Introduction

Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive system and originates from epithelial cells of the colon or rectum1. Globally, CRC is the third most common malignant tumor and the second leading cause of cancer-related death2. According to the World Health Organization's global cancer statistics (GLOBOCAN 2020), there were 1.93 million new cases of CRC and 935,200 deaths in 2020. By 2030, the incidence of CRC is expected to increase by 60%, reaching 2.2 million cases annually2,3. Currently, both the incidence and mortality of CRC are increasing, and CRC is becoming increasingly common in younger patients, posing a serious threat to human health worldwide and placing a considerable burden on patients and healthcare systems4,5. Unfortunately, the detailed pathogenesis of CRC is still unclear, though more than 35% of patients initially diagnosed with CRC develop liver metastases, resulting in poor prognosis6. Despite continuous development of new treatments such as chemotherapy, immunotherapy, and targeted therapy, the prognosis for patients remains unsatisfactory. Therefore, a deeper understanding of the pathogenesis of CRC is highly important for improving its diagnosis and treatment.

Programmed cell death (PCD) is crucial for eliminating risk factors7. Pyroptosis, apoptosis and necroptosis are three types of PCD that play important roles in normal physiological processes, such as maintaining tissue homeostasis and clearing damaged and aging cells8. PANoptosis (P, pyroptosis; A, apoptosis; N, necroptosis), which shows features of the three pathways9, is a unique pattern of integrated cell death in which the crosstalk and coordination that occurs between three of the pathways is highlighted10,11. PANoptosis is closely related to cytokine storms and host immune responses and can control pathogen invasion and overcome pathogen immune evasion10. Recent studies have shown that PANoptosis is closely related to occurrence and development of human digestive system tumors12.

To reveal the clinical significance and potential application of PANoptosis-related genes (PRGs) in CRC, relationships between PANoptosis dysregulation and CRC occurrence and development were systematically analyzed. In this study, multiple datasets were used to identify key PRGs, including BCL10, CDKN2A, DAPK1, PYGM and TIMP1, which are associated with progression of CRC. Next, the pathogenic regions of these key PRGs were determined, and their relationships with the immune microenvironment and chemotherapy drug sensitivity in CRC were explored. The potential signaling pathways influenced by the key PRGs involved in CRC progression, as well as their regulatory relationships with transcription factors and tumor progression genes related to CRC, were also analyzed. Moreover, expression of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 in CRC cells and tissues were verified using RT-PCR, and a novel prognostic nomogram model for CRC was successfully constructed by combining important clinical indicators and the PRGs. Clinical significance and prognostic value of key genes were further verified by multiple datasets. Our findings provide new insights into predicting CRC prognosis and offer multiple therapeutic targets for future treatment.

Materials and methods

Clinical specimen collection

Thirty paired CRC tissues along with adjacent normal tissues were obtained from surgical patients at The First Affiliated Hospital of Ningbo University during the period from 2022 to 2024. Prior to surgery, no patients underwent chemotherapy or radiotherapy, and all cancerous tissues were validated through pathological examination. This study was performed in accordance with Chinese clinical guidelines and relevant regulations. The collected tissues were preserved in RNA-fixer Reagent (Bioteke, Beijing, China) and subsequently kept at − 80 °C until use. The ethics committee of the First Affiliated Hospital of Ningbo University approved this study (IRB No. KY20220101). Written informed consent was obtained from the patients.

TCGA data and GEO acquisition

Original CRC mRNA expression data and clinical information data were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). A total of 701 samples were collected for subsequent analysis, including 51 normal samples and 650 tumor samples. The dataset files from the single-cell study GSE217774 were downloaded from the NCBI GEO public database (https://www.ncbi.nlm.nih.gov/geo/), and 3 samples with complete expression profiles were selected for analysis.

Differential gene expression analysis

The Limma package is an R software package used for differential expression analysis of expression profiles to identify significantly differentially expressed genes (DEGs) between various group comparisons. The R package "Limma" was used to analyze the molecular mechanisms of the data and identify DEGs between normal and tumor samples using cutoffs of P < 0.05 and |log2FC|> 0.585. Volcano plots and heatmaps were generated to visualize the differentially expressed genes.

