Abstract

Simple Summary

   Hepatocellular carcinoma (HCC) remains a major health concern.
   Immunotherapy combined with targeted therapy brings hope to patients
   with HCC, but its primary beneficiaries have not been identified.
   Recent studies have found that copper induces cell death, which is
   named cuproptosis. As we know, cell death is closely related to tumor
   therapy, in which some non-coding RNAs involved. Therefore, we focused
   on whether some long non-coding RNAs (LncRNAs) are related to
   cuproptosis, and the cuproptosis-related LncRNAs (crLncRNAs) can
   classify tumor treatment-sensitive populations. In the study, we
   explore a model of crLncRNAs with excellent specificity and sensitivity
   that is capable of predicting the prognosis of HCC patients and
   classifying tumor immunotherapy-sensitive populations, thereby
   providing new insights for the development of appropriate clinical
   strategies.

Abstract

   Immunotherapy has shown strong anti-tumor activity in a subset of
   patients. However, many patients do not benefit from the treatment, and
   there is no effective method to identify sensitive immunotherapy
   patients. Cuproptosis as a non-apoptotic programmed cell death caused
   by excess copper, whether it is related to tumor immunity has attracted
   our attention. In the study, we constructed the prognostic model of 9
   cuproptosis-related LncRNAs (crLncRNAs) and assessed its predictive
   capability, preliminarily explored the potential mechanism causing
   treatment sensitivity difference between the high-/low-risk group. Our
   results revealed that the risk score was more effective than
   traditional clinical features in predicting the survival of HCC
   patients (AUC = 0.828). The low-risk group had more infiltration of
   immune cells (B cells, CD8^+ T cells, CD4^+ T cells), mainly with
   anti-tumor immune function (p < 0.05). It showed higher sensitivity to
   immune checkpoint inhibitors (ICIs) treatment (p < 0.001) which may
   exert the effect through the [42]AL365361.1/hsa-miR-17-5p/NLRP3 axis.
   In addition, NLRP3 mutation-sensitive drugs (VNLG/124, sunitinib,
   linifanib) may have better clinical benefits in the high-risk group.
   All in all, the crLncRNAs model has excellent specificity and
   sensitivity, which can be used for classifying the therapy-sensitive
   population and predicting the prognosis of HCC patients.

   Keywords: cuproptosis-related LncRNAs (crLncRNAs), immunotherapy, tumor
   immune microenvironment (TIME), immune checkpoint inhibitors (ICIs),
   [43]AL365361.1

1. Introduction

   Liver cancer is the sixth most common cancer worldwide and the third
   leading cause of cancer death [[44]1]. Hepatocellular carcinoma (HCC)
   is the most common type of liver cancer, accounting for approximately
   80% of cases, and its 5-year survival rate is less than 20% [[45]2].
   Chronic hepatitis B virus infection is the main pathogenic factor for
   HCC in China. After chronic hepatitis progresses to cirrhosis, about
   85% of patients with cirrhosis are diagnosed with HCC [[46]3]. Early
   diagnosis of HCC is difficult, with two-thirds of patients being unable
   to undergo radical surgery, and for remaining one-third, the recurrence
   rates are high, and the survival rate is low. Targeted therapies have
   emerged, but with low treatment response rate and high drug resistance
   [[47]4]. The increase in drug toxicity and side effects aggravates the
   poor prognosis of patients with advanced HCC. Immune checkpoint
   inhibitors (ICIs) are promising therapies for HCC based on early
   efficacy data. Therefore, the pembrolizumab monotherapy [[48]5], the
   combination of ezetimibe and bevacizumab [[49]6], and nivolumab plus
   ipilimumab combination [[50]7] have received the US Food and Drug
   Administration (FDA) approval successively. Although the application of
   ICIs has improved the prognosis of HCC patients who showed a good
   response [[51]8], the response rate is less than 30% [[52]9].
   Biomarkers such as tumor immune microenvironment (TIME) [[53]10], tumor
   mutation load (TMB) [[54]11], immune checkpoint genes [[55]12], immune
   score, and IPS score [[56]13] may predict sensitivity to ICI treatment,
   providing the possibility of identifying patients who are sensitive to
   ICI treatment, but this approach still lacks specificity and accuracy.
   Immunotherapy combined with targeted therapy has a low response rate
   and high drug resistance. Therefore, it is particularly important to
   identify susceptible individuals [[57]14,[58]15]. Given the limited
   therapeutic strategies for HCC, new prognostic models need to be
   developed for prognosis and to identify sensitive treatment options,
   which may improve the survival rate of patients with HCC by guiding
   treatment decisions.

   Long non-coding RNAs (LncRNAs) are one kind of non-coding RNA with more
   than 200 nucleotides in length, which play a variety of roles in
   regulating immune response and affecting tumor progression [[59]16].
   For example, LncRNA MIR155HG upregulates the expression of PD-L1
   through the miR-223-STAT1 axis and promotes the immune escape of HCC
   [[60]17]. Previous studies have found that LncRNA is involved in the
   regulation of pyroptosis, ferroptosis, and other common programmed cell
   death modes during tumor progression, so it can be used as a biomarker
   to predict the prognosis of cancer patients and the response to
   immunotherapy. For instance, pyroptosis-related LncRNAs (HPN-AS1,
   MED8-AS1, SREBF2-AS1, MKLN1-AS, and ZNF232-AS1) can predict the
   response to immunotherapy in HCC patients [[61]18]. Similarly,
   ferroptosis-related LncRNAs (MKLN1-AS, LINC01224, LNCSRLR, LINC01063,
   PRRT3-AS1, and POLH-AS1) are reliable in predicting the prognosis and
   immunotherapy response in HCC patients [[62]19]. Hence, we believe
   LncRNAs may play a significant role in multiple forms of cell death,
   and the recent discovery of cuproptosis has attracted our attention.
   Cuproptosis is a newly defined programmed cell death type [[63]20].
   Copper ions induce a proteotoxic stress response by binding to
   thioacylated tricarboxylic acid cycling-related enzymes that ultimately
   lead to cell death [[64]21]. A disorder of copper metabolism can lead
   to the metabolites disrupting the tricarboxylic acid cycle. Copper ion
   levels are significantly changed in the tumor tissues of many cancer
   patients [[65]22,[66]23], and they affect tumor progression by
   affecting mitochondrial energy metabolism [[67]24,[68]25,[69]26]. It
   has been demonstrated that blocking Cu^2+ transport induces an ATP
   decrease, activates AMP-activated protein kinase, and ultimately
   inhibits the proliferation of tumor cells [[70]27]. However, whether
   LncRNAs participate in the regulation of tumor cuproptosis has not been
   reported previously. Therefore, we hope to clarify the relationship
   between them and assess how the LncRNAs might affect tumor progression
   by regulating cuproptosis. In addition, we attempted to use the
   cuproptosis-related LncRNAs (crLncRNAs) as novel biomarkers for
   immunotherapy and targeted therapy in patients with hepatocellular
   carcinoma to establish a prognostic model.

