{"paper_id":"c0b9d502-ddd5-4102-9a6c-beeffc9a6dc5","body_text":"Nuclear receptor corepressor 1 is a Potential Diagnostic and Prognostic Biomarker in Clear Cell Renal Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nuclear receptor corepressor 1 is a Potential Diagnostic and Prognostic Biomarker in Clear Cell Renal Cell Carcinoma Luri Bao, Wuniri Gao, Xi-feng Wang, Peng-cheng Ma, Min Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8086725/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the potential of nuclear receptor corepressor 1 (NCOR1) as a diagnostic and prognostic biomarker for clear cell renal cell carcinoma (ccRCC). Through the analysis of data from The Cancer Genome Atlas and Gene Expression Omnibus databases, along with immunohistochemical testing of clinical samples, this study revealed that NCOR1 expression was significantly downregulated in ccRCC tissues when compared to normal tissues. This downregulation was also more pronounced in 11 other types of tumors. The results of functional enrichment analysis indicated that NCOR1-related differentially expressed genes played a role in cell cycle regulation. These findings imply that the downregulation of NCOR1 expression may facilitate the progression of ccRCC through cell cycle activation. Correlation analysis revealed a significant association between NCOR1 expression and immune cell infiltration in ccRCC tissues. Specifically, NCOR1 expression was positively correlated with natural killer cells, γδ T cells, and mast cells, and negatively correlated with NK CD56 bright cells and cytotoxic cells. Moreover, NCOR1 expression was positively correlated with immune checkpoint genes, including TIGIT, CTLA-4, TP53, and PTEN. Analysis of the DNA methylation status revealed an association between the methylation levels of four CpG islands within the NCOR1 gene and the prognosis of patients with ccRCC. Elevated methylation levels were indicative of poor overall survival (OS). Conversely, NCOR1 gene mutations were not common in ccRCC and were not associated with survival rates. Clinicopathological correlation analysis demonstrated that in patients with ccRCC, decreased NCOR1 expression was significantly associated with advanced T stage, pathological stage, histological grade, as well as poor OS, disease-specific survival, and progression-free interval. Multivariate Cox regression analysis further confirmed that NCOR1 was an independent protective factor for the prognosis of ccRCC. Additionally, ROC curve analysis demonstrated that NCOR1 had diagnostic value (AUC = 0.673). In the nomogram model, combining NCOR1 expression with clinical parameters effectively predicted the 1-year, 3-year, and 5-year survival rates of ccRCC patients. In summary, the expression of NCOR1 is reduced in ccRCC, and its expression level and methylation status are closely related to the progression, immune microenvironment, and prognosis of ccRCC. These findings indicate that NCOR1 has the potential to become a viable diagnostic and prognostic biomarker as well as therapeutic target for ccRCC. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology NCOR1 clear cell renal cell carcinoma clinical outcome immune cell infiltration DNA methylation tumor prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Renal cancer is a common type of urinary system cancer, accounting for 2–3% of adult malignancies, and clear cell renal cell carcinoma (ccRCC) comprises 75–80% of all renal cancer cases [ 1 ] . Although surgical intervention is the primary treatment approach, postoperative recurrence affects 20–40% of patients [ 2 ] . Early detection is crucial for reducing the tumor burden. However, existing diagnostic methods rely on clinicopathologic alterations, imaging examinations, and clinical manifestations, lacking molecular indicators for early diagnosis. Therefore, there is an urgent need to develop reliable diagnostic biomarkers for the early detection of ccRCC. Nuclear receptor corepressor 1 (NCOR1), a key transcriptional regulator associated with the N-CoR nuclear receptor corepressor family, is located at a specific chromosomal locus (17p12-p11.2 in humans and chromosome 11 in mice) [ 3 ] . NCOR1 interacts with multiple transcription factors and plays a crucial role in nuclear receptor signaling pathways [ 4 ] . In the absence of ligands, NCOR1 cooperates with nuclear receptors, such as thyroid hormone receptors and retinoic acid receptors, to suppress transcription, thus regulating gene expression. NCOR1 inhibits nuclear receptor activity by promoting chromatin remodeling and suppressing transcription [ 5 ] . NCOR1 deficiency is associated with increased cancer cell invasion, tumor growth, and metastatic potential, along with the downregulation of genes related to enhanced metastasis and poor prognosis [ 6 ] . Reduced expression of NCOR1 has been observed in various tumor types, including prostate cancer [ 7 ] , non-small cell lung cancer [ 8 ] , and gastrointestinal stromal tumors [ 9 ] , indicating its role as a tumor suppressor. Nevertheless, the expression and function of NCOR1 in ccRCC tissues have not been extensively studied. The precise role of NCOR1 in regulating tumor immune cell infiltration, immune checkpoints, aberrant DNA methylation, and gene mutations remains to be fully elucidated. Moreover, its influence on the diagnosis and prognosis of ccRCC also needs further study. To fill this knowledge gap, we conducted comprehensive bioinformatics research using datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to evaluate the diagnostic and prognostic value of NCOR1 in ccRCC. Materials and methods Data collection and ethics statement TCGA is a collaborative initiative between the National Cancer Institute and the National Institute of Human Genetics, aggregating relevant information from over 20,000 samples representing 33 distinct types of cancer. This comprehensive database encompasses transcriptomic, genomic variation, methylation, and clinical data. This study acquired RNA-seq data and clinicopathological information from 613 ccRCC patients through TCGA database. The initially provided RNA-seq data in fragments per kilobase of transcript per million mapped reads (FPKM) format were transformed into transcripts per million (TPM) format for analysis. The GEO database was established by the National Center for Biotechnology Information (NCBI) in 2000 and serves as a repository for high-throughput gene expression data provided by global research institutions. We chose two datasets from the database, specifically GSE53757 and GSE100666. The GSE53757 dataset is derived from high-throughput gene array analysis, which measured gene expression in the tumor tissues of 72 ccRCC patients and compared it with that in matched normal kidney tissues. Similarly, the GSE100666 dataset employed high-throughput gene microarray technology to assess gene expression in the tumor and normal kidney tissues of three ccRCC patients. Both TCGA and GEO databases offer publicly accessible data, thus obviating the need for approval from medical ethics committees when using the data. Paraffin-embedded tissue sections were obtained from patients who underwent surgical treatment at the Affiliated Hospital of Inner Mongolia Medical University between January 2023 and January 2025, including cancerous tissues and adjacent normal tissues, and informed consent was obtained from all participants and their legal guardians. This study was conducted in line with medical ethical standards and was approved by the Ethics Committee of Inner Mongolia Medical University (Approval No. YKD202403009), and commit that all research was conducted in accordance with relevant regulations. Bioinformatics analysis of NCOR1 mRNA expression levels in ccRCC and adjacent normal tissues Data on NCOR1 mRNA expression levels were collected from TCGA database, which includes information on 33 types of human cancers, including ccRCC. For the analysis of NCOR1 expression in ccRCC samples, gene expression data from the GSE53757 and GSE100666 datasets in the GEO database were utilized. Based on the median expression of NCOR1, the 613 ccRCC patients from TCGA were classified into high-expression and low-expression groups according to their NCOR1 mRNA levels. Differential expression analysis between these groups was conducted using the DESeq function in the R package [ 10 ] , with thresholds set at an absolute log fold change > 1.5 and an adjusted P -value < 0.05. Volcano plots and heatmaps were generated using the \"ggplot2\" R package to visualize differentially expressed genes. Immunohistochemistry for the detection of NCOR1 expression levels in ccRCC tissues and adjacent normal tissues This study enrolled 20 patients diagnosed with ccRCC at the Affiliated Hospital of Inner Mongolia Medical University from 2023 to 2025. The inclusion criteria included initial diagnosis and treatment, patients being 18 years of age or older, confirmed ccRCC pathology with radical surgery performed, voluntary participation after signing informed consent, and availability of comprehensive clinicopathologic and follow-up data. During the surgical procedure, the tumor and adjacent normal kidney tissues were obtained, fixed in 10% neutral formaldehyde, and embedded in paraffin. After dewaxing the samples with xylene and rehydrating them with graded ethanol, EDTA was employed for antigen retrieval. Subsequently, the samples were treated with 3% hydrogen peroxide for 10 minutes to inhibit endogenous peroxidase activity. Next, the samples were incubated at room temperature with a 1:100 dilution of the primary antibody NCOR1 (Ruiying Biotechnology Co., Ltd.) for 1 hour, followed by incubation with the secondary antibody (Fuzhou Maixin) for 10 minutes. The samples were stained with DAB for 5 minutes and counterstained with hematoxylin. After dehydration and clearing processes, the slides were sealed with neutral gum for microscopic analysis. Two pathologists independently assessed the slides using a double-blind approach and conducted semi-quantitative scoring based on staining intensity and area. The staining intensity was classified as none, 1 (weak), or 2 (strong), and the staining area was scored proportionally, with a threshold of 50%. The two scores were multiplied. If the product was greater than 2, the result was considered positive; if it was less than or equal to 2, the result was considered negative. Functional enrichment analysis of NCOR1-related differentially expressed genes in ccRCC The \"org.Hs.eg.db\" R package was used to convert Entrez IDs into gene symbols. Subsequently, functional annotation and gene set enrichment analysis (GSEA) [ 11 ] of differentially expressed genes were carried out using the \"ClusterProfiler\" R package [ 12 ] . This analysis utilized the gene set c2.cp.v7.5.1.symbols.gmt from the MSigDB database. When the false discovery rate (FDR) was below 0.25 and the P -value was below 0.05, the enrichment of the gene set was regarded as significant. Correlation analysis of the relationship between NCOR1 expression levels and immune cell infiltration in ccRCC The single sample gene enrichment analysis (ssGSEA) algorithm in the \"GSVA\" package [ 13 ] was used to perform Spearman's correlation analysis to investigate the association between NCOR1 expression and immune cell infiltration [ 14 ] . This analysis assessed the infiltration levels of 24 different immune cell subtypes, including regulatory T cells (Tregs), cytotoxic cells, type 1 T helper (Th1) cells, T cells, activated dendritic cells (aDCs), macrophages, type 2 T helper (Th2) cells, CD56bright natural killer (NK) cells, plasmacytoid dendritic cells (pDCs), neutrophils, NK cells, mast cells, eosinophils, CD56dim NK cells, dendritic cells (DCs), γδ T cells, central memory T cells, T helper cells, and type 17 T helper (Th17) cells. Correlation analysis of the relationship between NCOR1 expression levels and immune checkpoint markers in ccRCC This study examined the correlations among NCOR1 expression, immune checkpoint markers (TIGIT, CTLA4, and TP53), and the oncogene PTEN in samples from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Spearman's rank correlation analysis was utilized to evaluate these associations, and the results were visualized using the \"ggplot2\" package in R. Associations with a significance level of P < 0.05 were deemed statistically significant. DNA methylation status analysis of the CpG island of the NCOR1 gene The DNA methylation status of the CpG island of the NCOR1 gene in the TCGA-KIRC dataset was verified using the MethSurv database. The MethSurv database is an online survival analysis platform based on CpG methylation profiles. This platform integrates data from over 700 methylation sites across 25 types of human cancers and applies the Cox proportional hazards model. Furthermore, this platform assessed the prognostic significance of NCOR1 CpG methylation in the TCGA-KIRC cohort. Gene mutations in ccRCC samples NCOR1 gene mutations were comprehensively evaluated across multiple datasets, including those from the Beijing Genomics Institute (BGI), Nat Genet 2012, and TCGA, Firehose Legacy. This assessment was carried out using the cBioPortal online platform. The prognostic relevance of NCOR1 gene mutations was evaluated through Kaplan-Meier survival analysis and the log-rank test. Statistical significance was defined as P < 0.05. Correlation between NCOR1 expression levels and clinicopathological features in ccRCC This study utilized the R package to examine the association between clinicopathological features (including race, gender, age, T stage, N stage, M stage, histological grade, pathological grade, overall survival (OS) events, and disease-specific survival (DSS) events) and NCOR1 expression levels in TCGA-KIRC samples. T stage denotes the size of primary tumor lesions. T1 stage indicates tumors with a diameter of ≤ 7 cm that are confined to the kidneys, T2 stage indicates tumors with a diameter of > 7 cm that are confined to the kidneys, T3 stage indicates tumors that invade the renal vein or perirenal tissue (excluding the ipsilateral adrenal gland without penetrating the perirenal fascia), and T4 stage indicates tumors that infiltrate the perirenal fascia (including the ipsilateral adrenal gland adjacent to the tumor). N stage represents the status of regional lymph node metastases, where N0 denotes the absence of regional lymph node metastases and N1-N3 denote increasing involvement of regional lymph nodes. M stage indicates the presence of distant metastases, where M0 denotes the absence of distant metastases and M1 denotes the presence of distant metastases. Based on the morphological and cytological features observed under a microscope, tumor tissues are categorized into four histological grades. G1 grade denotes a high level of tumor differentiation, characterized by tumor cells that are highly similar in morphology and function to normal cells, slow growth, and extremely low invasiveness. G2 grade denotes that the tumor is moderately differentiated. Compared with normal cells, tumor cells display some abnormalities, a moderately accelerated growth rate, and moderate invasiveness. G3 grade indicates that the tumor is poorly differentiated. The tumor cells differ significantly in morphology and function from normal cells, grow rapidly, and have strong invasiveness. G4 grade denotes extremely low tumor differentiation, with significant disparities in morphology, function, growth rate, and the extent of invasion of surrounding tissues between tumor cells and normal cells. The pathological stage of ccRCC is determined by tumor size and metastasis status. Stage I comprises tumors confined to the kidneys with a diameter of ≤ 7 cm, without lymph node or distant organ metastasis. In stage III, there is no restriction on the size of kidney tumors, and they have started to metastasize to adjacent lymph nodes, blood vessels near the kidneys, renal collecting systems, or perirenal fat. In pathological stage IV, the cancer has extended beyond the perirenal adipose tissue and may have spread to the adrenal gland or distant anatomical locations. Statistical analysis was performed using Pearson's χ 2 test, and Fisher's exact test might have been utilized if necessary. Logistic regression analysis was conducted to evaluate the correlation between NCOR1 expression levels and clinicopathological features in patients with ccRCC. Prognostic value of NCOR1 expression levels in patients with ccRCC The data analysis was visualized using the \"survminer\" and \"ggplot2\" packages. Diagnostic receiver operating characteristic (ROC) curves, time-dependent survival ROC curves, and nomogram models were constructed using the \"pROC,\" \"timeROC,\" and \"rms\" R packages to evaluate the predictive value of NCOR1 expression levels in the diagnosis of ccRCC. Prognostic assessment of subgroups of ccRCC patients was conducted using Kaplan-Meier survival curves, with the sample sizes (%), hazard ratios (HRs), confidence intervals (CIs), and P values displayed. The \"ggplot2\" R package was employed to generate forest plots. Results 1. Significantly downregulated NCOR1 expression in various tumor tissues, including ccRCC NCOR1 expression was detected in 33 cancer datasets sourced from TCGA database. Notably, NCOR1 exhibited significant downregulation in 12 analyzed cancer types, including bladder cancer (BLCA), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), thyroid cancer (THCA), and uterine corpus endometrial carcinoma (UCEC). Conversely, NCOR1 was upregulated in cholangiocarcinoma (CHOL) and stomach adenocarcinoma (STAD) (Fig. 1 A). Furthermore, after analyzing the GSE53757 and GSE100666 datasets in the GEO database, we found that the expression of NCOR1 was significantly reduced in ccRCC tissues compared with normal tissues ( P < 0.05, Fig. 1 B, C). The immunohistochemical (IHC) staining results of ccRCC cancer tissues and adjacent normal tissues confirmed the differential expression of NCOR1 at the protein level. We observed that NCOR1 expression was significantly lower in ccRCC cancer tissues, and it was mainly distributed on cell membranes (Fig. 1 D). Compared with adjacent normal kidney tissues, the IHC score of NCOR1 in ccRCC tissues was significantly reduced ( P < 0.01, Fig. 1 E), corroborating the downregulation of NCOR1 at the protein level in ccRCC. Subsequently, based on the median expression of NCOR1, 613 ccRCC patients were divided into a high-expression group and a low-expression group of NCOR1. We applied a threshold parameter of an absolute logFC value > 1.5 and set an adjusted P -value < 0.05 to identify 418 genes that exhibited differential expression between ccRCC tissues and adjacent normal tissues (Fig. 1 F). Among these genes, 8 genes were upregulated and 410 genes were downregulated. The heatmap in Fig. 1 G illustrates the co-expression of individual genes, highlighting the 10 genes with the most significant differential expression. 