A Predictive Model for Prognosis and Therapeutic Response in early-stage Hepatocellular Carcinoma: emphasis on program cell death genes

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A comprehensive predictive model for the progression and drug response of post-surgery HCC patients remains elusive. Various programmed cell death (PCD) patterns significantly influence tumor advancement, offering potential as prognostic and drug sensitivity indicators for postsurgery HCC. The analysis in this study utilized integrated data from 12 different types of PCD, multi-omics data from TCGA-HCC and other cohorts in the International Cancer Genome Consortium (ICGC), as well as clinical information of HCC patients. A PCD score was calculated using a four-gene signature determined through cox regression analysis. Validation in independent datasets revealed that HCC patients with high PCD scores had poorer prognoses post-surgery. Furthermore, an unsupervised clustering model identified two distinct molecular subtypes of HCC with unique biological processes. A nomogram exhibiting high predictive accuracy was developed by integrating a PCD signature with clinical characteristics. The association between programmed cell death, immune checkpoint genes and key components of the tumor microenvironment. was established. Patients with HCC displaying elevated CDI levels demonstrated resistance to traditional adjuvant chemotherapy and immune checkpoint inhibitor therapies. Additionally, the oncogenic function of four PCD genes was identified in an inpatient cohort. A novel scoring methodology for PCD was devised through the examination of genes linked to diverse PCD subtypes, providing valuable insights into the prognosis and drug responsiveness of HCC patients. Early-stage HCC patients may potentially derive therapeutic benefits from immune therapy directed at programmed cell death. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Biomarkers Health sciences/Oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Hepatocellular carcinoma (HCC) is a common form of primary liver cancer, ranking as the fourth leading cause of cancer-related mortality and the sixth most frequently occurring malignant tumor. Annually, over 700,000 new cases of HCC are diagnosed worldwide, with approximately half originating from China 1 . In recent decades, advancements in the treatment of HCC have been made through various methods including surgical interventions, liver transplants, local ablation, transarterial chemoembolization, and radiation therapy. However, a notable obstacle in precision treatment lies in the accurate staging of tumors using the TNM stage and clinical grade, which inform therapeutic recommendations. Emerging systemic treatments for HCC, including regorafenib, lenvatinib, ramucirumab, carbozantinib, and immune checkpoint inhibitors, show promise 2 . Given the limited treatment options for HCC, it is imperative to explore novel targets to improve patient outcomes, underscoring the importance of developing effective models for successful targeted therapy. Immunotherapy, a medical treatment modality, has the capacity to modulate the immune system's response to combat diseases. The efficacy of immunotherapy in treating HCC is attributed to its high immunogenicity 3 . However, tumor cells can develop resistance to immunotherapy through adaptive mechanisms. Additionally, the variability in response to immunotherapy among individuals with HCC underscores the complexity of treatment outcomes. Reprogramming immune cells and the tumor microenvironment (TME) can promote immune evasion by HCC cells, thereby contributing to disease progression 4 . The impact of the tumor microenvironment on the progression of hepatocellular carcinoma is widely acknowledged. Cell death occurs through two primary mechanisms: accidental cell death, which is uncontrolled, and programmed cell death (PCD), which is intricately regulated and involves multiple mechanisms. Dysregulation of apoptosis is crucial for the survival and advancement of cancer cells. Understanding the diverse mechanisms of PCD, including apoptosis, necroptosis, ferroptosis, pyroptosis, entotic cell death, lysosome-dependent cell death, netotic cell death, parthanatos, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and cuproptosis, is essential for the development of effective therapies for HCC 5 . Recent research suggests that the pathways governing programmed cell death play a role in modulating the immunosuppressive tumor microenvironment and influencing the efficacy of anti-cancer interventions 6 . A signature associated with PCD has been established for the prediction of clinical outcomes, alterations in the tumor microenvironment, and responses to therapy in cases of lung adenocarcinoma and osteosarcoma 7 , 8 . The initiation and dissemination of each PCD pattern exhibit significant associations that are essential for maintaining homeostasis, impacting disease advancement, and influencing therapeutic strategies. The recognition of intricate crosstalk among diverse programmed cell death pathways and the utilization of all identified programmed cell death-related genes for prognostic purposes in patients with HCC may present a novel approach to the management of HCC. Furthermore, the probability of tumor recurrence within 2 years post-surgery, referred to as early recurrences and comprising more than 70% of tumor reappearance cases, is associated with the aggressiveness of the primary tumor 9 . On the other hand, late recurrence, defined as tumor reappearance occurring after 2 years post-surgery, is often linked to de novo carcinogenesis or underlying liver conditions. Recognizing individuals at elevated risk of early-stage HCC following liver resection can enable healthcare providers to promptly initiate treatment and optimize treatment efficacy. Numerous studies have examined the relationship between specific molecular markers and early recurrence in HCC, yet their clinical utility is limited by the heterogeneity of the disease 10 . A comprehensive model incorporating multiple factors would greatly improve practicality in clinical settings compared to single biomarker approaches. Models predicting prognosis in early-stage HCC are scarce, prompting this study to emphasize the significance of the International Cancer Genome Consortium (ICGC) sample size and tissue source in developing accurate diagnostic and prognostic models for HCC. Patients diagnosed with TNM stage I and stage II HCC from The Cancer Genome Atlas (TCGA) and the ICGC were chosen for the creation of comprehensive diagnostic and prognostic models. Therefore, investigating the relationship between alterations in PCD-related gene expression and the oncogenic risk score in early-stage HCC may provide insights into the molecular mechanisms driving the progression and evolution of early HCC. Methods 1. Data acquiring We conducted a thorough examination of two gene expression datasets of HCC patients sourced from the ICGC. We specifically acquired the Liver Hepatocellular Carcinoma dataset, encompassing stage I and stage II HCC patients from TCGA database. In our study, the TCGA-HCC datasets were utilized as the training cohorts, while the ICGC-LIRI dataset served as the validation cohort. To address potential batch effects in the normalized data from TCGA and ICGC, we employed t-distributed stochastic neighbor embedding (tSNE) analysis, followed by adjustment using the ComBat algorithm from the R package sva. The integrated data set was employed for further analysis, revealing that the key regulatory genes associated with twelve PCD patterns are contained within the PCD-related gene pool. These genes were sourced from GSEA gene sets, KEGG databases, literature reviews, and manual compilation. The resultant gene list represents a compilation of regulatory genes from the aforementioned twelve distinct PCD patterns. A total of 1078 genes associated with PCD were identified, including 580 genes related to apoptosis, 52 genes related to pyroptosis, 87 genes related to ferroptosis, 367 genes related to autophagy, 15 genes related to entotic cell death, 14 genes related to cuproptosis, 9 genes related to parthanatos, 8 genes related to netotic cell death, 7 genes related to alkaliptosis, 220 genes related to lysosome-dependent cell death, and 5 genes related to oxeiptosis. The limma R package was utilized to identify differentially expressed genes (DEGs) between control and tumor samples in the TCGA-HCC database. Candidate DEGs were defined as those with a p-value log2(0.5). Venn diagram analyses were performed using the R package 'VennDiagram' to identify common DEGs, which were subsequently considered reliable DEGs related to PCD across all datasets. 