role of NRAV in the process of ferroptosis in vitro and in vivo. Our findings establish a lncRNAs prognostic model related to ferroptosis based on the expression of lncRNA, which complements the mechanism of lncRNA regulating ferroptosis in HCC.

Data collection

The RNA sequences of 422 patients (50 normal tissues and 374 tumor tissues) were extracted from the TCGA-HCC database, and the RNA sequences of 445 patients (202 normal tissues and 243 tumor tissues) were extracted from the ICGC-LIRI-JP database. Ferroptosis-related genes were acquired from FerrDb, an online source that supplies a comprehensive and updated checklist of ferroptosis markers, regulative molecules, and ferroptosis-disease associations[12]. In general, we identified 175 (Driver:76; suppressor:47; marker:75) ferroptosis-related genes (Table S1). Pearson correlation evaluation was used to identify the relationships between ferroptosis-related genes and lncRNAs. Connections with the value of correlation coefficients |R| > 0.5 as well as a P-value < 0.001 were taken into consideration as statistically substantial. The clinicopathological attributes of HCC people, consisting of age, gender, stage, TMN, survival condition, and survival time, were accumulated. In this study, we identified significant differentially expressed lncRNAs (DElncRNAs) between HCC tumor and normal liver tissue (absolute log2FC >= 1 and FDR < 0.05). After screening differentially expressed genetics (DEGs), we performed functional enrichment evaluation and KEGG analysis using upregulated and downregulated ferroptosis-related differentially expressed genetics (DEGs). Enrichment analysis using metascape for EDGs data.

Building of the ferroptosis-related lncRNAs prognostic signature

Univariate Cox regression, LASSO, and multivariate Cox regression evaluations were used to establish the last variables for building the prognostic threat score model. stratified based on risk rating (Coefficient lncRNA1 × expression of lncRNA1) + (Coefficient lncRNA2 × expression of lncRNA2) + ⋯ + (Coefficient lncRNAn × expression lncRNAn). Based on the threat scores for OS, HCC clients with fibrosis were separated into high- or low-risk groups making use of the typical score as a cut-off, and also Kaplan-Meier contours were calculated for both teams.

Predictive Nomogram building and validation

To execute genetics established enrichment evaluations stabilized, all genetics matters were imported right into the GSEA software application as well as the Molecular Signature Database (MSigDB) Trademark, KEGG, and also Genetics Ontology gene libraries were utilized to identify enriched gene sets[13], which were then searched in the TCGA-HCC database. Enriched gene sets were selected on the basis of statistical significance (incorrect exploration rate FDR q value < 0.25, and normalized p-value < 0.05)[14]. We built a nomogram incorporating typical medical variables such as age, gender, grade, tumor stage, and the risk rating originated from the prognostic trademark to assess the possible 3- and 5-year OS of clients with HCC.

Gene expression and Immunity analysis

At the same time, the CIBERSORT[15, 16], ESTIMATE[17], quanTIseq[18], xCell[19], MCP-counter (or mMCP-counter for mouse)[20], EPIC[21], and TIMER[22] formulas were analyzed the CC or cell sorts of immune reactions in heterogeneous examples among risky as well as low-risk teams based on FRLs trademark. The differences in immune feedback under various formulas were revealed using a Heatmap. In enhancement, ssGSEA was utilized to quantify the tumor-infiltrating immune cell subgroups between both groups and assess their immune function. Many possible immune check factor molecules were determined that could be targeted and incorporated with potential medication therapy[23].

Tissue specimens

86 matched hepatocellular carcinoma tissues and paracancerous tissues were obtained from patients with hepatocellular carcinoma diagnosed by Fujian Provincial Hospital and The First Affiliated Hospital of Zhengzhou University. All experiments involving human samples and clinical data were approved by the Accreditation Committee of Fujian Provincial Hospital and The First Affiliated Hospital of Zhengzhou University.

RNA extraction and quantitative real-time PCR (qRT‑PCR) analysis

According to the manufacturer's protocol, total RNA from HCC cells, tissues, and matched non-cancerous tissues was isolated using the TRIzol Reagent (Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription was performed using the PrimeScript RT Reagent Kit (Takara, Dalian, China). Bulge-loop™ miRNA RT-qPCR Primers were applied to determine the level of miRNAs. The real-time PCR reactions were performed using StepOnePlus™ Real-Time PCR System (Thermo Fisher Scientific, MA, US). The program settings on temperature cycling were followed as instructed by the manufacturer. The sequences of primers are listed in Table S2.

