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

Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma Yikai Wang, Dingyang Lv, Wei Zhang, Weibing Shuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5041616/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Background 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 Methods 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 (|log 2 fold 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 = Σ n 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 −ΔΔCt method[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 Results 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). 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 GATA 2. 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) . 4. Discussion 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. 5 Conclusion 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. Abbreviations 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 GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes PPI Protein-protein interaction LASSO Least absolute selection and shrinkage operator PH Proportional hazards ROC Receiver operating characteristic AUC Area under the curve OS Overall survival DCA Decision curve analysis TIDE Tumor immune dysfunction and exclusion TMB Tumor mutational burden ceRNA competitive endogenous RNA TFs Transcription factors CC Cellular component MF Molecular function OXPHOS Oxidative phosphorylation PTPRG Protein tyrosine phosphatase receptor gamma Declarations 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. References 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. 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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. Additional Declarations No competing interests reported. Supplementary Files 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5041616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357644098,"identity":"85b9ebdc-f1b0-4fd9-8cd3-5de0f4722b20","order_by":0,"name":"Yikai Wang","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yikai","middleName":"","lastName":"Wang","suffix":""},{"id":357644099,"identity":"ad206c67-eb54-4a3b-86a7-b162c5a78e4c","order_by":1,"name":"Dingyang Lv","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dingyang","middleName":"","lastName":"Lv","suffix":""},{"id":357644100,"identity":"e7f9aa21-ed60-4052-9b1d-22e19dcc5a81","order_by":2,"name":"Wei Zhang","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":357644101,"identity":"9f15ba9b-c591-4e40-8f6f-009a840a21d0","order_by":3,"name":"Weibing Shuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCSjNJsHYwMBQISHHT6KWMxbGkg3EagEzGNsqEjcQ0iI/u8fwc8Gvw3J80s1tD7/Ok2DcwMD88NENPFoM7pwxlp7Zd9iYTeZgu7HsNglmcwY2Y+McfFokcgykeXtuJ7ZJJLZJS26TYLNs4GGTxqdFfkaO8W+glnqIljkSPAYHCGhhuJFjJs3z43YCG1CL5McGCQmCWgxupJVZ8zb8N2yTOdgmzXBMwkCymYBf5Gckb77N8ydNXn52+zPJHzV19f3szQ8f43UYCDC2QWhmHjBJSDkY/IFq/UGU6lEwCkbBKBhpAADIvkiPgyLxrgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Weibing","middleName":"","lastName":"Shuang","suffix":""}],"badges":[],"createdAt":"2024-09-06 05:10:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5041616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5041616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67196589,"identity":"e31e65bb-ee0f-4eed-8e1b-898bedd06854","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14272117,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of candidate MPT-driven necrosis-related genes in KIRC.\u003cstrong\u003e (a-b)\u003c/strong\u003e Volcanic and thermal maps of DEGs-KIRC.\u003cstrong\u003e (c)\u003c/strong\u003eMPTDNRGs scores were significant in Control samples and KIRC samples discrepancy.\u003cstrong\u003e (d-e)\u003c/strong\u003eVolcano and heat maps of DEGs-MPT.\u003cstrong\u003e (f) \u003c/strong\u003eVenn map of candidate gene identification. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eKIRC, kidney renal clear cell carcinoma; DEGs, differentially expressed genes; MPTDNRGs, MPT driven necrosis-related genes).\u003c/p\u003e","description":"","filename":"Figure139.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/c162e226b0875a6f85b78bc2.png"},{"id":67196598,"identity":"1b474949-414f-425a-9335-f9cf94508ed4","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25617338,"visible":true,"origin":"","legend":"\u003cp\u003eFurther screening of candidate MPT-driven necrosis-related genes in KIRC.\u003cstrong\u003e (a-b)\u003c/strong\u003eCandidate gene GO and KEGG enrichment analyses. \u003cstrong\u003e(c)\u003c/strong\u003e Candidate genes PPI interaction network; (\u003cstrong\u003ed)\u003c/strong\u003e 56 candidate gene PPI interaction networks.