Abstract

Background

   Pyroptosis, an emerging type of programmed cell death. The mechanisms
   of pyroptosis mainly include inflammasome-activated pyroptosis and
   non-inflammasome-activated pyroptosis. Multiple prognostic scoring
   systems that utilize pyroptosis-related gene expression have been
   validated as effective predictors of patient outcomes. But the
   relationship between pyroptosis and colorectal cancer remains unclear.
   This study has established a gene signature associated with pyroptosis
   to forecast the prognosis of CRC patients.

Methods

   An analysis of 52 pyroptosis genes was conducted in both CRC and normal
   colorectal tissues, leading to the discovery of differentially
   expressed genes (DEGs). Core pyroptosis-related genes were identified
   using least absolute shrinkage and selection operator (LASSO) Cox
   regression to establish a prognostic risk score (PRS) for predicting
   CRC patient outcomes. The TCGA cohort was split into high-risk and
   low-risk groups based on the PRS, followed by Gene Ontology (GO) and
   KEGG pathway analyses. Additionally, differences in the enrichment
   scores of 16 immune cell types and the activity of 13 immune-related
   pathways were compared. The role of SPTBN5, a core pyroptosis-related
   gene, was validated through functional experiments on human colorectal
   adenocarcinoma cells (SW480).

Results

   40 differentially expressed genes were identified from 52 pyroptosis
   genes. A risk model was subsequently developed using 25 core
   pyroptosis-related genes identified through LASSO Cox regression
   analysis, and this model was validated in GEO cohorts. GO and KEGG
   pathway analyses showed that the DEGs are predominantly associated with
   mineral absorption, thyroid hormone synthesis, and pancreatic
   secretion. Functional experiments demonstrated that down-regulation of
   SPTBN5 expression through transfection led to significant decreases in
   the proliferation, migration, and clonogenicity of SW480 cells.

Conclusion

   The PRS can identify high-risk CRC patient groups and predict patient
   prognosis. SPTBN5 may present a potential therapeutic target for CRC.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-024-01691-w.

   Keywords: Pyroptosis, CRC, Prognosis, SPTBN5

Introduction

   Colorectal cancer (CRC) is the second leading cause of cancer-related
   mortality in America [[30]1]. Treatment for individual CRC patients
   predominantly relies on prognostic factors validated by prior research.
   However, the efficacy of current prognostic markers remains limited.
   For instance, Tumor-Associated Trypsin Inhibitor(TATI), a trypsin
   inhibitor that functions mainly in the pancreas, has been recognized as
   an inflammatory marker and a standalone prognostic factor for CRC, with
   elevated preoperative s-TATI levels indicating a poorer prognosis
   [[31]2, [32]3]. A recent study revealed that TATI could not serve as an
   independent prognostic biomarker following multivariate analysis of
   patient cohorts [[33]4]. Furthermore, increased TATI levels may arise
   from tissue damage, inflammation, or the tumor itself [[34]5].

   Pyroptosis, an emerging type of programmed cell death, typically
   instigated by inflammasomes and executed via gasdermin proteins. This
   process is characterized by cell swelling, membrane perforation, and
   the subsequent release of cellular contents. Under normal physiological
   conditions, pyroptosis serves a crucial role in host defense against
   pathogen infection. However, an excessive activation of pyroptosis can
   lead to uncontrolled and persistent inflammatory responses, thereby
   contributing to the onset of inflammatory diseases [[35]6]. Pyroptosis
   significantly influences the tumor microenvironment and is closely
   linked to both the initiation and progression of cancer. Research has
   shown that genes associated with pyroptosis can serve as standalone
   prognostic markers in various cancers, including ovarian cancer (OC)
   and gastric cancer (GC) [[36]7, [37]8]. In light of these findings, we
   conducted an extensive study to explore the role of pyroptosis-related
   genes and assess their prognostic value for patients with colorectal
   cancer (CRC). This was achieved by analyzing core pyroptosis-related
   genes and constructing a multi-gene risk model. Further investigation
   of Spectrin beta, non-erythrocytic 5 (SPTBN5), which was identified
   from a pool of 25 pyroptosis-related core genes using LASSO regression
   analysis, revealed its potential as a therapeutic target for CRC.

Materials and methods

Data

   RNA sequencing (RNA-seq) data along with clinical characteristics such
   as Sex, Age, TNM stage and survival information for 446 CRC patients
   were sourced from the TCGA database
   ([38]https://portal.gdc.cancer.gov/repository). Additionally, clinical
   data (Sex, Age, TNM stage and survival information) and RNA-seq data
   for the external validation cohort were retrieved from the GEO database
   ([39]https://www.ncbi.nlm.nih.gov/geo/, ID: [40]GSE40967).

Identification of differentially expressed cell pyroptosis-related genes

   52 pyroptosis genes, which were identified from the review, were listed
   in table S1 [[41]8]. GTEx data of 384 normal colon samples were
   analyzed to identify DEG between normal colon tissues and tumor
   tissues. To facilitate comparison, expression data in both datasets
   were standardized to fragments per kilobase million (FPKM). The 'limma'
   package was then used to identify DEGs (P-value < 0.05) (* for
   P < 0.05, ** for P < 0.01, *** for P < 0.001). The PPI network of the
   DEGs was generated by Search Tool for the Retrieval of Interacting
   Genes (STRING) version 11.0 ([42]https://string-db.org/).

