Exploring the Role of Key Gene PTTG1 in Clear Cell Renal Carcinoma Based on Bioinformatics Analysis and In-vitro Cell Experiments

Exploring the Role of Key Gene PTTG1 in Clear Cell Renal Carcinoma Based on Bioinformatics Analysis and In-vitro Cell Experiments | 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 Exploring the Role of Key Gene PTTG1 in Clear Cell Renal Carcinoma Based on Bioinformatics Analysis and In-vitro Cell Experiments Li-hui Guan, Yu-dong Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4267396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aimed at exploring the expression characteristics and functional roles of PTTG1 in ccRCC by bioinformatics analysis and in-vitro experiments, as well as its potential to be a new type of therapeutic target. Methods: TCGA-KIRC data and relevant information of the samples were obtained from UCSC Xena. Data of GSE66271, GSE168845 and GSE105261 were obtained from the GEO database. Differentially expressed genes were screened based on TCGA-KIRC and GSE168845 and the protein-protein interaction network was constructed. The risk regression model was constructed by Lasso regression and the key prognostic genes were obtained by combining immune infiltration and pathway enrichment analysis. Genes and proteins were quantified using RT-qPCR and western blot. MTT assay was used to detect the vitality of cells. Cell apoptosis and cell cycle were detected by flow cytometry. The comet assay was adopted to detect the damage degree of cell DNA. Results: Six significant DDR-relevant prognostic genes (CCNA2, CDC45, CTLA4, FOXM1, PLK1, and PTTG1) were obtained. A risk model was constructed using Lasso regression, and it was verified in multiple data sets that the renal cell carcinoma group had a higher risk score and was mainly enriched in multiple T cell-related pathways. Immune infiltration results showed that CTLA4 was significantly positively correlated to T cells CD8. Besides, PTTG1 was negatively correlated to T cells CD4 memory resting, but remarkably positively correlated with both T cells CD8 and T cells regulatory. Compared with normal renal proximal tubular epithelial cells, the protein expression of PTTG1 was up-regulated at both mRNA and protein levels in ccRCC tissues. PTTG1could notably promote the proliferation of 786-O cells, and significantly inhibited apoptosis, cycle arrest and DNA damage of 786-O cells. Conclusion: PTTG1 may play a carcinogenic role by promoting the proliferation of ccRCC cells and inhibiting apoptosis. PTTG1 is expected to become a potential diagnostic and prognostic biomarker as well as an immunotherapy target for ccRCC. Clear cell renal carcinoma PTTG1 Biomarker Immune infiltration DNA damage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Renal cell carcinoma (RCC), originating from the proximal tubular epithelial cells of renal parenchyma, is a group of tumor entities with obvious tissue heterogeneity [1]. According to the statistics, cell renal carcinoma is one of the most common malignant tumors, ranking 13 th in the world [2]. Among them, clear cell renal carcinoma (ccRCC) is the most common pathological sub-type. Due to its highly complicated tumor micro-environment heterogeneity. The mortality remains high despite the fact that the treatment of ccRCC has improved in the past several years [3]. Its therapy resistance or post-treatment recurrence, in particular, is a great challenge. All of the current clinical intervention strategies, such as targeted therapy and immunotherapy, failed to obtain good therapeutic effect [4]. Therefore, improving current diagnosis and treatment methods is a sore need. The therapeutic decisions and prognosis for patients with ccRCC mainly related to tumor size, metastasis, TNM staging system and molecular sub-types [5]. However, patients with the same neoplasm staging and molecular sub-type have varied clinical outcomes even if they received similar therapeutic schemes. This indicates that the current staging system can not correctly predict the prognostic outcomes and therapeutic benefits of patients with ccRCC [6]. In most cases, RCC at an early stage is found accidentally by imaging examination such as abdominal ultrasound or CT scan. However, 25%–30% of patients with RCC have distant metastasis at the first visit, resulting in poor prognosis of these patients [7]. With the rapid development of genomics, proteomics, transcriptomics, metabolomics and other technical means, people are allowed to gain a deeper and more precise understanding of the genetic abnormalities that lead to the transformation of renal tubular epithelial cells into cancer cells. Useful biomarkers can be thus screened for early diagnosis, prognosis evaluation and treatment detection of ccRCC, providing potential therapeutic targets. These biomarkers and targets may radically alter the diagnosis and treatment of RCC [8]. Pituitary tumor-transforming gene 1 (PTTG1) is a tumorigenic gene isolated from pituitary tumors [9]. Compared with human normal cells, the expression of PTTG1 in many malignant tumor tissues is extremely high [9-12]. PTTG1 primarily regulates the cell cycle and induces human cell transformation [13]. It is relevant to tumor differentiation, invasion and metastasis. It is highly expressed in colorectal cancer, hepatocellular carcinoma, small cell lung cancer and prostate cancer [14-17]. This study intended to screen prognostic factors related to KIRC by analyzing data from the TCGA and GEO databases using bioinformatic analysis, explore the expression and prognosis of PTTG1 in KIRC and their correlation with immune infiltration by constructing models of prognosis, and predict the molecular mechanism of PTTG1 in the occurrence and development of KIRC. It provides ideas for seeking new KIRC diagnostic and prognostic markers and offers new targets for the development of tumor drugs. Data and material 1.1 Data sources In this study, RNA-seq (n = 607), Phenotype (n = 985) and survival data (n = 979) of ccRCC (TCGA-KIRC) were obtained from UCSC Xena. GSE66271, GSE168845 and GSE105261 data were obtained from the GEO database. A total of 526 RRC samples and 72 control samples were obtained by screening TCGA-KIRC data. GSE168845 was Agilent microarray data with 4 cases of RCC and 4 control samples respectively. GSE66271 was Affymetrix gene expression data, with 13 groups of control samples and 13 groups of primary RCC samples respectively. GSE105261 was Illumina microarray data, with 9 cases of RCC and 9 cases of control samples. The DNA damage repair-related gene set was obtained from GSEA (https://www.gsea-msigdb.org/gsea/index.jsp), and a total of 908 related genes were obtained. 1.2 DEGs screening The DESeq2 package was used to analyze the differential expression of TCGA-KIRC data, and the limma package was used to analyze the differential expression of GSE168845 data. The screening condition for differential genes was padj 1. Finally, the intersection of differentially expressed gene (DEG) was obtained through the Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) as a common DEG. 1.3 Protein-protein interaction network The protein-protein interaction network of the differentially expressed common gene was constructed based on String (https://string-db.org/) database. Unconnected proteins were neglected. A score of 0.7 (high confidence) was selected for the interaction score. Scores of different modules were calculated using the MCODE plugin in Cytoscape, and nodes in key modules were obtained as core genes. 1.4 Construction of significant prognostic genes After obtaining the core genes in the key modules, single factor Cox regression analysis was performed first to screen for genes with p < 0.05 that were significantly related to survival, and then intersecting with DNA damage repair genes to obtain significant prognostic genes related to DDR. 1.5 Risk model construction by Lasso regression On the basis of the obtained significant prognostic genes, Lasso regression analysis was further performed. The cox.zph method was used to test the Lasso regression results. The coefficient obtained by the Lasso regression model was used to construct the sample risk score formula: Risk score = ExpressionmRNA1 × CoefmRNA1 + ExpressionmRNA2 × CoefmRNA2 + ... ExpressionmRNAn × CoefmRNAn. The risk scores of each sample were calculated respectively, and according to the median of these scores, the samples were divided into the high-risk group or the low-risk group respectively. Survival analysis was performed according to different groupings. 1.6 Immune infiltration analysis CIBERSORT was adopted to analyze the immune infiltration of the TCGA data set, and the correlation between 22 kinds of immune cells and samples was obtained. The pearson method was used to calculate the correlation between hub genes and T cells CD4 memory resting, Tregs and T cells CD8. The data was screened by |R| > 0.3, and p < 0.05 was considered significantly relevant. 1.7 Modeling and grouping of in-vitro cell experiment In this study, human renal proximal tubular epithelial cell line HK-2 and renal clear cell carcinoma cell line 786-O were used. 786-O cells were taken and divided into four experiment groups, which were the Vehicle group: transfected with negative overexpression vector pcDNA3.1; the pTTG1 group: transfected with PKN2 overexpression vector pcDNA3.1-PTTG1; th esiNC group: transfected with negative siRNA; and the si-PTTG1 group: PTTG1 siRNA was transfected. 1.8 RT-qPCR detection The RT-qPCR method was used to detect the expression level of mRNA. RNA extraction kit and RNA reverse transcription cDNA reagent were used to extract RNA and synthesize cDNA. ViiA™ 7 Real-Time PCR System was adopted for qRT-PCR analysis. Forty cycles were repeated at 95℃ for 30 s, 95℃ for 10 s, and 60℃ for 32 s. The relative expression of the target gene was calculated using β-Actin as an internal reference, and qRT-PCR was repeated three times and averaged. 