\u003c/p\u003e\n\u003cp\u003eGO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure226.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/dd2bbe9bf45c1e26237d44a8.png"},{"id":67198334,"identity":"9ac5431b-b472-49fa-854e-e0b299cc89f0","added_by":"auto","created_at":"2024-10-22 09:28:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5301288,"visible":true,"origin":"","legend":"\u003cp\u003eScreening and identification of key genes. \u003cstrong\u003e(a)\u003c/strong\u003e The forest maps of univariate Cox regression candidate MPTDNRGs.\u003cstrong\u003e (b-c)\u003c/strong\u003e LASSO analysis of candidate MPTDNRGs.\u003cstrong\u003e (d) \u003c/strong\u003eThe Forest maps of multivariate Cox regression candidate MPTDNRGs. \u003cstrong\u003e(e-f)\u003c/strong\u003e Expression levels in the \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e datasets\u003cstrong\u003e (e\u003c/strong\u003e, TCGA-KIRC dataset; \u003cstrong\u003ef\u003c/strong\u003e, GSE40435 dataset. *p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001; ****P\u0026lt;0.0001\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eMPTDNRGs, MPT driven necrosis-related genes; LASSO, Least absolute selection and shrinkage operator.\u003c/p\u003e","description":"","filename":"Figure326.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/e18955a200883050e042f901.png"},{"id":67198333,"identity":"40e783c0-9738-461e-bb5a-5684642273ce","added_by":"auto","created_at":"2024-10-22 09:28:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4932203,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a prognostic risk model and its prognostic value.\u003cstrong\u003e (a)\u003c/strong\u003e The ROC curves of key MPTDNRGs (TCGA-KIRC dataset).\u003cstrong\u003e (b-c)\u003c/strong\u003e Risk curves for key genes and gene expression chi-square plots for high- and low-risk groups (TCGA-KIRC dataset).\u003cstrong\u003e (d)\u003c/strong\u003e Kaplan-Meier curves for the overall survival of patients in the high- and low-risk groups(TCGA-KIRC dataset).\u003cstrong\u003e (e) \u003c/strong\u003eThe ROC curves of key MPTDNRGs(GSE40435 dataset). \u003cstrong\u003e(f-h)\u003c/strong\u003e Risk curves for key MPTDNRGs, chi-square plots of key gene expression for high and low risk groups, and Kaplan-Meier curves of overall survival for patients in high and low risk groups (GSE40435 dataset).\u003c/p\u003e\n\u003cp\u003eROC, Receiver operating characteristic; KIRC, Kidney renal clear cell carcinoma.\u003c/p\u003e","description":"","filename":"Figure422.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/2e9cda5a696c66f51852ef8c.png"},{"id":67196591,"identity":"0e2020d3-3a46-4a2a-8e46-aa596aada3ea","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3813126,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of independent prognostic factors. \u003cstrong\u003e(a)\u003c/strong\u003e The result of univariate Cox regression analysis. \u003cstrong\u003e(b)\u003c/strong\u003eThe result of multivariate Cox regression analysis.\u003cstrong\u003e (c)\u003c/strong\u003e Nomogram of independent prognostic factors. (\u003cstrong\u003ed)\u003c/strong\u003eThe nomogram correction curve.\u003cstrong\u003e (e-g) \u003c/strong\u003e1-year, 3-year, and 5-year nomogram model DCA curves.\u003c/p\u003e\n\u003cp\u003eDCA, Decision curve analysis.\u003c/p\u003e","description":"","filename":"Figure59.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/c28dddf36897a4f7818c89bc.png"},{"id":67196594,"identity":"78baf3e6-7b9b-4be1-91b8-6552c8ebffc0","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1831756,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment analysis in high and low risk groups.\u003c/p\u003e\n\u003cp\u003eGSEA, gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure67.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/33d5fe8fd0534d87d5799e52.png"},{"id":67196597,"identity":"3b4fe0b6-b635-4f06-859f-8a4b90cec2d8","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15940371,"visible":true,"origin":"","legend":"\u003cp\u003eImmune analysis. \u003cstrong\u003e(a) \u003c/strong\u003eHeat map of immune cell infiltration.\u003cstrong\u003e (b)\u003c/strong\u003e Box plots of 28 types of immune cell infiltration in high and low risk groups.\u003cstrong\u003e (c)\u003c/strong\u003e Differential immune cell correlation heat map. (\u003cstrong\u003ed) \u003c/strong\u003eHeatmap of correlation between key genes and differential immune cells.\u003c/p\u003e","description":"","filename":"Figure76.