Construction and validation of a prognostic model based on
pyroptosis-associated genes

   The correlation between each gene and the survival status of patients
   in the TCGA cohort was evaluated using Cox regression analysis to
   assess the potential value of pyroptosis-related genes in CRC
   prognosis. To enhance the reliability of our findings, we established a
   significance threshold of 0.2, which allowed us to identify 158 genes
   (Table S2) associated with survival for further analysis. Subsequently,
   we screened candidate genes using a least absolute shrinkage and
   selection operator (LASSO) Cox regression model (using the R package
   “glmnet”) and established a prognostic model. In the end, we identified
   25 genes along with their corresponding coefficients (Supplementary
   Table S4). The penalty parameter (λ) was selected based on the minimum
   criterion. After applying centralization and standardization to the
   TCGA expression data (utilizing the 'scale' function in R), the risk
   score was computed using the formula: Risk Score = \ (\mathop {\ sum}
   olimits_i^25 {Xi \times Yi} \) (where X represents the gene coefficient
   and Y corresponds to the gene expression level). CRC patients in the
   TCGA cohort were stratified into low-risk and high-risk groups
   according to the median risk score, and survival times between the two
   groups were evaluated using Kaplan–Meier analysis. Principal component
   analysis (PCA) utilizing twenty-five gene features was conducted using
   the "prcomp" function from the R package "stats". Additionally, the R
   packages "survival", "survminer", and "timeROC" were employed to carry
   out a three-year ROC curve analysis. To further validate our results,
   we utilized a CRC cohort obtained from the GEO database ([43]GSE40967).
   In the [44]GSE40967 cohort, we standardized the expression levels of
   each pyroptosis-associated gene employing the "scale" function, and
   subsequently derived risk scores for these genes by applying the
   identical formula that was utilized for the TCGA cohort. Utilizing the
   median risk score derived from the TCGA cohort as a threshold, we
   stratified patients within the [45]GSE40967 cohort into low-risk and
   high-risk subgroups. Subsequently, we conducted a comparative analysis
   between these groups to verify the gene model.

Independent prognostic analysis of risk scores

   Clinical data, encompassing age, tumor grade, and AJCC 8th edition
   staging information, alongside survival status, were extracted for
   individuals within the TCGA cohort. Subsequently, these variables were
   integrated with risk scores within the framework of both univariate and
   multivariate Cox regression models. This analytical approach aimed to
   comprehensively evaluate the predictive capabilities of risk scores,
   while also considering the potential impact of other clinical factors
   on patient outcomes.

Functional enrichment analysis of DEGs between low-risk and high-risk groups

   In the TCGA cohort of CRC patients, we stratified individuals into two
   distinct subgroups based on the median risk score as a threshold.
   Subsequently, we applied rigorous criteria (|log2FC|≥ 1 and FDR < 0.05)
   to filter out differentially expressed genes (DEGs) that exhibited
   significant variations between the low-risk and high-risk groups.
   Utilizing the "clusterProfiler" package, we conducted comprehensive GO
   and KEGG analyses to investigate the functions and pathways of these
   DEGs. The immune cell infiltration scores were evaluated by ssGSEA
   calculation through "gsva" package, thereby evaluating the
   functionality and dynamics of immunological pathways. To obtain more
   information of the immune characteristics and biological differences of
   risk grouping in CRC.

Cell culture and transfection

   SW480 cells were maintained in RPMI 1640 medium, under conditions of
   37 °C and 5% CO2. Prior to transfection, these cells were plated onto a
   twelve-well dish and allowed to proliferate for 24 h until achieving a
   70% confluency. Subsequently, transfection was performed by diluting
   lipo3000 and siRNA (SPTBN5) with 100 μl OPTI-MEM and left to stand at
   room temperature for 5 min. The siRNA and Lipo3000 were mixed and left
   to stand at room temperature for 20 min. Subsequently, the SW480 cells
   seeded in the twelve-well plate were washed with PBS and replenished
   with serum-deprived medium. Finally, the above mixture was added to a
   12-well plate.

Cellular immunofluorescence

   After culturing SW480 cells in a laser confocal dish for approximately
   48 h, they were rinsed three times with PBS. Subsequently, fixation was
   performed at ambient temperature for a duration of 15 min, utilizing
   1 ml of 4% paraformaldehyde as the fixative agent. Following this
   fixation step, the cells were again subjected to three rounds of PBS
   washing to eliminate the fixative. Lastly, permeabilization was carried
   out at ambient temperature for 15 min, employing a PBS solution that
   contained 0.2% Triton X. After another three washes with PBS, the cells
   were blocked at ambient temperature for 30 min with a PBS solution
   containing 4% BSA. Discard the blocking solution and add 100μL of
   SPTBN5 primary antibody diluted with blocking solution (ABMART,
   PA5987,1:1000), and incubate at 4℃ in a humid box for a night. After
   discarding the primary antibody, the cells were again subjected to
   three rounds of PBS washing. Under light-protected conditions, add the
   fluorescent secondary antibody solution ([46]A48254, diluted 1:250) of
   the same species as the primary antibody and incubate at ambient
   temperature for 1 h. After discarding the secondary antibody, the cells
   underwent a thorough washing procedure, involving three sequential
   rinses with PBS, to ensure the removal of any residual antibody. An
   appropriate amount of DAPI-containing mounting medium was added to
   cover the cells, and after 10 min, the stained results were scanned
   using an LSM900 laser confocal microscope.