1.9 Western blot detection Western blot was adopted to detect the expression level of proteins. According to the instructions of the kit (APPLYGEN, P1250-50), the total protein of each group was extracted and the total protein concentration was determined. A total of 30 μg of total protein was subjected to SDS-PAGE electrophoresis and transferred to the Nylon membrane. After blocking with 5% skimmed milk powder for 1 h, it was incubated with the primary antibody of the corresponding protein (1:5000, mouse, Sigma) at 4℃ overnight. After washing with PBS, the cells were incubated with the corresponding HRP-labeled secondary antibody (HRP-labeled anti-mouse IgG, Sigma) at room temperature for 1 h. ECL (Thermo, item number 32106) was used to develop the immunoblotting signal. The Nylon membrane was scanned using a chemiluminescence imaging system. The gray value of each protein band was analyzed by Image J software, and the expression of Serpine1 relative to β-Actin was calculated. 1.10 Cell vitality detection by MTT assay The 786-O cells in each group in the logarithmic growth phase were re-suspended into a cell suspension using the RPMI-1640 complete medium and counted by a cell counter after digested with trypsin and centrifuged. In 96-well plates, 100 μL cell suspension containing 2000 cells/well was inoculated, and 5 replicate wells were set up for each group. After the cells were completely precipitated, the cell density was observed under a microscope, and then cultured in an incubator. From the second day after plating, 20 μL of 5 mg/mL MTT was added to each well at 4 h before the end of the culture, without changing the medium. After 4 h, the culture medium was completely removed, and 100 μL of DMSO solution was added to dissolve formazan particles. The 96-well plate to be tested was placed in an oscillator and oscillated for 2–5 min. The OD values were detected by a microplate reader at 490 nm and 570 nm under dark conditions, and the data were statistically analyzed. 1.11 Apoptosis and cell cycle detection by flow cytometry In this study, Annexin V/PI was used to detect the apoptosis level of HL-1 cells in each group. The experimental methods were based on the kit instructions (Annexin V-FITC/PI Apoptosis Detection Kit, No.: A211-01). The excitation wavelength of flow cytometry was 488 nm. Cells in the assay can be divided into three subgroups: living cells, which were double negative (Annexin V-FITC-/PI-), early apoptotic cells, which were Annexin V-FITC single positive (Annexin V-FITC+/PI-), and late apoptotic cells, which were Annexin V-FITC and PI double positive (Annexin V-FITC+/PI+). In addition, cell cycle analysis of 786-O ccRCC cell line was carried out using flow cytometry. Cells at G0/G1 stage, S stage and G2/M stage were accurately distinguished by fixing and staining cell DNA, thus, the effect of PTTG1 gene expression on cell cycle progression was evaluated. 1.12 DNA damage level detection by comet assay The first layer of 1% agarose was spread on the completely polished glass slide. After curing, the second layer of the mixture of 1% low melting point agarose and cell suspension (volume ratio of 5:3) was paved; the third layer of 0.8% low melting point agarose was laid after the second layer of gel was solidified. The prepared gel was placed in a cold cracking solution and cracked at 4℃ for 2 h. Then it was rinsed in distilled water and put in the electrophoresis tank. The freshly configured electrophoresis buffer was poured into the electrophoresis tank and allowed to stand for 20 min to facilitate double-stranded DNA unwinding. The height of the electrophoresis liquid level was adjusted, and electrophoresis was performed at 18 V and 175 mA for 20 min. After electrophoresis, the slide was taken out and immersed in Tris buffer (PH7.5) and neutralized for 30 min. After staining with acridine orange (20 mg/L) for 5 min, the dye on the surface was washed away with double distilled water and observed under a fluorescence microscope within 24 h. Fifty cells were randomly selected for analysis. The internationally recognized comet tail moment was used as an index to evaluate the degree of DNA breakage. 1.13 Statistical analysis The statistical analysis was conducted using the statistics software SPSS. For continuous variables, the independent sample t test or Mann-Whitney U test (according to whether the data distribution conformed to the normal distribution or not) was used to compare the differences between the two groups. If more than two groups of comparisons were involved, one-way analysis of variance followed by Tukey's HSD (Honest Significant Difference) test was used for subsequent multiple comparative analysis. For categorical variables, the chi-square test or Fisher's exact probability test was used to assess inter-group differences. Survival analysis was performed by the Kaplan-Meier method and the Cox proportional hazard model to explore the correlation between risk score and patient survival. CIBERSORT algorithm was adopted for the immune infiltration analysis to reveal the correlation between different immune cell types and prognosis. The significance level was set at p < 0.05. Results 2.1 Screening results of DEGs TCGA-KIRC data was screened to 526 RRC samples and 72 control samples, and differential expression analysis was performed to the data using Deseq2 package. A total of 5883 DEGs were obtained, among which 4300 were up-regulated genes and 1583 were down-regulated genes. For GSE168845 micro-array data, the differential expression was analyzed using the limma package. A total of 5674 DEGs were obtained, among which 2812 were significantly up-regulated genes and 2862 were significantly down-regulated genes. Lastly, the Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was adopted to obtain the intersection of the different genes (Figure 1A). There were altogether 1344 common DEGs in the two data sets. The protein-protein interaction network of the common DEGs was constructed based on the String database. Unconnected proteins were neglected. A score of 0.7 (high confidence) was selected for the interaction score. Scores of different modules were calculated using the MCODE plugin in Cytoscape, as shown in Figure 1B , and a total of 27 modules whose score > 3 were obtained. Altogether 72 nodes were screened from three modules whose score > 8 were screened as the core genes. 2.2 Construction of significant prognostic genes Based on the clinical information obtained from TCGA-KIRC, a single factor Cox regression analysis was first conducted on 72 core genes, and a total of 33 genes that were significantly related to survival ( p < 0.05) were screened. The intersection between 33 genes significantly related to survival and 908 DNA damage repair genes ( Figure 1C ) were taken to obtain 6 DDR-relevant significant prognostic genes (CCNA2, CDC45, CTLA4, FOXM1, PLK1, PTTG1). Subsequently, Lasso regression analysis was performed to these genes whose result was tested using the cox.zph method. The result indicated that the p values of 4 genes were all above 0.05 (CDC45, CTLA4, PLK1, PTTG1), which met the assumption of proportional hazards, that is, the regression result was significant. 2.3 Risk model construction by Lasso regression The coefficient obtained by the Lasso regression model was used to construct the sample risk score formula: Risk Score = (CDC45*-0.197) + (CTLA4*0.031) + (PLK1*0.456) + (PTTG1*0.182), and the risk score of each sample was calculated respectively ( Figure 2A–B ). The samples were grouped into the high-risk group and the low-risk group according to the median risk scores, and the survival analysis was performed according to the grouping ( Figure 2C ). A forest plot of the clinical feature risk scores was constructed ( Figure 2D ). GSE66271 was the RCC gene expression data from Affymetrix platform. The 13 control samples and 13 primary RCC samples of the data set were selected as the validation set. The risk scores in TCGA, GSE168845 and GSE105261 were calculated. The differences in different groups ( Figure 2E–G ) were illustrated in the box plot ( Figure 2E–G ). All three data sets showed that the RCC group had a higher risk score. 2.4 KEGG analysis and immune infiltration analysis In GSE168845 and TCGA-KIRC data sets, differential expression analysis was performed on the high-risk and low-risk groups, and 475 common up-regulated genes and 642 common down-regulated genes in these two groups of data were obtained. Enrichment analysis based on the KEGG database was carried out on these two groups respectively, as shown in Figure 3A . It was found that the enrichment was mainly on the pathway relevant to multiple kinds of T cells. Immune infiltration analysis was performed on TCGA data set using Cibersort and the relation between 22 kinds of immune cells and samples was found. Similar to the enrichment analysis result, it showed that there were significant differences in various T cells between the high expression and low expression groups (Figure 3B). The correlation coefficient between T cells CD4 memory resting, Tregs and T cells CD8 and 4 genes was calculated. |R| > 0.3 and p < 0.05 were considered and screened as significantly relevant. As shown in Figure 3C – F , the results showed that CTLA4 and T cells CD8 were significantly positively correlated. PTTG1 was negatively correlated to T cells CD4 memory resting, but significantly positively related to both T cells CD8 and Tregs. 2.5 The up-regulation of key genes CDC45, CTLA4, PLK1, and PTTG1 in clear cell renal carcinoma In this study, qRT-PCR and western blot were used to detect the expressions of CDC45, CTLA4, PLK1 and PTTG1 in human renal proximal tubular epithelial cell line HK-2 and renal clear cell carcinoma cell line 786-O respectively. Compared with the HK-2 group, mRNA expression levels of CDC45, CTLA4, PLK1 and PTTG1 in cells of the 786-O group were significantly increased ( Figure 4A ). Moreover, Western blotting results also demonstrated that the protein expressions of CDC45, CTLA4, PLK1 and PTTG1 in 786-O group were up-regulated ( Figure 4B ). Therefore, the expressions of CDC45, CTLA4, PLK1 and PTTG1 in ccRCC cells were up-regulated. 