Systematic analysis identifies CDKN2A as a prognostic biomarker for hepatocellular carcinoma

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Disruptions in copper homeostasis adversely affect liver function. Cuproptosis, a recently defined form of regulated cell death triggered by intracellular copper accumulation, disrupts the tricarboxylic acid cycle and mitochondrial respiration. However, the specific roles and mechanisms of cuproptosis-related genes (CRGs) in HCC pathogenesis remain incompletely understood. Material and methods We systematically evaluated the expression of 10 CRGs in HCC tissues versus adjacent normal tissues. Bioinformatics analyses included Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and Gene Set Enrichment Analysis (GSEA) were performed. Immune infiltration levels within the tumor microenvironment were assessed. The prognostic significance of CDKN2A was evaluated using Kaplan-Meier (KM) survival analysis and univariate/multivariate Cox proportional hazards regression. CDKN2A protein expression was validated using immunohistochemistry (IHC). Results CDKN2A was significantly overexpressed in HCC compared to normal tissues. Bioinformatics analyses implicated CDKN2A in DNA replication, organelle fission, and cell cycle checkpoint signaling. Immune-related analysis revealed that high CDKN2A expression correlated positively with dendritic cell (DC) and Th2 cell infiltration, but negatively with CD8 + T cell and natural killer (NK) cell infiltration. KM analysis demonstrated that high CDKN2A expression predicted significantly shorter overall survival in HCC patients. Univariate and multivariate Cox regression identified CDKN2A as an independent prognostic risk factor. Conclusions This study demonstrates that CDKN2A is significantly overexpressed in HCC and plays a role in immune microenvironment modulation. CDKN2A serves as a promising independent prognostic biomarker for HCC, associated with poorer patient survival. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma (HCC) is ranked as the sixth most prevalent malignancy globally, causing 600,000 deaths annually, with an overall five-year survival rate of about 20% 1 . Although in recent years, advances in immunotherapy based on immune checkpoint inhibitors (ICIs) and molecular-targeted drugs have provided HCC patients with several treatment options and improved the prognosis to a certain extent 2 , 3 . Nevertheless, the survival rate remains unsatisfactory, attributed to challenges such as low early detection rates, limited treatment efficacy, high recurrence rates, and chemotherapy resistance 4 . Consequently, the development of effective prognostic predictors and therapeutic targets is urgently needed. Maintaining copper homeostasis is vital for the physiological function of cells, and disruptions in this balance can influence tumor growth and lead to irreversible cellular damage 5 . In 2022, Tsvetkov et al. raised “cuproptosis” for the first time and explained the term systematically 6 . Cuproptosis, a novel form of cell death reliant on copper, distinguishes itself from apoptosis, necrosis, cell pyroptosis, and iron death 7 . There is a significant relationship between this model and the tricarboxylic acid (TCA) cycle. The mechanism can be briefly expressed as the direct binding of copper ions (including Cu 2+ and Cu + ) to the lipoacylated components in the mitochondrial respiration process, resulting in lipoacylated proteins aggregating. Then, the expression of iron-sulfur cluster proteins is downregulated, inducing protein toxic stress and eventually causing cell death. Cuproptosis-related genes (CRGs) play a critical role in various cancers, such as skin melanoma 8 , pancreatic cancer 9 , clear cell renal cell carcinoma 10 , bladder cancer 11 , triple-negative breast cancer 12 , lung cancer 13 , and head and neck squamous cell carcinoma 14 . Some studies have shown that imbalance of copper homeostasis induces drug resistance and tumor progression in HCC cells 15 , 16 . In addition, mitochondrial respiration rate and TCA activity are closely related to the progress, metastasis, and drug sensitivity of HCC 17 , 18 . Recent studies reported that the prognosis of HCC can be effectively predicted by CRGs 5 , 19 – 22 . However, the expression pattern and clinical value of CRGs in HCC are yet to be clarified. CDKN2A encodes the cell cycle inhibitor p16, which mediates cell cycle arrest in both G1 and G2 phases by inhibiting the oncogenic activities of CDK4/6 and MDM2 23 . CDKN2A expression and mutation are closely associated with several malignancies, including biliary tract carcinoma, intrahepatic cholangiocarcinoma, anaplastic thyroid carcinoma, and pancreatic cancer 24 – 27 . Specifically, p16 deletion promotes liver metastases in pancreatic ductal adenocarcinoma 28 . A meta-analysis further identified CDKN2A promoter methylation in blood samples as a biomarker linked to increased HCC risk and poor prognosis in patients 29 . In HCC, CDKN2A also significantly influences immune microenvironment regulation; its elevated expression correlates with poor progression-free interval (PFI) and overall survival (OS) 30 . Given the dual role of CDKN2A in cell cycle regulation and immune modulation, we hypothesize that its dysregulation critically contributes to HCC pathogenesis and serves as a determinant of poor prognosis. Despite this, the expression pattern and prognostic significance of CDKN2A in HCC remain incompletely characterized. This study first compared the expression levels of 10 CRGs in HCC versus adjacent normal tissues and assessed their correlations with overall survival (OS) and progression-free interval (PFI). We subsequently developed a prognostic nomogram integrating clinicopathological parameters and CDKN2A mRNA expression. The molecular function of CDKN2A was further investigated through enrichment analysis. Additionally, based on the critical role of immune cell infiltration in determining patient OS, we examined the association between CDKN2A expression and tumor immune infiltration. Our results demonstrate that CDKN2A exerts pleiotropic effects in HCC pathogenesis. Its overexpression significantly correlates with adverse clinical outcomes, supporting its potential as a promising prognostic biomarker for HCC. Materials and Methods Data acquisition and pretreatment Data on CRGs and clinical information for 424 HCC sufferers were collected from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Raw read counts obtained using STAR were used to measure gene expression, and these counts were then converted into transcripts per million (TPM). The log2 (TPM + 1) transformation was used to normalize the transcriptome data. Clinical covariates were sourced from prior associated researches 31 . For confirming differentially expressed CRGs, the HCC expression profiling via array was obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/ ), including GSE36376 (Platform: GPL10558) and GSE76724 (Platform: GPL10558). GSE36376 enrolled 193 adjacent non-tumor tissues and 240 HCC tissues, while GSE76427 included 52 normal and 115 HCC tissues. Gene alterations of CDKN2A in TCGA HCC datasets were analyzed using the cBioPortal database ( http://www.cbioportal.org/ ) 32 . Expression analysis of CRGs We divided the samples into disease states (tumor or normal) and made scatter plots and boxplots to show the variations in CRGs expression between HCC and normal samples. CDKN2A expression levels were classified as CDKN2A -Low or CDKN2A -High based on statistical ranking, depending on whether they were below or above the median value. Correlation and enrichment analyses For analyzing the potential interactions of CDKN2A with other genes in HCC, Pearson’s correlation coefficient was calculated using the TCGA data. Next, we selected the top 300 genes correlating with CDKN2A positively for enrichment analysis through Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) analysis, and Gene Set Enrichment Analysis (GSEA) to understand the gene's function 33 . Immune cell infiltration Previously, CIBERSORT was adopted to estimate the immune cell infiltration score in TCGA liver cancer 34 , 35 . For comparison of immune cell infiltration levels, TCGA samples were classified into high and low CDKN2A groups based on the median expression level. The correlation between the immune cell abundance and the CRG expression was validated via a TIMER (Tumor Immune Estimation Resource; cistrome.shinyapps.io/timer) 36 . Also, we detected three critical immune checkpoints (ICKs), comprising CD274, HAVCR2, and PDCD1 . Prognostic evaluation After excluding cases lacking clinical information on tumor stage, histological grade, or OS outcomes, we obtained gene expression and clinical data for 373 HCC patients from the TCGA database. Kaplan Meier plotter was used to analyze the relationship between CDKN2A expression and survival in HCC, and log-rank test was