Western blot analysis

In brief, proteins were isolated from HCC cells using RIPA buffer (Solarbio, Beijing, China) supplemented with proteinase inhibitors, and the protein concentration was determined with a BCA reagent (Beyotime, Beijing, China). Cell lysates were separated on SDS-polyacrylamide gels and then transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore). After the membranes were blocked in 5% skim powdered milk for 2 h, they were incubated with primary antibodies overnight at 4°C. The primary antibodies used in this study included: ACSL4 (ET7111-43, HUABIO), GPX4 (ET1706-45, HUABIO), SLC7A11 (Abcam, ab175186), and GAPDH (Proteintech, 80570-1-RR). Next, the membranes were incubated with secondary antibodies (HUABIO, Hangzhou, China) at room temperature for 1h. After washing three times, the targeted proteins were visualized using enhanced chemiluminescence (ECL) reagent (Millipore, MA, USA). GAPDH was used as the loading control in this study.

Transwell migration assays

Transwell assays were conducted using 24-well plates with chamber inserts with 8.0 μm pores (Corning). The cells were digested 24 h after transfection and resuspended in a serum-free medium. Then, 3 × 104 cells per well were placed into the upper chambers. The lower chamber was filled with a 500 μl medium with 10% FBS, like a cell nutritional attractant. After incubation at 37°C for 24 h, cells in the upper chamber were gently removed with a cotton swab. The migrated or invaded cells were fixed with 4% polyformaldehyde and visualized by staining with crystal violet for 20 min. Last, the migrated cells were imaged and quantified by capturing five randomly chosen microscopic fields using an inverted microscope (Olympus, Japan). All experiments were performed in triplicate.

Cell proliferation assays

For the Cell Counting Kit-8 (CCK-8) assay, the treated HCC cells were seeded into 96-well plates at a concentration of 3 × 10³ /well. Then, 10 μL of CCK-8 assay solution (Dojindo, Japan) was added and incubated in the dark for 2 h. The absorbance at 450 nm was measured every 24 h with a microplate reader (BioTek Instruments, USA). All experiments were repeated three times.

RNA immunoprecipitation (RIP)

RNA immunoprecipitation (RIP) assay was performed using Magna RIP™ RNA-binding protein immunoprecipitation kit (Millipore, Billerica, MA, USA) following the manufacturer's protocol. The cell extract was incubated with magnetic beads conjugated with anti-Argonaute 2 (AGO2) or anti-IgG antibody (Millipore, Billerica, MA, USA) for 6h at 4°C. The beads were washed and incubated with Proteinase K to remove proteins. Finally, isolated RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific, Waltham, MA, USA), then the purified RNA was subjected to qRT-PCR analysis.

Dual-luciferase reporter assay

The full-length wild-type (WT) sequence of NRAV and the indicated mutant NRAV containing the predicted miR-615-3p binding sites were separately synthesized and cloned into the dual-luciferase reporter vector (Genechem, Shanghai, China). The resulting dual-luciferase reporter plasmids (WT or Mut) were co-transfected with the miR-375-3p mimic or inhibitor into hepG2 or HuH-7 cells, respectively, using Lipofectamine 3000. After 48h of incubation, the relative firefly luciferase activities concerning the corresponding Renilla luciferase activities were measured and analyzed using a Dual-Luciferase Assay System (Promega, Fitchburg, WI, USA) following the manufacturer's protocol.

Iron assay

Intracellular ferrous iron (Fe²⁺) level was determined using the iron assay kit (ab83366, Abcam) according to the manufacturer's instructions. Cells were collected and washed in ice-cold PBS and homogenized in 5× volumes of iron assay buffer on ice, then centrifuged (13,000 × g, 10 min) at 4°C to remove insoluble material. The supernatant was collected, iron reducer was added to each sample before mixing, and incubated at room temperature for 30 min. Then, 100 μL of the iron probe was added to each sample, mixing and incubating the reaction for 1 h at room temperature in the dark. The absorbance at 593 nm was measured immediately using a colorimetric microplate reader.