2.6 PTTG1 can promote 786-O cell proliferation, inhibit apoptosis and regulate the cell cycle In this study, the key gene PTTG1 was selected as the representative, and its effect on ccRCC was verified via in-vitro experiment. Firstly, we over-expressed and knocked down the expression of PTTG1 by cell transfection, and observed the transfection efficiency by qRT-PCR and WB experiments. Results showed that compared with Vehicle group, mRNA expression of PTTG1 in the PTTG1 group was increased significantly, while compared with siNC, the expression of si-PTTG1 was notably decreased ( Figure 5A ). WB experiment showed consistent results ( Figure 5B ), indicating that we have successfully over-expressed and knocked down PTTG1. Subsequently, a series of cell function assays were performed. MTT test results exhibited that compared with the Vehicle group, the vitality of 786-O cells in the PTTG1 group was remarkably increased. Compared with the siNC group, the vitality of 786-O cells in the si-PTTG1 group was significantly decreased. The difference between the Vehicle group and the siNC group had no statistical significance. With the progression of time, the cell vitality of each group showed a rising trend ( Figure 5C ). Flow cytometry detection results found that compared with the Vehicle group, the apoptosis levels of 78-O cells in the PTTG1 group were notably decreased. Compared with the siNC group, the apoptosis levels of 78-O cells in the si-PTTG1 group were significantly increased ( Figure 5D ). Similarly, compared to the Vehicle group, the percentage of G0/G1 stage in 786-O cells of the PTTG1 group was remarkably increased, the percentage of G2/M stage was notably decreased, and that of the S stage was slightly decreased but was not statistically significant. Compared with the siNC group, the percentage of G0/G1 stage in 786-O cells of si-PTTG1 was significantly decreased, the percentage of G2/M stage was remarkably increased, and that of the S stage was slightly increased but was not statistically significant ( Figure 5E ). The above results demonstrated the role of PTTG1 in ccRCC. PTTG1 could promote 786-O cell proliferation, inhibited apoptosis and regulated the cell cycle. 2.7 PTTG1 can inhibit DNA damage of 786-O cells The nucleus of the control group was mostly round, and occasionally comet-like. The number of comet-like nuclei increased in the si-PTTG1 group ( Figure 6A–B ). CASP software was used to measure various indicators of the comet, and the tail moment was selected as the indicator for evaluating DNA damage. Results showed that compared with the Vehicle group, the comet tail moment of 78-O cells in the PTTG1 group was notably decreased. Compared with the siNC group, the comet tail moment of 78-O cells of si-PTTG1 group was significantly increased. The difference between the Vehicle group and the siNC group had no statistical significance. By detecting the protein levels of DNA damage markers γH2AX, P53, and P21 using western blot, it was further found that, compared with the Vehicle group, the protein levels of γH2AX, P53, and P21 in 786-O cells of the PTTG1 group were remarkably decreased. Compared with the siNC group, the protein levels of γH2AX, P53, and P21 in 786-O cells of the si-PTTG1 group were significantly increased. The difference between the Vehicle group and the siNC group had no statistical significance ( Figure 6C ). It was demonstrated that PTTG1 could inhibit DNA damage of 786-O cells. Discussion As the most common RCC, ccRCC is difficult to diagnose at an early stage, and its operation is limited. Postoperative metastasis and recurrence are the main causes of its high mortality. Developing second tier therapy which primarily focuses on targeted treatment and immunotherapy is a new direction in recent oncotherapy. However, ccRCC has high intrinsic heterogeneity and lacks the biomarkers for early diagnosis and prognosis. For these reasons, seeking new biomarkers for diagnosis and prognosis and new treatment targets and developing new anti-tumor drugs and immunotherapy are vital ways to improve the survival of patients with ccRCC. In this study, we focused on exploring the potential role of PTTG1 in ccRCC and its clinical significance through comprehensive bioinformatics analysis and in-vitro cell experiments. Our study revealed that PTTG1 not only was abnormally expressed in the ccRCC, but also could remarkably inhibit the cell vitality of RCC and promote apoptosis by its down-regulation, which illustrated that it might play a core role in the pathogenic mechanism of ccRCC. This study adopted bioinformatics analysis. According to TCGA-KIRC data and GEO micro-array data, 6 significant prognostic genes related to DDR (CCNA2, CDC45, CTLA4, FOXM1, PLK1, and PTTG1) were obtained. The risk models were constructed using Lasso regression, and it was verified in multiple data sets that the RCC group had a higher risk score. It is known that PTTG1 is abnormally expressed in a variety of malignant tumors, such as gastric cancer [18], cholangiocarcinoma [19], adrenocortical carcinoma[20], hepatocellular carcinoma [21], glioblastoma [22], esophageal squamous cell carcinoma [23] and prostate cancer [24]. In this study, we found that PTTG1 was up-regulated at both mRNA and protein levels in ccRCC cells compared with normal renal proximal tubular epithelial cells. After detection, it was found that the expression of PTTG1 in ccRCC cell line was higher than in normal cells, indicating that PTTG1 might play an important role in ccRCC. WangF et al found that the over-expression of PTTG1 was positively correlated to the degree of differentiation, clinical stage, lymph node metastasis and distant metastasis of non-small cell lung cancer [25]. It is also correlated to colorectal cancer [26] and the malignant degree of colorectal cancer [20]. The expression of PTTG1 is associated with the prognosis of cancer and is an independent poor factor of prognosis for patients with colorectal cancer. In this study, we found that PTTG1 could promote the proliferation of 786-O cells, inhibit apoptosis, regulate cell cycles and inhibit DNA damage, which were consistent with existing relevant studies. Pingjiang analyzed genes related to ccRCC prognosis based on bioinformatics and found that 4 up-regulated DEGs ( BUB1 , CCNB2 , PTTG1 and RRM2 ) may be the key predictive genes for the prognosis of patients with ccRCC [27]. Similarly, Wei Can et al found that the expression of PTTG1 was unusually increased in RCCC, and the prognosis of PTTG1-positive patients was poor [28]. The expression of PTTG1 is up-regulated in metastatic prostate cancer tumor tissues, and PTTG1 regulates the progression and metastasis of prostate malignant tumors as the downstream target of androgen receptor [29]. In colorectal cancer, PTTG1 is the target gene of FoxM1 and can affect the metastasis and invasion of colorectal cancer by participating in the FoxM1-PTTG1 pathway [30]. PTTG1 may not only act as an important biomarker of ccRCC, but also become a new target for treatment, providing new strategies for the diagnosis, prognostic evaluation and treatment of ccRCC. The molecular mechanism of PTTG1 in the pathogenesis and progress of ccRCC was related to cell cycles. Besides, it may also be associated with the immune infiltration cells during ccRCC development regulation. For example, the protein expression of PTTG1 is significantly up-regulated in lung adenocarcinoma, and it is positively correlated to the lymphatic infiltration of the tumor [31]. Knocking down PTTG1 genes can decrease the ratio of Tregs, weakening the immunosuppression degree. This study analyzed the expression of PTTG1 in ccRCC and its relation with tumor immune cell infiltration using the TCGA database. We found that in ccRCC, CTLA4 was positively correlated to T cells CD8, and PTTG1 was negatively correlated in T cells CD4 memory resting but remarkably positively correlated both in T cells CD8 and Tregs. The infiltration of Tregs in the PTTG1 high expression group was significantly higher than that in the PTTG1 low expression group. Tregs, as a subset of inhibitory CD4 + T cells, also play an important role in maintaining immune balance and tumor immune escape. Tregs are usually enriched in the tumor microenvironment, and a large number of Tregs often have a poor prognosis [32], which may be the reason for the large DNA damage of 786-O cells in the si-PTTG1 group. In addition, by analyzing the gene function enrichment of PTTG1, we found that PTTG1 was associated with the signal pathway related to multiple T cells, such as Th17 cell differentiation, Th1 and Th2 cell differentiation and T cell receptor signaling pathway, etc. Among them, IFN-γ and IL-2 secreted by Th1 cells mediated anti-tumor effects, IL-4 and IL-10 secreted by Th2 cells promoted tumor growth by inhibiting the host immune system [33]. This may be the reason why PTTG1 regulated immune infiltrating cells during the development of ccRCC. Although this study made use of a large-scale public database, these data were mainly derived from specific populations and might result in geographical and racial biases. Therefore, it is necessary to further verify the study results among various populations. In addition, this study paid primary attention to the role of PTTG1 in ccRCC and its potential therapeutic values. However, there is insufficient exploration of the molecular mechanism of how PTTG1 specifically regulates ccRCC. In future studies, its molecular mechanism needs to be analyzed in a deeper way. Last but not least, although in-vitro experiments have demonstrated the role of PTTG1 in ccRCC, the direct validation of clinical samples fell short. In future studies, it is necessary to further determine the correlation between the expression level of PTTG1 and the clinical features and prognosis of ccRCC patients through clinical samples. Conclusion To sum up, this study found the up-regulated expression of PTTG1 in the cell line 786-O of ccRCC, which can promote 786-O cell proliferation and inhibit apoptosis, cell cycle arrest and DNA damage. It may be a marker in the early diagnosis of patients with ccRCC. PTTG1 can promote the pathogenesis