Functional annotation of differentially expressed genes

To determine the biological functions and signaling pathways involved in tumor development, the Metascape database (www.Metascape.org) was used for annotation and visualization. Gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed on intersecting genes. A minimum overlap of ≥ 3 and P ≤ 0.01 were considered to indicate statistical significance.

Random survival forest algorithm

RSF is a machine learning algorithm employed for survival analysis. The random ForestSRC package was used to perform the RSF algorithm on feature genes and rank the importance of prognosis-related genes (nrep = 1000, indicating that the number of iterations in the Monte Carlo simulation was 1000).

GWAS analysis

The Gene Atlas gene map database (http://geneatlas.roslin.ed.ac.uk/) is a large database that uses the UK BioBank cohort to record associations between hundreds of traits and millions of variants. These associations were calculated using 452,264 individuals from the UK BioBank database, with a total of 778 phenotypes and 30 million loci included.

Immune cell infiltration analysis

The CIBERSORT method is widely used for evaluating immune cell types in the microenvironment. This method is based on the support vector regression principle and performs deconvolution analysis on the expression matrix of immune cell subtypes. It includes 547 biomarkers and distinguishes 22 human immune cell phenotypes, including T cells, B cells, plasma cells and myeloid subgroups. In this study, CIBERSORT was used to analyze patient data to infer the relative proportions of 22 infiltrating immune cells, and Pearson correlation analysis was performed on gene expression levels and immune cell contents.

Drug sensitivity analysis

The chemosensitivity of each tumor sample was predicted using the R package "pRRophetic" based on the largest cancer pharmacogenomic database (GDSC cancer drug sensitivity genomics database, https://www.cancerrxgene.org/). IC50 estimates for each specific chemotherapy drug were obtained using regression methods, and tenfold cross-validation was performed on the GDSC training set to assess the regression and prediction accuracy. All parameters were selected with default values, including "combat", to remove batch effects and obtain the average of repeated gene expression.

Gene set variation analysis (gsva)

GSVA converts gene-level changes into pathway-level changes by scoring a comprehensive set of genes of interest and then determining the biological functions of genes of interest. In this study, gene sets were downloaded from Molecular Signatures Database, and the GSVA algorithm was used to score each gene set and evaluate potential biological function changes in different samples.

GSEA

Patients were divided into high- and low-expression groups based on expression levels of the key genes, and GSEA was used to analyze differences in signaling pathways between the two groups. The background gene set was obtained from version 7.0 of the MsigDB database and used as an annotated gene set for subtype pathways to perform differential expression analysis of pathways between subtypes. Then, sorting of significantly enriched gene sets was performed based on consistency scores (adjusted P < 0.05). GSEA is commonly used to explore the combination of disease subtyping and biological significance.

Nomogram model construction

A nomogram is based on regression analysis and uses a scale-lined line to plot the relationship between variables in a prediction model based on gene expression and clinical symptoms. This is achieved by drawing a line with scales on it using a certain proportion on the same plane to express the relationship between variables in the predictive model. A multiple regression model is built to determine the contribution of each influencing factor to the outcome variable based on the size of the regression coefficients. Each value level of the influencing factor is assigned a score, which is then added to obtain a total score and calculate the predicted value.

Transcriptional regulation analysis of key genes

This study used the R package "RcisTarget" to predict transcription factors. All calculations performed by RcisTarget are based on motifs. The normalized enrichment score (NES) of the motif depends on the total number of motifs in the database. In addition to motifs annotated by the source data, further annotation files were inferred based on motif similarity and gene sequences. The first step in estimating overexpression of each motif in the gene set is to calculate the area under the ROC curve (AUC) for each motif-gene set pair by computing the recovery curve based on the ranking of the gene set for the motif. The NES for each motif is then calculated based on the AUC distribution of all motifs in the gene set.

miRNA network construction

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression by promoting mRNA degradation or inhibiting mRNA translation. Therefore, we analyzed whether some miRNAs regulate the transcription or degradation of certain risk genes among the key genes. miRNAs related to the key genes were obtained from the miRcode database, and the miRNA network of genes was visualized using Cytoscape software.

Single-cell analysis

The expression profile was read using the Seurat package, and genes with low expression (nFeature_RNA > 500 and percent.mt < 50) were filtered out. Then, the data were standardized, normalized and subjected to PCA. The optimal number of PCs was determined using ElbowPlot, and the position relationship between each cluster was obtained via TSNE analysis. The clusters were then annotated using the annotation file Human Primary Cell Atlas Data provided by the celldex package and assigned to cells that are important in the development of CRC.