used. Logistic regression analysis was performed to reveal the correlation between CDKN2A mRNA expression and clinicopathologic factors, such as tumor-node-metastasis stage, age, sex, histologic grade, margin status, vascular invasion, and tumor mutation burden (TMB). To assess the impact of CDKN2A expression and clinicopathological factors on overall survival, univariate and multivariate analyses were performed, and the cox proportional hazards model was used to generate risk ratios (HR) and 95% confidence intervals (CI). Development of the nomogram for HCC patients We integrated the clinical parameters and the expression of CDKN2A to develop the nomogram to facilitate clinical application. A nomogram for predicting personal survival likelihood was established using the “rms” package in R software. Calibration curves predicting 1-, 3-, and 5-year survival rates were drawn to appraise the agreement between the nomogram's predicted prognosis and actual survival time 10 . Immunohistochemistry (IHC) Immunohistochemistry studies on HCC tissues were performed according to the manufacturer’s instructions (using antibody Cell signal technology #23200 ). Rehydration, deparaffinization, antigen retrieval, quenching endogenous peroxidase activity, and blocking were involved. After that, the tissues were subjected to overnight incubation at 4°C with the primary antibody (diluted 1:400) and for 30 min at 37°C with the horseradish peroxidase-tagged secondary antibody. The CDKN2A expression was evaluated through the H-SCORE method, calculated as follows: (1×percentage of weak staining) + (2×percentage of moderate staining) + (3×percentage of strong staining) within the target region, ranging from 0 to 300. Statistical analysis Mean ± standard deviation was used for describing continuous variables, and frequency and proportion were for displaying categorical variables. A Wilcoxon rank-sum test was recruited for examining the differences in CRG expression in HCC and adjacent normal tissue. P < 0.05 in the Kaplan–Meier survival curve indicated a significant difference. R version 4.2.1 (The R Foundation) was employed for all statistical analyses. Two-sided P -values were used, and a P-value of < 0.05 indicated a statistically significant difference. Results CDKN2A is more highly expressed in liver cancer than in adjacent normal tissue. First, we assessed the expression of 10 CRGs (FDX1, CDKN2A, LIAS, LIPT1, DLAT, DLD, PDHA, PDHB, MTF1, and GLS) closely involved in cuproptosis 10 . As stated in Fig. 1A, the nine genes expression was significantly upregulated, and FDX1 was downregulated in the tumor tissue (Fig. 1A). In the GSE36376 database, all genes except FDX1 and LIAS in the tumor group were considerably raised relative to those in the normal group, while FDX1 and LIAS expression was substantially decreased in the tumor group. In GSE76427 database, the other 9 genes expression levels were consistent with GSE36376 except the expression of FDX1 (Figure S1 and Figure S2). Data from the three databases showed that the expression of CRGs did change significantly in tumor tissue samples. Furthermore, strong correlations were discovered among different gene expression levels (Fig. 1B). As an illustration, DLD showed a high positive contact with MTF1 (r = 0.567, P < 0.001, Fig. 1C). Copper overload due to mutations triggered serious consequences 10 . The mutation frequency of 10 CRGs in HCC was analyzed (www.cbioportal.org), and the results showed that CDKN2A had up to 8% genetic alterations, which has the highest among the 10 CRGs (Fig. 1D), deeming it as a major factor leading to the disorder of copper metabolism in liver cells, thus inducing tumor formation and affecting the prognosis of patients. The expression levels of CDKN2A varied across pathological stages and histological grades of HCC, indicating an ascending tendency of CDKN2A expression with increased histological grade or tumor stage (Fig. 1E). The above findings disclosed that the CDKN2A expression was interrelated with disease grade. Correlation and enrichment analyses For predicting biological functions of CDKN2A, along with concomitant pathways, the analysis for correlation was carried out by GO and KEGG databases using HCC TCGA data. GO analysis and functional enrichment disclosed that CDKN2A was predominantly related to biological processes, such as DNA replication, organelle fission, chromosome segregation, mitotic nuclear division and nuclear division (Fig. 2A), and cell component, including centromeric region, chromosome, kinetochore, condensed chromosome kinetochore, chromosome region, condense chromosome (Fig. 2B), and molecular function (MF) (such as single-stranded DNA-dependent DNA helicase activity, single-stranded DNA-dependent ATPase activity, ATPase activity, catalytic activity, DNA-dependent ATPase activity, and acting on DNA) (Fig. 2C). Additionally, KEGG pathway analysis displayed a crosstalk and an enrichment among the top 300 genes in the Fanconi anemia pathway, homologous recombination, progesterone-mediated oocyte maturation, DNA replication and cell cycle (Fig. 2D). GSEA revealed that the Rho-GTPases, M phase, transcriptional regulation by P53, DNA repair, and cell cycle checkpoint signaling pathways were significantly enriched (Fig. 2E). Correlation between CDKN2A and immune cell infiltration Next, we assessed the immune cell infiltration score in TCGA HCC. Referring to the assessment results, the high CDKN2A expression group disclosed a rise in the activated dendritic cell (aDC) infiltration level, while a drop in neutrophils and CD8 + T cell. CDKN2A expression was positively related to DC cell infiltration and Th2 cells while negatively related to CD8 + T and natural killer (NK) cells (Fig. 3A and 3B). Furthermore, we proved our outcomes with the TIMER2 database and observed a positive relationship between CDKN2A expression and DC infiltration level under different algorithms (Figure S3). Moreover, CDKN2A expression was markedly linked ro DC markers in HCC, including CD83, CD40, CD80, and CD86 (Fig. 3C). Also, our findings divulged a positive association of CDKN2A expression in HCC with CD274 (r = 0.255, P < 0.001), HAVCR2 (r = 0.211, P0.001), and PDCD1 (r = 0.195 P < 0.001) in TIMER2 and TCGA data (Fig. 3D and Figure S4). Association with clinicopathological variables These patients were divided into groups with high or low CDKN2A expression based on the median CDKN2A expression value, and any potential correlations between CDKN2A expression and clinical characteristics were evaluated (Table 1). Logistic regression analysis revealed that the expression of CDKN2A was correlated with that of AFP (p = 0.003, Table 2) and but not with tumor stage, age, histologic grade, vascular invasion, BMI, liver fibrosis Ishak score, MarginStatus, sex and TMB (P > 0.05, Table 2). Table 1 Relationship between CDKN2A mRNA expression and clinical characteristics in HCC Characteristics Low expression of CDKN2A High expression of CDKN2A P value n 186 187 AJCC Stage,n(%) 0.041 Stage I 101(27.2%) 80(21.6%) Stage I 37(10.0%) 55(14.8%) Stage III 43(11.6%) 50(13.5%) Stage IV 4(1.1%) 1(1.1%) Race,n(%) 0.214 Asian 73(20.1%) 86(23.7%) White 102(28.1%) 83(22.9%) Black or African American 6(1.7%) 11(3.0%) American Indian or Alaska Native 1(0.3%) 1(0.3%) Age,n(%) 0.35 ≤60 93(25.0%) 84(22.6%) >60 93(25.0%) 102(27.4%) Histologic Grade,n(%) 0.123 G1&G2 123(33.4%) 110(29.9%) G3&G4 60(16.3%) 75(20.4%) Vascular Invasion,n(%) 0.857 None 105(33.1%) 102(32.2%) Micro 48(15.1%) 46(14.5%) Macro 7(2.2%) 9(2.8%) AFP,n(%) 0.003 Normal 74(26.5%) 48(17.2%) Abnormal 67(24.0%) 90(32.3%) BMI,n(%) 0.386 <24 82(24.4%) 75(22.3%) ≥24 85(25.3%) 94(28.0%) Liver Fibrosis Ishak Score,n(%) 0.457 0:No Fibrosis 44(20.6%) 31(14.5%) 1–2:Portal Fibrosis 13(6.1%) 18(8.4%) 3–4:Fibrous Speta 14(6.5%) 14(6.5%) 5–6:Nodular Formation and Incomplete Cirrhosis, or Established Cirrhosis 43(20.1%) 37(17.3%) MarginStatus,n(%) 0.588 R0 165(48.0%) 161(46.8%) R1 8(2.3%) 9(2.6%) R2 1(0.3%) 0(0.0%) Sex,n(%) 0.141 Male 119(31.9%) 133(35.7%) Female 67(18.0%) 54(14.5%) TMB,n(%) 0.708 <5 163(44.4%) 158(43.1%) ≥5 22(6.0%) 24(6.5%) AJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden Table 2 CDKN2A mRNA expression association with clinical pathological characteristics in HCC (logistic regression) Characteristics Total (N) OR (95% CI) P value AJCC Stage (StageIII & StageIV vs StageI & Stage II) 371 0.902(0.568–1.431) 0.660 Age (> 60 vs ≤ 60) 372 1.214(0.808–1.825) 0.350 Histologic Grade (G3&G4 vs G1&G2) 368 0.398(0.913–2.140) 0.123 Vascular Invasion (Micro & Macro vs None) 317 1.029(0.648–1.635) 0.902 AFP(Abnormal vs Normal) 279 2.071(1.279–3.352) 0.003 BMI (≥ 24 vs < 24) 336 1.209(0.787–1.857) 0.386 Liver Fibrosis Ishak Score (Score 3–6 vs Score 0–2) 214 0.970(0.557–1.690) 0.914 MarginStatus (R1 & R2 vs R0) 344 1.025(0.397–2.648) 0.960 Sex(Female vs Male) 373 0.721(0.467–1.115) 0.141 TMB (≥ 5 vs < 5) 367 1.125(0.606–2.089) 0.708 AJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden Association between CRG expression and HCC patient prognosis The analysis of CRG expression in relation to OS and PFI in the TCGA cohort was conducted to estimate the prognostic significance of CRGs in HCC patients. As stated by univariate logistic regression analysis, the expression of CDKN2A [odds ratio (OR) = 1.790 (1.262–2.538), P = 0.001], DLAT [OR = 1.689 (1.191–2.396), P = 0.003], and pathological stage [OR = 2.734 (1.792–4.172), P < 0.001] were closely connected to OS (Table 3 and Fig. 4). The expression of CDKN2A [OR = 1.646 (1.229–2.204), P = 0.002], DLAT [OR = 1.427 (1.067–1.911), P = 0.017], and pathological stage [OR = 2.678 (1.857–3.861), P < 0.001] was associated with PFI (Table 3 and Fig. 4). In addition, univariate logistic regression analysis showed that GLS expression was negatively correlated with OS, but high FDX1 expression was positively correlated with PFI. Furthermore, multivariate logistic regression analysis demonstrated that CDKN2A and pathologic stage independently served as the prognostic factors of OS [OR = 1.520 (1.032–2.231), P = 0.032 for CDKN2A and OR = 2.281 (1.471–3.539), P < 0.001 for tumor stage III) and PFI [OR = 1.371 (0.999–1.882), P = 0.05 for CDKN2A and OR = 2.222 (1.517–3.254), P < 0.001) for tumor stage III, respectively] (Table 3). Table 3 Univariate and multivariate analyses (overall survival) for prognostic factors in HCC Characteristics Total (N) Univariate analysis Multivariate analysis HR (95% CI) P value HR (95% CI) P value AJCC Stage 370 Stage I & Stage II 272 Reference Stage III & Stage IV 98 2.590(1.824–3.677) 60 196 1.219(0.860–1.728) 0.267 Histologic Grade 367 G1&G2 233 Reference G3&G4 134 1.110(0.774–1.592) 0.57 Vascular Invasion 316 None 207 Reference Micro & Macro 109 1.330(0.878–2.014) 0.178 AFP 278 Normal 122 Reference Abnormal 156 1.822(1.144–2.901) 0.012 1.587(0.963–2.617) 0.070 BMI 335 <24 157 Reference ≥24 178 0.668(0.461–0.969) 0.033 1.016(0.633–1.629) 0.949 Liver Fibrosis Ishak Score 213 Score 0–2 134 Reference Score 3–6 79 0.815(0.475-1.400) 0.459 MarginStatus (R1 & R2 vs R0) 343 R0 325 Reference R1 & R2 18 1.591(0.805–3.143) 0.182 Sex 373 Male 252 Reference Female 121 1.249(0.877–1.780) 0.218 TMB 366 <5 320 Reference ≥5 46 2.575(1.652–40.14) < 0.001 3.199(1.805–5.672) < 0.001 CDKN2A 373 Low Expression 187 Reference High Expression 186 1.790(1.262–2.538) 0.001 1.520(1.036–2.231) 0.032 AJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden HR:Hazard ratio Nomogram development and validation for HCC To achieve the precise estimation for individual survival in HCC patients, we built a nomogram model to predict 1-, 3-, and 5-year OS and PFI based on CDKN2A expression and pathological stage. The analysis outcomes proved that the nomograms are quantitative tools to assess the prognosis of HCC patients. Moreover, calibration curves demonstrated good consistency between the predicted and observed prognosis (Fig. 5). CDKN2A is overexpressed in patients with HCC To validate the consistency of CDKN2A expression in HCC tissues, IHC was conducted, comparing CDKN2A expression between patients with HCC (n = 20) and their normal controls (n = 20). The findings showed that HCC had higher CDKN2A expression than the control samples, suggesting that CDKN2A may play a crucial role in HCC (Fig. 6). Discussion This study investigated 10 copper metabolism-related genes (CRGs) implicated in copper homeostasis dysregulation. We found elevated expression of most CRGs (excluding FDX1 and LIAS) in HCC tissues compared to adjacent normal tissue. Multivariate analysis identified CDKN2A expression as significantly correlated with advanced tumor stage, poor prognosis, and distinct immune infiltration patterns in HCC patients. CDKN2A, (encoding p16/INK4a/MTS1) is a well-established tumor suppressor and cell cycle regulator 37 . Our findings confirm significantly higher CDKN2A expression in HCC versus controls, aligning with its upregulation observed in THCA, LIHC, KICH, KIRP, KIRC, and COAD 37 . While CDKN2A's prognostic significance across diverse cancers is well-documented 38 – 41 . our study specifically links its overexpression in HCC to poorer patient outcomes. Importantly, elevated CDKN2A emerged as an independent predictor of adverse prognosis. To translate this finding clinically, we developed a prognostic nomogram integrating CDKN2A expression and tumor stage. Higher nomogram scores predict significantly decreased 1-, 3-, and 5-year survival probabilities. Mechanistically, CDKN2A normally induces G1/S cell cycle arrest 37 , 38 , 42 . Its dysfunction via mutation/deletion, observed in up to 8% of cases in our cohort and permits uncontrolled proliferation 43 . Derived from functional analysis, genes associated with CDKN2A depended on biological pathways and functions pertaining to the organelle fission and cell cycle. Figures 4 C and 4 D show that majority of the genes exhibited a positive link to CDKN2A expression and had a relationship with cell cycle and proliferation, and are thus involved in critical physiological activities; the imbalance in their expression might increase the probability of tumor development 44 . The tumor immune microenvironment (TIME), comprising monocytes/macrophages, dendritic cells (DCs), neutrophils, NK cells, and T cells, critically influences HCC progression and response to therapy. While T-cell responses to immune checkpoint (ICI) inhibition dominate current research 45 , 46 . Our data reveal a novel association: CDKN2A expression positively correlated with DC infiltration and Th2 cells but negatively correlated with neutrophils, CD8 + T cell and natural killer (NK) cells. Specifically, high CDKN2A expression was associated with increased activated DC (aDC) populations, suggesting its potential role in modulating tumor immunity. DCs serve pivotal roles as central antigen-presenting cells, initiating anti-tumor immune responses by presenting tumor antigens and activating cytotoxic T cells 47 . As a crucial part of the tumor microenvironment, DCs are the most important antigen-presenting cells in the body. They are capable of monitoring and killing tumors by identifying tumor cell-specific antigens and signaling T cells to execute their cytotoxic function 47 . Coordination with other different immune cells is the essential condition for DCs to exert the anti-tumor role, and the imbalance between different immune cells is a sign of poor tumor prognosis. The present article demonstrated a connection of CDKN2A expression with varying immune cells and immune infiltration levels, suggesting its potential influence on HCC immunology. In addition, CDKN2A expression had a significant positive impact on ICKs, suggesting the effectiveness of immunotherapy PD-1 and PD-L1 in the treatment of liver cancer. Collectively, our results position CDKN2A not only as a robust prognostic biomarker but also as a putative immunotherapeutic target influencing tumor cell viability and the TIME. This study provides a foundation for future investigations into CDKN2A-directed therapeutic strategies in HCC. Limitations of the study Nevertheless, the present study had several limitations. First, although CDKN2A has a strong prognostic effect in HCC patients, there are only 50 samples from the paracarcinoma normal tissue group in the TCGA database. In future studies, the large sample size and prognostic clinical dataset need to be expanded for further verification. Second, despite a detailed analysis of the prognostic effect of CDKN2A in HCC, this study did not consider other genes that have been reported to be associated with HCC. Third, given that predictive signals are established and validated using data from public databases, basic biological experiments are needed for further validation. Conclusions In conclusion, CDKN2A is a useful biomarker for determining the HCC patients’ prognosis. Furthermore, CDKN2A exhibits a positive association with immune cell infiltration and prognosis, which is highly expressed in HCC. Therefore, future studies need to expand the sample size and accumulate biological evidence. Declarations Ethics approval and informed consent Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This study received no funding. Authors' contributions Shil-in He carried out the studies, participated in collecting data, and drafted the manuscript. Yan-ling Zhang and Jun-jie Xu performed the statistical analysis and participated in its design. Xiao Liang participated in interpretation of data and draft the manuscript. All authors read and approved the final manuscript. Acknowledgements None. References Siegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022. CA Cancer J Clin 2022, 72:7-33. Sangro B, Sarobe P, Hervas-Stubbs S, Melero I: Advances in immunotherapy for hepatocellular carcinoma. Nature reviews Gastroenterology & hepatology 2021, 18:525-43. Zongyi Y, Xiaowu L: Immunotherapy for hepatocellular carcinoma. 