2. Consensus clustering analysis of PCD The Pam method, a form of unsupervised clustering using Euclidean distance and Ward's linkage, was employed to differentiate distinct molecular subtypes by examining the expression of reliable DEGs linked to PCD. The 'ConsensusClusterPlus' R package was utilized for this analysis to determine the number of clusters in the TCGA-LIHC cohort, with 1000 repetitions to ensure classification consistency. A heatmap was produced to visualize the variability in distribution among various coagulation subtypes. To assess the clinical significance of coagulation subtypes, we investigated the associations between PCD subtypes and prognosis. Kaplan-Meier survival analysis was employed to evaluate overall survival (OS) across different clusters, with statistical significance denoted by a P-value below 0.05. Utilizing the ggplot R package, we generated a visualization to depict the diverse expression patterns of PCD, immunomodulatory, and immune checkpoint genes within distinct subgroups (P < 0.05). Generation and validation of prognostic signature Significant genes impacting the prognosis of HCC were identified using reliable PCD-associated DEGs through univariate Cox regression analysis with a significance level of P < 0.05. Prognostic genes linked to PCD were further identified through stepwise multivariate regression analysis utilizing stepwise Akaike information criterion (stepAIC). The prognostic signature related to PCD was calculated using the formula Riskscore=∑βi∗Ei, where β represents the coefficient value obtained from Cox regression analysis and Ei represents the expression level. The risk scores of each sample in the TCGA-HCC training set were calculated using a defined formula within a risk model, followed by z-score standardization. Subsequently, the samples were stratified into high and low-risk groups based on the median score. Survival analysis was performed using Kaplan-Meier curves, and a receiver operating characteristic (ROC) curve was generated using the 'timeROC' R package. Validation datasets were employed to assess the robustness of the risk model. Development of a nomogram integscore model and clinical features A nomogram calibration plot was employed to illustrate the relationship between survival outcomes and dummy observations. The nomogram incorporated risk categories and clinical characteristics, assigning a score to each factor which were then aggregated to determine a final comprehensive risk score. Analysis of immune microenvironment The CIBERSORT algorithm is frequently employed to estimate the distribution of 22 immune cell types based on gene expression data. In this study, the CIBERSORT algorithm was utilized to analyze the proportions of 22 immune cells in order to investigate the relationship between risk score and the immune microenvironment in patients with HCC. Subsequently, the correlation between risk scores of patients with HCC from TCGA and various immune cell fractions was assessed using the Spearman correlation test. Functional enrichment analysis The GSVA approach was employed to conduct a comparative analysis of various biological processes in high- and low-risk groups utilizing the 'c2.cp.kegg.v7.4.symbols.gmt' dataset, implemented in R with the 'GSVA' and 'GSEABase' packages. Drug responsiveness in patients with hepatocellular carcinoma was investigated using the PCD model, and the oncoPredict package in R software was used to predict the sensitivity of multiple drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Furthermore, a comparison of the half maximal inhibitory concentration (IC50) between the high- and low-risk score group was conducted through differential expression analysis. The role of PCD model in the prediction of immunotherapeutic benefits A total of 348 cases of urothelial cancer were examined in the IMvigor210 dataset, which included expression data, survival data, follow-up information, and immunotherapy effect data sourced from the IMvigor210CoreBiologies R package. Patients within the IMvigor210 cohort were stratified into four groups based on their response to immunotherapy: progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). The PCD risk score for patients in the IMvigor210 study was calculated based on normalized raw count data using the specified formula, and its impacts on both prognosis and the efficacy of the PD-L1 inhibitor were assessed. Immunohistochemistry After obtaining ethical approval from China-Japan Union Hospital of Jilin University, we selected the tumor tissues and adjacent tissues of 15 patients who met the criteria for HCC liver cancer for the following experiments. The prepared paraffin-embedded sections of HCC and non-tumor liver tissues were sequentially deparaffinized, rehydrated, and antigen retrieval was performed by by heating treatment in citrate buffer. After blocking with 3% bovine serum albumin, the sections were stained with the primary antibody of polyclonal rabbit anti-S100A9 (1:200, Affinity) and polyclonal rabbit anti-IL33 (1:200, Affinity), and kept overnight at 4°C. The following day after the primary antibody incubation, the sections were washed and stained with horseradish peroxidase-conjugated secondary antibody (1:200, Abcam) for one hour at 37°C and visualized using diaminobenzidine 1 , 11 . Western Blotting Western Blotting The total protein quantity from HCC tissue was determined by the bicinchoninic acid assay. The proteins were separated on sodium dodecyl sulfate gel and transferred onto the PVDF membranes. Proteins on membranes were blocked with 5% non-fat milk for 2 hours, probed with primary antibodies (1: 1000) overnight at 4°C and thereafter incubated with the secondary antibody (1: 10000) for 1 hour. The enhanced chemiluminescence (Beyotime) was used to visualize the brands 1 , 11 . RNA Extraction and Quantitative Real-Time Polymerase Chain Reaction Total RNA from cells was isolated by the TRIzol method (Invitrogen, USA) and dissolved in 50ul RNase-free water. The RNA (1 ug) was reverse-transcribed using the reverse transcription kit (Takara, Japan). The qRT-PCR reaction was performed using the SYBR Premix Ex Taq kit (Takara, Japan) according to the manufacturer’s instructions 1 , 11 . Statistical analysis Statistical analyses were performed using R software (v.4.3.0), with Student's t-test or Wilcoxon test utilized for comparisons between two groups, the Kruskal-Wallis test for comparisons among multiple groups, and Kaplan-Meier plots with log-rank test for survival curve analysis. A significance level of P < 0.05 was applied to determine statistical significance. ETHICS STATEMENT The Ethics Committee of China-Japan Union Hospital of Jilin University approved the human studies. Informed consent was obtained from all participants. All participants approved for publication. All methods were performed in accordance with the relevant guidelines and regulations . Results Landscape of PCD in HCC patients and Identification of the PCD-related molecular subtypes A total of 344 genes exhibiting differential expression related to programmed cell death were detected in stage I and II patients from the TCGA-HCC cohort, demonstrating statistical significance with a P value < 0.05 (supplementary Figure S1 ). Subsequently, a consensus clustering analysis was conducted on these 344 genes to ascertain potential molecular subtypes within the TCGA-HCC patient population. The analysis unveiled two distinct subtypes, labeled as Cluster 1 and 2 (Fig. 1 A). Discrimination between the samples was easily discernible through the CDF curve in depicted Fig. 1 B- 1 C, consensus value > 0.8 (Fig. 1 D), and heatmap matrix (Fig. 1 E). Significant differences in overall survival time were observed between the two clusters (P < 0.001, Fig. 1 F). Patients in Cluster 2 exhibited a poorer prognosis compared to those in Cluster 1, with disparities in survival rates. Additionally, the expression level of common immune checkpoint genes (Fig. 2 A) and immunomodulatory factors (Fig. 2 B) were significantly elevated in Cluster 2 compared to Cluster 1. To explore the differences in the tumor microenvironment between these two subtypes, ESTIMATE analysis was employed. The results revealed that individuals categorized in Cluster C2 exhibited elevated levels of TME score, specifically immune score and ESTIMATE score (Fig. 2 C). Additionally, the immune cell landscape exhibited high heternetity (Fig. 2 D- 2 E). These findings suggest that immunotherapy and immune response were more prominent in Cluster C2 compared to Cluster C1, indicating heightened sensitivity and efficacy. Developing a predictive gene panel for individuals with hepatocellular carcinoma Survival data from TCGA-HCC and ICGC-HCC patients was collected and subsequently analyzed. The data dimension discrepancies between the two databases were adjusted using the combat method and visualized through boxplots (Fig. 3 A- 3 B) and UMAP plots (Fig. 3 C and 3 D). Survival-related genes were identified through univariate Cox regression analysis (Fig. 3 E). Utilizing the identified survival-related gene signature, a PCD score was developed for the TCGA-HCC group using the formula: PCD score = (GBA × 0.541) + (G6PD × 0.222) + (S100A9 × -1.789) + (IL33 × 0.383) (Fig. 3 F). Subsequently, we stratified the HCC individuals within he merged cohort of TCGA-HCC and ICGC-LIRI dataset was characterized by high- and low-PCD scores, utilizing the median value as the delineating threshold. Subsequent to this classification, we conducted a comparative analysis of the OS of HCC patients within the TCGA-HCC cohort based on their PCD scores. A graphical representation was generated to illustrate the distribution of PCD scores and the corresponding OS data (Fig. 4 H), revealing that individuals with elevated PCD scores exhibited diminished OS duration and heightened mortality rates in comparison to those with low PCD scores. Principal component analysis (PCA) analysis (Fig. 4 F) was utilized to validate the precision and reliability of the Prognostic and Classification Diagnostic (PCD) model. Patients with elevated PCD scores exhibited more unfavorable outcomes compared to those with lower scores. To further validate the predictive accuracy of the scoring system, individuals in the TCGA-HCC cohort were stratified based on various T, N, M, and TNM levels and a stratified analysis was conducted using their PCD scores. The findings revealed that patients in the high-PCD score category had significantly shorter overall survival times across nearly all subgroups (Fig. 4 A- 4 E). The prognostic scoring system demonstrated its predictive ability through AUC values of 0.76, 0.68 (Fig. 4 I), and 0.73 for 1-, 3-, and 5-year OS, respectively (Fig. 4 G). Furthermore, PCD scores exhibited a significant correlation with various clinical characteristics, including distinct T and clinical stage in HCC (Fig. 5 A- 5 B). Highlighting the predictive significance of programmed cell death and combining a nomogram risk model with clinical data To mitigate the limitations of relying on sole PCD for prognostic forecasting in HCC patients, our study expanded the scope of analysis to include five clinical indicators (Age, Gender, Tumor size, Lymph node involvement, Metastasis, and Cancer stage) in conjunction with PCD. Utilizing data from the TCGA-HCC cohort, we employed a multivariable-Cox regression analysis and step-wise regression analysis to develop a comprehensive