Cell culture

Three human CRC cell lines (HCT116, SW480 and RKO) and a normal epithelial colon cell line (NCM460) were obtained from Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, China. All cell lines were cultured in DMEM Medium (Gibco, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (Gibco) at 37 °C with 5% CO2.

Quantitative real‐time reverse transcription‐polymerase chain reaction detection

Total RNA was extracted using TRIzol reagent (Ambion, Carlsbad, CA, USA) and reverse transcribed to cDNA with a GoScript Reverse Transcription (RT) System (Promega, Madison, WI, USA). qRT‐PCR detection was conducted with GoTaq qPCR Master Mix (Promega). The qRT‐PCR conditions were as follows: 95 °C for 5 min, followed by 40 cycles of 94 °C for 15 s, 53 °C for 30 s, and 72 °C for 30 s. The mRNA expression level of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 was normalized to that of GAPDH. Primers used for these genes are listed in Supplementary Table 1.

Statistical analysis

Statistical analyses were conducted using R (version 4.2.2) or SPSS, with P < 0.05 considered to indicate statistical significance.

Results

Identification and functional enrichment of differentially expressed genes (DEGs) associated with CRC-related PANoptosis

Raw CRC mRNA expression data were downloaded and integrated from the TCGA database, resulting in a total of 701 samples, which included 51 samples from the normal group and 650 samples from the tumor group. Differential genes between the two groups were identified using the Limma package (P < 0.05 and |log2FC|> 0.585). This resulted in a total of 5048 differentially expressed genes (DEGs), including 2639 upregulated genes and 2409 downregulated genes (Fig. 1A,B). PRGs were obtained from previous literature13. The intersection of the 5048 DEGs and PRGs resulted in 151 intersecting genes (Fig. 1C). Functional annotation of these 151 genes revealed enrichment mainly in signaling pathways such as necroptosis, apoptosis, and the NOD-like receptor signaling pathway (Fig. 1D,E).

Fig. 1
figure 1

Identification and functional enrichment of DEGs associated with CRC-related PANoptosis. (A) Volcano plot of differential gene expression. Blue and pink indicate downregulated and upregulated differential expression, respectively (screening conditions: P < 0.05 and |Log2FC|> 0.585). (B) Heatmap of differential gene expression. (C) Venn diagram of differentially expressed genes (DEGs) in CRC and PANoptosis-related genes (PRGs). (D,E) Functional enrichment of intersecting genes. GO-KEGG enrichment analysis of intersection genes from the Metascape database (D). A cluster network of enriched pathways in which nodes that share the same cluster are often located close to each other (E).

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Screening of key PRGs associated with CRC progression and survival analysis

To further determine the key genes that affect progression of CRC, random survival forest analysis was performed on the 151 intersecting genes, and genes with a relative importance greater than 0.2 were selected as feature genes, revealing the importance sequence of 12 genes (Fig. 2A,B). Finally, associations between survival and expression of these 12 key genes were analyzed, and BCL10, CDKN2A, DAPK1, PYGM and TIMP1 were found to be significantly associated with survival, indicating that they are key genes (Fig. 2C–N).

Fig. 2
figure 2

Screening of key PRGs associated with CRC progression and survival analysis. (A) Random survival forest analysis plot of 151 differentially expressed genes. (B) Importance ranking of 12 feature genes that met the criteria among intersecting genes. (C–N) Survival analysis of 12 characteristic genes. Kaplan–Meier analysis of the overall survival difference between BCL10 (C), CDKN2A (D), CLU (E), CTNNB1 (F), DAPK1 (G), GPX3 (H), GSN (I), GSTM1 (J), LEF1 (K), PIK3CD (L), PYGM (M) and TIMP1 (N) high- and low-expression groups.

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Identification of disease-causative regions for key PRGs in CRC

CRC GWAS data were analyzed to confirm the disease-causing regions for the 5 key genes. A Q-Q plot shows the significant disease-associated single-nucleotide polymorphisms (SNPs) identified in the GWAS data (Supplementary Fig. 1A–F). Precise mapping of the GWAS data revealed the important SNP loci in the enriched regions. The disease-causing regions corresponding to the key genes are shown, with BCL10 located on chromosome 1, CDKN2A on chromosome 9, DAPK1 on chromosome 9 and PYGM on chromosome 11. The significant SNP loci corresponding to the key genes are shown in the Supplementary material (Supplementary GWAS data).

