Platelet Count as an Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma: Insights from Multi-source Data Analysis

Platelet Count as an Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma: Insights from Multi-source Data Analysis | 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 Platelet Count as an Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma: Insights from Multi-source Data Analysis Cong Huang, Jialong Zhang, Hongzhi Wang, Mei zhang, Xiwei Lu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6196668/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: Clear Cell Renal Cell Carcinoma (ccRCC) is one of the most common and aggressive forms of kidney cancer, and identifying reliable prognostic indicators remains a critical challenge. While various biomarkers have been explored, platelet count has not been comprehensively evaluated as an independent prognostic factor in ccRCC. Given its clinical accessibility, platelet count could be a valuable tool for predicting patient outcomes. Objective: This study aims to evaluate the potential of platelet count as an independent prognostic marker for ccRCC patients using multi-source data analysis. Methods: We collected summary data from four large-scale genome-wide association studies (GWAS), constructed a bidirectional Mendelian randomization (MR) framework, used statistical methods such as inverse variance weighted (IVW), MR Egger regression, and weighted median, and analyzed the relationship between platelet count and the risk and prognosis of clear cell renal cell carcinoma (ccRCC) by propensity score matching to reduce selection bias. Then, we retrospectively collected clinical data from 231 ccRCC patients who underwent partial or radical nephrectomy at the First Affiliated Hospital of Anhui Medical University from 2014 to 2020 to verify the accuracy of the results. Results: We found through MR analysis that an increase in platelet count is positively correlated with the risk of kidney cancer (OR=1.001, 95% CI: 1.000-1.001, P=0.035). In 231 ccRCC patients, high platelet count was significantly correlated with later tumor staging (T, N, AJCC) and higher Fuhrman grade (P<0.05). In addition, in the TCGA cohort, the overall survival rate (OS) and disease-free survival rate (DFS) of patients with high platelet counts were significantly lower than those with low platelet counts (P<0.05). Patients with high platelet counts have a higher burden of tumor mutations, especially in key genes such as VHL and PBRM1. GO enrichment analysis revealed gene expression changes related to cell proliferation and extracellular matrix. Conclusions: Platelet count is a simple, non-invasive, and independent prognostic marker for ccRCC. This study supports the clinical utility of platelet count in risk stratification, offering the potential for integrating it into personalized treatment strategies. By predicting patient outcomes, platelet count can significantly improve clinical decision-making and guide therapeutic interventions for ccRCC patients. Platelet count Clear Cell Renal Cell Carcinoma (ccRCC) Prognostic indicator Multi-source data analysis Clinical prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Kidney cancer ranks among the top 10 most prevalent cancers in the United States, with renal cell carcinoma (RCC) accounting for over 90% of cases. Notably, the global incidence of RCC has been on the rise, positioning it as the 14th most common cancer worldwide. Clear cell renal cell carcinoma (ccRCC), the predominant subtype, constitutes approximately 70–80% of all kidney cancer cases. Despite remarkable progress in treatment and patient management, a considerable proportion of patients are diagnosed with advanced or metastatic disease. This underscores the pressing need for reliable prognostic markers to guide therapeutic decisions in RCC, particularly in the context of ccRCC 1 , 2 . Platelets, the second most abundant cell type in the blood, are well-known for their roles in hemostasis and thrombosis. However, emerging evidence has elucidated their pivotal involvement in various pathophysiological processes, including tumor progression, metastasis, immune modulation, and chemoresistance 3 , 4 . Specifically, elevated platelet counts have been implicated in poor prognosis across multiple cancer types, including RCC 5 . Platelets are thought to contribute to cancer progression through direct interactions with tumor cells, facilitating tumor growth, metastasis, immune evasion, and resistance to treatment 6 , 7 . This multifaceted role of platelets in cancer biology highlights their potential as a therapeutic target to improve cancer outcomes and underscores the importance of further exploring their prognostic significance. Despite the growing body of literature linking platelet count to cancer prognosis, the specific relationship between platelet count and ccRCC prognosis has not been comprehensively examined using robust methodological approaches, such as Mendelian randomization. This study aims to fill this research gap by first establishing the association between platelet count and RCC prognosis, with a particular focus on ccRCC, using Mendelian randomization. This technique allows for the assessment of causal relationships by leveraging genetic variants as instrumental variables, thereby minimizing confounding and reverse causality 8 , 9 . To validate this relationship, we utilized a publicly available dataset and complemented it with real-world clinical data. This dual approach enabled us to explore the connection between platelet count and patient outcomes in a more comprehensive and generalizable manner. Furthermore, we analyzed differential gene expression in tumors with varying platelet counts to gain insights into the underlying biological mechanisms that may mediate the association between platelet count and ccRCC prognosis. Our findings are expected to deepen the understanding of platelets' role in RCC, particularly ccRCC, and highlight their potential as a clinically relevant biomarker for prognosis and personalized treatment strategies. By shedding light on the biological underpinnings of this relationship, our study not only contributes to the existing literature but also paves the way for future research to explore the therapeutic potential of targeting platelets in ccRCC. Material and Methods Patients and Samples In this study, we employed a bidirectional Mendelian Randomization (MR) framework, utilizing summary-level data from four large-scale Genome-Wide Association Studies (GWAS) to explore the relationship between platelet count and clear cell renal cell carcinoma (ccRCC). The genetic instruments for platelet count were derived from four independent GWASs comprising 1,534,377 European individuals. Summary statistics for the platelet-associated Single Nucleotide Polymorphisms (SNPs) were obtained from the IEU GWAS database ( https://gwas.mrcieu.ac.uk/ ). Summary genetic data were also sourced from the same database for kidney cancer. SNPs that were associated with platelet count at a genome-wide significance level (P < 5 × 10 − 8 ) and were not in linkage disequilibrium (LD) with other SNPs (r 2 < 0.01 within a 10,000 kb window) were used as instruments. If a specific exposure SNP was absent from the outcome dataset, proxy SNPs were selected using LD tagging. Ultimately, 596 platelet-associated SNPs were included in the MR analysis. Additionally, we conducted a retrospective study involving 231 ccRCC patients who underwent partial nephrectomy or radical nephrectomy at the First Affiliated Hospital of Anhui Medical University between 2014 and 2020. Patients with complete clinical and pathological data were included. Clinical data were extracted from medical records, including demographic information, comorbidities, tumor characteristics, and surgical details. Pathological data such as tumor stage, grade, and histological subtype were recorded for each patient. Furthermore, to ensure the reliability and accuracy of the data, patients with non-clear cell renal cell carcinoma (non-ccRCC) tumors, those diagnosed with ccRCC tumors but with distant metastases, and those with severe inflammatory and tumorous processes that are confounding factors were excluded from this study. This exclusion measure is crucial because the conditions of these patients may introduce additional variables, thereby affecting the accuracy and reliability of the research results. Ensuring that the study focuses on a group with relatively consistent characteristics will lead to more precise and meaningful conclusions. In the course of collecting the data, we additionally scrutinized several constraints inherent in these datasets. Notably, our study encompassed merely 231 patients, a figure that may be considered relatively modest when juxtaposed against extensive multicenter studies. This limited sample size could potentially restrict the statistical power of our analysis, thereby impeding our ability to discern nuanced yet clinically significant disparities. In addition, all patient data in our study were obtained from a single hospital, which may not comprehensively summarize the diverse characteristics of a broader ccRCC patient population. This narrow data collection scope carries the risk of limited representativeness, potentially undermining the generalizability of our research findings in other contexts. Specifically, our study results may not be fully applicable to different ethnic groups, geographical regions, or healthcare systems. Furthermore, as this study is a retrospective study relying on medical records, there may be problems in terms of data completeness, accuracy, or reporting bias. This may affect the reliability and validity of our results. To overcome these limitations, future studies should aim to expand the sample size and incorporate patients from multiple centers. Additionally, refining data collection and validation processes is crucial to guarantee the completeness and precision of the data. By implementing these measures, we can significantly enhance the reliability and generalizability of our findings, ultimately providing more robust and informative evidence to guide clinical practice. mRNA Expression Data Gene expression data (raw counts) for 614 ccRCC patients, along with matched clinical annotations (e.g., tumor stage, gender, and recurrence-free survival), were downloaded from the Cancer Genome Atlas (TCGA) database. For mutation burden analysis, somatic mutation data (MAF files) for ccRCC were also obtained from TCGA. Mendelian Randomization Validation For the MR analysis, data on exposure variables were retrieved from a GWAS database to identify SNPs associated with platelet count. Data for the outcome variable, kidney cancer, were sourced from a separate GWAS dataset to confirm the presence of corresponding SNPs. Eligible SNPs were selected based on genome-wide significance (P < 5 × 10 − 8 ), and various statistical methods were applied to assess the causal relationship between platelet count and the risk of kidney cancer. All MR analyses were conducted using the MR-Base web app and the TwoSampleMR R package in R software (version 4.2.2). We used the inverse-variance weighted (IVW) MR method to estimate the causal association between platelet count and kidney cancer. Cochran's Q test was applied to evaluate heterogeneity among genetic instruments. In cases where heterogeneity was present (P < 0.05), a random-effects IVW model was used; otherwise, a fixed-effects IVW model was applied. We utilized MR-Egger regression to assess potential horizontal pleiotropy through the intercept term and the weighted median method to verify the stability of the results. A leave-one-out (LOO) analysis was performed to identify any individual SNPs that might have disproportionately influenced the results due to horizontal pleiotropy. The F-statistic (F = β 2 /se 2 ) was calculated to evaluate the strength of the instruments, with an F-value > 10 indicating sufficient strength to avoid weak instrument bias. Potential genetic effect size bias due to participant overlap was addressed by established methods using UK Biobank (UKB) data. All MR results are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Statistical Analysis To assess differences between groups, we performed independent sample t-tests. Specifically, we compared mean platelet counts