RT-qPCR

   The specified tissue or cellular material undergoes collection,
   subsequently undergoing total RNA extraction utilizing Trizol reagent
   (Beyotime brand) subsequent to pre-cooling at 4 °C. This RNA, derived
   from either cell or tissue samples, is then subjected to reverse
   transcription, with mRNA converted to cDNA. The reverse transcription
   system is prepared according to the kit's instructions, and reverse
   transcription is performed. After the reverse transcription is
   completed, the product is placed in a −80 ℃ refrigerator. mRNA is used
   for fluorescent quantitative PCR operation according to the user manual
   of SYBR company, PCR kit, which consists of a reaction system composed
   of 10 μL: 5 μL 2 × NovoStartSYBR QPCR SuperMix Plus, 1μL cDNA template,
   0.2 μL upstream and downstream primers, 0.2 μL ROXI and 3.4 μL sterile
   water. The execution of the experimental protocol involves utilization
   of the ABI 7500 real-time PCR apparatus, sourced from ABI Corporation
   in the USA. As an internal control for mRNA quantification, β-actin
   serves as the reference gene, enabling the computation of the target
   gene's relative transcriptional level through application of the
   comparative quantification approach, specifically the 2-ΔΔCT
   methodology. The primer sequences are listed in Table S5.

Cell scratch assay

   SW480 cells were planted onto six-well plates and subjected to
   transfection procedures. Following this, a clear demarcation line was
   created on the plate's surface utilizing a 200 μl pipette tip, and
   subsequently, the medium was replenished with 2% FBS. To assess the
   migratory capacity of these cells, microscopic images were captured at
   three distinct time points: 0 h, 12 h, and 48 h post-scratching. The
   analysis of cell migration was then conducted by quantifying the extent
   of scratch closure or healing area over this period. The area was
   quantified using the software ImageJ (Version 1.53 m).

CCK8

   SW480 cells, stratified into distinct groups, underwent trypsinization
   and were subsequently dispensed into a 96-well plate, ensuring an even
   distribution at a cellular density of 6 × 10^3 cells per well.
   Following a 48-h incubation period, 10 µl of CCK8 solution was
   introduced into each well, and the plates were further incubated at
   37 °C. The OD values were measured at 6 h, 12 h, 24 h, 48 h, and 60 h
   respectively.

Plate cloning experiment

   The processed SW480 cells were disseminated onto a 6-well plate and
   subjected to cultivation until discernible clusters of colonies became
   evident. Methanol was added, followed by fixation for 20 min. Then, 1%
   crystal violet staining solution was applied for 15 min. Afterwards,
   photographs were taken, and the number of cell clones formed was
   observed.

Statistical analysis

   In order to assess the differential gene expression patterns between
   normal colon tissues and CRC tissues, a one-way ANOVA analysis was
   conducted. For the categorical variable comparisons, Pearson's
   chi-squared test was applied. Subsequently, the Kaplan–Meier survival
   curves, accompanied by two-sided log-rank tests, were employed to
   analyze the overall survival (OS) disparities among patient subgroups.
   To evaluate the independent prognostic significance of the risk models,
   both univariate and multivariate Cox regression analyses were
   undertaken. Additionally, when comparing immune cell infiltration
   levels and immune pathway activation status between two distinct
   groups, the Mann–Whitney U test was utilized. All statistical
   computations and analyses were executed utilizing the R statistical
   software package, version 4.0.2, adhering to rigorous academic
   standards. The overall flowchart is depicted as Fig. [47]1 and
   Supplementary Fig. 1.

Fig. 1.

   [48]Fig. 1
   [49]Open in a new tab

   Flowchart of all data analysis in the experiment

Results

Identification of DEGs between normal and tumor tissues

   After analyzing the GTEx and TCGA data of 384 normal and 446 tumor
   tissues, we conducted a comparative analysis of the expression levels
   of 52 pyroptosis genes, resulting in the identification of 40
   differentially expressed genes (all P < 0.01). Among them, CHMP2A,
   CHMP6, NLRP2, NLRP7, GZMA, TP63, NLRP1, NLRC4, NLRP3, TIRAP, ELANE,
   PRKACA, IL18, CASP5, GSDMB, BAK1, CASP9, CHMP3, CHMP2B, CYCS, CASP3,
   IRF2, CHMP7, a total of 23 genes, were downregulated in tumor tissues,
   and BAX, GPX4, PJVK, NOD2, NOD1, PLCG1, CHMP4C, HMGB1, CASP8, GZMB,
   IL1A, IL1B, GSDMC, IL6, CASP4, GSDMA, TP53, a total of 17 genes, were
   upregulated in tumor tissues (Fig. [50]2A) (Blue represents low
   expression level, and red represents high expression level.). In order
   to delve deeper into the interplay among these genes associated with
   pyroptosis, we conducted a protein–protein interaction (PPI) analysis.
   The lowest interaction score was set to 0.9 (highest confidence), as
   shown in Fig. (Fig. [51]2B). The hub genes were NLRP3, NLRC4, CASP3,
   CYCS, CASP8, TP53, IL18, IL1B, CHMP3, CHMP2A, CHMP7 and CHMP5. The
   network depicting the relationships among all genes associated with
   pyroptosis is illustrated in Fig (Fig. [52]2C).

Fig. 2.