Relationships between key genes and the immune microenvironment

The microenvironment, which significantly affects disease diagnosis, survival outcomes and clinical treatment sensitivity, is composed mainly of fibroblasts, immune cells, the extracellular matrix, various growth factors, inflammatory factors and specific physicochemical characteristics. Distributions of immune infiltration levels and immune cell correlations are displayed in different forms in Fig. 3A,B. Furthermore, differences in immune cells between the two groups were explored, and significant differences were found in the numbers of naive B cells, plasma T cells, CD8 + T cells, activated memory CD4 + T cells, gamma delta T cells, resting NK cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting dendritic cells, resting mast cells, activated mast cells, eosinophils and neutrophils (Fig. 3C). Relationships between key genes and immune cells were further investigated, revealing that multiple key genes correlated highly with immune cells (Fig. 3D–H).

Fig. 3
figure 3

The landscape of immune infiltration between CRC and normal groups. (A) Relative percentages of 22 immune cells across all samples. (B) Heatmap showing the correlation of infiltration of 22 immune cell types. The colored squares represent the strength of the correlation; red represents a positive correlation, whereas purple represents a negative correlation. The deeper the color is, the stronger the correlation is. (C) Differences in immune cell content between normal patients (blue) and patients with CRC (yellow). P < 0.05 was considered to indicate statistical significance. (D–H) Spearman correlations of BCL10 (D), CDKN2A (E), DAPK1 (F), PYGM (G), and TIMP1 (H) gene expression with immune cell content.

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Analysis of drug sensitivity for key genes

The effectiveness of surgery combined with chemotherapy for early CRC treatment is well established. We used drug sensitivity data from the GDSC database and the R package "pRRophetic" to predict the chemotherapy sensitivity of each tumor sample to explore the relationship between key genes and common chemotherapy drugs. The results showed that BCL10 expression correlated significantly with sensitivity to AKT inhibitors (Supplementary Fig. 2A). CDKN2A expression correlated significantly with sensitivity to AKT inhibitor VIII, dasatinib and gefitinib (Supplementary Fig. 2B). DAPK1 expression correlated with sensitivity to cisplatin, dasatinib, erlotinib and gefitinib (Supplementary Fig. 2C), and that of PYGM correlated with sensitivity to AKT inhibitor VIII, cisplatin, dasatinib, erlotinib, gefitinib and gemcitabine (Supplementary Fig. 2D). Sensitivity to AKT inhibitors VIII, cisplatin, dasatinib and gefitinib correlated significantly with TIMP1 expression (Supplementary Fig. 2E).

Association of key genes with GSVA and GSEA in CRC

Our research evaluated the specific signaling pathways in which the 5 key genes are enriched to explore the potential molecular mechanisms by which these genes affect progression of CRC. GSVA results revealed enrichment of signaling pathways such as PI3K_AKT_MTOR_SIGNALING and IL2_STAT5_SIGNALING with high expression of BCL10 (Fig. 4A), P53_PATHWAY and WNT_BETA_CATENIN_SIGNALING with high expression of CDKN2A (Fig. 4B), IL2_STAT5_SIGNALING and HEME_METABOLISM with high expression of DAPK1 (Fig. 4C), HEDGEHOG_SIGNALING and BILE_ACID_METABOLISM with high expression of PYGM (Fig. 4D), and TGF_BETA_SIGNALING and DNA_REPAIR with high expression of TIMP1 (Fig. 4E). In addition, GSEA showed that pathways such as the cAMP signaling pathway, necroptosis pathway and T-cell receptor signaling pathway are related to BCL10 (Fig. 4F,K), the Hippo signaling pathway, IL-17 signaling pathway and Wnt signaling pathway to CDKN2A (Fig. 4G,L), the PI3K-Akt signaling pathway, Rap1 signaling pathway and TGF-beta signaling pathway to DAPK1 (Fig. 4H,M), the cGMP-PKG signaling pathway, Hedgehog signaling pathway and oxytocin signaling pathway to PYGM (Fig. 4I,N), and apoptosis, cholesterol metabolism and the relaxin signaling pathway to TIMP1 (Fig. 4J,O). These findings suggest that progression of CRC may be affected by the key genes through these pathways.