Lipid ROS measurement

Lipid ROS level was analyzed by flow cytometry using BODIPY-C11 (GLPBIO, GC40165) dye. Cells were seeded at 2.5 × 10⁵ per well in a six-well dish and grown overnight for 12 h. Cells were washed once with PBS. Cells were then stained with 2 mL medium containing 5 µM of BODIPY-C11 and incubated at 37°C for 20 min in the dark. Cells were washed twice with PBS to remove excess labeling mixture, followed by resuspending in 500 μL fresh PBS (DPBS, Gibco). The cell suspension was filtered through a 0.4 μM cell filter and subjected to flow cytometric analysis to detect the amount of intracellular lipid ROS. The fluorescence intensities of cells per sample were determined by flow cytometry using the BD FACSAria cytometer (BD Biosciences). A minimum of 10,000 cells was analyzed for each sample. Data analysis was evaluated using the FlowJo Software.

Subcutaneous xenograft model

The male nude mice (BALB/c, aged 4-6 weeks, 18-20 g) were randomly divided into two groups and inoculated with cells as follows: sh-NC stable transfected hepG2 Cell (1 × 10⁷ cells); sh-NRAV stable transfected hepG2 Cell (1 × 10⁷ cells); Cells were mixed with matrigel (1:2) and inoculated subcutaneously at the right rear back region. Tumors were measured weekly with calipers, and tumor volume was calculated as =(L× W² )/2.

Statistical analysis

The experimental data were analyzed using statistical analysis software, including GraphPad Prism 8.0 software (GraphPad) and the R package (V.3.3.4). Data are reported, including estimation of variation within each group. Unpaired t-test or one-way ANOVA was used to measure differences between groups. Chi-square (χ2 ) tests compared categorical variables. Statistical significance was determined at P < 0.05.

Identification of robust DEGs

The FRLs signature was constructed following the flowchart shown in Figure 1A. We uncovered 174 ferroptosis-related DEGs (38 downregulated and 136 upregulated; Fig 1B-D, Table S1). Enrichment analysis revealed the over-expressed genes were mainly involved in ferroptosis, cellular response to chemical, oxidative, and oxidative stress. (Fig. 1E)

Construction and validation of ferroptosis-related lncRNA prognostic model

A prognostic risk evaluation model based on only 6 FRLs was then constructed using the optimal penalty parameter (λ=-1.8) for the LASSO model from the FRLs group. The cvfit and lambda curve are shown in Figures 2A, B. Kaplan-Meier curve analysis also demonstrated that patients with the high-risk lncRNA signature had much poorer survival than those with the low-risk lncRNA signature (P<0.001, Fig. 2C). Figure 2D demonstrated the variation of risk scores between the low-risk and high-risk groups and more fatalities and fewer years of survival in the high-risk group. As shown in Figure 2E, the RNA expression of the six ferroptosis-associated lncRNAs was lower in the low-risk group than in the high-risk group. Meanwhile, the AUC of the signature lncRNAs was 0.795. Then, we demonstrated that the 6-gene signature was an independent prognostic factor for HCC patients superior to traditional clinicopathological factors and verified their survival prediction ability in an external HCC cohort in TCGA (Fig. 2F, G). The AUC predictive value of the novel lncRNAs signature for 1, 3, and 5-year survival rates was 0.795, 0.749, and 0.748, respectively (Fig. 2H).

Subsequently, we used univariate Cox analysis, Robust likelihood-based survival model, and multivariate Cox analysis to construct a signature composed of lncRNAs and tumor stage, independent prognosis factors of OS of HCC patients (P < 0.001; Fig. 3A, B). The relationship between the novel lncRNAs and Ferroptosis-related genes is shown in Figure 3C. The heatmap for the association between FRLs prognostic signature and clinicopathological manifestations was also analyzed (Fig. 3D). The hybrid nomogram incorporating clinicopathological characteristics and the novel FRLs prognostic signature (Fig. 3E) was stable and accurate, thus may be applied in surgical pathology or clinical management of HCC patients. Gene set enrichment analyses (GSEA) revealed that most of the novel FRLs of high-risk groups' prognostic signature regulated cell cycle and tumor-related pathways (Fig. 3F). On the other hand, most of the novel FRLs of low-risk groups' prognostic signatures regulated multiple metabolic processes of cells (Fig. 3G). The heatmap of immune responses based on CIBERSORT, ESTIMATE, quanTIseq, xCell, MCP-counter (or mMCP-counter for mouse), EPIC, and TIMER algorithms is shown in Figure S1A. To further explore the relationships between the risk scores and immune cells and functions, we quantified the enrichment scores of 16 immune cell subpopulations and their related functions with the ssGSEA R package, which revealed that APC co-stimulation, cytolytic activity, MHC class I, type I INF response and type II INF response were significantly different between the low-risk and high-risk groups (Fig. S1B). Given the importance of checkpoint inhibitor-based immunotherapies, we further explored the difference in the expression of immune checkpoints between the two groups. We found a significant difference in the expression of HHLA2, TNFRSF14, and CTLA4among other groups of patients (Fig. S1C).