across the T stage, N stage, AJCC stage, and Fuhrman grade. Statistical significance was defined as P < 0.05. Chi-squared tests were used to compare the observed and expected frequencies of categorical variables, employing the Pearson chi-square test via the "chisq.test" function in R software(version 4.3.0). A P-value less than 0.05 was considered statistically significant. Correlation Analysis Descriptive statistics are used to summarize and characterize the basic features of a population. They can help us understand the average level of platelet count and its variation range within the study population.The independent samples t-test is used to determine whether there are significant differences in the means of continuous variables between two independent groups. It can be used to test whether there are significant differences in the mean platelet counts between different groups.Furthermore, multivariate logistic regression can be used to evaluate whether platelet count is independently associated with the risk of clear cell renal cell carcinoma (ccRCC) after controlling for other factors such as age and gender. Through the coefficients of the model, we can understand the degree of change in the risk of ccRCC when the platelet count increases by a certain amount. Prognostic Statistical Analysis The Kaplan-Meier method is a non-parametric statistical tool specifically used to estimate the survival probability or recurrence-free survival probability of a patient population at specific time points. In the survival data analysis of patients with ccRCC, we used this method to predict the survival status of patients at different time points. The log-rank test is a statistical method used to compare the differences in survival distributions between two or more patient populations. It mainly tests whether there are significant differences in survival probabilities between different patient populations. In the results of statistical tests, the P-value is a very important indicator. When the P-value is less than 0.05, we consider this difference to be statistically significant, that is, there are indeed significant differences in survival probabilities between different groups. Tumor Mutational Burden (TMB) Analysis Tumor mutational burden (TMB) refers to the number of mutations per million bases and is an indicator for measuring the mutation frequency in the tumor genome. We downloaded and collated mutation data from The Cancer Genome Atlas (TCGA). We used maftools to read and process these mutation data and calculate the TMB of each sample. Finally, we compared the differences in TMB between different groups. Pathway and Enrichment Analysis GO(Gene Ontology) enrichment analysis is an important tool aimed at delving into the biological pathways potentially involved in genes associated with chemokine signatures. By analyzing the enrichment of these genes, we can reveal the biological processes they may participate in, the molecular functions they possess, or the cellular components to which they belong. To conduct this analysis, we selected the clusterProfiler R package. This is a very powerful R package that has been widely used in the field of functional annotation and enrichment analysis of genes and proteins. It not only supports multiple biological databases but also provides rich visualization functions, making the analysis results more intuitive and understandable. Results Platelet Count Correlates with Kidney Cancer Incidence The analysis of platelet parameters revealed a positive association between platelet count and kidney cancer incidence. As shown in Table 1 , platelet count and platelet distribution width (PDW) were significantly associated with the risk of developing kidney cancer. Specifically, the odds ratio (OR) for platelet count was 1.001 (95% confidence interval [CI]: 1.000–1.001, P = 0.035), suggesting a slight but statistically significant increase in the risk of kidney cancer with higher platelet counts. In contrast, PDW showed a modest decrease in risk, with an OR of 0.999 (95% CI: 0.999–1.000, P = 0.042). However, other platelet parameters, such as mean platelet volume (MPV) and platelet crit (PCT), did not show any significant association with kidney cancer, as their statistical measures were insignificant. These findings indicate that while some platelet-related metrics may influence kidney cancer risk, others do not play a substantial role. Table 1 The Putative Causal Effect of Platelet Parameters on Kidney Cancer: Results of Mendelian Randomization analysis investigating the causal effect of various platelet parameters (e.g., platelet count, mean platelet volume, platelet distribution width) on the risk of kidney cancer using the inverse variance weighted (IVW), weighted median, and MR-Egger approaches (where horizontal pleiotropy is present). CI, Confidence Interval; OR, Odds Ratio. exposure outcome nsnp method pval OR (95% CI) Mean platelet volume id: ebi − a−GCST90002345 Type of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis id: ukb − b−1316 4 Inverse variance weighted (multiplicative random effects) 0.808 1.000 (1.000 to 1.001) 4 Inverse variance weighted (fixed effects) 0.867 1.000 (0.999 to 1.001) 4 MR Egger 0.538 1.002 (0.996 to 1.009) 4 Weighted median 0.755 1.000 (0.999 to 1.001) 4 Simple mode 0.693 1.000 (0.999 to 1.001) 4 Weighted mode 0.719 1.000 (0.999 to 1.001) Platelet count id: ebi − a−GCST90028999 Type of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis id: ukb − b−1316 249 Inverse variance weighted (multiplicative random effects) 0.035 1.001 (1.000 to 1.001) 249 Inverse variance weighted (fixed effects) 0.036 1.001 (1.000 to 1.001) 249 MR Egger 0.148 1.001 (1.000 to 1.001) 249 Weighted median 0.230 1.001 (1.000 to 1.001) 249 Simple mode 0.272 1.001 (0.999 to 1.003) 249 Weighted mode 0.536 1.000 (0.999 to 1.001) Platelet distribution width id: ebi − a−GCST90029000 Type of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis id: ukb − b−1316 180 Inverse variance weighted (multiplicative random effects) 0.042 0.999 (0.999 to 1.000) 180 Inverse variance weighted (fixed effects) 0.041 0.999 (0.999 to 1.000) 180 MR Egger 0.561 1.000 (0.999 to 1.001) 180 Weighted median 0.388 0.999 (0.998 to 1.001) 180 Simple mode 0.778 1.000 (0.998 to 1.002) 180 Weighted mode 0.405 1.000 (0.998 to 1.001) Platelet crit id: ukb − d−30090_irnt Type of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis id: ukb − b−1316 163 Inverse variance weighted (multiplicative random effects) 0.163 1.000 (1.000 to 1.001) 163 Inverse variance weighted (fixed effects) 0.200 1.000 (1.000 to 1.001) 163 MR Egger 0.083 1.001 (1.000 to 1.002) 163 Weighted median 0.315 1.001 (1.000 to 1.002) 163 Simple mode 0.827 1.000 (0.998 to 1.003) 163 Weighted mode 0.449 1.000 (0.999 to 1.002) High Platelet Count Correlates with Adverse Pathological Outcomes in Renal Cell Carcinoma Further investigation into the relationship between platelet count and renal cell carcinoma (RCC) pathological features was conducted using data from 231 patients diagnosed between 2014 and 2020 at the First Affiliated Hospital of Anhui Medical University. We found that higher platelet counts were associated with more advanced tumor stages, including T stage (P = 0.001), N stage (P = 0.0253), overall stage (P = 0.000927), and Fuhrman grade (P = 0.00524), as shown in Fig. 1 A. Additionally, correlation analysis confirmed a positive association between platelet count and pathological staging, further supporting the link between platelet count and tumor progression (Fig. 1 B). We performed a chi-square test to assess differences in clinical and pathological characteristics based on platelet count groups: low platelet count ( 300 × 10 9 /L) (Table 2 ). The gender distribution was balanced, with 41.13% female and 58.87% male, and no significant association was found between platelet count and gender (P = 0.8606). However, age showed a significant association (P = 0.0109), with 53.68% of patients aged 60 years or younger in the low platelet count group, while 67.5% of those in the high platelet count group were 60 or younger. Table 2 Chi-Square Tests for the Relationship between Various Renal Cell Carcinoma Characteristics and Platelet Counts: Statistical analysis of the association between platelet counts and clinical characteristics of renal cell carcinoma (e.g., age, tumor size, stage) using chi-square tests. Covariates Type All low platelet count normal platelet count high platelet count P value Gender female 95(41.13%) 2(40%) 75(40.32%) 18(45%) 0.8606 male 136(58.87%) 3(60%) 111(59.68%) 22(55%) Age 60 107(46.32%) 5(100%) 89(47.85%) 13(32.5%) T stage 1 171(74.03%) 3(60%) 147(79.03%) 21(52.5%) 0.0315 2 40(17.32%) 1(20%) 26(13.98%) 13(32.5%) 3 18(7.79%) 1(20%) 12(6.45%) 5(12.5%) 4 2(0.87%) 0(0%) 1(0.54%) 1(2.5%) N stage 0 228(98.7%) 5(100%) 185(99.46%) 38(95%) 0.075 1 3(1.3%) 0(0%) 1(0.54%) 2(5%) AJCC 1 171(74.03%) 3(60%) 147(79.03%) 21(52.5%) 0.0345 stage 2 39(16.88%) 1(20%) 26(13.98%) 12(30%) 3 19(8.23%) 1(20%) 12(6.45%) 6(15%) 4 2(0.87%) 0(0%) 1(0.54%) 1(2.5%) Fuhrman 1 9(4.46%) 0(0%) 8(4.71%) 1(3.45%) 0.0766 grade 2 149(73.76%) 3(100%) 130(76.47%) 16(55.17%) 3 38(18.81%) 0(0%) 29(17.06%) 9(31.03%) 4 6(2.97%) 0(0%) 3(1.76%) 3(10.34%) Regarding tumor size (T stage), 74.03% of patients were classified as T1, and there was a significant difference in platelet count distribution across T stages (P = 0.0315). The low platelet count group had the lowest proportion of T1 patients (60%), while the high platelet count group had the highest proportion of T1 patients (52.5%). For lymph node involvement (N stage), the high platelet count group had a higher proportion of patients with lymph node metastasis (5%), although this difference was not statistically significant (P = 0.075). The distribution of disease stage also varied significantly among platelet count groups (P = 0.0345), with a lower proportion of stage I patients in both the low and high platelet count groups. Regarding histological grade, most patients were classified as grade 2 (73.76%), but no significant association was observed between grade and platelet count (P = 0.0766). Notably, the high platelet count group had a disproportionately high proportion of grade 4 patients (10.34%). In conclusion, our analysis suggests that age, tumor size, and disease stage are significantly associated with platelet count, while gender and lymph node involvement do not appear to correlate strongly. These findings highlight the potential clinical relevance of platelet count as a prognostic marker and warrant further investigation into the factors influencing platelet levels(Table S1-S3). Platelet Count and Prognosis in ccRCC The relationship between platelet count and patient prognosis was further examined using clinical data from 510 ccRCC patients obtained from the TCGA database. Chi-square tests confirmed the consistency of our findings with the clinical data we collected (Table 3 ). Regarding mortality, patients in the high platelet count group had the highest risk, while those in the normal platelet count group had the lowest risk (Fig. 2 A). The recurrence rate was significantly higher in the high platelet count group than in the low and normal platelet count groups (Fig. 2 B). No significant difference in recurrence was observed between the low and normal platelet count groups. The analysis of clinical factors in the cohort also revealed interesting patterns, particularly in the distribution of platelet counts across various clinical variables (Fig. 2 C). Table 3 Chi-Square Tests for the Relationship between Various Kidney Clear Cell Carcinoma Characteristics and Platelet Counts: Statistical analysis of the association between platelet counts and clinical features of clear cell renal cell carcinoma (e.g., AJCC stage, Fuhrman grade, recurrence) using chi-square tests. Covariates Type All low platelet count normal platelet count high platelet count P value Gender female 288(35.12%) 23(25.56%) 224(33.94%) 41(58.57%) 2.88E-05 male 532(64.88%) 67(74.44%) 436(66.06%) 29(41.43%) Age 60 420(51.22%) 56(62.22%) 325(49.24%) 39(55.71%) T stage 1 392(47.8%) 47(52.22%) 335(43%) 10(14.29%) 1.00E-09 2 202(12.32%) 11(12.22%) 81(12.27%) 9(12.86%) 3 915(37.2%) 32(35.56%) 230(34.85%) 43(61.43%) 4 88(2.68%) 0(0%) 9(2.12%) 8(11.43%) N stage 0 370(93.91%) 51(96.23%) 277(95.52%) 42(82.35%) 0.001 1 21(6.09%) 