Fig. 4
figure 4

Functional and pathway enrichment analysis of key genes. (A–E) Correlation analysis results of GSVA for BCL10 (A), CDKN2A (B), DAPK1 (C), PYGM (D) and TIMP1 (E) in CRC. (F–O) Correlation analysis results of GSEA for BCL10, CDKN2A, DAPK1, PYGM and TIMP1 in CRC and molecular interaction networks between various pathways.

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Construction of a clinical prognostic prediction model for key PRGs in CRC

Multivariate regression analysis revealed that in all of our samples, the values of different clinical indicators of CRC and the distribution of key gene expression contributed to the entire scoring process to varying degrees (Fig. 5A). Moreover, predictive analysis of OS was performed for three-year and five-year periods (Fig. 5B), and the results showed that the key gene-related nomogram model had good predictive performance.

Fig. 5
figure 5

Establishment and validation of the prognostic nomogram. (A) Nomogram based on the key gene signature and clinical information for predicting 3- and 5-year OS in patients with CRC. (B) Calibration curves were used to verify the consistency of the predicted and actual 3- and 5-year outcomes (x‐axis: predicted survival probabilities; y‐axis: actual observed survival probabilities). OS, overall survival.

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Enrichment analysis of transcription factors for key genes and the miRNA network

The 5 key genes were used as the gene set for this analysis, and it was found that they are regulated by common mechanisms such as multiple transcription factors. Therefore, enrichment analysis of transcription factors was performed using a cumulative recovery curve. The motif with the highest standardized enrichment score (NES: 9.09) was cisbp__M4892, as shown by motif-TF annotation, and the results of selection analysis for important genes and all the motifs enriched in the key genes and their corresponding transcription factors are displayed in Supplementary Fig. 3A–E. In addition, 71 miRNAs were obtained through reverse prediction of the 5 key genes using the miRcode database, resulting in a total of 146 mRNA-miRNA relationships, which were visualized using Cytoscape (Supplementary Fig. 3F).

Correlation analysis between key genes and disease-regulating genes

Genes involved in development and progression of diseases are called disease-regulating genes. In this study, tumor progression genes related to CRC were obtained from the GeneCards database (https://www.genecards.org/). Expression levels of the top 20 genes based on the relevance score and intergroup differences in expression of tumor genes were analyzed, revealing significant differences in expression of MSH6, BRCA2, BRCA1, MSH2, POLE, PMS2, APC, TP53, PALB2, CHEK2, POLD1, BRIP1, CDH1, AXIN2, BARD1, MUTYH, PTEN and KRAS between the two groups of patients. Furthermore, significant correlations were found between expression levels of the 5 key genes and those of multiple tumor progression genes, with DAPK1 and AXIN2 showing a significant negative correlation (r = − 0.504) and BCL10 and KRAS a significant positive correlation (r = 0.644) (Fig. 6A,B). Analysis of the impact of tumor immunotherapy revealed differences in expression of key genes between high- and low-expression groups (Supplementary Fig. 4A–E). (For responder analysis, the Hexp group refers to samples with gene expression greater than the median value, and the Lexp group refers to samples with gene expression not greater than the median value).

Fig. 6
figure 6

Correlation analysis of CRC-related genes. (A) Differential analysis of disease-regulating genes. Differences in expression of CRC-related genes between normal (yellow) and with CRC (blue) samples. (B) Correlation analysis of key genes and differentially expressed regulatory genes. The first plot indicates that DAPK1 correlated significantly negatively with AXIN2, the second plot indicates the Pearson correlation between regulatory genes and key genes, and the third plot indicates that BCL10 correlated significantly positively with KRAS. Purple and red indicate negative and positive correlations, respectively. The Pearson correlation coefficients and P values are shown at the top of the graphs (P < 0.01 indicates a significant correlation).

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Single-cell sample subtyping and annotation

Single-cell data from GSE217774 were downloaded, and single-cell analysis was performed using the Seurat package. Cells were clustered using the tSNE algorithm, resulting in 30 subtypes obtained through tSNE (Fig. 7A). Then, annotation of each cluster was performed using the R package SingleR. All clusters were annotated into 7 cell categories: Monocyte, Macrophage, Epithelial_cells, Endothelial_cells, T_cells, Tissue_stem_cells and B_cell (Fig. 7B). In addition, expression of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 in Monocyte, Macrophage, Epithelial_cells, Endothelial_cells, T_cells, Tissue_stem_cells and B_cell was explored (Fig. 7C–E).

