Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma

doi:10.21203/rs.3.rs-5041616/v1

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Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma

https://doi.org/10.21203/rs.3.rs-5041616/v1

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Background: Mitochondrial permeability transition (MPT) driven necrosis may play a key role in the proliferation, death and spread of kidney renal clear cell carcinoma (KIRC). However, few studies have investigated key genes of MPT driven necrosis-related genes (MPTDNRGs) and KIRC using bioinformatics methods. Consequently, this study aims to create a precise prognostic tool for forecasting patient outcomes of KIRC patients.

Methods: First, differentially expressed genes (DEGs) were acquired from KIRC and control samples in TCGA-KIRC dataset, as well as between high and low MPTDNRGs scores groups. Then, candidate MPTDNRGs were acquired by overlapping both DEGs. Next, key MPTDNRGs were obtained by Cox regression analysis. Subsequently, risk model and nomogram were constructed, along with enrichment analysis, immune analysis, and regulatory network were completed. Finally, the expression of key MPTDNRGs was validated clinically using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: Three key MPTDNRGs, namely IL2RA, CD7, and CXCL13, were obtained to construct the risk model. The ROC analysis results showed that the AUC for 1 year, 3 years, and 5 years were 0.658, 0.614, and 0.625, respectively, indicating that the risk model has good effectiveness. Besides, risk score and age are independent prognostic factors. Next, we constructed a nomogram with a decent potential for clinical utility over risk score and age alone. Among the high-risk group, there was a significant concentration of pathways related to immune functions, particularly systemic lupus erythematosus, while the low-risk group was largely enriched in pathways associated with metabolic processes, such as butanoate metabolism. A sum of 25 immune cells exhibited significant differences from different risk groups, and high-risk group patients revealed significantly higher TIDE score, which indicated a higher likelihood of tumor immune escape in high risk group. Moreover, ceRNA network showed complex interaction pairs such as CXCL13-hsa-miR-670-5p-AL121985.1, IL2RA-hsa-miR-6088-AL513497.1, and in total 25 TFs were predicted for key MPTDNRGs.

Conclusion: In this study, three key genes (IL2RA, CD7 and CXCL13) were integrated into a newly constructed prognostic model for KIRC, which offers clinicians a novel framework for more accurately forecasting patient outcomes and also presents a fresh target and approach for the management of KIRC.

driven necrosis

renal cell carcinoma

enrichment analysis

MPT

competitive endogenous RNA

The incidence of renal cell carcinoma (RCC) is increasing worldwide and ranks first among cancers of the upper urinary tract system[1]. Kidney renal clear cell carcinoma (KIRC) is the most common type of RCC, accounting for almost 75-80% of RCC[2]. The study found that KIRC is both the most invasive subtype of kidney cancer and one of the solid tumor subtypes that are most resistant to conventional chemotherapy protocols[3]. KIRC is not sensitive to radiotherapy and chemotherapy[4], so surgical resection is still the main treatment method for KIRC patients. However, after radical treatment of local RCC, up to 30% of patients experience tumor recurrence[5]. Therefore, targeted therapy has become one of the hot research directions of KIRC therapy[6]. In addition, early KIRC is often asymptomatic, which makes it difficult to detect, adding challenges for subsequent treatment. When patients are diagnosed with advanced metastasis, 30% of patients have a 5-year survival rate of less than 15%[7]. Therefore, exploring key genes is necessary for early diagnosis and treatment, and has positive significance for improving the prognosis of KIRC.

Necrosis triggered by mitochondrial permeability transition (MPT) is a type of cell demise that occurs outside of the apoptotic pathway, caused by oxidative stress and an excessive accumulation of calcium ions in the cell membrane. It mainly participated in the release of mitochondrial materials during cell death. Recent studies have shown that mitochondria have a role in the governance of mechanisms controlling cell death[8], and this regulation is related to the transformation of membrane permeability. Once the transformation of mitochondrial membrane permeability occurs, cells will eventually die due to apoptosis or necrosis[9, 10]. Scientific research has confirmed that MPT-driven necrosis is related to a multitude of diseases, based on the alterations in the molecular mechanisms that oversee cell death, such as brain injury, amyotrophic lateral sclerosis, fulminant death receptor-induced hepatitis[11], and liver cancer predispositions[12]. Few studies have been conducted on necrotic and clear cell renal carcinoma driven by MPT. Therefore by identifying the genes that drive MPT, this might contribute to the formulation of new predictive strategies for the clinical course of KIRC patients.

The study leveraged transcriptome data to identify the key genes involved in MPT-mediated cell death in KIRC, followed by a validation process using clinical samples from patients with KIRC. Ultimately, a predictive model was formulated utilizing pivotal genes, laying a theoretical foundation for investigating the influence of MPT-associated necrosis genes in KIRC. This also holds substantial importance for the prediction of outcomes and the treatment strategies for patients.