   [53]Fig. 2
   [54]Open in a new tab

   Expressions of the 40 pyroptosis-related genes and the interactions
   among them. A A heatmap illustrating the expression levels of these
   genes (green: low expression level; red: high expression level) of the
   pyroptosis-related genes between the normal (N, brilliant blue) and the
   tumour tissues (T, red). P values were indicated as: **P < 0.01;
   ***P < 0.001; B The PPI network demonstrating the interactions among
   pyroptosis-related genes (interaction score = 0.9); C The correlation
   network of pyroptosis-related genes is illustrated as follows: red
   lines indicate positive correlations, while blue lines denote negative
   correlations. The intensity of the colors reflects the strength of the
   associations

Considering the tumor classification of DEGs

   To investigate the association between the manifestation of 40 DEGs
   pertaining to pyroptosis and the diverse subtypes of CRC, we conducted
   a consensus clustering approach encompassing the entire pool of 446 CRC
   patients sourced from the TCGA database. By varying the number of
   clusters (k) from 2 to 10, we observed that k = 2 yielded the highest
   intragroup correlation and the lowest intergroup correlation. The
   result suggests that 446 CRC patients could be well divided into two
   clusters based on 40 DEGs (Fig. [55]3A). The gene expression profiles
   and clinical features, including N, M, T stage of colorectal cancer,
   stage, age (≤ 60 or > 60 years old) and survival status (alive or dead)
   were visualized using a heatmap. Notably, significant differences were
   observed between the two clusters with respect to the N, M, and T
   stages of CRC and tumor stage (Fig. [56]3B). In addition, we conducted
   a comparative analysis of the OS duration between the two distinct
   clusters (Fig. [57]3C), and found obvious discrepancy (P = 0.042).

Fig. 3.

   [58]Fig. 3
   [59]Open in a new tab

   Tumor classification utilizing the expression profiles of
   pyroptosis-related DEGs. A Based on the consensus clustering matrix
   with k = 2, the 446 CRC patients were categorized into two distinct
   clusters; B Heatmap and the clinicopathologic characters of the two
   clusters classified by these DEGs; C Kaplan–Meier OS curves for the two
   clusters

Establishment of a prognostic gene model within the TCGA cohort

   In total, 446 CRC samples were matched with the homologous patients who
   had comprehensive survival information. Genes associated with
   pyroptosis were selected by univariate Cox regression analysis. The
   results showed that 158 genes (Table S2) met the criteria of P < 0.2
   and were reserved for ulteriorly investigating. Notably, 156 of these
   genes (Table S3) exhibited a positive correlation with elevated hazard
   ratios (HRs > 1), indicating an increased risk factor. In contrast, the
   remaining two genes (CCL22, FENDRR), stood out as protective factors,
   characterized by HRs less than 1 (Fig. [60]4A). A total of 25 core
   genes related to pyroptosis (IL20RB, MID2, IFITM10, LAMP5, CALB2,
   BANK1, ANGPTL4, CCL22, EGFL7, UPK3B, SPTBN5, TMPRSS11E, LINGO1, FENDRR,
   TRAF1, RNF207, ROBO3, TMEM88, GRP, SYNGR3, HOXC11, CHGB, HEYL, P2RX5,
   TOX2) were screened out by univariate Cox and LASSO Cox regression
   analysis. According to the optimal λ value, a total of 25 gene markers
   were constructed (Fig. [61]4B, C), and 446 CRC patients were divided
   into low and high risk subgroups by the median score calculated by the
   risk scoring formula (Fig. [62]4D). As verified by PCA, CRC patients
   with diverse risks were well separated into two groups (Fig. [63]4E).
   The high risk subgroup had more deaths and shorter survival times
   (Fig. [64]4F, High risk subgroups on the right of the dashed line). A
   notable disparity in OS was observed between the low risk subgroup and
   high risk subgroup (Fig. [65]4G, P < 0.001). The sensitivity and
   specificity of the prognostic model were assessed using time-dependent
   ROC analysis. The result shows that the AUC of the ROC curve was 0.800
   for one year, 0.789 for two years, 0.777 for three years (Fig. [66]4H).

Fig. 4.

   [67]Fig. 4
   [68]Open in a new tab

   Development of a risk signature within the TCGA cohort. A Univariate
   Cox regression analysis was conducted to assess OS in relation to each
   pyroptosis-associated gene, and 158 genes with P < 0.2; B Through LASSO
   regression analysis, 25 OS-related genes were screened out; C
   Cross-validation was utilized to fine-tune parameter selection in the
   LASSO regression analysis; D Patients were categorized according to
   their risk scores; E PAC plot for CRC samples was generated based on
   the risk scores; F Survival status of all patients (low-risk
   population: on the left side of the dotted line; high-risk population:
   on the right side of the dotted line); G Kaplan–Meier survival curves
   were plotted to compare (OS) between patients in the high-risk and
   low-risk groups; H ROC curves were utilized to evaluate the predictive
   accuracy of the risk score

External inspection of risk characteristics

   The validation set used 579 CRC patients from the GEO ([69]GSE40967).
   Prior to next analysis, the gene expression data was standardized using
   the "Scale" function. Utilizing the median risk score derived from the
   TCGA cohort as a benchmark, the GEO cohort was subsequently stratified,
   with 267 patients were categorized into the low risk group and the
   remaining 312 patients were allocated to the high risk group
   (Fig. [70]5A). PCA validation demonstrated that the CRC patients were
   well segregated into two distinct groups. (Fig. [71]5B). And it was
   found that low risk subgroup had fewer deaths and longer survival time
   (Fig. [72]5C, low risk subgroup on the left of the dashed line).
   Kaplan–Meier analysis showed that there was an obvious discrepancy in
   survival rate in the two subgroups (P = 0.002, Fig. [73]5D). The AUC of
   the ROC curve was 0.640 for one year, 0.607 for two years, 0.594 for
   three years. This result also indicates that our model exhibits robust
   predictive performance. (Fig. [74]5E).