nomogram scoring system integrating both clinical and genomic information for enhanced prognostic prediction in HCC patients. The total score for each patient was meticulously calculated utilizing the nomogram formula as depicted in Fig. 5 C. The calibration curve provided a visual representation of the nomogram model's predictive accuracy for outcomes at 1, 3, and 5 years, as shown in Fig. 5 D. The significance of the nomogram was underscored by consistently achieving the highest Area Under the Curve (AUC) for the total score at specific time points, as demonstrated in Fig. 5 E. Exploring possible medication sensitivity for individuals with HCC To analyze and predict the efficacy of chemotherapy drugs in our patient-derived xenograft model for HCC, we calculated the IC50 values of various medications for each patient within the TCGA-HCC cohort. Subsequently, we conducted a differential expression analysis on the IC50 matrix of therapeutic drugs to identify 19 potentially significant therapeutic agents. As illustrated in Figs. 6 A- 6 B, the IC50 values for these 19 medications were found to be higher in the high-CDI group, suggesting that individuals with low PCD scores may exhibit heightened sensitivity to chemotherapy. Dissection of tumor microenvironment and molecular features based on PCD signature An analysis was conducted to assess variations in additional important characteristics within the two categories of PCD. Initially, we investigated the potential correlation between immune checkpoints and PCD score values. The bar graph displayed a potential relationship that higher PCD values had increased drug response activity (Fig. 6 A and 6 B). Furthermore, PCD scores were found to be significantly associated with LAIR1, TNFRSF14, HAVCR2, LGALS9, CD160, CD44 and TNFRSF18 in TCGA-HCC samples (Fig. 6 C and 6 E). Subsequently, CIBERSOR algorithms were utilized to compute the enrichment scores of immune-associated cells. Significantly, the proportion of memory CD4 + T cells in TCGA-HCC cohorts was found to be significantly higher in the low-PCD group, demonstrating a strong inverse correlation with PCD score (Fig. 6 D). Conversely, M0 macrophages and neutrophils were significantly decreased in the low-PCD group (Fig. 6 D). In the correlation analysis, the PCD score exhibited different correlations with each immune cells in TCGA-HCC samples (Fig. 6 F). Furthermore, an analysis was conducted to examine the differences in tumor-related pathways between high and low PCD score categories. In the low-risk group, a majority of metabolism-related pathways exhibited increased activity compared to the high-risk group, as depicted in Fig. 7 . These findings suggest a noteworthy metabolic heterogeneity in HCC. Accurately predicting the response to immunotherapy in PCD models We conducted an investigation into the predictive abilities of our PCD model for drug response in HCC and expanded our analysis to predict response in a cohort resistant to immune checkpoint inhibitors, with response to immunotherapy serving as the ultimate endpoint. Additionally, recent advancements in medical treatment have highlighted the efficacy of immunotherapy not only in HCC but also in a variety of other cancer types. Consequently, the scope of the predictive feature in our PCD model was expanded to encompass additional types of cancer. A cohort of 298 patients with bladder urothelial carcinoma (BLCA) from the IMvigor210 immunotherapy groups was assembled. The PCD model demonstrated a significant role in predicting outcomes in the IMvigor210 cohort (Fig. 8 A), indicating that patients with high PCD had a poor prognosis. Additionally, the low-PCD group showed a higher response rate (Fig. 8 B and 8 C). The PCD score also displayed strong prognostic predictive capabilities (Fig. 8 D). The validation of the gene and protein expression for SL100A9 and IL33 in the HCC tissues. According to the IHC staining and western blot, the SL100A9 protein expression was enhanced in HCC samples (Figs. 9 A and 9 E); while the IL33 protein expression was decreased in HCC samples (Figs. 9 B and 9 D). The mRNA expression of IL33 was significantly decreased in in-hospital HCC tissues relative to the adjacent non-tumor liver tissues (Fig. 9 C); while the SL100A9 mRNA expression was enhanced in HCC samples (Fig. 9 F). The results of western blotting were consistent with IHC (Fig. 9 G). Discussion The unfavorable prognosis of HCC can be attributed primarily to delayed diagnosis. As such, the exploration of innovative approaches for early intervention in early-stage HCC holds promise for improving patient outcomes 11 . This study represents a comprehensive analysis of twelve distinct patterns of PCD, the establishment of a cell death signature specific to early-stage TCGA-HCC cases, and validation of its robustness across four external cohorts (ICGC-HCC). A graphical representation was constructed to integrate patient data with PCD information, demonstrating the efficacy of the proposed strategy. Ultimately, we examined the potential correlations between PCD and immune checkpoints, tumor microenvironment, immune therapy and drug sensitivity. Dysregulated PCD processes can contribute to the progression of carcinogenesis. Apoptosis, a programmed cell death mechanism, involves a systematic elimination of damaged or unnecessary cells characterized by nuclear condensation, fragmentation, and nucleolus breakdown 12 . Macrophages efficiently clear apoptotic vesicles without inducing inflammation in surrounding cells, thereby maintaining tissue homeostasis. Initially, necrosis was perceived as unregulated cell death. Emerging research suggests that necrosis can be induced and occurs regularly, a process referred to as necroptosis. The formation of necrosomes, a complex series of events, is crucial in the execution of necroptosis. Pyroptosis is a type of programmed cell death marked by cellular swelling, membrane rupture, and the release of various pro-inflammatory mediators 13 . Ferroptosis is characterized by the accumulation of lipid hydroperoxides at toxic levels, a process that is reliant on iron 14 . Cuproptosis, a recently identified mode of cell deamise triggered by copper exposure, has been strongly associated with severe pathological conditions. Entotic cell death is a distinct cellular process characterized by the active invasion and occurrence within living cells and adjacent regions, distinct from apoptotic pathways that do not require activation of apoptotic executioner 15 . Netotic cell demise is initiated by the release of neutrophil extracellular traps, reticulated structures released in response to infection or injury 16 . Parthanatos represents a tightly regulated form of cell death triggered by excessive stimulation of the enzyme PARP-1 17 . Hydrolases play a crucial role in promoting lysosome-mediated cell death by facilitating membrane permeabilization and subsequent transfer of cellular contents to the cytosol. Autophagy-dependent cell death is characterized by a sequence of events involving lysosomal degradation, which contributes to metabolic adaptation and nutrient recycling 18 . Alkaliptosis represents a novel form of programmed cell death regulated by intracellular alkalinization 19 . In our study, we initially analyzed 344 differentially expressed genes associated with programmed cell death that demonstrated a significant correlation with hepatocellular carcinoma in the TCGA-HCC datasets. The expression levels of these 344 genes were utilized to stratify patients with HCC into two distinct molecular subtypes within the TCGA-HCC cohorts. Patients classified in Cluster C2 displayed a more unfavorable prognosis and advanced clinicopathological features in comparison to those in Cluster C1. We hypothesized that these differences may be partially attributed to varying responses to the heterogeneous immune microenvironment. As anticipated, Cluster C2 exhibited elevated expression levels of various immune checkpoint molecules commonly implicated in facilitating immune evasion by cancer cells, potentially contributing to their poorer prognosis. Our study identified a significant association between the PCD score and response to cancer therapy, with patients possessing high PCD scores, indicative of reduced survival rates, displaying decreased levels of CD4 + T cells compared to those with low PCD scores. Furthermore, individuals with high PCD scores exhibited diminished metabolism scores and suppression of metabolism-related pathways. Additionally, the group with high PCD scores exhibited a decreased proportion of CD4 T cells. CD4 T cells are known to secrete cytokines such as IFN-γ and TNFα, which play a role in promoting apoptosis in cancer cells and enhancing immune cell-mediated cytotoxicity against cancer cells. Previous studies have underscored the critical role of CD4 T lymphocytes in the immune response to tumors, underscoring their significance as pivotal contributors to cancer treatment via immunotherapy 12 , 20 . Our study revealed that individuals with lower PCD scores demonstrated heightened levels of CD4 T cell infiltration, suggesting a more robust response to immunotherapy. Moreover, the results of the IC50 analysis indicated that individuals with a lower PCD score may exhibit heightened sensitivity to chemotherapy and targeted therapies. Additionally, our PCD model revealed distinct responses to immunotherapy among the two PCD-related HCC groups. Notably, our investigation of the immunotherapy cohorts demonstrated that individuals with decreased PCD scores consistently displayed improved responses to immunotherapy in BLCA 21 . In conclusion, PCD may serve as a valuable tool in predicting which HCC patients could benefit from immunotherapy before starting treatment. In our study, we encountered constraints related to the diverse high-throughput sequencing platforms used in various public datasets to collect our study cohorts, leading to the inevitable presence of intratumor or intrapatient tumor heterogeneity. Previous research has indicated that tumor diversity may influence the efficacy of immunotherapy and chemotherapy. Unfortunately, due to data limitations, the extensive diversity of hepatocellular carcinoma had to be disregarded. Conclusion Our research demonstrated that the PCD model functioned effectively as a predictive tool for HCC, exhibiting correlations with immune cell profiles and immunotherapeutic responses. The PCD framework represents a valuable resource for prognosticating survival outcomes and guiding therapeutic strategies for HCC. Implementation of the PCD model has the potential to enhance prognostic accuracy for individuals with HCC and identify patients likely to benefit from anticancer immunotherapy. Our thorough analysis of PCD promises to yield valuable insights into its relevance and impact in the context of HCC. Declarations Funding: Supported in part by the international cooperation fund of the Science and Technology Bureau of Jilin Province (20220402075GH), Jilin Province Health Research Talent Project (2022SCZ06), and NSFC regional innovation and development fund (U20A20360). Author Contribution Chang Liu wrote the main manuscript text , Xiaojun Jin, Yuan An and Wei Li prepared the figures. All authors reviewed the manuscript. Data Availability Data is provided within the manuscript or supplementary information files References Jin, X. et al. A predictive model for prognosis and therapeutic response in hepatocellular carcinoma based on a panel of three MED8-related immunomodulators. Front. Oncol. 