2.1 Data source

The Cancer Genome Atlas-KIRC (TCGA-KIRC) transcriptomic and clinical information were obtained from TCGA database (http://cancergenome.nih.gov/). Totally 598 samples were acquired, comprising 526 KIRC tumor tissue samples (only 522 samples with survival information) and 72 paracancerous samples as training cohort. The 101 KIRC samples in transcriptom dataset (E-MTAB-1980) were gained from ArrayExpres database (https://www.ebi.ac.uk/biostudies/arrayexpress) as validation cohort[13]. The KIRC transcriptom dataset (GSE40435) was acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds), including 101 KIRC tumor tissue samples and 101 paracancerous samples as validation cohort[14]. Moreover, 39 MPT driven necrosis-related genes (MPTDNRGs) were procured from M17902, M3873 and M16257 in Molecular signatures database (MSigDB) database (https://www.gsea-msigdb.org/gsea/msigdb).

2.2 Identification of candidate MPTDNRGs

In TCGA-KIRC dataset, differentially expressed genes (DEGs) were screened out from KIRC and paracancerous samples (|log₂fold change (FC)| > 1 and adj.P < 0.05) by DESeq2 package (v 1.36.0)[15], denoted as DEGs-KIRC. The scores of MPTDNRGs were calculated for both KIRC and paracancerous samples by single sample gene set enrichment analysis (ssGSEA) algorithm of gene set variation analysis (GSVA) package (v 1.48.3)[16]. Then, differences in MPTDNRGs scores between KIRC and paracancerous samples were compared. The KIRC samples were divided into high and low score groups according to MPTDNRGs score's median value. Afterwards, DEGs were screened out from different score groups, denoted as DEGs-MPT. Volcano plot and heatmap of KIRC-DEGs and MPT-DEGs were plotted using ggplot2 package (v 3.3.6)[17] and circlize package (v 0.4.15)[18], respectively. Thereafter, candidate MPTDNRGs were acquired by overlapping KIRC-DEGs and MPT-DEGs using eulerr package (v 7.0.0)[19]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of candidate MPTDNRGs were analyzed by ClusterProfiler package (v 4.8.2)[20](adj.P < 0.05). GO and KEGG results were presented via ggpubr package (v 0.6.0)[21] and treemap package (v 2.4-4)[22], respectively. Moreover, a protein-protein interaction (PPI) network was constructed according to candidate MPTDNRGs using STRING database (confidence score > 0.4). And the MCODE in Cytoscape software (v 3.9.1)[23] was utilized to identify and visualize significant gene clusters within PPI network.

2.3 Identification of key MPTDNRGs

First, univariate Cox regression analysis was implemented on candidate MPTDNRGs that were screened out from PPI network to initially identify survival-related genes. Subsequently, the glmnet package (v 4.1-6)[24] was used for least absolute selection and shrinkage operator (LASSO) Cox regression analysis on the genes that passed the proportional hazards (PH) hypothesis test for further key MPTDNRGs. Finally, multivariate Cox analysis was executed to obtain key MPTDNRGs. In addition, the expression levels of key MPTDNRGs in KIRC and paracancerous samples from both TCGA-KIRC dataset and GSE40435 dataset were analyzed.

2.4 Construction and validation of risk model

The risk score for each KIRC patient (N = 522) was determined according to relative expression of key MPTDNRGs and their associated LASSO Cox coeffcient. The formula was risk score = Σⁿ_i=1(coefi * Xi), where X-i was relative expression of key MPTDNRGs-i, coefi was LASSO Cox coeffcient of key MPTDNRGs-i. To further assess validity of risk model, receiver operating characteristic (ROC) curve was plotted to determine area under the curve (AUC) by survivalROC package(v 4.1-6)[25]. Thereafter, KIRC patients were dichotomized into high and low risk groups according to risk score median value. The different risk groups underwent Kaplan-meier (K-M) survival analysis to evaluate overall survival (OS) by survminer package (v 0.4.9)[26]. Furthermore, risk model was validated in E-MTAB-1980 dataset.

2.5 Independent prognostic analysis

To begin with, we merged risk scores and clinical features—like age, grade, gender, and tumor stage—from KIRC patients within the TCGA-KIRC dataset for constructing a univariate Cox regression model. Afterward, we derived independent prognostic factors from a multivariate Cox regression analysis of the variables that passed the PH hypothesis test. We then leveraged the rms package (version 6.7-0) to formulate a nomogram with these independent prognostic factors for predicting survival rates[27]. The predictive performance of the nomogram was gauged via calibration curves. Furthermore, decision curve analysis (DCA) was applied to determine if the nomogram had a greater clinical advantage compared to the independent prognostic factors alone.

2.6 Functional and annotation analyses

To delve into the pathways related to various risk groups, we began by analyzing the differential gene expression across the groups using the DESeq2 package, ranking the genes based on their log2 fold change (log2FC). Subsequently, using the ranked list, GSEA was carried out with the ClusterProfiler package, considering results with an adjusted P-value below 0.05 as significant. The reference gene sets for this analysis were obtained from the MSigDB database, namely the c2.cp.kegg.v2023.1.Hs.symbols.