2(3.77%) 13(4.48%) 9(17.65%) AJCC 1 383(46.94%) 45(50%) 328(49.85%) 10(14.71%) 2.50E-09 stage 2 79(9.68%) 9(10%) 64(9.73%) 6(8.82%) 3 207(25.37%) 30(33.33%) 154(23.4%) 23(33.82%) 4 147(18.01%) 6(6.67%) 122(17.02%) 29(42.65%) Fuhrman 1 10(1.23%) 3(3.41%) 7(1.07%) 0(0%) 4.53E-08 grade 2 335(41.1%) 35(39.77%) 283(43.07%) 17(24.29%) 3 324(39.75%) 38(43.18%) 265(40.33%) 21(30%) 4 146(17.91%) 12(13.64%) 102(15.53%) 32(45.71%) Somatic Mutations and Platelet Count Next, we explored the association between platelet count and somatic mutations in ccRCC. Using simple nucleotide variation data from the TCGA database, we observed a higher somatic mutation rate in the high platelet count group (96.77%) compared to the low platelet count group (90.48%) and normal platelet count group (85.3%) (Figs. 2 D-F). The most frequently mutated genes in the high platelet count group were VHL (58%), PBRM1 (23%), and MUC (13%), while in the low platelet count group, the top mutations were in VHL (43%), PBRM1 (38%), and KDM5C (13%). In the normal platelet count group, the top mutations were in VHL (51%), PBRM1 (28%), and TTN (15%). These findings suggest that higher platelet counts are associated with a higher somatic mutation rate, particularly in key genes such as VHL and PBRM1, which are implicated in ccRCC pathogenesis. Platelet Count and Cancer Pathways To further investigate the mechanisms underlying the effect of platelet count on kidney cancer, we performed a Gene Ontology (GO) enrichment analysis. Differential gene expression analysis revealed several key pathways associated with platelet count. Among individuals with low platelet counts, significant gene expression changes were observed in genes involved in cardiovascular system development, which may have implications for cancer progression (Fig. 3 A-B). In contrast, individuals with normal to high platelet counts exhibited altered gene expression patterns linked to cell proliferation and organelle division (Fig. 3 C-D). Higher platelet counts may promote tumor initiation and progression through enhanced cell proliferation dynamics. Additionally, significant differences were observed in genes related to the extracellular matrix and basal cellular components between individuals with low and high platelet counts (Fig. 3 E-F). These results point to potential alterations in the tumor microenvironment mediated by platelet count, which could influence the progression of ccRCC. Discussion The current research examining the relationship between platelets and kidney cancer remains incomplete. However, our study underscores the significant role of platelet count in the progression and prognosis of kidney cancer. These findings suggest that platelet count could serve as a biomarker for diagnosis and prognosis and a potential target for therapeutic interventions 10 , 11 , 12 . To further elaborate on the potential molecular mechanisms underlying platelet count as a prognostic biomarker in clear cell renal cell carcinoma (ccRCC), several aspects warrant discussion. Platelets have been shown to release various growth factors and cytokines, such as vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), which are known to promote tumor angiogenesis and growth 13 . In ccRCC, where the tumor microenvironment plays a crucial role, the interaction between platelets and cancer cells may enhance these pro-tumorigenic pathways, contributing to disease progression. Additionally, the activation of the interleukin-6 (IL-6) pathway by elevated platelet counts could lead to an inflammatory response that favors tumor growth and metastasis, as demonstrated by increased T lymphocyte and macrophage activity 14 . These molecular mechanisms highlight the potential of platelet count not only as a prognostic indicator but also as a therapeutic target to modulate the tumor microenvironment.In particular, we observed a positive correlation between platelet count and kidney cancer progression, with higher platelet levels being more frequently associated with advanced tumor stages and worse pathological outcomes. This effect was not observed with other platelet parameters, such as mean platelet volume (MPV), platelet distribution width (PDW), and platelet crit (PCT), reinforcing the idea that platelet count specifically plays a key role in kidney cancer progression. These results align with previous studies, emphasizing the importance of platelet count over other platelet parameters in predicting disease outcomes 15 – 18 . The clinical application of platelet count as a biomarker in ccRCC holds great promise for improving diagnosis, prognosis, and therapeutic strategies. Platelet count is a readily available and cost-effective parameter that can be easily measured in routine blood tests, making it an attractive candidate for clinical use. However, it is important to acknowledge potential limitations of platelet count in clinical practice. For instance, the sensitivity and specificity of platelet count as a biomarker may vary across different patient populations and disease stages. Additionally, while platelet count is readily available in most routine clinical settings, variations in laboratory techniques and equipment may affect the consistency and accuracy of measurements. These factors need to be considered when interpreting platelet count data in the context of ccRCC. Our study underscores the potential of platelet count as a diagnostic and prognostic biomarker in ccRCC. Higher platelet levels were more frequently associated with advanced tumor stages and worse pathological outcomes, suggesting that platelet count could be used to stratify patients based on their risk of disease progression. When comparing our findings with other studies on biomarkers for ccRCC, platelet count emerges as a promising prognostic factor alongside more established markers such as tumor size, grade, and stage. For example, a study found that elevated platelet count was independently associated with poorer survival outcomes in ccRCC patients 19 , further supporting our observations. The added value of platelet count lies in its simplicity and cost-effectiveness, as it can be easily incorporated into routine clinical practice without the need for additional specialized tests or equipment. Furthermore, platelet count may provide complementary information to other prognostic factors, enhancing the overall accuracy of disease prediction and patient stratification. Incorporating platelet count into existing prognostic models, such as the TNM staging system, may improve the accuracy of predicting disease outcomes and guide personalized treatment decisions. Platelet count may also serve as a potential target for therapeutic interventions in ccRCC. In the clinical setting, antiplatelet therapy could be explored as an adjuvant treatment to conventional therapies, such as surgery, radiation, and chemotherapy, to improve therapeutic outcomes and prevent metastasis. Platelet count can be combined with other diagnostic or prognostic tools to enhance the accuracy and reliability of clinical decision-making. For example, integrating platelet count with imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), may improve the detection and staging of ccRCC. Additionally, combining platelet count with molecular markers, such as genetic mutations or protein expression levels, may provide a more comprehensive understanding of the tumor biology and guide targeted therapy. Extensive literature supports the critical involvement of platelets in cancer metastasis, which represents a key factor in the malignancy and poor prognosis of kidney cancer 20 . Metastasis, a primary cause of cancer-related mortality, complicates the effectiveness of therapeutic strategies 21 . During metastasis, tumor thrombus formation, which involves the aggregation of cancer cells with platelets, plays a critical role in facilitating the dissemination of cancer cells into distant sites 22 . As key mediators of hemostasis, platelets contribute to the adhesion and aggregation of circulating tumor cells, fostering the formation of tumor thrombi within blood vessels 23 – 25 . This process is particularly relevant in kidney cancer, where platelet levels have been shown to correlate with tumor thrombus formation 26 – 29 . Kidney cancer’s propensity for invading large blood vessels, such as the renal vein and inferior vena cava 30 , 31 , further promotes tumor cell entry into the bloodstream and increases the likelihood of metastasis. Elevated platelet activity in kidney cancer patients contributes not only to tumor thrombus formation but also to the aggressive nature of the disease and poorer overall prognosis 13 . These findings underscore the potential clinical value of targeting platelet function in preventing metastasis and improving therapeutic outcomes, particularly in metastatic kidney cancer. Although the mechanisms linking thrombocytosis to cancer prognosis remain speculative, our study suggests a potential association with cell proliferation. Platelets may influence tumor progression by releasing growth factors that promote cellular proliferation and metastasis. Previous studies have shown that platelets release growth factors that support the survival and spread of cancer cells 32 . For example, the anti-cancer drug Ticagrelor, which inhibits platelet aggregation, has been found to reduce tumor metastasis by inducing platelet apoptosis 33 . This observation implies that reducing platelet activity and quantity could inhibit tumor cell proliferation and metastasis, providing a potential therapeutic avenue 34 , 35 . Additionally, gene mutations in kidney cancer are significant factors influencing tumor behavior. Our study reveals a positive correlation between VHL gene mutations and elevated platelet counts, suggesting that VHL mutations may regulate tumor microenvironment processes such as inflammation and angiogenesis, which are known to contribute to tumor progression 36 , 37 . Mutations in VHL lead to the loss of function of the VHL protein, which normally regulates hypoxia-induced pathways and cell growth, contributing to the development of ccRCC. In contrast, we found a negative correlation between platelet count and PBRM1 gene mutations 38 , 39 . PBRM1, a key player in chromatin remodeling, is frequently mutated in ccRCC and has been shown to interact with transcription factors involved in tumor progression 40 , 41 . These findings suggest a complex interplay between VHL and PBRM1 mutations, where VHL mutations may suppress the activity of PBRM1, thereby influencing platelet count and the progression of ccRCC. This interaction warrants further investigation to explore the mechanisms underlying the regulation of platelet function in the tumor microenvironment. Despite these promising findings, there are several limitations to our study. First, although a range of methodologies was employed to assess the prognostic relationship between platelet count and renal cell carcinoma, data on specific subgroups of patients, such as those with different genetic backgrounds or treatment regimens, were unavailable. Second, the tumor-promoting role of platelets in kidney cancer requires further validation through experimental studies. Our future research will experimentally investigate how platelet activity influences tumor progression in renal cancer models. Lastly, the retrospective nature of the data limits our ability to draw prospective conclusions about the role of platelet count in kidney cancer prognosis. Conclusion Our study provides robust evidence linking platelet count with the progression and prognosis of kidney cancer, particularly clear cell renal cell carcinoma (ccRCC). As a readily accessible and cost-effective biomarker, platelet count could offer significant clinical utility for prognosis assessment and therapeutic decision-making in kidney cancer. However, additional prospective studies are needed to confirm these findings and establish platelet count as a standard clinical marker. Our results lay the groundwork for future investigations into the potential therapeutic targeting of platelets in metastatic kidney cancer and other malignancies characterized by thrombocytosis. Declarations Acknowledgements Not Applicable Funding No funding was received. