ITPKA as a biomarker for proliferation, migration and metastasis in renal clear cell carcinoma

ITPKA as a biomarker for proliferation, migration and metastasis in 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 ITPKA as a biomarker for proliferation, migration and metastasis in renal clear cell carcinoma Ziang Si, Jianping Liu, Shuaizhi Zhu, Zengshun Kou, Hai Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7374587/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 Inositol-Trisphosphate3-Kinase A (ITPKA), a phosphorylated kinase that acts primarily on cellular metabolism, is highly expressed in renal clear cell carcinoma (ccRCC) and is associated with tumor progression and prognosis, but the action mechanism of ITPKA in renal cell carcinoma is not yet fully understood. The differential expression pattern of ITPKA was investigated using Cancer Genome Atlas (TCGA) and GEPIA2 databases. The expression of ITPKA in ccRCC patients was further verified by immunohistochemical (IHC) examination of 20 clinicopathologic specimens. A protein-protein interaction (PPI) network was established to include ITPKA and differentially expressed genes. The role of ITPKA in the PPAR pathway was predicted by functional enrichment analysis. Our results showed that ITPKA was overexpressed in ccRCC, and the higher the expression, the worse the clinical outcomes such as TNM staging and pathological grading. Immune infiltration analysis suggested a potential link between ITPKA expression and immune infiltration. In addition, patients with high ITPKA expression had worse survival compared with patients with low expression. Finally, to validate our earlier studies, we performed cellular functional tests and protein imprinting assays on ccRCC cell line 786-O. The experimental results showed that ITPKA knockdown significantly reduced the invasion and migration rates of renal cell carcinoma tumor cells, while PPAR pathway activity was also significantly inhibited. Overall, our study revealed that ITPKA is a promising biomarker with prognostic potential in ccRCC. Its key regulatory role in the PPAR signaling pathway provides research value and lays the foundation for future targeted therapy research. Clear cell renal cell carcinoma ITPKA biomarkers molecular mechanisms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Renal cancer (RCC) is one of the deadliest malignant tumors of the kidney in adults, the 6th most frequently diagnosed cancer in men,[1] and the highest incidence is in Western countries, accounting for about 3% of all cancers.[2] Among the histological subtypes of RCC, clear cell renal cell carcinoma is the most common, representing approximately 70–75% of all cases.[3, 4] Although many patients with early-stage renal cell carcinoma (RCC) have had remarkable results after surgical treatment in the last decade, the prognosis is still poor once they enter the advanced stage, with a 5-year survival rate less than 10% on average.[5, 6] As RCC exhibits resistance to conventional radiotherapy, chemotherapy, and hormonal therapy, the search for new biomarkers and molecular targets is crucial for the treatment, early diagnosis, and prognostic evaluation of ccRCC. Ins (1,4,5) P3-kinase-A (ITPKA or InsP3kinase) was first described and characterized by Irvine et al. in 1986, and cloning was achieved by Takazawa et al. in 1990.[7, 8] ITPKA is a cell motility-promoting protein that increases the metastatic potential of tumor cells.[9] ITPKA is localized on chromosome 15q15, its C-terminal amino acid sequence exhibits high similarity, while the N-terminal region shows relatively low conservation. In addition to regulating InsP4 production, ITPKA can also modulate cellular plasticity by controlling F-actin binding.[10] And its expression is stimulated by methylation in tumor tissues such as hepatocellular carcinomas, lung carcinomas, ovarian carcinomas, and breast carcinomas, and is elevated as the malignancy of these tumors increases.[10–12] Since there are no clear therapeutic options for metastatic lung or breast tumors, blocking ITPKA activity may provide new options for patients with these tumors.[13] The mechanisms underlying the increased expression of ITPKA in tumors are complex and may be regulated by a variety of molecular mechanisms, including DNA methylation, microRNAs, or aberrant transcription factor signaling.[14] Although ITPKA has been identified as an oncogene, little is known about the role of ITPKA in cancer progression compared to the related PI3K family. Peroxisome proliferator-activated receptors (PPARs) function as nuclear hormone receptors and are triggered by fatty acids and related molecules. There are three subtypes of PPARs in vertebrates (PPARα, PPARβ, and PPARγ) that differ in their expression patterns.[15] They are encoded by different genes and bind fatty acids and prostaglandins, respectively. PPARα regulates genes involved in lipid metabolism in the liver and skeletal muscle, thereby facilitating the clearance of circulating and intracellular lipids, promoting adaptive responses to fasting. PPARβ increases glucose and lipid metabolism by up-regulating mitochondrial function and fatty acid desaturation; PPARγ promotes adipose cell differentiation, fatty acid uptake, and lipid droplet storage, thereby increasing systemic insulin sensitivity and reducing ectopic lipid deposition.[16–18] Although the main target genes of the PPAR signaling pathway are related to FAO (fatty acid oxidation) and lipid metabolism, PPAR also plays important roles in tumor and immune cells, especially in metabolic reprogramming of cells, lipid droplet formation, and macrophage differentiation.[4] In the pre-analysis experiments of this study, we found that the results of enrichment analysis showed that ITPKA was closely linked to the PPAR signaling pathway with a significant correlation, which suggests that ITPKA may be similar to PI-3K and act in a common downstream pathway, that is, the PPAR signaling pathway. The present study consisted of analyzing the expression of ITPKA and its potential association with various clinical features. In addition, we verified its expression in clinical specimens from 20 patients with renal clear cell carcinoma (KIRC) by immunohistochemistry (IHC), cell scratch assay, Transwell assay, and protein blotting (Western Blot, WB). In addition, we constructed a PPI network involving ITPKA and its related differentially expressed genes. We predicted the effect of ITPKA in the PPAR pathway and its role in promoting the development of KIRC by integrating cell signaling pathway enrichment analysis and immune infiltration analysis. Finally, we aimed to reveal the specific biological mechanisms of ITPKA in ccRCC development and assess its feasibility as a potential therapeutic target. Methods Test sources and pretreatment We obtained mRNA expression profiles of ITPKA in pan-cancer and corresponding normal tissues through TCGA and GTEx databases. We collected RNA-seq data from unpaired and paired samples from KIRC in the TCGA database and performed subsequent processing.[19] 'limma' and other R (v3.6.3) packages were used for normalization, standardization and visualization of the processing. ITPKA differential expression analysis The Wilcoxon rank sum test was used to evaluate the differential expression of ITPKA in pan-cancer tissues. The expression profile data of ITPKA in paired and unpaired samples were subjected to Shapiro-Wilk normality analysis followed by Wilcoxon rank sum test. The chi-square test was used to analyze the relationship between ITPKA expression and clinical data of TCGA-KIRC patients. All of the above analyses were considered statistically significant at p < 0.05. Differential expression analysis was performed, adjusted for p-value 1, and the screened differential genes were plotted as volcano plots. And 100 co-expressed genes were found with the help of Genemania database and STRING database to analyze the PPI network, and Cytoscape software was used to screen the related genes.[20] Functional enrichment analysis ITPKA was entered into the "General" module of the GEPIA database, and the 100 genes most similar to ITPKA were filtered out. These 100 genes were subjected to gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the "clusterProfiler, GOplot package". ITPKA differential expression analysis was performed on the TCGA-KIRC cohort, and gene set enrichment analysis (GSEA) was performed on the results of the differential analysis,[21] and the reference gene set used for GSEA was 'h.all.v2022.1.Hs.symbols.gmt[Hallmarks]'. ITPKA immune infiltration analysis 24 immune cell infiltration levels were analyzed in the ITPKA high expression group and the ITPKA low expression group, and the corresponding enrichment scores were calculated by the ssGSEA algorithm. We defined the significant relative threshold as p-value < 0.001. Prognosis analysis and model construction The Wilcoxon signed rank sum test was used to compare the clinicopathologic features of ITPKA and ccRCC. Kaplan-Meier (KM) survival analysis was performed on the clinical information data of prostate adenocarcinoma samples in the TCGA database using the "survival" package, and the results were analyzed by the "survminer" package and the "ggplot2" package. The results were visualized using the "survminer" and "ggplot2" packages. ROC analysis was performed using the "pROC" package, and AUC values were calculated to evaluate the efficacy of ITPKA in the diagnosis of ccRCC. the closer the AUC value was to 1, the better the diagnostic efficacy. Multifactorial Cox analysis was performed to evaluate the impact of ITPKA expression and clinical characteristics on patient survival. Using multivariate analysis and Cox regression modeling, we created nomogram plots with independent prognostic indicators and predicted survival at 1, 3, and 5 years. Corrective analyses and corrective plots determined the predictive accuracy of the plots. Immunohistochemistry (IHC) Twenty pairs of clear cell renal cell carcinoma (ccRCC) and matched adjacent tissues were collected from surgical patients at Qingdao Municipal Hospital, all of whom had received no prior anticancer treatment. Pathological diagnosis was confirmed for all specimens, which were stored at − 80°C. The study was approved by the hospital’s ethics committee (No: KTLL2024-062), and informed consent was obtained from all patients. Tissues were fixed in 10% formalin for 24 h, dehydrated through graded ethanol, cleared in xylene, embedded in paraffin, and sectioned at 4–5 µm. After deparaffinization, rehydration, and antigen retrieval in 0.01 M sodium citrate buffer (95°C, 15–20 min), endogenous activity was blocked with 5% fetal bovine serum for 30 min at room temperature. Sections were incubated overnight at 4°C with primary antibody, followed by HRP-conjugated secondary antibody for 30–60 min at room temperature. Signals were developed using DAB, counterstained with hematoxylin, dehydrated, cleared, and mounted. Photographs of the relevant sections were taken