2.7 Immune infiltration analysis

To further understand the differences from different risk groups of immune cells, the 28 immune cell[28] enrichment scores for KIRC sample were determined by ssGSEA algorithm of GSVA package and to compare differences from different risk groups (P < 0.05). And Spearman correlation analyses were performed between differential immunity cells as well as between differential immunity cells and key MPTDNRGs by psych package (v 2.3.6)[29]. Besides, the expression of immune checkpoints [PD-L1 (CD274), PD-1 (PDCD1), CTLA-4 (CTLA4), TIGIT, LAG-3 (LAG3), GAL9 (LGALS9), TIM-3 (HAVCR2), PD-L2 (PDCD1LG2), SIRPα (SIRPA), BTLA and Siglec-7 (SIGLEC7)][30] were compared (P < 0.05). And to further assess the effectiveness of immunotherapeutic response, tumor immune dysfunction and exclusion (TIDE) scores were compared from different risk groups (P < 0.05)[31]. Tumor mutational burden (TMB) indicated the extent of genetic variation within the tumor cell genome. Consequently, somatic mutations in KIRC samples were analyzed utilizing the maftools package (v 2.16.0)[32].

2.8 Construction of regulatory network

The miRNAs of key MPTDNRGs were predicted in miRDB database. And the lncRNAs corresponding to miRNAs were predicted in starBase database (clipExpNum > 7). Finally, a competitive endogenous RNA (ceRNA) network was created to explore molecular regulatory mechanisms of key MPTDNRGs. Additionally, the Jaspar database was utilized to predict transcription factors (TFs) associated with key MPTDNRGs.

2.9 Expression validation of key MPTDNRGs

KIRC tumor tissue samples and paracancerous tissue samples of 5 KIRC patients from the First Hospital of Shanxi Medical University were taken as the experimental group and the control group, respectively. All samples underwent reverse transcription quantitative polymerase chain reaction (RT-qPCR). This study received approval from the Scientific Research Ethics Review Committee of Shanxi Medical University, and informed consent was obtained from all participants. To validate the expression of key MPTDNRGs, total RNA was extracted from the 10 samples using TRIzol (Ambion, Austin, USA) following the manufacturer’s instructions. Reverse transcription to cDNA was performed using the SureScript First-Strand cDNA Synthesis Kit (Servicebio, Wuhan, China) according to the provided guidelines. RT-qPCR was conducted using the 2xUniversal Blue SYBR Green qPCR Master Mix (Servicebio, Wuhan, China). Primer sequences for the PCR were listed in Additional file 1. GAPDH was used as an internal reference gene. Gene expression levels were calculated using the 2^−ΔΔCtmethod[33].

2.10 Statistical Analysis

The entire analytical process was carried out in R (version 4.2.3). Group differences were assessed using the Wilcoxon statistical test. A p-value below 0.05 was considered to signify statistical relevance.

3.1 A sum of 56 candidate MPTDNRGs in PPI network were screen out

We screened out 3,314 DEGs-KIRCs, including 2,141 up-regulated and 1,173 down-regulated between KIRC and paracancerous samples from TCGA-KIRC dataset (Figure 1a, 1b). Next, the scores of MPTDNRGs were calculated in KIRC and paracancerous samples, revealing a significantly higher score in KIRC samples (P < 0.0001) (Figure 1c). Totally 408 DEGs-MPT, containing 267 up-regulated and 141 down-regulated were acquired from different scoring groups (Figure 1d, 1e). Ultimately, we identified 339 candidate MPTDNRGs by overlapping DEGs-KIRC and DEGs-MPT (Figure 1f). To uncover the biological roles and pathways linked to potential candidate MPTDNRGs, GO and KEGG enrichment analyses were performed (Figure 2a, 2b). To be more specific, the potential MPTDNRGs were correlated with biological processes including T cell differentiation and the control of T cell stimulation, as indicated in the biological process (BP) entries. They were also related to cellular components such as endocytic vesicles and their membranes, as listed in the cellular component (CC) entries. Their molecular functions, noted in the molecular function (MF) entries, included cytokine receptor activity and immune receptor activity, and they were enriched in KEGG pathways including rheumatoid arthritis. Moreover, to delve into the mutual influences of candidate MPTDNRGs at the protein level, a PPI network was constructed, which contained 339 candidate MPTDNRGs and 3,615 interaction pairs (Figure 2c). And to further identify key MPTDNRGs, we analyzed significant gene clusters within PPI network, and acquired 56 candidate MPTDNRGs with 1,314 interactions for subsequent analysis (Figure 2d).