   In this study, we designed to provide a promising biomarker to predict
   the prognosis of HCC patients, the tumor microenvironment, classify the
   treatment-sensitive population, and provide new insights for developing
   appropriate clinical strategies.

2. Materials and Methods

2.1. Data Acquisition and Screening

   We downloaded RNA-seq data and corresponding clinical characteristics
   from the Cancer Genome Atlas (TCGA) database (424 HCC samples,
   including 50 normal samples and 374 tumor samples), which updated its
   data after April 2022 ([71]https://portal.gdc.cancer.gov/, accessed on
   31 May 2022). Considering the possibility of non-cancer death, HCC
   patients whose survival time was <30 days or uncertain were excluded.
   Subsequently, data from 343 patients were analyzed. The baseline
   characteristics of patients are summarized in [72]Supplementary Table
   S1. TCGA is a public database and does not require ethical approval.

2.2. Identification of CrLncRNAs

   A validated list of genes (NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1,
   LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT,
   GCSH, and DLST) associated with cuproptosis was obtained from a recent
   study by Tsvetkov et al. [[73]28]. Spearman correlation coefficients
   were calculated based on cuproptosis-related mRNA and LncRNA expression
   profiles to identify crLncRNAs (|R| > 0.4 and p < 0.001).

2.3. Construction of the Prognostic Model of CrLncRNAs

   The crLncRNAs correlated with HCC survival time were determined by
   univariate Cox regression analysis (p < 0.05). The 343 patients were
   randomly assigned to either the training cohort or the test cohort in a
   ratio of 3:2. [74]Supplementary Table S1 summarizes the baseline
   characteristics of the two groups. For the training cohort, LASSO
   regression analysis was performed using the R project ‘Glmnet’ package.
   Parameter selection was adjusted by a round of cross-validation to
   prevent overfitting, and the partial likelihood deviation met the
   minimum criterion. Then, the multivariate Cox regression was performed
   on the 9-crLncRNA generated, and the corresponding coefficients were
   multiplied to obtain the score of each sample. The formula was
   determined as follows: Risk score = [75]AC026412.3 * 2.54199 +
   [76]AC026356.1 * 1.87356 + SLC6A1-AS1 * (−0.71370) + [77]AC011462.4 *
   1.19584 + MIR548XHG * 0.62675 + AL031985.3 * 0.96845 + [78]AL117336.2 *
   0.77657 + MCM3AP-AS1 * 2.69411 + [79]AL365361.1 * (−2.99107).

2.4. Confirmation of the Prognostic Signatures of 9-CrLncRNA

   Patients in the training and validation cohorts were divided into high-
   and low-risk groups according to the median risk score. Principal
   component analysis (PCA) of the 9-crLncRNA was performed by the
   ‘scatterplot3d’ R package. The Kaplan–Meier survival analysis was
   conducted using the ‘survminer’ R package, with the log-rank test
   comparing the overall survival (OS) and progression-free survival (PFS)
   between high- and low-risk groups. ROC curves were constructed to
   verify the predictive power of features, and the area under the ROC
   curves (AUC) values for 1, 3, and 5 years were calculated by the
   ‘timeROC’ R package. Risk curves, survival status, and heatmaps of risk
   gene expression profiles were generated by the ‘pheatmap’ R package. In
   addition, univariate and multivariate Cox regression analyses were
   performed, and a conformance index (C-index) was used to assess whether
   the risk score could be an independent predictor of OS in HCC patients.

2.5. Nomogram Construction and Assessment

   The R packages ‘regplot’ and ‘rms’ were used to construct the nomogram
   of the risk score, and the prognosis of HCC patients in it at years 1,
   3, and 5 were estimated. Finally, the ROC curve, decision curve
   analysis (DCA) curve, and calibration curve were used to evaluate the
   nomogram’s accuracy and reliability.

2.6. Enrichment Analysis

   High- and low-risk groups of differentially expressed genes (DEGs) were
   filtered through ‘limma’ R package (log2 |FC| > 1, FDR < 0.05). Gene
   Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) pathway analysis were performed using the
   ‘clusterProfiler’ R package (FDR < 0.05, p < 0.05).

2.7. Evaluation of Immune Cell Infiltration and Immune Microenvironment

   CIBERSORT is an analysis tool that transforms expression data into the
   absolute abundance of immune and stromal cell expression profiles,
   assessing the proportion of 22 human immune cell subpopulations
   ([80]http://CIBERSORT.stanford.edu/, accessed on 31 May 2022). The
   CIBERSORT algorithms were used to quantify the relative infiltration
   levels of different immune cell subsets. ESTIMATION algorithms were
   used to calculate the immune/estimate/stromal scores and the purity of
   tumor in HCC patients [[81]29,[82]30]. Finally, we used the single
   sample gene set enrichment analysis (ssGSEA) method to analyze
   immune-related functions and immune cell infiltration profiles between
   the high-risk and low-risk groups.

2.8. Assessment of the Options of Specific ICIs Treatment

   The immunophenoscore (IPS) of HCC patients was obtained from the Cancer
   Immunome Atlas ([83]https://tcia.at/, accessed on 31 May 2022) to
   compare the potential utilization of immunotherapy in the high- and
   low-risk subgroups. Immune checkpoint gene expression is associated
   with ICIs response, so we analyzed differences in immune checkpoint
   gene expression between the two subgroups. In addition, Spearman
   correlation analysis identified 9 crLncRNAs that are most closely
   associated with immune checkpoints.

2.9. GSVA Analysis

   GSVA analysis boosts the ability to detect subtle changes in pathway
   activity in sample populations. The ‘GSEABase’ and ‘GSVA’ R packages
   were used to analyze the correlation between the KEGG pathway and the
   prognostic signatures of 9 crLncRNAs, paving the way for the
   construction of pathway-based biological models.