under a ×200 or ×400 microscope. Protein expression was evaluated based on staining intensity in malignant/epithelial cells and the proportion of immunoreactive cells. The specific scoring method was as follows: unstained tissue was scored as 0, 20% of cells with weak staining or moderate to strong staining as 1, 20–40% of cells with moderate or strong staining as 2, and > 40% of cells with strong staining as 3. Cell culture and transfection The 786-O cell line (Shanghai Jikai Biotechnology Co., Ltd.) was cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1% nonessential amino acids, and 1% penicillin–streptomycin at 37°C in a humidified atmosphere containing 5% CO₂. Cells were passaged at 80–90% confluence using 0.25% trypsin. For transfection, cells were seeded in 6-well plates and transfected at 50–70% confluence with pLKO.1-Scramble or pLKO.1-shITPKA lentiviral plasmids (shITPKA sequence: 5′-TGGTCAATCTGCCGGGTCATAA-3′; shScramble sequence: 5′-GTATAAGTCAACTGTTGAC-3′) using Lipofectamine 2000. Lentiviral supernatant (100 µL) was mixed with transfection reagent, incubated for 15–20 min, and added to cultures, followed by medium replacement after 48 h. Successfully transfected cells were selected with puromycin until stable cell lines were established. Scratch experiment Cells were inoculated on six-well plates at a density of 3 × 105 cells per well. Linear cuts were gently scraped with a 200 mL pipette tip. Samples should be rinsed with phosphate buffered saline (PBS) to eliminate any loose cells. Photographs were taken at three time points after the experimental procedure, i.e., 0, 8, and 24 respectively hours, using a digital camera and optical microscope manufactured by Motic. Transwell experiment 786-O cells were cultured in appropriate medium and used at 80% confluence during the logarithmic growth phase. For migration, 2 × 10⁴ to 5 × 10⁴ cells were seeded into the upper chamber of a Transwell without fetal bovine serum (FBS) to induce migration. The lower chamber was filled with medium containing 10% FBS to attract the cells. For invasion assays, the upper chamber was pre-coated with 1:8 diluted Matrigel and incubated at 37°C for 30–60 min to solidify. Cells were incubated for 24–48 hours at 37°C, 5% CO₂. After incubation, non-migrated or non-invaded cells were removed, and the remaining cells on the lower membrane were fixed with 4% paraformaldehyde and stained with crystal violet for 30 min. After washing with PBS, cells were counted under a microscope in five randomly selected fields. The number of migrated or invaded cells was compared between experimental and control groups. Western Blot (WB) Cell samples were washed twice with PBS and lysed with RIPA buffer containing 1 mM PMSF on ice for 10–15 min. The cell suspension was sonicated (20 cycles of 40W, 1s each, 2s intervals) and centrifuged at 12,000 × g for 15 min at 4°C. The supernatant was collected, and protein concentration was measured using the BCA method. Samples were adjusted to 2 µg/µL by adding lysate. To prepare for SDS-PAGE, 1/5 volume of 6X loading buffer was added, mixed, and boiled at 100°C for 10 min. After brief centrifugation, samples were stored at − 80°C. For Western blotting, proteins were separated by SDS-PAGE and transferred onto a PVDF membrane at 200 mA for 120 min at 4°C. The membrane was blocked with TBST containing 5% skimmed milk for 1 hour at room temperature or overnight at 4°C. Primary antibody incubation was done for 2 hours at room temperature or overnight at 4°C, followed by four washes with TBST (8 min each). Chemiluminescent detection was performed using the LumiGLO® reagent (mixing 20X LumiGLO and 20X Peroxide #7003 Reagents in a 1:1 ratio). The membrane was exposed to the ECL solution for several seconds to minutes for signal detection. Statistical Analysis R language (v3.6.3) was used for data processing and statistical analysis. SPSS 24 (IBM, USA) was used to analyze the data. Protein blotting bands were initially processed by Image Lab software and subsequently processed using Photoshop 2024 and ImageJ software. If the data of two groups conformed to normal distribution, T-test could be used; for data not conforming to normal distribution, Wilcoxon test could be used for comparison between two groups. The overall test (Kruskal-Wallis Test) and multiple hypothesis test (Dunn's test) were used for multiple groups of data. p < 0.05 indicated statistical significance. * p < 0.05; ** p < 0.01; *** p < 0.001. Results Differential expression of ITPKA in KIRC First, we found that ITPKA was significantly highly expressed in 16 malignant tumors, including clear cell renal cell carcinoma (KIRC), papillary renal cell carcinoma (KIRP), and prostate cancer (PRAD), after differential expression of ITPKA in pan-cancers (p < 0.05, Fig. 1A). And among the paired pan-cancer samples, there were 13 sets of paired experiments of different malignancies showing significantly elevated ITPKA expression compared to controls, which also included KIRC (Fig. 1B). In the TCGA-KIRC cohort, significantly elevated ITPKA expression in KIRC tumor tissues was again verified in both unpaired and paired sample experiments (p 1). 427 were lowly expressed (negative logFC<-1). Volcano plots were used to depict the results of single-gene differential analysis (Fig. 1E). Figure 1 ITPKA is highly expressed in pan-cancer and KIRC. According to the TCGA database, there are significant differences in ITPKA expression levels between (A) unmatched samples and (B) matched samples. According to the TCGA-KIRC cohort, there are significant differences in ITPKA expression levels between (C) unmatched samples and (D) matched samples. (E) Single-gene differential analysis of ITPKA, with blue genes indicating downregulation and red genes indicating upregulation. ns; * p < 0.05; ** p < 0.01; *** p < 0.001. Plotting the ROC curve, it was obvious that the expression level of ITPKA had a significant discriminatory value for KIRC (AUC = 0.898, Fig. 2 A), which indicated that ITPKA had the ability to serve as a biomarker for predicting clear cell renal cell carcinoma. To verify the expression of ITPKA in ccRCC, we collected 20 specimens pathologically diagnosed as clear cell renal cell carcinoma and performed immunohistochemical staining experiments (Fig. 2 B). The protein expression level of ITPKA was significantly elevated in the 20 samples, a result consistent with the query in the TCGA database. Table 1 Clinical information of 20 patients with clear cell renal cell carcinoma in a three-line table. * p < 0.05; ** p < 0.01; *** p < 0.001. Patient number Age Pathologic stage Pathologic T stage IHC 1 70 Ⅰ T1 + 2 65 Ⅰ T1 + 3 74 Ⅰ T1 + 4 62 Ⅱ T2 + 5 59 Ⅲ T2 + 6 73 Ⅱ T2 + 7 82 Ⅱ T2 + 8 63 Ⅳ T3 + 9 65 Ⅲ T3 ++ 10 86 Ⅲ T3 + 11 77 Ⅲ T3 + 12 84 Ⅳ T3 ++ 13 60 Ⅲ T3 ++ 14 71 Ⅳ T3 +++ 15 76 Ⅲ T3 ++ 16 83 Ⅳ T4 +++ 17 77 Ⅳ T4 ++ 18 69 Ⅳ T4 ++ 19 75 Ⅳ T4 +++ 20 81 Ⅳ T4 +++ Correlation analysis of ITPKA in KIRC The top 100 related genes co-expressed with ITPKA were searched based on the Genemania database and the STRING database, and the protein interactions network maps were plotted, respectively (Fig. 3A and B). With the help of GEPIA2 database (threshold setting: |Pearson R|>0.55), 100 genes significantly associated with ITPKA were screened in this study to draw the coexpression heatmap (Fig. 3C), and the correlation heatmap between the differential gene expression levels and the clinical TNM staging and pathologic grading is shown in Fig. 3D. Figure 3 Co-expressed genes and pathway analysis of ITPKA. (A) Co-expressed gene network diagram of ITPKA; (B) Protein-protein interaction (PPI) network diagram of ITPKA; (C) Expression heatmap of 100 ITPKA-related genes; (D) Clinical relevance heatmap of co-expressed genes. Enrichment analysis and the search of ITPKA-related pathways GSEA enrichment analysis of ITPKA differential genes and search for related pathways were performed, and the five pathways with the strongest negative correlation were screened, including PPAR Signaling Pathway, Fatty Acid Metabolism and Peroxisome, etc., and the five pathways with the strongest positive correlation, including P53 Signaling Pathway, Ribosome and Maturity Onset Diabetes of the Young, etc. (Fig. 4A, B). After that, we performed GSEA enrichment analysis on PPAR pathway alone (NES=-2.165, Fig. 4C) and GO and KEGG enrichment analysis on ITPKA differentially expressed genes, which showed that ITPKA-related genes were involved in Regulation of Transport, Synaptic Signaling, Neuron Projection, Somatodendritic Compartment, Metal Ion Transmembrane Transporter Activity, Oxytocin Signaling Pathway and Circadian Entrainment etc. (Fig. 4D-H). We then verified the correlation between ITPKA expression level and PPAR pathway separately, and we found that PPARG had the highest correlation among the three subpathways (R=-0.457, Fig. 4I). Figure 4 Functional enrichment analysis of ITPKA-related differentially expressed genes in KIRC. (A, B) KEGG pathway enrichment analysis, showing the top and bottom five pathways closely associated with PKMYT1; (C) PPAR signaling pathway enrichment in ITPKA low-expression conditions. (D–F) GO enrichment analysis of ITPKA-associated differentially expressed genes, (G, H) KEGG enrichment analysis of ITPKA-associated differentially expressed genes. (I) ITPKA expression shows a significant negative correlation with the PPARG pathway (R = − 0.457). Relationship between ITPKA expression and immune infiltration The difference in immune infiltration scores between high and low expression groups of ITPKA in Th2 cells, aDC cells, DC cells, macrophages, neutrophils, NK cells, Th1 cells, Th17 cells, and Treg cells was statistically significant (p < 0.05, Fig. 5A). The relationship between ITPKA expression and Treg cells, Th2 cells, Th1 cells, macrophages, and other immune cells and negatively correlated with Th17 cells and neutrophils (Fig. 5B-D, p < 0.05). We produced a correlation heat map based on ITPKA expression and the infiltration score of each immune cell (Fig. 5E). Among them, ITPKA correlated significantly with tumor-associated Treg cells and Th2 cells (p < 0.001, Fig. 5F, G). The above results indicated that ITPKA was associated with the immune activation status of the tumor. Figure 5 Immune infiltration analysis of ITPKA. (A) Comparison of enrichment scores for 24 immune cells between high- and low-expression groups of ITPKA, presented as box plots; (B, C) Paired-sample experiments show significant differences in enrichment scores for Th2 cells and Treg cells between high- and low-expression groups of ITPKA (p < 0.001); (D) A bubble plot shows the correlation between 24 immune cell types and ITPKA; (E) A heatmap shows the correlation between ITPKA expression levels and immune cell infiltration scores for various immune cell types; (F, G) ITPKA expression levels are positively correlated with Th2 cell and Treg cell enrichment scores. * p < 0.05; ** p < 0.01; *** p < 0.001. The expression of ITPKA had a clear significance on the clinicopathologic grading of patients