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Authors ’ contributions XW.L and CZ.L supervised the entire project and approved of the final manuscript version to be published. C H,JL Z and M Z contributed to the data interpretation, data analysis,HZ W contributed to writing of the manuscript. C H and JL Z confirm the authenticity of all the raw data. Ethics approval and consent to participate Not Applicable Patient consent for publication Not Applicable Competing interests The authors declare that they have no competing interests. References Ferlay, J. et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International journal of cancer 127 , 2893-2917, doi:10.1002/ijc.25516 (2010). Capitanio, U. & Montorsi, F. Renal cancer. 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Platelet-derived MHC class I confers a pseudonormal phenotype to cancer cells that subverts the antitumor reactivity of natural killer immune cells. Cancer Res 72 , 440-448, doi:10.1158/0008-5472.CAN-11-1872 (2012). Casagrande, N. et al. In Ovarian Cancer Multicellular Spheroids, Platelet Releasate Promotes Growth, Expansion of ALDH+ and CD133+ Cancer Stem Cells, and Protection against the Cytotoxic Effects of Cisplatin, Carboplatin and Paclitaxel. Int J Mol Sci 22 , doi:10.3390/ijms22063019 (2021). Metelli, A. et al. Thrombin contributes to cancer immune evasion via proteolysis of platelet-bound GARP to activate LTGF-β. Sci Transl Med 12 , doi:10.1126/scitranslmed.aay4860 (2020). Schumacher, D., Strilic, B., Sivaraj, K. K., Wettschureck, N. & Offermanns, S. Platelet-derived nucleotides promote tumor-cell transendothelial migration and metastasis via P2Y2 receptor. Cancer Cell 24 , 130-137, doi:10.1016/j.ccr.2013.05.008 (2013). Haemmerle, M., Stone, R. L., Menter, D. G., Afshar-Kharghan, V. & Sood, A. K. The Platelet Lifeline to Cancer: Challenges and Opportunities. Cancer Cell 33 , 965-983, doi:10.1016/j.ccell.2018.03.002 (2018). RINI B I, ATKINS M B. Resistance to targeted therapy in renal-cell carcinoma [J]. The Lancet Oncology, 2009, 10(10): 992-1000. TAKENAWA J, KANEKO Y, FUKUMOTO M, et al. Enhanced expression of interleukin-6 in primary human renal cell carcinomas [J]. Journal of the National Cancer Institute, 1991, 83(22): 1668-72. Gu, L. et al. The association of platelet count with clinicopathological significance and prognosis in renal cell carcinoma: a systematic review and meta-analysis. PLoS One 10 , e0125538, doi:10.1371/journal.pone.0125538 (2015). Wu, Y. et al. Prognostic role of systemic inflammatory response in renal cell carcinoma: a systematic review and meta-analysis. J Cancer Res Clin Oncol 137 , 887-896, doi:10.1007/s00432-010-0951-3 (2011). Coban, E. Comment on "Mean platelet volume-to-lymphocyte ratio: a novel biomarker associated with overall survival in patients with nonmetastatic clear cell renal cell carcinoma treated with nephrectomy". Int Urol Nephrol 52 , 1703, doi:10.1007/s11255-020-02446-6 (2020). Lee, A. et al. Prognostic Significance of Inflammation-associated Blood Cell Markers in Nonmetastatic Clear Cell Renal Cell Carcinoma. Clin Genitourin Cancer 18 , 304-313, doi:10.1016/j.clgc.2019.11.013 (2020). YOUNG M, TAPIA J C, SZABADOS B, et al. NLR Outperforms Low Hemoglobin and High Platelet Count as Predictive and Prognostic Biomarker in Metastatic Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors [J]. Clinical genitourinary cancer, 2024, 22(3): 102072. Zhou, L. et al. The critical role of platelet in cancer progression and metastasis. Eur J Med Res 28 , 385, doi:10.1186/s40001-023-01342-w (2023). Gerstberger, S., Jiang, Q. & Ganesh, K. Metastasis. Cell 186 , 1564-1579, doi:10.1016/j.cell.2023.03.003 (2023). Zhou, S., Zheng, J., Zhai, W. & Chen, Y. Spatio-temporal heterogeneity in cancer evolution and tumor microenvironment of renal cell carcinoma with tumor thrombus. Cancer Lett 572 , 216350, doi:10.1016/j.canlet.2023.216350 (2023). Shi, Q., Ji, T., Tang, X. & Guo, W. The role of tumor-platelet interplay and micro tumor thrombi during hematogenous tumor metastasis. Cell Oncol (Dordr) 46 , 521-532, doi:10.1007/s13402-023-00773-1 (2023). Holinstat, M. Normal platelet function. Cancer Metastasis Rev 36 , 195-198, doi:10.1007/s10555-017-9677-x (2017). Dudiki, T. et al. Mechanism of Tumor-Platelet Communications in Cancer. Circ Res 132 , 1447-1461, doi:10.1161/CIRCRESAHA.122.321861 (2023). Tathireddy, H., Rice, D., Martens, K., Shivakumar, S. & Shatzel, J. Breaking down tumor thrombus: Current strategies for medical management. Thromb Res 230 , 144-151, doi:10.1016/j.thromres.2023.09.004 (2023). Kaptein, F. H. J. et al. Prevalence, Treatment, and Prognosis of Tumor Thrombi in Renal Cell Carcinoma. JACC CardioOncol 4 , 522-531, doi:10.1016/j.jaccao.2022.07.011 (2022). Reese, A. C., Whitson, J. M. & Meng, M. V. Natural history of untreated renal cell carcinoma with venous tumor thrombus. Urol Oncol 31 , 1305-1309, doi:10.1016/j.urolonc.2011.12.006 (2013). Horynecka, Z. et al. Analysis of surgical outcomes in 102 patients with renal cell carcinoma with venous tumor thrombus: A retrospective observational single-center study. Medicine (Baltimore) 101 , e30808, doi:10.1097/MD.0000000000030808 (2022). Rodriguez Faba, O. et al. Impact of Microscopic Wall Invasion of the Renal Vein or Inferior Vena Cava on Cancer-specific Survival in Patients with Renal Cell Carcinoma and Tumor Thrombus: A Multi-institutional Analysis from the International Renal Cell Carcinoma-Venous Thrombus Consortium. Eur Urol Focus 4 , 435-441, doi:10.1016/j.euf.2017.01.009 (2018). Blute, M. L., Leibovich, B. C., Lohse, C. M., Cheville, J. C. & Zincke, H. The Mayo Clinic experience with surgical management, complications and outcome for patients with renal cell carcinoma and venous tumour thrombus. BJU Int 94 , 33-41 (2004). Schlesinger, M. Role of platelets and platelet receptors in cancer metastasis. J Hematol Oncol 11 , 125, doi:10.1186/s13045-018-0669-2 (2018). Meng, X. et al. Ticagrelor prevents tumor metastasis via inhibiting cell proliferation and promoting platelet apoptosis. Anticancer Drugs 31 , 1012-1017, doi:10.1097/CAD.0000000000000925 (2020). Ramsey, S., Lamb, G. W. A., Aitchison, M. & McMillan, D. C. Prospective study of the relationship between the systemic inflammatory response, prognostic scoring systems and relapse-free and cancer-specific survival in patients undergoing potentially curative resection for renal cancer. BJU Int 101 , 959-963, doi:10.1111/j.1464-410X.2007.07363.x (2008). Bromwich, E. J. et al. The relationship between T-lymphocyte infiltration, stage, tumour grade and survival in patients undergoing curative surgery for renal cell cancer. British Journal of Cancer 89 , 1906-1908 (2003). Yang, J. et al. A Positive Feedback Loop between Inactive VHL-Triggered Histone Lactylation and PDGFRβ Signaling Drives Clear Cell Renal Cell Carcinoma Progression. International Journal of Biological Sciences 18 , 3470-3483, doi:10.7150/ijbs.73398 (2022). Linehan, W. M. et al. The Metabolic Basis of Kidney Cancer. Cancer Discov 9 , 1006-1021, doi:10.1158/2159-8290.CD-18-1354 (2019). Linehan, W. M. & Rathmell, W. K. Kidney cancer. Urol Oncol 30 , 948-951, doi:10.1016/j.urolonc.2012.08.021 (2012). Carril-Ajuria, L., Santos, M., Roldán-Romero, J. M., Rodriguez-Antona, C. & de Velasco, G. Prognostic and Predictive Value of PBRM1 in Clear Cell Renal Cell Carcinoma. Cancers (Basel) 12 , doi:10.3390/cancers12010016 (2019). Gad, S. et al. Involvement of PBRM1 in VHL disease-associated clear cell renal cell carcinoma and its putative relationship with the HIF pathway. Oncol Lett 22 , 835, doi:10.3892/ol.2021.13096 (2021). Yao, X. et al. PBRM1-deficient PBAF complexes target aberrant genomic loci to activate the NF-κB pathway in clear cell renal cell carcinoma. Nat Cell Biol 25 , 765-777, doi:10.1038/s41556-023-01122-y (2023). Supplemental Tables Supplementary Tables S1-S3 are not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6196668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427145910,"identity":"1c126bfb-14b5-45ad-9e6a-1cfaf1532a25","order_by":0,"name":"Cong Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Huang","suffix":""},{"id":427145911,"identity":"72b79ec9-7670-4375-914a-445546dfa55f","order_by":1,"name":"Jialong Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jialong","middleName":"","lastName":"Zhang","suffix":""},{"id":427145912,"identity":"15e61654-e4c1-4098-8f3d-5d84d6dd4f24","order_by":2,"name":"Hongzhi Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Wang","suffix":""},{"id":427145913,"identity":"2553ceae-88be-44d9-b376-b0cec0306146","order_by":3,"name":"Mei zhang","email":"","orcid":"","institution":"Zhangshu people's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"zhang","suffix":""},{"id":427145914,"identity":"804aab1d-f569-4974-b095-47d2222ac2b1","order_by":4,"name":"Xiwei Lu","email":"","orcid":"","institution":"Zhangshu people's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiwei","middleName":"","lastName":"Lu","suffix":""},{"id":427145915,"identity":"fe0150e1-282b-417a-9d15-da58a85b7b82","order_by":5,"name":"Chaozhao Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACA2YYA4hBbDk29vYDpGkx5uM5k4BfCzIDpCVxnoSDAW71IJXszM8efmFgkDdnP3z4c0GNTXqbBEMCw4+KbXgcxmZuLMPAYLizJy3BeMaxtNw26cYDjD1nbuPzi5m0BANDgsGBHINkHrbDuW0yBxKYGdvwaWH/BtFy/o3BYZ5/h9PZJBIMCGjhMZP8ANJyI8ewmbftcAIxWsqkgbThhhvPkpl5+9IM24CBfBCfX+z7j2+T/AEMMYPzyYc/83yzkZdvbz/44EcFbi0gwMz77z+qyAG86oGA8QchFaNgFIyCUTCyAQA+5kwoSlAoYAAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chaozhao","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-03-10 14:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6196668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6196668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78731997,"identity":"a1a546a3-c7a2-4fab-95ec-ba07600d1f55","added_by":"auto","created_at":"2025-03-18 07:42:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePlatelet Count and t-Test Analysis of Renal Cell Carcinoma Pathology: The relationship between platelet count and various pathological characteristics of renal cell carcinoma (RCC) is assessed using a t-test.\u003cstrong\u003e(B)\u003c/strong\u003e Correlation Analysis between Platelet Count and RCC Pathology: Correlation between platelet count and key pathological factors of RCC, including tumor grade and stage.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6196668/v1/c87634efa3334073f33aa309.png"},{"id":78731990,"identity":"e96a3729-59d2-42bc-ba3a-babf7b81df14","added_by":"auto","created_at":"2025-03-18 07:42:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":456242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-B) \u003c/strong\u003eComparative Prognosis of Patients among Different Platelet Count Groups in the TCGA Cohort: Survival analysis comparing patient outcomes (overall survival and recurrence) across low, normal, and high platelet count groups within the TCGA cohort.\u003cstrong\u003e(C) \u003c/strong\u003eSankey Plot Summarizing the Relationship among Platelet Count Clusters and Key Pathological Stages: Visualization of how platelet count clusters correlate with AJCC stage, T stage, M stage, and N stage in RCC patients.\u003cstrong\u003e(D-F)\u003c/strong\u003e Oncoprint of Mutation Status of Top 20 Genes in Platelet Count Clusters: Oncoprint of mutation status for the top 20 most frequently mutated genes in the high (D), normal (E), and low \u003cstrong\u003e(F) \u003c/strong\u003eplatelet count groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6196668/v1/21d85e7300f968ef04e87e9f.png"},{"id":78731998,"identity":"d3504223-9f18-4220-a7b9-61561466d351","added_by":"auto","created_at":"2025-03-18 07:42:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":703331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e Analysis of Differential Genes and Gene Ontology (GO) Enrichment in Low and Normal Platelet Count Clusters: Differential gene expression analysis between low and normal platelet count groups, with GO enrichment results highlighting the biological processes affected.\u003cstrong\u003e(C-D)\u003c/strong\u003eAnalysis of Differential Genes and GO Enrichment in Normal and High Platelet Count Clusters: Differential gene expression and GO enrichment analysis between normal and high platelet count groups.\u003cstrong\u003e(E-F) \u003c/strong\u003eAnalysis of Differential Genes and GO Enrichment in Low and High Platelet Count Clusters: Examination of differential genes and GO enrichment between low and high platelet count groups, indicating potential mechanistic pathways involved in RCC progression.