12 (2022). Magen, A. et al. Intratumoral dendritic cell–CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat. 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A machine learning computational framework develops a multiple programmed cell death index for improving clinical outcomes in bladder cancer. Biochem. Genet. https://doi.org/10.1007/s10528-024-10683-y (2024). Additional Declarations No competing interests reported. Supplementary Files s1.png Supplementary Figure S1: The screening for different genes of program cell death in TCGA-HCC samples. A: The heatmap of all difference expression genes between tumor and non-tumor samples. B: The volcano plot of all difference expression genes between tumor and non-tumor samples. C: The intersection of all differential genes with programmed cell death genes. Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Feb, 2025 Reviews received at journal 28 Jan, 2025 Reviewers agreed at journal 21 Jan, 2025 Reviews received at journal 20 Nov, 2024 Reviewers agreed at journal 06 Nov, 2024 Reviewers invited by journal 02 Oct, 2024 Editor assigned by journal 02 Oct, 2024 Editor invited by journal 21 Sep, 2024 Submission checks completed at journal 21 Sep, 2024 First submitted to journal 18 Aug, 2024 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-4935574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":417563344,"identity":"64b03acc-946d-4b71-9399-6ac3a8da0373","order_by":0,"name":"Chang Liu","email":"","orcid":"","institution":"Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":417563345,"identity":"450758c2-f6da-4c2f-bf62-2a66b5193fee","order_by":1,"name":"Xiaojun Jin","email":"","orcid":"","institution":"School of Medicine, Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Jin","suffix":""},{"id":417563346,"identity":"dc970b03-e643-4d1a-9343-d7bfb6df5466","order_by":2,"name":"Yuan An","email":"","orcid":"","institution":"Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"An","suffix":""},{"id":417563348,"identity":"e4ca0ca8-48cf-4e05-aa32-b5480fa9d660","order_by":3,"name":"Wei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIie3RMQrCMBSA4VcEuxRc00HPUCiIUMGrJItdxLmDwxOhjq4eo+AFXijYJe7dtDdwdBKTKriFjIL5lxB4H0kIgM/3kwVb4sV8MoIa9W7gRJC4WqYxSmcCQEFZi4oCR5I0O6SbPiWVsmRQZALDC9mJkti/ZUqGqFxgtOZWMm3F+y09MTdEFiV2cu2QhJ48oSFPF9IGb1KBIehAFupzMUZyN+PnPC2jlZ3E+6brHuYrj7Vs75tsfAiVnXxjBMD1OnSc143Qfdbn8/n+qxd1ElS4bPRGrQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-19 03:28:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4935574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4935574/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98634-4","type":"published","date":"2025-04-22T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78880868,"identity":"67259e3b-70ca-4b39-81a1-f2ade309f05b","added_by":"auto","created_at":"2025-03-20 08:31:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":502509,"visible":true,"origin":"","legend":"\u003cp\u003eThe landscape of the molecular cluster in the TCGA-HCC samples.\u003c/p\u003e\n\u003cp\u003eA: Consensus matrix heatmap under k = 2 reflected optimal clustering. B: The CDF score for diverse clusters. C: Cumulative distribution curves under cluster counts of 2-10. D: The consensus score for diverse clusters. E: The heat map distribution under cluster counts of 2-10. F: The overall survival difference between clusters.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/9f58f0478944dff3aa1a545b.png"},{"id":78880013,"identity":"7796339c-da28-47e4-a187-c8d31f5e1e36","added_by":"auto","created_at":"2025-03-20 08:23:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":642544,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune-related landscape of the molecular cluster in the TCGA-HCC samples.\u003c/p\u003e\n\u003cp\u003eA: The gene expression of immune checkpoints differences between clusters. B: The gene expression of HLA molecules differences between clusters. C: ESTIMATE scores differences between clusters. D: The heat map of immune cell proportion distribution under cluster. E: The immune cell proportion differences between clusters. *: P \u0026lt; 0.05, **: P \u0026lt; 0.01, ***: P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/7f0e0825e2d7a1466d742409.png"},{"id":78880021,"identity":"0cabbf2a-774a-44a6-84d9-f54d14e5da94","added_by":"auto","created_at":"2025-03-20 08:23:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530519,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a predictive model based on program cell death genes.\u003c/p\u003e\n\u003cp\u003e(A) The boxplot presenting differences in batches before merging. (B) The boxplot presenting differences in batches before merging. (C) UMAP analysis of the differences in batches before merging. (D) UMAP analysis of the differences in batches after merging. (E): Univariate Cox regression analysis of program cell death genes. F: Forest plot exhibiting the multivariate Cox regression results.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/8cd8e35b0f0c08ceec7d125c.png"},{"id":78880862,"identity":"8da80c72-345d-42d4-a53f-086fe787a8ec","added_by":"auto","created_at":"2025-03-20 08:31:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":333429,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic values of the predictive model in the merged HCC cohort.\u003c/p\u003e\n\u003cp\u003eA-E: Overall survival analyses between low-risk and high-risk subgroups in T1 stage, N0 stage, M0 stage, pathological stage 1 and histological grade 1. F: PCA analysis of merged HCC cohort. J: ROC analyses of 1-, 3- and 5-years overall survival in training HCC cohort. G: Risk score, survival status and expression of PCD-related genes in high-risk and low-risk groups of training HCC cohort. H: Overall survival analysis in high-risk and low-risk groups of testing HCC cohort. I: ROC analyses of 1-, 3- and 5-years overall survival in testing HCC cohort.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/e33741d751e3ab1f86d85eba.png"},{"id":78880864,"identity":"4f4d4410-f093-41ed-a0ef-6d1d479eb413","added_by":"auto","created_at":"2025-03-20 08:31:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":259715,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram\u003c/p\u003e\n\u003cp\u003eA-B: Difference of risk scores in T stage (A) and pathological stage (B). C: The nomogram for predicting 1-, 3- and 5-year overall survival in merged HCC cohort. D: Calibration curve for validation of the nomogram. E: ROC analyses of the nomogram predicting of 1-, 3- and 5-year overall survival.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/b1f74ad63a986cd336ad9d00.png"},{"id":78880017,"identity":"8dba1c9b-6096-4825-ae36-d82ecb27e3cd","added_by":"auto","created_at":"2025-03-20 08:23:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":578962,"visible":true,"origin":"","legend":"\u003cp\u003eThe linking to PCD model and immune landscape and drug sensitivity in HCC samples.\u003c/p\u003e\n\u003cp\u003eA-B: Sensitivity of high-risk and low-risk groups of HCC cohort to drug therapy. C: The difference between high-risk and low-risk groups of HCC cohort to immune checkpoints gene expression. D: The differences of immune cell proportion between high-risk and low-risk groups of HCC cohort. E: The spearman correlation between risk score and immune checkpoints gene expression. F: The spearman correlation between risk score and immune cell proportion.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/a305e53e88cfed2b0bec4885.png"},{"id":78880866,"identity":"7df6659b-7519-4189-a2d3-56c7b54fc9f2","added_by":"auto","created_at":"2025-03-20 08:31:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":526062,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in molecular landscape between high- and low-risk groups\u003c/p\u003e\n\u003cp\u003eA-C: Enrichment score heatmap for metabolism pathways between high- and low-risk groups. D-F: Enrichment score boxplot for metabolism pathways between high- and low-risk groups. *: P \u0026lt; 0.05, **: P \u0026lt; 0.01, ***: P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/7883db3f4a595069a6786cf1.png"},{"id":78881471,"identity":"0d4931df-29cf-470d-b441-a39f2a3433f3","added_by":"auto","created_at":"2025-03-20 08:39:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":269671,"visible":true,"origin":"","legend":"\u003cp\u003eThe responsiveness of PCD risk score to PD-L1 blockade immunotherapy in IMvigor210 cohort.\u003c/p\u003e\n\u003cp\u003eA: Prognostic differences among low- and high-risk score groups in the IMvigor210 cohort. B: Differences in low- and high-risk scores among immunotherapy responses in the IMvigor210 cohort. C: Distribution of immunotherapy responses among risk score groups in the IMvigor210 cohort. D: ROC curves of risk model constructed by four genes in IMvigor210 cohort.