Fig. 5.

   [75]Fig. 5
   [76]Open in a new tab

   Validation of risk models by GEO cohort. A Patients in the GEO cohort
   were categorized based on the median risk score derived from the TCGA
   cohort; B PCA plot for CRC samples; C The survival status of each
   patient was depicted with the low-risk population on the left side of
   the dotted line and the high-risk population on the right side; D
   Kaplan–Meier curves were generated to compare OS between the low-risk
   and high-risk groups; E Time-dependent ROC curves for CRC patients

Assessment of the independent prognostic value of risk models

   Univariate and multivariate Cox regression analyses were employed to
   determine if the risk score obtained from the gene signature model
   could act as an independent prognostic factor. Univariate Cox
   regression analysis showed that in TCGA and GEO cohorts, the risk score
   was an independent factor predicting poor survival (HR = 4.349, 95% CI
   3.288–5.752 and HR: 1.886, 95% CI 1.465–2.429) (Fig. [77]6A, C).
   Multivariate analysis further indicated that after adjusting for other
   confounding factors, the risk score remained an independent prognostic
   factor (HR: 3.452, 95% CI 2.532–4.708and HR: 1.374, 95% CI 1.091–1.731)
   (Fig. [78]6B, D) for CRC patients in both cohorts. Additionally, a
   heatmap of clinical characteristics was created for the TCGA cohort.
   (Fig. [79]6E). It was found that there were no significant differences
   in the distribution of N, M, T stages of colorectal cancer, Stage, age
   (≤ 60 or > 60 years), and survival status (alive or dead) between
   low-risk subgroup and high-risk subgroup.

Fig. 6.

   [80]Fig. 6
   [81]Open in a new tab

   Univariate and multivariate Cox regression analyses for the risk score.
   A Univariate analysis for the TCGA cohort; B Multivariate analysis for
   the TCGA cohort; C Univariate analysis for the GEO cohort; D
   Multivariate analysis for the GEO cohort; E A heatmap was generated to
   illustrate the relationships between clinicopathologic features and the
   risk groups (blue: low expression; red: high expression) (*P < 0.05)

Gene function and pathway analysis based on risk models

   Using the "limma" R package (FDR < 0.05 and |log2FC|≥ 1) to screen DEGs
   in low and high risk subgroups of the TCGA cohort, to further explore
   differences in gene function and pathways between subgroups classified
   by the risk model.Then, GO enrichment analysis and KEGG pathway mapping
   were conducted using DEGs. It is shown that DEGs were primarily related
   to Mineral absorption, Thyroid hormone synthesis, Pancreatic secretion
   (Fig. [82]7A, B).

Fig. 7.

   [83]Fig. 7
   [84]Open in a new tab

   Gene function and pathway analysis based on the DEGs between the
   high-risk and low-risk groups in the TCGA cohort. A A bubble graph was
   used to illustrate GO enrichment analysis. In the graph, larger bubbles
   indicate a greater number of enriched genes, and deeper shades of red
   signify more pronounced differences. The q-value represents the
   adjusted p-value; B A bar plot was created to display the KEGG pathway
   enrichment analysis. In this graph, longer bars indicate a higher
   number of enriched genes, while deeper shades of red represent more
   pronounced differences

Comparison of immune activity between high- and low-risk subgroups

   Based on functional analysis, the ssGSEA was used to further compare
   the enrichment scores of 16 distinct immune cell subsets and the
   activity levels of 13 pertinent immune-mediated pathways in the low
   risk subgroup and high risk subgroup in the TCGA and GEO cohorts.
   Compared with the low-risk subgroup, the levels of infiltration of
   immune cells were generally lower in the high-risk subgroup of TCGA
   cohort, especially for dendritic cells (DCs), induced dendritic cells
   (iDCs), helper T cells (Th1 and Th2), and regulatory T cells (Treg).
   Furthermore, the intensity of the T cell co-stimulation cascade and the
   type II IFN response mechanism, within the realm of immune signaling
   pathways, was markedly diminished in the high risk subgroup, as
   compared to their counterparts in the low risk subgroup (Fig. [85]8A,
   B). The same conclusion was drawn from the data analysis of the GEO
   cohort (Fig. [86]8C, D).

Fig. 8.