Identification of NRAV in HCC

Next, to further confirm the role of lncRNAs in the HCC prognostic model. By analyzing the data of 424 cases of liver cancer in the TCGA database, the expression levels of 6 FRLs are shown in Figure 4A. Through the prognostic analysis of three molecules in the Gepia database, we found that NRAV was significant for OS (Fig. 4B), while DANCR, MKLN1-AS1, and ZFPM2-AS1 showed little difference (Fig. S2A-C), and no difference between AL137186.2 and LNCSRLR. So we selected NRAVs with the most significant difference in HCC prognosis for further validation. NRAV is located on chromosome 12. In different liver cancer cell lines, the expression of NRAV is shown in Figure 4C. Compared with Paracancer, NRAV is generally upregulated in cancer and can be stably expressed in various commercial liver cancer cells (Fig. 4D). The subcellular localization of NRAV in hepatoma cells was detected by nuclear and cytoplasmic separation experiments, and it was found that NRAV was located not only in the cytoplasm but also in the nucleus (Fig. 4E). With the deepening of the research, researchers have found that many noncoding RNAs can also work by translating into proteins. To study the action mode of NRAV, we use software to predict and analyze the protein-coding ability of NRAV. ORF reading frame prediction shows a reading frame, and ORF12 is greater than 300bp (Fig. S2D), arousing our strong curiosity. However, it is a pity that coding-potential computer (CPC) analysis shows that the coding possibility of NRAV is very little (Fig. S2E), and we can't detect any special protein bands through in vitro translation experiments, while the control gene can detect band (Fig. 4F), which also supports that NRAV is a noncoding RNA.

NRAV promotes HCC tumorigenesis in vitro and in vivo

Next, we studied the carcinogenic function of NRAV in HCC. To evaluate the biological function of NRAV in HCC cells, we designed and constructed three siRNA targeting NRAV to change their expression specifically. QRT-PCR results showed that all three siRNAs effectively silenced NRAV (Fig. 5A). In addition, the introduction of the recombinant human NRAV gene successfully realized the overexpression of the gene (Fig 5B). The results of CCK-8 and clone formation experiments showed that knockout of the NRAV gene effectively inhibited the proliferation of hepG2, while up-regulation of NRAV significantly enhanced the proliferation of HuH-7 (Fig. 5C-F). Transwell's experiment showed that the down-regulation of NRAV significantly decreased the migration ability of hepG2, while the up-regulation of NRAV enhanced the migration ability of HuH-7 cells (Fig. 5G, H). Next, we constructed a CDX model to examine the carcinogenic function of NRAV in vivo. Compared with the control group, the tumor volume and weight in the knockout group decreased significantly, and the tumor growth rate slowed down (Fig. 5I-K). To sum up, these experimental results show that NRAV has a carcinogenic function in HCC.

NRAV promotes iron export and ferroptosis resistance as a sponge of miR-375-3P

Iron accumulation was increased in hepG2 cells infected with sh-NRAV lentivirus compared with control and decreased in HuH-7 cells infected with oe-NRAV compared with Vector (Fig. 6A). Analysis of ROS by flow cytometry showed a similar trend as the iron accumulation results (Fig. 6B, C). These results suggested that NRAV plays a negative regulatory role in ferroptosis in HCC. Furthermore, western blot analysis in Figure 6D revealed that SLC7A11 and GPX4 expressions were decreased in hepG2 cells infected with sh-NRAV lentivirus compared with control and increased in HuH-7 cells infected with lv-NRAV compared with Vector. The opposite trends were observed in ACSL4 expressions, and the most obvious change is SLC7A11. The potential downstream miRNA of NRAV was found through the annolnc2 database, in which miR-375-3P was reported to play a role in regulating the key molecule SLC7A11 of ferroptosis[24]. However, the specific mechanism by which NRAV regulates the miR-375-3P/SLC7A11 axis has not been explored. Through bioinformatics methods, we discovered the binding site of NRAV and miR-375-3P (Fig. 6E). We conducted immunoprecipitation assays using the AGO2 antibody and found that the AGO2 antibody was able to enrich both endogenous miR-375-3p and NRAV (Fig. 6F). To further verify the binding of NRAV and miR- 375-3p, we constructed two NRAV luciferase reporter plasmids: wild type and miR-375-3p binding site mutant (Fig. 6G). The luciferase activity of wild type can be significantly inhibited by miR-375-3p mimics, but the luciferase activity of mutant has no significant change (Fig. 6H).