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Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA: Robust enumeration of cell subsets from tissue expression profiles. Nature methods 2015, 12:453-7. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS: TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer research 2017, 77:e108-e10. Chen Z, Guo Y, Zhao D, Zou Q, Yu F, Zhang L, Xu L: Comprehensive Analysis Revealed that CDKN2A is a Biomarker for Immune Infiltrates in Multiple Cancers. Frontiers in cell and developmental biology 2021, 9:808208. Zeng H, Jorapur A, Shain AH, Lang UE, Torres R, Zhang Y, McNeal AS, Botton T, Lin J, Donne M, Bastian IN, Yu R, North JP, Pincus L, Ruben BS, Joseph NM, Yeh I, Bastian BC, Judson RL: Bi-allelic Loss of CDKN2A Initiates Melanoma Invasion via BRN2 Activation. Cancer cell 2018, 34:56-68 e9. Christodoulou E, Nell RJ, Verdijk RM, Gruis NA, van der Velden PA, van Doorn R: Loss of Wild-Type CDKN2A Is an Early Event in the Development of Melanoma in FAMMM Syndrome. The Journal of investigative dermatology 2020, 140:2298-301 e3. Ji Z, Huo C, Yang P: Genistein inhibited the proliferation of kidney cancer cells via CDKN2a hypomethylation: role of abnormal apoptosis. International urology and nephrology 2020, 52:1049-55. Xande JG, Dias AP, Tamura RE, Cruz MC, Brito B, Ferreira RA, Strauss BE, Costanzi-Strauss E: Bicistronic transfer of CDKN2A and p53 culminates in collaborative killing of human lung cancer cells in vitro and in vivo. Gene therapy 2020, 27:51-61. Kimura H, Klein AP, Hruban RH, Roberts NJ: The Role of Inherited Pathogenic CDKN2A Variants in Susceptibility to Pancreatic Cancer. Pancreas 2021, 50:1123-30. Schulze K, Imbeaud S, Letouze E, Alexandrov LB, Calderaro J, Rebouissou S, Couchy G, Meiller C, Shinde J, Soysouvanh F, Calatayud AL, Pinyol R, Pelletier L, Balabaud C, Laurent A, Blanc JF, Mazzaferro V, Calvo F, Villanueva A, Nault JC, Bioulac-Sage P, Stratton MR, Llovet JM, Zucman-Rossi J: Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nature genetics 2015, 47:505-11. Cotter TG: Apoptosis and cancer: the genesis of a research field. Nature reviews Cancer 2009, 9:501-7. Powles T, Eder JP, Fine GD, Braiteh FS, Loriot Y, Cruz C, Bellmunt J, Burris HA, Petrylak DP, Teng SL, Shen X, Boyd Z, Hegde PS, Chen DS, Vogelzang NJ: MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 2014, 515:558-62. Mahmoud SM, Paish EC, Powe DG, Macmillan RD, Grainge MJ, Lee AH, Ellis IO, Green AR: Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2011, 29:1949-55. Steinman RM: Decisions about dendritic cells: past, present, and future. Annual review of immunology 2012, 30:1-22. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureLegends.docx FigureS1.jpg FigureS2.jpg FigureS3.jpg FigureS4.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers invited by journal 18 Jul, 2025 Editor invited by journal 16 Jul, 2025 Editor assigned by journal 12 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 08 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7074933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488700064,"identity":"55550835-e802-4b3d-8e40-3211921cece4","order_by":0,"name":"Shi-lin He","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shi-lin","middleName":"","lastName":"He","suffix":""},{"id":488700065,"identity":"672b95ff-702b-4f50-8dba-daaf7f8700fb","order_by":1,"name":"Yan-ling Zhang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yan-ling","middleName":"","lastName":"Zhang","suffix":""},{"id":488700066,"identity":"15ec8296-0e8d-4580-b516-5aa5bd692b1e","order_by":2,"name":"Jun-jie Xu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Jun-jie","middleName":"","lastName":"Xu","suffix":""},{"id":488700067,"identity":"b07910c9-4714-4d88-a1f2-cfca0dac2212","order_by":3,"name":"Xiao Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYJACZoYCBjkGhgNAJhvRWgwYjEnXktgAZhKjRX722cefCwwOp89vPGPA8KHsMAP/7Ab8WgzOpZtJzzBIy21sOGPAOOPcYQaJOwcIaOFhY2PmMbDJbWY4Y8DM23aYwUAigYDDetiYP/MYSKSzgbT8JUYLwxk2BmmgLQk8IC2MxGgxOMPGBtSSZjiD4VjBwZ5z6TwSN4hyWMVhefkZhzc++FFmLcc/g5DD4EDiADgyeYhVDwT8DSQoHgWjYBSMghEFAB1DOeI3YRdeAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-07-08 12:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7074933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7074933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87347037,"identity":"f1ef5a03-d441-4bce-b593-2e2129519cb1","added_by":"auto","created_at":"2025-07-23 02:36:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and genetic variation of 10 CRGs in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Expression of 10 CRGs in HCC and normal tissues. (B-C). Correlation analysis of the 10 genes with respect to the expression of cuproptosis genes. (D). Mutation frequency of 10 CRGs in HCC. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001; (E). Expression of CDKN2A in different pathological stages of HCC patients. CRGs: cuproptosis-related genes, HCC: hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/6d853ac1144635ce2a4ecf52.png"},{"id":87347760,"identity":"7ef94ac1-ae61-41af-ab5a-423afc827f21","added_by":"auto","created_at":"2025-07-23 02:44:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment of CRGs in HCC patients of TCGA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–C). Results of GO analysis, including BP, MF, and CC. (D) Enriched items in the KEGG analysis. The size of the circles represents the number of enriched genes. (E) Significant GSEA results of the top 300 genes most positively associated with FCGBP.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/268fc8ffc43a5eeb3649d03a.png"},{"id":87346610,"identity":"0dd61c56-0aed-4c9b-9488-5a8779634d73","added_by":"auto","created_at":"2025-07-23 02:28:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":349083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between immune cell infiltration and CDKN2A in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immunoinfiltration level in the high and low CDKN2A expression groups in the TCGA. (B) Lollipop chart of the correlation between CDKN2A and levels of immune cell infiltration. (C) Correlation between CDKN2A expression and DC marker. (D) Association between CDKN2A and CD274, HAVCR2 and PDCD1 expression in HCC patients in TIME.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/6a8c164fdb2754195886cc79.png"},{"id":87347040,"identity":"58c94ab2-45a1-4942-b158-e4c460714343","added_by":"auto","created_at":"2025-07-23 02:36:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":442909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between CDKN2A expression and HCC patient prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Kaplan–Meier analysis of OS of HCC in TCGA. (B). Kaplan–Meier analysis of PFI of HCC in TCGA. (C). Top 50 genes most positively associated with CDKN2A are shown in the heatmap. (D). Top 50 genes most negatively associated with CDKN2A are shown in the heatmap.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/3f7b3e03d410668beaa0a1fe.png"},{"id":87347046,"identity":"bcbe67e9-0126-4f0c-b0f6-73302480e32c","added_by":"auto","created_at":"2025-07-23 02:36:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":249136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive significance of CDKN2A expression was verified in the nomogram model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B). Nomogram to predict the 1-, 3-, and 5-year OS and PFI rate of HCC patients. (C-D). Calibration plots of 1-, 2-, and 3-year OS and PFI.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/5017f07debe5057c87e3d0af.png"},{"id":87347764,"identity":"64d8e2cf-ed51-453c-897e-68071aca448b","added_by":"auto","created_at":"2025-07-23 02:44:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":172114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh CDKN2A expression was observed in patients with HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Representative immunohistochemistry images of CDKN2A expression in the liver tissues of patients with and without HCC. (B). The staining intensity of CDKN2A expression was assessed using H-SCORE analysis (n = 20 in the HCC group, n = 20 in the normal control group).