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ORIGINAL ARTICLE
Year : 2022  |  Volume : 18  |  Issue : 7  |  Page : 1952-1960

CASP4 and CASP8 as newly defined autophagy-pyroptosis-related genes associated with clinical and prognostic features of renal cell carcinoma


Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China

Date of Submission15-Jan-2022
Date of Decision27-Jun-2022
Date of Acceptance11-Jul-2022
Date of Web Publication11-Jan-2023

Correspondence Address:
Guangyuan Zhang
Department of Urology, Affiliated Zhongda Hospital of Southeast University, No. 87 Dingjiaqiao, Gulou District, Nanjing - 210 009
China
Ming Chen
Department of Urology, Affiliated Zhongda Hospital of Southeast University, No. 87 Dingjiaqiao, Gulou District, Nanjing - 210 009
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.jcrt_126_22

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 > Abstract 


Objective: The rapid discoveries of autophagy and pyroptosis have opened new avenues for treating renal cell carcinoma (RCC). The objective was to identify potential autophagy-pyroptosis-related drug targets and plausible prognostic biomarkers crucial for disease detection.
Materials and Methods: Gene expression data were downloaded from Gene Expression Omnibus (GSE168845), and autophagy-pyroptosis-related differentially expressed genes (DEGs) were identified. The prognostic values of DEGs were assessed using differential expression analysis and Kaplan–Meier curves, a prognostic nomogram was constructed using the DEG data, and the correlation between DEGs and infiltrating immune cells was evaluated. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) were carried out to verify the expression levels of DEGs.
Results: CASP4 and CASP8 were identified as RCC-associated autophagy-pyroptosis-related genes, and CASP4 and CASP8 were found to be highly expressed in RCC tumor tissues. High expression of CASP4 and CASP8 was associated with higher pathological staging and poorer prognosis, whereas a prognostic nomogram constructed based on CASP4 and CASP8 could better predict RCC patient survival rates. Additionally, increased expression of CASP4 and CASP8 was highly correlated with the expression levels of multiple infiltrating immune cell types. Moreover, qRT-PCR and IHC validated the increased expression of CASP4 and CASP8 in RCC.
Conclusion: CASP4 and CASP8 were autophagy-pyroptosis-related genes associated with immunotherapy in RCC. CASP4 and CASP8 were identified as potential targets and effective prognostic biomarkers for RCC.

Keywords: Autophagy, biomarkers, nomogram, pyroptosis, renal cell carcinoma


How to cite this article:
Li T, Liu N, Zhang G, Chen M. CASP4 and CASP8 as newly defined autophagy-pyroptosis-related genes associated with clinical and prognostic features of renal cell carcinoma. J Can Res Ther 2022;18:1952-60

How to cite this URL:
Li T, Liu N, Zhang G, Chen M. CASP4 and CASP8 as newly defined autophagy-pyroptosis-related genes associated with clinical and prognostic features of renal cell carcinoma. J Can Res Ther [serial online] 2022 [cited 2023 Jan 27];18:1952-60. Available from: https://www.cancerjournal.net/text.asp?2022/18/7/1952/367459




 > Background Top


Renal cell carcinoma (RCC) is among the most common malignant tumors of the urinary system. According to the cancer-related statistics, it is predicted that there will be nearly 73,750 new cases and 14,830 deaths in the United States in 2020.[1],[2] Despite breakthroughs in the diagnosis, treatment, and prognosis of RCC, the detection rate in patients with RCC is still unsatisfactory, with approximately 30% of patients experiencing tumor recurrences after being considered disease-free.[3],[4],[5] At present, the pathogenesis of RCC has not yet been elucidated, and no sensitive tumor biomarkers, which could help identify the disease well in advance, have been identified.[6] Therefore, it is crucial to identify effective therapeutic targets and promising prognostic biomarkers for the timely detection of RCC.

Autophagy is a dynamic process of catabolism in cells, which is considered an expression of programmed cell death.[7],[8] With the understanding of the pathogenesis of RCC and the underlying mechanism of autophagy, the quantum of research on autophagy and RCC has increased drastically.[9] Numerous studies indicate that the expression of LC3 protein was higher in moderately and poorly differentiated RCC, lower in differentiated RCC, and even lower in progressive and metastatic RCC, as compared to a localized RCC.[10] Autophagy regulation could be closely related to the development and progression of RCC.

