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CASP5 associated with PANoptosis promotes tumorigenesis and progression of clear cell renal cell carcinoma

Cancer Cell International volume 25, Article number: 8 (2025) Cite this article

Abstract

Clear cell renal cell carcinoma (ccRCC) is a globally severe cancer with an unfavorable prognosis. PANoptosis, a form of cell death regulated by PANoptosomes, plays a role in numerous cancer types. However, the specific roles of genes associated with PANoptosis in the development and advancement of ccRCC remain unclear. Our study developed a risk model utilizing three PANoptosis-associated genes (Caspase 4 (CASP4), TLR3, and CASP5). This model demonstrated a high degree of precision in predicting the prognosis for patients with ccRCC. ccRCC patients in the high-risk group had the strongest immune cell activity, experiencing immune evasion, and might potentially derive advantages from treatment involving combined immune checkpoint inhibitors. CASP5 was highly expressed in ccRCC tissues by RT-qPCR, western blotting, and immunofluorescence. Stable CASP5 knockdown cell lines were constructed by lentivirus in vitro transfection technique. Reducing CASP5 level suppressed the growth, migration, and invasion of ccRCC cells, while encouraging cell apoptosis. In addition, the results of in vivo tumorigenesis experiments showed that down-regulating CASP5 expression inhibited the tumorigenic ability of 786-O cells. Together, the innovative risk model using PANoptosis-associated genes effectively forecasts the tumor microenvironment and survival rates for ccRCC, offering a novel approach to the early, precise diagnosis of ccRCC and the advancement of personalized treatment strategies.

Introduction

Around 85% renal cancer patients are diagnosed with renal cell carcinoma (RCC), with approximately 70% exhibiting the histological characteristic of clear cell RCC (ccRCC) [1]. Nearly 30% of patients with RCC have metastases when initial diagnosis, which poses additional challenges for subsequent treatment [2]. Due to the continuous improvement in comprehending the development mechanism of ccRCC and the constant upgrading and remodeling of immune checkpoint inhibitors, combination therapy has become the treatment standard for metastatic and advanced ccRCC and has improved the survival rate of ccRCC patients [3,4,5]. As ccRCC is a highly heterogeneous cancer, it lacks effective biomarkers for diagnosis, which prevents precise treatment and hinders improvements in its clinical efficacy. Therefore, identifying novel biomarkers of ccRCC and establishing reliable prognostic features are highly beneficial for early treatment and improving the outcome of ccRCC.

Programmed cell death (PCD) is a fundamental physiological process, which regulates embryonic development, aging, and coordinating immune responses and autoimmune reactions [6, 7]. In PCD pathways, apoptosis, necroptosis, and pyroptosis are the major three distinct pathways, involving intricate molecular mechanisms [8, 9]. The three PCD pathways are not independent but a complex intricate crosstalk among them [8]. Apoptosis is the primary type of cell death under normal physiological conditions, and it is a non-inflammatory process distinguished by the formation of apoptosomes [10]. Severing as immunologically active forms of cell death, necroptosis, and pyroptosis actively trigger the release of inflammatory factors and rupture of the cell membrane [6, 11]. PANoptosis is an inflammatory PCD activated by the simultaneous participation of apoptosis, pyroptosis, and/or necrotic apoptotic components, which emphasizes the interplay and coordination between apoptosis, necroptosis, and pyroptosis [12, 13]. PANoptosis possesses three basic features of PCD, but cannot be explained by any of them in isolation [14]. The dysfunction of PANoptosis and inflammatory responses are involved in tumorigenesis. Interferon regulatory factor 1 serves as a key upstream controller of PANoptosis, reducing cell death during colitis-related tumor development [15]. After quantifying the individual PANoptosis patterns of gastric cancer patients using the PANscore system, it has been observed that patients with a low PANscore exhibit a higher effectiveness rate of immunotherapy and a more favorable prognosis [16]. The prognosis and tumor immune landscape of colon cancer patients are analyzed by utilizing molecular clustering and prognostic features of PANoptosis [17]. However, the functions of PANoptosis-related genes in the onset and progression of ccRCC are inscrutable.

In this study, we constructed a novel prognostic model based on three differentially PANoptosis-related genes (CASP4, TLR3, and CASP5). Comprehensive bioinformatics analysis was performed to predict prognosis, diagnostic value, immune characteristics, and immunotherapy in patients with ccRCC. Furthermore, we collected tumor and adjacent normal tissues from ccRCC patients to systematically analyze the CASP5 levels. Cell lines 786-O and 769-P with consistently reduced CASP5 expression were generated using a lentiviral transfection method. The effect of CASP5 on the tumorigenesis and development of ccRCC were explored in vivo and in vitro. This systematic study provides potential insights and perspectives for treatment strategies for ccRCC.