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/d021ffd57631217ce7a98fc0.png"},{"id":78880023,"identity":"c478b041-38b8-4d02-9663-3830fe04cdd7","added_by":"auto","created_at":"2025-03-20 08:23:02","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2646344,"visible":true,"origin":"","legend":"\u003cp\u003eThe validation results of SL100A9 and IL33 expression in HCC tissue samples.\u003c/p\u003e\n\u003cp\u003eA-B: The immunohistochemistry staining of tumor and non-tumor liver tissues. P: non-tumor liver tissues, T: tumor tissue. C: The levels of IL33 mRNA expression in tumor and non-tumor liver tissues. D: The positive area of immunohistochemistry staining for IL33. E: The levels of SL100A9 mRNA expression in tumor and non-tumor liver tissues. F: The positive area of immunohistochemistry staining for SL100A9. G: The western blotting results of SL100A9 and IL33 protein expression in tumor and non-tumor liver tissues. T: tumor, N: non-tumor liver tissues.\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/c2bd81cb2cb9042972686e93.png"},{"id":81569878,"identity":"198b7df3-ba8d-43f5-af9f-640c6fff2839","added_by":"auto","created_at":"2025-04-28 16:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7580226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/412fa01a-5a52-4f39-974f-655cdbf41757.pdf"},{"id":78881470,"identity":"0134e2a4-9027-4ee4-83a0-ed8d4130ceff","added_by":"auto","created_at":"2025-03-20 08:39:01","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":473168,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure S1: The screening for different genes of program cell death in TCGA-HCC samples.\u003c/p\u003e\n\u003cp\u003eA: The heatmap of all difference expression genes between tumor and non-tumor samples. B: The volcano plot of all difference expression genes between tumor and non-tumor samples. C: The intersection of all differential genes with programmed cell death genes.\u003c/p\u003e","description":"","filename":"s1.png","url":"https://assets-eu.researchsquare.com/files/rs-4935574/v1/6728cbdcc23592ef487c58ec.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Predictive Model for Prognosis and Therapeutic Response in early-stage Hepatocellular Carcinoma: emphasis on program cell death genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is a common form of primary liver cancer, ranking as the fourth leading cause of cancer-related mortality and the sixth most frequently occurring malignant tumor. Annually, over 700,000 new cases of HCC are diagnosed worldwide, with approximately half originating from China\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent decades, advancements in the treatment of HCC have been made through various methods including surgical interventions, liver transplants, local ablation, transarterial chemoembolization, and radiation therapy. However, a notable obstacle in precision treatment lies in the accurate staging of tumors using the TNM stage and clinical grade, which inform therapeutic recommendations. Emerging systemic treatments for HCC, including regorafenib, lenvatinib, ramucirumab, carbozantinib, and immune checkpoint inhibitors, show promise\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Given the limited treatment options for HCC, it is imperative to explore novel targets to improve patient outcomes, underscoring the importance of developing effective models for successful targeted therapy.\u003c/p\u003e \u003cp\u003eImmunotherapy, a medical treatment modality, has the capacity to modulate the immune system's response to combat diseases. The efficacy of immunotherapy in treating HCC is attributed to its high immunogenicity\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, tumor cells can develop resistance to immunotherapy through adaptive mechanisms. Additionally, the variability in response to immunotherapy among individuals with HCC underscores the complexity of treatment outcomes. Reprogramming immune cells and the tumor microenvironment (TME) can promote immune evasion by HCC cells, thereby contributing to disease progression\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The impact of the tumor microenvironment on the progression of hepatocellular carcinoma is widely acknowledged.\u003c/p\u003e \u003cp\u003eCell death occurs through two primary mechanisms: accidental cell death, which is uncontrolled, and programmed cell death (PCD), which is intricately regulated and involves multiple mechanisms. Dysregulation of apoptosis is crucial for the survival and advancement of cancer cells. Understanding the diverse mechanisms of PCD, including apoptosis, necroptosis, ferroptosis, pyroptosis, entotic cell death, lysosome-dependent cell death, netotic cell death, parthanatos, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and cuproptosis, is essential for the development of effective therapies for HCC\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Recent research suggests that the pathways governing programmed cell death play a role in modulating the immunosuppressive tumor microenvironment and influencing the efficacy of anti-cancer interventions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A signature associated with PCD has been established for the prediction of clinical outcomes, alterations in the tumor microenvironment, and responses to therapy in cases of lung adenocarcinoma and osteosarcoma\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The initiation and dissemination of each PCD pattern exhibit significant associations that are essential for maintaining homeostasis, impacting disease advancement, and influencing therapeutic strategies. The recognition of intricate crosstalk among diverse programmed cell death pathways and the utilization of all identified programmed cell death-related genes for prognostic purposes in patients with HCC may present a novel approach to the management of HCC.\u003c/p\u003e \u003cp\u003eFurthermore, the probability of tumor recurrence within 2 years post-surgery, referred to as early recurrences and comprising more than 70% of tumor reappearance cases, is associated with the aggressiveness of the primary tumor\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. On the other hand, late recurrence, defined as tumor reappearance occurring after 2 years post-surgery, is often linked to de novo carcinogenesis or underlying liver conditions. Recognizing individuals at elevated risk of early-stage HCC following liver resection can enable healthcare providers to promptly initiate treatment and optimize treatment efficacy. Numerous studies have examined the relationship between specific molecular markers and early recurrence in HCC, yet their clinical utility is limited by the heterogeneity of the disease\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A comprehensive model incorporating multiple factors would greatly improve practicality in clinical settings compared to single biomarker approaches. Models predicting prognosis in early-stage HCC are scarce, prompting this study to emphasize the significance of the International Cancer Genome Consortium (ICGC) sample size and tissue source in developing accurate diagnostic and prognostic models for HCC. Patients diagnosed with TNM stage I and stage II HCC from The Cancer Genome Atlas (TCGA) and the ICGC were chosen for the creation of comprehensive diagnostic and prognostic models. Therefore, investigating the relationship between alterations in PCD-related gene expression and the oncogenic risk score in early-stage HCC may provide insights into the molecular mechanisms driving the progression and evolution of early HCC.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003e1. Data acquiring\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe conducted a thorough examination of two gene expression datasets of HCC patients sourced from the ICGC. We specifically acquired the Liver Hepatocellular Carcinoma dataset, encompassing stage I and stage II HCC patients from TCGA database. In our study, the TCGA-HCC datasets were utilized as the training cohorts, while the ICGC-LIRI dataset served as the validation cohort. To address potential batch effects in the normalized data from TCGA and ICGC, we employed t-distributed stochastic neighbor embedding (tSNE) analysis, followed by adjustment using the ComBat algorithm from the R package sva. The integrated data set was employed for further analysis, revealing that the key regulatory genes associated with twelve PCD patterns are contained within the PCD-related gene pool. These genes were sourced from GSEA gene sets, KEGG databases, literature reviews, and manual compilation. The resultant gene list represents a compilation of regulatory genes from the aforementioned twelve distinct PCD patterns. A total of 1078 genes associated with PCD were identified, including 580 genes related to apoptosis, 52 genes related to pyroptosis, 87 genes related to ferroptosis, 367 genes related to autophagy, 15 genes related to entotic cell death, 14 genes related to cuproptosis, 9 genes related to parthanatos, 8 genes related to netotic cell death, 7 genes related to alkaliptosis, 220 genes related to lysosome-dependent cell death, and 5 genes related to oxeiptosis. The limma R package was utilized to identify differentially expressed genes (DEGs) between control and tumor samples in the TCGA-HCC database. Candidate DEGs were defined as those with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; log2(0.5). Venn diagram analyses were performed using the R package 'VennDiagram' to identify common DEGs, which were subsequently considered reliable DEGs related to PCD across all datasets.