2. Functional enrichment analysis of NCOR1-related differentially expressed genes in ccRCC Through differential expression analysis, we identified differentially expressed genes between cancer tissues and adjacent normal tissues of ccRCC, which revealed the impact of ccRCC on gene expression. Subsequently, Gene Ontology (GO) analysis was conducted to classify these differentially expressed genes according to genomic annotation data, aiming to reveal the affected biological functions and pathways. The \"clusterProfiler\" R package was used to annotate the differentially expressed genes related to NCOR1 in ccRCC patients. The results of the GO enrichment analysis encompassed biological processes, cellular components, and molecular functions (Fig. 2 A). Key biological processes included lipid catabolic processes, negative regulation of signaling receptor activity, regulation of fatty acid biosynthetic processes, negative regulation of peptidase activity, and positive regulation of fatty acid biosynthetic processes. Predominant cellular components comprised the secretory granule lumen, endoplasmic reticulum lumen, intermediate filament cytoskeleton, intermediate filaments, and keratin filaments. The most highly enriched molecular functions included serine hydrolase activity, peptidase regulator activity, endopeptidase activity, receptor ligand activity, and passive transmembrane transporter activity. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the differentially expressed genes were significantly enriched in key pathways, including complement and coagulation cascades, tyrosine metabolism, synaptic vesicle cycle, linoleic acid metabolism, and cholesterol metabolism. GSEA analysis revealed that the differentially expressed genes related to NCOR1 were significantly enriched in clusters related to cell proliferation (Fig. 2 B–I). These clusters included cell cycle checkpoint (NES = 3.037, P adj < 0.001, FDR < 0.001), G1 phase and G1-S transition (NES = 3.002, P adj < 0.001, FDR < 0.001), G2-M checkpoints (NES = 2.901, P adj < 0.001, FDR < 0.001), S phase (NES = 2.790, P adj < 0.001, FDR < 0.001), mitotic spindle checkpoint (NES = 2.772, P adj < 0.001, FDR < 0.001), cell cycle (NES = 2.686, P adj < 0.001, FDR < 0.001), G1-S specific transcription (NES = 2.626, P adj < 0.001, FDR < 0.001), and cyclin A/B-regulated G2-M transition (NES = 2.484, P adj < 0.001, FDR < 0.001). 3. Association of NCOR1 expression levels with the infiltration of multiple immune cells and the expression of immune checkpoint genes in ccRCC tissues The functionality of immune cells is characterized by continual adaptation and dynamic equilibrium when interacting with various human and environmental factors. When immune cells malfunction or their performance is impaired, it can increase an individual's vulnerability to infectious diseases and cancer. On the other hand, excessive activity or an imbalance within specific subsets of immune cells can lead to the onset of autoimmune disorders [ 15 , 16 ] . This study explored the correlation between NCOR1 expression and immune cell infiltration in ccRCC tissues. Statistical analysis revealed significant differences in the abundance levels of 14 types of immune cells between the high-expression group and the low-expression group of NCOR1, including CD56bright NK cells, cytotoxic cells, Tregs, CD8 T cells, pDCs, NK cells, γδ T cells, mast cells, effector memory T (Tem) cells, Th17 cells, neutrophils, T helper cells, eosinophils, and central memory T (Tcm) cells. The correlation analysis revealed significant associations between NCOR1 expression and various types of immune cells. Specifically, NK cells (r = 0.094, P < 0.05), γδ T cells (r = 0.167, P < 0.001), mast cells (r = 0.190, P < 0.001), Tem cells (r = 0.203, P < 0.001), Th17 cells (r = 0.218, P < 0.001), neutrophils (r = 0.237, P < 0.001), T helper cells (r = 0.317, P < 0.001), eosinophils (r = 0.320, P < 0.001), and Tcm cells (r = 0.458, P < 0.001) were positively correlated with NCOR1 expression. Conversely, CD56bright NK cells (r = -0.266, P < 0.001), cytotoxic cells (r = -0.226, P < 0.001), Tregs (r = -0.180, P < 0.001), CD8 T cells (r = -0.102, P < 0.05), and pDCs (r = -0.091, P < 0.05) were negatively correlated with NCOR1 expression. The tumor infiltration levels of CD56bright NK cells (Fig. 3 B), cytotoxic cells (Fig. 3 C), Tregs (Fig. 3 D), Th17 cells (Fig. 3 E), neutrophils (Fig. 3 F), and Tcm cells (Fig. 3 G) were consistent with the results of the Spearman's correlation analysis depicted in Fig. 3 A. TIGIT and CTLA-4 are significant immune checkpoint proteins associated with immune evasion by tumors [ 17 ] . TP53, a tumor suppressor gene, exhibits low expression in normal cells but high expression in malignant tumors [ 18 ] . The PTEN gene plays a crucial role in cell growth, development, signal transduction, and apoptosis, and its mutation or deletion is linked to various human tumors [ 19 ] . This study discovered that NCOR1 expression in ccRCC samples from TCGA dataset was positively correlated with the levels of TIGIT, CTLA-4, TP53, and PTEN (Fig. 4 A-D). 4. Association between NCOR1 gene methylation status and prognosis in ccRCC patients DNA methylation levels within the NCOR1 gene and the prognostic value of CpG islands within NCOR1 were assessed using the MetSurv online tool. The analysis results identified five CpG islands that had undergone methylation, with cg20155837 exhibiting a relatively higher level of DNA methylation (Fig. 5 ). Moreover, the methylation status of four CpG islands (cg20155837, cg04223442, cg21211144, and cg13465826) was significantly associated with patient prognosis ( P < 0.05) (Table 1 ). Among these four CpG islands, the NCOR1 methylation levels were elevated, particularly in cg04223442 and cg13465826. These elevated methylation levels were linked to lower OS rates in ccRCC patients compared to those with lower NCOR1 CpG methylation levels. Table 1 Effect of the methylation level of the CpG locus of the NCOR1 gene on the prognosis of ccRCC patients. CpG island HR P .value 1stExon;5'UTR-Island-cg00253204 0.703 0.085615631 5'UTR-N_Shore-cg04223442 0.56 0.018749972 5'UTR-N_Shore-cg13465826 0.482 0.001584027 Body-Open_Sea-cg20155837 2.799 0.00080388 TSS200-Island-cg21211144 1.949 0.016814657 5. NCOR1 gene mutations were not associated with survival outcomes in ccRCC patients NCOR1 gene mutations were analyzed in a cohort of 636 patients with ccRCC sourced from two datasets: BGI, Nat Genet 2012 (n = 98), and TCGA, Firehose Legacy (n = 538). The findings revealed that only 1.1% of the patients with ccRCC exhibited NCOR1 gene mutations (Fig. 6 A). Despite the low proportion of patients with these alterations, subsequent analysis using Kaplan-Meier survival curves and log-rank tests indicated no significant differences in OS ( P = 0.728) and DSS ( P = 0.308) between individuals with and without NCOR1 gene mutations (Fig. 6 B, C). 6. Association between NCOR1 expression levels and multiple clinicopathological features of ccRCC The TCGA-KIRC dataset was utilized to analyze the relationship between clinicopathological features and NCOR1 expression levels among ccRCC patients, as presented in Table 2 . Notably, there was no significant correlation between NCOR1 expression and patient race or N stage ( P > 0.05). However, significant associations were found between NCOR1 expression and gender (Fig. 7 A), age (Fig. 7 B), T stage (Fig. 7 C), pathological stage (Fig. 7 D), histological grade (Fig. 7 E), OS (Fig. 7 F), DSS (Fig. 7 G), and progression-free interval (PFI) (Fig. 7 H). Logistic regression analysis further demonstrated a positive correlation between NCOR1 expression levels and age, T stage, M stage, pathological stage, and histological grade among ccRCC patients, as detailed in Table 3 . Table 2 Clinicopathological characteristics of ccRCC patients in the high and low NCOR1 expression groups. Characteristics Low expression of NCOR1 High expression of NCOR1 P value n 270 271 Race, n (%) 0.095 Asian 1 (0.2%) 7 (1.3%) Black or African American 30 (5.6%) 27 (5.1%) White 237 (44.4%) 232 (43.4%) Gender, n (%) 0.132 Female 85 (15.7%) 102 (18.9%) Male 185 (34.2%) 169 (31.2%) Age, n (%) 0.023 <= 60 121 (22.4%) 148 (27.4%) > 60 149 (27.5%) 123 (22.7%) Pathologic T stage, n (%) < 0.001 T1 117 (21.6%) 162 (29.9%) T2 36 (6.7%) 35 (6.5%) T3 111 (20.5%) 69 (12.8%) T4 6 (1.1%) 5 (0.9%) Pathologic N stage, n (%) 0.923 N0 118 (45.7%) 124 (48.1%) N1 8 (3.1%) 8 (3.1%) Pathologic M stage, n (%) 0.007 M0 201 (39.6%) 228 (44.9%) M1 50 (9.8%) 29 (5.7%) Pathologic stage, n (%) 0.001 Stage I 115 (21.4%) 158 (29.4%) Stage II 29 (5.4%) 30 (5.6%) Stage III 72 (13.4%) 51 (9.5%) Stage IV 52 (9.7%) 31 (5.8%) Histologic grade, n (%) 0.004 G1 3 (0.6%) 11 (2.1%) G2 106 (19.9%) 130 (24.4%) G3 106 (19.9%) 101 (18.9%) G4 49 (9.2%) 27 (5.1%) OS event, n (%) < 0.001 Alive 152 (28.1%) 214 (39.6%) Dead 118 (21.8%) 57 (10.5%) DSS event, n (%) < 0.001 No 184 (34.7%) 237 (44.7%) Yes 78 (14.7%) 31 (5.8%) PFI event, n (%) < 0.001 No 169 (31.2%) 210 (38.8%) Yes 101 (18.7%) 61 (11.3%) Table 3 Logistic regression analysis between NCOR1 expression levels and clinicopathological characteristics of ccRCC patients. Characteristics Total (N) P value Race (White vs. Asian&Black or African American) 534 0.893 (0.531–1.500) 0.668 Gender (Male vs. Female) 541 0.761 (0.534–1.086) 0.133 Age (> 60 vs. <= 60) 541 0.675 (0.481–0.947) 0.023 Pathologic T stage (T3&T4 vs. T1&T2) 541 0.491 (0.343–0.704) < 0.001 Pathologic N stage (N1 vs. N0) 258 0.952 (0.346–2.618) 0.923 Pathologic M stage (M1 vs. M0) 508 0.511 (0.312–0.839) 0.008 Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II) 538 0.507 (0.356–0.721) < 0.001 Histologic grade (G3&G4 vs. G1&G2) 533 0.638 (0.453–0.899) 0.010 7. NCOR1 as a potential biomarker for prognostic and diagnostic assessment in ccRCC Kaplan-Meier survival analysis indicated that, when compared with patients with high levels of NCOR1 expression in ccRCC, those with low levels of NCOR1 expression had significantly lower OS ( P < 0.001), DSS ( P < 0.001), and PFI ( P < 0.001) (Fig. 8 A-C). Univariate Cox regression analysis identified T stage, N stage, M stage, pathological stage, and low NCOR1 expression as adverse prognostic factors. Furthermore, multivariate Cox analysis confirmed that NCOR1 is an independent protective factor for predicting OS (HR = 0.511, P < 0.01), DSS (HR = 0.340, P < 0.001), and PFI (HR = 0.511, P < 0.01). M stage emerged as an independent risk factor for OS and PFI, while pathological stage was independently associated with DSS and PFI. Additionally, histologic grading showed significant predictive value for PFI (Table 4 ). Using data from TCGA database, ROC curve analysis was conducted on NCOR1 gene expression data with the R packages pROC, rms, and survival to evaluate its diagnostic utility. The area under the curve (AUC) obtained from the analysis was 0.673, indicating its moderate diagnostic efficacy (Fig. 9 A). Time-dependent ROC curve analysis revealed that the AUC values for predicting the 1-year, 3-year, and 5-year survival rates of ccRCC patients based on NCOR1 expression levels were below 0.35 (Fig. 9 B). Through multivariate Cox regression analysis, NCOR1 expression levels were incorporated into the nomogram along with clinical parameters, and T stage, pathological stage, histological grade, and NCOR1 expression levels were verified as significant prognostic indicators (Fig. 9 C). Columnar line plots underscored the clinical relevance of the prognostic nutritional index in predicting the 1-year, 3-year, and 5-year survival rates in ccRCC patients. Table 4 Cox regression analysis of NCOR1 levels and other factors affecting the prognosis of patients with ccRCC. Characteristics HR for overall survival (95%CI) HR for disease-specific survival (95%CI) HR for progression-free interval (95%CI) Univariate Multivariate Univariate Multivariate Univariate Multivariate Gender (Female vs. male) 0.924 (0.679–1.257) 1.183 (0.786–1.781) 1.476 (1.043–2.090)* 1.249 (0.798–1.955) Age ( < = 60 vs. >60) 1.791 (1.319–2.432)*** 1.562 (1.018–2.395)* 1.351 (0.926–1.971) 1.285 (0.942–1.754) Pathologic T stage (T1&T2 vs. T3&T4) 3.210 (2.373–4.342)*** 1.333 (0.575–3.090) 5.606 (3.697–8.502)*** 1.059 (0.441–2.541) 4.569 (3.306–6.314)*** 1.040 (0.509–2.127) Pathologic N stage (N0 vs. N1) 3.422 (1.817–6.446)*** 1.686 (0.841–3.381) 3.864 (1.831–8.157)*** 1.461 (0.671–3.185) 3.697 (1.899–7.198)*** 1.319 (0.653–2.662) Pathologic M stage (M0 vs. M1) 4.401 (3.226–6.002)*** 2.600 (1.525–4.432)*** 9.219 (6.294–13.504)*** 3.509 (1.920–6.412)*** 9.081 (6.554–12.582)*** 4.266 (2.492–7.303)*** Pathologic stage (Stage I&Stage II vs. Stage III&Stage IV) 3.910 (2.852–5.360)*** 1.370 (0.535–3.508) 9.937 (5.989–16.486)*** 3.384 (1.110–10.313)* 6.877 (4.813–9.826)*** 3.330 (1.357–8.174)** Histologic grade (G1&G2 vs. G3&G4) 2.665 (1.898–3.743)*** 1.633 (0.993–2.686) 4.850 (2.925–8.043)*** 1.932 (0.973–3.837) 3.684 (2.530–5.364)*** 1.695 (1.015–2.833)* NCOR1(Low vs. High) 0.431 (0.314–0.592)*** 0.511 (0.330–0.792)** 0.357 (0.235–0.542)*** 0.340 (0.190–0.609)*** 0.541 (0.392–0.746)*** 0.511 (0.325–0.804)** ccRCC, renal clear cell carcinoma; HR, hazard ratio; CI, confidence interval; * P < 0.05; ** P < 0.01; *** P < 0.001. 