2.10. Prediction of Targeted MiRNAs

   We predicted microRNA targets through TargetScan
   ([84]https://www.targetscan.org/vert_71/, accessed on 18 October 2022).
   The miRNA–LncRNA relationship was predicted through DIANA Tools
   ([85]https://diana.e-ce.uth.gr/lncbasev3/interactions, accessed on 18
   October 2022). Some diagrams were used online graphic drawing sites
   ([86]https://app.rawgraphs.io/, accessed on 18 October 2022)
   ([87]https://www.omicstudio.cn/tool, accessed on 18 October 2022).
   Based on the online Venn diagram tool, we screened two common genes
   ([88]https://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on
   18 October 2022).

2.11. Copy Number Variation (CNV) Analysis

   Statistical data of heterozygous and homozygous CNV mutation were
   displayed as pie charts, and the gene expression significantly affected
   by CNV was obtained via the Pearson correlation analysis.
   ([89]http://bioinfo.life.hust.edu.cn/web/GSCALite, accessed on 22 June
   2022).

2.12. Drug Sensitivity Analysis

   The gene set drug resistance analysis from Genomics of Drug Sensitivity
   in Cancer (GDSC) IC50 drug data. The Spearman correlation analysis was
   used to determine the drug sensitivity and gene expression profile data
   of tumor cell lines. ([90]http://bioinfo.life.hust.edu.cn/web/GSCALite,
   accessed on 22 June 2022).

2.13. HCC Tissue Specimens

   With the informed consent of HCC patients in Xijing Hospital, 10 pairs
   of fresh frozen tumors and adjacent normal tissues were collected.
   These patients did not receive any treatment before surgery. This
   research was approved by the Medical Ethics Committee of the First
   Military Medical University.

2.14. Real-Time Quantitative PCR Analysis

   TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract
   total RNA from fresh HCC tissues from Xijing Hospital according to the
   instructions. 1 μg of RNA was employed to synthesize cDNA using the
   PrimeScript RT Reagent Kit Perfect Real Time (TaKaRa, Dalian, China) or
   the miScript II RT Kit (Qiagen, Hilden, Germany). In order to detect
   the mRNA and LncRNA levels of target genes, fluorescent qRT-PCR was
   used, with GAPDH as the internal control. The expression level of
   miR-17-5p in HCC tissues was detected by qRT-PCR, and the expression of
   U6 snRNA (sn-RNU6) was used as the internal control. All the primers
   were obtained from AuGCT (Beijing, China), and the reactions were
   repeated three times. The relative expression levels of LncRNA, miRNA,
   or mRNA were obtained and analyzed using the Bio-Rad C1000 thermal
   cycling apparatus (Bio-Rad, Hercules, CA, USA). The primer sequences
   are listed in [91]Supplementary Table S2.

2.15. Statistical Analysis

   We used R version 4.2.0 to statistically analyze our data and graph
   visualization. The Kaplan–Meier curve was adopted to plot the
   prognostic survival curve for the subgroups, and the log-rank test was
   performed to evaluate if the differences in OS were statistically
   significant. The Spearman method was used to calculate the correlation
   between two variables. The differences in the proportions of clinical
   characteristics were analyzed by the chi-squared test. The Wilcoxon
   test was used for the analysis of differences between the two
   independent groups. p < 0.05 was considered statistically significant.

3. Results

3.1. Construction and Validation of 9-CrLncRNA Prognostic Model in HCC

   The flow chart of our study is shown in [92]Figure 1. The TCGA
   transcriptome data included 50 normal cases and 374 HCC patients. A
   total of 16,876 LncRNAs, 19,938 mRNAs, and 19 cuproptosis-related
   genes’ transcriptome data were obtained from TCGA. The co-expression
   network of mRNAs and crLncRNAs is plotted in [93]Figure 2A. Finally, a
   total of 394 crLncRNAs were obtained by Spearman correlation analysis,
   and 147 of them are closely related to patient survival. The cvfit and
   lambda curves are shown in [94]Figure 2B,C. The prognostic risk model
   was based on 9 crLncRNAs ([95]AC026412.3, [96]AC026356.1, MCM3AP-AS1,
   AL031985.3, [97]AL117336.2, [98]AL365361.1, SLC6A1-AS1, MIR548XHG,
   [99]AC011462.4). The cuproptosis-related mRNAs and the 9-crLncRNA
   signatures were significantly correlated, among which, the 9-crLncRNA
   signatures significantly correlated with GLS, LIPT1, MTF1, ATP7A, and
   NLRP3 ([100]Figure 2D).

Figure 1.

   [101]Figure 1
   [102]Open in a new tab

   Flow chart of this study. A total of 343 HCC patients with complete
   survival information in the TCGA database were divided into two
   cohorts, i.e., training and testing. Using the expression data of 147
   prognostic crLncRNA genes in the training cohort, the 9-crLncRNA
   signatures based on LASSO and COX regression analyses were obtained,
   and the optimal penalty parameter (λ) of the LASSO model was
   constructed. The KM curves, ROC curves, PCA analysis, DCA curves, and
   nomogram were applied to evaluate the accuracy and reliability of the
   9-crLncRNA prognostic model. Subsequently, a series of analyses,
   including ssGSEA, KEGG, GO, GSVA, immune-related,
   immunecheckpoint-related genes, somatic mutations, and drug sensitivity
   analyses, were applied to explore the potential of classifying the
   population as sensitive to treatment and its mechanism.

Figure 2.

   [103]Figure 2
   [104]Open in a new tab

   Identification of crLncRNAs in HCC patients, the prognostic model of
   crLncRNAs and its prediction potential. (A) The co-expression network
   of mRNAs and LncRNAs associated with cuproptosis, which was visualized
   by Sankey diagram according to Spearman correlation analysis (|R| >
   0.4, p < 0.001). (B) A cross-validation to adjust the parameter
   selection in the LASSO Cox regression model. (C) The LASSO coefficient
   curve of 9 crLncRNAs. (D) The heatmap of correlation analysis of
   cuproptosis-related genes and the 9-crLncRNA signatures. * p < 0.05, **
   p < 0.01, and *** p < 0.001. Principal components analysis between low-
   and high-risk groups based on the expressions of (E) entire genes (F)
   19 cuproptosis-related genes (G)147-crLncRNA, and (H) 9-crLncRNA.

   In this model, the following formula was used to calculate the risk
   score for each HCC patient in the TCGA database: Risk score =
   [105]AC026412.3 * 2.54199 + [106]AC026356.1 * 1.87356 + SLC6A1-AS1 *
   (−0.71370) + [107]AC011462.4 * 1.19584 + MIR548XHG * 0.62675 +
   AL031985.3 * 0.96845 + [108]AL117336.2 * 0.77657 + MCM3AP-AS1 * 2.69411
   + [109]AL365361.1 * (−2.99107) (note: the name of LncRNA indicates
   their expression level in TCGA database).