3.2 IL2RA, CD7 and CXCL13 with high expression levels in KIRC samples were identified as key MPTDNRGs

We identified 19 genes with survival-related [hazard ratio (HR)≠1 and P < 0.05] (Figure 3a), and PH hypothesis test revealed that these 19 genes satisfied the hypothesis (P > 0.05). Then, 7 genes were further screened out (Lambdamin = 0.015), namely IL2RA, CD7, CXCL13, CTLA4, CD38, IL2RG and IL10RA (Figure 3b, 3c). Afterwards, IL2RA, CD7, CXCL13 and IL2RG were screened out by means of multivariate Cox analysis (HR≠1 and P < 0.05) (Figure 3d). Nevertheless, IL2RG exhibited a HR of less than 1 (HR = 0.69), contradicting the results of univariate Cox regression analysis (HR = 1.2). Accordingly, IL2RA, CD7 and CXCL13 were designated as key MPTDNRGs. And key MPTDNRGs exhibited high expression levels in KIRC samples from both TCGA-KIRC dataset and GSE40435 dataset (P < 0.0001) (Figure 3e, 3f).

3.3 A risk model was constructed according key MPTDNRGs

Consequently, a risk model was constructed by key MPTDNRGs, with RiskScore calculated as follows: = IL2RA*0.3233 + CD7*0.3673 + CXCL13*(-0.2197). In TCGA-KIRC dataset, the model was assessed through time-dependent ROC analysis, and AUC were 0.658, 0.614 and 0.625 at 1, 3 and 5 years, respectively (Figure 4a). These findings indicated the favorable efficacy of our risk model. Figure 4b, 4c illustrated distribution of samples in different risk groups. Clearly and unequivocally, high risk patients had significantly worst OS than low risk group (P = 0.00014) (Figure 4d). And we also carried out verification in E-MTAB-1980 dataset. Notably, the AUC were 0.810, 0.653 and 0.633 at 1, 3 and 5 years, respectively (Figure 4e). And likewise, high risk patients had significantly worst OS (P = 0.036) (Figure 4 f-h). The results were consistent with training cohort.

3.4 Only risk score and age were independent factors of prognosis

This research endeavored to determine if risk scores could act as a standalone predictor for the prognosis of patients with KIRC. As a consequence, we determined that risk scores, age, tumor grade, and stage are influential variables that affect the overall survival (OS) of KIRC patients (HR≠1 and P < 0.05) (Figure 5a). Nonetheless, the PH hypothesis test indicated that neither tumor grade nor stage fulfilled the required assumptions (P < 0.05). As a result, we proceeded with further analysis considering only risk score and age. Ultimately, we established that the risk score and age were the sole independent prognostic factors(HR≠1 and P < 0.05) (Figure 5b). Consequently, a nomogram was developed incorporating these two factors, risk score and age (Figure 5c). The calibration plots, which closely matched the reference line, suggested that the nomogram had a favorable predictive accuracy (Figure 5d). Furthermore, the decision curve analysis (DCA) at both 1 and 5 years showed that the nomogram had a clinical utility compared to using risk score and age in isolation (Figure 5e-g).

3.5 Different risk groups-related signaling pathways

Conducting GSEA aimed to provide a more profound understanding of the associated signaling pathways and the potential biological processes that characterize the distinct risk groups. The detail results of GSEA could be found in Additional file 2 Specifically, high risk group was mainly enriched in systemic lupus erythematosus, leishmania infection etc., and low risk group was mainly enriched in oxidative phosphorylation, valine leucine and isoleicine, etc. (Figure 6). GSEA uncovered distinct signaling pathways linked to various risk groups, thereby broadening our in-depth comprehension of KIRC.

3.6 Immune analysis of KIRC patients

The heatmap illustrated the scores of 28 immune cells (Figure 7a). Evidently, in high risk group, except for CD56bright natural killer cells, eosinophils and neutrophils, the proportion of 25 immune cells were significantly higher (P < 0.05) (Figure 7b). Then, we observed that the strongest correlation among differential immune cells was between T follicular helper cell and activated MDSC (r = 0.869 and P < 0.001) (Figure 7c-d). Further, the strongest correlation was observed between CD7 and activated CD8 T cells (r = 0.856, P < 0.001), exhibiting significantly higher expression in high risk group (P < 0.0001) (Figure 8a-c). Moving forward, we sought to determine if there existed any potential disparities in the levels of immune checkpoint expression across different risk categories. The results revealed that gene expression of 11 immune checkpoints was significantly higher in high risk group, like BTLA, CD274 and CTLA4 (P < 0.001) (Figure 8d). TIDE score was analyzed to access the potential for tumor immune evasion. Obviously, high risk group patients exhibited significantly higher TIDE score (P < 0.05) (Figure 8e). Additionally, the waterfall plot illustrated the top 20 mutations in tumor cells of different risk groups (Figure 8f, 8g). The results indicated that VHL and PBRM1 mutations were more prevalent in different risk groups. The higher frequency mutations in high risk group were frame shift del mutation and missense mutation, while the most common mutations in low risk group were nonsense mutation, missense mutation and frame shift del mutation.