8. Prognostic value of NCOR1 in a clinicopathological subgroup of patients with ccRCC Table 5 and Fig. 10 present the results of the Cox regression analysis that investigated the predictive value of NCOR1 in different subgroups of clinicopathological features in ccRCC patients. Higher NCOR1 expression was associated with better OS, particularly in subgroups characterized by white race (HR = 0.45, P < 0.001), stages T1 and T2 (HR = 0.43, P < 0.001), N (HR = 0.46, P < 0.001), M (HR = 0.40, P < 0.001), clinicopathological stages I and II (HR = 0.44, P = 0.004), stages III and IV (HR = 0.64, P = 0.018), and histological grades G3 and G4 (HR = 0.45, P < 0.001) (Fig. 10 A). Furthermore, high NCOR1 expression was linked to favorable DSS (Fig. 10 B) and PFI (Fig. 10 C), particularly in white patients and those with stages T1 and T2, N, M, and high-grade histological tumors. These findings suggest that ccRCC patients with high NCOR1 expression have significantly better survival outcomes than those with low NCOR1 expression. Table 5 Prognostic performance of the NCOR1 gene on clinical outcomes in a subgroup of ccRCC patients based on Cox regression analysis. Characteristics n (%) HR for OS (95%CI) HR for DSS (95%CI) HR for PFI (95%CI) Race Asian 65(12.1%) 0.35(0.10–1.18) 0.27(0.05–1.41) 1.01(0.34–3.01) White 474(87.9%) 0.45(0.32–0.62)*** 0.38(0.25–0.59)*** 0.51(0.37–0.72)*** T stage T1&T2 353(64.7%) 0.43(0.26–0.71)*** 0.28(0.12–0.65)** 0.54(0.32–0.93)* T3&T4 193(35.3%) 0.70(0.48–1.04) 0.68(0.44–1.07) 0.76(0.51–1.12) N stage N0 242(93.4%) 0.46(0.29–0.72)*** 0.30(0.16–0.56)*** 0.47(0.29–0.76)** N1 17(6.6%) 0.44(0.12–1.54) 0.22(0.04–1.14) 0.28(0.07–1.17) M stage M0 434(84.6%) 0.40(0.27–0.60)*** 0.27(0.14–0.52)*** 0.56(0.36–0.85)** M1 79(15.4%) 0.84(0.51–1.37) 0.87(0.52–1.46) 0.90(0.56–1.45) Pathologic stage stage I&stage II 335(61.7%) 0.44(0.25–0.78)** 0.20(0.06–0.68)* 0.49(0.26–0.95)* stage III&stage IV 208(38.3%) 0.64(0.44–0.93)* 0.61(0.40–0.94)* 0.73(0.50–1.05) Histologic grade G1&G2 252(46.8%) 0.58(0.32–1.07) 0.28(0.09–0.84)* 0.44(0.22–0.91)* G3&G4 286(53.2%) 0.45(0.32–0.65)*** 0.44(0.29–0.68)*** 0.61(0.42–0.87)** ccRCC, renal clear cell carcinoma; CI, confidence interval; HR, hazard ratio; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; * P < 0.05; ** P < 0.01; *** P < 0.001. Discussion Clear cell renal cell carcinoma (ccRCC), a prevalent urological malignancy, is characterized by a high mortality rate, presenting substantial challenges to clinical treatment. Global cancer data indicate a significant disparity in 5-year survival rates between early-stage ccRCC cases (exceeding 70%) and advanced or metastatic cases (less than 10%), underscoring the crucial significance of early detection in improving patient prognosis [ 20 ] . However, the subtle nature of ccRCC frequently results in the absence of specific clinical symptoms during the early stages of the disease. Consequently, most patients are diagnosed in the intermediate to late stages, thereby missing the optimal treatment opportunity. Therefore, urologists face the crucial task of overcoming the barriers to timely and accurate diagnosis of early ccRCC. Biomarkers, serving as detectable indicators of disease onset and progression, have demonstrated promising applications in early-stage tumor diagnosis [ 21 ] . Ideal biomarkers should be detectable in the early stages of the disease and in readily accessible samples, such as blood and urine, offering convenient means for clinical diagnosis [ 22 ] . The most recent advancements in molecular biology and high-throughput sequencing technology enable us to identify potential diagnostic biomarkers at the genomic, transcriptomic, and proteomic levels of ccRCC. For instance, CDK1, which is involved in cell cycle regulation [ 23 ] , Bcl-2 family members, which are associated with apoptosis pathways [ 24 ] , and VEGF, which plays a role in angiogenesis [ 25 ] . The discovery of these biomarkers provides a novel approach for the diagnosis of early ccRCC and lays the groundwork for further exploration of the molecular mechanisms of the disease. This study aims to explore the most recent advancements in biomarker research for ccRCC so as to optimize early-stage clinical diagnostic strategies. In this study, we employed high-throughput RNA sequencing data from TCGA and validated the research findings via IHC analysis of tissue specimens, suggesting NCOR1 as an emerging prognostic indicator for ccRCC. After analyzing the data of 613 ccRCC patients in TCGA, we found that the expression level of NCOR1 in tumor samples was significantly lower than that in normal tissues. The reduction in NCOR1 expression was significantly correlated with OS and DSS. Furthermore, in ccRCC, reduced NCOR1 expression levels were linked to advanced T stage, pathological stage, and histological grade. The notable correlation between NCOR1 expression levels and clinical features in ccRCC patients motivated us to explore the potential molecular mechanisms underlying ccRCC pathogenesis induced by alterations in NCOR1 expression. Our GSEA results revealed that the reduction in NCOR1 expression might be associated with the heightened activation of cell cycle regulatory processes. The accurate regulation of the cell cycle is crucial for maintaining normal cell proliferation and genomic stability, and its dysregulation is pivotal in the progression of ccRCC. The key checkpoints in cell cycle advancement, such as the G1/S transition and the G2/M checkpoint, rely on the intricate balance of the Cyclin-CDK complex, which is significantly disrupted in ccRCC. Notably, the inactivation of the VHL gene (present in approximately 80% of ccRCC cases) upregulates Cyclin D1 and CDK4/6 via the HIF-α pathway, accelerating the cell cycle progression from the G1 phase to the S phase [ 26 ] . Conversely, CDK inhibitors (such as p27 and p21) alleviate cell cycle suppression by counteracting the functional loss resulting from the enhanced SKP2-mediated ubiquitin-dependent degradation [ 27 ] . Previous research has indicated that kidney cancer patients often experience immune suppression during tumor progression, which may be ascribed to the high immunogenicity of ccRCC [ 28 ] , potentially leading to the production of inhibitory cells such as Tregs. Our research suggests that NCOR1 expression may be associated with immune cell infiltration in tumors. In ccRCC samples, we observed that NCOR1 expression was inversely correlated with CD56bright NK cells, cytotoxic cells, and Tregs, while positively correlated with Tcm, Th17 cells, and neutrophils. Neutrophils are vital in the immune response against tumors by activating immune responses and directly lysing tumor cells [ 29 ] . Tregs, which produce immunosuppressive cytokines to inhibit immune responses, and Th17 cells, which enhance immune responses through the release of inflammatory cytokines, originate from the same precursor T cells [ 30 ] . The Tregs/Th17 cell ratio is associated with various immune disorders and cancers [ 31 ] . Our results suggest that decreased NCOR1 expression significantly facilitates immune evasion in ccRCC cells, thereby promoting the growth and progression of ccRCC. CTLA-4 is a protein that is crucial in tumor immune evasion [ 17 ] , and CTLA-4 inhibitors can substantially improve the OS rate of patients with melanoma and advanced liver cancer [ 32 – 34 ] . TP53 mutations are associated with poor prognosis in various human cancers [ 18 ] , as they suppress anti-tumor immunity and diminish the efficacy of tumor immunotherapy [ 35 , 36 ] . Recent research has demonstrated that TIGIT can inhibit the activity of NK cells and CD8 + T cells by binding to its ligand, thereby suppressing anti-tumor immune responses [ 37 ] . PTEN regulates central memory T cells and T helper cells in the tumor microenvironment. The expression level of PTEN can impact immune cell infiltration, thereby affecting the function of T cells in the tumor microenvironment and exerting anti-tumor effects. Therefore, we examined the relationship between NCOR1 expression levels and immune checkpoint genes, including TIGIT, CTLA-4, PTEN, and TP53. Our results demonstrated that the NCOR1 expression levels in ccRCC tissues were positively correlated with the expression levels of immune checkpoint genes TIGIT, CTLA-4, PTEN, and TP53. This suggests that NCOR1 may be a promising target for improving the efficacy of immunotherapy in ccRCC patients. DNA methylation, a crucial epigenetic modification, can impact gene expression independently of DNA sequence alterations and plays a significant role in tumor development, such as in cervical cancer [ 38 ] , lung cancer [ 39 ] , and thyroid cancer [ 40 ] . In this study, we explored the relationship between NCOR1 methylation levels and the prognosis of ccRCC patients. We discovered that the higher the methylation levels of four specific CpG islands (cg20155837, cg04223442, cg21211144, and cg13465826), the poorer the OS of patients. Additionally, the research findings indicate that NCOR1 gene mutations are infrequent in ccRCC tissues, occurring in only 1.1% of cases, and are not correlated with OS or DSS. The findings from multivariate Cox regression analysis suggest that NCOR1 can independently impact the prognosis of ccRCC. Compared to patients with higher levels of NCOR1 protein, those with lower levels of NCOR1 protein had significantly lower OS, DSS, and PFI rates. Nomograms incorporating NCOR1 expression accurately predicted the 1-year, 3-year, and 5-year survival rates of ccRCC patients. Previous research has verified that NCOR1 is linked to a poor prognosis in bladder cancer [ 41 ] , prostate cancer [ 7 ] , and lung cancer [ 8 ] . These findings suggest that NCOR1 is a promising prognostic biomarker for ccRCC. Therefore, routine IHC analysis of cancer tissue samples can be aided by nomograms for diagnosis, prognosis assessment, and optimization of follow-up and treatment strategies. This study is constrained by the RNA-seq data of ccRCC tissues in TCGA database, which impedes direct assessment of downstream signaling pathways or NCOR1 protein levels in ccRCC. Therefore, it is essential to perform additional in vitro experiments to clarify the mechanism of action of NCOR1 in ccRCC. Conclusion This study emphasizes the significant value of NCOR1 in the diagnosis and prognosis of ccRCC. NCOR1 is significantly downregulated in ccRCC tumor tissues and facilitates the progression of ccRCC by cell cycle activation. Moreover, NCOR1 expression levels are correlated with the infiltration of various immune cell types, suggesting its potential impact on the response to ccRCC immunotherapy. The methylation status and expression levels of NCOR1 are closely associated with the prognosis of ccRCC. Therefore, NCOR1 emerges as a promising therapeutic target and a crucial biomarker for the diagnosis and management of ccRCC. Abbreviations Abbreviations Full Name ccRCC clear cell renal cell carcinoma RCC Renal clear cell carcinoma NCOR1 nuclear receptor corepressor 1 TCGA the cancer genome atlas GEO gene expression omnibus K‒M kaplan‒meier OS overall survival DFS disease-free survival DSS disease-specific survival PFI progression-free interval GSEA gene set enrichment analysis HR hazard ratio CI confidence interval COAD colon adenocarcinoma KIRC kidney renal clear cell carcinoma KICH renal suspicious cell carcinoma LUSC lung squamous cell carcinoma BLCA bladder cancer BRCA breast invasive carcinoma CHOL cholangiocarcinoma LIHC liver hepatocellular carcinoma LUAD lung adenocarcinoma STAD stomach adenocarcinoma THCA thyroid cancer UCEC uterine corpus endometrial carcinoma PRAD prostate cancer READ rectal adenocarcinoma Treg regulatory T cells pDC plasmacytoid dendritic cell NK cells natural killer cells Th17 cells type 17 Th cells PTEN phosphatase and tensin homolog TIGIT T-cell immunoreceptor with Ig and ITIM domains CTL4 cytotoxic t-lymphocyte-associated protein 4 TP53 tumor protein 53 Tcm central memory T cell Declarations Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the General Project of Inner Mongolia Natural Science Foundation (Grant No. 2022MS08063), the Key Research and Development and Achievement Transformation Project in the Social Welfare Field of the 14th Five-Year Plan in the Inner Mongolia Autonomous Region (Grant No. 2022YFSH0087), Inner Mongolia Medical University Affiliated Hospital Talent Training Project - Sailing Series, the Inner Mongolia Health Science and Technology Project in 2022 (Grant No. 202201293), the National Natural Science Foundation of China (Grant No. 81960143), the Inner Mongolia Grassland Talents Program Young Innovative Talent Project (Grant No. Q2022082), the Shanghai Key Laboratory of Kidney and Blood Purification (Grant No. 14DZ226022, 20DZ2271600), the Science and Technology Commission of Shanghai, Science and Technology Program of the Joint Fund of Scientific Research for the Public Hospitals of Inner Mongolia Academy of Medical Sciences (Grant No. 2024GLLH0337), and the Trinity College Students Innovation and Entrepreneurship Cultivation Project of Inner Mongolia Medical University (Grant No. SWYT2020008). Data availability statement Publicly available datasets were analyzed in this study. These data can be found at https://portal.gdc.cancer.gov/(TCGA-KIRC), https://www.ncbi.nlm.nih.gov/geo/(GSE53757 and GSE100666), https://biit.cs.ut.ee/methsurv/, https://www.cbioportal.org/(BGI, Nat Genet 2012 and TCGA, Firehose Legacy). Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of Inner Mongolia Medical University (No. YKD202403009). The patients/participants provided written informed consent to participate in this study. Author contributions L.B. collected the cases and obtained tissue specimens and wrote a revised version of the paper; W.G. conducted data analysis and created pictures and tables; X.W. and P.M. wrote the original draft of the manuscript; M.Z. and S.Z. reviewed the draft; L.Y. and L.Y. coordinates the work of various departments and controls the quality of data; Y.M. was responsible for project administration, supervision, and editing. All the authors approved the final draft of the manuscript. Disclosure statement No potential conflicts of interest were reported by the author(s). Consent for publication Not applicable. References Grammatikaki, S. et al. An Overview of Epigenetics in Clear Cell Renal Cell Carcinoma. Vivo 37 (1), 1–10 (2023). Speed, J. M., Trinh, Q. D., Choueiri, T. K. & Sun, M. Recurrence in Localized Renal Cell Carcinoma: a Systematic Review of Contemporary Data. Curr. Urol. Rep. 18 (2), 15 (2017). Müller, L., Hainberger, D., Stolz, V. & Ellmeier, W. 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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-8086725\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":554406309,\"identity\":\"76e54a59-292f-4b1c-a5b7-bbdf1946b1b8\",\"order_by\":0,\"name\":\"Luri Bao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Inner Mongolia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luri\",\"middleName\":\"\",\"lastName\":\"Bao\",\"suffix\":\"\"},{\"id\":554406312,\"identity\":\"7a95fa25-56d6-4434-8fb5-7dc51fc3920b\",\"order_by\":1,\"name\":\"Wuniri 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(B,C) NCOR1 expression levels were significantly higher in the ccRCC tissues than in the adjacent peritumoral kidney tissues in the GSE53757(B), GSE100666 (C). (D) Representative images of Immunohistochemically-stained tumor tissues and normal tissues. (E) Comparison of the IHC scores of ccRCC tissues and adjacent normal tissues. (F,G) According to the median NCOR1 level, 613 ccRCC patients from the TCGA–KIRC project were divided into high- and low-NCOR1 expression groups. (F) The volcano plots and (G) the heatmaps show the expression levels of specific mRNAs in the ccRCC patients with high- and low-NCOR1 expression (n = 613) from the TCGA-KIRC project, F and G were created by the “ggplot2” R package.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/48182334b21c8f5548b59895.png\"},{\"id\":97434646,\"identity\":\"1cc025da-8b27-4396-85ac-a3b0e3a679ab\",\"added_by\":\"auto\",\"created_at\":\"2025-12-04 10:54:19\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2830591,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunctional enrichment analysis of differentially expressed genes between the NCOR1 expression groups in ccRCC. \\u003c/strong\\u003e(A) GO and KEGG enrichment analysis results of NCOR1-related DEGs. (B-I) Gene Set Enrichment Analysis (GSEA) of the altered signaling pathways in the ccRCC tissues based on the NCOR1-associated DEGs between the high- and low-NCOR1 expression groups in ccRCC.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/bf55afd50ee2b9090c4d60e1.png\"},{\"id\":97667555,\"identity\":\"4bc377ac-e3d6-4785-bf27-64210da55c8b\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 09:23:45\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2688389,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation analysis of immune cell infiltration and \\u003c/strong\\u003eNCOR1\\u003cstrong\\u003eexpression in ccRCC.\\u003c/strong\\u003e (A) Spearman’s correlation analysis results between the infiltration levels of 24 immune cell types and NCOR1 expression levels in ccRCC tissues. (B–G) The infiltration levels of (B) NK CD56bright cells, (C) cytotoxic cells, (D) Tregs, (E) Th17 cells, (F) neutrophils, and (G) Tcm in the high- and low-NCOR1 expression groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/155eb5dc4aaed66f3bd51cb7.png\"},{\"id\":97667811,\"identity\":\"9b2f66e8-fe1e-489b-9e7c-6c8b99dc950d\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 09:24:17\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1020178,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation analysis between the expression levels of NCOR1, TIGIT, CTLA4, TP53 and PTEN in ccRCC.