Materials and methods

Patient cohorts

The RNA-seq data and corresponding clinicopathological features of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. The term “entire cohort” refers to the total number of 526 ccRCC patients. In addition, 526 ccRCC patients were randomly divided into the validating cohort 1 and validating cohort 2 with an approximately 7:3 by “caret” package in R software. The expression data of GSE15641, GSE16449, GSE36895, GSE40435, GSE53000, GSE53757, GSE66272, GSE68417, GSE71963 and GSE76351 were obtained from the Gene Expression Omnibus (GEO) database. A total of 66 PANoptosis-related genes were compiled from previous research, integrating gene lists from necrosis, apoptosis, and necroptosis. The genes were sourced from Reactome, AmiGO 2, and KEGG databases, with duplicates removed to finalize the list [16].

Analysis of differentially expressed genes (DEGs)

The DEGs between ccRCC tumor and adjacent normal tissues were screened using the “DESeq2” package with |Log2 (Foldchange)| > 1 and p-value < 0.05. By intersecting DEGs with PANoptosis-related genes, the PANoptosis-related DEGs were successfully identified.

Construction and evaluation of PANoptosis-related risk model

The impact of PANoptosis-related DEGs on prognosis was assessed through univariate Cox regression with the “survival” package. LASSO-Cox regression was conducted using the “glmnet” package. The “timeROC” package was employed to assess the predictive accuracy of the risk model. Utilizing the median risk score as the threshold, patients were divided into high- and low-risk groups. Survival differences between the two groups were analyzed using the Kaplan-Meier (K-M) curve method.

Construction of nomogram

Univariate and multivariate Cox regression analyses were conducted to investigate the independent impact of risk score and clinical parameters (age, stage, grade, and histological types) on prognosis. We then combined the risk score with clinical parameters into a predictive chart for overall survival (OS) using the “rms” package. The “survcomp” package was used to assess the model’s predictive power with a concordance index (C-index).

Analysis of immune-related gene signatures

We calculated the immune infiltration degrees of 28 immune signatures in the entire cohort by ssGSEA algorithm using the “GSVA” package, and the scores were standardized for each immune cell type [18]. The CIBERSORT was applied to assess infiltration levels of high- and low-risk groups [19]. Additionally, the immune microenvironment of the entire cohort was also analyzed using the ESTIMATE method by the “estimate” package, including stromal score, immune score, ESTIMATE score, and tumor purity [20]. The correlation between risk score and immune checkpoint was analyzed by the “corrplot” package.

Analysis of drug susceptibility

The immunophenoscore (IPS) was downloaded from The Cancer Immunome Atlas (TCIA) [21]. The package “oncoPredict” was utilized to calculate half inhibitory concentration (IC50) of drugs commonly employed for ccRCC.

Cell culture and establishment of CASP5 knockdown cells

The 786-O and 769-P cells were purchased from the Cell Bank of the Shanghai Life Science Institution, Chinese Academy of Sciences (Shanghai, China). Cells were cultured in RPMI 1640 medium (Keygen BioTECH, China) with 10% fetal bovine serum (FBS, ExCell Bio, China), 100 U/ml penicillin-streptomycin (Seven, Beijing, China). Cells were maintained in a cell incubator (Thermo Fisher Scientific, United States) at 37 °C with 5% CO2.

The pLKO.1 short hairpin RNA (shRNA) lentivirus system was used to generate shRNA lentivirus against CASP5 purchased from GenePharma (Shanghai, China). Cells were transduced with the solution containing 6 µg/ml polybrene (Sigma-Aldrich, Germany). CASP5 knockdown (sh-CASP5) cells were obtained by puromycin (1 µg/ml) (Solarbio, China) screening in 786-O cells and obtained by puromycin (3 µg/ml) screening in 769-P cells.

Western blotting

Proteins were extracted using RIPA buffer (Beyotime, China) and protein concentrations were determined with BCA kit (Beyotime, China). The proteins were fractionated by SDS-PAGE and transferred to a PVDF membrane (0.45 μm, Pall Corporation, USA), followed by blocking with 5% non-fat milk. The primary antibodies (anti-CASP5, Affinity Biosciences, 1:2000; anti-Cleaved caspase3, Proteintech, 1:1000; anti-Bax, Proteintech, 1:5000; anti-Bcl2, Proteintech, 1:2000; anti-GSDMD, Proteintech, 1:5000; anti-β-Actin, Proteintech, 1:8000; and anti-GAPDH, Proteintech, 1:8000) were used to incubate at 4℃ overnight. The membrane was washed with TBST and incubated with corresponding secondary antibodies for 1 hour (h). The expression of specific proteins was ultimately detected and visualized by using an ECL chemiluminescence detection system (Bio-Rad, CA, USA).