and progression of tumors by regulating cell cycles, immune-relevant signal pathways and promoting immune cell infiltration. Therefore, PTTG1 is expected to become a potential biomarker for the diagnosis and prognosis of ccRCC and a new target for the development of anti-tumor drugs. Abbreviations ccRCC clear cell renal carcinoma PTTG1 Pituitary tumor-transforming gene 1 TCGA The Cancer Genome Atlas KIRC Kidney renal clear cell carcinoma UCSC The University of California Santa Cruz GEO Gene Expression Omnibus RT-qPCR Real-time reverse transcriptase-polymerase chain reaction MTT Tetrazolium component DDR DNA damage repair CCNA2 Cyclin A2 CDC45 Cell division cycle 45 CTLA4 Cytotoxic T lymphocyte antigen 4 FOXM1 Forkhead box M1 PLK1 Polo-like kinase RCC Renal cell carcinoma TNM Tumour, Node, Metastasis RNA-seq Ribonucleic acid sequencing DEG Differentially expressed gene MCODE Molecular complex detection Cox Cyclooxygenase CIBERSORT Cell-type identification by estimating relative subsets of RNA transcripts PKN2 Protein kinase N2 qRT-PCR Quantitative real-time polymerase chain reaction SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis PBS Polybutylene Succinate HRP Horseradish peroxidase ECL Enterochromaffin-like RPMI Roswell Park Memorial Institute DMSO Dimethyl sulfoxide HL-1 Heart Like-1 HSD Honest Significant Difference KEGG Kyoto Encyclopedia of Genes and Genomes BUB1 Budding uninhibited by benzimidazoles-1 CCNB2 Cyclin B2 RRM2 Ribonucleotide reductase M2 Th17 T helper type 17 Th1 T-helper 1 Th2 Type 2 helper cell IL Interleukin Declarations Acknowledgements Not applicable. Authors’ contribution statements GLH and WYD contributed to conception and design; both authors were involved in drafting the manuscript and revising it critically for important intellectual content; both authors made substantial contributions to acquisition and analysis of data; and both authors gave final approval of the version to be published. Funding Not applicable. Data availability TCGA-KIRC data and relevant information of the samples were obtained from UCSC Xena. Data of GSE66271, GSE168845 and GSE105261 were obtained from the GEO database. Ethics approval and consent Not applicable. Consent for publication Not applicable. Conflicts of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. References Prakasam G, Mishra A, Christie A, Miyata J, Carrillo D, Tcheuyap VT, et al. Comparative genomics incorporating translocation renal cell carcinoma mouse model reveals molecular mechanisms of tumorigenesis. 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Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. Cancer Treat Rev. 2008;34(3):193-205. Ma G. The role and mechanic study of proteomic-based differential protein FRL1 in clear cell renal cell carcinoma. Doctorate. 2023. Perramon M, Jimenez W. Pituitary Tumor-Transforming Gene 1/Delta like Non-Canonical Notch Ligand 1 Signaling in Chronic Liver Diseases. Int J Mol Sci. 2022;23(13). Wu NP, Huang HL, Zhou KL, Zhou CF, Tang JF. Advances on the role of PTTG1 in the pathogenesis of tumors. Basic and Clinical Medicine. 2023;43(9):1448-52. Xu J, Zhou X, Zhang T, Zhang B, Xu PX. Smarca4 deficiency induces Pttg1 oncogene upregulation and hyperproliferation of tubular and interstitial cells during kidney development. Front Cell Dev Biol. 2023;11:1233317. Huang S, Liao Q, Li W, Deng G, Jia M, Fang Q, et al. The lncRNA PTTG3P promotes the progression of CRPC via upregulating PTTG1. Bull Cancer. 2021;108(4):359-68. Zhang X, Ji H, Huang Y, Zhu B, Xing Q. Elevated PTTG1 predicts poor prognosis in kidney renal clear cell carcinoma and correlates with immunity. Heliyon. 2023;9(2):e13201. Ren Q, Jin B. The clinical value and biological function of PTTG1 in colorectal cancer. Biomed Pharmacother. 2017;89:108-15. Kong J, Wang T, Zhang Z, Yang X, Shen S, Wang W. Five Core Genes Related to the Progression and Prognosis of Hepatocellular Carcinoma Identified by Analysis of a Coexpression Network. DNA Cell Biol. 2019;38(12):1564-76. Yang S, Wang X, Liu J, Ding B, Shi K, Chen J, et al. Distinct expression pattern and prognostic values of pituitary tumor transforming gene family genes in non-small cell lung cancer. Oncol Lett. 2019;18(5):4481-94. Ersvaer E, Kildal W, Vlatkovic L, Cyll K, Pradhan M, Kleppe A, et al. Prognostic value of mitotic checkpoint protein BUB3, cyclin B1, and pituitary tumor-transforming 1 expression in prostate cancer. Mod Pathol. 2020;33(5):905-15. Weng W, Ni S, Wang Y, Xu M, Zhang Q, Yang Y, et al. PTTG3P promotes gastric tumour cell proliferation and invasion and is an indicator of poor prognosis. J Cell Mol Med. 2017;21(12):3360-71. Hu ZG, Zheng CW, Su HZ, Zeng YL, Lin CJ, Guo ZY, et al. MicroRNA-329-mediated PTTG1 downregulation inactivates the MAPK signaling pathway to suppress cell proliferation and tumor growth in cholangiocarcinoma. J Cell Biochem. 2019;120(6):9964-78. Romero Arenas MA, Whitsett TG, Aronova A, Henderson SA, LoBello J, Habra MA, et al. Protein Expression of PTTG1 as a Diagnostic Biomarker in Adrenocortical Carcinoma. Ann Surg Oncol. 2018;25(3):801-7. Lin X, Yang Y, Guo Y, Liu H, Jiang J, Zheng F, et al. PTTG1 is involved in TNF-alpha-related hepatocellular carcinoma via the induction of c-myc. Cancer Med. 2019;8(12):5702-15. Cui L, Ren T, Zhao H, Chen S, Zheng M, Gao X, et al. Suppression of PTTG1 inhibits cell angiogenesis, migration and invasion in glioma cells. Med Oncol. 2020;37(8):73. Chen SW, Zhou HF, Zhang HJ, He RQ, Huang ZG, Dang YW, et al. The Clinical Significance and Potential Molecular Mechanism of PTTG1 in Esophageal Squamous Cell Carcinoma. Front Genet. 2020;11:583085. Fraune C, Yehorov S, Luebke AM, Steurer S, Hube-Magg C, Buscheck F, et al. Upregulation of PTTG1 is associated with poor prognosis in prostate cancer. Pathol Int. 2020;70(7):441-51. Wang F, Liu Y, Chen Y. Pituitary tumor transforming gene-1 in non-small cell lung cancer: Clinicopathological and immunohistochemical analysis. Biomed Pharmacother. 2016;84:1595-600. Feng ZZ, Chen JW, Yang ZR, Lu GZ, Cai ZG. Expression of PTTG1 and PTEN in endometrial carcinoma: correlation with tumorigenesis and progression. Med Oncol. 2012;29(1):304-10. Jiang P, Sun TT, Chen CW, Huang RS, Zhong ZM, Lou XJ, et al. Identification of Prognostic Related Hub Genes in Clear-Cell Renal Cell Carcinoma via Bioinformatical Analysis. Chin Med Sci J. 2021;36(2):127-34. Wei C, Zhang YB, Yang XL, Xi JH, Wu W, Yang ZX, et al. Expression of pituitary transforming gene 1 in renal clear cell carcinoma and its association with prognosis. Chinese Journal of Experimental Surgery. 2018;35:1610-2. Zhang Z, Jin B, Jin Y, Huang S, Niu X, Mao Z, et al. PTTG1, A novel androgen responsive gene is required for androgen-induced prostate cancer cell growth and invasion. Exp Cell Res. 2017;350(1):1-8. Zheng Y, Guo J, Zhou J, Lu J, Chen Q, Zhang C, et al. FoxM1 transactivates PTTG1 and promotes colorectal cancer cell migration and invasion. BMC Med Genomics. 2015;8:49. Li WH, Chang L, Xia YX, Wang L, Liu YY, Wang YH, et al. Knockdown of PTTG1 inhibits the growth and invasion of lung adenocarcinoma cells through regulation of TGFB1/SMAD3 signaling. Int J Immunopathol Pharmacol. 2015;28(1):45-52. Zou W. Regulatory T cells, tumour immunity and immunotherapy. Nat Rev Immunol. 2006;6(4):295-307. Romagnani S. Th1/Th2 cells. Inflamm Bowel Dis. 1999;5(4):285-94. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4267396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291889839,"identity":"b6c3cc04-eda8-4764-8d65-053cee18486b","order_by":0,"name":"Li-hui Guan","email":"","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li-hui","middleName":"","lastName":"Guan","suffix":""},{"id":291889840,"identity":"6f0a9c88-1f36-43f1-8570-f2284dd3407d","order_by":1,"name":"Yu-dong Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3PMWsCMRTA8UhAOzzJesF+iCcFiyB3X+VJwMlB6OKYItx9hRT8EqXQORJwCu16UAcnJ4fc3A5VB13a88ZC84dkeLwfJIzFYn81OBzBuUXCEQihGxJZ5DQLs8mtNLYhQe+xMsGNUFP9NpbTQbKfb1JdUv8F8B2Q2VaopvVELv1OPRpSd4AfcM81l0+vv5PBgfS6uVM8ofWJDLVt824T0k7GxSfgG6ClZiQFcKxv0F4nmd89DJfeUdLJGQZUIM1qUfsXWajncj93WeZEQPpKMyEWq1DVEMZu8HiP9WXS0j9vnutsTw+8shWLxWL/uW+c4lc1F34p6AAAAABJRU5ErkJggg==","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu-dong","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-04-15 05:41:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4267396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4267396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54884973,"identity":"496adb54-1473-44db-8d9d-118775b5f694","added_by":"auto","created_at":"2024-04-18 06:05:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":527524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of prognostic genes\u003c/strong\u003e A DEGs calculated was performed on TCGA-KIRCH and GSE168845 data sets and the intersection was taken. B The protein-protein interaction network was constructed and the red modules were the key genes. C The candidate genes screened by single factor regression were intersected with DNA damage repair genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/eeed7c4b2f18c781f59ba5e6.png"},{"id":54884640,"identity":"77f03ac5-68aa-4141-a132-2bd120350d59","added_by":"auto","created_at":"2024-04-18 05:57:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLasso regression coefficient plot\u003c/strong\u003e A Lasso regression parameter plot; B Regression CV plot, in which the red dots represented different; C Cox survival analysis, in which the red represented the high-risk group, the green represented low-risk group; D The forest plot of final model features; E\u003cstrong\u003e–\u003c/strong\u003eG The box plot of data set risk score verification.