and patient prognosis The survival rate was significantly lower in the ITPKA-High group, and the OS rate differed significantly from that of the ITPKA-Low group (HR = 2.46, p < 0.001, Fig. 6 B), and the results of the PFI (HR = 3.11, p < 0.001) and DSS (HR = 3.90, p < 0.001) indicated that ccRCC with high expression of ITPKA patients had shorter survival (Fig. 6 A, C). Clinical baseline information sheets (Table 2 ) were obtained from 613 KIRC samples in the TCGA database, and ITPKA expression was associated with clinicopathologic features. Figure 6 D, E shows that in pathologic grading, ITPKA levels were significantly elevated in the late progressive stages of the disease (Stage III, Stage IV) compared to the early stages (Stage I, Stage II) (Fig. 6 D). In TNM staging, the expression of ITPKA was also correlated with the clinical data, and the expression was significantly higher in T3 and T4 stages than in T1 and T2 stages (Fig. 6 E). Plotting the ROC curve based on the above clinical data showed a high predictive efficiency of ITPKA (AUC = 0.699, Fig. 6 F). Table 2 Clinical baseline information table showing the correlation between ITPKA expression in the TCGA database and different clinical pathological characteristics in KIRC patients. * p < 0.05; ** p < 0.01; *** p < 0.001. Characteristics Low expression of ERBB2 High expression of ERBB2 pvalue n 270 271 Pathologic T stage, n (%) < 0.001 T1 111 (20.5%) 168 (31.1%) T2 37 (6.8%) 34 (6.3%) T3 113 (20.9%) 67 (12.4%) T4 9 (1.7%) 2 (0.4%) Pathologic N stage, n (%) 0.302 N0 119 (46.1%) 123 (47.7%) N1 10 (3.9%) 6 (2.3%) Pathologic M stage, n (%) < 0.001 M0 208 (40.9%) 221 (43.5%) M1 55 (10.8%) 24 (4.7%) Pathologic stage, n (%) < 0.001 Stage I 107 (19.9%) 166 (30.9%) Stage II 29 (5.4%) 30 (5.6%) Stage III 74 (13.8%) 49 (9.1%) Stage IV 58 (10.8%) 25 (4.6%) Primary therapy outcome, n (%) 0.037 SD 1 (0.7%) 5 (3.4%) PR 0 (0%) 2 (1.4%) CR 45 (30.6%) 83 (56.5%) PD 8 (5.4%) 3 (2%) Gender, n (%) 0.006 Male 192 (35.5%) 162 (29.9%) Female 78 (14.4%) 109 (20.1%) Age, n (%) 0.366 60 141 (26.1%) 131 (24.2%) Histologic grade, n (%) < 0.001 G1 1 (0.2%) 13 (2.4%) G2 98 (18.4%) 138 (25.9%) G3 112 (21%) 95 (17.8%) G4 56 (10.5%) 20 (3.8%) Serum calcium, n (%) 0.515 Low 105 (28.6%) 99 (27%) Normal 81 (22.1%) 72 (19.6%) Elevated 7 (1.9%) 3 (0.8%) Hemoglobin, n (%) 0.112 Low 151 (32.8%) 113 (24.5%) Normal 91 (19.7%) 101 (21.9%) Elevated 3 (0.7%) 2 (0.4%) Laterality, n (%) 0.438 Left 131 (24.3%) 122 (22.6%) Right 139 (25.7%) 148 (27.4%) Multiple prognostic factors were selected for further investigation, leading to multivariate Cox regression analysis. Based on the multivariate Cox regression analysis, we created a prognostic nomogram using TNM staging, age, and ITPKA level to quantify the prognosis of KIRC patients with a 0.773 (0.750–0.796) C-index (Fig. 6 G). Afterwards, we produced a calibration plot as shown in Fig. 6 H to test the predictive accuracy of the model. The results showed that the deviation correction line was close to the ideal curve (45°) and the projected values matched the actual values. Knockdown of ITPKA inhibits the growth of 786-O cell line We constructed an ITPKA knockdown 786-O cell line using si-ITPKA RNA and performed functional experiments. In the scratch assay, cell healing was significantly slowed down in the knockdown group (Fig. 7 A). Transwell assay showed that the invasion and migration abilities of the ITPKA knockdown group were significantly inhibited (Fig. 7 B). Notably, the results of Western blot experiments showed that the expression of relevant proteins of the PPAR pathway was elevated after ITPKA was knocked down compared with the control group, especially the elevation of PPARA and PPARG was more obvious (Fig. 7 C, G). This showed that in 786-O cell line, when ITPKA was knocked down, the invasive migration ability of tumor cells was significantly and statistically decreased (Fig. 7 D-F). Discussion Renal cell carcinoma (RCC) is a common human renal malignancy, especially in adults. Clear cell renal cell carcinoma (ccRCC) has a worse survival outcome compared to other RCC subtypes.[22, 23] Metabolic disorders have been found in multiple tumors. Imbalance of multiple metabolites is closely associated with the development and progression of ccRCC,[24] and exploring metabolism-related molecular indicators to identify and predict the presence and prognosis of KIRC appears to be crucial, which can also help to formulate more targeted and effective therapeutic strategies. We observed that ITPKA was highly expressed in KIRC and significantly elevated in metastatic KIRC patients, suggesting that ITPKA may play a key role in the development of KIRC. We confirmed this by immunohistochemistry in carcinoma and paracarcinoma tissues of 20 KIRC patients. The expression of ITPKA was significantly higher in T3 and T4 stages than in T1 and T2 stages (p < 0.05), suggesting that the high expression of ITPKA is closely related to the development, metastasis and invasion of KIRC. This is consistent with the results of our in vitro cellular experiments. In addition, we clarified the differential genes co-expressed with ITPKA in the occurrence and development of KIRC, including PLCB1, INPP5A, CAMK2, and CALMs through protein interaction network. Among them, phospholipase C b1 (PLCB1) is associated with hyperactive disorders, such as schizophrenia, epileptic encephalopathy and myotonic dystrophy,[25] PLCB1 is also involved in the cell cycle and cellular proliferation and has been shown to act as a tumor-initiating factor in small-cell lung, breast and colorectal cancers.[26] Therefore, we found that ITPKA may promote the development of ccRCC by driving PLCB1. INPP5A has been associated with melanoma and esophageal carcinogenesis,[27, 28] and aberrant expression of INPP5A leads to the accumulation of intracellular inositol trisphosphate (IP3), overactivation of IP3 receptor, and increase of p53-dependent apoptosis, and it has also been reported to correlate with tumor cell proliferation. Calcium/calmodulin-dependent protein kinase 2 (CAMK2) is highly expressed in triple-negative breast cancer and is involved in the inhibition of iron death to weaken the efficacy of immune checkpoint blockade therapy (ICB).[29] The PPI network has shown that calmodulin family of proteins (CALMs) are associated with neurodevelopment,[30–33] and are also involved in the development of steatohepatopathies and pancreatic cancers; therefore, calmodulin may have a synergistic role with ITPKA in cell metabolism. Role. These results suggest that ITPKA can alter cell division and cell cycle progression and therefore plays a key role in the progression and propagation of KIRC. To better understand the molecular processes associated with ITPKA in tumor growth, functional enrichment analysis was performed. The results showed that ITPKA is involved in biological processes such as intercellular signaling, neurotransmitter release, molecular transmembrane transport, ionic and gated channel activity, insulin secretion and calcium signaling pathway. It has been shown that calcium signaling, as a dynamic process, plays an important role in cellular activities under both normal and pathological conditions. Calcium ions strictly regulate gene transcription and proliferation and neovascularization in tumors, and remodel the tumor microenvironment by regulating the metabolic programs of immune cells.[34] Metabolic reprogramming is one of the hallmark features of tumorigenesis and development,[35] and the GSEA results of the present study indicated that ITPKA was significantly enriched in biometabolism-related pathways. Among them, dysregulation of the PPAR signaling pathway, which plays a key role in fatty acid metabolism, glucose metabolism, and amino acid degradation, may lead to metabolic reprogramming of tumor cells to adapt to rapid proliferation.[36] Abnormalities in the PPAR signaling pathway may provide a metabolic advantage for tumor cells, which may drive their rapid proliferation and enhance their aggressiveness.[37, 38] Tumor microenvironment (TME) is a key factor influencing tumorigenesis, progression, and therapeutic response. Among them, immune cells, as an important component of TME, play a double-edged role in tumor immune escape,[39, 40] drug resistance formation and tumor progression. In TME, different subpopulations of CD4 + T cells have important roles in antitumor immunoregulation, especially Th2 cells, which play a key regulatory role in tumor inflammatory response and immune escape.[41] The presence of macrophages in solid tumors is usually associated with treatment resistance and poor prognosis. In the present study, we found that high expression of ITPKA was closely associated with infiltration of Treg cells, Th2 cells and macrophages, and the shift from Th1-dominant to Th2-dominant accelerated the immunosuppressive response in the tumor microenvironment, which was consistent with the positive correlation between high expression of ITPKA and infiltration of Th2 cells. This finding suggests that ITPKA may play a key role in the immunoregulatory process of ccRCC by promoting the recruitment or activation of Treg cells, Th2 cells.[42] We investigated the prognostic value of ITPKA by examining the correlation between ITPKA and clinicopathological features through the TCGA database. The expression of ITPKA was closely correlated with the TNM stage and pathological stage of the tumors. The high expression of ITPKA was correlated with the likelihood of lymphatic and distant metastasis, suggesting that the prognosis of KIRC patients was poor. Survival analysis showed that patients with high ITPKA expression had significantly shorter OS, PFI and DSS. The results of subgroup prognostic analysis were consistent. The results of multivariate Cox regression were also utilized to establish a nomogram map as a clinical prognostic prediction tool and to test the accuracy of the model. The calibration plots showed that the actual OS values at 1, 3, and 5 years closely matched the predicted values. Thus, the nomogram map created for this study may eventually become a new and useful tool for prognostic analysis. This suggests that ITPKA could serve as a potential biomarker providing important information for early diagnosis and treatment selection. To verify the effect of ITPKA on the biological behavior of KIRC cells, we also performed functional experiments. The results demonstrated that ITPKA knockdown markedly suppressed the proliferation, migration, and invasion of 786O cells. In addition, Western Blot experiments showed that knockdown of ITPKA significantly down-regulated the expression of PPAR pathway-related proteins. The experimental validation further demonstrated the critical role of PPAR in the malignant progression of renal clear cell carcinoma and supported the negative regulatory effect of ITPKA on PPAR. Our study still has some limitations. Although this study provides important preliminary findings, its sample size is relatively small and further validation of these