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/b46c9689f89ebf73424232a7.png"},{"id":67197341,"identity":"ff06da90-ae1b-448a-916b-df4e7aa3bb45","added_by":"auto","created_at":"2024-10-22 09:20:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8921066,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoassay results in high and low risk groups. \u003cstrong\u003e(a) \u003c/strong\u003eScatterplot of correlation between CD7 and activated CD8 T-cells,\u003cstrong\u003e (b) \u003c/strong\u003eBox plot of CD7 expression in high and low risk groups. \u003cstrong\u003e(c) \u003c/strong\u003eBox plot of activated CD8 T-cells expression in high and low risk groups (*p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001; ****P\u0026lt;0.0001). \u003cstrong\u003e(d)\u003c/strong\u003e Immune checkpoint gene expression between high and low risk groups. \u003cstrong\u003e(e)\u003c/strong\u003e TIDE scores were different between high and low risk groups (*p\u0026lt;0.05; **, p\u0026lt;0.01; ***, p\u0026lt;0.001; ****P\u0026lt;0.0001). \u003cstrong\u003e(f-g)\u003c/strong\u003e The top 20 genes with the highest mutation frequencies in the \u003cstrong\u003e(f) \u003c/strong\u003ehigh-risk\u003cstrong\u003e \u003c/strong\u003eand\u003cstrong\u003e (g)\u003c/strong\u003e low-risk groups.\u003c/p\u003e","description":"","filename":"Figure84.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/8fe0ca03372b1b3d601bfd74.png"},{"id":67196595,"identity":"fb450d48-0135-43b7-80c2-2a7e6e4ca30e","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4393538,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory network and clinical validation of key MPTDNRGs.\u003cstrong\u003e (a)\u003c/strong\u003e The ceRNA network. \u003cstrong\u003e(b) \u003c/strong\u003eThe TF-mRNA-miRNA network.\u003cstrong\u003e (c-e)\u003c/strong\u003e RT-qPCR expression validation of \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e, \u003cem\u003eCXCL13\u003c/em\u003ein KIRC samples.\u003c/p\u003e\n\u003cp\u003eMPTDNRGs, MPT driven necrosis-related genes; RT-qPCR, Reverse transcription quantitative polymerase chain reaction; TF-mRNA-miRNA, transcription factors-messenger RNA-microRNAs; KIRC, Kidney renal clear cell carcinoma.\u003c/p\u003e","description":"","filename":"Figure94.png","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/1339b70a39247a5dc92223dc.png"},{"id":67786322,"identity":"bdb1e248-3bc6-4389-864d-29b6fd8cfc37","added_by":"auto","created_at":"2024-10-29 17:02:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":79410138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/7cd2bac8-06db-4acd-9b74-5e5aaa895175.pdf"},{"id":67196588,"identity":"db043ac9-749e-4a85-93a3-19441152398c","added_by":"auto","created_at":"2024-10-22 09:12:20","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1 \u003c/strong\u003eList of primer sequences.\u003c/p\u003e","description":"","filename":"Additionalfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/a58383074f8ad536da0744de.xls"},{"id":67197339,"identity":"fd50ba6a-243c-42f2-8ccd-15c30430ec7d","added_by":"auto","created_at":"2024-10-22 09:20:20","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2 \u003c/strong\u003eThe detail results of Gene set variation analysis (GSEA).\u003c/p\u003e","description":"","filename":"Additionalfile2.xls","url":"https://assets-eu.researchsquare.com/files/rs-5041616/v1/4e6b1947346b28d0dee10d6d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma","fulltext":[{"header":"1\tBackground","content":"\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNecrosis triggered by\u0026nbsp;mitochondrial permeability transition (MPT)\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"2\tMethods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Data source\u003c/h2\u003e\n\u003cp\u003eThe 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).\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Identification of candidate MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eIn TCGA-KIRC dataset, differentially expressed genes (DEGs) were screened out from KIRC and paracancerous samples (|log\u003csub\u003e2\u003c/sub\u003efold change (FC)| \u0026gt; 1 and adj.P \u0026lt; 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\u0026apos;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]\u0026nbsp;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 \u0026lt; 0.05). GO and KEGG results were presented via ggpubr package (v 0.6.0)[21]\u0026nbsp;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 \u0026gt; 0.4). And the MCODE in Cytoscape software (v 3.9.1)[23]\u0026nbsp;was utilized to identify and visualize significant gene clusters within PPI network.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Identification of key MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eFirst, 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]\u0026nbsp;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.