Pyroptosis is a programmed cell death mediated by caspase-1,-4,-5, or -11caspase-1/4/5/11, which belongs to a class of crucial, natural immune responses.[11],[12] The inflammatory mediators released because of the inflammatory response induced by pyroptosis were found to be closely associated with tumorigenesis, development, and drug resistance.[13] Recent studies have reported that tumor cells undergo pyroptosis without infection.[14] Liu et al.[15] stated that the tumorigenesis occurs as a consequence of an inflammatory reaction and that the pro-inflammatory nature of pyroptosis is associated with the pathogenesis of many other chronic inflammatory diseases. Therefore, it may be speculated that pyroptosis promotes tumorigenesis and progression.

However, the function and mechanisms of autophagy and pyroptosis happening in RCC remain unclear. Thus, with our current study, we wish to get a clear view of the expression levels of autophagy-pyroptosis-related genes between the normal kidney and RCC tissues to explore their prognostic value and find their correlation with the tumor immune microenvironment. Through this study, we also intend to explore the underlying mechanisms to utilize the gathered knowledge to elucidate a therapeutic basis for clinical treatment and find new therapeutic targets.


 > Materials and Methods Top


Micro-array data analysis and screening of autophagy-pyroptosis-related differential expressed genes

To perform a comparative study of autophagy-pyroptosis-related differential expressed genes (DEGs) in KIRC, we used the GSE168845 data set from the Gene Expression Omnibus (GEO) database. A total of 223 human autophagy-related genes were downloaded from the Human Autophagy Database (HADb) (http://autophagy.lu/clustering/index.html). Also, based on the previously published reports on pyroptosis,[16],[17] we obtained 33 more pyroptosis-related genes.

The cut-off condition was set to an adjusted P value <0.05, and the absolute value of log-fold change | log2FC| ≥1 was observed to be statistically significant for the DEGs. Venn diagrams were created online using ImageGP.

Functional enrichment analysis of autophagy- pyroptosis-related DEGs

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by the ClusterProfiler software package to explore functional annotation and enrichment pathways of DEGs.

Survival analysis

The expression and prognostic value of autophagy-pyroptosis-related DEGs in RCC were evaluated through differential analysis and Kaplan–Meier curve prognostic analysis using Survival Package.

Construction and validation of the autophagy-pyroptosis-related DEG prognostic nomogram

Based on the initial screening of DEGs with differential expression and significant prognosis, signatures containing DEGs were constructed. Eventually, based on the median risk score, calculated from the signatures, all the patients were divided into high-risk and low-risk groups, and the prognosis of both groups was assessed using the Kaplan–Meier curve. Uni-variate and multi-variate Cox regression analyses first assessed the prognostic performance of the signatures. Subsequently, the overall survival (OS) prognostic nomogram was constructed based on the results of the multi-variate regression analysis, and the prognostic predictive ability of the prognostic nomogram for RCC patients was validated.

Relationship between DEGs and the immune micro-environment

The correlation between the expression of autophagy-pyroptosis-related DEGs and immune-infiltrating cells (B-cells, CD8+ T-cells, CD4+ T-cells, macrophages, neutrophils, and dendritic cells) was assessed using the online software TIMER (https://cistrome.shinyapps.io/timer/).

Clinical specimens and cell lines

Twenty-five pairs of freshly biopsied RCC tumor tissues and the corresponding adjacent normal kidney tissues were collected from RCC patients who had undergone laparoscopic nephrectomy from the Department of Urology at the Zhongda Hospital from 2019 to 2020. All patients were diagnosed to have RCC and did not receive any anti-tumor therapy before surgery. Ethical approval for this study was obtained from the Ethics Committee and Clinical Research Institutional Review Board of Zhongda Hospital. Informed consent was obtained from all patients or their relatives.

Human RCC cell lines 786-O and Caki-1 and normal renal tubular epithelial cells, HK-2, were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The 786-O and Caki-1 cells were cultured in Dulbecco's modified Eagle's medium, whereas the HK-2 cells were cultured in a keratinocyte medium supplemented with 1% keratinocyte. All cell cultures were supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (Yeasen, Shanghai, China). The cells were all incubated at 37°C containing 5% CO2. RCC cell lines were stored at -80°C using the instrument CELLSAVING (NCM, Suzhou, China).