Fig. 7
figure 7

Single-cell analysis. (A) Cell clustering yielded 30 subtypes. (B) Annotating each subtype by “SingleR” from the R package. (C–E) Scatter plot (C), violin plot (D) and bubble plot (E) shows expression of five key genes in Monocyte, Macrophage, Epithelial_cells, Endothelial_cells, T_cells, Tissue_stem_cells and B_cell.

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Validation of the differential expression of key genes in CRC cells and tissues, and clinical significance verification

To verify the results of our data analysis, we extracted total RNA from CRC cell lines (HCT116, SW480 and RKO) and the normal epithelial colon cell line NCM460 and measured mRNA expression levels of BCL10, CDKN2A, DAPK1, PYGM and TIMP1. qRT-PCR assays showed that the levels of BCL10, CDKN2A and TIMP1 were significantly increase in CRC cells than in NCM460 cells but that DAPK1 was expressed at low levels in cancer cells (Fig. 8A–C,E). In addition to those in RKO cells, mRNA levels of PYGM in HCT116 and SW480 cells were significantly increased (Fig. 8D).

Fig. 8
figure 8

Validation of the differential expression of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 in CRC cells and tissues. (A–E) mRNA expression levels of key genes in different cell lines (NCM460, HCT116, SW480 and RKO) were measured by qRT-PCR. The results were normalized to the reference gene GAPDH. Data are shown as means ± SDs, and two-tailed unpaired t tests were used for statistical analysis of each marker; n = 4 independent experiments. (F–J) The expression levels of key genes in CRC tissues (n = 30) and adjacent normal tissues (n = 30). (K–T) Analysis of the overall survival (OS) and disease-specific survival (DSS) of the key genes. (*P < 0.05, **P < 0.01, ***P < 0.001).

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Then, we detected expression levels of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 in CRC tissues and paired adjacent normal tissues (Fig. 8F–J). Compared with adjacent normal tissues, the expression levels of BCL10, CDKN2A and TIMP1 were significantly increased in CRC tissues, whereas DAPK1 and PYGM expression levels were decreased (Fig. 8F–J). As expected, the expression changes of these key genes in CRC tissues were consistent with the cell results. Combined with clinicopathological features of CRC patients, BCL10 levels in CRC tissues were significantly associated with differentiation (P = 0.022) and perineural invasion (P = 0.043) (Supplementary Table 2), CDKN2A were associated with age (P = 0.001) and differentiation (P = 0.048) (Supplementary Table 3), DAPK1 were associated with tumor location (P = 0.001), dukes’ stage (P = 0.049) and lymphatic metastasis (P = 0.049) (Supplementary Table 4), PYGM were associated with differentiation (P = 0.020) (Supplementary Table 5), TIMP1 were associated with age (P = 0.010) and perineural invasion (P = 0.028) (Supplementary Table 6).

More interestingly, the expression of 5 key genes in CRC tissue are also associated with the overall survival (OS) and disease-specific survival (DSS) of patients. Kaplan–Meier survival plots were used to analyze patients’ survival data from our study, TCGA-COAD and TCGA-READ projects. Our results showed that CRC patients with low expression levels of CDKN2A, PYGM and TIMP1 had longer OS and DSS times than those in high group (Fig. 8L, N,O,Q,S,T). On the contrary, CRC patients with high BCL10 expression level had longer OS and DSS times than those in low group (Fig. 8K,P). Additionally, DAPK1 expression level was associated with OS in CRC patients (Fig. 8M), but not associated with DSS (Fig. 8R).