\\u003c/strong\\u003e (A–D) Correlation between the expression levels of NCOR1 and the expression levels of (A)TIGIT , (B)CTLA4 , (C)TP53 and (D) PTEN in the TCGA-KIRC dataset.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/0277cb4a34f4ec77e5e6df75.png\"},{\"id\":97434642,\"identity\":\"72bfafa3-c4d9-4675-986f-d9f16def8a38\",\"added_by\":\"auto\",\"created_at\":\"2025-12-04 10:54:19\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":65201,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe DNA methylation level of the NCOR1 gene correlates with the prognosis of ccRCC patients.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/b94477b0706240a03d4f7eb7.png\"},{\"id\":97666743,\"identity\":\"34dbd37c-fec2-4a03-97fe-dbefab741a0e\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 09:22:02\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":209015,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNCOR1 gene alterations are not associated with the survival outcomes in ccRCC.\\u003c/strong\\u003e (A) OncoPrint visual summary of the alterations in the NCOR1 gene. (B,C) Kaplan–Meier survival curves show the (B) overall survival and (C) disease-free survival rates of ccRCC patients with or without NCOR1 gene alterations.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/ea1b8507d2abaa98c7ec2a83.png\"},{\"id\":97667707,\"identity\":\"94b3c2b2-83dd-4921-8aca-46897b63c860\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 09:24:06\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1416523,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNCOR1 expression levels and clinicopathological features of ccRCC patients. \\u003c/strong\\u003e(A–H) Correlation analysis between NCOR1 expression levels and (A) gender, (B) age, (C) T stage, (D) pathological stage, (E) histological grade, (F) OS, (G) DSS, and (H) PFI of ccRCC patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/a91fdb8bd61637b52490faf0.png\"},{\"id\":97434649,\"identity\":\"4e2b8c6f-62dc-460a-9978-989778dfe6ab\",\"added_by\":\"auto\",\"created_at\":\"2025-12-04 10:54:19\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":245362,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNCOR1 shows a high prognostic prediction value in ccRCC patients. \\u003c/strong\\u003eThe Kaplan–Meier plotter database analysis shows the differences in (A) overall survival, (B) disease-specific survival, and (C) progression-free interval of ccRCC patients with high- and low-NCOR1 expression levels. \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05 indicates statistical significance. The red and green curves represent high and low NCOR1 expressing ccRCC patients, respectively.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/14d680058b70185974e7ba41.png\"},{\"id\":97668582,\"identity\":\"66d4b7b8-87f5-4fb2-ad56-36886bb287e3\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 09:25:50\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":257577,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe nomogram model with NCOR1 shows a superior diagnostic and prognostic performance in ccRCC. \\u003c/strong\\u003e(A) Diagnostic ROC curves to distinguish ccRCC tissues and normal tissues based on the NCOR1 expression levels. (B) Time-dependent survival ROC curves to predict 1-, 3-, and 5-year survival rates of ccRCC patients based on the NCOR1 expression levels. (C) ROC curve analysis to evaluate the prediction efficacy of the nomogram model that includes clinicopathological factors (T stages, pathologic stages, and histological grade) and NCOR1 expression levels to predict the 1-, 3-, and 5-year survival rates of ccRCC patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/d1693bd510d9bb4205f55d58.png\"},{\"id\":97434671,\"identity\":\"43cba8ab-8584-4d42-84e0-66afbf56dbb2\",\"added_by\":\"auto\",\"created_at\":\"2025-12-04 10:54:20\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2621643,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePrognostic performance of NCOR1 expression in subgroups of ccRCC patients. NCOR1 patients were classified into different subgroups according to race, T stage, N stage, M stage, pathological stage, and histological grade.\\u003c/strong\\u003e (A–C) Cox regression analysis results showing the prognostic performance of NCOR1 expression levels in terms of (A) overall survival, (B) disease-specific survival, and (C) progression-free survival in different subgroups of ccRCC patients. The results are represented by the hazard ratio (HR). The bar represents the 95% confidence interval (CI) of the HR values, and the size of the triangle represents the significance of the prognostic performance of NCOR1.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/e1ef0d2b40fa4029fcc77b52.png\"},{\"id\":101690442,\"identity\":\"d9c65794-c905-408f-ae6d-8615f1a0b0b7\",\"added_by\":\"auto\",\"created_at\":\"2026-02-02 16:02:58\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":13719994,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8086725/v1/a029fcad-8e9a-422d-ad6f-893970ef2f9f.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Nuclear receptor corepressor 1 is a Potential Diagnostic and Prognostic Biomarker in Clear Cell Renal Cell Carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eRenal cancer is a common type of urinary system cancer, accounting for 2\\u0026ndash;3% of adult malignancies, and clear cell renal cell carcinoma (ccRCC) comprises 75\\u0026ndash;80% of all renal cancer cases\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. Although surgical intervention is the primary treatment approach, postoperative recurrence affects 20\\u0026ndash;40% of patients\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. Early detection is crucial for reducing the tumor burden. However, existing diagnostic methods rely on clinicopathologic alterations, imaging examinations, and clinical manifestations, lacking molecular indicators for early diagnosis. Therefore, there is an urgent need to develop reliable diagnostic biomarkers for the early detection of ccRCC.\\u003c/p\\u003e\\u003cp\\u003eNuclear receptor corepressor 1 (NCOR1), a key transcriptional regulator associated with the N-CoR nuclear receptor corepressor family, is located at a specific chromosomal locus (17p12-p11.2 in humans and chromosome 11 in mice)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. NCOR1 interacts with multiple transcription factors and plays a crucial role in nuclear receptor signaling pathways\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. In the absence of ligands, NCOR1 cooperates with nuclear receptors, such as thyroid hormone receptors and retinoic acid receptors, to suppress transcription, thus regulating gene expression. NCOR1 inhibits nuclear receptor activity by promoting chromatin remodeling and suppressing transcription\\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e. NCOR1 deficiency is associated with increased cancer cell invasion, tumor growth, and metastatic potential, along with the downregulation of genes related to enhanced metastasis and poor prognosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Reduced expression of NCOR1 has been observed in various tumor types, including prostate cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, non-small cell lung cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e, and gastrointestinal stromal tumors\\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e, indicating its role as a tumor suppressor. Nevertheless, the expression and function of NCOR1 in ccRCC tissues have not been extensively studied.\\u003c/p\\u003e\\u003cp\\u003eThe precise role of NCOR1 in regulating tumor immune cell infiltration, immune checkpoints, aberrant DNA methylation, and gene mutations remains to be fully elucidated. Moreover, its influence on the diagnosis and prognosis of ccRCC also needs further study. To fill this knowledge gap, we conducted comprehensive bioinformatics research using datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to evaluate the diagnostic and prognostic value of NCOR1 in ccRCC.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData collection and ethics statement\\u003c/h2\\u003e\\u003cp\\u003eTCGA is a collaborative initiative between the National Cancer Institute and the National Institute of Human Genetics, aggregating relevant information from over 20,000 samples representing 33 distinct types of cancer. This comprehensive database encompasses transcriptomic, genomic variation, methylation, and clinical data. This study acquired RNA-seq data and clinicopathological information from 613 ccRCC patients through TCGA database. The initially provided RNA-seq data in fragments per kilobase of transcript per million mapped reads (FPKM) format were transformed into transcripts per million (TPM) format for analysis.\\u003c/p\\u003e\\u003cp\\u003eThe GEO database was established by the National Center for Biotechnology Information (NCBI) in 2000 and serves as a repository for high-throughput gene expression data provided by global research institutions. We chose two datasets from the database, specifically GSE53757 and GSE100666. The GSE53757 dataset is derived from high-throughput gene array analysis, which measured gene expression in the tumor tissues of 72 ccRCC patients and compared it with that in matched normal kidney tissues. Similarly, the GSE100666 dataset employed high-throughput gene microarray technology to assess gene expression in the tumor and normal kidney tissues of three ccRCC patients. Both TCGA and GEO databases offer publicly accessible data, thus obviating the need for approval from medical ethics committees when using the data.\\u003c/p\\u003e\\u003cp\\u003e Paraffin-embedded tissue sections were obtained from patients who underwent surgical treatment at the Affiliated Hospital of Inner Mongolia Medical University between January 2023 and January 2025, including cancerous tissues and adjacent normal tissues, and informed consent was obtained from all participants and their legal guardians. This study was conducted in line with medical ethical standards and was approved by the Ethics Committee of Inner Mongolia Medical University (Approval No. YKD202403009), and commit that all research was conducted in accordance with relevant regulations.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eBioinformatics analysis of NCOR1 mRNA expression levels in ccRCC and adjacent normal tissues\\u003c/h3\\u003e\\n\\u003cp\\u003eData on NCOR1 mRNA expression levels were collected from TCGA database, which includes information on 33 types of human cancers, including ccRCC. For the analysis of NCOR1 expression in ccRCC samples, gene expression data from the GSE53757 and GSE100666 datasets in the GEO database were utilized. Based on the median expression of NCOR1, the 613 ccRCC patients from TCGA were classified into high-expression and low-expression groups according to their NCOR1 mRNA levels. Differential expression analysis between these groups was conducted using the DESeq function in the R package\\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e, with thresholds set at an absolute log fold change\\u0026thinsp;\\u0026gt;\\u0026thinsp;1.5 and an adjusted \\u003cem\\u003eP\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Volcano plots and heatmaps were generated using the \\\"ggplot2\\\" R package to visualize differentially expressed genes.\\u003c/p\\u003e\\n\\u003ch3\\u003eImmunohistochemistry for the detection of NCOR1 expression levels in ccRCC tissues and adjacent normal tissues\\u003c/h3\\u003e\\n\\u003cp\\u003eThis study enrolled 20 patients diagnosed with ccRCC at the Affiliated Hospital of Inner Mongolia Medical University from 2023 to 2025. The inclusion criteria included initial diagnosis and treatment, patients being 18 years of age or older, confirmed ccRCC pathology with radical surgery performed, voluntary participation after signing informed consent, and availability of comprehensive clinicopathologic and follow-up data. During the surgical procedure, the tumor and adjacent normal kidney tissues were obtained, fixed in 10% neutral formaldehyde, and embedded in paraffin. After dewaxing the samples with xylene and rehydrating them with graded ethanol, EDTA was employed for antigen retrieval. Subsequently, the samples were treated with 3% hydrogen peroxide for 10 minutes to inhibit endogenous peroxidase activity. Next, the samples were incubated at room temperature with a 1:100 dilution of the primary antibody NCOR1 (Ruiying Biotechnology Co., Ltd.) for 1 hour, followed by incubation with the secondary antibody (Fuzhou Maixin) for 10 minutes. The samples were stained with DAB for 5 minutes and counterstained with hematoxylin. After dehydration and clearing processes, the slides were sealed with neutral gum for microscopic analysis. Two pathologists independently assessed the slides using a double-blind approach and conducted semi-quantitative scoring based on staining intensity and area. The staining intensity was classified as none, 1 (weak), or 2 (strong), and the staining area was scored proportionally, with a threshold of 50%. The two scores were multiplied. If the product was greater than 2, the result was considered positive; if it was less than or equal to 2, the result was considered negative.\\u003c/p\\u003e\\n\\u003ch3\\u003eFunctional enrichment analysis of NCOR1-related differentially expressed genes in ccRCC\\u003c/h3\\u003e\\n\\u003cp\\u003eThe \\\"org.Hs.eg.db\\\" R package was used to convert Entrez IDs into gene symbols. Subsequently, functional annotation and gene set enrichment analysis (GSEA)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e of differentially expressed genes were carried out using the \\\"ClusterProfiler\\\" R package\\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. This analysis utilized the gene set c2.cp.v7.5.1.symbols.gmt from the MSigDB database. When the false discovery rate (FDR) was below 0.25 and the \\u003cem\\u003eP\\u003c/em\\u003e-value was below 0.05, the enrichment of the gene set was regarded as significant.\\u003c/p\\u003e\\n\\u003ch3\\u003eCorrelation analysis of the relationship between NCOR1 expression levels and immune cell infiltration in ccRCC\\u003c/h3\\u003e\\n\\u003cp\\u003eThe single sample gene enrichment analysis (ssGSEA) algorithm in the \\\"GSVA\\\" package\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e was used to perform Spearman's correlation analysis to investigate the association between NCOR1 expression and immune cell infiltration\\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. This analysis assessed the infiltration levels of 24 different immune cell subtypes, including regulatory T cells (Tregs), cytotoxic cells, type 1 T helper (Th1) cells, T cells, activated dendritic cells (aDCs), macrophages, type 2 T helper (Th2) cells, CD56bright natural killer (NK) cells, plasmacytoid dendritic cells (pDCs), neutrophils, NK cells, mast cells, eosinophils, CD56dim NK cells, dendritic cells (DCs), γδ T cells, central memory T cells, T helper cells, and type 17 T helper (Th17) cells.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCorrelation analysis of the relationship between NCOR1 expression levels and immune checkpoint markers in ccRCC\\u003c/h2\\u003e\\u003cp\\u003eThis study examined the correlations among NCOR1 expression, immune checkpoint markers (TIGIT, CTLA4, and TP53), and the oncogene PTEN in samples from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Spearman's rank correlation analysis was utilized to evaluate these associations, and the results were visualized using the \\\"ggplot2\\\" package in R. Associations with a significance level of \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were deemed statistically significant.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eDNA methylation status analysis of the CpG island of the NCOR1 gene\\u003c/h3\\u003e\\n\\u003cp\\u003eThe DNA methylation status of the CpG island of the NCOR1 gene in the TCGA-KIRC dataset was verified using the MethSurv database. The MethSurv database is an online survival analysis platform based on CpG methylation profiles. This platform integrates data from over 700 methylation sites across 25 types of human cancers and applies the Cox proportional hazards model. Furthermore, this platform assessed the prognostic significance of NCOR1 CpG methylation in the TCGA-KIRC cohort.