RNA extraction and quantitative real-time polymerase chain reaction

Total RNA was extracted from cells or human frozen tissues, as required, using Trizol reagent as per the manufacturer's instructions. cDNA kits (R312, Vazyme Biotech, Nanjing, China) and SYBR Green PCR kits (Q141, Vazyme Biotech, Nanjing, China) were used for reverse transcription and quantitative real-time polymerase chain reaction (qRT-PCR) to determine CT values as per the manufacturer's instructions; GAPDH was used as an internal standard. The primer sequences for CASP4 and CASP8 used were as follows: CASP4-F 5' CAAGAGAAGCAACGTATGGCA 3'; CASP4-R 5' AGGCAGATGGTCAAACTCTGTA 3'; CASP8-F 5' GAAGATAATCAACGACTATG 3'; CASP8-R 5' TTCACTATCCTGTTCTCT 3'; GAPDH-F 5' AACGGATTTGGTCGTATTG 3'; GAPDH-R 5' GGAAGATGGTGATGGGATT 3'. The relative expression of CASP4 and CASP8 was calculated using the 2-ΔΔCt method.

Immunohistochemistry

Immunohistochemistry (IHC) was performed according to a protocol described previously,[18],[19] and histological sections were incubated with CASP4 (ab25898, Abcam) and CASP8 (ab25901, Abcam) antibodies. The images were then recorded by microscopy (Leica Microsystems, Germany).

Statistical analysis

The statistical analysis was carried out using R software (version 4.0.2). The Perl programming language (Version 5.30.2) was used for data processing. A difference of P < 0.05 was considered statistically significant.


 > Results Top


Identification of autophagy-pyroptosis-related DEGs

Based on the screening criteria, we screened 1425 up-regulated and 1672 down-regulated DEGs in GSE168845. Thirty-three pyroptosis-related genes and 223 human autophagy-related genes were analyzed by constructing a Venn diagram, and four co-expressed genes were analyzed: CASP1, CASP4, CASP8, and NLRC4 [Figure 1]a. In the GO and KEGG analysis, the functions of these four co-expressed genes were found to be primarily associated with “cysteine-type endopeptidase activity involved in the apoptotic process” and “inflammasome complex” [Figure 1]b.
Figure 1: Identification of autophagy-pyroptosis-related DEGs. (a) Venn diagram identifying GSE168845, autophagy, and pyroptosis co-expressing DEGs. (b) GO and KEGG-based analysis of four autophagy-pyroptosis-related DEGs. (c) Expression of four autophagy pyroptosisrelated DEGs in RCC tumor tissues (n=539), and normal tissues (n=72). (d) Expression of four autophagy pyroptosisrelated DEGs in RCC tumor tissues (n=72) and paired normal tissues (n=72)

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Subsequently, examination of the expression levels of the four DEGs in the TCGA-KIRC database revealed that CASP1, CASP4, CASP8, and NLRC4 were up-regulated in tumor tissues compared to normal tissues [Figure 1]c and [Figure 1]d.

Survival analysis of autophagy-pyroptosis-related DEGs

Survival analysis of the four DEGs from the TCGA-KIRC database was performed, which revealed that high expression of CASP4 and CASP8 was statistically associated with OS, disease-specific survival (DSS), and progress-free interval (PFI) in RCC patients [Figure 2]b and [Figure 2]c. Moreover, high CASP1 expression was associated with poor prognosis of DSS but not with OS and PFI, whereas NLRC4 expression level was not associated with OS, DSS, or PFI [Figure 2]a and [Figure 2]d.
Figure 2: Survival analysis of autophagy-pyroptosis-related DEGs. (a) OS, DSS, and PFI survival curves of CASP1. (b) OS, DSS, and PFI survival curves of CASP4. (c) OS, DSS, and PFI survival curves of CASP8. (d) OS, DSS, and PFI survival curves of NLRC4

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Relationship between CASP4 and CASP8 and clinicopathological factors

Based on the aforementioned survival analysis results, CASP4 and CASP8 were targeted in this study. The relationship between CASP4 and CASP8 expression as well as various clinicopathological variables in the TCGA database are shown in [Table 1] and [Table 2]. The relationship between CASP4 and CASP8 and clinicopathological factors were examined, which revealed that a high expression of CASP4 and CASP8 was associated with a higher TNM stage [Figure 3]a, [Figure 3]b, [Figure 3]c, [Figure 3]d, [Figure 3]g, [Figure 3]h, [Figure 3]i, grade [Figure 3]d and [Figure 3]j, and pathological stage [Figure 3]e and [Figure 3]k. In addition, the area under the curve (AUC) of CASP4 and CASP8 was 0.942 and 0.891 [Figure 3]f, [Figure 3]l, respectively, indicating the utility of CASP4 and CASP8 as potential and ideal biomarkers to distinguish RCC from normal tissues.
Table 1: Relationship between the expression of CASP4 and various clinicopathological variables in the TCGA database

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Table 2: Relationship between the expression of CASP8 and various clinicopathological variables in the TCGA database.