   The PCA showed that our prognostic prediction model could separate the
   two groups’ patients (p < 0.05, [110]Figure 2E–H). We first validated
   the predictive ability of the prognostic model in HCC patients with
   different clinical characteristics. There were significant differences
   between G1 and G3 and grades G2 and G3, between stages I and II and III
   and IV, and between T1 and 2, and T3 and 4 (p < 0.05) ([111]Figure
   3A–C). Interestingly, the OS rate of HCC patients can be well predicted
   by the risk scores for age (<65, ≥65), sex (female, male), grade (G1-2,
   G3-4), stage (III–IV), stage T (T3-4), M0, and N0 (p < 0.05). And,
   within these clinical-characteristic groups, patients in the high-risk
   group had a worse prognosis when compared to the low-risk group
   ([112]Figure 3D,I,K,L,N,O). However, patients in stages I–II and T1–2
   had a better prognosis when compared to the low-risk group and may not
   be classified by risk scores (p > 0.05) ([113]Figure 3J,M). These
   results indicate that the survival of patients with advanced HCC can be
   predicted based on risk scores in different clinical characteristic
   groups.

Figure 3.

   [114]Figure 3
   [115]Open in a new tab

   Validation of the 9-crLncRNA prognostic model by clinical
   characteristics. Based on the median risk score, the entire sample was
   divided into high-risk group (165 cases) and low-risk group (178
   cases). The differences in (A) grade, (B) stage, and (C) T stage were
   analyzed in HCC patients. * p < 0.05, ** p < 0.01, and *** p < 0.001,
   and ns means no significance. The K-M plotters of clinical
   characteristics: (D) <65, (E) ≥65, (F) FEMALE, (G) MALE, (H) G1-G2, (I)
   G3-G4, (J) stage I–II, (K) stage III–IV, (L) M0, (M) T1-T2, (N) T3-T4,
   and (O) N0. p < 0.05 was considered statistically significant.

3.2. The 9-CrLncRNA Model Has High Reliability in Predicting the OS and
Application Value in Clinical

   Subsequently, we assessed the prognostic value of the 9-crLncRNA.
   According to the risk and survival status distributions of the
   visualized risk score, the sample distribution was reasonable. The
   up-regulated genes of [116]AC026412.3, [117]AC026356.1, MCM3AP-AS1,
   AL031985.3, [118]AL117336.2, MIR548XHG, and [119]AC011462.4 in the
   high-risk groups of the TCGA-HCC database were considered risk
   crLncRNAs. [120]AL365361.1 and SLC6A1-AS1 were protective crLncRNAs, as
   they were downregulated in the high-risk group (blue: low expression
   level; red: high expression level, [121]Figure 4A). The OS rate of HCC
   patients in the high-risk group was lower than that in the low-risk
   group ([122]Figure 4D). In addition, the areas under the curve (AUC) in
   the training group were 0.836, 0.780, and 0.794 in 1, 3, and 5 years,
   respectively, indicating that features had a strong ability to predict
   survival time ([123]Figure 4G). The multi-indicator ROC curve indicated
   that the risk score was significantly better than traditional clinical
   characteristics at predicting the OS rate (AUC = 0.836) ([124]Figure
   4J). To further evaluate the predictive validity of this 9-crLncRNA
   model, dual validation was performed in both the validation group and
   the entire group using distribution maps, heat maps, Kaplan–Meier
   survival curves, and ROC curves. Samples from the two risk groups were
   also reasonably distributed in the validation group ([125]Figure
   4B,E,H,K) and the entire group ([126]Figure 4C,F,I,L). As expected, the
   high-risk group has a higher mortality rate than the low-risk group.
   All of the above results indicate that our model has a good specificity
   and sensitivity.

Figure 4.

   [127]Figure 4
   [128]Open in a new tab

   Validation of the 9-crLncRNA prognostic model in the training,
   validation and entire groups. The training group was further divided
   into the high-risk group (101 cases) and the low-risk group (105 cases)
   based on the median risk score. The validation sample was then divided
   into the high-risk group (64 cases) and the low-risk group (73 cases).
   Each sub-graph of [129]Figure 4 shows the risk score curves, survival
   status distribution maps, the 9-crLncRNA expression heatmaps (A–C),
   Kaplan–Meier survival curves (D–F), the ROC curves of the overall
   survival at years 1, 3, and 5 (G–I), and the ROC curves of the risk
   score and other relevant clinical characteristics of the 9-crLncRNA
   (J–L) for the training, validation and entire groups, respectively. p <
   0.05 was considered statistically significant.

   First, the univariate Cox regression analysis showed that the stage, T
   staging, and risk scores of the 9-crLncRNA characteristics were
   correlated with the OS rate (p < 0.001) ([130]Figure 5A). Furthermore,
   risk score was an independent risk prognostic factor for predicting the
   OS rate (HR > 1, p < 0.001) ([131]Figure 5B). Risk score concordance is
   higher than other clinical features, suggesting that the model could
   predict survival better than others ([132]Figure 5C). The nomogram
   calculated the likelihood of survival for these patients by summing
   scores of clinical characteristics ([133]Figure 5D). The calibration
   plot of 1-, 3-, and 5-year survival probabilities showed that the
   nomogram based on risk score had good predictive ability in clinical
   application ([134]Figure 5E). Moreover, the multi-indicator ROC curves
   and DCA curves showed that while both nomogram (AUC = 0.805) and risk
   score were good predictors of OS, the latter was better (AUC = 0.824)
   ([135]Figure 5F,G). Therefore, the risk score of our model has a high
   application value in clinical settings and is reliable in predicting
   the OS of HCC patients.

Figure 5.

   [136]Figure 5
   [137]Open in a new tab

   Evaluation of the predictive ability of the 9-crLncrNA prognostic
   model. The univariate (A) and multivariate (B) Cox regression analyses
   were performed to evaluate the independent predictive potential of OS
   of the risk score and relevant clinical characteristics. *** p < 0.001.
   (C) The C-index was used to evaluate the predictive power of the model.
   (D) The nomogram was used for the prediction of 1-, 3-, and 5-year
   survival. (E) The calibration curves were used to examine the
   capability of predicting the OS at 1, 3, and 5 years. (F) The
   multi-indicator ROC curve and (G) the DCA curve were used to evaluate
   the predictive ability of the nomogram and risk score.