\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Construction and validation of risk model\u003c/h2\u003e\n\u003cp\u003eThe 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 = \u0026Sigma;\u003csup\u003en\u003c/sup\u003e\u003csub\u003ei=1\u003c/sub\u003e(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.\u003c/p\u003e\n\u003ch2\u003e2.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Independent prognostic analysis\u003c/h2\u003e\n\u003cp\u003eTo begin with, we merged risk scores and clinical features\u0026mdash;like age, grade, gender, and tumor stage\u0026mdash;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.\u003c/p\u003e\n\u003ch2\u003e2.6\u0026nbsp; \u0026nbsp; \u0026nbsp;Functional and annotation analyses\u003c/h2\u003e\n\u003cp\u003eTo 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.\u003c/p\u003e\n\u003ch2\u003e2.7\u0026nbsp; \u0026nbsp; \u0026nbsp;Immune infiltration analysis\u003c/h2\u003e\n\u003cp\u003eTo 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 \u0026lt; 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 (\u003cem\u003eCD274\u003c/em\u003e), PD-1 (\u003cem\u003ePDCD1\u003c/em\u003e), CTLA-4 (\u003cem\u003eCTLA4\u003c/em\u003e), TIGIT, LAG-3 (\u003cem\u003eLAG3\u003c/em\u003e), GAL9 (\u003cem\u003eLGALS9\u003c/em\u003e), TIM-3 (\u003cem\u003eHAVCR2\u003c/em\u003e), PD-L2 (\u003cem\u003ePDCD1LG2\u003c/em\u003e), SIRP\u0026alpha; (\u003cem\u003eSIRPA\u003c/em\u003e),\u003cem\u003e\u0026nbsp;BTLA\u003c/em\u003e and Siglec-7 (\u003cem\u003eSIGLEC7\u003c/em\u003e)][30] were compared (P \u0026lt; 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 \u0026lt; 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].\u003c/p\u003e\n\u003ch2\u003e2.8\u0026nbsp; \u0026nbsp; \u0026nbsp;Construction of regulatory network\u003c/h2\u003e\n\u003cp\u003eThe miRNAs of key MPTDNRGs were predicted in miRDB database. And the lncRNAs corresponding to miRNAs were predicted in starBase database (clipExpNum \u0026gt; 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.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.9\u0026nbsp; \u0026nbsp; \u0026nbsp;Expression validation of key MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eKIRC 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\u0026rsquo;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 \u003cstrong\u003eAdditional file 1\u003c/strong\u003e. GAPDH was used as an internal reference gene. Gene expression levels were calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u0026nbsp;\u003c/sup\u003emethod[33].\u003c/p\u003e\n\u003ch2\u003e2.10\u0026nbsp; \u0026nbsp;Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;A sum of 56 candidate MPTDNRGs in PPI network were screen out\u003c/h2\u003e\n\u003cp\u003eWe 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\u003cstrong\u003e\u0026nbsp;(Figure 1a, 1b)\u003c/strong\u003e. Next, the scores of MPTDNRGs were calculated in KIRC and paracancerous samples, revealing a significantly higher score in KIRC samples (P \u0026lt; 0.0001)\u003cstrong\u003e\u0026nbsp;(Figure 1c)\u003c/strong\u003e. Totally 408 DEGs-MPT, containing 267 up-regulated and 141 down-regulated were acquired from different scoring groups \u003cstrong\u003e(Figure 1d, 1e)\u003c/strong\u003e. Ultimately, we identified 339 candidate MPTDNRGs by overlapping DEGs-KIRC and DEGs-MPT \u003cstrong\u003e(Figure 1f)\u003c/strong\u003e. To uncover the biological roles and pathways linked to potential candidate MPTDNRGs, GO and KEGG enrichment analyses were performed \u003cstrong\u003e(Figure 2a, 2b)\u003c/strong\u003e. 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\u003cstrong\u003e\u0026nbsp;(Figure 2c)\u003c/strong\u003e. 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\u003cstrong\u003e\u0026nbsp;(Figure 2d)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e with high expression levels in KIRC samples were identified as key MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eWe identified 19 genes with survival-related [hazard ratio (HR)\u0026ne;1 and P \u0026lt; 0.05]\u003cstrong\u003e\u0026nbsp;(Figure 3a)\u003c/strong\u003e, and PH hypothesis test revealed that these 19 genes satisfied the hypothesis (P \u0026gt; 0.05). Then, 7 genes were further screened out (Lambdamin = 0.015), namely \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e, \u003cem\u003eCXCL13\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e,\u003cem\u003e\u0026nbsp;CD38\u003c/em\u003e, \u003cem\u003eIL2RG\u003c/em\u003e and\u003cem\u003e\u0026nbsp;IL10RA\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;(Figure 3b, 