Real-time quantitative PCR (RT-qPCR)

Total RNA was extracted using TRIzol™ reagent (Invitrogen, USA) and the Prime Script RT-PCR Kit (Seven, Beijing, China) was utilized for the reverse transcription of RNA into cDNA. The amplification of cDNA is carried out using an Applied Biosystems Prism 7000 Sequence Detection System along with SYBR qPCR Master Mix. Relative fold changes of CASP5 gene amplification were calculated according to 2−ΔΔCt. The primers used were listed:

CASP5: forward, 5’-TCACCTGCCTGCAAGGAATG-3’ and reverse, 5’-TCTTTTCGTCAACCACAGTGTAG-3’; GAPDH: forward, 5’-GGAGCGAGATCCCTCCAAAAT-3’ and reverse, 5’-GGCTGTTGTCATACTTCTCATGG-3’.

Cell counting kit-8 (CCK-8) assay

A total of 3,000 cells per well were planted into 96-well plate and cultured for various periods: 0, 12, 24, 48, and 72 hs. Then, 10 µl CCK-8 reagent (Beyotime, China) was added and incubated for an additional 2 hs. Subsequently, the absorbance at 450 nm was measured.

Cell colony formation assay

A total of 2,000 cells were plated in each well of a 6-well plate and grown for two weeks. After, the cells were fixed for 30 min and then dyed with 0.1% crystal violet (Solarbio, China) for 30 min. The cell colonies were subsequently imaged and counted.

Transwell assays

For invasion assay, the upper chamber was treated with 40 µl of matrigel (Corning, USA) and incubated for 2 hs at 37℃, while the migration test omitted this step. A total of 2 × 104 cells in 200 µl of FBS-free medium were added in the upper chamber of a 24-well Transwell (Jet Biofil, China), with 550 µl of medium containing 10% FBS in the lower chamber. After 48 hs, the upper chamber was cleaned with PBS, fixed with 4% paraformaldehyde, and stained with 0.1% crystal violet (Solarbio, China) for 20 min. The cells were then photographed and counted.

Wound healing assay

Approximately 5 × 105 cells were plated in each well of 6-well plates and allowed to reach complete confluence. A 200 µl sterile pipette was used to create a wound. The cells were incubated for 6 hs for 786-O cells and 24 hs for 769-P cells in FBS-free medium, after which images of the same areas were taken.

Cell apoptosis assay

Apoptosis levels were measured using a FITC-Annexin V/propidium iodide (PI) apoptosis assay kit (Beyotime, China). Cells (1 × 105) from the negative control (NC) and sh-CSAP5 groups were respectively harvested, rinsed thrice with PBS, and re-suspended in 500 µl binding buffer. The cell suspension was then mixed with 5 µl FITC-Annexin V and 10 µl PI, and incubated for 10 min in the dark at room temperature. Cells were analyzed using flow cytometer (AccuriTM C6 Plus Flow Cytometer, BD, USA). Apoptotic rates were determined using FlowJo software.

Tissue immunofluorescence

After deparaffinized, hydrated, and blocked, tissue sections were incubated overnight at 4 °C with anti-CASP5 (1:250, Affinity Biosciences, China) and anti-Ki67 (1:200, Affinity Biosciences, China) antibodies. Sections were incubated with Alexa Fluor 594-conjugated goat anti-rabbit IgG (1:500, Beyotime, China) for 1 h at 37 °C and the nucleus was stained with 2-(4-Amidinophenyl)-6-indolecarbamidine (DAPI) dihydrochloride (Beyotime, China). Finally, the slices were observed and imaged under a fluorescence microscope (Leica, Germany).

Terminal Deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay

The DNA fragmentation was assessed using an in-situ apoptosis detection kit (Beyotime, China). After deparaffinized, hydrated, and blocked, tissue sections were treated with proteinase k for 20 min before being exposed to a reaction mix containing fluorescein-labeled dUTP. Nuclei were subsequently stained with DAPI. The apoptosis rate was visualized and captured using a fluorescence microscope (Leica, Germany).

Xenograft tumor model

A total of 1 × 107 of NC and sh-CASP5 786-O cells were suspended in 100 µl PBS and subcutaneously injected into six-week-old male athymic nude mice (Beijing vital river laboratory animal technology, China). The nude mice were sacrificed on the 28th day. Tumor size in mice groin was measured using a vernier caliper and volumes were calculated applying the formula: 1/2 × (length × witdth2). All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Dalian Medical University (No: AEE19081), and performed in accordance with the University Policies on the Use and Care of Animals.

Statistical analysis

All statistical analyses and visualization were performed with GraphPad Prism 8.0, R 4.1.3, Image J, FlowJo, and SPSS 26.0. Data were presented as mean ± standard deviation (SD) from at least 3 repeated experiments. Student’s t-test was analyzed for comparison between the two groups. One-way ANOVA and two-way ANOVA were used between multiple groups to evaluate whether there were statistical differences between the groups. Differences with p < 0.05 were considered statistically significant.