3.7 Regulatory network of key MPTDNRGs

The ceRNA network showed that 3 mRNAs of the risk model could interact with 27 miRNAs, which could in turn interact with 36 lncRNAs. The complex interaction pairs were formed, such as CXCL13-hsa-miR-670-5p-AL121985.1, IL2RA-hsa-miR-6088-AL513497.1 (Figure 9a). A total of 25 TFs were predicted for key MPTDNRGs. Notably, IL2RA, CD7 and CXCL13 collectively predicted GATA2. CD7 and CXCL13 collectively predicted MAX and GATA3. CD7 and IL2RA collectively predicted USF2 (Figure 9b). The results indicated the regulatory mechanism for key MPTDNRGs in KIRC.

3.8 Verification of key MPTDNRGs expression

In the previous studies, we observed that IL2RA, CD7 and CXCL13 exhibited significantly higher expression levels in KIRC samples in both TCGA-KIRC and GSE40435 (P < 0.0001) (Figure 3e, 3f). This prompted us to further employ RT-qPCR techniques to validate the clinical expression levels of key MPTDNRGs in patients with KIRC. Remarkably, RT-qPCR revealed that both IL2RA and CD7 showed significantly higher expression in KIRC samples (P < 0.05), while CXCL13 also showed an up-regulation trend in KIRC (P = 0.0820), consistent with our previous findings (Figure 9c-e).

The poor prognosis of KIRC has always been a big problem, and many patients still relapse after treatment. In an attempt to better prognosticate renal cancer, researchers have engaged in numerous studies. MPT-induced necrosis-related genes might play a crucial role in the processes of tumor cell proliferation, death, and dissemination[34]. Consequently, utilizing the clinical data and transcriptome information of KIRC available in public repositories, this study explored the necrotic genes driven by mitochondrial permeability transfer and key genes of KIRC through bioinformatics technology, and conducted clinical validation of key genes (IL2RA, CD7 and CXCL13), and constructed a new risk prognosis model, providing a new reference for clinical diagnosis and treatment of KIRC.

We identified the pivotal genes in KIRC that are linked to MPT-induced necrosis. Through the integrated analysis of the tumor microenvironment of KIRC, it was found that the expression of the immune-related gene IL2RA is related to the prognosis of clear cell carcinoma[35], which is consistent with the results of our study. NK cell marker genes, including CD7 and six additional genes, can serve as a standalone biomarker for forecasting the prognosis and therapeutic responses in patients with KIRC, and are intimately associated with immunosuppression[36]. These studies have proved that IL-2RA and CD7 are correlated with the prognosis of patients with KIRC. However, the specific mechanism is still relatively limited and further exploration is needed. Researchers have more studies on CXCL13, and a large number of evidences show that CXCL13 is highly expressed in clear cell carcinoma[37]. In addition, circHIPK3 can promote the proliferation and metastasis of clear cell renal cell carcinoma (ccRCC) cells by altering miR-5083p/CXCL13 signaling[38]; M2 macrophages in the immune environment can secrete CXCL13, thus promoting the proliferation, migration, invasion and epithelial-mesenchymal transformation of ccRCC cells[39]; CXCL13 can activate the PI3K/AKT/mTOR signaling pathway by binding with CXCR5 to promote the proliferation and migration of ccRCC cells[40]. In conclusion, CXCL13 is closely related to KIRC, and increased CXCL13 expression is associated with poor survival outcomes in KIRC patients.

Evidence suggests that the presence of systemic lupus erythematosus correlates with a reduced likelihood of renal cancer, in an inverse relationship[41]. Through GSEA in our research, the systemic lupus erythematosus pathway was found to be enriched in the high-risk group. Our results do not contradict earlier studies, implying that the connection between systemic lupus erythematosus and KIRC is multifaceted, not merely a simple inverse association. It is possible that patients with KIRC have a poor prognosis when the systemic lupus erythematosus pathway is active. Or it may be caused by the use of immunotherapy. The relationship between leishmania infection and clear cell carcinoma of the kidney has not been reported. The oxidative phosphorylation pathway has been extensively studied in KIRC, in which oxidative phosphorylation is reported to play an important role. Deletion of chromosome 3p has also been associated with down-regulation of oxidative phosphorylation (OXPHOS)[42]. Clear cell cancer cells metabolize glucose primarily through glycolysis even when oxygen is plentiful. Thus, there is activation of the hypoxic response pathway in KIRC under normal oxygen conditions[43]. It has been demonstrated that the HIF1 signaling pathway influences mitochondrial behavior and shifts the metabolic pathway in cancer cells from oxidative phosphorylation to glycolysis[44]. The findings indicate a reduction in the activity of the Krebs cycle and the electron transport chain (OXPHOS) related to the Warburg effect at the protein level. Yet, it is noteworthy that the downregulation of these Krebs cycle elements and the majority of nuclear-encoded OXPHOS proteins was not evident at the mRNA level and was not detected by RNA-seq analysis alone. The researchers hypothesized that maintaining OXPHOS transcription levels similar to that of orthoxic cells, while it is beneficial for meeting the energy requirements of tumors, may provide a mechanism for rapidly inducing OXPHOS activity, which needs more and further experiments and studies to verify[42]. Overexpression of protein tyrosine phosphatase receptor gamma(PTPRG) can activate oxidative phosphorylation, inhibit apoptosis, inhibit epithelial-mesenchymal transformation, promote G1/S cell cycle arrest, and have anticancer effects[45]. Therefore, the metabolic pathways associated with mitochondria may promote the development of KIRC, which is not conducive to the prognosis and treatment of KIRC patients.