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6196668/v1/f8d7221576b8f25de44ef9a5.png"},{"id":78736105,"identity":"8d4c0c21-1707-408b-95b5-ab0abcf0d471","added_by":"auto","created_at":"2025-03-18 08:14:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2689897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6196668/v1/eabdd800-9e5c-4943-b491-a0def4103c3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Platelet Count as an Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma: Insights from Multi-source Data Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney cancer ranks among the top 10 most prevalent cancers in the United States, with renal cell carcinoma (RCC) accounting for over 90% of cases. Notably, the global incidence of RCC has been on the rise, positioning it as the 14th most common cancer worldwide. Clear cell renal cell carcinoma (ccRCC), the predominant subtype, constitutes approximately 70\u0026ndash;80% of all kidney cancer cases. Despite remarkable progress in treatment and patient management, a considerable proportion of patients are diagnosed with advanced or metastatic disease. This underscores the pressing need for reliable prognostic markers to guide therapeutic decisions in RCC, particularly in the context of ccRCC\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePlatelets, the second most abundant cell type in the blood, are well-known for their roles in hemostasis and thrombosis. However, emerging evidence has elucidated their pivotal involvement in various pathophysiological processes, including tumor progression, metastasis, immune modulation, and chemoresistance\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Specifically, elevated platelet counts have been implicated in poor prognosis across multiple cancer types, including RCC\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Platelets are thought to contribute to cancer progression through direct interactions with tumor cells, facilitating tumor growth, metastasis, immune evasion, and resistance to treatment\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This multifaceted role of platelets in cancer biology highlights their potential as a therapeutic target to improve cancer outcomes and underscores the importance of further exploring their prognostic significance.\u003c/p\u003e \u003cp\u003eDespite the growing body of literature linking platelet count to cancer prognosis, the specific relationship between platelet count and ccRCC prognosis has not been comprehensively examined using robust methodological approaches, such as Mendelian randomization. This study aims to fill this research gap by first establishing the association between platelet count and RCC prognosis, with a particular focus on ccRCC, using Mendelian randomization. This technique allows for the assessment of causal relationships by leveraging genetic variants as instrumental variables, thereby minimizing confounding and reverse causality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo validate this relationship, we utilized a publicly available dataset and complemented it with real-world clinical data. This dual approach enabled us to explore the connection between platelet count and patient outcomes in a more comprehensive and generalizable manner. Furthermore, we analyzed differential gene expression in tumors with varying platelet counts to gain insights into the underlying biological mechanisms that may mediate the association between platelet count and ccRCC prognosis.\u003c/p\u003e \u003cp\u003eOur findings are expected to deepen the understanding of platelets' role in RCC, particularly ccRCC, and highlight their potential as a clinically relevant biomarker for prognosis and personalized treatment strategies. By shedding light on the biological underpinnings of this relationship, our study not only contributes to the existing literature but also paves the way for future research to explore the therapeutic potential of targeting platelets in ccRCC.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Samples\u003c/h2\u003e \u003cp\u003eIn this study, we employed a bidirectional Mendelian Randomization (MR) framework, utilizing summary-level data from four large-scale Genome-Wide Association Studies (GWAS) to explore the relationship between platelet count and clear cell renal cell carcinoma (ccRCC). The genetic instruments for platelet count were derived from four independent GWASs comprising 1,534,377 European individuals. Summary statistics for the platelet-associated Single Nucleotide Polymorphisms (SNPs) were obtained from the IEU GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Summary genetic data were also sourced from the same database for kidney cancer. SNPs that were associated with platelet count at a genome-wide significance level (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and were not in linkage disequilibrium (LD) with other SNPs (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 within a 10,000 kb window) were used as instruments. If a specific exposure SNP was absent from the outcome dataset, proxy SNPs were selected using LD tagging. Ultimately, 596 platelet-associated SNPs were included in the MR analysis.\u003c/p\u003e \u003cp\u003eAdditionally, we conducted a retrospective study involving 231 ccRCC patients who underwent partial nephrectomy or radical nephrectomy at the First Affiliated Hospital of Anhui Medical University between 2014 and 2020. Patients with complete clinical and pathological data were included. Clinical data were extracted from medical records, including demographic information, comorbidities, tumor characteristics, and surgical details. Pathological data such as tumor stage, grade, and histological subtype were recorded for each patient. Furthermore, to ensure the reliability and accuracy of the data, patients with non-clear cell renal cell carcinoma (non-ccRCC) tumors, those diagnosed with ccRCC tumors but with distant metastases, and those with severe inflammatory and tumorous processes that are confounding factors were excluded from this study. This exclusion measure is crucial because the conditions of these patients may introduce additional variables, thereby affecting the accuracy and reliability of the research results. Ensuring that the study focuses on a group with relatively consistent characteristics will lead to more precise and meaningful conclusions.\u003c/p\u003e \u003cp\u003eIn the course of collecting the data, we additionally scrutinized several constraints inherent in these datasets. Notably, our study encompassed merely 231 patients, a figure that may be considered relatively modest when juxtaposed against extensive multicenter studies. This limited sample size could potentially restrict the statistical power of our analysis, thereby impeding our ability to discern nuanced yet clinically significant disparities. In addition, all patient data in our study were obtained from a single hospital, which may not comprehensively summarize the diverse characteristics of a broader ccRCC patient population. This narrow data collection scope carries the risk of limited representativeness, potentially undermining the generalizability of our research findings in other contexts. Specifically, our study results may not be fully applicable to different ethnic groups, geographical regions, or healthcare systems. Furthermore, as this study is a retrospective study relying on medical records, there may be problems in terms of data completeness, accuracy, or reporting bias. This may affect the reliability and validity of our results.\u003c/p\u003e \u003cp\u003eTo overcome these limitations, future studies should aim to expand the sample size and incorporate patients from multiple centers. Additionally, refining data collection and validation processes is crucial to guarantee the completeness and precision of the data. By implementing these measures, we can significantly enhance the reliability and generalizability of our findings, ultimately providing more robust and informative evidence to guide clinical practice.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003emRNA Expression Data\u003c/h3\u003e\n\u003cp\u003eGene expression data (raw counts) for 614 ccRCC patients, along with matched clinical annotations (e.g., tumor stage, gender, and recurrence-free survival), were downloaded from the Cancer Genome Atlas (TCGA) database. For mutation burden analysis, somatic mutation data (MAF files) for ccRCC were also obtained from TCGA.\u003c/p\u003e\n\u003ch3\u003eMendelian Randomization Validation\u003c/h3\u003e\n\u003cp\u003eFor the MR analysis, data on exposure variables were retrieved from a GWAS database to identify SNPs associated with platelet count. Data for the outcome variable, kidney cancer, were sourced from a separate GWAS dataset to confirm the presence of corresponding SNPs. Eligible SNPs were selected based on genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), and various statistical methods were applied to assess the causal relationship between platelet count and the risk of kidney cancer. All MR analyses were conducted using the MR-Base web app and the TwoSampleMR R package in R software (version 4.2.2). We used the inverse-variance weighted (IVW) MR method to estimate the causal association between platelet count and kidney cancer. Cochran's Q test was applied to evaluate heterogeneity among genetic instruments. In cases where heterogeneity was present (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a random-effects IVW model was used; otherwise, a fixed-effects IVW model was applied. We utilized MR-Egger regression to assess potential horizontal pleiotropy through the intercept term and the weighted median method to verify the stability of the results. A leave-one-out (LOO) analysis was performed to identify any individual SNPs that might have disproportionately influenced the results due to horizontal pleiotropy. The F-statistic (F\u0026thinsp;=\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e/se\u003csup\u003e2\u003c/sup\u003e) was calculated to evaluate the strength of the instruments, with an F-value\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating sufficient strength to avoid weak instrument bias. Potential genetic effect size bias due to participant overlap was addressed by established methods using UK Biobank (UKB) data. All MR results are reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo assess differences between groups, we performed independent sample t-tests. Specifically, we compared mean platelet counts across the T stage, N stage, AJCC stage, and Fuhrman grade. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Chi-squared tests were used to compare the observed and expected frequencies of categorical variables, employing the Pearson chi-square test via the \"chisq.test\" function in R software(version 4.3.0). A P-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics are used to summarize and characterize the basic features of a population. They can help us understand the average level of platelet count and its variation range within the study population.The independent samples t-test is used to determine whether there are significant differences in the means of continuous variables between two independent groups. It can be used to test whether there are significant differences in the mean platelet counts between different groups.Furthermore, multivariate logistic regression can be used to evaluate whether platelet count is independently associated with the risk of clear cell renal cell carcinoma (ccRCC) after controlling for other factors such as age and gender. Through the coefficients of the model, we can understand the degree of change in the risk of ccRCC when the platelet count increases by a certain amount.