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Figure 3: Relationship between CASP4 and CASP8 and clinicopathological factors. Relative expression levels of CASP4 in the TCGA database with the T stage (a), N stage (b), M stage (c), tumor grade (d), pathological stage (e), and receiver operating characteristic (ROC) curve (f). Relative expression levels of CASP8 in the TCGA database with the T stage (g), N stage (h), M stage (i), tumor grade (j), pathological stage (k), and ROC curve (l)

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Construction and validation of the prognostic nomogram

Based on CASP4 and CASP8, we constructed a signature containing CASP4 and CASP8: risk score = (0.6373) *CASP4+(-0.4014) *CASP8. The cut-off value of the signature was 0.991, and all patients were subsequently divided into high- and low-risk groups [Figure 4]a. Kaplan–Meier survival curves showed that patients in the high-risk group had poorer survival than those in the low-risk group (HR = 0.66, 95% CI 0.47–0.91; P = 0.013) [Figure 4]b. Multi-variate Cox regression analysis showed that risk signature, metastasis, stage, and grade were independent prognostic factors for OS [Figure 4]c. Furthermore, based on the results of the above multi-variate Cox regression analysis, we constructed a prognostic nomogram for RCC patients and verified that the nomogram had high accuracy in predicting 1-, 3-, and 5-year survival [Figure 4]d, [Figure 4]e. In addition, both uni-variate and multi-variate Cox regression analyses showed that the nomogram was a better prognostic factor for OS in RCC patients [Figure 4]f.
Figure 4: Construction and validation of the prognostic nomogram. (a) High-risk and low-risk groupings based on CASP4 and CASP8. (b) Kaplan–Meier curve analysis of high- and low-risk groups. (c) Uni-variate and multi-variate Cox regression analysis of OS-related variables. (d) Construction of the prognostic nomogram. (e) Validation of the prognostic nomogram. (f) Uni-variate and multi-variate Cox regression analysis of prognostic risk-related variables

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Correlation between the expression of infiltrating immune cells in KIRC tissues and CASP4 and CASP8

The correlation between CASP4 and CASP8 expression and infiltrating immune cells was evaluated. CASP4 and CASP8 were found to be highly correlated with the expression level of B-cells, CD8+ T-cells, CD4+ T-cells, macrophages, neutrophils, and dendritic cells, among the infiltrating cells [Figure 5]a and [Figure 5]b. These findings suggest that CASP4 and CASP8 may be involved in RCC progression via levels of immune cell infiltration.
Figure 5: Correlation between the expression of immune infiltrating cells in KIRC tissues and CASP4 and CASP8. (a) Correlation between the expression of CASP4 and immune infiltrating cells. (b) Correlation between the expression of CASP8 and immune infiltrating cells

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Validation of the expression of CASP4 and CASP8 in clinical tissue samples

To examine the expression of two genes (CASP4 and CASP8) in RCC, qRT-PCR and IHC were performed on RCC cells and clinical tissue samples. The results from qRT-PCR showed elevated expression of CASP4 and CASP8 in RCC cell lines and tumor tissues [Figure 6]a, [Figure 6]b, [Figure 6]c, [Figure 6]d. IHC showed that compared to adjacent normal kidney tissues, CASP4 and CASP1 were significantly increased in KIRC tissues [Figure 6]e.
Figure 6: Validation of the expression of CASP4 and CASP8 in clinical tissue samples. (a, b) Relative expression of CASP4 by qRT-PCR in RCC cell lines and tumor tissues. (c, d) Relative expression of CASP8 by qRT-PCR in RCC cell lines and tumor tissues. (e) Expression of CASP4 and CASP4 in RCC tumor tissues and adjacent normal tissues was detected by IHC