3.3. The 9-CrLncRNA May Be Closely Related to Tumor Immunity

   We obtained 357 DEGs associated with cuproptosis, including 125
   up-regulated genes and 132 down-regulated genes ([138]Figure 6A). The
   KEGG pathway analysis indicated that cuproptosis-related DEGs were
   mainly enriched in the p53 signaling pathway (hsa04115), cell cycle
   (hsa04110), oocyte meiosis (hsa04114), etc. ([139]Figure 6B). In the
   biological process category, GO analysis showed that DEGs were mainly
   enriched in immune-response related pathways, such as cell recognition,
   complement activation, humoral immune response mediated by circulating
   immunoglobulin, phagocytosis (recognition and engulfment), B cell
   receptor signaling pathway, positive regulation of B cell activation,
   etc. In the cellular component category, the DEGs were mainly enriched
   in immunoglobulin complex, etc. In the molecular function category, the
   DEGs were mainly enriched in antigen binding, immunoglobulin receptor
   binding, etc. ([140]Figure 6C). All in all, the cuproptosis-related
   DEGs are mainly enriched in the cell cycle (hsa04110), antigen binding
   (GO:0034987) and immune response related pathway mediated by
   circulating immunoglobulin (GO:0042571) ([141]Figure 6D,E). Therefore,
   the DEGs obtained according to our 9-crLncRNA signatures grouping were
   mainly related to tumor immunity, which is convenient for further
   study. We have reasons to believe that 9-crLncRNA may be closely
   related to tumor immunity.

Figure 6.

   [142]Figure 6
   [143]Open in a new tab

   The GO biofunction and KEGG pathway enrichment analysis of two risk
   groups based on the 9-crLncRNA. (A) The volcano map reflects the 357
   differentially expressed genes between two risk groups (log2|FC| > 1, p
   < 0.05). Green, red, and gray represent downregulated, upregulated, and
   no difference genes, respectively. Bubble graphs for KEGG pathways (B)
   and GO enrichment (C) Circle diagrams of significant GO functional
   items (D) and significant KEGG pathways (E). The latter contains the
   name of the dataset, the number of genes in the dataset, and the
   proportion of crLncRNAs in the pathway. The outer ring is the name of
   the dataset, the inner circles are the number of genes in the dataset
   and the proportion of crLncRNAs in the pathway. p < 0.05 was considered
   statistically significant.

3.4. The Low-Risk Group Was More Likely to Have a Higher Immune Response

   To further explore the relationship between the prognostic signatures
   of 9-crLncRNA and tumor immunity in HCC patients, we used the CIBERSORT
   algorithm to determine the immune cell infiltration in all HCC patients
   from the TCGA database. We analyzed the stromal score (substrate cells
   in tumor tissue), immune score (infiltrating immune cells in tumor
   tissue), and estimated score (the sum of the stromal and immune scores
   from individual cases) in the high- and low-risk groups. A higher
   immune score indicates a better outcome, and as expected, the immune
   score was significantly higher in the low-risk group (p < 0.001,
   [144]Figure 7A). We compared the differences in each immune cell
   between the low- and high-risk groups and found that CD8^+ T cells and
   macrophage M0 were significantly different between the two groups
   ([145]Figure 7B). Subsequently, the ssGSEA was applied to the RNA
   sequencing data of HCC samples to assess immune cell infiltration and
   related functions. Immune cell populations, including naive B cells, B
   cell memory, plasma cells, CD8^+ T cells, CD4^+ T cell memory resting,
   M0 macrophages, and dendritic cells activation, were found to be
   significantly different between the two groups ([146]Figure 7C). The
   high-risk group had a poor prognosis, which was characterized by low
   ratio of B cell naive, resting B cell memory, plasma cells, CD8^+ T
   cells and CD4^+ T cells memory, and the high activation of M0
   macrophages and dendritic cells. In addition, a comparison of immune
   characteristics between high- and low-risk patients showed that
   low-risk patients have higher APC_co-inhibition, APC_co-stimulation,
   chemokines and chemokine receptors (CCR), checkpoint, cytolytic
   activity, HLA, inflammatory promotion, parainflammation,
   T_cell_co-inhibition, and T_cell_co-stimulation, type-I IFN response,
   and type-II IFN response ([147]Figure 7D). This means that patients in
   the low-risk group are more likely to have a higher immune response,
   and they can benefit more from immunotherapy. Finally, the expression
   level of each patient’s immune cells and immune cell-related functions
   between different risk groups is summarized in [148]Figure 7E. Patients
   in the low-risk group had higher expression levels. It is consistent
   with the conclusion that patients in the low-risk group may have a
   higher immune response. We speculate that the 9-crLncRNA may change the
   TIME, one of the biomarkers representing sensitivity to ICIs treatment,
   by influencing immune cell infiltration. Therefore, the risk score
   based on 9-crLncRNA may be able to identify the population susceptible
   to ICIs treatment.

Figure 7.

   [149]Figure 7
   [150]Open in a new tab

   Immune profiles between different risk groups. (A) The boxplots of the
   stroma, immune, and estimate scores of the two groups, and the Wilcoxon
   test is used for comparison. (B) The comparison of immune cell subtypes
   in the low- and high-risk groups. The differences of (C) immune cell
   infiltration and (D) immune function between the two risk groups
   according to ssGSEA. (E) The heatmap summarized stroma, immune,
   estimate scores, tumor purity, immune cell infiltration, and immune
   function between the two groups. * p < 0.05, ** p < 0.01, and *** p <
   0.001, ns means no significance.

3.5. Patients in the Low-Risk Group May Have a Higher Sensitivity to ICIs

   The TIME was different between the two risk groups in our study;
   therefore, we hypothesized that there may be differences in the
   efficacy of ICIs treatment. Due to the importance of ICI in the
   immunotherapy of hepatocellular carcinoma, we further analyzed the
   differential expression of immune checkpoint genes between the two
   groups, and found that the expression in the low-risk group is higher,
   while the results for TNFSF4 and CD276 are the opposite ([151]Figure
   8A). This means that patients in the low-risk group may benefit from
   ICIs treatment. Interestingly, the IPS, IPS_CTLA4 + PD1, IPS_CTLA4, and
   IPS_PD1 in low-risk groups had higher values ([152]Figure 8B–E). This
   means that patients in the low-risk group may have higher ICIs
   sensitivity and benefit from immune checkpoint inhibitor therapy,
   whether they are treated with a CTLA-4 inhibitor alone, a PD-1
   inhibitor, or both.