   [87]Fig. 8
   [88]Open in a new tab

   Immune cell and immune pathway analysis. A, B Comparison of enrichment
   scores for 16 types of immune cells and 13 immune-related pathways
   between the low-risk (blue box) and high-risk (red box) groups in the
   TCGA cohort; C, D Comparison of the tumour immunity between low- (blue
   box) and high-risk (red box) group in the GEO cohort. (P: ns not
   significant; *P < 0.05; **P < 0.01; ***P < 0.001)

Functional verification of the SPTBN5 gene

   Among the 25 survival-related core genes screened out by univariate and
   multivariate Cox regression analyses, only the SPTBN5 gene has not been
   studied in tumors. Therefore, we performed a comprehensive study of the
   SPTBN5 gene. Firstly, we conducted a study on five colorectal cancer
   cell lines (SW480, LOVO, HT29, RKO and NCM460), and detected the
   expression level of SPTBN5 gene in them by RT-qPCR and cellular
   immunofluorescence. It was found that SPTBN5 was most significantly
   expressed in colon adenocarcinoma cells (SW480) (Fig. [89]9A, B). Three
   different siRNAs (siRNA1, siRNA2, siRNA3, primer sequences are listed
   in Table S5) were used to transfect SW480 cells, and the siRNA2 with
   the most significant transfection effect was selected for subsequent
   transfection experiments (Fig. [90]9C). Immunofluorescence results
   demonstrated (Fig. [91]10A) that the Ki67 protein content in the cells
   of the NC group was markedly elevated compared to the transfection
   group. Ki67, a pivotal marker of cellular proliferation, exhibits a
   profound functional linkage to the mitotic process, rendering it an
   indispensable component in the proliferation of cells. The higher the
   Ki67 content, the more active cell proliferation will be. In addition,
   the CCK8 experiment, plate clone experiment, and cell scratch
   experiment results of SW480 transfection group and NC group proved that
   knocking down SPTBN5 by transfection would reduce the cell's
   proliferation and migration ability (Fig. [92]10B–D).

Fig. 9.

   [93]Fig. 9
   [94]Open in a new tab

   The expression difference of SPTBN5 in different colorectal cancer cell
   lines and the transfection effect of different primers. A, B SPTBN5
   mRNA expression levels in CRC cell lines; C Transfection effect of
   three SPTBN5 siRNA. (***P < 0.001)

Fig. 10.

   [95]Fig. 10
   [96]Open in a new tab

   Functional validation of SPTBN5. A Immunofluorescence assay; B CCK8
   analysis of the proliferation of SW480 cells upon the transfections; C
   The effect of SPTBN5 down-regulation on migration function was
   evaluated by scratch tests, *P < 0.05; D Colony formation in SW480
   cells was analyzed when SPTBN5 was knocked down compared with negative
   control, *P < 0.05

Discussion

   In our investigation, we delved into the mRNA expression profiles of 52
   pyroptosis genes, comparing their levels in CRC tissues against those
   in normal counterparts, and revealing differential expression in 40
   genes. A consensus clustering analysis on account of these DEGs
   successfully segregated CRC patients into low risk subgroup and high
   risk subgroup, which exhibited significant disparities in clinical
   characteristics and OS. In order to comprehensively assess the
   prognostic implications of the regulatory elements pertaining to
   pyroptosis, we formulated a risk prediction model, leveraging both
   univariate Cox and LASSO Cox regression analysis. The functional
   analysis suggested that the PRS derived from pyroptosis-related genes
   could act as an independent risk factor to identify high risk patient
   subgroup. DEGs between the two subgroups were associated with pathways
   such as mineral absorption, thyroid hormone synthesis, and pancreatic
   secretion. Furthermore, we performed an in-depth study of the SPTBN5
   gene, one of the 25 core pyroptosis-related genes identified through
   LASSO regression analysis. Our findings revealed that downregulation of
   SPTBN5 by transfection significantly decreased the proliferation,
   migration and colony formation ability of SW480 cells. These
   consequences imply the potential of SPTBN5 as a viable therapeutic
   target in the CRC.

   Pyroptosis, a novel type of programmed cellular demise, holds a pivotal
   position within the intricate tumor microenvironment, intricately
   intertwined with the onset and advancement of neoplastic processes. On
   one hand, pyroptosis can instigate apoptosis in cancer cells, thereby
   inhibiting the initiation and progression of tumors. Conversely,
   pyroptosis, a type of pro-inflammatory death, can generate a
   microenvironment conducive to tumor cell proliferation, thereby
   promoting tumor growth [[97]9–[98]11]. Furthermore, the expression
   levels of genes associated with pyroptosis, their interplay, and their
   influence on cancer patient prognosis may exhibit variations across
   different cancers. For instance, research has indicated that the
   expression of the factors associated with pyroptosis is reduced in
   hepatocellular carcinoma (HCC) [[99]12]. In a study on HCC, the
   pyroptosis-related differentially expressed genes (DEGs) between HCC
   and non-tumor tissues were identified. Using Cox univariate and LASSO
   Cox regression analysis, a risk signature consisting of 10 genes was
   established. It was found that this risk signature had predictive value
   for the prognosis of HCC patients and was verified in the GEO database
   [[100]13]. Therefore, it is pertinent to develop a risk score related
   to cellular pyroptosis for CRC patients.

   In this study, we compiled a list of 52 publicly reported pyroptosis
   genes and conducted a systematic analysis of their expression patterns
   in both normal and tumor tissues. Forty significantly differentially
   expressed pyroptosis genes were identified. Notably, several of these
   genes, including NLRP1, NLRP3, CASP5, IL18, and IL1B, have been
   established as crucial components in the cell pyroptosis triggering
   pathway [[101]14]. For example, as the core molecule of the
   inflammasome, NLRP3 expression was silenced in CRC, thereby limiting
   gasdermin D (GSDMD)-mediated pyroptosis. NLRP1 and NLRP3 are involved
   in the assembly of the inflammasome complex, which cleave caspase-1
   (CASP1) to mediate IL-18 production. IL-18 enhances epithelial barrier
   function and regeneration to resist CRC [[102]15, [103]16]. In our
   analysis results, the expression levels of NLRP1, NLRP3 and IL-18 genes
   in CRC patients were downregulated, which is consistent with the above
   research findings. Utilizing these 40 DEGS, we performed consensus
   clustering analysis to categorize CRC patients into low risk subgroup
   and high risk subgroup. Upon integrating clinical data analysis, we
   observed significant disparities in clinical features and OS between
   these two patient subgroups. This analytical approach bears resemblance
   to the research methodology employed in a study on ovarian cancer
   [[104]8].