Results

Construction and identification of prognostic models based on PANoptosis-related DEGs

To explore the role of PANoptosis-related DEGs in ccRCC, we screened 2,091 down-regulated genes and 3,800 up-regulated genes (Tumor versus Normal) from the entire cohort (Fig. 1A and Figure S1A). A total of 16 intersecting genes were screened from DEGs and PANoptosis-related genes (Fig. 1B). Additionally, the expression of 16 PANoptosis-related DEGs was increased in tissues of ccRCC (Fig. 1C). We identified 11 PANoptosis-related DEGs that were associated with patients’ OS through univariate Cox regression (Fig. 1D). A prognostic risk model was established based on three DEGs (CASP4, TLR3, and CASP5) utilizing LASSO-Cox regression analysis (Fig. 1E and F) and the high-risk group has worse OS (Fig. 1G). ROC curves were used to evaluate the prediction efficiency, showing area under the curve (AUC) values were 0.7184 in the first year, 0.6497 in the third year, and 0.6893 in the fifth year (Fig. 1H), indicating that this risk model had good prognostic predictability.

Fig. 1
figure 1

Construction and identification of prognostic models in ccRCC. (A) Histogram of differentially expressed genes in tumor tissues compared to normal tissues, with |Log2 (Foldchange)| > 1 and p-value < 0.05. (B) The Venn diagram showed the overlap between DEGs and PANoptoosis-related genes. (C) A volcano diagram of 16 PANoptosis-related DEGs in ccRCC patients from entire cohort. (D) Forest plot of the 16 PANoptosis-related DEGs associated with OS based on univariate Cox regression analysis. (E) Cross-validation using LASSO-Cox regression in entire cohort. (F) Coefficient profiles in LASSO-Cox regression model. (G) Kaplan–Meier analysis for OS curves of patients from entire cohorts in low- or high-risk subgroups. (H) Time-dependent ROC curves for predicting 1-, 3-, and 5-year OS in patients from entire cohort

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To ascertain the prognostic value, the entire cohort was randomly assigned to validating cohort 1 and validating cohort 2 with an approximately 7:3 ratio. Similar to the entire cohort, the number of fatalities and patients with high risk got higher as risk scores became higher in both validating cohort 1 and validating cohort 2 (Figure S2A and S2B). The K-M curve also revealed that the OS of patients in the high-risk group was shorter than those in the low-risk group (Figure S2C and S2D). The AUC in both groups was greater than 0.64 (Figure S2E and S2F). Taken together, the above results showed that the risk model based on CASP4, TLR3, and CASP5 had precisely predictive and discriminative capacity for ccRCC patients.

Analysis of independent prognostic value and construction of prognostic nomogram

To explore the correlation between the prognostic risk model and clinical characteristics, we analyzed risk score distribution across various clinicopathological stratifications (Fig. 2A). Next, ccRCC patients with higher grades (G) (G3 and G4), higher stages (stage ii, iii, and iv), distance metastasis (M) (M1), and tumor size (T) (T2, T3, and T4) had higher risk score (Figure S3A). However, no significant differences in risk scores were observed in age, gender, or node (N). Furthermore, a subgroup analysis of clinical parameters indicated that the high-risk group experienced inferior outcomes across various strata, including age, gender, tumor grade, and stage categories (Figure S3B).

Fig. 2
figure 2

Construction and identification of nomogram model. (A) The landscape of three PANoptosis-related DEGs (CASP4, TLR3, and CASP5), risk model, and clinicopathological features in ccRCC patients from entire cohort. (B) The relationship between risk score, clinical features and OS were analyzed by univariate and multivariate Cox regression. (C) The corresponding nomogram was constructed based on the risk model and clinical characteristics, which predicted 1-, 3-, and 5-year OS in ccRCC patients from entire cohort. (D) The calibration plots showed the comparison between predicted and actual OS for 1-, 3-, and 5-year survival probabilities in ccRCC patients from entire cohort

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To enhance the accuracy of predicting outcomes, we assessed the prognostic significance of risk model and different pathological parameters using univariate and multivariate Cox analysis. The results showed that risk score, age, grade, stage, M, and T serve as independent prognostic factors of ccRCC (Fig. 2B). The C-index (0.767) of nomogram that we constructed was reliable and accurate (Fig. 2C). The calibration curve and actual observed values on the nomogram demonstrate a satisfactory overlap, implying that this prognostic risk model has superior predictive abilities for prognosis (Fig. 2D).