Figure 8.

   [153]Figure 8
   [154]Open in a new tab

   The comparison of the expressions of immune checkpoint genes and
   sensitivity to immune checkpoint inhibitors between high- and low-risk
   groups. (A) The boxplots for comparing the immune checkpoints genes
   between the two risk groups. The violin figures are for comparing the
   two risk groups of the treatment for using (B) none of CTLA4 or PD1,
   (C) CTLA4 + PD1, (D) CTLA4 alone, and (E) PD1 alone. * p < 0.05, ** p <
   0.01, and *** p < 0.001.

3.6. [155]AL365361.1 May Be the Main Tumor Immune-Related Molecule of the
9-CrLncRNA Model

   Our 9-crLncRNA model has excellent specificity and sensitivity to
   identify the sensitive population of immune checkpoints inhibitor
   treatment. Therefore, further analysis of the potential function of the
   9 crLncRNAs may provide the possibility of finding valuable therapeutic
   targets. [156]Figure 9A shows the potential pathways involved in the
   prognostic signatures of 9-crLncRNA analyzed by GSVA. It was found that
   9-crLncRNA mainly mediated immune-related pathways (such as T cell
   receptor, B cell receptor, and JAK-STAT signaling pathway).
   Subsequently, we analyzed the correlation between 9 crLncRNAs and 22
   immune cells and their immune-related functions ([157]Figure 9B).
   [158]AL365361.1 was significantly correlated with T cells, APC, immune
   checkpoint, and CCR (p < 0.001). As mentioned above, immune checkpoint
   genes were differentially expressed between the low-risk and high-risk
   groups, so we analyzed the association between the 9 crLncRNAs and
   immune checkpoint genes ([159]Figure 9C). The data showed that
   [160]AL365361.1 was significantly correlated with immune checkpoints
   CD28 and CD40LG (p < 0.001), which have been reported to
   synergistically regulate exhaustion of tumor-infiltrating lymphocytes
   (TILs) and response to PD-1 blockade [[161]31,[162]32]. This implies
   that [163]AL365361.1 plays a tumor suppressive role in HCC patients. To
   confirm the possibility of this relationship, we collected fresh
   tissues and performed qRT-PCR to examine the expression level of
   [164]AL365361.1 in HCC tissues and adjacent normal tissues from the HCC
   patients ([165]Figure 9D). The result showed that the expression of
   [166]AL365361.1 in HCC tissues is significantly lower than that in
   adjacent normal tissues. According to the above results,
   [167]AL365361.1 may be the main tumor immune-related molecule between
   the two risk groups.

Figure 9.

   [168]Figure 9
   [169]Open in a new tab

   The connection between 9-crLncrNA prognostic signature and the immune
   landscape. (A) Correlation heatmap of KEGG pathways by GSVA. (B) The
   9-crLncrNA correlates with 22 immune cells and immune-related functions
   on a heatmap. (C) The heatmap of the correlation between the 9-crLncrNA
   and immune checkpoint genes. (D) The differential analysis plots show
   the expression of [170]AL365361.1 in HCC tissue and adjacent normal
   tissue from HCC patients. * p < 0.05, ** p < 0.01, and *** p < 0.001.

3.7. Sensitive Drugs for NLRP3 Mutation May Improve OS in High-Risk Group

   AL36536.1 is closely related to tumor immunity and screened from
   cuproptosis-related genes. Whether it affects tumor immunity by
   mediating cuproptosis-related genes has triggered our thinking. At the
   same time, although our model was able to distinguish the sensitive
   population of immune checkpoints inhibitor therapy well, we hope to use
   it not only to distinguish the sensitive population but also to propose
   new treatment options for patients in the high-risk group who are not
   sensitive to ICIs, thereby prolonging the OS of the high-risk group.
   Therefore, we analyzed the correlation between 9 cuproptosis-related
   genes and 22 immune cells and their immune-related functions
   ([171]Figure 10A). Interestingly, NLRP3 was also significantly
   associated with T cells, APC, immune checkpoints, and CCR (p < 0.001),
   like [172]AL365361.1. Our previous results showed that [173]AL365361.1
   and NLRP3 are highly expressed in the low-risk group. This supports our
   previous findings that patients in the low-risk group may be sensitive
   to ICIs.

Figure 10.

   [174]Figure 10
   [175]Open in a new tab

   The analysis of drug sensitivity in high-risk group. (A) The heatmap of
   correlation between cuproptosis-related genes and 22 immune cells and
   immune-related functions. (B) The pathways pie chart of
   cuproptosis-related genes, with red representing activation and green
   representing inhibition. (C) The pie charts of cuproptosis-related
   genes with CNV in HCC. (D) The CNV status of the cuproptosis-related
   genes in heterozygous and homozygous HCC patients was shown above and
   below the figure, respectively. (E) The differential analysis plots
   show the expression of NLRP3 in HCC tissue and adjacent normal tissue
   from HCC patients. (F) The correlation between cuproptosis-related gene
   expression and sensitive targeted therapy drugs obtained by the
   spearman correlation analysis. The red represents positive correlation,
   indicating that the gene is highly expressed and resistant to drugs,
   whereas the blue represents the drug sensitivity of the gene. * p <
   0.05, ** p < 0.01, and *** p < 0.001.

   We found that NLRP3 may be an important target for improving OS in
   high-risk patients. The proportion of NLRP3 activation/inhibition
   pathways was analyzed and visualized by GSCALite ([176]Figure 10B).
   NLRP3 mainly activates epithelial-to-mesenchymal transition (EMT) and
   hormone pathways. NLRP3 in HCC tumors and adjacent normal tissues were
   analyzed for copy number variation (CNV) ([177]Figure 10C,D). NLRP3 had
   the most significant changes, mainly with CNV mutations (>60%).
   Furthermore, the expression of NLRP3 in HCC tissues is significantly
   lower than that in adjacent normal tissues ([178]Figure 10E). Genomic
   resistance analysis showed that VNLG/124, sunitinib, and linifanib were
   sensitive drugs in the inflammatory body NLRP3 mutant population
   ([179]Figure 10F). Our previous results showed that NLRP3 was poorly
   expressed in the high-risk group; it is dominated by NLRP3 mutation and
   insensitive to immune checkpoint inhibitors. Thus, in high-risk groups,
   NLRP3 mutation increased susceptibility to VNLG/124, sunitinib, and
   linifanib. This may extend the survival of high-risk group patients who
   are not sensitive to ICIs.