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2. Consensus clustering analysis of PCD\u003c/h2\u003e \u003cp\u003eThe Pam method, a form of unsupervised clustering using Euclidean distance and Ward's linkage, was employed to differentiate distinct molecular subtypes by examining the expression of reliable DEGs linked to PCD. The 'ConsensusClusterPlus' R package was utilized for this analysis to determine the number of clusters in the TCGA-LIHC cohort, with 1000 repetitions to ensure classification consistency. A heatmap was produced to visualize the variability in distribution among various coagulation subtypes. To assess the clinical significance of coagulation subtypes, we investigated the associations between PCD subtypes and prognosis. Kaplan-Meier survival analysis was employed to evaluate overall survival (OS) across different clusters, with statistical significance denoted by a P-value below 0.05. Utilizing the ggplot R package, we generated a visualization to depict the diverse expression patterns of PCD, immunomodulatory, and immune checkpoint genes within distinct subgroups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneration and validation of prognostic signature\u003c/h3\u003e\n\u003cp\u003eSignificant genes impacting the prognosis of HCC were identified using reliable PCD-associated DEGs through univariate Cox regression analysis with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Prognostic genes linked to PCD were further identified through stepwise multivariate regression analysis utilizing stepwise Akaike information criterion (stepAIC). The prognostic signature related to PCD was calculated using the formula Riskscore=\u0026sum;βi\u0026lowast;Ei, where β represents the coefficient value obtained from Cox regression analysis and Ei represents the expression level. The risk scores of each sample in the TCGA-HCC training set were calculated using a defined formula within a risk model, followed by z-score standardization. Subsequently, the samples were stratified into high and low-risk groups based on the median score. Survival analysis was performed using Kaplan-Meier curves, and a receiver operating characteristic (ROC) curve was generated using the 'timeROC' R package. Validation datasets were employed to assess the robustness of the risk model.\u003c/p\u003e\n\u003ch3\u003eDevelopment of a nomogram integscore model and clinical features\u003c/h3\u003e\n\u003cp\u003eA nomogram calibration plot was employed to illustrate the relationship between survival outcomes and dummy observations. The nomogram incorporated risk categories and clinical characteristics, assigning a score to each factor which were then aggregated to determine a final comprehensive risk score.\u003c/p\u003e\n\u003ch3\u003eAnalysis of immune microenvironment\u003c/h3\u003e\n\u003cp\u003eThe CIBERSORT algorithm is frequently employed to estimate the distribution of 22 immune cell types based on gene expression data. In this study, the CIBERSORT algorithm was utilized to analyze the proportions of 22 immune cells in order to investigate the relationship between risk score and the immune microenvironment in patients with HCC. Subsequently, the correlation between risk scores of patients with HCC from TCGA and various immune cell fractions was assessed using the Spearman correlation test.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe GSVA approach was employed to conduct a comparative analysis of various biological processes in high- and low-risk groups utilizing the 'c2.cp.kegg.v7.4.symbols.gmt' dataset, implemented in R with the 'GSVA' and 'GSEABase' packages.\u003c/p\u003e \u003cp\u003eDrug responsiveness in patients with hepatocellular carcinoma was investigated using the PCD model, and the oncoPredict package in R software was used to predict the sensitivity of multiple drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Furthermore, a comparison of the half maximal inhibitory concentration (IC50) between the high- and low-risk score group was conducted through differential expression analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe role of PCD model in the prediction of immunotherapeutic benefits\u003c/h2\u003e \u003cp\u003eA total of 348 cases of urothelial cancer were examined in the IMvigor210 dataset, which included expression data, survival data, follow-up information, and immunotherapy effect data sourced from the IMvigor210CoreBiologies R package. Patients within the IMvigor210 cohort were stratified into four groups based on their response to immunotherapy: progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). The PCD risk score for patients in the IMvigor210 study was calculated based on normalized raw count data using the specified formula, and its impacts on both prognosis and the efficacy of the PD-L1 inhibitor were assessed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry\u003c/h3\u003e\n\u003cp\u003e After obtaining ethical approval from China-Japan Union Hospital of Jilin University, we selected the tumor tissues and adjacent tissues of 15 patients who met the criteria for HCC liver cancer for the following experiments. The prepared paraffin-embedded sections of HCC and non-tumor liver tissues were sequentially deparaffinized, rehydrated, and antigen retrieval was performed by by heating treatment in citrate buffer. After blocking with 3% bovine serum albumin, the sections were stained with the primary antibody of polyclonal rabbit anti-S100A9 (1:200, Affinity) and polyclonal rabbit anti-IL33 (1:200, Affinity), and kept overnight at 4\u0026deg;C. The following day after the primary antibody incubation, the sections were washed and stained with horseradish peroxidase-conjugated secondary antibody (1:200, Abcam) for one hour at 37\u0026deg;C and visualized using diaminobenzidine\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eWestern Blotting\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eWestern Blotting\u003c/div\u003e \u003cp\u003eThe total protein quantity from HCC tissue was determined by the bicinchoninic acid assay. The proteins were separated on sodium dodecyl sulfate gel and transferred onto the PVDF membranes. Proteins on membranes were blocked with 5% non-fat milk for 2 hours, probed with primary antibodies (1: 1000) overnight at 4\u0026deg;C and thereafter incubated with the secondary antibody (1: 10000) for 1 hour. The enhanced chemiluminescence (Beyotime) was used to visualize the brands\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA Extraction and Quantitative Real-Time Polymerase Chain Reaction\u003c/h2\u003e \u003cp\u003eTotal RNA from cells was isolated by the TRIzol method (Invitrogen, USA) and dissolved in 50ul RNase-free water. The RNA (1 ug) was reverse-transcribed using the reverse transcription kit (Takara, Japan). The qRT-PCR reaction was performed using the SYBR Premix Ex Taq kit (Takara, Japan) according to the manufacturer\u0026rsquo;s instructions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (v.4.3.0), with Student's t-test or Wilcoxon test utilized for comparisons between two groups, the Kruskal-Wallis test for comparisons among multiple groups, and Kaplan-Meier plots with log-rank test for survival curve analysis. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to determine statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e \u003cp\u003e The Ethics Committee of China-Japan Union Hospital of Jilin University approved the human studies. Informed consent was obtained from all participants. All participants approved for publication. All methods were performed in accordance with the relevant guidelines and regulations .\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLandscape of PCD in HCC patients and Identification of the PCD-related molecular subtypes\u003c/h2\u003e \u003cp\u003eA total of 344 genes exhibiting differential expression related to programmed cell death were detected in stage I and II patients from the TCGA-HCC cohort, demonstrating statistical significance with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Subsequently, a consensus clustering analysis was conducted on these 344 genes to ascertain potential molecular subtypes within the TCGA-HCC patient population. The analysis unveiled two distinct subtypes, labeled as Cluster 1 and 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Discrimination between the samples was easily discernible through the CDF curve in depicted Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, consensus value\u0026thinsp;\u0026gt;\u0026thinsp;0.8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and heatmap matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Significant differences in overall survival time were observed between the two clusters (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Patients in Cluster 2 exhibited a poorer prognosis compared to those in Cluster 1, with disparities in survival rates. Additionally, the expression level of common immune checkpoint genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and immunomodulatory factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) were significantly elevated in Cluster 2 compared to Cluster 1. To explore the differences in the tumor microenvironment between these two subtypes, ESTIMATE analysis was employed. The results revealed that individuals categorized in Cluster C2 exhibited elevated levels of TME score, specifically immune score and ESTIMATE score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Additionally, the immune cell landscape exhibited high heternetity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These findings suggest that immunotherapy and immune response were more prominent in Cluster C2 compared to Cluster C1, indicating heightened sensitivity and efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDeveloping a predictive gene panel for individuals with hepatocellular carcinoma\u003c/h2\u003e \u003cp\u003eSurvival data from TCGA-HCC and ICGC-HCC patients was collected and subsequently analyzed. The data dimension discrepancies between the two databases were adjusted using the combat method and visualized through boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and UMAP plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Survival-related genes were identified through univariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Utilizing the identified survival-related