Patients in the high-risk group have a higher level of immune infiltration

In light of tumor microenvironment (TME) in regulating tumorigenesis, we investigated whether the risk model was associated with TME (Fig. 3A). Next, immune score in high-risk group surpasses those the low-risk group, indicating a noteworthy activation of immune status in high-risk group (Fig. 3B). The fraction of most immune cells was higher in high-risk group, whereas immature dendritic cells and neutrophils were higher in low-risk group (Figure S4A). Meanwhile, patients with high risk had higher proportion of immune cells by CIBERSORT (Fig. 3C). Major histocompatibility complex (MHC) expression in high-risk group was often higher than that in low-risk group, indicating a correlation between risk score and antigen presentation function (Figure S4B). The ccRCC patients in high-risk group had a more active immune status and typically experienced greater infiltration of immune cells, specifically demonstrated through the activation of T cells.

Fig. 3
figure 3

Evaluation of immune infiltration landscape in risk model. (A) Heatmap represents the relationship between immune infiltration landscape and risk group. (B) Differences of the stromal score, immune score, ESTIMATE score, and tumor purity in low- and high-risk groups from entire cohort. (C) The proportion of 22 immune cells in low- and high-risk groups was analyzed by CIBERSORT. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001

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Estimation of immunotherapy and chemotherapy responses

We firstly evaluated classical immune checkpoints (ICPs) expression, including CTLA4, LAG3, PD1, TIGIT, CD80, CD86, CD28, and GZMB, which were up-regulated in high-risk group (Fig. 4A and B). The high expression of ICPs groups tended to have worse prognosis (Figure S5A). Patients in high-risk groups might be more sensitive when applying ICP inhibitors. The IPS scores were used to assess the immune system activation status of immunotherapy in different risk populations [22]. There was no significant difference in IPS between the two groups. However, patients in high-risk group, who received single CTLA4 blocker treatment, PD1 blocker treatment, or CTLA4/PD-1 combined therapy, could have a good response (Fig. 4C). Research suggested that high-risk patients were more suitable for combination therapy with ICP inhibitors. Next, we predicted the chemotherapy response based on the Genomics of Drug Sensitivity in Cancer (GDSC) database. Patients in low-risk group demonstrated a lower response to vinblastine, cisplatin, and rapamycin than high-risk group. The response to daporinad, BI-2536, and PD173074 was reduced in high-risk group (Fig. 4D). Together, the findings indicated that individuals across various risk categories exhibited distinct responsiveness to immunotherapy and chemotherapy, underscoring the potential for tailored treatment approaches in the management of ccRCC.

Fig. 4
figure 4

Estimation of immunotherapy and chemotherapy responses. (A) The relative expression of eight classical ICPs (CTLA4, LAG3, PD1, TIGIT, CD80, CD86, CD28, GZMB) in low- and high-risk groups. (B) Heatmap showing correlations between risk score and eight ICPs. Blue represents the positive correlation. (C) The relative expression of immunotherapy response with PD1-blocker, CTLA4-blocker, and CTLA4-PD1-blocker in low- and high-risk groups. (D) Drug sensitivity analysis for low- and high-risk groups. ****p < 0.0001

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The expression of CASP5 was up-regulated in ccRCC tissues

Furthermore, as a good candidate for the prognostic risk model, CASP5 mRNA level was increased in ccRCC tissues in the entire cohort (Fig. 5A). The ccRCC patients with high CASP5 expression have worse OS (Fig. 5B). The ROC results showed that CASP5 has a favorable predictive value (Fig. 5C). Additionally, the CASP5 levels were up-regulated in ccRCC in different GEO datasets (Fig. 5D). Moreover, a notable up-regulation of CASP5 expression was observed in correlation with the advancement of tumor grade (Fig. 5E) as well as the progression of disease stage (Fig. 5F). Next, we investigated CASP5 expression in tumor tissues by western blotting, RT-qPCR, and immunofluorescence. As shown in Fig. 5G, CASP5 protein and mRNA levels in ccRCC tissues were higher than adjacent normal tissues. The same results were confirmed by tissue immunofluorescence (Fig. 5H).

Fig. 5
figure 5

Comparison of CASP5 between healthy control and ccRCC patients. (A) The relative mRNA level of CASP5 between normal and tumor in ccRCC patients from entire cohort. (B) The K-M curve analysis between high- and low-expression of CASP5. (C) Evaluation of the prognostic utility of CASP5 using ROC curves. (D) The differential expression of CASP5 in normal and tumor in ccRCC patients from GEO databases. (E) The relative mRNA level of CASP5 in normal, benign, low grade, and high grade among ccRCC patients from the GSE68417 database. (F) The relative mRNA level of CASP5 in normal, low stage, and high stage among ccRCC patients from the GSE71963 database. (G) Expression of CASP5 protein and mRNA in tumor (T) tissues and adjacent normal (N) tissues were analyzed by western blotting and RT-qPCR. (H) The expression of CASP5 protein in tumor tissues and adjacent normal tissues was analyzed by immunofluorescence. Scale bar: 100 μm

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CASP5 knockdown suppressed the proliferation, migration, and invasion of ccRCC in vitro and in vivo