results in a broader patient population is needed. It is uncertain whether the use of a single biomarker provides sufficient predictive and diagnostic accuracy. Therefore, future studies will need to focus on combinations of many different biomarkers. In addition, the molecular mechanisms by which ITPKA promotes ccRCC cell behavior by modulating the PPAR pathway and the upstream molecules of ITPKA remain to be further explored. Conclusion Overall, ITPKA was overexpressed in renal clear cell carcinoma and was closely associated with tumor progression, infiltration of immune cells and poor prognosis. It can promote the proliferation, migration and invasion ability of KIRC tumor cells by affecting cellular ion exchange, information transduction and other mechanisms. In addition, ITPKA promotes tumor growth by inhibiting the activity of PPAR signaling pathway and disrupting the metabolic process of tumor cells. All of these will contribute to the development of friendlier therapeutic options for renal clear cell carcinoma and provide new horizons for unraveling the molecular mechanisms of renal clear cell carcinoma as well as developing novel targeted therapeutic strategies. Limitations This study has certain limitations. Although it provides important preliminary findings, the sample size is relatively small, and future studies are needed to validate these results in a broader patient population. It is currently unclear whether the use of a single biomarker can provide sufficient accuracy for prediction and diagnosis. Therefore, future research should focus on the combination of multiple different biomarkers. Additionally, the molecular mechanisms by which ITPKA regulates ccRCC cell behavior require further exploration. Abbreviations Abbreviation Full Term AUC Area Under the Curve BCA Bicinchoninic Acid CAMK2 Calcium/Calmodulin-Dependent Protein Kinase II ccRCC Clear Cell Renal Cell Carcinoma DSS Disease-Specific Survival FAO Fatty Acid Oxidation FBS Fetal Bovine Serum GO Gene Ontology GTEx Genotype-Tissue Expression GSEA Gene Set Enrichment Analysis IHC Immunohistochemistry INPP5A Inositol Polyphosphate-5-Phosphatase A IP3 Inositol 1,4,5-Trisphosphate ITPKA Inositol 1,4,5-Trisphosphate 3-Kinase A KIRC Kidney Renal Clear Cell Carcinoma KEGG Kyoto Encyclopedia of Genes and Genomes KM Kaplan–Meier KIRP Kidney Renal Papillary Cell Carcinoma mRNA Messenger Ribonucleic Acid NES Normalized Enrichment Score OS Overall Survival PBS Phosphate Buffered Saline PFI Progression-Free Interval PI3K Phosphatidylinositol 3-Kinase PLCB1 Phospholipase C Beta 1 PPAR Peroxisome Proliferator-Activated Receptor PPI Protein–Protein Interaction ROC Receiver Operating Characteristic shRNA Short Hairpin RNA siRNA Small Interfering RNA ssGSEA Single-Sample Gene Set Enrichment Analysis TCGA The Cancer Genome Atlas WB Western Blot Declarations Competing interests The final version submitted has been reviewed and approved by all authors of this research. The authors have disclosed that they have no potential conflicts of interest pertaining to the research and publishing of this article. Ethics approval and consent to participate The acquisition of postoperative pathological specimens was approved by the Ethics Committee of the Qingdao Municipal Hospital (No: KTLL2024-062), and informed consent was obtained from all patients. Consent to publish Not Applicable. Funding This work was funded by the Qingdao Key Medical and Health Discipline Project, the Natural Science Foundation of Shandong Province (No. ZR2023MH327) and the Natural Science Foundation of Qingdao Municipality (No. 23-2-1-193-zyyd-jch) Author Contribution SZA, ZSZ and KZS made a substantial contribution to the concept or design of the article. SZA and LJP drafted the article. SZA, LJP and ZSZ conducted the experiments and analyzed the data. LJP, ZSZ and KZS revised it critically for important intellectual content. SZA modified the language of this article to make it more readable. Data Availability The datasets used in this research are available in the following public databases: TCGA ( [https://portal.gdc.cancer.gov/](https:/portal.gdc.cancer.gov) ), GTEx ( [https://www.gtexportal.org/](https:/www.gtexportal.org) ), GEPIA ( [http://gepia.cancer-pku.cn/](http:/gepia.cancer-pku.cn) ), STRING ( [https://cn.string-db.org/](https:/cn.string-db.org) ), GeneMANIA ( [http://genemania.org/](http:/genemania.org) ). In addition to datasets from public databases, other datasets used and analyzed during the present study are available from the corresponding author on reasonable request. Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. References Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. Epidemiology of Renal Cell Carcinoma. Eur Urol. 2019;75(1):74-84. 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According to the TCGA database, there are significant differences in ITPKA expression levels between (A) unmatched samples and (B) matched samples. According to the TCGA-KIRC cohort, there are significant differences in ITPKA expression levels between (C) unmatched samples and (D) matched samples. (E) Single-gene differential analysis of ITPKA, with blue genes indicating downregulation and red genes indicating upregulation. ns; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/30bdd3e27290c10f6d5ee0ec.png"},{"id":91304882,"identity":"2496970a-3375-4a5a-bf6f-42277a089d4d","added_by":"auto","created_at":"2025-09-15 06:24:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5253428,"visible":true,"origin":"","legend":"\u003cp\u003eITPKA protein expression in KIRC. (A) ROC curve and AUC area for ITPKA in the TCGA-KIRC database; (B) comparison of IHC scores between adjacent and tumor tissues in 20 KIRC patients (Wilcoxon signed-rank test, p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/30d0457ecc3ef39b9cc70547.png"},{"id":91304944,"identity":"b29d99b6-88e3-44e7-a43e-0ebc3ba3f24f","added_by":"auto","created_at":"2025-09-15 06:24:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25217198,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expressed genes and pathway analysis of ITPKA. (A) Co-expressed gene network diagram of ITPKA; (B) Protein-protein interaction (PPI) network diagram of ITPKA; (C) Expression heatmap of 100 ITPKA-related genes; (D) Clinical relevance heatmap of co-expressed genes.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/90ea83f63fecdf2cfbf1ef3e.png"},{"id":91304941,"identity":"9867a59b-ba90-4b26-a89b-bf44392364e9","added_by":"auto","created_at":"2025-09-15 06:24:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4502712,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of ITPKA-related differentially expressed genes in KIRC. (A, B) KEGG pathway enrichment analysis, showing the top and bottom five pathways closely associated with PKMYT1; (C) PPAR signaling pathway enrichment in ITPKA low-expression conditions. (D–F) GO enrichment analysis of ITPKA-associated differentially expressed genes, (G, H) KEGG enrichment analysis of ITPKA-associated differentially expressed genes. (I) ITPKA expression shows a significant negative correlation with the PPARG pathway (R = −0.457).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/90715a60bb0aa20b7d55241c.png"},{"id":91304877,"identity":"143a8005-f31a-49bc-8634-bc034515d495","added_by":"auto","created_at":"2025-09-15 06:24:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6480376,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis of ITPKA. (A) Comparison of enrichment scores for 24 immune cells between high- and low-expression groups of ITPKA, presented as box plots; (B, C) Paired-sample experiments show significant differences in enrichment scores for Th2 cells and Treg cells between high- and low-expression groups of ITPKA (p \u0026lt; 0.001); (D) A bubble plot shows the correlation between 24 immune cell types and ITPKA; (E) A heatmap shows the correlation between ITPKA expression levels and immune cell infiltration scores for various immune cell types; (F, G) ITPKA expression levels are positively correlated with Th2 cell and Treg cell enrichment scores. * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/6ec42a987d9d515321eac1fb.png"},{"id":91306371,"identity":"f9e4c32a-18dc-4570-9c6f-fc9304f60e62","added_by":"auto","created_at":"2025-09-15 06:32:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2811119,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between ITPKA expression and clinical pathological factors in the TCGA-KIRC database. (A–C) K-M analysis of PFI, OS, and DSS between the ITPKA low-expression group and the ITPKA high-expression group in KIRC. (D, E) Relationship between ITPKA and pathological grade and TNM staging. (F) AUC curve showing the predictive efficiency of ITPKA for KIRC. (G, H) Nomogram and calibration curve predicting 1-year, 3-year, and 5-year OS survival rates for KIRC. * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/8cb10a9849d4fddf1197a331.png"},{"id":91304884,"identity":"33b2d674-3ec3-4c94-8ed4-31542aca7618","added_by":"auto","created_at":"2025-09-15 06:24:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":13865767,"visible":true,"origin":"","legend":"\u003cp\u003eKnocking down ITPKA reduces the proliferation, migration, and invasion capabilities of KIRC cells. (A, D) Wound healing assays show that ITPKA knockdown groups exhibit lower tumor cell healing capacity; (B, E, F) Transwell assays demonstrate that ITPKA knockdown groups exhibit significantly inhibited invasion and migration capacity; (C, G) Western Blot assays indicate that ITPKA knockdown upregulates the expression of proteins associated with the PPAR signaling pathway. * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/146154ea1fb5c37a272fc059.png"},{"id":96249595,"identity":"6b323bce-262d-438d-b912-3a92cd8dfc58","added_by":"auto","created_at":"2025-11-19 07:35:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":57287699,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/611e4930-5ba2-46d1-9da4-22402d989ea8.pdf"},{"id":91304957,"identity":"9c20f899-1f78-40aa-a5a6-b8d8f73ca1f0","added_by":"auto","created_at":"2025-09-15 06:24:26","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6820498,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.zip","url":"https://assets-eu.researchsquare.com/files/rs-7374587/v1/ea039dc2ead57075035fe88e.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"ITPKA as a biomarker for proliferation, migration and metastasis in renal clear cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cancer (RCC) is one of the deadliest malignant tumors of the kidney in adults, the 6th most frequently diagnosed cancer in men,[1] and the highest incidence is in Western countries, accounting for about 3% of all cancers.[2] Among the histological subtypes of RCC, clear cell renal cell carcinoma is the most common, representing approximately 70\u0026ndash;75% of all cases.[3, 4] Although many patients with early-stage renal cell carcinoma (RCC) have had remarkable results after surgical treatment in the last decade, the prognosis is still poor once they enter the advanced stage, with a 5-year survival rate less than 10% on average.