gene signature, a PCD score was developed for the TCGA-HCC group using the formula: PCD score = (GBA \u0026times; 0.541) + (G6PD \u0026times; 0.222) + (S100A9 \u0026times; -1.789) + (IL33 \u0026times; 0.383) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Subsequently, we stratified the HCC individuals within he merged cohort of TCGA-HCC and ICGC-LIRI dataset was characterized by high- and low-PCD scores, utilizing the median value as the delineating threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequent to this classification, we conducted a comparative analysis of the OS of HCC patients within the TCGA-HCC cohort based on their PCD scores. A graphical representation was generated to illustrate the distribution of PCD scores and the corresponding OS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), revealing that individuals with elevated PCD scores exhibited diminished OS duration and heightened mortality rates in comparison to those with low PCD scores. Principal component analysis (PCA) analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) was utilized to validate the precision and reliability of the Prognostic and Classification Diagnostic (PCD) model. Patients with elevated PCD scores exhibited more unfavorable outcomes compared to those with lower scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further validate the predictive accuracy of the scoring system, individuals in the TCGA-HCC cohort were stratified based on various T, N, M, and TNM levels and a stratified analysis was conducted using their PCD scores. The findings revealed that patients in the high-PCD score category had significantly shorter overall survival times across nearly all subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The prognostic scoring system demonstrated its predictive ability through AUC values of 0.76, 0.68 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), and 0.73 for 1-, 3-, and 5-year OS, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Furthermore, PCD scores exhibited a significant correlation with various clinical characteristics, including distinct T and clinical stage in HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHighlighting the predictive significance of programmed cell death and combining a nomogram risk model with clinical data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo mitigate the limitations of relying on sole PCD for prognostic forecasting in HCC patients, our study expanded the scope of analysis to include five clinical indicators (Age, Gender, Tumor size, Lymph node involvement, Metastasis, and Cancer stage) in conjunction with PCD. Utilizing data from the TCGA-HCC cohort, we employed a multivariable-Cox regression analysis and step-wise regression analysis to develop a comprehensive nomogram scoring system integrating both clinical and genomic information for enhanced prognostic prediction in HCC patients. The total score for each patient was meticulously calculated utilizing the nomogram formula as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. The calibration curve provided a visual representation of the nomogram model's predictive accuracy for outcomes at 1, 3, and 5 years, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. The significance of the nomogram was underscored by consistently achieving the highest Area Under the Curve (AUC) for the total score at specific time points, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExploring possible medication sensitivity for individuals with HCC\u003c/h2\u003e \u003cp\u003eTo analyze and predict the efficacy of chemotherapy drugs in our patient-derived xenograft model for HCC, we calculated the IC50 values of various medications for each patient within the TCGA-HCC cohort. Subsequently, we conducted a differential expression analysis on the IC50 matrix of therapeutic drugs to identify 19 potentially significant therapeutic agents. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, the IC50 values for these 19 medications were found to be higher in the high-CDI group, suggesting that individuals with low PCD scores may exhibit heightened sensitivity to chemotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDissection of tumor microenvironment and molecular features based on PCD signature\u003c/h2\u003e \u003cp\u003eAn analysis was conducted to assess variations in additional important characteristics within the two categories of PCD. Initially, we investigated the potential correlation between immune checkpoints and PCD score values. The bar graph displayed a potential relationship that higher PCD values had increased drug response activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Furthermore, PCD scores were found to be significantly associated with LAIR1, TNFRSF14, HAVCR2, LGALS9, CD160, CD44 and TNFRSF18 in TCGA-HCC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Subsequently, CIBERSOR algorithms were utilized to compute the enrichment scores of immune-associated cells. Significantly, the proportion of memory CD4\u0026thinsp;+\u0026thinsp;T cells in TCGA-HCC cohorts was found to be significantly higher in the low-PCD group, demonstrating a strong inverse correlation with PCD score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Conversely, M0 macrophages and neutrophils were significantly decreased in the low-PCD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In the correlation analysis, the PCD score exhibited different correlations with each immune cells in TCGA-HCC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Furthermore, an analysis was conducted to examine the differences in tumor-related pathways between high and low PCD score categories. In the low-risk group, a majority of metabolism-related pathways exhibited increased activity compared to the high-risk group, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. These findings suggest a noteworthy metabolic heterogeneity in HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAccurately predicting the response to immunotherapy in PCD models\u003c/h2\u003e \u003cp\u003eWe conducted an investigation into the predictive abilities of our PCD model for drug response in HCC and expanded our analysis to predict response in a cohort resistant to immune checkpoint inhibitors, with response to immunotherapy serving as the ultimate endpoint. Additionally, recent advancements in medical treatment have highlighted the efficacy of immunotherapy not only in HCC but also in a variety of other cancer types. Consequently, the scope of the predictive feature in our PCD model was expanded to encompass additional types of cancer. A cohort of 298 patients with bladder urothelial carcinoma (BLCA) from the IMvigor210 immunotherapy groups was assembled. The PCD model demonstrated a significant role in predicting outcomes in the IMvigor210 cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), indicating that patients with high PCD had a poor prognosis. Additionally, the low-PCD group showed a higher response rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The PCD score also displayed strong prognostic predictive capabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe validation of the gene and protein expression for SL100A9 and IL33 in the HCC tissues.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e According to the IHC staining and western blot, the SL100A9 protein expression was enhanced in HCC samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE); while the IL33 protein expression was decreased in HCC samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). The mRNA expression of IL33 was significantly decreased in in-hospital HCC tissues relative to the adjacent non-tumor liver tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC); while the SL100A9 mRNA expression was enhanced in HCC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF). The results of western blotting were consistent with IHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe unfavorable prognosis of HCC can be attributed primarily to delayed diagnosis. As such, the exploration of innovative approaches for early intervention in early-stage HCC holds promise for improving patient outcomes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This study represents a comprehensive analysis of twelve distinct patterns of PCD, the establishment of a cell death signature specific to early-stage TCGA-HCC cases, and validation of its robustness across four external cohorts (ICGC-HCC). A graphical representation was constructed to integrate patient data with PCD information, demonstrating the efficacy of the proposed strategy. Ultimately, we examined the potential correlations between PCD and immune checkpoints, tumor microenvironment, immune therapy and drug sensitivity.