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/9583b260203e8591d7400224.png"},{"id":54884642,"identity":"f37cf57f-c72d-467e-a503-150b92a2a6a6","added_by":"auto","created_at":"2024-04-18 05:57:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG analysis and immune clustering analysis\u003c/strong\u003e A KEGG enrichment analysis of differentially expressed genes. B Immune score box plot of different risk groups; C–F The correlation between genes and immune cell groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/80ececb02eebf3a2d6eb6bf3.png"},{"id":54884972,"identity":"900067a3-c3e2-4181-86f7-12f1169fade7","added_by":"auto","created_at":"2024-04-18 06:05:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of CDC45, CTLA4, PLK1 and PTTG1 in HK-2 group and 786-O group\u003c/strong\u003e A mRNA expression levels; B Protein expression levels. **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, vs. HK-2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/bfea76e4187382055014b2df.png"},{"id":54884644,"identity":"1e881702-409a-425c-b68a-0f43c47d4d7e","added_by":"auto","created_at":"2024-04-18 05:57:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1580175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of PTTG1 on cell proliferation, apoptosis and cell cycle in different experiment groups \u003c/strong\u003eA mRNA expression levels of PTTG1; B The protein expression levels of PTTG1; C MMT detection results. D Flow cytometry results and apoptosis ratio. E Each cell proliferation stage ratio. ** denoted comparison with Vehicle; \u003csup\u003e##\u003c/sup\u003e denoted comparison with siNC.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/14338f36f1e45cc48b526898.png"},{"id":54884645,"identity":"4fa8d5f1-6059-4bb9-9d8d-091535e16b3f","added_by":"auto","created_at":"2024-04-18 05:57:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1451888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTTG1 inhibits DNA damage of cells\u003c/strong\u003e A Comet experiment images under a fluorescence microscope. B The tail moments were measured for each experiment group. C The protein levels of DNA damage markers γH2AX, P53, P21. ** denoted comparison with Vehicle, \u003csup\u003e##\u003c/sup\u003e denoted comparison with siNC.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/03fa310be8277ecc58dfbf9c.png"},{"id":61181405,"identity":"09ae8dfb-40b8-4491-b44f-9f44acf073a7","added_by":"auto","created_at":"2024-07-26 16:47:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5163901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4267396/v1/77c9f757-d432-41c0-97f2-9ad0348a076d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Role of Key Gene PTTG1 in Clear Cell Renal Carcinoma Based on Bioinformatics Analysis and In-vitro Cell Experiments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC), originating from the proximal tubular epithelial cells of renal parenchyma, is a group of tumor entities with obvious tissue heterogeneity\u0026nbsp;[1]. According to the statistics, cell renal carcinoma is one of the most common malignant tumors, ranking 13\u003csup\u003eth\u003c/sup\u003e in the world\u0026nbsp;[2]. Among them, clear cell renal carcinoma (ccRCC) is the most common pathological sub-type. Due to its highly complicated tumor micro-environment heterogeneity. The mortality remains high despite the fact that the treatment of ccRCC has improved in the past several years\u0026nbsp;[3]. Its therapy resistance or post-treatment recurrence, in particular, is a great challenge. All of the current clinical intervention strategies, such as targeted therapy and immunotherapy, failed to obtain good therapeutic effect\u0026nbsp;[4]. Therefore, improving current diagnosis and treatment methods is a sore need. The therapeutic decisions and prognosis for patients with ccRCC mainly related to tumor size, metastasis, TNM staging system and molecular sub-types\u0026nbsp;[5]. However, patients with the same neoplasm staging and molecular sub-type have varied clinical outcomes even if they received similar therapeutic schemes. This indicates that the current staging system can not correctly predict the prognostic outcomes and therapeutic benefits of patients with ccRCC\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003eIn most cases, RCC at an early stage is found accidentally by imaging examination such as abdominal ultrasound or CT scan. However, 25%\u0026ndash;30% of patients with RCC have distant metastasis at the first visit, resulting in poor prognosis of these patients\u0026nbsp;[7]. With the rapid development of genomics, proteomics, transcriptomics, metabolomics and other technical means, people are allowed to gain a deeper and more precise understanding of the genetic abnormalities that lead to the transformation of renal tubular epithelial cells into cancer cells. Useful biomarkers can be thus screened for early diagnosis, prognosis evaluation and treatment detection of ccRCC, providing potential therapeutic targets. These biomarkers and targets may radically alter the diagnosis and treatment of RCC\u0026nbsp;[8].\u003c/p\u003e\n\u003cp\u003ePituitary tumor-transforming gene 1 (PTTG1) is a tumorigenic gene isolated from pituitary tumors [9]. Compared with human normal cells, the expression of PTTG1 in many malignant tumor tissues is extremely high [9-12]. PTTG1 primarily regulates the cell cycle and induces human cell transformation [13]. It is relevant to tumor differentiation, invasion and metastasis. It is highly expressed in colorectal cancer, hepatocellular carcinoma, small cell lung cancer and prostate cancer [14-17]. This study intended to screen prognostic factors related to KIRC by analyzing data from the TCGA and GEO databases using bioinformatic analysis, explore the expression and prognosis of PTTG1 in KIRC and their correlation with immune infiltration by constructing models of prognosis, and predict the molecular mechanism of PTTG1 in the occurrence and development of KIRC. It provides ideas for seeking new KIRC diagnostic and prognostic markers and offers new targets for the development of tumor drugs. \u0026nbsp;\u003c/p\u003e"},{"header":"Data and material","content":"\u003cp\u003e\u003cstrong\u003e1.1 Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, RNA-seq (n = 607), Phenotype (n = 985) and survival data (n = 979) of ccRCC (TCGA-KIRC) were obtained from UCSC Xena. GSE66271, GSE168845 and GSE105261 data were obtained from the GEO database. A total of 526 RRC samples and 72 control samples were obtained by screening TCGA-KIRC data. GSE168845 was Agilent microarray data with 4 cases of RCC and 4 control samples respectively. GSE66271 was Affymetrix gene expression data, with 13 groups of control samples and 13 groups of primary RCC samples respectively. GSE105261 was Illumina microarray data, with 9 cases of RCC and 9 cases of control samples.\u0026nbsp;The DNA damage repair-related gene set was obtained from GSEA (https://www.gsea-msigdb.org/gsea/index.jsp), and a total of 908 related genes were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 DEGs screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DESeq2 package was used to analyze the differential expression of TCGA-KIRC data, and the limma package was used to analyze the differential expression of GSE168845 data. The screening condition for differential genes was padj \u0026lt; 0.05, |Log2FC| \u0026gt; 1. Finally, the intersection of differentially expressed gene (DEG) was obtained through the Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) as a common DEG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Protein-protein interaction network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein-protein interaction network of the differentially expressed common gene was constructed based on String (https://string-db.org/) database. Unconnected proteins were neglected. A score of 0.7 (high confidence) was selected for the interaction score. Scores of different modules were calculated using the MCODE plugin in Cytoscape, and nodes in key modules were obtained as core genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Construction of significant prognostic genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter obtaining the core genes in the key modules, single factor Cox regression analysis was performed first to screen for genes with p \u0026lt; 0.05 that were significantly related to survival, and then intersecting with DNA damage repair genes to obtain significant prognostic genes related to DDR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Risk model construction by Lasso regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the basis of the obtained significant prognostic genes, Lasso regression analysis was further performed. The cox.zph method was used to test the Lasso regression results. The coefficient obtained by the Lasso regression model was used to construct the sample risk score formula: Risk score = ExpressionmRNA1 \u0026times; CoefmRNA1 + ExpressionmRNA2 \u0026times; CoefmRNA2 + ... ExpressionmRNAn \u0026times; CoefmRNAn. The risk scores of each sample were calculated respectively, and according to the median of these scores, the samples were divided into the high-risk group or the low-risk group respectively. Survival analysis was performed according to different groupings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.6 Immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT was adopted to analyze the immune infiltration of the TCGA data set, and the correlation between 22 kinds of immune cells and samples was obtained. The pearson method was used to calculate the correlation between hub genes and T cells CD4 memory resting, Tregs and T cells CD8. The data was screened by |R| \u0026gt; 0.3, and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered significantly relevant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.7 Modeling and grouping of in-vitro cell experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, human renal proximal tubular epithelial cell line HK-2 and renal clear cell carcinoma cell line 786-O were used. 786-O cells were taken and divided into four experiment groups, which were the Vehicle group: transfected with negative overexpression vector pcDNA3.1; the pTTG1 group: transfected with PKN2 overexpression vector pcDNA3.1-PTTG1; th esiNC group: transfected with negative siRNA; and the si-PTTG1 group: PTTG1 siRNA was transfected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.8 RT-qPCR detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RT-qPCR