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier method is a non-parametric statistical tool specifically used to estimate the survival probability or recurrence-free survival probability of a patient population at specific time points. In the survival data analysis of patients with ccRCC, we used this method to predict the survival status of patients at different time points. The log-rank test is a statistical method used to compare the differences in survival distributions between two or more patient populations. It mainly tests whether there are significant differences in survival probabilities between different patient populations. In the results of statistical tests, the P-value is a very important indicator. When the P-value is less than 0.05, we consider this difference to be statistically significant, that is, there are indeed significant differences in survival probabilities between different groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTumor Mutational Burden (TMB) Analysis\u003c/h3\u003e\n\u003cp\u003eTumor mutational burden (TMB) refers to the number of mutations per million bases and is an indicator for measuring the mutation frequency in the tumor genome. We downloaded and collated mutation data from The Cancer Genome Atlas (TCGA). We used maftools to read and process these mutation data and calculate the TMB of each sample. Finally, we compared the differences in TMB between different groups.\u003c/p\u003e\n\u003ch3\u003ePathway and Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eGO(Gene Ontology) enrichment analysis is an important tool aimed at delving into the biological pathways potentially involved in genes associated with chemokine signatures. By analyzing the enrichment of these genes, we can reveal the biological processes they may participate in, the molecular functions they possess, or the cellular components to which they belong. To conduct this analysis, we selected the clusterProfiler R package. This is a very powerful R package that has been widely used in the field of functional annotation and enrichment analysis of genes and proteins. It not only supports multiple biological databases but also provides rich visualization functions, making the analysis results more intuitive and understandable.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlatelet Count Correlates with Kidney Cancer Incidence\u003c/h2\u003e \u003cp\u003eThe analysis of platelet parameters revealed a positive association between platelet count and kidney cancer incidence. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, platelet count and platelet distribution width (PDW) were significantly associated with the risk of developing kidney cancer. Specifically, the odds ratio (OR) for platelet count was 1.001 (95% confidence interval [CI]: 1.000\u0026ndash;1.001, P\u0026thinsp;=\u0026thinsp;0.035), suggesting a slight but statistically significant increase in the risk of kidney cancer with higher platelet counts. In contrast, PDW showed a modest decrease in risk, with an OR of 0.999 (95% CI: 0.999\u0026ndash;1.000, P\u0026thinsp;=\u0026thinsp;0.042). However, other platelet parameters, such as mean platelet volume (MPV) and platelet crit (PCT), did not show any significant association with kidney cancer, as their statistical measures were insignificant. These findings indicate that while some platelet-related metrics may influence kidney cancer risk, others do not play a substantial role.\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\u003eThe Putative Causal Effect of Platelet Parameters on Kidney Cancer: Results of Mendelian Randomization analysis investigating the causal effect of various platelet parameters (e.g., platelet count, mean platelet volume, platelet distribution width) on the risk of kidney cancer using the inverse variance weighted (IVW), weighted median, and MR-Egger approaches (where horizontal pleiotropy is present). CI, Confidence Interval; OR, Odds Ratio.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ensnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean platelet volume id: ebi\u0026thinsp;\u0026minus;\u0026thinsp;a\u0026minus;GCST90002345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis\u003c/p\u003e \u003cp\u003eid: ukb\u0026thinsp;\u0026minus;\u0026thinsp;b\u0026minus;1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (multiplicative random effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003cp\u003e(0.996 to 1.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003cp\u003eid: ebi\u0026thinsp;\u0026minus;\u0026thinsp;a\u0026minus;GCST90028999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis\u003c/p\u003e \u003cp\u003eid: ukb\u0026thinsp;\u0026minus;\u0026thinsp;b\u0026minus;1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (multiplicative random effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003cp\u003e(fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(0.999 to 1.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet distribution width\u003c/p\u003e \u003cp\u003eid: ebi\u0026thinsp;\u0026minus;\u0026thinsp;a\u0026minus;GCST90029000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis\u003c/p\u003e \u003cp\u003eid: ukb\u0026thinsp;\u0026minus;\u0026thinsp;b\u0026minus;1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (multiplicative random effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e(0.999 to 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003cp\u003e(fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e(0.999 to 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e(0.998 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.998 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.998 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet crit\u003c/p\u003e \u003cp\u003eid: ukb\u0026thinsp;\u0026minus;\u0026thinsp;d\u0026minus;30090_irnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of cancer: ICD10: C64 Malignant neoplasm of kidney, except renal pelvis\u003c/p\u003e \u003cp\u003eid: ukb\u0026thinsp;\u0026minus;\u0026thinsp;b\u0026minus;1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted (multiplicative random effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003cp\u003e(fixed effects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000 to 1.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.998 to 1.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(0.999 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHigh Platelet Count Correlates with Adverse Pathological Outcomes in Renal Cell Carcinoma\u003c/h2\u003e \u003cp\u003eFurther investigation into the relationship between platelet count and renal cell carcinoma (RCC) pathological features was conducted using data from 231 patients diagnosed between 2014 and 2020 at the First Affiliated Hospital of Anhui Medical University. We found that higher platelet counts were associated with more advanced tumor stages, including T stage (P\u0026thinsp;=\u0026thinsp;0.001), N stage (P\u0026thinsp;=\u0026thinsp;0.0253), overall stage (P\u0026thinsp;=\u0026thinsp;0.000927), and Fuhrman grade (P\u0026thinsp;=\u0026thinsp;0.00524), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Additionally, correlation analysis confirmed a positive association between platelet count and pathological staging, further supporting the link between platelet count and tumor progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed a chi-square test to assess differences in clinical and pathological characteristics based on platelet count groups: low platelet count (\u0026lt;\u0026thinsp;100 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L), normal platelet count (100\u0026ndash;300 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L), and high platelet count (\u0026gt;\u0026thinsp;300 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The gender distribution was balanced, with 41.13% female and 58.87% male, and no significant association was found between platelet count and gender (P\u0026thinsp;=\u0026thinsp;0.8606). However, age showed a significant association (P\u0026thinsp;=\u0026thinsp;0.0109), with 53.68% of patients aged 60 years or younger in the low platelet count group, while 67.5% of those in the high platelet count group were 60 or younger.\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\u003eChi-Square Tests for the Relationship between Various Renal Cell Carcinoma Characteristics and Platelet Counts: Statistical analysis of the association between platelet counts and clinical characteristics of renal cell carcinoma (e.g., age, tumor size, stage) using chi-square tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elow\u003c/p\u003e \u003cp\u003eplatelet\u003c/p\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003cp\u003eplatelet\u003c/p\u003e \u003cp\u003ecount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ehigh platelet count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\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\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(41.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75(40.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(58.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111(59.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e22(55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124(53.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(52.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e27(67.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(46.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89(47.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13(32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(74.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147(79.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21(52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(17.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(13.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e13(32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(7.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228(98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185(99.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e38(95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(74.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147(79.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21(52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003estage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(16.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(13.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e12(30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(8.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFuhrman\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(4.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(4.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1(3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149(73.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130(76.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e16(55.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(18.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(17.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9(31.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3(10.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding tumor size (T stage), 74.03% of patients were classified as T1, and there was a significant difference in platelet count distribution across T stages (P\u0026thinsp;=\u0026thinsp;0.0315). The low platelet count group had the lowest proportion of T1 patients (60%), while the high platelet count group had the highest proportion of T1 patients (52.5%). For lymph node involvement (N stage), the high platelet count group had a higher proportion of patients with lymph node metastasis (5%), although this difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.075). The distribution of disease stage also varied significantly among platelet count groups (P\u0026thinsp;=\u0026thinsp;0.0345), with a lower proportion of stage I patients in both the low and high platelet count groups. Regarding histological grade, most patients were classified as grade 2 (73.76%), but no significant association was observed between grade and platelet count (P\u0026thinsp;=\u0026thinsp;0.0766). Notably, the high platelet count group had a disproportionately high proportion of grade 4 patients (10.34%).