\u003c/p\u003e \u003cp\u003eDysregulated PCD processes can contribute to the progression of carcinogenesis. Apoptosis, a programmed cell death mechanism, involves a systematic elimination of damaged or unnecessary cells characterized by nuclear condensation, fragmentation, and nucleolus breakdown\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Macrophages efficiently clear apoptotic vesicles without inducing inflammation in surrounding cells, thereby maintaining tissue homeostasis. Initially, necrosis was perceived as unregulated cell death. Emerging research suggests that necrosis can be induced and occurs regularly, a process referred to as necroptosis. The formation of necrosomes, a complex series of events, is crucial in the execution of necroptosis. Pyroptosis is a type of programmed cell death marked by cellular swelling, membrane rupture, and the release of various pro-inflammatory mediators\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Ferroptosis is characterized by the accumulation of lipid hydroperoxides at toxic levels, a process that is reliant on iron\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Cuproptosis, a recently identified mode of cell deamise triggered by copper exposure, has been strongly associated with severe pathological conditions. Entotic cell death is a distinct cellular process characterized by the active invasion and occurrence within living cells and adjacent regions, distinct from apoptotic pathways that do not require activation of apoptotic executioner\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Netotic cell demise is initiated by the release of neutrophil extracellular traps, reticulated structures released in response to infection or injury\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Parthanatos represents a tightly regulated form of cell death triggered by excessive stimulation of the enzyme PARP-1\u003csup\u003e17\u003c/sup\u003e. Hydrolases play a crucial role in promoting lysosome-mediated cell death by facilitating membrane permeabilization and subsequent transfer of cellular contents to the cytosol. Autophagy-dependent cell death is characterized by a sequence of events involving lysosomal degradation, which contributes to metabolic adaptation and nutrient recycling\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Alkaliptosis represents a novel form of programmed cell death regulated by intracellular alkalinization\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, we initially analyzed 344 differentially expressed genes associated with programmed cell death that demonstrated a significant correlation with hepatocellular carcinoma in the TCGA-HCC datasets. The expression levels of these 344 genes were utilized to stratify patients with HCC into two distinct molecular subtypes within the TCGA-HCC cohorts. Patients classified in Cluster C2 displayed a more unfavorable prognosis and advanced clinicopathological features in comparison to those in Cluster C1. We hypothesized that these differences may be partially attributed to varying responses to the heterogeneous immune microenvironment. As anticipated, Cluster C2 exhibited elevated expression levels of various immune checkpoint molecules commonly implicated in facilitating immune evasion by cancer cells, potentially contributing to their poorer prognosis.\u003c/p\u003e \u003cp\u003eOur study identified a significant association between the PCD score and response to cancer therapy, with patients possessing high PCD scores, indicative of reduced survival rates, displaying decreased levels of CD4\u0026thinsp;+\u0026thinsp;T cells compared to those with low PCD scores. Furthermore, individuals with high PCD scores exhibited diminished metabolism scores and suppression of metabolism-related pathways. Additionally, the group with high PCD scores exhibited a decreased proportion of CD4 T cells. CD4 T cells are known to secrete cytokines such as IFN-γ and TNFα, which play a role in promoting apoptosis in cancer cells and enhancing immune cell-mediated cytotoxicity against cancer cells. Previous studies have underscored the critical role of CD4 T lymphocytes in the immune response to tumors, underscoring their significance as pivotal contributors to cancer treatment via immunotherapy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Our study revealed that individuals with lower PCD scores demonstrated heightened levels of CD4 T cell infiltration, suggesting a more robust response to immunotherapy. Moreover, the results of the IC50 analysis indicated that individuals with a lower PCD score may exhibit heightened sensitivity to chemotherapy and targeted therapies.\u003c/p\u003e \u003cp\u003eAdditionally, our PCD model revealed distinct responses to immunotherapy among the two PCD-related HCC groups. Notably, our investigation of the immunotherapy cohorts demonstrated that individuals with decreased PCD scores consistently displayed improved responses to immunotherapy in BLCA\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In conclusion, PCD may serve as a valuable tool in predicting which HCC patients could benefit from immunotherapy before starting treatment.\u003c/p\u003e \u003cp\u003eIn our study, we encountered constraints related to the diverse high-throughput sequencing platforms used in various public datasets to collect our study cohorts, leading to the inevitable presence of intratumor or intrapatient tumor heterogeneity. Previous research has indicated that tumor diversity may influence the efficacy of immunotherapy and chemotherapy. Unfortunately, due to data limitations, the extensive diversity of hepatocellular carcinoma had to be disregarded.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research demonstrated that the PCD model functioned effectively as a predictive tool for HCC, exhibiting correlations with immune cell profiles and immunotherapeutic responses. The PCD framework represents a valuable resource for prognosticating survival outcomes and guiding therapeutic strategies for HCC. Implementation of the PCD model has the potential to enhance prognostic accuracy for individuals with HCC and identify patients likely to benefit from anticancer immunotherapy. Our thorough analysis of PCD promises to yield valuable insights into its relevance and impact in the context of HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003e Supported in part by the international cooperation fund of the Science and Technology Bureau of Jilin Province (20220402075GH), Jilin Province Health Research Talent Project (2022SCZ06), and NSFC regional innovation and development fund (U20A20360).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChang Liu wrote the main manuscript text , Xiaojun Jin, Yuan An and Wei Li prepared the figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJin, X.\u003cem\u003e et al.\u003c/em\u003e A predictive model for prognosis and therapeutic response in hepatocellular carcinoma based on a panel of three MED8-related immunomodulators. \u003cem\u003eFront. Oncol.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003eMagen, A.\u003cem\u003e et al.\u003c/em\u003e Intratumoral dendritic cell\u0026ndash;CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma. \u003cem\u003eNat. 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Genet.\u003c/em\u003e https://doi.org/10.1007/s10528-024-10683-y (2024).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4935574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4935574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe principal cause of treatment ineffectiveness inhepatocellular carcinoma (HCC) patients stems from post-surgery stagnation and treatment resistance. A comprehensive predictive model for the progression and drug response of post-surgery HCC patients remains elusive. Various programmed cell death (PCD) patterns significantly influence tumor advancement, offering potential as prognostic and drug sensitivity indicators for postsurgery HCC. The analysis in this study utilized integrated data from 12 different types of PCD, multi-omics data from TCGA-HCC and other cohorts in the International Cancer Genome Consortium (ICGC), as well as clinical information of HCC patients. A PCD score was calculated using a four-gene signature determined through cox regression analysis. Validation in independent datasets revealed that HCC patients with high PCD scores had poorer prognoses post-surgery. Furthermore, an unsupervised clustering model identified two distinct molecular subtypes of HCC with unique biological processes. A nomogram exhibiting high predictive accuracy was developed by integrating a PCD signature with clinical characteristics. The association between programmed cell death, immune checkpoint genes and key components of the tumor microenvironment. was established. Patients with HCC displaying elevated CDI levels demonstrated resistance to traditional adjuvant chemotherapy and immune checkpoint inhibitor therapies. Additionally, the oncogenic function of four PCD genes was identified in an inpatient cohort. A novel scoring methodology for PCD was devised through the examination of genes linked to diverse PCD subtypes, providing valuable insights into the prognosis and drug responsiveness of HCC patients.\u003c/p\u003e \u003cp\u003eEarly-stage HCC patients may potentially derive therapeutic benefits from immune therapy directed at programmed cell death.\u003c/p\u003e","manuscriptTitle":"A Predictive Model for Prognosis and Therapeutic Response in early-stage Hepatocellular Carcinoma: emphasis on program cell death genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 08:22:56","doi":"10.21203/rs.3.rs-4935574/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-10T06:31:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T08:33:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120086352299551759832368132454522272087","date":"2025-01-22T02:02:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T13:26:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168618052631411256616785850645564103340","date":"2024-11-07T02:32:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T00:47:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-03T00:46:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-21T12:42:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-21T12:21:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-19T03:26:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6604c442-b78b-42bf-ba08-d8f651170e35","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44518729,"name":"Biological sciences/Cancer"},{"id":44518730,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":44518731,"name":"Biological sciences/Immunology"},{"id":44518732,"name":"Health sciences/Biomarkers"},{"id":44518733,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-04-28T16:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-4935574","link":"https://doi.org/10.1038/s41598-025-98634-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-22 15:58:07","publishedOnDateReadable":"April 22nd, 2025"},"versionCreatedAt":"2025-03-20 08:22:56","video":"","vorDoi":"10.1038/s41598-025-98634-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98634-4","workflowStages":[]},"version":"v1","identity":"rs-4935574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4935574","identity":"rs-4935574","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}