3.8. The Potential Regulation Axis between [180]AL365361.1 and NLRP3

   The risk score based on 9-crLncRNA can identify the sensitive
   population for ICIs treatment, and the mechanism involved in the
   regulation of core molecules has aroused our attention. Our previous
   results showed that [181]AL365361.1 was positively correlated with
   NLRP3 ([182]Figure 11A), and both of them were positively correlated
   with anti-tumor immunity. In conclusion, we speculated that NLRP3 may
   cooperate with [183]AL365361.1 to regulate the tumor microenvironment,
   ICIs response, and play a tumor suppressor role in HCC. We consider
   that microRNAs (miRNAs) also play a crucial bridging role between
   [184]AL365361.1 and NLRP3. Thereafter, target gene prediction software
   was used to find the possible hub between them. The sunburst diagram
   shows 51 miRNAs may target NLRP3, 61 miRNAs may be targeted by
   [185]AL365361.1, and 6 common miRNAs among them ([186]Figure 11B).
   There were 77 and 209 miRNAs co-expressed with TILs and ICIs related
   miRNAs, respectively (|R| > 0.2 and p < 0.001). Based on the online
   Venn diagram tool, we screened two common miRNAs from miRNA-NLRP3,
   [187]AL365361.1-miRNA, TILs-, and ICIs-related miRNAs ([188]Figure
   11C). The two common genes were hsa-miR-17-5p and hsa-miR-93-5p. The
   correlation heatmap between the two common genes and the main immune
   checkpoint genes showed that hsa-miR-17-5p and hsa-miR-93-5p might play
   a carcinogenic role in HCC ([189]Figure 11D). In addition, by
   visualizing miRNAs targeting NLRP3 ([190]Figure 11E), we found a
   significant interaction between hsa-miR-17-5p and NLRP3. Meanwhile, we
   performed qRT-PCR to examine the expression level of hsa-miR-17-5p in
   HCC tissues and adjacent normal tissues from the HCC patients. The
   result showed that the hsa-miR-17-5p expression in HCC tissues is
   significantly higher than that in adjacent normal tissues ([191]Figure
   11F). The correlation of three molecules between the expression levels
   in 10 pairs of HCC patients’ tissues ([192]Figure 11G–I) was analyzed,
   respectively. The results showed that [193]AL365361.1 was negatively
   correlated with hsa-miR-17-5p, NLRP3 was negatively correlated with
   hsa-miR-17-5p, and [194]AL365361.1 was positively correlated with
   NLRP3. These are consistent with our predictions. Therefore, we
   hypothesized that the [195]AL365361.1/hsa-miR-17-5p/NLRP3 axis may be
   related to the ICIs sensitivity of HCC patients, which is the main
   reason for the difference in immune response and OS between the two
   risk groups.

Figure 11.

   [196]Figure 11
   [197]Open in a new tab

   The regulatory network between [198]AL365361.1 and NLRP3. (A) The
   scatter plot of correlation showing the correlation of the expression
   levels between [199]AL365361.1 and NLRP3. (B) The sunburst diagram
   shows predicted microRNA targets of NLRP3 and [200]AL365361.1. (C) The
   Venn diagram containing four lists of miRNAs. (D) The correlation
   heatmap between the 2 common miRNAs and the main immune checkpoint
   genes. (E) The network of the potential regulation of miRNAs to NLRP3.
   (F) The differential analysis plots show the expression of miR-17-5p in
   HCC tissue and adjacent normal tissue from HCC patients. The scatter
   plot of correlation shows the correlation of the expression levels
   between [201]AL365361.1 and hsa-miR-17-5p (G), NLRP3 and hsa-miR-17-5p
   (H), [202]AL365361.1 and NLRP3 (I). * p < 0.05, ** p < 0.01. p < 0.05
   was considered statistically significant.

4. Discussion

   Patients with hepatocellular carcinoma have a poor prognosis, most of
   them lost the opportunity for surgery when they are in the advanced
   stage [[203]33]. Immunotherapy combined with targeted therapy brings
   hope to these patients, but it is difficult to identify sensitive
   populations and patients prone to drug resistance now [[204]14].
   LncRNAs have been reported to be used as biomarkers to predict the
   efficacy of chemotherapy, targeted therapy, and immunotherapy, but the
   potential clinical value and regulatory mechanism of crLncRNAs in HCC
   have not been studied [[205]34,[206]35]. There, we hope to provide a
   sensitive biomarker to predict the prognosis of HCC patients and
   immunotherapy efficacy, which will guide the selection of ICIs and
   targeted drugs by constructing the prognostic model of crLncRNAs, thus
   serving as a basis for formulating appropriate clinical strategies. The
   preliminary exploration of the potential mechanism of 9-crLncRNA in
   dividing the ICIs-sensitive population also provides a research
   direction for basic research.

   Recently, researchers have verified that cuproptosis-related genes
   could assess prognosis in patients with clear cell renal cell carcinoma
   [[207]36], melanoma [[208]37], and hepatocellular carcinoma. Compared
   with the recently established crLncRNA prognostic model in HCC, our
   model, which consists of nine crLncRNAs ([209]AC026412.3,
   [210]AC026356.1, MCM3AP-AS1, AL031985.3, [211]AL117336.2,
   [212]AL365361.1, SLC6A1-AS1, MIR548XHG, [213]AC011462.4), has higher
   specificity and sensitivity (1-, 3-, and 5-year AUC of 0.828, 0.781,
   and 0.779, respectively) [[214]38,[215]39]. Patients are divided into
   high- and low-risk groups based on the risk score, and the OS in each
   group can be predicted. The low expressions of [216]AL365361.1 and
   SLC6A1-AS1 in the high-risk group may play an anti-tumor role, while
   the other seven crLncRNAs have the opposite effect. It has been
   reported that [217]AC026412.3 can predict patient survival in
   hepatocellular carcinoma and is associated with immune invasion and the
   tumor microenvironment [[218]40]. MCM3AP-AS1 promotes hepatocellular
   carcinoma growth by targeting the miR-194-5p/FOXA1 axis [[219]41].
   AL031985.3 is associated with ferroptosis-related hepatocellular
   carcinoma and predicts patient survival and immunotherapy response
   [[220]42]. It is possible to predict early recurrence of hepatocellular
   carcinoma after curative resection with [221]AL365361.1 [[222]43].
   Although some of the nine crLncRNAs have been reported to be able to
   predict the prognosis of HCC by other studies, these predictive LncRNAs
   were not used to jointly construct the prognosis model. Therefore, we
   have constructed the model to predict the prognosis and ICIs treatment
   efficacy of HCC patients, and explored the potential function of the
   nine crLncRNAs in the model. This is the first time nine crLncRNAs have
   been used as a biomarker to predict the efficacy of ICIs therapy in
   patients with HCC.