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/1808a278ad2bdbe715b471f6.png"},{"id":87348012,"identity":"4c5284b1-7de4-4d3a-a1ab-eb2180d33752","added_by":"auto","created_at":"2025-07-23 02:52:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3144767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/d0932b4f-9e46-4af3-8596-91dc4977681c.pdf"},{"id":87346603,"identity":"71a7f328-2910-496c-aa01-625d422fc12c","added_by":"auto","created_at":"2025-07-23 02:28:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14146,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/a7676908dcb88d26e40f5156.docx"},{"id":87347039,"identity":"6e52d11e-44f0-465e-aabc-25c6ef3858f6","added_by":"auto","created_at":"2025-07-23 02:36:03","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":394706,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/d3e0d32ca936e8ec035cfdbc.jpg"},{"id":87347049,"identity":"0b73d0d4-60fc-4374-a750-21fabbcbb5c0","added_by":"auto","created_at":"2025-07-23 02:36:04","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":399105,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/f0c9ca077fd4547f6df7e0f5.jpg"},{"id":87346627,"identity":"decd250c-557f-4db6-9ecc-df081e70b297","added_by":"auto","created_at":"2025-07-23 02:28:04","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":638273,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/b9b3ad043d9aac0ece81efae.jpg"},{"id":87346619,"identity":"d52df31e-6f5f-4885-839e-7a9c51563cdf","added_by":"auto","created_at":"2025-07-23 02:28:04","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":536734,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7074933/v1/9258dc726990ba512b96184d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic analysis identifies CDKN2A as a prognostic biomarker for hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is ranked as the sixth most prevalent malignancy globally, causing 600,000 deaths annually, with an overall five-year survival rate of about 20% \u003csup\u003e1\u003c/sup\u003e. Although in recent years, advances in immunotherapy based on immune checkpoint inhibitors (ICIs) and molecular-targeted drugs have provided HCC patients with several treatment options and improved the prognosis to a certain extent\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the survival rate remains unsatisfactory, attributed to challenges such as low early detection rates, limited treatment efficacy, high recurrence rates, and chemotherapy resistance \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, the development of effective prognostic predictors and therapeutic targets is urgently needed.\u003c/p\u003e\u003cp\u003eMaintaining copper homeostasis is vital for the physiological function of cells, and disruptions in this balance can influence tumor growth and lead to irreversible cellular damage \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In 2022, Tsvetkov et al. raised \u0026ldquo;cuproptosis\u0026rdquo; for the first time and explained the term systematically \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Cuproptosis, a novel form of cell death reliant on copper, distinguishes itself from apoptosis, necrosis, cell pyroptosis, and iron death \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. There is a significant relationship between this model and the tricarboxylic acid (TCA) cycle. The mechanism can be briefly expressed as the direct binding of copper ions (including Cu\u003csup\u003e2+\u003c/sup\u003e and Cu\u003csup\u003e+\u003c/sup\u003e) to the lipoacylated components in the mitochondrial respiration process, resulting in lipoacylated proteins aggregating. Then, the expression of iron-sulfur cluster proteins is downregulated, inducing protein toxic stress and eventually causing cell death. Cuproptosis-related genes (CRGs) play a critical role in various cancers, such as skin melanoma \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, pancreatic cancer \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, clear cell renal cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, bladder cancer \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, triple-negative breast cancer \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, lung cancer \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and head and neck squamous cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Some studies have shown that imbalance of copper homeostasis induces drug resistance and tumor progression in HCC cells \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition, mitochondrial respiration rate and TCA activity are closely related to the progress, metastasis, and drug sensitivity of HCC \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Recent studies reported that the prognosis of HCC can be effectively predicted by CRGs \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the expression pattern and clinical value of CRGs in HCC are yet to be clarified.\u003c/p\u003e\u003cp\u003eCDKN2A encodes the cell cycle inhibitor p16, which mediates cell cycle arrest in both G1 and G2 phases by inhibiting the oncogenic activities of CDK4/6 and MDM2 \u003csup\u003e23\u003c/sup\u003e. CDKN2A expression and mutation are closely associated with several malignancies, including biliary tract carcinoma, intrahepatic cholangiocarcinoma, anaplastic thyroid carcinoma, and pancreatic cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Specifically, p16 deletion promotes liver metastases in pancreatic ductal adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A meta-analysis further identified CDKN2A promoter methylation in blood samples as a biomarker linked to increased HCC risk and poor prognosis in patients \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In HCC, CDKN2A also significantly influences immune microenvironment regulation; its elevated expression correlates with poor progression-free interval (PFI) and overall survival (OS) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Given the dual role of CDKN2A in cell cycle regulation and immune modulation, we hypothesize that its dysregulation critically contributes to HCC pathogenesis and serves as a determinant of poor prognosis.\u003c/p\u003e\u003cp\u003eDespite this, the expression pattern and prognostic significance of CDKN2A in HCC remain incompletely characterized. This study first compared the expression levels of 10 CRGs in HCC versus adjacent normal tissues and assessed their correlations with overall survival (OS) and progression-free interval (PFI). We subsequently developed a prognostic nomogram integrating clinicopathological parameters and CDKN2A mRNA expression. The molecular function of CDKN2A was further investigated through enrichment analysis. Additionally, based on the critical role of immune cell infiltration in determining patient OS, we examined the association between CDKN2A expression and tumor immune infiltration. Our results demonstrate that CDKN2A exerts pleiotropic effects in HCC pathogenesis. Its overexpression significantly correlates with adverse clinical outcomes, supporting its potential as a promising prognostic biomarker for HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eData acquisition and pretreatment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData on CRGs and clinical information for 424 HCC sufferers were collected from The Cancer Genome Atlas (TCGA) database (\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). Raw read counts obtained using STAR were used to measure gene expression, and these counts were then converted into transcripts per million (TPM). The log2 (TPM\u0026thinsp;+\u0026thinsp;1) transformation was used to normalize the transcriptome data. Clinical covariates were sourced from prior associated researches \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For confirming differentially expressed CRGs, the HCC expression profiling via array was obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including GSE36376 (Platform: GPL10558) and GSE76724 (Platform: GPL10558). GSE36376 enrolled 193 adjacent non-tumor tissues and 240 HCC tissues, while GSE76427 included 52 normal and 115 HCC tissues. Gene alterations of CDKN2A in TCGA HCC datasets were analyzed using the cBioPortal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression analysis of CRGs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe divided the samples into disease states (tumor or normal) and made scatter plots and boxplots to show the variations in CRGs expression between HCC and normal samples. CDKN2A expression levels were classified as CDKN2A -Low or CDKN2A -High based on statistical ranking, depending on whether they were below or above the median value.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation and enrichment analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor analyzing the potential interactions of CDKN2A with other genes in HCC, Pearson\u0026rsquo;s correlation coefficient was calculated using the TCGA data. Next, we selected the top 300 genes correlating with CDKN2A positively for enrichment analysis through Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) analysis, and Gene Set Enrichment Analysis (GSEA) to understand the gene's function \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmune cell infiltration\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreviously, CIBERSORT was adopted to estimate the immune cell infiltration score in TCGA liver cancer \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. For comparison of immune cell infiltration levels, TCGA samples were classified into high and low CDKN2A groups based on the median expression level. The correlation between the immune cell abundance and the CRG expression was validated via a TIMER (Tumor Immune Estimation Resource; cistrome.shinyapps.io/timer) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Also, we detected three critical immune checkpoints (ICKs), comprising CD274, HAVCR2, and PDCD1 .