\\u003c/p\\u003e\\n\\u003ch3\\u003eGene mutations in ccRCC samples\\u003c/h3\\u003e\\n\\u003cp\\u003eNCOR1 gene mutations were comprehensively evaluated across multiple datasets, including those from the Beijing Genomics Institute (BGI), Nat Genet 2012, and TCGA, Firehose Legacy. This assessment was carried out using the cBioPortal online platform. The prognostic relevance of NCOR1 gene mutations was evaluated through Kaplan-Meier survival analysis and the log-rank test. Statistical significance was defined as \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCorrelation between NCOR1 expression levels and clinicopathological features in ccRCC\\u003c/h2\\u003e\\u003cp\\u003eThis study utilized the R package to examine the association between clinicopathological features (including race, gender, age, T stage, N stage, M stage, histological grade, pathological grade, overall survival (OS) events, and disease-specific survival (DSS) events) and NCOR1 expression levels in TCGA-KIRC samples. T stage denotes the size of primary tumor lesions. T1 stage indicates tumors with a diameter of \\u0026le;\\u0026thinsp;7 cm that are confined to the kidneys, T2 stage indicates tumors with a diameter of \\u0026gt;\\u0026thinsp;7 cm that are confined to the kidneys, T3 stage indicates tumors that invade the renal vein or perirenal tissue (excluding the ipsilateral adrenal gland without penetrating the perirenal fascia), and T4 stage indicates tumors that infiltrate the perirenal fascia (including the ipsilateral adrenal gland adjacent to the tumor). N stage represents the status of regional lymph node metastases, where N0 denotes the absence of regional lymph node metastases and N1-N3 denote increasing involvement of regional lymph nodes. M stage indicates the presence of distant metastases, where M0 denotes the absence of distant metastases and M1 denotes the presence of distant metastases. Based on the morphological and cytological features observed under a microscope, tumor tissues are categorized into four histological grades. G1 grade denotes a high level of tumor differentiation, characterized by tumor cells that are highly similar in morphology and function to normal cells, slow growth, and extremely low invasiveness. G2 grade denotes that the tumor is moderately differentiated. Compared with normal cells, tumor cells display some abnormalities, a moderately accelerated growth rate, and moderate invasiveness. G3 grade indicates that the tumor is poorly differentiated. The tumor cells differ significantly in morphology and function from normal cells, grow rapidly, and have strong invasiveness. G4 grade denotes extremely low tumor differentiation, with significant disparities in morphology, function, growth rate, and the extent of invasion of surrounding tissues between tumor cells and normal cells. The pathological stage of ccRCC is determined by tumor size and metastasis status. Stage I comprises tumors confined to the kidneys with a diameter of \\u0026le;\\u0026thinsp;7 cm, without lymph node or distant organ metastasis. In stage III, there is no restriction on the size of kidney tumors, and they have started to metastasize to adjacent lymph nodes, blood vessels near the kidneys, renal collecting systems, or perirenal fat. In pathological stage IV, the cancer has extended beyond the perirenal adipose tissue and may have spread to the adrenal gland or distant anatomical locations. Statistical analysis was performed using Pearson's χ\\u003csup\\u003e2\\u003c/sup\\u003e test, and Fisher's exact test might have been utilized if necessary. Logistic regression analysis was conducted to evaluate the correlation between NCOR1 expression levels and clinicopathological features in patients with ccRCC.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePrognostic value of NCOR1 expression levels in patients with ccRCC\\u003c/h2\\u003e\\u003cp\\u003eThe data analysis was visualized using the \\\"survminer\\\" and \\\"ggplot2\\\" packages. Diagnostic receiver operating characteristic (ROC) curves, time-dependent survival ROC curves, and nomogram models were constructed using the \\\"pROC,\\\" \\\"timeROC,\\\" and \\\"rms\\\" R packages to evaluate the predictive value of NCOR1 expression levels in the diagnosis of ccRCC. Prognostic assessment of subgroups of ccRCC patients was conducted using Kaplan-Meier survival curves, with the sample sizes (%), hazard ratios (HRs), confidence intervals (CIs), and \\u003cem\\u003eP\\u003c/em\\u003e values displayed. The \\\"ggplot2\\\" R package was employed to generate forest plots.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003e1. Significantly downregulated NCOR1 expression in various tumor tissues, including ccRCC\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eNCOR1 expression was detected in 33 cancer datasets sourced from TCGA database. Notably, NCOR1 exhibited significant downregulation in 12 analyzed cancer types, including bladder cancer (BLCA), breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), thyroid cancer (THCA), and uterine corpus endometrial carcinoma (UCEC). Conversely, NCOR1 was upregulated in cholangiocarcinoma (CHOL) and stomach adenocarcinoma (STAD) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Furthermore, after analyzing the GSE53757 and GSE100666 datasets in the GEO database, we found that the expression of NCOR1 was significantly reduced in ccRCC tissues compared with normal tissues (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB, C). The immunohistochemical (IHC) staining results of ccRCC cancer tissues and adjacent normal tissues confirmed the differential expression of NCOR1 at the protein level. We observed that NCOR1 expression was significantly lower in ccRCC cancer tissues, and it was mainly distributed on cell membranes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD). Compared with adjacent normal kidney tissues, the IHC score of NCOR1 in ccRCC tissues was significantly reduced (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE), corroborating the downregulation of NCOR1 at the protein level in ccRCC. Subsequently, based on the median expression of NCOR1, 613 ccRCC patients were divided into a high-expression group and a low-expression group of NCOR1. We applied a threshold parameter of an absolute logFC value\\u0026thinsp;\\u0026gt;\\u0026thinsp;1.5 and set an adjusted \\u003cem\\u003eP\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 to identify 418 genes that exhibited differential expression between ccRCC tissues and adjacent normal tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eF). Among these genes, 8 genes were upregulated and 410 genes were downregulated. The heatmap in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eG illustrates the co-expression of individual genes, highlighting the 10 genes with the most significant differential expression.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e2. Functional enrichment analysis of NCOR1-related differentially expressed genes in ccRCC\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eThrough differential expression analysis, we identified differentially expressed genes between cancer tissues and adjacent normal tissues of ccRCC, which revealed the impact of ccRCC on gene expression. Subsequently, Gene Ontology (GO) analysis was conducted to classify these differentially expressed genes according to genomic annotation data, aiming to reveal the affected biological functions and pathways. The \\\"clusterProfiler\\\" R package was used to annotate the differentially expressed genes related to NCOR1 in ccRCC patients. The results of the GO enrichment analysis encompassed biological processes, cellular components, and molecular functions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Key biological processes included lipid catabolic processes, negative regulation of signaling receptor activity, regulation of fatty acid biosynthetic processes, negative regulation of peptidase activity, and positive regulation of fatty acid biosynthetic processes. Predominant cellular components comprised the secretory granule lumen, endoplasmic reticulum lumen, intermediate filament cytoskeleton, intermediate filaments, and keratin filaments. The most highly enriched molecular functions included serine hydrolase activity, peptidase regulator activity, endopeptidase activity, receptor ligand activity, and passive transmembrane transporter activity. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the differentially expressed genes were significantly enriched in key pathways, including complement and coagulation cascades, tyrosine metabolism, synaptic vesicle cycle, linoleic acid metabolism, and cholesterol metabolism. GSEA analysis revealed that the differentially expressed genes related to NCOR1 were significantly enriched in clusters related to cell proliferation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB\\u0026ndash;I). These clusters included cell cycle checkpoint (NES\\u0026thinsp;=\\u0026thinsp;3.037, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), G1 phase and G1-S transition (NES\\u0026thinsp;=\\u0026thinsp;3.002, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), G2-M checkpoints (NES\\u0026thinsp;=\\u0026thinsp;2.901, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), S phase (NES\\u0026thinsp;=\\u0026thinsp;2.790, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), mitotic spindle checkpoint (NES\\u0026thinsp;=\\u0026thinsp;2.772, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), cell cycle (NES\\u0026thinsp;=\\u0026thinsp;2.686, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), G1-S specific transcription (NES\\u0026thinsp;=\\u0026thinsp;2.626, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and cyclin A/B-regulated G2-M transition (NES\\u0026thinsp;=\\u0026thinsp;2.484, \\u003cem\\u003eP\\u003c/em\\u003eadj\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e3. Association of NCOR1 expression levels with the infiltration of multiple immune cells and the expression of immune checkpoint genes in ccRCC tissues\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe functionality of immune cells is characterized by continual adaptation and dynamic equilibrium when interacting with various human and environmental factors. When immune cells malfunction or their performance is impaired, it can increase an individual's vulnerability to infectious diseases and cancer. On the other hand, excessive activity or an imbalance within specific subsets of immune cells can lead to the onset of autoimmune disorders\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. This study explored the correlation between NCOR1 expression and immune cell infiltration in ccRCC tissues. Statistical analysis revealed significant differences in the abundance levels of 14 types of immune cells between the high-expression group and the low-expression group of NCOR1, including CD56bright NK cells, cytotoxic cells, Tregs, CD8 T cells, pDCs, NK cells, γδ T cells, mast cells, effector memory T (Tem) cells, Th17 cells, neutrophils, T helper cells, eosinophils, and central memory T (Tcm) cells. The correlation analysis revealed significant associations between NCOR1 expression and various types of immune cells. Specifically, NK cells (r\\u0026thinsp;=\\u0026thinsp;0.094, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), γδ T cells (r\\u0026thinsp;=\\u0026thinsp;0.167, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), mast cells (r\\u0026thinsp;=\\u0026thinsp;0.190, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), Tem cells (r\\u0026thinsp;=\\u0026thinsp;0.203, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), Th17 cells (r\\u0026thinsp;=\\u0026thinsp;0.218, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), neutrophils (r\\u0026thinsp;=\\u0026thinsp;0.237, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), T helper cells (r\\u0026thinsp;=\\u0026thinsp;0.317, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), eosinophils (r\\u0026thinsp;=\\u0026thinsp;0.320, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and Tcm cells (r\\u0026thinsp;=\\u0026thinsp;0.458, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were positively correlated with NCOR1 expression. Conversely, CD56bright NK cells (r = -0.266, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), cytotoxic cells (r = -0.226, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), Tregs (r = -0.180, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), CD8 T cells (r = -0.102, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), and pDCs (r = -0.091, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) were negatively correlated with NCOR1 expression. The tumor infiltration levels of CD56bright NK cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), cytotoxic cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC), Tregs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD), Th17 cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE), neutrophils (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF), and Tcm cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG) were consistent with the results of the Spearman's correlation analysis depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA. TIGIT and CTLA-4 are significant immune checkpoint proteins associated with immune evasion by tumors\\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e. TP53, a tumor suppressor gene, exhibits low expression in normal cells but high expression in malignant tumors\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. The PTEN gene plays a crucial role in cell growth, development, signal transduction, and apoptosis, and its mutation or deletion is linked to various human tumors\\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. This study discovered that NCOR1 expression in ccRCC samples from TCGA dataset was positively correlated with the levels of TIGIT, CTLA-4, TP53, and PTEN (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-D).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e4. Association between NCOR1 gene methylation status and prognosis in ccRCC patients\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eDNA methylation levels within the NCOR1 gene and the prognostic value of CpG islands within NCOR1 were assessed using the MetSurv online tool. The analysis results identified five CpG islands that had undergone methylation, with cg20155837 exhibiting a relatively higher level of DNA methylation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Moreover, the methylation status of four CpG islands (cg20155837, cg04223442, cg21211144, and cg13465826) was significantly associated with patient prognosis (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Among these four CpG islands, the NCOR1 methylation levels were elevated, particularly in cg04223442 and cg13465826. These elevated methylation levels were linked to lower OS rates in ccRCC patients compared to those with lower NCOR1 CpG methylation levels.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eEffect of the methylation level of the CpG locus of the NCOR1 gene on the prognosis of ccRCC patients.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCpG island\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e.value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1stExon;5'UTR-Island-cg00253204\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.703\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.085615631\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5'UTR-N_Shore-cg04223442\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.56\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.018749972\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5'UTR-N_Shore-cg13465826\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.482\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.001584027\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBody-Open_Sea-cg20155837\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2.799\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.00080388\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eTSS200-Island-cg21211144\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.949\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.016814657\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e5. NCOR1 gene mutations were not associated with survival outcomes in ccRCC patients\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eNCOR1 gene mutations were analyzed in a cohort of 636 patients with ccRCC sourced from two datasets: BGI, Nat Genet 2012 (n\\u0026thinsp;=\\u0026thinsp;98), and TCGA, Firehose Legacy (n\\u0026thinsp;=\\u0026thinsp;538). The findings revealed that only 1.1% of the patients with ccRCC exhibited NCOR1 gene mutations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). Despite the low proportion of patients with these alterations, subsequent analysis using Kaplan-Meier survival curves and log-rank tests indicated no significant differences in OS (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.728) and DSS (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.308) between individuals with and without NCOR1 gene mutations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB, C).