[5, 6] As RCC exhibits resistance to conventional radiotherapy, chemotherapy, and hormonal therapy, the search for new biomarkers and molecular targets is crucial for the treatment, early diagnosis, and prognostic evaluation of ccRCC.\u003c/p\u003e\u003cp\u003eIns (1,4,5) P3-kinase-A (ITPKA or InsP3kinase) was first described and characterized by Irvine et al. in 1986, and cloning was achieved by Takazawa et al. in 1990.[7, 8] ITPKA is a cell motility-promoting protein that increases the metastatic potential of tumor cells.[9] ITPKA is localized on chromosome 15q15, its C-terminal amino acid sequence exhibits high similarity, while the N-terminal region shows relatively low conservation. In addition to regulating InsP4 production, ITPKA can also modulate cellular plasticity by controlling F-actin binding.[10] And its expression is stimulated by methylation in tumor tissues such as hepatocellular carcinomas, lung carcinomas, ovarian carcinomas, and breast carcinomas, and is elevated as the malignancy of these tumors increases.[10\u0026ndash;12] Since there are no clear therapeutic options for metastatic lung or breast tumors, blocking ITPKA activity may provide new options for patients with these tumors.[13] The mechanisms underlying the increased expression of ITPKA in tumors are complex and may be regulated by a variety of molecular mechanisms, including DNA methylation, microRNAs, or aberrant transcription factor signaling.[14] Although ITPKA has been identified as an oncogene, little is known about the role of ITPKA in cancer progression compared to the related PI3K family.\u003c/p\u003e\u003cp\u003ePeroxisome proliferator-activated receptors (PPARs) function as nuclear hormone receptors and are triggered by fatty acids and related molecules. There are three subtypes of PPARs in vertebrates (PPARα, PPARβ, and PPARγ) that differ in their expression patterns.[15] They are encoded by different genes and bind fatty acids and prostaglandins, respectively. PPARα regulates genes involved in lipid metabolism in the liver and skeletal muscle, thereby facilitating the clearance of circulating and intracellular lipids, promoting adaptive responses to fasting. PPARβ increases glucose and lipid metabolism by up-regulating mitochondrial function and fatty acid desaturation; PPARγ promotes adipose cell differentiation, fatty acid uptake, and lipid droplet storage, thereby increasing systemic insulin sensitivity and reducing ectopic lipid deposition.[16\u0026ndash;18] Although the main target genes of the PPAR signaling pathway are related to FAO (fatty acid oxidation) and lipid metabolism, PPAR also plays important roles in tumor and immune cells, especially in metabolic reprogramming of cells, lipid droplet formation, and macrophage differentiation.[4] In the pre-analysis experiments of this study, we found that the results of enrichment analysis showed that ITPKA was closely linked to the PPAR signaling pathway with a significant correlation, which suggests that ITPKA may be similar to PI-3K and act in a common downstream pathway, that is, the PPAR signaling pathway.\u003c/p\u003e\u003cp\u003eThe present study consisted of analyzing the expression of ITPKA and its potential association with various clinical features. In addition, we verified its expression in clinical specimens from 20 patients with renal clear cell carcinoma (KIRC) by immunohistochemistry (IHC), cell scratch assay, Transwell assay, and protein blotting (Western Blot, WB). In addition, we constructed a PPI network involving ITPKA and its related differentially expressed genes. We predicted the effect of ITPKA in the PPAR pathway and its role in promoting the development of KIRC by integrating cell signaling pathway enrichment analysis and immune infiltration analysis. Finally, we aimed to reveal the specific biological mechanisms of ITPKA in ccRCC development and assess its feasibility as a potential therapeutic target.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTest sources and pretreatment\u003c/p\u003e\u003cp\u003eWe obtained mRNA expression profiles of ITPKA in pan-cancer and corresponding normal tissues through TCGA and GTEx databases. We collected RNA-seq data from unpaired and paired samples from KIRC in the TCGA database and performed subsequent processing.[19] 'limma' and other R (v3.6.3) packages were used for normalization, standardization and visualization of the processing.\u003c/p\u003e\u003cp\u003eITPKA differential expression analysis\u003c/p\u003e\u003cp\u003eThe Wilcoxon rank sum test was used to evaluate the differential expression of ITPKA in pan-cancer tissues. The expression profile data of ITPKA in paired and unpaired samples were subjected to Shapiro-Wilk normality analysis followed by Wilcoxon rank sum test. The chi-square test was used to analyze the relationship between ITPKA expression and clinical data of TCGA-KIRC patients. All of the above analyses were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differential expression analysis was performed, adjusted for p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |Log2-fold change|\u0026gt;1, and the screened differential genes were plotted as volcano plots. And 100 co-expressed genes were found with the help of Genemania database and STRING database to analyze the PPI network, and Cytoscape software was used to screen the related genes.[20]\u003c/p\u003e\u003cp\u003eFunctional enrichment analysis\u003c/p\u003e\u003cp\u003eITPKA was entered into the \"General\" module of the GEPIA database, and the 100 genes most similar to ITPKA were filtered out. These 100 genes were subjected to gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the \"clusterProfiler, GOplot package\". ITPKA differential expression analysis was performed on the TCGA-KIRC cohort, and gene set enrichment analysis (GSEA) was performed on the results of the differential analysis,[21] and the reference gene set used for GSEA was 'h.all.v2022.1.Hs.symbols.gmt[Hallmarks]'.\u003c/p\u003e\u003cp\u003eITPKA immune infiltration analysis\u003c/p\u003e\u003cp\u003e24 immune cell infiltration levels were analyzed in the ITPKA high expression group and the ITPKA low expression group, and the corresponding enrichment scores were calculated by the ssGSEA algorithm. We defined the significant relative threshold as p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003ePrognosis analysis and model construction\u003c/p\u003e\u003cp\u003eThe Wilcoxon signed rank sum test was used to compare the clinicopathologic features of ITPKA and ccRCC. Kaplan-Meier (KM) survival analysis was performed on the clinical information data of prostate adenocarcinoma samples in the TCGA database using the \"survival\" package, and the results were analyzed by the \"survminer\" package and the \"ggplot2\" package. The results were visualized using the \"survminer\" and \"ggplot2\" packages. ROC analysis was performed using the \"pROC\" package, and AUC values were calculated to evaluate the efficacy of ITPKA in the diagnosis of ccRCC. the closer the AUC value was to 1, the better the diagnostic efficacy. Multifactorial Cox analysis was performed to evaluate the impact of ITPKA expression and clinical characteristics on patient survival. Using multivariate analysis and Cox regression modeling, we created nomogram plots with independent prognostic indicators and predicted survival at 1, 3, and 5 years. Corrective analyses and corrective plots determined the predictive accuracy of the plots.\u003c/p\u003e\u003cp\u003eImmunohistochemistry (IHC)\u003c/p\u003e\u003cp\u003eTwenty pairs of clear cell renal cell carcinoma (ccRCC) and matched adjacent tissues were collected from surgical patients at Qingdao Municipal Hospital, all of whom had received no prior anticancer treatment. Pathological diagnosis was confirmed for all specimens, which were stored at \u0026minus;\u0026thinsp;80\u0026deg;C. The study was approved by the hospital\u0026rsquo;s ethics committee (No: KTLL2024-062), and informed consent was obtained from all patients.\u003c/p\u003e\u003cp\u003eTissues were fixed in 10% formalin for 24 h, dehydrated through graded ethanol, cleared in xylene, embedded in paraffin, and sectioned at 4\u0026ndash;5 \u0026micro;m. After deparaffinization, rehydration, and antigen retrieval in 0.01 M sodium citrate buffer (95\u0026deg;C, 15\u0026ndash;20 min), endogenous activity was blocked with 5% fetal bovine serum for 30 min at room temperature. Sections were incubated overnight at 4\u0026deg;C with primary antibody, followed by HRP-conjugated secondary antibody for 30\u0026ndash;60 min at room temperature. Signals were developed using DAB, counterstained with hematoxylin, dehydrated, cleared, and mounted.\u003c/p\u003e\u003cp\u003ePhotographs of the relevant sections were taken under a \u0026times;200 or \u0026times;400 microscope. Protein expression was evaluated based on staining intensity in malignant/epithelial cells and the proportion of immunoreactive cells. The specific scoring method was as follows: unstained tissue was scored as 0, 20% of cells with weak staining or moderate to strong staining as 1, 20\u0026ndash;40% of cells with moderate or strong staining as 2, and \u0026gt;\u0026thinsp;40% of cells with strong staining as 3.\u003c/p\u003e\u003cp\u003eCell culture and transfection\u003c/p\u003e\u003cp\u003eThe 786-O cell line (Shanghai Jikai Biotechnology Co., Ltd.) was cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS), 1% nonessential amino acids, and 1% penicillin\u0026ndash;streptomycin at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂. Cells were passaged at 80\u0026ndash;90% confluence using 0.25% trypsin. For transfection, cells were seeded in 6-well plates and transfected at 50\u0026ndash;70% confluence with pLKO.1-Scramble or pLKO.1-shITPKA lentiviral plasmids (shITPKA sequence: 5\u0026prime;-TGGTCAATCTGCCGGGTCATAA-3\u0026prime;; shScramble sequence: 5\u0026prime;-GTATAAGTCAACTGTTGAC-3\u0026prime;) using Lipofectamine 2000. Lentiviral supernatant (100 \u0026micro;L) was mixed with transfection reagent, incubated for 15\u0026ndash;20 min, and added to cultures, followed by medium replacement after 48 h. Successfully transfected cells were selected with puromycin until stable cell lines were established.\u003c/p\u003e\u003cp\u003eScratch experiment\u003c/p\u003e\u003cp\u003eCells were inoculated on six-well plates at a density of 3 \u0026times; 105 cells per well. Linear cuts were gently scraped with a 200 mL pipette tip. Samples should be rinsed with phosphate buffered saline (PBS) to eliminate any loose cells. Photographs were taken at three time points after the experimental procedure, i.e., 0, 8, and 24 respectively hours, using a digital camera and optical microscope manufactured by Motic.