\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrognostic evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter excluding cases lacking clinical information on tumor stage, histological grade, or OS outcomes, we obtained gene expression and clinical data for 373 HCC patients from the TCGA database. Kaplan Meier plotter was used to analyze the relationship between CDKN2A expression and survival in HCC, and log-rank test was used. Logistic regression analysis was performed to reveal the correlation between CDKN2A mRNA expression and clinicopathologic factors, such as tumor-node-metastasis stage, age, sex, histologic grade, margin status, vascular invasion, and tumor mutation burden (TMB). To assess the impact of CDKN2A expression and clinicopathological factors on overall survival, univariate and multivariate analyses were performed, and the cox proportional hazards model was used to generate risk ratios (HR) and 95% confidence intervals (CI).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDevelopment of the nomogram for HCC patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe integrated the clinical parameters and the expression of CDKN2A to develop the nomogram to facilitate clinical application. A nomogram for predicting personal survival likelihood was established using the \u0026ldquo;rms\u0026rdquo; package in R software. Calibration curves predicting 1-, 3-, and 5-year survival rates were drawn to appraise the agreement between the nomogram's predicted prognosis and actual survival time \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunohistochemistry (IHC)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImmunohistochemistry studies on HCC tissues were performed according to the manufacturer\u0026rsquo;s instructions (using antibody Cell signal technology \u003cb\u003e#23200\u003c/b\u003e). Rehydration, deparaffinization, antigen retrieval, quenching endogenous peroxidase activity, and blocking were involved. After that, the tissues were subjected to overnight incubation at 4\u0026deg;C with the primary antibody (diluted 1:400) and for 30 min at 37\u0026deg;C with the horseradish peroxidase-tagged secondary antibody. The CDKN2A expression was evaluated through the H-SCORE method, calculated as follows: (1\u0026times;percentage of weak staining) + (2\u0026times;percentage of moderate staining) + (3\u0026times;percentage of strong staining) within the target region, ranging from 0 to 300.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used for describing continuous variables, and frequency and proportion were for displaying categorical variables. A Wilcoxon rank-sum test was recruited for examining the differences in CRG expression in HCC and adjacent normal tissue. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the Kaplan\u0026ndash;Meier survival curve indicated a significant difference. R version 4.2.1 (The R Foundation) was employed for all statistical analyses. Two-sided \u003cem\u003eP\u003c/em\u003e-values were used, and a P-value of \u0026lt;\u0026thinsp;0.05 indicated a statistically significant difference.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCDKN2A is more highly expressed in liver cancer than in adjacent normal tissue.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we assessed the expression of 10 CRGs (FDX1, CDKN2A, LIAS, LIPT1, DLAT, DLD, PDHA, PDHB, MTF1, and GLS) closely involved in cuproptosis \u003csup\u003e10\u003c/sup\u003e. As stated in Fig. 1A, the nine genes expression was significantly upregulated, and FDX1 was downregulated in the tumor tissue (Fig. 1A). In the GSE36376 database, all genes except FDX1 and LIAS in the tumor group were considerably raised relative to those in the normal group, while FDX1 and LIAS expression was substantially decreased in the tumor group. In GSE76427 database, the other 9 genes expression levels were consistent with GSE36376 except the expression of FDX1 (Figure S1 and Figure S2). Data from the three databases showed that the expression of CRGs did change significantly in tumor tissue samples. Furthermore, strong correlations were discovered among different gene expression levels (Fig. 1B). As an illustration, DLD showed a high positive contact with MTF1 (r = 0.567, P \u0026lt; 0.001, Fig. 1C).\u003c/p\u003e\n\u003cp\u003eCopper overload due to mutations triggered serious consequences \u003csup\u003e10\u003c/sup\u003e. The mutation frequency of 10 CRGs in HCC was analyzed (www.cbioportal.org), and the results showed that \u003cem\u003eCDKN2A\u003c/em\u003e had up to 8% genetic alterations, which has the highest among the 10 CRGs (Fig. 1D), deeming it as a major factor leading to the disorder of copper metabolism in liver cells, thus inducing tumor formation and affecting the prognosis of patients.\u003c/p\u003e\n\u003cp\u003eThe expression levels of CDKN2A varied across pathological stages and histological grades of HCC, indicating an ascending tendency of CDKN2A expression with increased histological grade or tumor stage (Fig. 1E). The above findings disclosed that the CDKN2A expression was interrelated with disease grade.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation and enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor predicting biological functions of CDKN2A, along with concomitant pathways, the analysis for correlation was carried out by GO and KEGG databases using HCC TCGA data. GO analysis and functional enrichment disclosed that CDKN2A was predominantly related to biological processes, such as DNA replication, organelle fission, chromosome segregation, mitotic nuclear division and nuclear division (Fig. 2A), and cell component, including centromeric region, chromosome, kinetochore, condensed chromosome kinetochore, chromosome region, condense chromosome (Fig. 2B), and molecular function (MF) (such as single-stranded DNA-dependent DNA helicase activity, single-stranded DNA-dependent ATPase activity, ATPase activity, catalytic activity, DNA-dependent ATPase activity, and acting on DNA) (Fig. 2C). Additionally, KEGG pathway analysis displayed a crosstalk and an enrichment among the top 300 genes in the Fanconi anemia pathway, homologous recombination, progesterone-mediated oocyte maturation, DNA replication and cell cycle (Fig. 2D). GSEA revealed that the Rho-GTPases, M phase, transcriptional regulation by P53, DNA repair, and cell cycle checkpoint signaling pathways were significantly enriched (Fig. 2E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between CDKN2A and immune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we assessed the immune cell infiltration score in TCGA HCC. Referring to the assessment results, the high CDKN2A expression group disclosed a rise in the activated dendritic cell (aDC) infiltration level, while a drop in neutrophils and CD8 + T cell. CDKN2A expression was positively related to DC cell infiltration and Th2 cells while negatively related to CD8 + T and natural killer (NK) cells (Fig. 3A and 3B).\u003c/p\u003e\n\u003cp\u003eFurthermore, we proved our outcomes with the TIMER2 database and observed a positive relationship between CDKN2A expression and DC infiltration level under different algorithms (Figure S3). Moreover, CDKN2A expression was markedly linked ro DC markers in HCC, including CD83, CD40, CD80, and CD86 (Fig. 3C). Also, our findings divulged a positive association of CDKN2A expression in HCC with CD274 (r = 0.255, P \u0026lt; 0.001), HAVCR2 (r = 0.211, P0.001), and PDCD1 (r = 0.195 P \u0026lt; 0.001) in TIMER2 and TCGA data (Fig. 3D and Figure S4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation with clinicopathological variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese patients were divided into groups with high or low CDKN2A expression based on the median CDKN2A expression value, and any potential correlations between CDKN2A expression and clinical characteristics were evaluated (Table 1). Logistic regression analysis revealed that the expression of CDKN2A was correlated with that of AFP (p = 0.003, Table 2) and but not with tumor stage, age, histologic grade, vascular invasion, BMI, liver fibrosis Ishak score, MarginStatus, sex and TMB (P \u0026gt; 0.05, Table 2).