method was used to detect the expression level of mRNA. RNA extraction kit and RNA reverse transcription cDNA reagent were used to extract RNA and synthesize cDNA. ViiA\u0026trade; 7 Real-Time PCR System was adopted for qRT-PCR analysis. Forty cycles were repeated at 95℃ for 30 s, 95℃ for 10 s, and 60℃ for 32 s. The relative expression of the target gene was calculated using \u0026beta;-Actin as an internal reference, and qRT-PCR was repeated three times and averaged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.9 Western blot detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWestern blot was adopted to detect the expression level of proteins. According to the instructions of the kit (APPLYGEN, P1250-50), the total protein of each group was extracted and the total protein concentration was determined. A total of 30 \u0026mu;g of total protein was subjected to SDS-PAGE electrophoresis and transferred to the Nylon membrane. After blocking with 5% skimmed milk powder for 1 h, it was incubated with the primary antibody of the corresponding protein (1:5000, mouse, Sigma) at 4℃ overnight. After washing with\u0026nbsp;PBS, the cells were incubated with the corresponding HRP-labeled secondary antibody (HRP-labeled anti-mouse IgG, Sigma) at room temperature for 1 h. ECL (Thermo, item number 32106) was used to develop the immunoblotting signal. The Nylon membrane was scanned using a chemiluminescence imaging system. The gray value of each protein band was analyzed by Image J software, and the expression of Serpine1 relative to \u0026beta;-Actin was calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.10 Cell vitality detection by MTT assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 786-O cells in each group in the logarithmic growth phase were re-suspended into a cell suspension using the RPMI-1640 complete medium and counted by a cell counter after digested with trypsin and centrifuged. In 96-well plates, 100 \u0026mu;L cell suspension containing 2000 cells/well was inoculated, and 5 replicate wells were set up for each group. After the cells were completely precipitated, the cell density was observed under a microscope, and then cultured in an incubator. From the second day after plating, 20 \u0026mu;L of 5 mg/mL MTT was added to each well at 4 h before the end of the culture, without changing the medium. After 4 h, the culture medium was completely removed, and 100 \u0026mu;L of DMSO solution was added to dissolve formazan particles. The 96-well plate to be tested was placed in an oscillator and oscillated for 2\u0026ndash;5 min. The OD values were detected by a microplate reader at 490 nm and 570 nm under dark conditions, and the data were statistically analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.11 Apoptosis and cell cycle detection by flow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, Annexin V/PI was used to detect the apoptosis level of HL-1 cells in each group. The experimental methods were based on the kit instructions (Annexin V-FITC/PI Apoptosis Detection Kit, No.: A211-01). The excitation wavelength of flow cytometry was 488 nm. Cells in the assay can be divided into three subgroups: living cells, which were double negative (Annexin V-FITC-/PI-), early apoptotic cells, which were Annexin V-FITC single positive (Annexin V-FITC+/PI-), and late apoptotic cells, which were Annexin V-FITC and PI double positive (Annexin V-FITC+/PI+). In addition, cell cycle analysis of 786-O ccRCC cell line was carried out using flow cytometry. Cells at G0/G1 stage, S stage and G2/M stage were accurately distinguished by fixing and staining cell DNA, thus, the effect of PTTG1 gene expression on cell cycle progression was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.12 DNA damage level detection by comet assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first layer of 1% agarose was spread on the completely polished glass slide. After curing, the second layer of the mixture of 1% low melting point agarose and cell suspension (volume ratio of 5:3) was paved; the third layer of 0.8% low melting point agarose was laid after the second layer of gel was solidified. The prepared gel was placed in a cold cracking solution and cracked at 4℃ for 2 h. Then it was rinsed in distilled water and put in the electrophoresis tank. The freshly configured electrophoresis buffer was poured into the electrophoresis tank and allowed to stand for 20 min to facilitate double-stranded DNA unwinding. The height of the electrophoresis liquid level was adjusted, and electrophoresis was performed at 18 V and 175 mA for 20 min. After electrophoresis, the slide was taken out and immersed in Tris buffer (PH7.5) and neutralized for 30 min. After staining with acridine orange (20 mg/L) for 5 min, the dye on the surface was washed away with double distilled water and observed under a fluorescence microscope within 24 h. Fifty cells were randomly selected for analysis. The internationally recognized comet tail moment was used as an index to evaluate the degree of DNA breakage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.13 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis was conducted using the statistics software SPSS. For continuous variables, the independent sample t test or Mann-Whitney U test (according to whether the data distribution conformed to the normal distribution or not) was used to compare the differences between the two groups. If more than two groups of comparisons were involved, one-way analysis of variance followed by Tukey\u0026apos;s HSD (Honest Significant Difference) test was used for subsequent multiple comparative analysis. For categorical variables, the chi-square test or Fisher\u0026apos;s exact probability test was used to assess inter-group differences. Survival analysis was performed by the Kaplan-Meier method and the Cox proportional hazard model to explore the correlation between risk score and patient survival. CIBERSORT algorithm was adopted for the immune infiltration analysis to reveal the correlation between different immune cell types and prognosis. The significance level was set at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Screening results of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCGA-KIRC data was screened to 526 RRC samples and 72 control samples, and differential expression analysis was performed to the data using Deseq2 package. A total of 5883 DEGs were obtained, among which 4300 were up-regulated genes and 1583 were down-regulated genes. For GSE168845 micro-array data, the differential expression was analyzed using the limma package. A total of 5674 DEGs were obtained, among which 2812 were significantly up-regulated genes and 2862 were significantly down-regulated genes. Lastly, the Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was adopted to obtain the intersection of the different genes (Figure 1A). There were altogether 1344 common DEGs in the two data sets. The protein-protein interaction network of the common DEGs was constructed based on the String database. Unconnected proteins were neglected. A score of 0.7 (high confidence) was selected for the interaction score. Scores of different modules were calculated using the MCODE plugin in Cytoscape, as shown in \u003cstrong\u003eFigure 1B\u003c/strong\u003e, and a total of 27 modules whose score \u0026gt; 3 were obtained. Altogether 72 nodes were screened from three modules whose score \u0026gt; 8 were screened as the core genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Construction of significant prognostic genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the clinical information obtained from TCGA-KIRC, a single factor Cox regression analysis was first conducted on 72 core genes, and a total of 33 genes that were significantly related to survival (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) were screened. The intersection between 33 genes significantly related to survival and 908 DNA damage repair genes (\u003cstrong\u003eFigure 1C\u003c/strong\u003e) were taken to obtain 6 DDR-relevant significant prognostic genes (CCNA2, CDC45, CTLA4, FOXM1, PLK1, PTTG1). Subsequently, Lasso regression analysis was performed to these genes whose result was tested using the cox.zph method. The result indicated that the p values of 4 genes were all above 0.05 (CDC45, CTLA4, PLK1, PTTG1), which met the assumption of proportional hazards, that is, the regression result was significant. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Risk model construction by Lasso regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coefficient obtained by the Lasso regression model was used to construct the sample risk score formula: Risk Score = (CDC45*-0.197) + (CTLA4*0.031) + (PLK1*0.456) + (PTTG1*0.182), and the risk score of each sample was calculated respectively (\u003cstrong\u003eFigure 2A\u0026ndash;B\u003c/strong\u003e). The samples were grouped into the high-risk group and the low-risk group according to the median risk scores, and the survival analysis was performed according to the grouping (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). A forest plot of the clinical feature risk scores was constructed (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). GSE66271 was the RCC gene expression data from Affymetrix platform. The 13 control samples and 13 primary RCC samples of the data set were selected as the validation set. The risk scores in TCGA, GSE168845 and GSE105261 were calculated. The differences in different groups (\u003cstrong\u003eFigure 2E\u0026ndash;G\u003c/strong\u003e) were illustrated in the box plot (\u003cstrong\u003eFigure 2E\u0026ndash;G\u003c/strong\u003e). All three data sets showed that the RCC group had a higher risk score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 KEGG analysis and immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn GSE168845 and TCGA-KIRC data sets, differential expression analysis was performed on the high-risk and low-risk groups, and 475 common up-regulated genes and 642 common down-regulated genes in these