\u003c/p\u003e\u003cp\u003eTranswell experiment\u003c/p\u003e\u003cp\u003e786-O cells were cultured in appropriate medium and used at 80% confluence during the logarithmic growth phase. For migration, 2 \u0026times; 10⁴ to 5 \u0026times; 10⁴ cells were seeded into the upper chamber of a Transwell without fetal bovine serum (FBS) to induce migration. The lower chamber was filled with medium containing 10% FBS to attract the cells. For invasion assays, the upper chamber was pre-coated with 1:8 diluted Matrigel and incubated at 37\u0026deg;C for 30\u0026ndash;60 min to solidify.\u003c/p\u003e\u003cp\u003eCells were incubated for 24\u0026ndash;48 hours at 37\u0026deg;C, 5% CO₂. After incubation, non-migrated or non-invaded cells were removed, and the remaining cells on the lower membrane were fixed with 4% paraformaldehyde and stained with crystal violet for 30 min. After washing with PBS, cells were counted under a microscope in five randomly selected fields. The number of migrated or invaded cells was compared between experimental and control groups.\u003c/p\u003e\u003cp\u003eWestern Blot (WB)\u003c/p\u003e\u003cp\u003eCell samples were washed twice with PBS and lysed with RIPA buffer containing 1 mM PMSF on ice for 10\u0026ndash;15 min. The cell suspension was sonicated (20 cycles of 40W, 1s each, 2s intervals) and centrifuged at 12,000 \u0026times; g for 15 min at 4\u0026deg;C. The supernatant was collected, and protein concentration was measured using the BCA method. Samples were adjusted to 2 \u0026micro;g/\u0026micro;L by adding lysate. To prepare for SDS-PAGE, 1/5 volume of 6X loading buffer was added, mixed, and boiled at 100\u0026deg;C for 10 min. After brief centrifugation, samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor Western blotting, proteins were separated by SDS-PAGE and transferred onto a PVDF membrane at 200 mA for 120 min at 4\u0026deg;C. The membrane was blocked with TBST containing 5% skimmed milk for 1 hour at room temperature or overnight at 4\u0026deg;C. Primary antibody incubation was done for 2 hours at room temperature or overnight at 4\u0026deg;C, followed by four washes with TBST (8 min each). Chemiluminescent detection was performed using the LumiGLO\u0026reg; reagent (mixing 20X LumiGLO and 20X Peroxide #7003 Reagents in a 1:1 ratio). The membrane was exposed to the ECL solution for several seconds to minutes for signal detection.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eR language (v3.6.3) was used for data processing and statistical analysis. SPSS 24 (IBM, USA) was used to analyze the data. Protein blotting bands were initially processed by Image Lab software and subsequently processed using Photoshop 2024 and ImageJ software. If the data of two groups conformed to normal distribution, T-test could be used; for data not conforming to normal distribution, Wilcoxon test could be used for comparison between two groups. The overall test (Kruskal-Wallis Test) and multiple hypothesis test (Dunn's test) were used for multiple groups of data. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDifferential expression of ITPKA in KIRC\u003c/p\u003e\u003cp\u003eFirst, we found that ITPKA was significantly highly expressed in 16 malignant tumors, including clear cell renal cell carcinoma (KIRC), papillary renal cell carcinoma (KIRP), and prostate cancer (PRAD), after differential expression of ITPKA in pan-cancers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;1A). And among the paired pan-cancer samples, there were 13 sets of paired experiments of different malignancies showing significantly elevated ITPKA expression compared to controls, which also included KIRC (Fig.\u0026nbsp;1B). In the TCGA-KIRC cohort, significantly elevated ITPKA expression in KIRC tumor tissues was again verified in both unpaired and paired sample experiments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;1C, D). After ITPKA single-gene differential expression analysis, we screened 2349 genes that met the criteria under which 1922 genes were strongly expressed (positive logFC\u0026gt;1). 427 were lowly expressed (negative logFC\u0026lt;-1). Volcano plots were used to depict the results of single-gene differential analysis (Fig.\u0026nbsp;1E).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;1 ITPKA is highly expressed in pan-cancer and KIRC. According to the TCGA database, there are significant differences in ITPKA expression levels between (A) unmatched samples and (B) matched samples. According to the TCGA-KIRC cohort, there are significant differences in ITPKA expression levels between (C) unmatched samples and (D) matched samples. (E) Single-gene differential analysis of ITPKA, with blue genes indicating downregulation and red genes indicating upregulation. ns; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003ePlotting the ROC curve, it was obvious that the expression level of ITPKA had a significant discriminatory value for KIRC (AUC\u0026thinsp;=\u0026thinsp;0.898, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), which indicated that ITPKA had the ability to serve as a biomarker for predicting clear cell renal cell carcinoma. To verify the expression of ITPKA in ccRCC, we collected 20 specimens pathologically diagnosed as clear cell renal cell carcinoma and performed immunohistochemical staining experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The protein expression level of ITPKA was significantly elevated in the 20 samples, a result consistent with the query in the TCGA database.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical information of 20 patients with clear cell renal cell carcinoma in a three-line table. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePathologic stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePathologic T stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIHC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅠ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅡ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅡ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅡ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅢ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eⅣ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e+++\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCorrelation analysis of ITPKA in KIRC\u003c/p\u003e\u003cp\u003eThe top 100 related genes co-expressed with ITPKA were searched based on the Genemania database and the STRING database, and the protein interactions network maps were plotted, respectively (Fig.\u0026nbsp;3A and B). With the help of GEPIA2 database (threshold setting: |Pearson R|\u0026gt;0.55), 100 genes significantly associated with ITPKA were screened in this study to draw the coexpression heatmap (Fig.\u0026nbsp;3C), and the correlation heatmap between the differential gene expression levels and the clinical TNM staging and pathologic grading is shown in Fig.\u0026nbsp;3D.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;3 Co-expressed genes and pathway analysis of ITPKA. (A) Co-expressed gene network diagram of ITPKA; (B) Protein-protein interaction (PPI) network diagram of ITPKA; (C) Expression heatmap of 100 ITPKA-related genes; (D) Clinical relevance heatmap of co-expressed genes.\u003c/p\u003e\u003cp\u003eEnrichment analysis and the search of ITPKA-related pathways\u003c/p\u003e\u003cp\u003eGSEA enrichment analysis of ITPKA differential genes and search for related pathways were performed, and the five pathways with the strongest negative correlation were screened, including PPAR Signaling Pathway, Fatty Acid Metabolism and Peroxisome, etc., and the five pathways with the strongest positive correlation, including P53 Signaling Pathway, Ribosome and Maturity Onset Diabetes of the Young, etc. (Fig.\u0026nbsp;4A, B). After that, we performed GSEA enrichment analysis on PPAR pathway alone (NES=-2.165, Fig.\u0026nbsp;4C) and GO and KEGG enrichment analysis on ITPKA differentially expressed genes, which showed that ITPKA-related genes were involved in Regulation of Transport, Synaptic Signaling, Neuron Projection, Somatodendritic Compartment, Metal Ion Transmembrane Transporter Activity, Oxytocin Signaling Pathway and Circadian Entrainment etc. (Fig.\u0026nbsp;4D-H). We then verified the correlation between ITPKA expression level and PPAR pathway separately, and we found that PPARG had the highest correlation among the three subpathways (R=-0.457, Fig.\u0026nbsp;4I).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;4 Functional enrichment analysis of ITPKA-related differentially expressed genes in KIRC. (A, B) KEGG pathway enrichment analysis, showing the top and bottom five pathways closely associated with PKMYT1; (C) PPAR signaling pathway enrichment in ITPKA low-expression conditions. (D\u0026ndash;F) GO enrichment analysis of ITPKA-associated differentially expressed genes, (G, H) KEGG enrichment analysis of ITPKA-associated differentially expressed genes. (I) ITPKA expression shows a significant negative correlation with the PPARG pathway (R\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.457).\u003c/p\u003e\u003cp\u003eRelationship between ITPKA expression and immune infiltration\u003c/p\u003e\u003cp\u003eThe difference in immune infiltration scores between high and low expression groups of ITPKA in Th2 cells, aDC cells, DC cells, macrophages, neutrophils, NK cells, Th1 cells, Th17 cells, and Treg cells was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;5A). The relationship between ITPKA expression and Treg cells, Th2 cells, Th1 cells, macrophages, and other immune cells and negatively correlated with Th17 cells and neutrophils (Fig.\u0026nbsp;5B-D, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We produced a correlation heat map based on ITPKA expression and the infiltration score of each immune cell (Fig.\u0026nbsp;5E). Among them, ITPKA correlated significantly with tumor-associated Treg cells and Th2 cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;5F, G). The above results indicated that ITPKA was associated with the immune activation status of the tumor.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;5 Immune infiltration analysis of ITPKA. (A) Comparison of enrichment scores for 24 immune cells between high- and low-expression groups of ITPKA, presented as box plots; (B, C) Paired-sample experiments show significant differences in enrichment scores for Th2 cells and Treg cells between high- and low-expression groups of ITPKA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (D) A bubble plot shows the correlation between 24 immune cell types and ITPKA; (E) A heatmap shows the correlation between ITPKA expression levels and immune cell infiltration scores for various immune cell types; (F, G) ITPKA expression levels are positively correlated with Th2 cell and Treg cell enrichment scores. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003eThe expression of ITPKA had a clear significance on the clinicopathologic grading of patients and patient prognosis\u003c/p\u003e\u003cp\u003eThe survival rate was significantly lower in the ITPKA-High group, and the OS rate differed significantly from that of the ITPKA-Low group (HR\u0026thinsp;=\u0026thinsp;2.