Moreover, the knockout of NRAV significantly increased miR-375-3P expression, while the overexpression of NRAV significantly reduced the expression of miR-615-3p (Fig. 6I, J). western blot analysis confirmed that the miR-375-3P inhibitor and mimic reversed the alterations in SLC7A11 caused by alterations in NRAV. In summary, these results prove that NRAV promotes iron export and ferroptosis resistance through the regulation of the miR- 375-3p/SLC7A11 axis.

High expression of NRAV is associated with poor prognosis in HCC patients

Through the analysis of q-PCR results of clinical samples, the results showed that the expression of NRAV was negatively correlated with miR-375-3P and positively correlated with SLC7A11 (Fig. 7A, B). To further study the clinical significance of NRAV in HCC, the clinical characteristics of patients with high NRAV expression in our hospital showed that the clinical outcome of HCC patients with high NRAV expression was worse than that of patients with low NRAV expression (Fig. 7C, D). The pattern of ferroptosis regulated by NRAV is shown in figure 7E. These results suggest that the high expression of NRAV may predict a poor prognosis in patients with HCC.

In recent years, great progress has been made in the clinical treatment of HCC. It is still one of the leading causes of cancer-related deaths worldwide, suggesting an urgent need to develop new treatments for HCC [4]. ferroptosis are thought to play an important role in the immunotherapy. Many kinds of lncRNA are thought to have abnormal expression and participate in cancer progression by binding to genetic material and encoding proteins or other small molecular peptides[25, 26]. Therefore, we developed a lncRNA prognostic risk model combined with ferroptosis. In this study, we first identified 84 genes and then identified 52 significant lncRNA associated with ferroptosis. Finally, 6 lncRNA characteristic models related to ferroptosis were established, most of which have been reported to affect the progression of liver cancer and associated with poor prognosis, including NRAV[27], He et al. found that ZFPM2-AS1 acts as a miRNA sponge and promotes cell invasion through regulation of miR-139/GDF10 in hepatocellular carcinoma [28], Ma et al. Found that DANCR was up-regulated in tumor tissues and plasma of patients with HCC, and its expression was highly correlated with microvascular and liver capsule invasion of HCC [29], MKLN1-AS[30], LNCSRLR[31] and AL137186.2[32] also have been shown to be associated with the prognosis of patients with HCC, which are consistent with our findings. Moreover, after correcting for traditional clinical risk indicators, the six-FRLs signature model was shown to be an independent predictive factor for HCC. This result suggested that the six-FRLs signature could reliably predict the prognosis of HCC patients.

Although adjuvant chemotherapy, immunotherapy, and other anticancer therapies have shown encouraging results in early cancer treatment, the middle and later stages of treatment are challenging in most cases. Therefore, it is imperative to find a more effective cancer treatment strategy. Ferroptosis has long been proved to be closely related to immunotherapy[33, 34], so we further explored the connection between the model and immunitywe. We studied the difference in immune checkpoint expression between the two groups and found that there were significant differences in T cell function, including checkpoint (inhibition), cytolysis, HLA, inflammatory regulation, co-stimulation, co-inhibition and type II INF response between low-risk and high-risk groups. Given the importance of immunotherapy based on checkpoint inhibitors, we further discussed the difference in immune checkpoint expression between the two groups. The results showed significant differences in the expression of PDCD-1 (PD-1), CTLA4, HHLA2, and CD44 etc. between the two groups. And most of them play a key role in immunotherapy.

To further study the regulation of lncRNA on ferroptosis, we selected the lncRNA NRAV with the most pronounced effect on prognosis for validation. It has been proved that NRAV has a clear relationship with the occurrence and development of hepatocellular carcinoma. Wang et al. have proved that NRAV can promote the occurrence and development of HCC through Wnt/ β-catenin signal pathway[27]. In addition, several HCC-related prognostic models have shown that NRAV is related to immunity and cell death[35-37]. Our results show that the change of NRAV expression can significantly affect the ferroptosis-related proteins SLC7A11, GPX4, and ACSL4, especially SLC7A11, and can change the Fe²⁺ content and ROS level in HCC cells. Through a series of tests, we found that NRAV can change the expression of SLC7A11 through binding to mir-375-3P. Our results show that NRAV promotes the occurrence and development of hepatocellular carcinoma and affects ferroptosis in hepatocellular carcinoma through the mir-375-3P/SLC7A11 axis.