Discussion

CRC is a complex and heterogeneous disease with multiple steps and variations, and its pathogenesis is closely related to gene mutations, epigenetic changes, abnormal signaling pathway activation, environmental factors and genetic factors14,15,16. With development of large-scale sequencing technology, multiple key gene mutations associated with CRC occurrence and development have been discovered, including APC, KRAS and TP53, which have a high mutation frequency in CRC that can lead to abnormal activation and inactivation of key signaling pathways, such as the Wnt/β-catenin, RAS/MAPK and p53 pathways, promoting tumor cell proliferation, apoptosis escape and enhanced metastatic ability17,18. At the epigenetic level, studies have revealed numerous DNA methylation abnormalities and histone modifications in CRC, which can lead to abnormal activation and inactivation of multiple key genes, affecting transcription regulation, chromatin structure and cell function19,20. Moreover, abnormal activation of multiple signaling pathways, such as the PI3K/AKT, Wnt/β-catenin and TGF-β pathways, participates in the occurrence and development of CRC18,21. Activation of these abnormal signaling pathways can change cell growth, differentiation and metastatic ability, thereby promoting tumor development and progression. Environmental and genetic factors also play important roles in CRC occurrence22. Dietary habits, obesity, inflammatory bowel disease, genetic susceptibility and other factors are all associated with an increased risk of CRC22,23. Despite significant progress in understanding the molecular pathogenesis of CRC in recent years, the origin, development and metastasis mechanisms of CRC are still not fully understood.

Recent studies have shown that cells can execute multiple regulated cell death programs through extensive crosstalk, which can be simultaneously activated under specific conditions, establishing the concept of PANoptosis24. PANoptosis is characterized by pyroptosis, apoptosis and necroptosis, and as a form of PCD, it plays a critical role in many diseases, such as infection, cancer, neurodegenerative diseases and inflammatory diseases8,25. Studies have shown that inhibition of PANoptosis is an important cause of the unlimited proliferation, invasion and metastasis of malignant cells. The PANoptosis pathway can enhance the killing effect of immune cells on CRC. For example, loss of the PANoptosis-related molecule caspase-8 not only promotes proliferation and invasion in CRC cells but also simultaneously inhibits the killing activity of immune cells, indicating that the PANoptosis pathway plays an important role in the occurrence of CRC and immune surveillance26. Another study revealed that the PANoptosis-related molecule Gasdermin E is highly expressed in CRC, with an expression level that is closely related to the degree of malignancy and patient prognosis27,28. Further research has shown that abnormal overexpression of Gasdermin E not only promotes the proliferation, migration and invasion of CRC cells but also significantly inhibits apoptosis, playing an important role in development of CRC27,28. The above studies indicate that PANoptosis plays an important role in CRC occurrence and development; thus, in-depth study of the relationship between PANoptosis regulation and CRC is expected to promote understanding of the origin, development and metastasis mechanisms of CRC and to provide target molecules for individualized diagnosis, treatment and prognosis assessment.

In this study, 5048 differentially expressed genes (DEGs), including 2639 upregulated genes and 2409 downregulated genes, were identified by downloading CRC mRNA expression data from the TCGA database. Further intersection analysis with genes related to PANoptosis revealed 151 intersecting genes, and functional annotation revealed that these genes were mainly enriched in signaling pathways such as necroptosis, apoptosis and the NOD-like receptor signaling pathway. Finally, through random survival forest algorithm analysis, 5 key PRGs associated with progression of CRC, namely, BCL10, CDKN2A, DAPK1, PYGM and TIMP1, were identified and explored in further research.

The genes BCL10, CDKN2A, DAPK1, PYGM and TIMP1 play important roles in CRC and are closely related to its occurrence and progression, which can aid in understanding the potential molecular mechanisms involved in CRC progression. The BCL10 gene is a regulatory gene for apoptosis located at chromosome 1p22 and encodes a protein with proapoptotic activity29. A previous study revealed that during development of CRC, BCL10 undergoes inactivating mutations, resulting in loss of the proapoptotic function of the protein, laying an important foundation for subgroups of CRC cells to escape apoptosis30. The CDKN2A (Cyclin-Dependent Kinase Inhibitor 2A) gene, also known as the p16 or INK4A gene, is a tumor-suppressor gene located at chromosome 9p21. The protein encoded by CDKN2A inhibits the cell cycle process and is an important factor in cell cycle regulation31,32. The DAPK1 (Death-Associated Protein Kinase 1) gene, also considered a tumor suppressor, is located at chromosome 9q21.3. The encoded protein is a cell death-related protein kinase involved in multiple biological processes, such as apoptosis, cell cycle regulation and cell movement33,34,35. Abnormally low DAPK1 expression can lead to CRC resistance to apoptosis, promote cell proliferation, migration and invasion, and accelerate tumor progression36. The TIMP (Tissue Inhibitor of Metalloproteinases) gene family includes members such as TIMP1, TIMP2, TIMP3 and TIMP4, and expression levels of TIMP1 and TIMP2 are related to the prognosis of CRC patients37,38. Studies have shown that TIMP1 is a sensitive biomarker for metastatic colon cancer patients, and its high expression is associated with lymph node metastasis, vascular infiltration and distant metastasis in CRC patients39,40,41. Furthermore, GSEA results showed that these 5 key PRGs are involved in multiple important cell signaling and metabolic pathways, such as the PI3K/AKT/mTOR signaling pathway, Wnt/β-catenin signaling pathway, and TGF-β signaling pathway, revealing their potential molecular mechanisms in progression of CRC. Additionally, there was a significant correlation between expression levels of these 5 key PRGs and tumor progression genes related to CRC, including MSH6, BRCA2, BRCA1, MSH2, POLE, PMS2, APC, TP53, PALB2, CHEK2, POLD1, BRIP1, CDH1, AXIN2, BARD1, MUTYH, PTEN and KRAS. These results confirm the close relationship between the key PRGs we identified and development of CRC.