   Prognostic scoring system based on gene expression has been established
   as effective predictors of patient outcomes [[105]17, [106]18]. To
   further establish a pyroptosis-related gene signature that can
   accurately predict the prognosis of CRC patients, we screened 25 core
   genes related to cell pyroptosis through univariate Cox and LASSO Cox
   regression analysis. Then, based on the risk score formula, we
   calculated the PRS and separated all patients into low risk subgroup
   and high risk subgroup. Consistent with our hypothesis, patients in the
   low risk subgroup had a markedly superior survival rate. And the risk
   prediction model and its independent prognostic value were validated in
   GEO and TCGA cohorts. When using the GEO cohort for risk model
   validation, the AUC value of predicting survival was lower than that of
   the TCGA cohort (0640 vs 0800 at 1 year), which may be due to the
   different clinical or biological characteristics of the GEO cohort from
   the TCGA cohort and the smaller sample size in the GEO cohort. In
   addition, we analyzed DEGs between the two subgroups. Based on our GO
   and KEGG analysis findings, it was found that DEGs were predominantly
   associated with mineral absorption pathways, thyroid hormone synthesis
   pathways, and pancreatic secretion pathways. Mineral absorption
   pathways are intricately linked to hormone synthesis pathways, and they
   play a pivotal role in the numerous diseases associated with the human
   body. For instance, in the context of thyroid hormone synthesis,
   iodine, iron, and copper emerge as indispensable elements, whereas
   selenium and zinc facilitate the conversion of T4 to T3 [[107]19].
   Iron, in particular, is an essential trace element that supports
   protein and enzyme activity across a broad spectrum of functions.
   Extensive epidemiological and experimental studys had shown that high
   levels of iron in the body have been correlated with the initiation,
   proliferation, and metastasis of malignancies [[108]20]. Iron is
   implicated in various tumor types, with colorectal cancer being the
   most prominent. Research indicates that excessive intestinal lumen iron
   can precipitate the onset and progression of colorectal cancer.
   Furthermore, prolonged exposure to iron compounds in drinking water can
   instigate and exacerbate ulcerative colitis and markedly elevate the
   prevalence of colorectal tumors. In mouse models, dietary heme has been
   shown to induce gut microbiota dysbiosis, lipid peroxidation, and
   exacerbated colitis, thereby fostering adenoma development [[109]21,
   [110]22]. Additionally, ferroptosis, a type of programmed cellular
   demise analogous to pyroptosis, has garnered increasing attention due
   to its potential association with various diseases [[111]23, [112]24].
   Thyroid hormones are instrumental in modulating normal metabolism,
   development, and growth while also promoting cancer cell proliferation.
   Several studies have pointed out that hyperthyroidism and
   supplementation with thyroid hormones (TH) are predisposing risk
   factors for CRC [[113]25–[114]27]. The thyroid hormone receptor
   interacting protein 6 (TRIP6) belongs to the LIM family and serves as
   an adaptor protein, it is overexpressed in several tumor types,
   including colorectal cancer. Numerous studies have revealed that TRIP6
   expression is markedly upregulated in CRC and correlates with tumor
   staging [[115]28, [116]29]. And the thyroid hormone T3 and its nuclear
   receptor TRα1 have certain effects on the phenotype of colon cancer
   stem cells and their response to chemotherapy. By analyzing the thyroid
   hormone status of patients, the chemotherapy treatment for CRC can be
   optimized [[117]30]. These findings align with our stratification of
   CRC patients based on PRS, wherein the high-risk subgroup exhibited
   markedly elevated tumor malignancy and reduced OS. This underscores the
   validity of the constructed risk model for colorectal cancer
   pyroptosis-related genes. Moreover, we hypothesize that the
   augmentation of the mineral absorption pathway underlies the
   enhancement of the thyroid hormone synthesis pathway, subsequently
   influencing the onset and progression of CRC. Consequently, these two
   signaling pathways may represent novel avenues for CRC treatment
   research.

   Furthermore, 25 of the core genes linked to pyroptosis have been proven
   to contribute to tumor progression. For example, IL20RB is involved in
   the JAK/STAT pathway that fosters tumor progression, and its elevated
   expression correlates with a decreased overall survival rate in
   patients [[118]31]. MID2, a ubiquitin-conjugating E2 enzyme, has been
   identified as a new interaction companion of breast cancer 1 (BRCA1).
   Elevated expression level of MID2 is intimately correlated with
   advanced clinical stages of breast cancer, as well as the T, N, and M
   staging, with all associations reaching statistical significance
   (P < 0.05) [[119]32]. CALB2 facilitates the activation of the
   TRPV2-Ca2 + -ERK1/2 signaling pathway, which induces metastasis in HCC
   cells. Additionally, research suggests that CALB2 could be a underlying
   target for gemcitabine (GEM) therapy in CRC [[120]33, [121]34].