3c)\u003c/strong\u003e. Afterwards, \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e, \u003cem\u003eCXCL13\u003c/em\u003e and\u003cem\u003e\u0026nbsp;IL2RG\u003c/em\u003e were screened out by means of multivariate Cox analysis (HR\u0026ne;1 and P \u0026lt; 0.05)\u003cstrong\u003e\u0026nbsp;(Figure 3d)\u003c/strong\u003e. Nevertheless, \u003cem\u003eIL2RG\u003c/em\u003e exhibited a HR of less than 1 (HR = 0.69), contradicting the results of univariate Cox regression analysis (HR = 1.2). Accordingly, \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e were designated as key MPTDNRGs. And key MPTDNRGs exhibited high expression levels in KIRC samples from both TCGA-KIRC dataset and GSE40435 dataset (P \u0026lt; 0.0001) \u003cstrong\u003e(Figure 3e, 3f)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;A risk model was constructed according key MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eConsequently, a risk model was constructed by key MPTDNRGs, with RiskScore calculated as follows: = \u003cem\u003eIL2RA\u003c/em\u003e*0.3233 + \u003cem\u003eCD7\u003c/em\u003e*0.3673 + \u003cem\u003eCXCL13\u003c/em\u003e*(-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\u003cstrong\u003e\u0026nbsp;(Figure 4a)\u003c/strong\u003e. These findings indicated the favorable efficacy of our risk model. \u003cstrong\u003eFigure 4b, 4c\u003c/strong\u003e 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) \u003cstrong\u003e(Figure 4d)\u003c/strong\u003e. 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\u003cstrong\u003e\u0026nbsp;(Figure 4e)\u003c/strong\u003e. And likewise, high risk patients had significantly worst OS (P = 0.036)\u003cstrong\u003e\u0026nbsp;(Figure 4 f-h)\u003c/strong\u003e. The results were consistent with training cohort.\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Only risk score and age were independent factors of prognosis\u003c/h2\u003e\n\u003cp\u003eThis 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\u0026ne;1 and P \u0026lt; 0.05) \u003cstrong\u003e(Figure 5a)\u003c/strong\u003e. Nonetheless, the PH hypothesis test indicated that neither tumor grade nor stage fulfilled the required assumptions (P \u0026lt; 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\u0026ne;1 and P \u0026lt; 0.05)\u003cstrong\u003e\u0026nbsp;(Figure 5b)\u003c/strong\u003e. Consequently, a nomogram was developed incorporating these two factors, risk score and age \u003cstrong\u003e(Figure 5c)\u003c/strong\u003e. The calibration plots, which closely matched the reference line, suggested that the nomogram had a favorable predictive accuracy\u003cstrong\u003e\u0026nbsp;(Figure 5d)\u003c/strong\u003e. 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 \u003cstrong\u003e(Figure 5e-g)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e3.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Different risk groups-related signaling pathways\u003c/h2\u003e\n\u003cp\u003eConducting 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 \u003cstrong\u003eAdditional file 2\u003c/strong\u003e 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. \u003cstrong\u003e(Figure 6)\u003c/strong\u003e. GSEA uncovered distinct signaling pathways linked to various risk groups, thereby broadening our in-depth comprehension of KIRC.\u003c/p\u003e\n\u003ch2\u003e3.6\u0026nbsp; \u0026nbsp; \u0026nbsp;Immune analysis of KIRC patients\u003c/h2\u003e\n\u003cp\u003eThe heatmap illustrated the scores of 28 immune cells \u003cstrong\u003e(Figure 7a)\u003c/strong\u003e. Evidently, in high risk group, except for CD56bright natural killer cells, eosinophils and neutrophils, the proportion of 25 immune cells were significantly higher (P \u0026lt; 0.05)\u003cstrong\u003e\u0026nbsp;(Figure 7b)\u003c/strong\u003e. 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 \u0026lt; 0.001) \u003cstrong\u003e(Figure 7c-d)\u003c/strong\u003e. Further, the strongest correlation was observed between \u003cem\u003eCD7\u003c/em\u003e and activated \u003cem\u003eCD8\u0026nbsp;\u003c/em\u003eT cells (r = 0.856, P \u0026lt; 0.001), exhibiting significantly higher expression in high risk group (P \u0026lt; 0.0001) \u003cstrong\u003e(Figure 8a-c)\u003c/strong\u003e. 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\u003cem\u003e\u0026nbsp;BTLA\u003c/em\u003e, \u003cem\u003eCD274\u003c/em\u003e and \u003cem\u003eCTLA4\u003c/em\u003e (P \u0026lt; 0.001)\u003cstrong\u003e\u0026nbsp;(Figure 8d)\u003c/strong\u003e. TIDE score was analyzed to access the potential for tumor immune evasion. Obviously, high risk group patients exhibited significantly higher TIDE score (P \u0026lt; 0.05) \u003cstrong\u003e(Figure 8e)\u003c/strong\u003e. Additionally, the waterfall plot illustrated the top 20 mutations in tumor cells of different risk groups \u003cstrong\u003e(Figure 8f, 8g)\u003c/strong\u003e. The results indicated that \u003cem\u003eVHL\u003c/em\u003e and \u003cem\u003ePBRM1\u003c/em\u003e 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.