As the expression of CASP5 in ccRCC tissues was significantly increased, we wondered whether it promoted the occurrence and development of ccRCC. Stable down-regulated CASP5 cell lines were constructed using lentiviral infection in vitro, and knockout efficiency was verified by RT-qPCR (Fig. 6A) and western blotting (Fig. 6B). Knockdown CASP5 was found to restrain the proliferation, migration, and invasion of 786-O and 769-P cells (Fig. 6C, D, E and F), while knockdown CASP5 promoted the apoptosis versus to NC group (Fig. 6G). We have validated the expression of Caspase3, Bax, and Bcl2 (Markers associated with apoptosis), and GSDMD (Marker associated with pyroptosis). We find that knockdown of CASP5 seems to be less strongly linked to the apoptosis pathway mediated by Caspase3, but more closely related to the Bax/Bcl2-induced apoptosis pathway. For cell pyroptosis, knockdown of CASP5 could also promote the up-regulation of GSDMD protein, thereby promoting the occurrence of pyroptosis (Figure S6A). Taken together, we believe that knocking down CASP5 indeed induces PANoptosis in ccRCC.

Fig. 6
figure 6

The effect of CASP5 on the occurrence and development of ccRCC. (A) RT-qPCR was used to verify CASP5 knockdown in 786-O and 769-P cells. (B) Western blotting was used to verify CASP5 knockdown. (C) CCK-8 assay was used to evaluate the effect of CASP5 knockdown on cell proliferation. (D) Cell colony formation assay showed that downregulation of CASP5 inhibited the proliferation of 786-O and 769-P cells. (E) Transwell assay was used to evaluate the effect of CASP5 knockdown on migration and invasion of 786-O and 769-P cells, scale bar: 500 μm. (F) The migration ability in different groups were detected by wound healing, scale bar: 500 μm. (G) The representative images of the apoptosis rate in each group were detected by flow cytometry

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To explore the influence of CASP5 on ccRCC in vivo, we further injected the NC and sh-CASP5 786-O cells into athymic male nude mice and found that the tumor volumes and weights in sh-CASP5 group were lower than that in NC group (Fig. 7A, B and C). In tumor tissues injected with sh-CASP5 cells, the expression level of CASP5 was significantly reduced (Fig. 7D and E). The Ki67 staining showed that the proliferation capacity of sh-CASP5 group was reduced (Fig. 7F). The apoptosis rate in sh-CASP5 group was increased compared to NC group, as evidenced by TUNEL analysis (Fig. 7G). These results suggested that knockdown CASP5 could inhibit tumor growth and progression of ccRCC in vivo and in vitro.

Fig. 7
figure 7

Knockdown CASP5 inhibited tumorigenicity of ccRCC in vivo. (A) Negative control (NC) and sh-CASP5 786-O cells were respectively injected into nude mice to detect tumor formation in vivo. (B) The visual characteristics of tumors were compared between NC and sh-CASP5 groups. (C) The tumor weights of NC and sh-CASP5 groups. (D) The protein and mRNA levels of CASP5 in tumor tissues of NC and sh-CASP5 groups were analyzed by western blotting and RT-qPCR assays. (E) The representative images of CASP5 in NC and sh-CASP5 groups were detected by immunofluorescence. (F) The representative images of Ki67 in NC and sh-CASP5 groups were detected by immunofluorescence. (G) TUNEL assay was used to evaluate the apoptosis rate of tumor tissues in NC and sh-CASP5 groups. Scale bar: 100 μm

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Discussion

Accounting for around 80% of RCC, ccRCC is a common histopathological type with highly malignant tumor [23]. Due to its insensitivity to chemotherapy and radiotherapy, the prognosis of ccRCC is often poor, and surgical resection is the first-line therapy for clinical therapy. However, the mortality rate of postoperative patients is still high [24]. Moreover, due to the heterogeneity and complexity of pathophysiology, ccRCC patients are involved in high risk of recurrence and metastasis, which severely affects the quality of human life [25]. Consequently, it is urgent to explore novel biomarkers for diagnosis and treatment. PANoptosis is defined as an inflammatory cell death pathway that is activated by specific triggering factors and regulated by the PANoptosome complex, exhibiting features of pyroptosis, apoptosis, and necroptosis, explaining biological phenomena that cannot be solely explained by a single cell death pathway [11, 26]. PANoptosis is a coordinated system where any of the three PCD pathways could compensate for the others, and they work together at different times in response to stimuli in the tumor microenvironment [27]. Considering that PANoptosis might regulate tumorigenesis, we explored the potential diagnostic values and immunotherapeutic effects of PANoptosis-related genes on ccRCC. A total of 66 PANoptosis-related genes were obtained from previous study, in which 16 PANoptosis-related genes were increased in ccRCC tissues. Three PANoptosis-related DEGs (CASP4, TLR3, and CASP5) were utilized to construct a prognostic risk model, having great clinical applicability as it could accurately predict ccRCC patient’s prognosis. Furthermore, it effectively applies to various stages, grades, gender, and age of ccRCC patients.