The GATA transcription factor family is a zinc finger transcription factor belonging to GATA family proteins 1-6, and GATA transcription factors have been found to contribute to cell proliferation, apoptosis and tumorigenesis in a multitude of solid cancers[46]. The regulatory network analysis we conducted predicted the significance of GATA2 and GATA3 in the GATA family of transcription factors, highlighting their involvement in the MPT process in KIRC. The mRNA levels and protein expression levels of GATA2/3/6 in KIRC tissues were significantly decreased compared with normal tissues. Moreover, univariate analysis showed that decreased GATA2 expression level was associated with advanced tumor disease, positive distant metastasis, and lymph node metastasis status[47]. The presence of infiltrating immune cells showed a strong correlation with the expression patterns of GATA, and our analysis of immune cell infiltration also noted an increase in the proportion of these immune cells. Moreover, GATA2 was negatively correlated with B cells and positively correlated with CD8+ T cells, CD4+ T cells and neutrophils. Interestingly, CD8+ T cells were also the immune cells most strongly associated with a key gene, CD7. Due to homologous inhibition of phosphatase and tensin, increased GATA2 expression levels may promote the proliferation of breast cancer cells by stimulating AKT phosphorylation[46]. The findings imply that members of the GATA family could potentially act as biomarkers for prognosis and as targets for therapeutic intervention in KIRC.

Previous studies have shown that 3p loss and VHL mutation of chromosome almost always occur in the early stage of KIRC, and then additional aneuploidy produced by PBRM1, SETD2 or BAP1 mutations and defects and errors in DNA repair and mitosis drive tumor development. The acquisition and loss of key chromosomes, mutations in PI3K pathway elements, and other cancer-driving mutations confer the lethal potential of intra-tumor cloning and increase the likelihood of metastasis[34].

Our study still has some shortcomings, relies on data from public databases, lacks validation of clinical data, and requires a combination of animal experiments and clinical trials to further demonstrate the mechanism and role of the key genes we screened for in KIRC. In the future, we will further increase the number of clinical samples to make the results more reliable.

In summary, by applying bioinformatics approaches, we discovered and clinically substantiated the key genes (IL2RA, CD7, and CXCL13) associated with cell necrosis driven by MPT in patients with KIRC, and subsequently formulated a fresh prognostic model. It offers clinicians a novel framework for more accurately forecasting patient outcomes and also presents a fresh target and approach for the management of KIRC. Nonetheless, additional research is required to assess the mechanisms through which MPT-driven necrosis influences tumor growth and advancement.

MPT

Mitochondrial permeability transition

KIRC

kidney renal clear cell carcinoma

MPTDNRGs

MPT driven necrosis-related genes

DEGs

Differentially expressed genes

RT-qPCR

Reverse transcription quantitative polymerase chain reaction

RCC

Renal cell carcinoma

GEO

Gene expression omnibus

MSigDB

Molecular signatures database

ssGSEA

single sample gene set enrichment analysis

GSVA

Gene set variation analysis

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

PPI

Protein-protein interaction

LASSO

Least absolute selection and shrinkage operator

Proportional hazards

ROC

Receiver operating characteristic

AUC

Area under the curve

Overall survival

DCA

Decision curve analysis

TIDE

Tumor immune dysfunction and exclusion

TMB

Tumor mutational burden

ceRNA

competitive endogenous RNA

TFs

Transcription factors

Cellular component

Molecular function

OXPHOS

Oxidative phosphorylation

PTPRG

Protein tyrosine phosphatase receptor gamma

Ethics approval and consent to participate

This study was conducted in accordance with the principles of the Helsinki Declaration and approved by the Scientific Research Ethics Review Committee of Shanxi Medical University (Ethics Review No: KYLL-2024-028). Informed consent was obtained from all participants.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and analysed during the current study are available in the [TCGA database, TCGA-KIRC] [http://cancergenome.nih.gov/]; [ArrayExpres database, E-MTAB-1980] [https://www.ebi.ac.uk/biostudies/arrayexpress]; [the Gene Expression Omnibus (GEO) database,GSE40435] [https://www.ncbi.nlm.nih.gov/gds]; [Molecular signatures database (MSigDB), MPTDNRGs]repository,[https://www.gsea-msigdb.org/gsea/msigdb]. Additional materials from this study are available by contacting the corresponding author at [email protected].