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e6. Association between NCOR1 expression levels and multiple clinicopathological features of ccRCC\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe TCGA-KIRC dataset was utilized to analyze the relationship between clinicopathological features and NCOR1 expression levels among ccRCC patients, as presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Notably, there was no significant correlation between NCOR1 expression and patient race or N stage (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). However, significant associations were found between NCOR1 expression and gender (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA), age (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB), T stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eC), pathological stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eD), histological grade (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE), OS (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eF), DSS (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eG), and progression-free interval (PFI) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eH). Logistic regression analysis further demonstrated a positive correlation between NCOR1 expression levels and age, T stage, M stage, pathological stage, and histological grade among ccRCC patients, as detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eClinicopathological characteristics of ccRCC patients in the high and low NCOR1 expression groups.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLow expression of NCOR1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHigh expression of NCOR1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\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\\u003en\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e270\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e271\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eRace, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.095\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsian\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (0.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e7 (1.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBlack or African American\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30 (5.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e27 (5.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e237 (44.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e232 (43.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGender, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.132\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e85 (15.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e102 (18.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e185 (34.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e169 (31.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;= 60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e121 (22.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e148 (27.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e149 (27.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e123 (22.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePathologic T stage, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e117 (21.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e162 (29.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e36 (6.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35 (6.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e111 (20.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e69 (12.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6 (1.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5 (0.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePathologic N stage, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.923\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e118 (45.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e124 (48.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8 (3.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8 (3.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePathologic M stage, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e201 (39.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e228 (44.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e50 (9.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29 (5.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePathologic stage, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage I\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e115 (21.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e158 (29.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage II\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e29 (5.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e30 (5.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage III\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e72 (13.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e51 (9.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStage IV\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e52 (9.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31 (5.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHistologic grade, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (0.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11 (2.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e106 (19.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e130 (24.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e106 (19.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e101 (18.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e49 (9.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e27 (5.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eOS event, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAlive\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e152 (28.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e214 (39.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDead\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e118 (21.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e57 (10.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDSS event, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e184 (34.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e237 (44.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e78 (14.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31 (5.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePFI event, n (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e169 (31.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e210 (38.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e101 (18.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e61 (11.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\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\\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\\u003eLogistic regression analysis between NCOR1 expression levels and clinicopathological characteristics of ccRCC patients.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal (N)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRace (White vs. Asian\\u0026amp;Black or African American)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e534\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.893 (0.531\\u0026ndash;1.500)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.668\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender (Male vs. Female)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e541\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.761 (0.534\\u0026ndash;1.086)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.133\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge (\\u0026gt;\\u0026thinsp;60 vs. \\u0026lt;= 60)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e541\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.675 (0.481\\u0026ndash;0.947)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic T stage (T3\\u0026amp;T4 vs. T1\\u0026amp;T2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e541\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.491 (0.343\\u0026ndash;0.704)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic N stage (N1 vs. N0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e258\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.952 (0.346\\u0026ndash;2.618)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.923\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic M stage (M1 vs. M0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e508\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.511 (0.312\\u0026ndash;0.839)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic stage (Stage III\\u0026amp;Stage IV vs. Stage I\\u0026amp;Stage II)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e538\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.507 (0.356\\u0026ndash;0.721)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistologic grade (G3\\u0026amp;G4 vs. G1\\u0026amp;G2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e533\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.638 (0.453\\u0026ndash;0.899)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e0.010\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e7. NCOR1 as a potential biomarker for prognostic and diagnostic assessment in ccRCC\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eKaplan-Meier survival analysis indicated that, when compared with patients with high levels of NCOR1 expression in ccRCC, those with low levels of NCOR1 expression had significantly lower OS (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), DSS (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and PFI (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA-C). Univariate Cox regression analysis identified T stage, N stage, M stage, pathological stage, and low NCOR1 expression as adverse prognostic factors. Furthermore, multivariate Cox analysis confirmed that NCOR1 is an independent protective factor for predicting OS (HR\\u0026thinsp;=\\u0026thinsp;0.511, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), DSS (HR\\u0026thinsp;=\\u0026thinsp;0.340, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and PFI (HR\\u0026thinsp;=\\u0026thinsp;0.511, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). M stage emerged as an independent risk factor for OS and PFI, while pathological stage was independently associated with DSS and PFI. Additionally, histologic grading showed significant predictive value for PFI (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eUsing data from TCGA database, ROC curve analysis was conducted on NCOR1 gene expression data with the R packages pROC, rms, and survival to evaluate its diagnostic utility. The area under the curve (AUC) obtained from the analysis was 0.673, indicating its moderate diagnostic efficacy (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eA). Time-dependent ROC curve analysis revealed that the AUC values for predicting the 1-year, 3-year, and 5-year survival rates of ccRCC patients based on NCOR1 expression levels were below 0.35 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eB). Through multivariate Cox regression analysis, NCOR1 expression levels were incorporated into the nomogram along with clinical parameters, and T stage, pathological stage, histological grade, and NCOR1 expression levels were verified as significant prognostic indicators (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eC). Columnar line plots underscored the clinical relevance of the prognostic nutritional index in predicting the 1-year, 3-year, and 5-year survival rates in ccRCC patients.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCox regression analysis of NCOR1 levels and other factors affecting the prognosis of patients with ccRCC.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"9\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"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=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eHR for overall survival\\u003c/p\\u003e\\u003cp\\u003e(95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eHR for disease-specific survival\\u003c/p\\u003e\\u003cp\\u003e(95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e\\u003cp\\u003eHR for progression-free interval (95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eMultivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eUnivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMultivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eUnivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eMultivariate\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender (Female vs. male)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.924 (0.679\\u0026ndash;1.257)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.183 (0.786\\u0026ndash;1.781)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.476 (1.043\\u0026ndash;2.090)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.249 (0.798\\u0026ndash;1.955)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge (\\u0026thinsp;\\u0026lt;\\u0026thinsp;=\\u0026thinsp;60 vs. \\u0026gt;60)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.791 (1.319\\u0026ndash;2.432)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.562 (1.018\\u0026ndash;2.395)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.351 (0.926\\u0026ndash;1.971)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.285 (0.942\\u0026ndash;1.754)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic T stage (T1\\u0026amp;T2 vs. T3\\u0026amp;T4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.210 (2.373\\u0026ndash;4.342)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.333 (0.575\\u0026ndash;3.090)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.606 (3.697\\u0026ndash;8.502)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.059 (0.441\\u0026ndash;2.541)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.569 (3.306\\u0026ndash;6.314)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.040 (0.509\\u0026ndash;2.127)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic N stage (N0 vs. N1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.422 (1.817\\u0026ndash;6.446)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.686 (0.841\\u0026ndash;3.381)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.864 (1.831\\u0026ndash;8.157)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.461 (0.671\\u0026ndash;3.185)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3.697 (1.899\\u0026ndash;7.198)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.319 (0.653\\u0026ndash;2.662)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic M stage (M0 vs. M1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.401 (3.226\\u0026ndash;6.002)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.600 (1.525\\u0026ndash;4.432)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9.219 (6.294\\u0026ndash;13.504)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.509 (1.920\\u0026ndash;6.412)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e9.081 (6.554\\u0026ndash;12.582)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4.266 (2.492\\u0026ndash;7.303)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic stage (Stage I\\u0026amp;Stage II vs. Stage III\\u0026amp;Stage IV)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.910 (2.852\\u0026ndash;5.360)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.370 (0.535\\u0026ndash;3.508)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9.937 (5.989\\u0026ndash;16.486)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.384 (1.110\\u0026ndash;10.313)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.877 (4.813\\u0026ndash;9.826)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e3.330 (1.357\\u0026ndash;8.174)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistologic grade (G1\\u0026amp;G2 vs. G3\\u0026amp;G4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.665 (1.898\\u0026ndash;3.743)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.633 (0.993\\u0026ndash;2.686)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.850 (2.925\\u0026ndash;8.043)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.932 (0.973\\u0026ndash;3.837)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3.684 (2.530\\u0026ndash;5.364)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.695 (1.015\\u0026ndash;2.833)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNCOR1(Low vs. High)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.431 (0.314\\u0026ndash;0.592)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.511 (0.330\\u0026ndash;0.792)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.357 (0.235\\u0026ndash;0.542)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.340 (0.190\\u0026ndash;0.609)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.541 (0.392\\u0026ndash;0.746)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.511 (0.325\\u0026ndash;0.804)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eccRCC, renal clear cell carcinoma; HR, hazard ratio; CI, confidence interval; *\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; **\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; ***\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003e8. Prognostic value of NCOR1 in a clinicopathological subgroup of patients with ccRCC\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e present the results of the Cox regression analysis that investigated the predictive value of NCOR1 in different subgroups of clinicopathological features in ccRCC patients. Higher NCOR1 expression was associated with better OS, particularly in subgroups characterized by white race (HR\\u0026thinsp;=\\u0026thinsp;0.45, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), stages T1 and T2 (HR\\u0026thinsp;=\\u0026thinsp;0.43, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), N (HR\\u0026thinsp;=\\u0026thinsp;0.46, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), M (HR\\u0026thinsp;=\\u0026thinsp;0.40, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), clinicopathological stages I and II (HR\\u0026thinsp;=\\u0026thinsp;0.44, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), stages III and IV (HR\\u0026thinsp;=\\u0026thinsp;0.64, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.018), and histological grades G3 and G4 (HR\\u0026thinsp;=\\u0026thinsp;0.45, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eA). Furthermore, high NCOR1 expression was linked to favorable DSS (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eB) and PFI (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eC), particularly in white patients and those with stages T1 and T2, N, M, and high-grade histological tumors. These findings suggest that ccRCC patients with high NCOR1 expression have significantly better survival outcomes than those with low NCOR1 expression.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003ePrognostic performance of the NCOR1 gene on clinical outcomes in a subgroup of ccRCC patients based on Cox regression analysis.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHR for OS (95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eHR for DSS (95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eHR for PFI (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\\u003eRace\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsian\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e65(12.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.35(0.10\\u0026ndash;1.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.27(0.05\\u0026ndash;1.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.01(0.34\\u0026ndash;3.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e474(87.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.45(0.32\\u0026ndash;0.62)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.38(0.25\\u0026ndash;0.59)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.51(0.37\\u0026ndash;0.72)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT stage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1\\u0026amp;T2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e353(64.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.43(0.26\\u0026ndash;0.71)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.28(0.12\\u0026ndash;0.65)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.54(0.32\\u0026ndash;0.93)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3\\u0026amp;T4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e193(35.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.70(0.48\\u0026ndash;1.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.68(0.44\\u0026ndash;1.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.76(0.51\\u0026ndash;1.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN stage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e242(93.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.46(0.29\\u0026ndash;0.72)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.30(0.16\\u0026ndash;0.56)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.47(0.29\\u0026ndash;0.76)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eN1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e17(6.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.44(0.12\\u0026ndash;1.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.22(0.04\\u0026ndash;1.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.28(0.07\\u0026ndash;1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM stage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e434(84.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.40(0.27\\u0026ndash;0.60)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.27(0.14\\u0026ndash;0.52)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.56(0.36\\u0026ndash;0.85)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eM1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e79(15.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.84(0.51\\u0026ndash;1.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.87(0.52\\u0026ndash;1.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.90(0.56\\u0026ndash;1.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePathologic stage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003estage I\\u0026amp;stage II\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e335(61.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.44(0.25\\u0026ndash;0.78)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.20(0.06\\u0026ndash;0.68)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.49(0.26\\u0026ndash;0.95)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003estage III\\u0026amp;stage IV\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e208(38.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.64(0.44\\u0026ndash;0.93)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.61(0.40\\u0026ndash;0.94)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.73(0.50\\u0026ndash;1.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHistologic grade\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG1\\u0026amp;G2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e252(46.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.58(0.32\\u0026ndash;1.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.28(0.09\\u0026ndash;0.84)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.44(0.22\\u0026ndash;0.91)*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eG3\\u0026amp;G4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e286(53.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.45(0.32\\u0026ndash;0.65)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.44(0.29\\u0026ndash;0.68)***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.61(0.42\\u0026ndash;0.87)**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eccRCC, renal clear cell carcinoma; CI, confidence interval; HR, hazard ratio; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; *\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; **\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; ***\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eClear cell renal cell carcinoma (ccRCC), a prevalent urological malignancy, is characterized by a high mortality rate, presenting substantial challenges to clinical treatment. Global cancer data indicate a significant disparity in 5-year survival rates between early-stage ccRCC cases (exceeding 70%) and advanced or metastatic cases (less than 10%), underscoring the crucial significance of early detection in improving patient prognosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e. However, the subtle nature of ccRCC frequently results in the absence of specific clinical symptoms during the early stages of the disease. Consequently, most patients are diagnosed in the intermediate to late stages, thereby missing the optimal treatment opportunity. Therefore, urologists face the crucial task of overcoming the barriers to timely and accurate diagnosis of early ccRCC.\\u003c/p\\u003e\\u003cp\\u003eBiomarkers, serving as detectable indicators of disease onset and progression, have demonstrated promising applications in early-stage tumor diagnosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. Ideal biomarkers should be detectable in the early stages of the disease and in readily accessible samples, such as blood and urine, offering convenient means for clinical diagnosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e. The most recent advancements in molecular biology and high-throughput sequencing technology enable us to identify potential diagnostic biomarkers at the genomic, transcriptomic, and proteomic levels of ccRCC. For instance, CDK1, which is involved in cell cycle regulation\\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e, Bcl-2 family members, which are associated with apoptosis pathways\\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e, and VEGF, which plays a role in angiogenesis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. The discovery of these biomarkers provides a novel approach for the diagnosis of early ccRCC and lays the groundwork for further exploration of the molecular mechanisms of the disease. This study aims to explore the most recent advancements in biomarker research for ccRCC so as to optimize early-stage clinical diagnostic strategies.\\u003c/p\\u003e\\u003cp\\u003eIn this study, we employed high-throughput RNA sequencing data from TCGA and validated the research findings via IHC analysis of tissue specimens, suggesting NCOR1 as an emerging prognostic indicator for ccRCC. After analyzing the data of 613 ccRCC patients in TCGA, we found that the expression level of NCOR1 in tumor samples was significantly lower than that in normal tissues. The reduction in NCOR1 expression was significantly correlated with OS and DSS. Furthermore, in ccRCC, reduced NCOR1 expression levels were linked to advanced T stage, pathological stage, and histological grade.\\u003c/p\\u003e\\u003cp\\u003eThe notable correlation between NCOR1 expression levels and clinical features in ccRCC patients motivated us to explore the potential molecular mechanisms underlying ccRCC pathogenesis induced by alterations in NCOR1 expression. Our GSEA results revealed that the reduction in NCOR1 expression might be associated with the heightened activation of cell cycle regulatory processes. The accurate regulation of the cell cycle is crucial for maintaining normal cell proliferation and genomic stability, and its dysregulation is pivotal in the progression of ccRCC. The key checkpoints in cell cycle advancement, such as the G1/S transition and the G2/M checkpoint, rely on the intricate balance of the Cyclin-CDK complex, which is significantly disrupted in ccRCC. Notably, the inactivation of the VHL gene (present in approximately 80% of ccRCC cases) upregulates Cyclin D1 and CDK4/6 via the HIF-α pathway, accelerating the cell cycle progression from the G1 phase to the S phase\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. Conversely, CDK inhibitors (such as p27 and p21) alleviate cell cycle suppression by counteracting the functional loss resulting from the enhanced SKP2-mediated ubiquitin-dependent degradation\\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003ePrevious research has indicated that kidney cancer patients often experience immune suppression during tumor progression, which may be ascribed to the high immunogenicity of ccRCC\\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e, potentially leading to the production of inhibitory cells such as Tregs. Our research suggests that NCOR1 expression may be associated with immune cell infiltration in tumors. In ccRCC samples, we observed that NCOR1 expression was inversely correlated with CD56bright NK cells, cytotoxic cells, and Tregs, while positively correlated with Tcm, Th17 cells, and neutrophils. Neutrophils are vital in the immune response against tumors by activating immune responses and directly lysing tumor cells\\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e. Tregs, which produce immunosuppressive cytokines to inhibit immune responses, and Th17 cells, which enhance immune responses through the release of inflammatory cytokines, originate from the same precursor T cells\\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. The Tregs/Th17 cell ratio is associated with various immune disorders and cancers\\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e. Our results suggest that decreased NCOR1 expression significantly facilitates immune evasion in ccRCC cells, thereby promoting the growth and progression of ccRCC.\\u003c/p\\u003e\\u003cp\\u003eCTLA-4 is a protein that is crucial in tumor immune evasion\\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e, and CTLA-4 inhibitors can substantially improve the OS rate of patients with melanoma and advanced liver cancer\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. TP53 mutations are associated with poor prognosis in various human cancers\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e, as they suppress anti-tumor immunity and diminish the efficacy of tumor immunotherapy\\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Recent research has demonstrated that TIGIT can inhibit the activity of NK cells and CD8\\u0026thinsp;+\\u0026thinsp;T cells by binding to its ligand, thereby suppressing anti-tumor immune responses\\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. PTEN regulates central memory T cells and T helper cells in the tumor microenvironment. The expression level of PTEN can impact immune cell infiltration, thereby affecting the function of T cells in the tumor microenvironment and exerting anti-tumor effects. Therefore, we examined the relationship between NCOR1 expression levels and immune checkpoint genes, including TIGIT, CTLA-4, PTEN, and TP53. Our results demonstrated that the NCOR1 expression levels in ccRCC tissues were positively correlated with the expression levels of immune checkpoint genes TIGIT, CTLA-4, PTEN, and TP53. This suggests that NCOR1 may be a promising target for improving the efficacy of immunotherapy in ccRCC patients.\\u003c/p\\u003e\\u003cp\\u003eDNA methylation, a crucial epigenetic modification, can impact gene expression independently of DNA sequence alterations and plays a significant role in tumor development, such as in cervical cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e, lung cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e, and thyroid cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e. In this study, we explored the relationship between NCOR1 methylation levels and the prognosis of ccRCC patients. We discovered that the higher the methylation levels of four specific CpG islands (cg20155837, cg04223442, cg21211144, and cg13465826), the poorer the OS of patients. Additionally, the research findings indicate that NCOR1 gene mutations are infrequent in ccRCC tissues, occurring in only 1.1% of cases, and are not correlated with OS or DSS.