   Our results showed that patients in the low-risk group, divided
   according to their risk score, had better OS, better treatment efficacy
   of ICIs, and were more likely to benefit from ICIs treatment. The
   reason may be that immune cell infiltration in the low-risk group
   enhanced their immune reactivity. The nine crLncRNAs are enriched in
   the tumor immune pathway and may be related to tumor immunity. The
   results also explain why our model can divide the population with
   different immunotherapy efficacies. Some studies have screened
   sensitive targeted therapeutic drugs by GDSC analysis and verified the
   effect of sensitive drugs on tumor progression through experiments
   [[223]44,[224]45]. Therefore, the genomic resistance analysis using
   Cancer Drug Sensitivity Genomics (GDSC) IC50 drug data is feasible for
   the patients in the high-risk group with low immunotherapy sensitivity.
   The results show that patients in the high-risk group may have higher
   sensitivity to VNLG/124, sunitinib, and linifanib and better clinical
   benefits. Therefore, our prognostic model can predict the OS of HCC
   patients, evaluate the sensitivity of ICIs, and choose the appropriate
   treatment for different groups.

   Interestingly, [225]AL365361.1 is the core molecule of crLncRNA in the
   model and may be the main factor affecting the difference in ICIs
   response between low- and high-risk populations. We found that
   [226]AL365361.1 may affect the interaction between immune checkpoints,
   probably mainly CD28 and CD40LG. CD28 is essential for maintaining
   immune homeostasis and ensuring T cell survival [[227]46,[228]47]. CD28
   may enhance effector T function and block the inhibitory function of
   Treg cells [[229]48]. CD40/CD40L molecular pairs can mediate
   bidirectional signaling between T cells and APC, through reverse
   signaling that leads to activation and differentiation of APC as well
   as positive signaling that leads to activation and differentiation of
   T/B cells [[230]49,[231]50]. Our results showed that [232]AL365361.1
   expression in HCC tissue is significantly lower than in adjacent normal
   tissue, as expected. This implies that higher expression of
   [233]AL365361.1 by affecting CD28 and CD40LG leads to a higher immune
   response in the low-risk group. Therefore, we further explored and
   verified the regulatory network of [234]AL365361.1 in HCC tissues. It
   is exciting to find that the [235]AL365361.1/hsa-miR-17-5p/NLRP3 axis
   may inhibit HCC progression and may be related to the ICIs sensitivity
   of HCC patients, which is the main reason for the difference in immune
   response and OS between the two risk groups. Moreover, it has been
   reported that hsa-miR-17-5p promotes the progression of HCC and is
   related to sorafenib resistance [[236]51]. Sorafenib is a receptor
   tyrosine kinase inhibitor that can significantly suppress HCC growth
   [[237]52]. Previous studies did not pay attention to the effect of
   [238]AL365361.1 on immunotherapy efficacy in HCC, or the effect of
   hsa-miR-17-5p and its target NLRP3, on drug resistance. By intervening
   with the [239]AL365361.1/hsa-miR-17-5p/NLRP3 axis, it is possible to
   increase the objective response rate of ICIs, while at the same time
   providing new treatment options for ICIs-insensitive populations.

   Although CD8+ cytotoxic T cells play key roles in eliminating tumor
   cells, they often experience exhaustion late in development and are
   unable to control tumor progression [[240]53]. NLRP3 may be a
   bidirectional valve for the interaction between inflammation and EMT
   [[241]54,[242]55]. Activation of NLRP3 can drive an inflammatory
   response, while inhibition of NLRP3 may aggravate immune exhaustion in
   patients with advanced HCC, which is not conducive to ICIs treatment
   and leads to poor efficacy of ICIs treatment in patients in the
   high-risk group [[243]56,[244]57,[245]58]. Genomic mutations that
   influence clinical responses to therapy can guide the selection of
   targeted drugs. NLRP3 may be a valve for reversing treatment outcomes
   in high-risk patients, and we screened sensitive drugs (VNLG/124,
   sunitinib, and linifanib) for NLRP3 mutation. VNLG/124 was reported to
   be able to enhance the anticancer activity of breast and prostate
   cancer cells in vitro [[246]59]. TACE plus sunitinib can prolong the
   survival of patients with unresectable HCC [[247]60]. Sunitinib can
   increase the anti-tumor immune response by inhibiting the hepatocyte
   growth factor (HGF) and vascular endothelial-derived growth factor
   (VEGF) signaling pathways [[248]61,[249]62]. Linifanib achieved OS
   similar to sorafenib in advanced HCC patients with sorafenib
   intolerance [[250]63]. These clearly verify the efficacy of VNLG/124,
   sunitinib, and linifanib in the treatment of HCC, and when NLRP3 is
   mutated, the sensitivity may be enhanced in the high-risk group of
   patients with low NLRP3 expression, thus bringing better clinical
   benefits.

   The research may predict patient survival and guide the selection of
   immunotherapy and targeted therapy for patients in high- and low-risk
   groups. Furthermore, our study could evaluate the efficacy of
   immunotherapy, and provide a feasible approach for identifying
   significant challenges in treating sensitive populations. Inevitably,
   our study has limitations, and more prospective data are needed to
   verify the clinical application value of our 9-crLncRNA prognostic
   model in guiding treatment selection.

5. Conclusions

   The study found that the prognostic model based on 9-crLncRNA had
   excellent specificity and sensitivity and was able to predict the
   prognosis of HCC patients and identify ICIs sensitive populations.
   Additionally, tumor immunity may be connected to tumor cuproptosis.
   According to the crLncRNA model, patients in the low-risk group may
   have higher ICIs sensitivity and benefit from ICIs therapy, and the
   crLncRNA [251]AL365361.1 may affect the immune response to ICIs through
   the [252]AL365361.1/hsa-miR-17-5p/NLRP3 axis. It is possible to improve
   the treatment outcomes by using NLRP3 mutation-sensitive drugs
   (VNLG/124, sunitinib, and linifanib) in the high-risk group of patients
   who are not sensitive to ICIs and have low expression of NLRP3. In a
   word, our study may provide new insights into the development of
   appropriate clinical strategies.

Acknowledgments