Competing interests

The authors declare that they have no competing interests.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contributions

W.S. and Y.W. wrote the main manuscript text. Y.W. and D.L. prepared figures 1-9. Y.W. and W.Z. prepared additional file 1-2. All authors reviewed the manuscript.

Acknowledgments

We thank the entire team for their support and assistance with this study.

Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. Epidemiology of Renal Cell Carcinoma. European urology. 2019;75(1):74-84.
Nabi S, Kessler ER, Bernard B, Flaig TW, Lam ET. Renal cell carcinoma: a review of biology and pathophysiology. F1000Research. 2018;7:307.
Vuong L, Kotecha RR, Voss MH, Hakimi AA. Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma. Cancer discovery. 2019;9(10):1349-57.
Wang Q, Tang H, Luo X, Chen J, Zhang X, Li X, et al. Immune-Associated Gene Signatures Serve as a Promising Biomarker of Immunotherapeutic Prognosis for Renal Clear Cell Carcinoma. Frontiers in immunology. 2022;13:890150.
Klatte T, Rossi SH, Stewart GD. Prognostic factors and prognostic models for renal cell carcinoma: a literature review. World journal of urology. 2018;36(12):1943-52.
Courtney KD, Infante JR, Lam ET, Figlin RA, Rini BI, Brugarolas J, et al. Phase I Dose-Escalation Trial of PT2385, a First-in-Class Hypoxia-Inducible Factor-2α Antagonist in Patients With Previously Treated Advanced Clear Cell Renal Cell Carcinoma. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2018;36(9):867-74.
Ochocki JD, Khare S, Hess M, Ackerman D, Qiu B, Daisak JI, et al. Arginase 2 Suppresses Renal Carcinoma Progression via Biosynthetic Cofactor Pyridoxal Phosphate Depletion and Increased Polyamine Toxicity. Cell metabolism. 2018;27(6):1263-80.e6.
Gogvadze V, Orrenius S, Zhivotovsky B. Mitochondria in cancer cells: what is so special about them? Trends in cell biology. 2008;18(4):165-73.
Armstrong JS. The role of the mitochondrial permeability transition in cell death. Mitochondrion. 2006;6(5):225-34.
Crompton M. Mitochondria and aging: a role for the permeability transition? Aging Cell. 2003;3(1):3-6.
Kupsch K, Parvez S, Siemen D, Wolf G. Modulation of the permeability transition pore by inhibition of the mitochondrial K(ATP) channel in liver vs. brain mitochondria. The Journal of membrane biology. 2007;215(2-3):69-74.
Rasola A, Bernardi P. The mitochondrial permeability transition pore and its involvement in cell death and in disease pathogenesis. Apoptosis : an international journal on programmed cell death. 2007;12(5):815-33.
Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nature genetics. 2013;45(8):860-7.
Wozniak MB, Calvez-Kelm FL, Abedi-Ardekani B, Byrnes G, Durand G, Carreira C, et al. Integrative genome-wide gene expression profiling of clear cell renal cell carcinoma in Czech Republic and in the United States. PloS one. 2013;8(3):e57886.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12):550.
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics. 2013;14:7.
Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics (Oxford, England). 2022;38(15):3844-6.
Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize Implements and enhances circular visualization in R. Bioinformatics (Oxford, England). 2014;30(19):2811-2.
Berker Y, Muti IH, Cheng LL. Visualizing metabolomics data with R. NMR in biomedicine. 2023;36(4):e4865.
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology. 2012;16(5):284-7.
Cheng Q, Chen X, Wu H, Du Y. Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis. Journal of translational medicine. 2021;19(1):18.
Gortler J, Schulz C, Weiskopf D, Deussen O. Bubble Treemaps for Uncertainty Visualization. IEEE transactions on visualization and computer graphics. 2018;24(1):719-28.
Liu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evidence-based complementary and alternative medicine : eCAM. 2020;2020:3023127.
Li Y, Lu F, Yin Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease. Scientific reports. 2022;12(1):11340.
Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56(2):337-44.
Ramsay IS, Ma S, Fisher M, Loewy RL, Ragland JD, Niendam T, et al. Model selection and prediction of outcomes in recent onset schizophrenia patients who undergo cognitive training. Schizophrenia research Cognition. 2018;11:1-5.
Sachs MC. plotROC: A Tool for Plotting ROC Curves. Journal of statistical software. 2017;79.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell reports. 2017;18(1):248-62.
Orifjon S, Jammatov J, Sousa C, Barros R, Vasconcelos O, Rodrigues P. Translation and Adaptation of the Adult Developmental Coordination Disorder/Dyspraxia Checklist (ADC) into Asian Uzbekistan. Sports (Basel, Switzerland). 2023;11(7).