The levels of CD8+ T, activated CD4+ T, activated B cells, and other immune cells were increased in high-risk group. Unlike most other types of cancer, RCC patients with increased expression of CD8+ T cells might have poor prognosis [28, 29]. According to previous research, infiltrating CD8+ T cells in ccRCC were in a dysfunctional state and contributed to the development of immune evasion [30]. This explains why patients in high-risk group display high CD8+ T cells and, conversely, have a poor prognosis. Patients of high-risk group exhibit a substantial expression of regulatory T cells, which belong to a specific type of T cells known for their potent immunosuppressive capabilities in dampening the immune response of other cells [31]. There was a positive relationship between risk score and ccRCC progression and a negative association with prognosis. Nonetheless, the high-risk group with a worse prognosis demonstrated increased levels of tumor-infiltrating immune cells, indicating that the patient’s immune function was further suppressed as the tumor grew, possibly due to immune escape in ccRCC.

ccRCC is a heterogeneous tumor with resistance to radiotherapy and chemotherapy [32]. It is essential to discover new potential targets to effectively treat ccRCC. Serving as immune inhibitory molecules, ICPs are expressed in immune cells, regulating immune activation [28, 33]. Excessive activation of ICPs could lead to immune cell dysfunction, thereby promoting the immune escape of tumors [34]. In our study, most ICPs, including PD-1, CTLA4, and LAG-3, increased in high-risk group, inhibiting immune cells’ function and interfering with tumor clearance. These findings further indicated that tumor immune escape occurred in high-risk groups. ICP blockade improved the prognosis of ccRCC patients and the combination of ipilimumab and nivolumab improved the survival of patients with advanced RCC [35]. We examined IPS under different immunotherapy regimens by using the TCIA database and found that patients in high-risk groups might be more sensitive to immunotherapy with CTLA4, PD1 and, CTLA4 + PD1 treatment. We also predicted the chemotherapy response of low- and high-risk groups to common chemotherapy agents. These results indicated that prognostic risk model of CASP4, TLR3, and CASP5 might have provided strong support for guiding precision treatment strategies for ccRCC.

Furthermore, it was reported that CASP4, TLR3, and CASP5 contributed to the risk model of various types of cancer. CASP4 was involved in cellular processes such as inflammation and apoptosis [36]. Although the specific mechanism remains unclear, up-regulation of CASP4 led to tumor resistance, and ccRCC patients with high CASP4 had a worse prognosis and knockdown of CASP4 inhibited the proliferation, migration, and invasion of ccRCC cells [37, 38]. Moreover, CASP4 in pancreatic cancer was higher than normal tissues, and when CASP4 expression was inhibited, the invasion and migration capacities of pancreatic cancer cells were reduced [39]. TLR3 was highly expressed in ccRCC, with only limited expression in a panel of normal tissues [40]. Although the high level of TLR3 in the TCGA database was associated with a favorable prognosis in ccRCC, further validation studies confirmed that elevated TLR3 expression in ccRCC tissues was correlated with poorer OS [41]. CASP5 is also an enzyme that cleaves proteins on aspartic acid residues. Although the biological function of CASP5 protein is poorly understood, it acts in various aspects of inflammation [42]. Inflammatory caspases, including CASP1 (human and mice), CASP4 and CASP5 (human), as well as CASP11 (mice), are activated during an inflammatory form of innate immune PCD known as pyroptosis (III-PCD) [43]. Unlike CASP1, CASP4, CASP5, and CASP11 could activate pore-forming molecule gasdermin D (GSDMD) without the upstream formation of an inflammasome upon sensing pathogen-associated molecular patterns such as lipopolysaccharide (LPS) [44, 45]. These caspases undergo dimerization, oligomerization, and self-cleavage, and are activated following binding to cytosolic LPS, leading to atypical inflammasome activation and CASP1 activation in a cell-intrinsic manner, which induces the maturation of IL-1β and IL-18 [46]. The maturation and release of these pro-inflammatory cytokines endow these cysteine-aspartate proteases with “inflammatory” characteristics. Additionally, the expression of CASP5 was downregulated in the highly metastatic lung cancer [47]. In our research, we found that CASP4, TLR3, and CASP5 were increased in tumor tissues of ccRCC patients.