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), and the results of the PFI (HR\u0026thinsp;=\u0026thinsp;3.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and DSS (HR\u0026thinsp;=\u0026thinsp;3.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicated that ccRCC with high expression of ITPKA patients had shorter survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C). Clinical baseline information sheets (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were obtained from 613 KIRC samples in the TCGA database, and ITPKA expression was associated with clinicopathologic features. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E shows that in pathologic grading, ITPKA levels were significantly elevated in the late progressive stages of the disease (Stage III, Stage IV) compared to the early stages (Stage I, Stage II) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In TNM staging, the expression of ITPKA was also correlated with the clinical data, and the expression was significantly higher in T3 and T4 stages than in T1 and T2 stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Plotting the ROC curve based on the above clinical data showed a high predictive efficiency of ITPKA (AUC\u0026thinsp;=\u0026thinsp;0.699, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical baseline information table showing the correlation between ITPKA expression in the TCGA database and different clinical pathological characteristics in KIRC patients. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLow expression of ERBB2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh expression of ERBB2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003epvalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e111 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e37 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e113 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e119 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e208 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221 (43.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e55 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e107 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e166 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e29 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e74 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e58 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e45 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e192 (35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e78 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;= 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e129 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e141 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistologic grade, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e98 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e112 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e56 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e105 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e81 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e7 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e151 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e91 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaterality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e131 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e139 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultiple prognostic factors were selected for further investigation, leading to multivariate Cox regression analysis. Based on the multivariate Cox regression analysis, we created a prognostic nomogram using TNM staging, age, and ITPKA level to quantify the prognosis of KIRC patients with a 0.773 (0.750\u0026ndash;0.796) C-index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Afterwards, we produced a calibration plot as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003eH to test the predictive accuracy of the model. The results showed that the deviation correction line was close to the ideal curve (45\u0026deg;) and the projected values matched the actual values.\u003c/p\u003e\u003cp\u003eKnockdown of ITPKA inhibits the growth of 786-O cell line\u003c/p\u003e\u003cp\u003eWe constructed an ITPKA knockdown 786-O cell line using si-ITPKA RNA and performed functional experiments. In the scratch assay, cell healing was significantly slowed down in the knockdown group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Transwell assay showed that the invasion and migration abilities of the ITPKA knockdown group were significantly inhibited (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Notably, the results of Western blot experiments showed that the expression of relevant proteins of the PPAR pathway was elevated after ITPKA was knocked down compared with the control group, especially the elevation of PPARA and PPARG was more obvious (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, G). This showed that in 786-O cell line, when ITPKA was knocked down, the invasive migration ability of tumor cells was significantly and statistically decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRenal cell carcinoma (RCC) is a common human renal malignancy, especially in adults. Clear cell renal cell carcinoma (ccRCC) has a worse survival outcome compared to other RCC subtypes.[22, 23] Metabolic disorders have been found in multiple tumors. Imbalance of multiple metabolites is closely associated with the development and progression of ccRCC,[24] and exploring metabolism-related molecular indicators to identify and predict the presence and prognosis of KIRC appears to be crucial, which can also help to formulate more targeted and effective therapeutic strategies. We observed that ITPKA was highly expressed in KIRC and significantly elevated in metastatic KIRC patients, suggesting that ITPKA may play a key role in the development of KIRC. We confirmed this by immunohistochemistry in carcinoma and paracarcinoma tissues of 20 KIRC patients. The expression of ITPKA was significantly higher in T3 and T4 stages than in T1 and T2 stages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the high expression of ITPKA is closely related to the development, metastasis and invasion of KIRC. This is consistent with the results of our in vitro cellular experiments.\u003c/p\u003e\u003cp\u003eIn addition, we clarified the differential genes co-expressed with ITPKA in the occurrence and development of KIRC, including PLCB1, INPP5A, CAMK2, and CALMs through protein interaction network. Among them, phospholipase C b1 (PLCB1) is associated with hyperactive disorders, such as schizophrenia, epileptic encephalopathy and myotonic dystrophy,[25] PLCB1 is also involved in the cell cycle and cellular proliferation and has been shown to act as a tumor-initiating factor in small-cell lung, breast and colorectal cancers.[26] Therefore, we found that ITPKA may promote the development of ccRCC by driving PLCB1. INPP5A has been associated with melanoma and esophageal carcinogenesis,[27, 28] and aberrant expression of INPP5A leads to the accumulation of intracellular inositol trisphosphate (IP3), overactivation of IP3 receptor, and increase of p53-dependent apoptosis, and it has also been reported to correlate with tumor cell proliferation. Calcium/calmodulin-dependent protein kinase 2 (CAMK2) is highly expressed in triple-negative breast cancer and is involved in the inhibition of iron death to weaken the efficacy of immune checkpoint blockade therapy (ICB).[29] The PPI network has shown that calmodulin family of proteins (CALMs) are associated with neurodevelopment,[30\u0026ndash;33] and are also involved in the development of steatohepatopathies and pancreatic cancers; therefore, calmodulin may have a synergistic role with ITPKA in cell metabolism. Role. These results suggest that ITPKA can alter cell division and cell cycle progression and therefore plays a key role in the progression and propagation of KIRC.\u003c/p\u003e\u003cp\u003eTo better understand the molecular processes associated with ITPKA in tumor growth, functional enrichment analysis was performed. The results showed that ITPKA is involved in biological processes such as intercellular signaling, neurotransmitter release, molecular transmembrane transport, ionic and gated channel activity, insulin secretion and calcium signaling pathway. It has been shown that calcium signaling, as a dynamic process, plays an important role in cellular activities under both normal and pathological conditions. Calcium ions strictly regulate gene transcription and proliferation and neovascularization in tumors, and remodel the tumor microenvironment by regulating the metabolic programs of immune cells.[34]\u003c/p\u003e\u003cp\u003eMetabolic reprogramming is one of the hallmark features of tumorigenesis and development,[35] and the GSEA results of the present study indicated that ITPKA was significantly enriched in biometabolism-related pathways. Among them, dysregulation of the PPAR signaling pathway, which plays a key role in fatty acid metabolism, glucose metabolism, and amino acid degradation, may lead to metabolic reprogramming of tumor cells to adapt to rapid proliferation.[36] Abnormalities in the PPAR signaling pathway may provide a metabolic advantage for tumor cells, which may drive their rapid proliferation and enhance their aggressiveness.[37, 38]\u003c/p\u003e\u003cp\u003eTumor microenvironment (TME) is a key factor influencing tumorigenesis, progression, and therapeutic response. Among them, immune cells, as an important component of TME, play a double-edged role in tumor immune escape,[39, 40] drug resistance formation and tumor progression. In TME, different subpopulations of CD4\u003csup\u003e+\u003c/sup\u003e T cells have important roles in antitumor immunoregulation, especially Th2 cells, which play a key regulatory role in tumor inflammatory response and immune escape.[41] The presence of macrophages in solid tumors is usually associated with treatment resistance and poor prognosis. In the present study, we found that high expression of ITPKA was closely associated with infiltration of Treg cells, Th2 cells and macrophages, and the shift from Th1-dominant to Th2-dominant accelerated the immunosuppressive response in the tumor microenvironment, which was consistent with the positive correlation between high expression of ITPKA and infiltration of Th2 cells. This finding suggests that ITPKA may play a key role in the immunoregulatory process of ccRCC by promoting the recruitment or activation of Treg cells, Th2 cells.