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 > Discussion Top


A few studies have shown the prognosis and treatment of RCC to have a relation with pyroptosis, autophagy, and immunotherapy. This association was attempted to be illustrated through this analysis to identify potential drug targets and effective prognostic biomarkers for RCC. To this end, 3097 DEGs from GSE168845 with 223 human autophagy-associated genes and 33 pyroptosis-associated genes were analyzed to obtain four co-expressed autophagy-pyroptosis-associated DEGs. Subsequent to survival analysis, we could identify CASP4 and CASP8 as autophagy-pyroptosis-associated DEGs. CASP4 and CASP8 were observed to be closely associated with the prognosis of RCC patients and with higher pathological staging and poor prognosis. Additionally, we constructed a nomogram to predict the prognosis of RCC patients after 1, 3, and 5 years based on the presence of CASP4 and CASP8 genes. Moreover, we also found that CASP4 and CASP8 were highly correlated with the expression levels of multiple infiltrating immune cells, and even qRT-PCR and IHC validated the increased expression of CASP4 and CASP8 in RCC. These findings suggested that CASP4 and CASP8 could be potential targets and also serve as effective prognostic biomarkers for RCC.

CASP4 is located on human chromosome 11 and belongs to the cysteine-aspartate acid protease (caspase) family.[20] Sequential activation of caspases plays a central role in the execution phase of apoptosis.[21] CASP4 is expressed in various tissues and cells, such as monocytes, macrophages, and keratinocytes, and is involved in the signaling pathways of apoptosis, necrosis, and inflammation.[22],[23] Nickles et al.[24] screened HEK293T and Hep G2 cell lines using genome-wide RNA interference and found CASP4 to be a novel positive regulator involved in regulating the tumor necrosis factor-α (TNF-α)-induced NF-KB signaling pathway. Abnormal expression of CASP4 has been associated with various diseases, including inflammatory bowel disease, Alzheimer's disease, and various malignancies.[25],[26],[27],[28] In addition, Meng et al.[29] found that CASP4 is over-expressed in RCC and can be used as a prognostic marker for RCC.

CASP8, another important member of the caspase family, encodes 479 amino acids and is present in the cytoplasm as an inactive 55 kD precursor protein, which upon activation is cleaved into two polypeptides of 20 kD and 12 kD and is assembled into a tetrameric structure.[30] CASP8 is a key promoter in the death receptor-mediated apoptotic pathway and exists mainly as an inactive zymogen.[31] When the apoptotic signal activates CASP8, it causes the inactive CASP8 precursor to self-hydrolyze and activates itself to form active CASP8, which further activates the other caspases and amplifies death signal issuance, eventually triggering a lethal proteolytic cascade of reactions.[32] CASP8 has also been found to be aberrantly expressed in cervical cancer, neuroblastoma, lung cancer, and other malignancies.[33],[34]

To date, there are two known modes of cell death: programmed death and non-programmed death.[35],[36] Programmed cell death could be classified into three types: autophagy, apoptosis, and pyroptosis.[37] The first two types are non-inflammatory, whereas pyroptosis is an inflammatory response.[11] Pyroptosis is a caspase-mediated inflammatory cell death that is pro-inflammatory and programmed as a natural immune response of the body.[11] Autophagy is a selective non-cystine-dependent programmed cell death closely related to cell growth, proliferation, senescence, and cell cycle regulation.[38] It has also been found that autophagy can be degraded by active CASP8, and chemotherapy-induced apoptosis could inhibit autophagy via CASP8-mediated cleavage of Beclin 1 during the execution phase after the release of cytochrome C.[39],[40] Additionally, Oral et al.[41] found that cleavage of Atg3 protein by CASP8 during receptor-activated cell death modulated autophagy. Furthermore, CASP4 is also involved in capsaicin and dihydrocapsaicin-induced endoplasmic reticulum stress autophagy/apoptosis processes.[42]


 > Conclusion Top


To conclude, our study identified two autophagy- decay-associated genes, CASP4 and CASP8, and found that CASP4 and CASP8 are highly expressed in RCC and are associated with higher pathological staging and poorer prognosis. Furthermore, we could construct a prognostic nomogram for the RCC patients based on CASP4 and CASP. Notably, our data suggest that CASP4 and CASP8 were potential targets and effective prognostic biomarkers for treating RCC. Additional studies are warranted to identify even more prognostic biomarkers and drug targets.

Acknowledgements

The authors thank Biobank of Zhongda Hospital for providing tissue samples. We thank Bullet Edits for editing this manuscript.

Authors' contributions

All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

This study was supported by the Jiangsu Provincial Key Research and Development Program (BE2019751), Innovative Team of Jiangsu Provincial (2017ZXKJQW07), and The National Key Research and Development Program of China (SQ2017YFSF090096).

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
 
 
    Tables

  [Table 1], [Table 2]



 

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