Prior research has typically centered on individual pyroptosis molecules or specific cell types within the tumor microenvironment. However, in recent years, there has been a shift in focus towards genes that exert diverse impacts and are associated with various tumor characteristics. Through systematic bioinformatics examination, the researcher identified three pyroptosis subtypes within ccRCC and developed a model reliant on pyroptosis-associated long noncoding RNA (lncRNA) to forecast immune infiltration, therapeutic response, and prognostic outcomes in ccRCC [48]. Furthermore, the researcher developed a PANoptosis-related microRNA profile and elucidated its potential implications in terms of clinical and pathological characteristics as well as tumor immunology [49]. In the present study, based on three PANoptosis-related genes, we established a novel prognostic risk model to predict its role in immune invasion, therapeutic effect, and prognosis of ccRCC. More importantly, we established 786-O and 769-P cell lines of stable knockdown CASP5. Through a series of molecular biology experiments and tumor formation experiments in nude mice, we found that CASP5 knockdown could obviously inhibit the tumorigenesis and progression of ccRCC.

Conclusion

In this study, we established a novel prognostic risk model based on three PANoptosis-related genes (CASP4, TLR3, and CASP5), which had well diagnostic sensitivity and prognostic value in ccRCC. Patients in high-risk group had worse prognosis and more infiltration of immunosuppressive cells that were conducive to immune escape and tumor progression. We predicted the response of low- and high-risk groups to current immunotherapy and chemotherapy treatment agents, providing new ideas for clinical guidance of individualized immunization and targeted therapy strategies for patients with ccRCC. Down-regulated CASP5 inhibited the growth, migration, and invasion of ccRCC cells.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ccRCC:

Clear cell Renal Cell Carcinoma

CASP:

Caspase

PCD:

Programmed Cell Death

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

DEGs:

Differentially Expressed Genes

K-M:

Kaplan-Meier

OS:

Overall Survival

ROC:

Receiver Operating Characteristic

C-index:

Concordance index

IPS:

Immunophenoscore

TCIA:

The Cancer Immunome Atlas

IC50:

Half Inhibitory Concentration

shRNA:

short hairpin RNA

CCK-8:

Cell Counting Kit-8

PI:

Propidium Iodide

NC:

Negative Control

DAPI:

2-(4-Amidinophenyl)-6-indolecarbamidine

TUNEL:

Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End Labeling

AUC:

Area Under the Curve

G:

Grades

M:

Metastasis

T:

Tumor Size

N:

Node

TME:

Tumor Microenvironment

MHC:

Major Histocompatibility Complex

ICPs:

Immune Checkpoints

GDSC:

Genomics of Drug Sensitivity in Cancer

lncRNA:

long noncoding RNA

RT-qPCR:

Real-Time quantitative PCR

GSDMD:

Gasdermin-D

LPS:

Lipopolysaccharide

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Acknowledgements

Not applicable.

Funding

This work was supported by the Dalian life and health field guidance program project (No.2022ZXYG23) and National Natural Science Foundation of China (No.82201817) and Youth Talent Cultivation Fund Project of Dalian Medical University (No.508027).

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Author notes

  1. Kangkang Yang, Yushuang Wang and Yuli Jian contributed equally to this work and shared first authorship.

Authors and Affiliations

  1. Institute for Genome Engineered Animal Models of Human Diseases, National Center of Genetically Engineered Animal Models for International Research, Dalian Medical University, 9 West Section Lvshun South Road, Dalian, 116044, China

    Kangkang Yang, Meihua Guo & Ning Wang

  2. Department of Clinical Laboratory, Central Hospital of Dalian University of Technology, 826 Southwest Road, Dalian, 116033, China

    Yushuang Wang, Bo Wang & Zhi Li

  3. Liaoning Provincial Core Lab of Glycobiology and Glycoengineering, College of Basic Medical Sciences, Dalian Medical University, 9 West Section Lvshun South Road, Dalian, 116044, China

    Yuli Jian

  4. Department of Urology, Central Hospital of Dalian University of Technology, 826 Southwest Road, Dalian, 116033, China

    Hao Du

  5. Pharmaceutical sciences, Massachusetts College of Pharmacy and Health Science University, 179 Longwood Avenue, Boston, Massachusetts, 02115, USA

    Yuqing Xia

  6. Department of Obstetrics and Gynecology, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Dalian, 116023, China

    Jianlei Bi

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Contributions

N.W. and Z.L. conceived the project and designed the research. K.K.Y., Y.S.W., and Y.L.J., wrote manuscript, analyzed data, and generated the figures. Y.S.W., B.W., and H.D. collected the clinical samples. Y.Q.X. revised the manuscript and improved the quality of the language in the manuscript. J.L.B. and M.H.G. performed western blotting for Supplementary data. All authors reviewed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Zhi Li or Ning Wang.

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The study was approved by the Ethics Committee of the Affiliated Central Hospital of Dalian University of Technology (Dalian Central Hospital) (No. YN2024-053-01). All the study subjects provided informed consent.

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Yang, K., Wang, Y., Jian, Y. et al. CASP5 associated with PANoptosis promotes tumorigenesis and progression of clear cell renal cell carcinoma. Cancer Cell Int 25, 8 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-024-03630-9

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Cancer Cell International

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