\u003c/p\u003e \u003cp\u003eIn conclusion, our analysis suggests that age, tumor size, and disease stage are significantly associated with platelet count, while gender and lymph node involvement do not appear to correlate strongly. These findings highlight the potential clinical relevance of platelet count as a prognostic marker and warrant further investigation into the factors influencing platelet levels(Table S1-S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePlatelet Count and Prognosis in ccRCC\u003c/h2\u003e \u003cp\u003eThe relationship between platelet count and patient prognosis was further examined using clinical data from 510 ccRCC patients obtained from the TCGA database. Chi-square tests confirmed the consistency of our findings with the clinical data we collected (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding mortality, patients in the high platelet count group had the highest risk, while those in the normal platelet count group had the lowest risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The recurrence rate was significantly higher in the high platelet count group than in the low and normal platelet count groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). No significant difference in recurrence was observed between the low and normal platelet count groups. The analysis of clinical factors in the cohort also revealed interesting patterns, particularly in the distribution of platelet counts across various clinical variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-Square Tests for the Relationship between Various Kidney Clear Cell Carcinoma Characteristics and Platelet Counts: Statistical analysis of the association between platelet counts and clinical features of clear cell renal cell carcinoma (e.g., AJCC stage, Fuhrman grade, recurrence) using chi-square tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elow platelet count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003enormal platelet count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehigh platelet count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\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\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288(35.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(25.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224(33.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41(58.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.88E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e532(64.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(74.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e436(66.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29(41.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400(48.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(37.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335(50.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31(44.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420(51.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(62.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325(49.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(55.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(52.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202(12.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(12.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(12.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(12.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e915(37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(35.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230(34.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43(61.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(2.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(2.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(11.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370(93.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(96.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e277(95.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(82.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(6.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(3.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(4.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383(46.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328(49.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(14.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.50E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003estage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64(9.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(8.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207(25.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23(33.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(18.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122(17.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29(42.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFuhrman\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(1.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(1.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.53E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003egrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(39.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283(43.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17(24.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324(39.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(43.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e265(40.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146(17.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(13.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102(15.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32(45.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSomatic Mutations and Platelet Count\u003c/h2\u003e \u003cp\u003eNext, we explored the association between platelet count and somatic mutations in ccRCC. Using simple nucleotide variation data from the TCGA database, we observed a higher somatic mutation rate in the high platelet count group (96.77%) compared to the low platelet count group (90.48%) and normal platelet count group (85.3%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). The most frequently mutated genes in the high platelet count group were VHL (58%), PBRM1 (23%), and MUC (13%), while in the low platelet count group, the top mutations were in VHL (43%), PBRM1 (38%), and KDM5C (13%). In the normal platelet count group, the top mutations were in VHL (51%), PBRM1 (28%), and TTN (15%). These findings suggest that higher platelet counts are associated with a higher somatic mutation rate, particularly in key genes such as VHL and PBRM1, which are implicated in ccRCC pathogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePlatelet Count and Cancer Pathways\u003c/h2\u003e \u003cp\u003eTo further investigate the mechanisms underlying the effect of platelet count on kidney cancer, we performed a Gene Ontology (GO) enrichment analysis. Differential gene expression analysis revealed several key pathways associated with platelet count. Among individuals with low platelet counts, significant gene expression changes were observed in genes involved in cardiovascular system development, which may have implications for cancer progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). In contrast, individuals with normal to high platelet counts exhibited altered gene expression patterns linked to cell proliferation and organelle division (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). Higher platelet counts may promote tumor initiation and progression through enhanced cell proliferation dynamics. Additionally, significant differences were observed in genes related to the extracellular matrix and basal cellular components between individuals with low and high platelet counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). These results point to potential alterations in the tumor microenvironment mediated by platelet count, which could influence the progression of ccRCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current research examining the relationship between platelets and kidney cancer remains incomplete. However, our study underscores the significant role of platelet count in the progression and prognosis of kidney cancer. These findings suggest that platelet count could serve as a biomarker for diagnosis and prognosis and a potential target for therapeutic interventions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. To further elaborate on the potential molecular mechanisms underlying platelet count as a prognostic biomarker in clear cell renal cell carcinoma (ccRCC), several aspects warrant discussion. Platelets have been shown to release various growth factors and cytokines, such as vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), which are known to promote tumor angiogenesis and growth\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In ccRCC, where the tumor microenvironment plays a crucial role, the interaction between platelets and cancer cells may enhance these pro-tumorigenic pathways, contributing to disease progression. Additionally, the activation of the interleukin-6 (IL-6) pathway by elevated platelet counts could lead to an inflammatory response that favors tumor growth and metastasis, as demonstrated by increased T lymphocyte and macrophage activity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These molecular mechanisms highlight the potential of platelet count not only as a prognostic indicator but also as a therapeutic target to modulate the tumor microenvironment.In particular, we observed a positive correlation between platelet count and kidney cancer progression, with higher platelet levels being more frequently associated with advanced tumor stages and worse pathological outcomes. This effect was not observed with other platelet parameters, such as mean platelet volume (MPV), platelet distribution width (PDW), and platelet crit (PCT), reinforcing the idea that platelet count specifically plays a key role in kidney cancer progression. These results align with previous studies, emphasizing the importance of platelet count over other platelet parameters in predicting disease outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe clinical application of platelet count as a biomarker in ccRCC holds great promise for improving diagnosis, prognosis, and therapeutic strategies. Platelet count is a readily available and cost-effective parameter that can be easily measured in routine blood tests, making it an attractive candidate for clinical use. However, it is important to acknowledge potential limitations of platelet count in clinical practice. For instance, the sensitivity and specificity of platelet count as a biomarker may vary across different patient populations and disease stages. Additionally, while platelet count is readily available in most routine clinical settings, variations in laboratory techniques and equipment may affect the consistency and accuracy of measurements. These factors need to be considered when interpreting platelet count data in the context of ccRCC. Our study underscores the potential of platelet count as a diagnostic and prognostic biomarker in ccRCC. Higher platelet levels were more frequently associated with advanced tumor stages and worse pathological outcomes, suggesting that platelet count could be used to stratify patients based on their risk of disease progression. When comparing our findings with other studies on biomarkers for ccRCC, platelet count emerges as a promising prognostic factor alongside more established markers such as tumor size, grade, and stage. For example, a study found that elevated platelet count was independently associated with poorer survival outcomes in ccRCC patients\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, further supporting our observations. The added value of platelet count lies in its simplicity and cost-effectiveness, as it can be easily incorporated into routine clinical practice without the need for additional specialized tests or equipment. Furthermore, platelet count may provide complementary information to other prognostic factors, enhancing the overall accuracy of disease prediction and patient stratification. Incorporating platelet count into existing prognostic models, such as the TNM staging system, may improve the accuracy of predicting disease outcomes and guide personalized treatment decisions. Platelet count may also serve as a potential target for therapeutic interventions in ccRCC. In the clinical setting, antiplatelet therapy could be explored as an adjuvant treatment to conventional therapies, such as surgery, radiation, and chemotherapy, to improve therapeutic outcomes and prevent metastasis. Platelet count can be combined with other diagnostic or prognostic tools to enhance the accuracy and reliability of clinical decision-making. For example, integrating platelet count with imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), may improve the detection and staging of ccRCC. Additionally, combining platelet count with molecular markers, such as genetic mutations or protein expression levels, may provide a more comprehensive understanding of the tumor biology and guide targeted therapy.