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRelationship between CDKN2A mRNA expression and clinical characteristics in HCC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow expression of CDKN2A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh expression of CDKN2A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAJCC Stage,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e101(27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43(11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50(13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≤60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHistologic Grade,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123(33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110(29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVascular Invasion,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105(33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMicro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMacro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAFP,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≥24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85(25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiver Fibrosis Ishak Score,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0:No Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1–2:Portal Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3–4:Fibrous Speta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5–6:Nodular Formation and Incomplete Cirrhosis, or Established Cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarginStatus,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165(48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161(46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSex,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119(31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133(35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTMB,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163(44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158(43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≥5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCDKN2A mRNA expression association with clinical pathological characteristics in HCC (logistic regression)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAJCC Stage (StageIII \u0026amp; StageIV vs StageI \u0026amp; Stage II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902(0.568–1.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (\u0026gt; 60 vs ≤ 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.214(0.808–1.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistologic Grade (G3\u0026amp;G4 vs G1\u0026amp;G2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398(0.913–2.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular Invasion (Micro \u0026amp; Macro vs None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.029(0.648–1.635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP(Abnormal vs Normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.071(1.279–3.352)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (≥ 24 vs \u0026lt; 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.209(0.787–1.857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver Fibrosis Ishak Score (Score 3–6 vs Score 0–2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970(0.557–1.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginStatus (R1 \u0026amp; R2 vs R0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.025(0.397–2.648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex(Female vs Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721(0.467–1.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMB (≥ 5 vs \u0026lt; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.125(0.606–2.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between CRG expression and HCC patient prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of CRG expression in relation to OS and PFI in the TCGA cohort was conducted to estimate the prognostic significance of CRGs in HCC patients. As stated by univariate logistic regression analysis, the expression of CDKN2A [odds ratio (OR) = 1.790 (1.262–2.538), P = 0.001], DLAT [OR = 1.689 (1.191–2.396), P = 0.003], and pathological stage [OR = 2.734 (1.792–4.172), P \u0026lt; 0.001] were closely connected to OS (Table 3 and Fig. 4). The expression of CDKN2A [OR = 1.646 (1.229–2.204), P = 0.002], DLAT [OR = 1.427 (1.067–1.911), P = 0.017], and pathological stage [OR = 2.678 (1.857–3.861), P \u0026lt; 0.001] was associated with PFI (Table 3 and Fig. 4). In addition, univariate logistic regression analysis showed that GLS expression was negatively correlated with OS, but high FDX1 expression was positively correlated with PFI. Furthermore, multivariate logistic regression analysis demonstrated that CDKN2A and pathologic stage independently served as the prognostic factors of OS [OR = 1.520 (1.032–2.231), P = 0.032 for CDKN2A and OR = 2.281 (1.471–3.539), P \u0026lt; 0.001 for tumor stage III) and PFI [OR = 1.371 (0.999–1.882), P = 0.05 for CDKN2A and OR = 2.222 (1.517–3.254), P \u0026lt; 0.001) for tumor stage III, respectively] (Table 3).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate and multivariate analyses (overall survival) for prognostic factors in HCC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAJCC Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I \u0026amp; Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStage III \u0026amp; Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.590(1.824–3.677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.199(1.805–5.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≤60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.219(0.860–1.728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHistologic Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.110(0.774–1.592)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVascular Invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMicro \u0026amp; Macro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.330(0.878–2.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.822(1.144–2.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.587(0.963–2.617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≥24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.668(0.461–0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016(0.633–1.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLiver Fibrosis Ishak Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScore 0–2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScore 3–6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.815(0.475-1.400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarginStatus (R1 \u0026amp; R2 vs R0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eR1 \u0026amp; R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.591(0.805–3.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.249(0.877–1.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e≥5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.575(1.652–40.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.199(1.805–5.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.790(1.262–2.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.520(1.036–2.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAJCC: American Joint Committee on Cancer, AFP: Alpha-Fetal Protein, BMI: Body Mass Index, TMB: Tumor Mutational Burden HR:Hazard ratio\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNomogram development and validation for HCC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo achieve the precise estimation for individual survival in HCC patients, we built a nomogram model to predict 1-, 3-, and 5-year OS and PFI based on CDKN2A expression and pathological stage. The analysis outcomes proved that the nomograms are quantitative tools to assess the prognosis of HCC patients. Moreover, calibration curves demonstrated good consistency between the predicted and observed prognosis (Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDKN2A is overexpressed in patients with HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the consistency of CDKN2A expression in HCC tissues, IHC was conducted, comparing CDKN2A expression between patients with HCC (n = 20) and their normal controls (n = 20). The findings showed that HCC had higher CDKN2A expression than the control samples, suggesting that CDKN2A may play a crucial role in HCC (Fig. 6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated 10 copper metabolism-related genes (CRGs) implicated in copper homeostasis dysregulation. We found elevated expression of most CRGs (excluding FDX1 and LIAS) in HCC tissues compared to adjacent normal tissue. Multivariate analysis identified CDKN2A expression as significantly correlated with advanced tumor stage, poor prognosis, and distinct immune infiltration patterns in HCC patients.