   SPTBN5 is a core gene associated with pyroptosis. Mammalian β-spectrin
   comprises two α subunits and five β subunits. The αI subunit is
   genetically determined by SPTA1, whereas the αII subunit is encoded by
   SPTAN1. And the βI-V spectrin subunits are encoded by SPTB, SPTBN1,
   SPTBN2, SPTBN4, and SPTBN5. The occurrence of pathological mutations
   within either the spectrin α or β subunits has the potential to
   precipitate a diverse spectrum of illnesses, encompassing conditions
   such as hemolytic anemia, neurodegenerative disorders, ataxia, cardiac
   ailments, and even malignancies [[122]35]. For instance, SPTBN1, the
   most highly expressed member of the β-spectrin within the brain, is
   crucial for the structural integrity of neurons. It plays significant
   roles in axon stability and transport, synaptic transmission,
   regulation of neurite outgrowth during synaptogenesis, and proper
   myelination of Schwann cells. Studies have shown that mice lacking
   SPTBN1 in all neural progenitor cells exhibit early postnatal death,
   impaired distal cortical and cerebellar connections, spontaneous
   seizures, and motor coordination defects [[123]36, [124]37]. Loss of
   SPTBN1 activates Wnt signaling and promotes tumorigenesis and invasion
   in HCC cells in our body [[125]38]. Furthermore, SPTBN1 is implicated
   in the onset and progression of various cancers as per recent reviews
   [[126]37, [127]39]. SPTBN2, predominantly expressed in cerebellar
   Purkinje cells, plays a crucial role in the maintenance of dendritic
   architecture. It also facilitates the trafficking and stabilization of
   various membrane proteins [[128]40]. Heterozygous mutations in SPTBN2
   can cause autosomal dominant spinocerebellar ataxia 5 (SCA5), while
   homozygous mutations are thought to cause autosomal recessive
   spinocerebellar ataxia 14 (SCAR14). SPTBN2 is also implicated in
   various cancers. For instance, it exhibits a positive correlation with
   poor prognosis in lung adenocarcinoma and ovarian cancer. Furthermore,
   research has indicated that the N6-methyladenosine (M6A) modification
   of SPTBN2 is associated with tumor growth and survival in CRC patients
   [[129]41–[130]43]. SPTBN4 is predominantly localized to the axon
   initial segments (AISs) and nodes of Ranvier, and assuming a pivotal
   function in the sustenance of membrane potential stability. It also
   initiates, propagates, and regulates action potentials by interacting
   with ankyrin-G, which clusters KCNQ2/3-potassium channels and
   NaV-sodium channels [[131]44]. Homozygous or compound heterozygous
   variants in SPTBN4 can lead to neurodevelopmental disorders,
   characterized by hypotonia, neuropathy, and deafness [[132]35].

   Within the β-spectrin family, SPTBN5 is the only gene that has not been
   extensively investigated in relation to diseases. Consequently, we
   conducted a comprehensive study on the SPTBN5 gene. Our investigation
   uncovered a conspicuously elevated expression of SPTBN5 in human SW480
   cells, which surpassed both the levels observed in non-neoplastic colon
   tissue cells and those documented in other colorectal cancer cell
   lines. As a result, we selected SW480 cells for the functional
   validation of the SPTBN5 gene. The findings indicated that following
   transfection and knockdown of SPTBN5, there was a significant reduction
   in the proliferation, migration, and clonogenicity of SW480 cells.
   Therefore, we propose that SPTBN5 could be one of the potential
   therapeutic targets for CRC.

   In conclusion, our study suggests a strong correlation between CRC and
   pyroptosis. The multi-gene risk model we developed, centered on core
   genes associated with pyroptosis, demonstrates potential in predicting
   the prognosis of CRC patients and could inform personalized treatment
   strategies. Moreover, we identified the SPTBN5 gene as a potential
   therapeutic target for CRC, which could guide the development of novel
   therapies. However, further studies are needed to validate these
   findings in larger, independent cohorts, and to explore the underlying
   mechanisms of pyroptosis in CRC progression. Future research should
   also investigate the clinical applicability of the multi-gene model in
   optimizing treatment plans for CRC patients.

Supplementary Information

   [133]12672_2024_1691_MOESM1_ESM.tif^ (2.2MB, tif)

   Supplementary material 1: Fig.1 Graphic abstract of this study.
   [134]Supplementary material 2.^ (8.9KB, xlsx)
   [135]Supplementary material 3.^ (19.2KB, xlsx)
   [136]Supplementary material 4.^ (19.1KB, xlsx)
   [137]Supplementary material 5.^ (9.4KB, xlsx)
   [138]Supplementary material 6.^ (8.7KB, xlsx)

Author contributions

   GYL: writing-original draft, conceptualization; PYW: methodology, data
   curation, investigation, visualization; XNF: software; YXL: supervision
   and editing.

Funding

   This research was funded by Anhui Provincial Health Commission
   Provincial Financial Support for Youth Programs (Guangyao Li, Grant No.
   AHWJ2023A30159).

Data availability

   No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

   The study was conducted according to the guidelines of the Declaration
   of Helsinki and approved by the Ethics Committee of The Second People’s
   Hospital of Wuhu (2023-KY-010). The written informed consent was
   obtained from each patient.

Consent for publication

   All authors approved the content and submission.

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher's Note

   Springer Nature remains neutral with regard to jurisdictional claims in
   published maps and institutional affiliations.

   Guangyao Li and Pingyu Wang have contributed equally to this work.

References