two groups of data were obtained. Enrichment analysis based on the KEGG database was carried out on these two groups respectively, as shown in \u003cstrong\u003eFigure 3A\u003c/strong\u003e. It was found that the enrichment was mainly on the pathway relevant to multiple kinds of T cells. Immune infiltration analysis was performed on TCGA data set using Cibersort and the relation between 22 kinds of immune cells and samples was found. Similar to the enrichment analysis result, it showed that there were significant differences in various T cells between the high expression and low expression groups (Figure 3B). The correlation coefficient between T cells CD4 memory resting, Tregs and T cells CD8 and 4 genes was calculated. |R| \u0026gt; 0.3 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were considered and screened as significantly relevant. As shown in \u003cstrong\u003eFigure 3C\u003c/strong\u003e\u0026ndash;\u003cstrong\u003eF\u003c/strong\u003e, the results showed that CTLA4 and T cells CD8 were significantly positively correlated. PTTG1 was negatively correlated to T cells CD4 memory resting, but significantly positively related to both T cells CD8 and Tregs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 The up-regulation of key genes\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCDC45, CTLA4, PLK1, and PTTG1 in clear cell renal carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, qRT-PCR and western blot were used to detect the expressions of\u0026nbsp;CDC45, CTLA4, PLK1 and PTTG1 in human renal proximal tubular epithelial cell line HK-2 and renal clear cell carcinoma cell line 786-O respectively. Compared with the HK-2 group, mRNA expression levels of CDC45, CTLA4, PLK1 and PTTG1 in cells of the 786-O group were significantly increased (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). Moreover, Western blotting results also demonstrated that the protein expressions of CDC45, CTLA4, PLK1 and PTTG1 in 786-O group were up-regulated (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). Therefore, the expressions of CDC45, CTLA4, PLK1 and PTTG1 in ccRCC cells were up-regulated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePTTG1 can promote 786-O cell proliferation, inhibit apoptosis and regulate the cell cycle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the key gene PTTG1 was selected as the representative, and its effect on ccRCC was verified via in-vitro experiment. Firstly, we over-expressed and knocked down the expression of PTTG1 by cell transfection, and observed the transfection efficiency by qRT-PCR and WB experiments. Results showed that compared with Vehicle group, mRNA expression of PTTG1 in the PTTG1 group was increased significantly, while compared with siNC, the expression of si-PTTG1 was notably decreased (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). WB experiment showed consistent results (\u003cstrong\u003eFigure 5B\u003c/strong\u003e), indicating that we have successfully over-expressed and knocked down PTTG1. Subsequently, a series of cell function assays were performed. MTT test results exhibited that compared with the Vehicle group, the vitality of 786-O cells in the PTTG1 group was remarkably increased. Compared with the siNC group, the vitality of 786-O cells in the si-PTTG1 group was significantly decreased. The difference between the Vehicle group and the siNC group had no statistical significance. With the progression of time, the cell vitality of each group showed a rising trend (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). Flow cytometry detection results found that compared with the Vehicle group, the apoptosis levels of 78-O cells in the PTTG1 group were notably decreased. Compared with the siNC group, the apoptosis levels of 78-O cells in the si-PTTG1 group were significantly increased (\u003cstrong\u003eFigure 5D\u003c/strong\u003e). Similarly, compared to the Vehicle group, the percentage of G0/G1 stage in 786-O cells of the PTTG1 group was remarkably increased, the percentage of G2/M stage was notably decreased, and that of the S stage was slightly decreased but was not statistically significant. Compared with the siNC group, the percentage of G0/G1 stage in 786-O cells of si-PTTG1 was significantly decreased, the percentage of G2/M stage was remarkably increased, and that of the S stage was slightly increased but was not statistically significant (\u003cstrong\u003eFigure 5E\u003c/strong\u003e). The above results demonstrated the role of PTTG1 in ccRCC. PTTG1 could promote 786-O cell proliferation, inhibited apoptosis and regulated the cell cycle.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 PTTG1 can inhibit DNA damage of 786-O cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nucleus of the control group was mostly round, and occasionally comet-like. The number of comet-like nuclei increased in the si-PTTG1 group (\u003cstrong\u003eFigure 6A\u0026ndash;B\u003c/strong\u003e). CASP software was used to measure various indicators of the comet, and the tail moment was selected as the indicator for evaluating DNA damage. Results showed that compared with the Vehicle group, the comet tail moment of 78-O cells in the PTTG1 group was notably decreased. Compared with the siNC group, the comet tail moment of 78-O cells of si-PTTG1 group was significantly increased. The difference between the Vehicle group and the siNC group had no statistical significance. By detecting the protein levels of DNA damage markers \u0026gamma;H2AX, P53, and P21 using western blot, it was further found that, compared with the Vehicle group, the protein levels of \u0026gamma;H2AX, P53, and P21 in 786-O cells of the PTTG1 group were remarkably decreased. Compared with the siNC group, the protein levels of \u0026gamma;H2AX, P53, and P21 in 786-O cells of the si-PTTG1 group were significantly increased. The difference between the Vehicle group and the siNC group had no statistical significance (\u003cstrong\u003eFigure 6C\u003c/strong\u003e). It was demonstrated that PTTG1 could inhibit DNA damage of 786-O cells.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the most common RCC, ccRCC is difficult to diagnose at an early stage, and its operation is limited. Postoperative metastasis and recurrence are the main causes of its high mortality. Developing second tier therapy which primarily focuses on targeted treatment and immunotherapy is a new direction in recent oncotherapy. However, ccRCC has high intrinsic heterogeneity and lacks the biomarkers for early diagnosis and prognosis. For these reasons, seeking new biomarkers for diagnosis and prognosis and new treatment targets and developing new anti-tumor drugs and immunotherapy are vital ways to improve the survival of patients with ccRCC. In this study, we focused on exploring the potential role of PTTG1 in ccRCC and its clinical significance through comprehensive bioinformatics analysis and in-vitro cell experiments. Our study revealed that PTTG1 not only was abnormally expressed in the ccRCC, but also could remarkably inhibit the cell vitality of RCC and promote apoptosis by its down-regulation, which illustrated that it might play a core role in the pathogenic mechanism of ccRCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study adopted bioinformatics analysis. According to TCGA-KIRC data and GEO micro-array data, 6 significant prognostic genes related to DDR (CCNA2, CDC45, CTLA4, FOXM1, PLK1, and PTTG1) were obtained. The risk models were constructed using Lasso regression, and it was verified in multiple data sets that the RCC group had a higher risk score. It is known that PTTG1 is abnormally expressed in a variety of malignant tumors, such as gastric cancer\u0026nbsp;[18], cholangiocarcinoma\u0026nbsp;[19], adrenocortical carcinoma[20], hepatocellular carcinoma\u0026nbsp;[21], glioblastoma\u0026nbsp;[22], esophageal squamous cell carcinoma\u0026nbsp;[23]\u0026nbsp;and prostate cancer\u0026nbsp;[24]. In this study, we found that PTTG1 was up-regulated at both mRNA and protein levels in ccRCC cells compared with normal renal proximal tubular epithelial cells. After detection, it was found that the expression of PTTG1 in ccRCC cell line was higher than in normal cells, indicating that PTTG1 might play an important role in ccRCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWangF et al found that the over-expression of PTTG1 was positively correlated to the degree of differentiation, clinical stage, lymph node metastasis and distant metastasis of non-small cell lung cancer\u0026nbsp;[25]. It is also correlated to colorectal cancer\u0026nbsp;[26]\u0026nbsp;and the malignant degree of colorectal cancer\u0026nbsp;[20]. The expression of PTTG1 is associated with the prognosis of cancer and is an independent poor factor of prognosis for patients with colorectal cancer. In this study, we found that PTTG1 could promote the proliferation of 786-O cells, inhibit apoptosis, regulate cell cycles and inhibit DNA damage, which were consistent with existing relevant studies. Pingjiang analyzed genes related to ccRCC prognosis based on bioinformatics and found that 4 up-regulated\u0026nbsp;DEGs\u0026nbsp;(\u003cem\u003eBUB1\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003eCCNB2\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003ePTTG1\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eRRM2\u003c/em\u003e\u003cem\u003e)\u0026nbsp;\u003c/em\u003emay be the key predictive genes for the prognosis of patients with\u0026nbsp;ccRCC\u0026nbsp;[27].\u0026nbsp;Similarly, Wei Can et al found that the expression of PTTG1 was unusually increased in RCCC, and the prognosis of PTTG1-positive patients was poor\u0026nbsp;[28].