\u003c/p\u003e \u003cp\u003eExtensive literature supports the critical involvement of platelets in cancer metastasis, which represents a key factor in the malignancy and poor prognosis of kidney cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Metastasis, a primary cause of cancer-related mortality, complicates the effectiveness of therapeutic strategies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. During metastasis, tumor thrombus formation, which involves the aggregation of cancer cells with platelets, plays a critical role in facilitating the dissemination of cancer cells into distant sites\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. As key mediators of hemostasis, platelets contribute to the adhesion and aggregation of circulating tumor cells, fostering the formation of tumor thrombi within blood vessels\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This process is particularly relevant in kidney cancer, where platelet levels have been shown to correlate with tumor thrombus formation\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Kidney cancer\u0026rsquo;s propensity for invading large blood vessels, such as the renal vein and inferior vena cava\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, further promotes tumor cell entry into the bloodstream and increases the likelihood of metastasis. Elevated platelet activity in kidney cancer patients contributes not only to tumor thrombus formation but also to the aggressive nature of the disease and poorer overall prognosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These findings underscore the potential clinical value of targeting platelet function in preventing metastasis and improving therapeutic outcomes, particularly in metastatic kidney cancer.\u003c/p\u003e \u003cp\u003eAlthough the mechanisms linking thrombocytosis to cancer prognosis remain speculative, our study suggests a potential association with cell proliferation. Platelets may influence tumor progression by releasing growth factors that promote cellular proliferation and metastasis. Previous studies have shown that platelets release growth factors that support the survival and spread of cancer cells\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. For example, the anti-cancer drug Ticagrelor, which inhibits platelet aggregation, has been found to reduce tumor metastasis by inducing platelet apoptosis\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This observation implies that reducing platelet activity and quantity could inhibit tumor cell proliferation and metastasis, providing a potential therapeutic avenue\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, gene mutations in kidney cancer are significant factors influencing tumor behavior. Our study reveals a positive correlation between VHL gene mutations and elevated platelet counts, suggesting that VHL mutations may regulate tumor microenvironment processes such as inflammation and angiogenesis, which are known to contribute to tumor progression\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Mutations in VHL lead to the loss of function of the VHL protein, which normally regulates hypoxia-induced pathways and cell growth, contributing to the development of ccRCC. In contrast, we found a negative correlation between platelet count and PBRM1 gene mutations\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. PBRM1, a key player in chromatin remodeling, is frequently mutated in ccRCC and has been shown to interact with transcription factors involved in tumor progression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. These findings suggest a complex interplay between VHL and PBRM1 mutations, where VHL mutations may suppress the activity of PBRM1, thereby influencing platelet count and the progression of ccRCC. This interaction warrants further investigation to explore the mechanisms underlying the regulation of platelet function in the tumor microenvironment.\u003c/p\u003e \u003cp\u003eDespite these promising findings, there are several limitations to our study. First, although a range of methodologies was employed to assess the prognostic relationship between platelet count and renal cell carcinoma, data on specific subgroups of patients, such as those with different genetic backgrounds or treatment regimens, were unavailable. Second, the tumor-promoting role of platelets in kidney cancer requires further validation through experimental studies. Our future research will experimentally investigate how platelet activity influences tumor progression in renal cancer models. Lastly, the retrospective nature of the data limits our ability to draw prospective conclusions about the role of platelet count in kidney cancer prognosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides robust evidence linking platelet count with the progression and prognosis of kidney cancer, particularly clear cell renal cell carcinoma (ccRCC). As a readily accessible and cost-effective biomarker, platelet count could offer significant clinical utility for prognosis assessment and therapeutic decision-making in kidney cancer. However, additional prospective studies are needed to confirm these findings and establish platelet count as a standard clinical marker. Our results lay the groundwork for future investigations into the potential therapeutic targeting of platelets in metastatic kidney cancer and other malignancies characterized by thrombocytosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW.L and CZ.L supervised the entire project and approved of the final manuscript version to be published. C H,JL Z and M Z contributed to the data interpretation, data analysis,HZ W contributed to writing of the manuscript. C H and JL Z confirm the authenticity of all the raw data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay, J.\u003cem\u003e et al.\u003c/em\u003e Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. \u003cem\u003eInternational journal of cancer\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 2893-2917, doi:10.1002/ijc.25516 (2010).\u003c/li\u003e\n\u003cli\u003eCapitanio, U. \u0026amp; Montorsi, F. 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J.\u003cem\u003e et al.\u003c/em\u003e The relationship between T-lymphocyte infiltration, stage, tumour grade and survival in patients undergoing curative surgery for renal cell cancer. \u003cem\u003eBritish Journal of Cancer\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 1906-1908 (2003).\u003c/li\u003e\n\u003cli\u003eYang, J.\u003cem\u003e et al.\u003c/em\u003e A Positive Feedback Loop between Inactive VHL-Triggered Histone Lactylation and PDGFR\u0026beta; Signaling Drives Clear Cell Renal Cell Carcinoma Progression. \u003cem\u003eInternational Journal of Biological Sciences\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 3470-3483, doi:10.7150/ijbs.73398 (2022).\u003c/li\u003e\n\u003cli\u003eLinehan, W. M.\u003cem\u003e et al.\u003c/em\u003e The Metabolic Basis of Kidney Cancer. \u003cem\u003eCancer Discov\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1006-1021, doi:10.1158/2159-8290.CD-18-1354 (2019).\u003c/li\u003e\n\u003cli\u003eLinehan, W. 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Prognostic and Predictive Value of PBRM1 in Clear Cell Renal Cell Carcinoma. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, doi:10.3390/cancers12010016 (2019).\u003c/li\u003e\n\u003cli\u003eGad, S.\u003cem\u003e et al.\u003c/em\u003e Involvement of PBRM1 in VHL disease-associated clear cell renal cell carcinoma and its putative relationship with the HIF pathway. \u003cem\u003eOncol Lett\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 835, doi:10.3892/ol.2021.13096 (2021).\u003c/li\u003e\n\u003cli\u003eYao, X.\u003cem\u003e et al.\u003c/em\u003e PBRM1-deficient PBAF complexes target aberrant genomic loci to activate the NF-\u0026kappa;B pathway in clear cell renal cell carcinoma. \u003cem\u003eNat Cell Biol\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 765-777, doi:10.1038/s41556-023-01122-y (2023).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplemental Tables","content":"\u003cp\u003eSupplementary Tables S1-S3 are not available with this version.\u003c/p\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":"Platelet count, Clear Cell Renal Cell Carcinoma (ccRCC), Prognostic indicator, Multi-source data analysis, Clinical prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6196668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6196668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eClear Cell Renal Cell Carcinoma (ccRCC) is one of the most common and aggressive forms of kidney cancer, and identifying reliable prognostic indicators remains a critical challenge. While various biomarkers have been explored, platelet count has not been comprehensively evaluated as an independent prognostic factor in ccRCC. Given its clinical accessibility, platelet count could be a valuable tool for predicting patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aims to evaluate the potential of platelet count as an independent prognostic marker for ccRCC patients using multi-source data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe collected summary data from four large-scale genome-wide association studies (GWAS), constructed a bidirectional Mendelian randomization (MR) framework, used statistical methods such as inverse variance weighted (IVW), MR Egger regression, and weighted median, and analyzed the relationship between platelet count and the risk and prognosis of clear cell renal cell carcinoma (ccRCC) by propensity score matching to reduce selection bias. Then, we retrospectively collected clinical data from 231 ccRCC patients who underwent partial or radical nephrectomy at the First Affiliated Hospital of Anhui Medical University from 2014 to 2020 to verify the accuracy of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe found through MR analysis that an increase in platelet count is positively correlated with the risk of kidney cancer (OR=1.001, 95% CI: 1.000-1.001, P=0.035). In 231 ccRCC patients, high platelet count was significantly correlated with later tumor staging (T, N, AJCC) and higher Fuhrman grade (P\u0026lt;0.05). In addition, in the TCGA cohort, the overall survival rate (OS) and disease-free survival rate (DFS) of patients with high platelet counts were significantly lower than those with low platelet counts (P\u0026lt;0.05). Patients with high platelet counts have a higher burden of tumor mutations, especially in key genes such as VHL and PBRM1. GO enrichment analysis revealed gene expression changes related to cell proliferation and extracellular matrix.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePlatelet count is a simple, non-invasive, and independent prognostic marker for ccRCC. This study supports the clinical utility of platelet count in risk stratification, offering the potential for integrating it into personalized treatment strategies. By predicting patient outcomes, platelet count can significantly improve clinical decision-making and guide therapeutic interventions for ccRCC patients.\u003c/p\u003e","manuscriptTitle":"Platelet Count as an Independent Prognostic Marker in Clear Cell Renal Cell Carcinoma: Insights from Multi-source Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 07:42:02","doi":"10.21203/rs.3.rs-6196668/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":"433a0193-6096-4a13-91b8-e179e82d0678","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-18T07:42:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 07:42:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6196668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6196668","identity":"rs-6196668","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}