\\u003c/p\\u003e\\u003cp\\u003eThe findings from multivariate Cox regression analysis suggest that NCOR1 can independently impact the prognosis of ccRCC. Compared to patients with higher levels of NCOR1 protein, those with lower levels of NCOR1 protein had significantly lower OS, DSS, and PFI rates. Nomograms incorporating NCOR1 expression accurately predicted the 1-year, 3-year, and 5-year survival rates of ccRCC patients. Previous research has verified that NCOR1 is linked to a poor prognosis in bladder cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e, prostate cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, and lung cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. These findings suggest that NCOR1 is a promising prognostic biomarker for ccRCC. Therefore, routine IHC analysis of cancer tissue samples can be aided by nomograms for diagnosis, prognosis assessment, and optimization of follow-up and treatment strategies.\\u003c/p\\u003e\\u003cp\\u003eThis study is constrained by the RNA-seq data of ccRCC tissues in TCGA database, which impedes direct assessment of downstream signaling pathways or NCOR1 protein levels in ccRCC. Therefore, it is essential to perform additional in vitro experiments to clarify the mechanism of action of NCOR1 in ccRCC.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study emphasizes the significant value of NCOR1 in the diagnosis and prognosis of ccRCC. NCOR1 is significantly downregulated in ccRCC tumor tissues and facilitates the progression of ccRCC by cell cycle activation. Moreover, NCOR1 expression levels are correlated with the infiltration of various immune cell types, suggesting its potential impact on the response to ccRCC immunotherapy. The methylation status and expression levels of NCOR1 are closely associated with the prognosis of ccRCC. Therefore, NCOR1 emerges as a promising therapeutic target and a crucial biomarker for the diagnosis and management of ccRCC.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"634\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eAbbreviations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eFull Name\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eccRCC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eclear cell renal cell carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eRCC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eRenal clear cell carcinoma\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eNCOR1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003enuclear receptor corepressor 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTCGA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ethe cancer genome atlas\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eGEO\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003egene expression omnibus\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eK‒M\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ekaplan‒meier\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eOS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eoverall survival\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eDFS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003edisease-free survival\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eDSS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003edisease-specific survival\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003ePFI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eprogression-free interval\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eGSEA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003egene set enrichment analysis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eHR\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ehazard ratio\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003econfidence interval\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eCOAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ecolon adenocarcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eKIRC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ekidney renal clear cell carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eKICH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003erenal suspicious cell carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eLUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003elung squamous cell carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eBLCA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ebladder cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eBRCA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ebreast invasive carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eCHOL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003echolangiocarcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eLIHC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eliver hepatocellular carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eLUAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003elung adenocarcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eSTAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003estomach adenocarcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTHCA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ethyroid cancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eUCEC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003euterine corpus endometrial carcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003ePRAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eprostate cancer \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eREAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003erectal adenocarcinoma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTreg\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eregulatory T cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003epDC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003eplasmacytoid dendritic cell\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eNK cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003enatural killer cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTh17 cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003etype 17 Th cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003ePTEN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ephosphatase and tensin homolog\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTIGIT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;T-cell immunoreceptor with Ig and ITIM domains \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eCTL4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ecytotoxic\\u0026nbsp;t-lymphocyte-associated\\u0026nbsp;protein 4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTP53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003etumor protein 53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eTcm\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 493px;\\\"\\u003e\\n \\u003cp\\u003ecentral memory T cell\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the General Project of Inner Mongolia Natural Science Foundation (Grant No. 2022MS08063), the Key Research and Development and Achievement Transformation Project in the Social Welfare Field of the 14th Five-Year Plan in the Inner Mongolia Autonomous Region (Grant No. 2022YFSH0087), Inner Mongolia Medical University Affiliated Hospital Talent Training Project - Sailing Series, the Inner Mongolia Health Science and Technology Project in 2022 (Grant No. 202201293), the National Natural Science Foundation of China (Grant No. 81960143), the Inner Mongolia Grassland Talents Program Young Innovative Talent Project (Grant No. Q2022082), the Shanghai Key Laboratory of Kidney and Blood Purification (Grant No. 14DZ226022, 20DZ2271600), the Science and Technology Commission of Shanghai, Science and Technology Program of the Joint Fund of Scientific Research for the Public Hospitals of Inner Mongolia Academy of Medical Sciences (Grant No. 2024GLLH0337), and the Trinity College Students Innovation and Entrepreneurship Cultivation Project of Inner Mongolia Medical University (Grant No. SWYT2020008).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePublicly available datasets were analyzed in this study. These data can be found at https://portal.gdc.cancer.gov/(TCGA-KIRC), https://www.ncbi.nlm.nih.gov/geo/(GSE53757 and GSE100666), https://biit.cs.ut.ee/methsurv/, https://www.cbioportal.org/(BGI, Nat Genet 2012 and TCGA, Firehose Legacy).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of Inner Mongolia Medical University (No. YKD202403009). The patients/participants provided written informed consent to participate in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eL.B. collected the cases and obtained tissue specimens and wrote a revised version of the paper; W.G. conducted data analysis and created pictures and tables; X.W. and P.M. wrote the original draft of the manuscript; M.Z. and S.Z. reviewed the draft; L.Y. and L.Y. coordinates the work of various departments and controls the quality of data; Y.M. was responsible for project administration, supervision, and editing. All the authors approved the final draft of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosure statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo potential conflicts of interest were reported by the author(s).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eGrammatikaki, S. et al. An Overview of Epigenetics in Clear Cell Renal Cell Carcinoma. \\u003cem\\u003eVivo\\u003c/em\\u003e \\u003cb\\u003e37\\u003c/b\\u003e (1), 1\\u0026ndash;10 (2023).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSpeed, J. M., Trinh, Q. D., Choueiri, T. K. \\u0026amp; Sun, M. Recurrence in Localized Renal Cell Carcinoma: a Systematic Review of Contemporary Data. \\u003cem\\u003eCurr. Urol. 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(2018).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLyu, H., Li, M., Jiang, Z., Liu, Z. \\u0026amp; Wang, X. Correlate the TP53 Mutation and the HRAS Mutation with Immune Signatures in Head and Neck Squamous Cell Cancer. \\u003cem\\u003eComput. Struct. Biotechnol. J.\\u003c/em\\u003e \\u003cb\\u003e17\\u003c/b\\u003e, 1020\\u0026ndash;1030 (2019).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBraun, D. A. et al. Beyond conventional immune-checkpoint inhibition - novel immunotherapies for renal cell carcinoma. \\u003cem\\u003eNat. Rev. Clin. Oncol.\\u003c/em\\u003e \\u003cb\\u003e18\\u003c/b\\u003e (4), 199\\u0026ndash;214 (2021).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBowden, S. J. et al. Protocol for a systematic review and meta-analysis of the diagnostic test accuracy of host and HPV DNA methylation in cervical cancer screening and management. \\u003cem\\u003eBMJ Open.\\u003c/em\\u003e \\u003cb\\u003e13\\u003c/b\\u003e (6), e071534 (2023).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRauch, T. A. et al. DNA methylation biomarkers for lung cancer. \\u003cem\\u003eTumour Biol.\\u003c/em\\u003e \\u003cb\\u003e33\\u003c/b\\u003e (2), 287\\u0026ndash;296 (2012).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWalter, M. J. et al. Recurrent DNMT3A mutations in patients with myelodysplastic syndromes. \\u003cem\\u003eLeukemia\\u003c/em\\u003e \\u003cb\\u003e25\\u003c/b\\u003e (7), 1153\\u0026ndash;1158 (2011).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAn, M. et al. Aberrant Nuclear Export of circNCOR1 Underlies SMAD7-Mediated Lymph Node Metastasis of Bladder Cancer. \\u003cem\\u003eCancer Res.\\u003c/em\\u003e \\u003cb\\u003e82\\u003c/b\\u003e (12), 2239\\u0026ndash;2253 (2022).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"NCOR1, clear cell renal cell carcinoma, clinical outcome, immune cell infiltration, DNA methylation, tumor prognosis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8086725/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8086725/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study investigates the potential of nuclear receptor corepressor 1 (NCOR1) as a diagnostic and prognostic biomarker for clear cell renal cell carcinoma (ccRCC).\\u003c/p\\u003e\\u003cp\\u003eThrough the analysis of data from The Cancer Genome Atlas and Gene Expression Omnibus databases, along with immunohistochemical testing of clinical samples, this study revealed that NCOR1 expression was significantly downregulated in ccRCC tissues when compared to normal tissues. This downregulation was also more pronounced in 11 other types of tumors. The results of functional enrichment analysis indicated that NCOR1-related differentially expressed genes played a role in cell cycle regulation. These findings imply that the downregulation of NCOR1 expression may facilitate the progression of ccRCC through cell cycle activation.\\u003c/p\\u003e\\u003cp\\u003eCorrelation analysis revealed a significant association between NCOR1 expression and immune cell infiltration in ccRCC tissues. Specifically, NCOR1 expression was positively correlated with natural killer cells, γδ T cells, and mast cells, and negatively correlated with NK CD56 bright cells and cytotoxic cells. Moreover, NCOR1 expression was positively correlated with immune checkpoint genes, including TIGIT, CTLA-4, TP53, and PTEN.\\u003c/p\\u003e\\u003cp\\u003eAnalysis of the DNA methylation status revealed an association between the methylation levels of four CpG islands within the NCOR1 gene and the prognosis of patients with ccRCC. Elevated methylation levels were indicative of poor overall survival (OS). Conversely, NCOR1 gene mutations were not common in ccRCC and were not associated with survival rates.\\u003c/p\\u003e\\u003cp\\u003eClinicopathological correlation analysis demonstrated that in patients with ccRCC, decreased NCOR1 expression was significantly associated with advanced T stage, pathological stage, histological grade, as well as poor OS, disease-specific survival, and progression-free interval. Multivariate Cox regression analysis further confirmed that NCOR1 was an independent protective factor for the prognosis of ccRCC. Additionally, ROC curve analysis demonstrated that NCOR1 had diagnostic value (AUC\\u0026thinsp;=\\u0026thinsp;0.673). In the nomogram model, combining NCOR1 expression with clinical parameters effectively predicted the 1-year, 3-year, and 5-year survival rates of ccRCC patients.\\u003c/p\\u003e\\u003cp\\u003eIn summary, the expression of NCOR1 is reduced in ccRCC, and its expression level and methylation status are closely related to the progression, immune microenvironment, and prognosis of ccRCC. These findings indicate that NCOR1 has the potential to become a viable diagnostic and prognostic biomarker as well as therapeutic target for ccRCC.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Nuclear receptor corepressor 1 is a Potential Diagnostic and Prognostic Biomarker in Clear Cell Renal Cell Carcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-04 10:54:14\",\"doi\":\"10.21203/rs.3.rs-8086725/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-12-15T06:52:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-07T13:19:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-05T03:56:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"128287749498198857982766812876519738480\",\"date\":\"2025-12-05T03:39:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"217328040718983041839754029698188894622\",\"date\":\"2025-12-03T04:12:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-02T17:32:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"90915124150078492276073836849420771323\",\"date\":\"2025-12-02T17:25:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-12-02T12:18:57+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-02T12:08:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-11-21T09:28:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-11-20T11:04:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-11-20T10:59:33+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"66d8255f-c1eb-4a30-9a35-0a626de4e6f7\",\"owner\":[],\"postedDate\":\"December 4th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":58996472,\"name\":\"Health sciences/Biomarkers\"},{\"id\":58996473,\"name\":\"Biological sciences/Cancer\"},{\"id\":58996474,\"name\":\"Biological sciences/Computational biology and bioinformatics\"},{\"id\":58996475,\"name\":\"Health sciences/Oncology\"}],\"tags\":[],\"updatedAt\":\"2026-02-02T16:00:35+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-8086725\",\"link\":\"https://doi.org/10.1038/s41598-026-37486-y\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2026-01-27 15:58:07\",\"publishedOnDateReadable\":\"January 27th, 2026\"},\"versionCreatedAt\":\"2025-12-04 10:54:14\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-026-37486-y\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-026-37486-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8086725\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8086725\",\"identity\":\"rs-8086725\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}