\u0026nbsp;The expression of PTTG1 is up-regulated in metastatic prostate cancer tumor tissues, and PTTG1 regulates the progression and metastasis of prostate malignant tumors as the downstream target of androgen receptor\u0026nbsp;[29]. In colorectal cancer, PTTG1 is the target gene of FoxM1 and can affect the metastasis and invasion of colorectal cancer by participating in the FoxM1-PTTG1 pathway\u0026nbsp;[30]. PTTG1 may not only act as an important biomarker of ccRCC, but also become a new target for treatment, providing new strategies for the diagnosis, prognostic evaluation and treatment of ccRCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe molecular mechanism of PTTG1 in the pathogenesis and progress of ccRCC was related to cell cycles. Besides, it may also be associated with the immune infiltration cells during ccRCC development regulation. For example, the protein expression of PTTG1 is significantly up-regulated in lung adenocarcinoma, and it is positively correlated to the lymphatic infiltration of the tumor\u0026nbsp;[31]. Knocking down PTTG1 genes can decrease the ratio of Tregs, weakening the immunosuppression degree. This study analyzed the expression of PTTG1 in ccRCC and its relation with tumor immune cell infiltration using the TCGA database. We found that in ccRCC, CTLA4 was positively correlated to T cells CD8, and PTTG1 was negatively correlated in T cells CD4 memory resting but remarkably positively correlated both in T cells CD8 and Tregs. The infiltration of Tregs in the PTTG1 high expression group was significantly higher than that in the PTTG1 low expression group. Tregs, as a subset of inhibitory CD4 + T cells, also play an important role in maintaining immune balance and tumor immune escape. Tregs are usually enriched in the tumor microenvironment, and a large number of Tregs often have a poor prognosis\u0026nbsp;[32], which may be the reason for the large DNA damage of 786-O cells in the si-PTTG1 group. In addition, by analyzing the gene function enrichment of PTTG1, we found that PTTG1 was associated with the signal pathway related to multiple T cells, such as Th17 cell differentiation, Th1 and Th2 cell differentiation and T cell receptor signaling pathway, etc. Among them, IFN-\u0026gamma; and IL-2 secreted by Th1 cells mediated anti-tumor effects, IL-4 and IL-10 secreted by Th2 cells promoted tumor growth by inhibiting the host immune system\u0026nbsp;[33]. This may be the reason why PTTG1 regulated immune infiltrating cells during the development of ccRCC.\u003c/p\u003e\n\u003cp\u003eAlthough this study made use of a large-scale public database, these data were mainly derived from specific populations and might result in geographical and racial biases. Therefore, it is necessary to further verify the study results among various populations. In addition, this study paid primary attention to the role of PTTG1 in ccRCC and its potential therapeutic values. However, there is insufficient exploration of the molecular mechanism of how PTTG1 specifically regulates ccRCC. In future studies, its molecular mechanism needs to be analyzed in a deeper way. Last but not least, although in-vitro experiments have demonstrated the role of PTTG1 in ccRCC, the direct validation of clinical samples fell short. In future studies, it is necessary to further determine the correlation between the expression level of PTTG1 and the clinical features and prognosis of ccRCC patients through clinical samples.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, this study found the up-regulated expression of PTTG1 in the cell line 786-O of ccRCC, which can promote 786-O cell proliferation and inhibit apoptosis, cell cycle arrest and DNA damage. It may be a marker in the early diagnosis of patients with ccRCC. PTTG1 can promote the pathogenesis and progression of tumors by regulating cell cycles, immune-relevant signal pathways and promoting immune cell infiltration. Therefore, PTTG1 is expected to become a potential biomarker for the diagnosis and prognosis of ccRCC and a new target for the development of anti-tumor drugs.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eccRCC clear cell renal carcinoma\u003c/p\u003e\n\u003cp\u003ePTTG1 Pituitary tumor-transforming gene 1\u003c/p\u003e\n\u003cp\u003eTCGA The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eKIRC Kidney renal clear cell carcinoma\u003c/p\u003e\n\u003cp\u003eUCSC The University of California Santa Cruz\u003c/p\u003e\n\u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eRT-qPCR Real-time reverse transcriptase-polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eMTT Tetrazolium component\u003c/p\u003e\n\u003cp\u003eDDR DNA damage repair\u003c/p\u003e\n\u003cp\u003eCCNA2 Cyclin A2\u003c/p\u003e\n\u003cp\u003eCDC45 Cell division cycle 45\u003c/p\u003e\n\u003cp\u003eCTLA4 Cytotoxic T lymphocyte antigen 4\u003c/p\u003e\n\u003cp\u003eFOXM1 Forkhead box M1\u003c/p\u003e\n\u003cp\u003ePLK1 Polo-like kinase \u003c/p\u003e\n\u003cp\u003eRCC Renal cell carcinoma \u003c/p\u003e\n\u003cp\u003eTNM Tumour, Node, Metastasis\u003c/p\u003e\n\u003cp\u003eRNA-seq Ribonucleic acid sequencing\u003c/p\u003e\n\u003cp\u003eDEG Differentially expressed gene\u003c/p\u003e\n\u003cp\u003eMCODE Molecular complex detection\u003c/p\u003e\n\u003cp\u003eCox Cyclooxygenase\u003c/p\u003e\n\u003cp\u003eCIBERSORT Cell-type identification by estimating relative subsets of RNA transcripts\u003c/p\u003e\n\u003cp\u003ePKN2 Protein kinase N2\u003c/p\u003e\n\u003cp\u003eqRT-PCR Quantitative real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eSDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis\u003c/p\u003e\n\u003cp\u003ePBS Polybutylene Succinate\u003c/p\u003e\n\u003cp\u003eHRP Horseradish peroxidase\u003c/p\u003e\n\u003cp\u003eECL Enterochromaffin-like\u003c/p\u003e\n\u003cp\u003eRPMI Roswell Park Memorial Institute\u003c/p\u003e\n\u003cp\u003eDMSO Dimethyl sulfoxide\u003c/p\u003e\n\u003cp\u003eHL-1 Heart Like-1\u003c/p\u003e\n\u003cp\u003eHSD Honest Significant Difference\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eBUB1 Budding uninhibited by benzimidazoles-1\u003c/p\u003e\n\u003cp\u003eCCNB2 Cyclin B2\u003c/p\u003e\n\u003cp\u003eRRM2 Ribonucleotide reductase M2\u003c/p\u003e\n\u003cp\u003eTh17 T helper type 17\u003c/p\u003e\n\u003cp\u003eTh1 T-helper 1\u003c/p\u003e\n\u003cp\u003eTh2 Type 2 helper cell\u003c/p\u003e\n\u003cp\u003eIL Interleukin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGLH and WYD contributed to conception and design; both authors were involved in drafting the manuscript and revising it critically for important intellectual content; both authors made substantial contributions to acquisition and analysis of data; and both authors gave final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCGA-KIRC data and relevant information of the samples were obtained from UCSC Xena. Data of GSE66271, GSE168845 and GSE105261 were obtained from the GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrakasam G, Mishra A, Christie A, Miyata J, Carrillo D, Tcheuyap VT, et al. Comparative genomics incorporating translocation renal cell carcinoma mouse model reveals molecular mechanisms of tumorigenesis. J Clin Invest. 2024;134(7).\u003c/li\u003e\n\u003cli\u003eCapitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. 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Inflamm Bowel Dis. 1999;5(4):285-94.\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 carcinoma, PTTG1, Biomarker, Immune infiltration, DNA damage","lastPublishedDoi":"10.21203/rs.3.rs-4267396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4267396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This study aimed at exploring the expression characteristics and functional roles of PTTG1 in ccRCC by bioinformatics analysis and in-vitro experiments, as well as its potential to be a new type of therapeutic target.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e TCGA-KIRC data and relevant information of the samples were obtained from UCSC Xena. Data of GSE66271, GSE168845 and GSE105261 were obtained from the GEO database. Differentially expressed genes were screened based on TCGA-KIRC and GSE168845 and the protein-protein interaction network was constructed. The risk regression model was constructed by Lasso regression and the key prognostic genes were obtained by combining immune infiltration and pathway enrichment analysis. Genes and proteins were quantified using RT-qPCR and western blot. MTT assay was used to detect the vitality of cells. Cell apoptosis and cell cycle were detected by flow cytometry. The comet assay was adopted to detect the damage degree of cell DNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Six significant DDR-relevant prognostic genes (CCNA2, CDC45, CTLA4, FOXM1, PLK1, and PTTG1) were obtained. A risk model was constructed using Lasso regression, and it was verified in multiple data sets that the renal cell carcinoma group had a higher risk score and was mainly enriched in multiple T cell-related pathways. Immune infiltration results showed that CTLA4 was significantly positively correlated to T cells CD8. Besides, PTTG1 was negatively correlated to T cells CD4 memory resting, but remarkably positively correlated with both T cells CD8 and T cells regulatory. Compared with normal renal proximal tubular epithelial cells, the protein expression of PTTG1 was up-regulated at both mRNA and protein levels in ccRCC tissues. PTTG1could notably promote the proliferation of 786-O cells, and significantly inhibited apoptosis, cycle arrest and DNA damage of 786-O cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e PTTG1 may play a carcinogenic role by promoting the proliferation of ccRCC cells and inhibiting apoptosis. PTTG1 is expected to become a potential diagnostic and prognostic biomarker as well as an immunotherapy target for ccRCC.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of Key Gene PTTG1 in Clear Cell Renal Carcinoma Based on Bioinformatics Analysis and In-vitro Cell Experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 05:57:41","doi":"10.21203/rs.3.rs-4267396/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":"6e1c80f8-37bb-4337-af5f-2eb16b6d2299","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-26T16:39:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 05:57:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4267396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4267396","identity":"rs-4267396","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}