\u003c/p\u003e\u003cp\u003eCDKN2A, (encoding p16/INK4a/MTS1) is a well-established tumor suppressor and cell cycle regulator \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our findings confirm significantly higher CDKN2A expression in HCC versus controls, aligning with its upregulation observed in THCA, LIHC, KICH, KIRP, KIRC, and COAD \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. While CDKN2A's prognostic significance across diverse cancers is well-documented \u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. our study specifically links its overexpression in HCC to poorer patient outcomes. Importantly, elevated CDKN2A emerged as an independent predictor of adverse prognosis. To translate this finding clinically, we developed a prognostic nomogram integrating CDKN2A expression and tumor stage. Higher nomogram scores predict significantly decreased 1-, 3-, and 5-year survival probabilities.\u003c/p\u003e\u003cp\u003eMechanistically, CDKN2A normally induces G1/S cell cycle arrest \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Its dysfunction via mutation/deletion, observed in up to 8% of cases in our cohort and permits uncontrolled proliferation \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Derived from functional analysis, genes associated with CDKN2A depended on biological pathways and functions pertaining to the organelle fission and cell cycle. Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eD show that majority of the genes exhibited a positive link to CDKN2A expression and had a relationship with cell cycle and proliferation, and are thus involved in critical physiological activities; the imbalance in their expression might increase the probability of tumor development \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe tumor immune microenvironment (TIME), comprising monocytes/macrophages, dendritic cells (DCs), neutrophils, NK cells, and T cells, critically influences HCC progression and response to therapy. While T-cell responses to immune checkpoint (ICI) inhibition dominate current research \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our data reveal a novel association: CDKN2A expression positively correlated with DC infiltration and Th2 cells but negatively correlated with neutrophils, CD8\u0026thinsp;+\u0026thinsp;T cell and natural killer (NK) cells. Specifically, high CDKN2A expression was associated with increased activated DC (aDC) populations, suggesting its potential role in modulating tumor immunity. DCs serve pivotal roles as central antigen-presenting cells, initiating anti-tumor immune responses by presenting tumor antigens and activating cytotoxic T cells \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. As a crucial part of the tumor microenvironment, DCs are the most important antigen-presenting cells in the body. They are capable of monitoring and killing tumors by identifying tumor cell-specific antigens and signaling T cells to execute their cytotoxic function \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Coordination with other different immune cells is the essential condition for DCs to exert the anti-tumor role, and the imbalance between different immune cells is a sign of poor tumor prognosis. The present article demonstrated a connection of CDKN2A expression with varying immune cells and immune infiltration levels, suggesting its potential influence on HCC immunology. In addition, CDKN2A expression had a significant positive impact on ICKs, suggesting the effectiveness of immunotherapy PD-1 and PD-L1 in the treatment of liver cancer. Collectively, our results position CDKN2A not only as a robust prognostic biomarker but also as a putative immunotherapeutic target influencing tumor cell viability and the TIME. This study provides a foundation for future investigations into CDKN2A-directed therapeutic strategies in HCC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNevertheless, the present study had several limitations. First, although CDKN2A has a strong prognostic effect in HCC patients, there are only 50 samples from the paracarcinoma normal tissue group in the TCGA database. In future studies, the large sample size and prognostic clinical dataset need to be expanded for further verification. Second, despite a detailed analysis of the prognostic effect of CDKN2A in HCC, this study did not consider other genes that have been reported to be associated with HCC. Third, given that predictive signals are established and validated using data from public databases, basic biological experiments are needed for further validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, CDKN2A is a useful biomarker for determining the HCC patients\u0026rsquo; prognosis. Furthermore, CDKN2A exhibits a positive association with immune cell infiltration and prognosis, which is highly expressed in HCC. Therefore, future studies need to expand the sample size and accumulate biological evidence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShil-in He carried out the studies, participated in collecting data, and drafted the manuscript. Yan-ling Zhang and Jun-jie Xu performed the statistical analysis and participated in its design. Xiao Liang participated in interpretation of data and draft the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A: Cancer statistics, 2022. CA Cancer J Clin 2022, 72:7-33.\u003c/li\u003e\n\u003cli\u003eSangro B, Sarobe P, Hervas-Stubbs S, Melero I: Advances in immunotherapy for hepatocellular carcinoma. Nature reviews Gastroenterology \u0026amp; hepatology 2021, 18:525-43.\u003c/li\u003e\n\u003cli\u003eZongyi Y, Xiaowu L: Immunotherapy for hepatocellular carcinoma. 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Annual review of immunology 2012, 30:1-22.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-7074933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7074933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHepatocellular carcinoma (HCC) ranks as the fifth most prevalent malignancy worldwide. Disruptions in copper homeostasis adversely affect liver function. Cuproptosis, a recently defined form of regulated cell death triggered by intracellular copper accumulation, disrupts the tricarboxylic acid cycle and mitochondrial respiration. However, the specific roles and mechanisms of cuproptosis-related genes (CRGs) in HCC pathogenesis remain incompletely understood.\u003c/p\u003e\u003ch2\u003eMaterial and methods\u003c/h2\u003e\u003cp\u003eWe systematically evaluated the expression of 10 CRGs in HCC tissues versus adjacent normal tissues. Bioinformatics analyses included Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and Gene Set Enrichment Analysis (GSEA) were performed. Immune infiltration levels within the tumor microenvironment were assessed. The prognostic significance of CDKN2A was evaluated using Kaplan-Meier (KM) survival analysis and univariate/multivariate Cox proportional hazards regression. CDKN2A protein expression was validated using immunohistochemistry (IHC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCDKN2A was significantly overexpressed in HCC compared to normal tissues. Bioinformatics analyses implicated CDKN2A in DNA replication, organelle fission, and cell cycle checkpoint signaling. Immune-related analysis revealed that high CDKN2A expression correlated positively with dendritic cell (DC) and Th2 cell infiltration, but negatively with CD8\u0026thinsp;+\u0026thinsp;T cell and natural killer (NK) cell infiltration. KM analysis demonstrated that high CDKN2A expression predicted significantly shorter overall survival in HCC patients. Univariate and multivariate Cox regression identified CDKN2A as an independent prognostic risk factor.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study demonstrates that CDKN2A is significantly overexpressed in HCC and plays a role in immune microenvironment modulation. CDKN2A serves as a promising independent prognostic biomarker for HCC, associated with poorer patient survival.\u003c/p\u003e","manuscriptTitle":"Systematic analysis identifies CDKN2A as a prognostic biomarker for hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 02:27:58","doi":"10.21203/rs.3.rs-7074933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-23T12:13:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T16:06:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208318253790367177296260062253432056733","date":"2025-07-21T15:24:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T16:22:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316768414013740281732144575014831588287","date":"2025-07-18T14:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-18T12:50:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-16T10:55:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-12T04:41:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-12T04:40:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-08T12:35:15+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":"fd9349cf-2e0f-426a-8481-aafe73dce39b","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T12:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 02:27:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7074933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7074933","identity":"rs-7074933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}