Shibru B, Fey K, Fricke S, Blaudszun A-R, Fürst F, Weise M, et al. Detection of Immune Checkpoint Receptors - A Current Challenge in Clinical Flow Cytometry. Frontiers in immunology. 2021;12:694055.
Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nature medicine. 2018;24(10):1550-8.
Mayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome research. 2018;28(11):1747-56.
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (San Diego, Calif). 2001;25(4):402-8.
Jonasch E, Walker CL, Rathmell WK. Clear cell renal cell carcinoma ontogeny and mechanisms of lethality. Nature reviews Nephrology. 2021;17(4):245-61.
Zheng B, Xie F, Cheng F, Wang J, Yao Z, He W, et al. Integrative Analysis of Immune-Related Genes in the Tumor Microenvironment of Renal Clear Cell Carcinoma and Renal Papillary Cell Carcinoma. Frontiers in Molecular Biosciences. 2021;8.
Wang K, Yu M, Zhang Z, Yin R, Chen Q, Zhao X, et al. Integrated analysis of single-cell and bulk transcriptome identifies a signature based on NK cell marker genes to predict prognosis and therapeutic response in clear cell renal cell carcinoma. Translational cancer research. 2023;12(5):1270-89.
Shen J, Wang R, Chen Y, Fang Z, Tang J, Yao J, et al. Prognostic significance and mechanisms of CXCL genes in clear cell renal cell carcinoma. Aging. 2023;15(16):7974-96.
Han B, Shaolong E, Luan L, Li N, Liu X. CircHIPK3 Promotes Clear Cell Renal Cell Carcinoma (ccRCC) Cells Proliferation and Metastasis via Altering of miR-508-3p/CXCL13 Signal. OncoTargets and therapy. 2020;13:6051-62.
Xie Y, Chen Z, Zhong Q, Zheng Z, Chen Y, Shangguan W, et al. M2 macrophages secrete CXCL13 to promote renal cell carcinoma migration, invasion, and EMT. Cancer cell international. 2021;21(1):677.
Zheng Z, Cai Y, Chen H, Chen Z, Zhu D, Zhong Q, et al. CXCL13/CXCR5 Axis Predicts Poor Prognosis and Promotes Progression Through PI3K/AKT/mTOR Pathway in Clear Cell Renal Cell Carcinoma. Frontiers in oncology. 2018;8:682.
Ma KS-K, Liu P, Luo J, Zhao L, Fu Q, Chen Y, et al. Causal relationship between several autoimmune diseases and renal malignancies: A two-sample mendelian randomization study. Plos One. 2024;19(2).
Clark DJ, Dhanasekaran SM, Petralia F, Pan J, Song X, Hu Y, et al. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma. Cell. 2019;179(4):964-83.e31.
Akhtar M, Al-Bozom IA, Hussain TA. Molecular and Metabolic Basis of Clear Cell Carcinoma of the Kidney. Advances in anatomic pathology. 2018;25(3):189-96.
Simon MC. Coming up for air: HIF-1 and mitochondrial oxygen consumption. Cell metabolism. 2006;3(3):150-1.
Huang L, Xie Y, Han W, Jiang S, Zeng L. Oxidative Phosphorylation-Related Signature Participates in Cancer Development, and PTPRG Overexpression Suppresses the Cancer Progression in Clear Cell Renal Cell Carcinoma. Journal of immunology research. 2022;2022:8300187.
Peters I, Dubrowinskaja N, Tezval H, Kramer MW, Klot CAv, Hennenlotter J, et al. Decreased mRNA expression of GATA1 and GATA2 is associated with tumor aggressiveness and poor outcome in clear cell renal cell carcinoma. Targeted oncology. 2015;10(2):267-75.
Yang X, Mei C, Nie H, Zhou J, Ou C, He X. Expression profile and prognostic values of GATA family members in kidney renal clear cell carcinoma. Aging. 2023;15(6):2170-88.

No competing interests reported.

Additionalfile1.xls
Additional file 1 List of primer sequences.
Additionalfile2.xls
Additional file 2 The detail results of Gene set variation analysis (GSEA).

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Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma

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Abstract

Figures

1 Background

2 Methods

2.1 Data source

2.2 Identification of candidate MPTDNRGs

2.3 Identification of key MPTDNRGs

2.4 Construction and validation of risk model

2.5 Independent prognostic analysis

2.6 Functional and annotation analyses

2.7 Immune infiltration analysis

2.8 Construction of regulatory network

2.9 Expression validation of key MPTDNRGs

2.10 Statistical Analysis

3 Results

3.1 A sum of 56 candidate MPTDNRGs in PPI network were screen out

3.2 IL2RA, CD7 and CXCL13 with high expression levels in KIRC samples were identified as key MPTDNRGs

3.3 A risk model was constructed according key MPTDNRGs

3.4 Only risk score and age were independent factors of prognosis

3.5 Different risk groups-related signaling pathways

3.6 Immune analysis of KIRC patients

3.7 Regulatory network of key MPTDNRGs

3.8 Verification of key MPTDNRGs expression

4. Discussion

5 Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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