\u003c/p\u003e\n\u003ch2\u003e3.7\u0026nbsp; \u0026nbsp; \u0026nbsp;Regulatory network of key MPTDNRGs\u003c/h2\u003e\n\u003cp\u003eThe 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 \u003cem\u003eCXCL13\u003c/em\u003e-hsa-miR-670-5p-AL121985.1, \u003cem\u003eIL2RA\u003c/em\u003e-hsa-miR-6088-AL513497.1 \u003cstrong\u003e(Figure 9a)\u003c/strong\u003e. A total of 25 TFs were predicted for key MPTDNRGs. Notably, \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e collectively predicted \u003cem\u003eGATA\u003c/em\u003e2. \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e collectively predicted\u003cem\u003e\u0026nbsp;MAX\u003c/em\u003e and \u003cem\u003eGATA3\u003c/em\u003e. \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eIL2RA\u003c/em\u003e collectively predicted USF2\u003cstrong\u003e\u0026nbsp;(Figure 9b)\u003c/strong\u003e. The results indicated the regulatory mechanism for key MPTDNRGs in KIRC.\u003c/p\u003e\n\u003ch2\u003e3.8\u0026nbsp; \u0026nbsp; \u0026nbsp;Verification of key MPTDNRGs expression\u003c/h2\u003e\n\u003cp\u003eIn the previous studies, we observed that \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e exhibited significantly higher expression levels in KIRC samples in both TCGA-KIRC and GSE40435 (P \u0026lt; 0.0001)\u003cstrong\u003e\u0026nbsp;(Figure 3e, 3f)\u003c/strong\u003e. 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 \u003cem\u003eIL2RA\u003c/em\u003e and \u003cem\u003eCD7\u003c/em\u003e showed significantly higher expression in KIRC samples (P \u0026lt; 0.05), while \u003cem\u003eCXCL13\u003c/em\u003e also showed an up-regulation trend in KIRC (P = 0.0820), consistent with our previous findings \u003cstrong\u003e(Figure 9c-e)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe 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 (\u003cem\u003eIL2RA\u003c/em\u003e,\u003cem\u003e\u0026nbsp;CD7\u003c/em\u003e and\u003cem\u003e\u0026nbsp;CXCL13\u003c/em\u003e), and constructed a new risk prognosis model, providing a new reference for clinical diagnosis and treatment of KIRC.\u003c/p\u003e\n\u003cp\u003eWe 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 \u003cem\u003eIL2RA\u003c/em\u003e is related to the prognosis of clear cell carcinoma[35], which is consistent with the results of our study. NK cell marker genes, including \u003cem\u003eCD7\u003c/em\u003e 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 \u003cem\u003eIL-2RA\u003c/em\u003e and \u003cem\u003eCD7\u003c/em\u003e are correlated with the prognosis of patients with KIRC. However, the specific mechanism is still relatively limited and further exploration is needed.\u0026nbsp;Researchers have more studies on \u003cem\u003eCXCL13\u003c/em\u003e, and a large number of evidences show that \u003cem\u003eCXCL13\u003c/em\u003e 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/\u003cem\u003eCXCL13\u003c/em\u003e signaling[38]; M2 macrophages in the immune environment can secrete \u003cem\u003eCXCL13\u003c/em\u003e, thus promoting the proliferation, migration, invasion and epithelial-mesenchymal transformation of ccRCC cells[39]; \u003cem\u003eCXCL13\u003c/em\u003e can activate the PI3K/AKT/mTOR signaling pathway by binding with \u003cem\u003eCXCR5\u003c/em\u003e to promote the proliferation and migration of ccRCC cells[40]. In conclusion, \u003cem\u003eCXCL13\u0026nbsp;\u003c/em\u003eis closely related to KIRC, and increased \u003cem\u003eCXCL13\u003c/em\u003e expression is associated with poor survival outcomes in KIRC patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence 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.\u003c/p\u003e\n\u003cp\u003eThe 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, \u003cem\u003eCD7\u003c/em\u003e. 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.\u003c/p\u003e\n\u003cp\u003ePrevious 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].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur 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.