The 5 key PRGs identified in this study are closely related to the tumor microenvironment (TME) and drug sensitivity. The TME plays an important role in tumor formation and consists of various cell types, mainly cancer-associated fibroblasts, immune cells, the extracellular matrix, multiple growth factors, inflammatory factors and special physicochemical characteristics. These cell types have a symbiotic relationship and can nonautonomously control tumor cell proliferation, cell death, evasion of growth inhibitory genes, energy metabolism, immune evasion, angiogenesis and invasion42. Our analysis revealed significant differences in immune cells, such as naive B cells, plasma cells, activated CD8 T cells, activated memory CD4 + T cells, activated gamma delta T cells, resting NK cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting dendritic cells, resting mast cells, activated mast cells, eosinophils and neutrophils, between the normal group and CRC group (Fig. 3C). These differences correlated strongly with expression of multiple key PRGs (Fig. 3D–H), suggesting that these PRGs may play important roles in regulating immune cell function and participating in immune responses. For chemotherapy drug sensitivity, it was found that the key PRGs are generally related to sensitivity to chemotherapy drugs. Among these genes, BCL10 correlated significantly with sensitivity to AKT inhibitor VIII, cisplatin, dasatinib, gefitinib and gemcitabine (Supplementary Fig. 2A), CDKN2A with sensitivity to AKT inhibitor VIII, dasatinib and gefitinib (Supplementary Fig. 2B), DAPK1 with sensitivity to cisplatin, dasatinib, erlotinib and gefitinib (Supplementary Fig. 2C), PYGM with sensitivity to AKT inhibitor VIII, cisplatin, dasatinib, erlotinib, gefitinib, and gemcitabine (Supplementary Fig. 2D), and TIMP1 with sensitivity to AKT inhibitor VIII, cisplatin, dasatinib and gefitinib (Supplementary Fig. 2E). These findings contribute to a deeper understanding of the treatment response mechanism of CRC and provide potential biomarkers for personalized treatment and drug selection.

In recent years, nomograms have been widely used for prognosis prediction in cancer patients. Multivariate regression analysis revealed that the values of different clinical indicators and distribution of BCL10, CDKN2A, DAPK1, PYGM and TIMP1 gene expression contributed differently to the entire scoring process among all samples (Fig. 5A). Based on this, a nomogram was established to predict the 3-year and 5-year survival of CRC patients in combination with common clinical factors (Fig. 5B). The results showed that the nomogram prediction was consistent with the actual prognosis, indicating that the PANoptosis gene-related nomogram has good predictive performance.

In conclusion, this study systematically analyzed the clinical significance and potential application of PRGs in CRC. We identified 5 key PRGs associated with progression of CRC, namely, BCL10, CDKN2A, DAPK1, PYGM and TIMP1, and systematically analyzed their relationships with the TME, immune cell infiltration, chemotherapy drug sensitivity, tumor progression genes, single-cell subpopulations, signaling pathways, transcription factor regulation and miRNA regulatory networks. The findings provide new insights into understanding the pathogenesis of CRC, predicting its prognosis, and identifying multiple therapeutic targets for future treatment.