[42]\u003c/p\u003e\u003cp\u003eWe investigated the prognostic value of ITPKA by examining the correlation between ITPKA and clinicopathological features through the TCGA database. The expression of ITPKA was closely correlated with the TNM stage and pathological stage of the tumors. The high expression of ITPKA was correlated with the likelihood of lymphatic and distant metastasis, suggesting that the prognosis of KIRC patients was poor. Survival analysis showed that patients with high ITPKA expression had significantly shorter OS, PFI and DSS. The results of subgroup prognostic analysis were consistent. The results of multivariate Cox regression were also utilized to establish a nomogram map as a clinical prognostic prediction tool and to test the accuracy of the model. The calibration plots showed that the actual OS values at 1, 3, and 5 years closely matched the predicted values. Thus, the nomogram map created for this study may eventually become a new and useful tool for prognostic analysis. This suggests that ITPKA could serve as a potential biomarker providing important information for early diagnosis and treatment selection.\u003c/p\u003e\u003cp\u003eTo verify the effect of ITPKA on the biological behavior of KIRC cells, we also performed functional experiments. The results demonstrated that ITPKA knockdown markedly suppressed the proliferation, migration, and invasion of 786O cells.\u003c/p\u003e\u003cp\u003eIn addition, Western Blot experiments showed that knockdown of ITPKA significantly down-regulated the expression of PPAR pathway-related proteins. The experimental validation further demonstrated the critical role of PPAR in the malignant progression of renal clear cell carcinoma and supported the negative regulatory effect of ITPKA on PPAR.\u003c/p\u003e\u003cp\u003eOur study still has some limitations. Although this study provides important preliminary findings, its sample size is relatively small and further validation of these results in a broader patient population is needed. It is uncertain whether the use of a single biomarker provides sufficient predictive and diagnostic accuracy. Therefore, future studies will need to focus on combinations of many different biomarkers. In addition, the molecular mechanisms by which ITPKA promotes ccRCC cell behavior by modulating the PPAR pathway and the upstream molecules of ITPKA remain to be further explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, ITPKA was overexpressed in renal clear cell carcinoma and was closely associated with tumor progression, infiltration of immune cells and poor prognosis. It can promote the proliferation, migration and invasion ability of KIRC tumor cells by affecting cellular ion exchange, information transduction and other mechanisms. In addition, ITPKA promotes tumor growth by inhibiting the activity of PPAR signaling pathway and disrupting the metabolic process of tumor cells. All of these will contribute to the development of friendlier therapeutic options for renal clear cell carcinoma and provide new horizons for unraveling the molecular mechanisms of renal clear cell carcinoma as well as developing novel targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThis study has certain limitations. Although it provides important preliminary findings, the sample size is relatively small, and future studies are needed to validate these results in a broader patient population. It is currently unclear whether the use of a single biomarker can provide sufficient accuracy for prediction and diagnosis. Therefore, future research should focus on the combination of multiple different biomarkers. Additionally, the molecular mechanisms by which ITPKA regulates ccRCC cell behavior require further exploration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAbbreviation\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFull Term\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBicinchoninic Acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAMK2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCalcium/Calmodulin-Dependent Protein Kinase II\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eccRCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eClear Cell Renal Cell Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-Specific Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFAO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFatty Acid Oxidation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFetal Bovine Serum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eINPP5A\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInositol Polyphosphate-5-Phosphatase A\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIP3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInositol 1,4,5-Trisphosphate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eITPKA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInositol 1,4,5-Trisphosphate 3-Kinase A\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKidney Renal Clear Cell Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKIRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKidney Renal Papillary Cell Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMessenger Ribonucleic Acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNormalized Enrichment Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphate Buffered Saline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgression-Free Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePI3K\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphatidylinositol 3-Kinase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLCB1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhospholipase C Beta 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeroxisome Proliferator-Activated Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtein\u0026ndash;Protein Interaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eshRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShort Hairpin RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003esiRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSmall Interfering RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle-Sample Gene Set Enrichment Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWestern Blot\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003e The final version submitted has been reviewed and approved by all authors of this research. The authors have disclosed that they have no potential conflicts of interest pertaining to the research and publishing of this article.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The acquisition of postoperative pathological specimens was approved by the Ethics Committee of the Qingdao Municipal Hospital (No: KTLL2024-062), and informed consent was obtained from all patients.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was funded by the Qingdao Key Medical and Health Discipline Project, the Natural Science Foundation of Shandong Province (No. ZR2023MH327) and the Natural Science Foundation of Qingdao Municipality (No. 23-2-1-193-zyyd-jch)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSZA, ZSZ and KZS made a substantial contribution to the concept or design of the article. SZA and LJP drafted the article. SZA, LJP and ZSZ conducted the experiments and analyzed the data. LJP, ZSZ and KZS revised it critically for important intellectual content. SZA modified the language of this article to make it more readable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used in this research are available in the following public databases: TCGA ( [https://portal.gdc.cancer.gov/](https:/portal.gdc.cancer.gov) ), GTEx ( [https://www.gtexportal.org/](https:/www.gtexportal.org) ), GEPIA ( [http://gepia.cancer-pku.cn/](http:/gepia.cancer-pku.cn) ), STRING ( [https://cn.string-db.org/](https:/cn.string-db.org) ), GeneMANIA ( [http://genemania.org/](http:/genemania.org) ). In addition to datasets from public databases, other datasets used and analyzed during the present study are available from the corresponding author on reasonable request. Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCapitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. Epidemiology of Renal Cell Carcinoma. Eur Urol. 2019;75(1):74-84. Epub 2018/09/24. https://doi.org/10.1016/j.eururo.2018.08.036. PubMed PMID: 30243799; PubMed Central PMCID: PMCPMC8397918.\u003c/li\u003e\n\u003cli\u003eLjungberg B, Albiges L, Abu-Ghanem Y, Bedke J, Capitanio U, Dabestani S, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol. 2022;82(4):399-410. Epub 2022/03/30. https://doi.org/10.1016/j.eururo.2022.03.006. PubMed PMID: 35346519.\u003c/li\u003e\n\u003cli\u003eLi Y, Lih TM, Dhanasekaran SM, Mannan R, Chen L, Cieslik M, et al. 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PubMed PMID: 17343606.\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":"Clear cell renal cell carcinoma, ITPKA, biomarkers, molecular mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-7374587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7374587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInositol-Trisphosphate3-Kinase A (ITPKA), a phosphorylated kinase that acts primarily on cellular metabolism, is highly expressed in renal clear cell carcinoma (ccRCC) and is associated with tumor progression and prognosis, but the action mechanism of ITPKA in renal cell carcinoma is not yet fully understood. The differential expression pattern of ITPKA was investigated using Cancer Genome Atlas (TCGA) and GEPIA2 databases. The expression of ITPKA in ccRCC patients was further verified by immunohistochemical (IHC) examination of 20 clinicopathologic specimens. A protein-protein interaction (PPI) network was established to include ITPKA and differentially expressed genes. The role of ITPKA in the PPAR pathway was predicted by functional enrichment analysis. Our results showed that ITPKA was overexpressed in ccRCC, and the higher the expression, the worse the clinical outcomes such as TNM staging and pathological grading. Immune infiltration analysis suggested a potential link between ITPKA expression and immune infiltration. In addition, patients with high ITPKA expression had worse survival compared with patients with low expression. Finally, to validate our earlier studies, we performed cellular functional tests and protein imprinting assays on ccRCC cell line 786-O. The experimental results showed that ITPKA knockdown significantly reduced the invasion and migration rates of renal cell carcinoma tumor cells, while PPAR pathway activity was also significantly inhibited. Overall, our study revealed that ITPKA is a promising biomarker with prognostic potential in ccRCC. Its key regulatory role in the PPAR signaling pathway provides research value and lays the foundation for future targeted therapy research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"ITPKA as a biomarker for proliferation, migration and metastasis in renal clear cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 06:23:20","doi":"10.21203/rs.3.rs-7374587/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":"104d9917-be96-46f7-bdfe-1fc36e33ce51","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T10:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 06:23:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7374587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7374587","identity":"rs-7374587","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}