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, by applying bioinformatics approaches, we discovered and clinically substantiated the key genes (\u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e, and \u003cem\u003eCXCL13\u003c/em\u003e) 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eMPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eMitochondrial permeability transition\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003ekidney renal clear cell carcinoma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eMPTDNRGs\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eMPT driven necrosis-related genes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eReverse transcription quantitative polymerase chain reaction\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eRCC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eRenal cell carcinoma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eGene\u0026nbsp;expression\u0026nbsp;omnibus\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eMolecular signatures database\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003essGSEA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003esingle sample gene set enrichment analysis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eGene set variation analysis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eProtein-protein interaction\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eLeast absolute selection and shrinkage operator\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eProportional hazards\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eTIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eTumor immune dysfunction and exclusion\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eTumor mutational burden\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eceRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003ecompetitive endogenous RNA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eTFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eTranscription factors\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eCellular component\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eMolecular function\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003eOXPHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eOxidative phosphorylation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8198%;\"\u003e\n \u003cp\u003ePTPRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80.1802%;\"\u003e\n \u003cp\u003eProtein tyrosine phosphatase receptor gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis 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\u0026nbsp;(Ethics Review No:\u0026nbsp;KYLL-2024-028).\u0026nbsp;Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe 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].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eW.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.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the entire team for their support and assistance with this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCapitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. 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Coming up for air: HIF-1 and mitochondrial oxygen consumption. Cell metabolism. 2006;3(3):150-1.\u003c/li\u003e\n \u003cli\u003eHuang 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.\u003c/li\u003e\n \u003cli\u003ePeters 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.\u003c/li\u003e\n \u003cli\u003eYang 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.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"driven necrosis, renal cell carcinoma, enrichment analysis, MPT, competitive endogenous RNA","lastPublishedDoi":"10.21203/rs.3.rs-5041616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5041616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Three key MPTDNRGs, namely \u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e, and \u003cem\u003eCXCL13\u003c/em\u003e, 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 \u003cem\u003eCXCL13\u003c/em\u003e-hsa-miR-670-5p-AL121985.1, \u003cem\u003eIL2RA\u003c/em\u003e-hsa-miR-6088-AL513497.1, and in total 25 TFs were predicted for key MPTDNRGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e In this study, three key genes (\u003cem\u003eIL2RA\u003c/em\u003e, \u003cem\u003eCD7\u003c/em\u003e and \u003cem\u003eCXCL13\u003c/em\u003e) 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.\u003c/p\u003e","manuscriptTitle":"Explore the key genes and prognosis related to mitochondrial permeability transition driving necrosis gene in kidney renal clear cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 09:12:15","doi":"10.21203/rs.3.rs-5041616/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66420131-bfb8-4534-9b9d-12a35c1b3c77","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T04:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 09:12:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5041616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5041616","identity":"rs-5041616","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}