Compared with other lncRNA prognostic models related to ferroptosis in HCC [38-40], this study has more in-depth basic research, larger sample size, and stronger prediction accuracy. Through lasso regression analysis and the performance of lncRNA in liver cancer, we selected the most representative FRLs-NRAV and verified it, and first found the regulation of NRAV via miR-375-3P/SLC7A11 axis on ferroptosis, but this study also had some defects, including the lack of research depth, and the mechanism research is relatively simple. At the same time, due to the difficulty of clinical data collection and verification, the number of related clinical samples is relatively small. In the future, we will continue to study the role of NRAV in immunity to improve the mechanism of ferroptosis and immunotherapy.

In conclusion we developed a new model that can be used for clinical prognosis and demonstrated that NRAV could affect ferroptosis in HCC cells through the mir-375-3P/SLC7A11 axis and then affect the occurrence and development of HCC. Our findings expand the mechanism of ferroptosis regulation in HCC and provide new markers for HCC prognosis.

HCC: Hepatocellular carcinoma;

GEO: Gene Expression Omnibus;

ICGC: International Cancer Genome Consortium

RIP: RNA immunoprecipitation;

RCD: Regulatory cell death;

TCGA: The Cancer Genome Atlas;

DEGs: Differentially expressed genes;

KEGG: Kyoto Encyclopedia of Genetics and Genomes;

GO: Genetics Ontology;

MSigDB: Molecular Signature Database;

ECL: Enhanced chemiluminescence;

CDX: Cell line-derived xenograft;

OS: Overall survival;

DFS: Disease-free survival;

CCK-8: Cell Counting Kit-8;

FRLs: Ferroptosis-related lncRNAs;

GSEA: Gene set enrichment analyses;

CPC: Coding-potential computer;

Ethical Approval and Consent to participate

All participants provided informed written consent. This study was approved by the Human Research Ethics Committees of the Fujian Provincial Hospital and was carried out following the principles embodied in the Declaration of Helsinki. The patients provided their written informed consent to participate in this study. All in vivo animal experiments were approved by the Committee on the Ethics of Animal Experiments at the Shanghai University of Traditional Chinese Medicine.

Consent for publication

All authors have given their consent for publication.

Availability of supporting data

All data in our study are available upon request.

Competing interests

The authors declare that they have no competing interests.
Funding

This work was supported by The Key Scientific Research Project Plan of Henan University (Grant No.20A320037)

Authors' contributions

RF. L. and S. C. designed the study. K.Z. performed the study and wrote the paper. CF.L. and JF. H. conducted experiments and collected the samples and clinical data. K. L. participated in data analysis. YL. D. assisted with experiments. HF. W. collected the samples and clinical data. All authors read and approved the final manuscript. No potential conflicts of interest were disclosed.
Acknowledgments

We thank Dr. Yi Huang for permitting us to conduct our experiments at the Center for Experimental Research in Clinical Medicine for this study.
Authors' information

Ke Zong, Cai-Feng Lin, Shi Chen, and Ren-Feng Li contributed equally to this work.

Preprint statement：

This manuscript has been preprinted [//doi.org/10.21203/rs.3.rs-1749498/v1]

Supplementary Materials

Figure S1. A. Heatmap for immune responses based on CIBERSORT, ESTIMATE, quanTIseq, xCell, MCP-counter, EPIC, and TIMER algorithms among the high and low-risk groups. B. The correlation between immune cell subpopulations and related functions was evaluated using ssGSEA. C. Expression of common immune checkpoints between the high and low-risk HCC patients. Figure S2. A-C. Overall survival of HCC patients (N=362) from the Gepia project with high or low expression levels. D. The protein-coding potential analysis of NRAV was performed using the ORF finder from NCBI. E. Using CPC 2.0 to predict the coding capability of NRAV. Table S1. The list of 174 ferroptosis-related DEGs. Table S2. The list of primer sequences. (Supplementary Materials)

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No competing interests reported.

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Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via miR-375-3P/SLC7A11 axis

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