Nuclear receptor corepressor 1 is a potential diagnostic and prognostic biomarker in clear cell renal cell carcinoma

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.

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 cases¹. Although surgical intervention is the primary treatment approach, postoperative recurrence affects 20–40% of patients². 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. Notably, the chromosomal region 17p12-p11.2 has been reported to exhibit frequent copy number variations and genetic alterations in ccRCC⁴, which raises the hypothesis that NCOR1, as a resident gene in this unstable region, may be functionally disrupted during ccRCC tumorigenesis. NCOR1 interacts with multiple transcription factors and plays a crucial role in nuclear receptor signaling pathways⁵, a pathway closely linked to ccRCC progression, as nuclear receptors (e.g., peroxisome proliferator-activated receptors) are known to regulate metabolic reprogramming⁶, a hallmark of ccRCC characterized by dysregulated glycolysis and lipid metabolism^7,8. 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 transcription⁹, a mechanism that may counteract the aberrant gene expression programs driving ccRCC development.

Accumulating evidence supports NCOR1’s role as a tumor suppressor: 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 prognosis¹⁰. Reduced expression of NCOR1 has been observed in various tumor types, including prostate cancer¹¹, non-small cell lung cancer¹², and gastrointestinal stromal tumors¹³, Importantly, systematic analysis of NCOR1 expression patterns in clinical ccRCC tissues and its correlation with patient outcomes remains lacking. Given that ccRCC is driven by complex interactions between transcriptional dysregulation¹⁴, metabolic dysfunction, and immune evasion¹⁵, and NCOR1 is a central mediator of transcriptional repression, and metabolic pathway regulation¹⁶, it represents a particularly promising candidate biomarker—one that may not only reflect early molecular alterations in ccRCC but also link core pathogenic processes of the disease.

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 package¹⁷, 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 and interpretation of the results

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 min 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 h, followed by incubation with the secondary antibody (Fuzhou Maixin) for 10 min. The samples were stained with DAB for 5 min and counterstained with hematoxylin. After dehydration and clearing processes, the slides were sealed with neutral gum for microscopic analysis.

Results interpretation: The evaluation of NCOR1 protein expression in renal clear cell carcinoma tissues was performed by double-blind method, which was done independently by two pathologists with senior titles. The scoring criteria were as follows: first, the results were classified into four grades based on the intensity of strong immunohistochemical staining results: unstained (0 points), weakly positive (light yellow, 1 point), moderately positive (light brown, 2 points) and strongly positive (dark brown, 3 points). Next, quantitative grading was based on the percentage of positively stained area in the tumor tissue: ≤25% was scored as 1 point, 26% − 50% was scored as 2 points, 51% − 75% was scored as 3 points, and > 75% was scored as 4 points. Immunoreactivity score (IRS) was calculated by multiplying the staining percentage and intensity score, and this comprehensive scoring system can objectively reflect the expression level of NCOR1 protein. To ensure the reliability of the results, the two pathologists interpreted the results separately without knowing the clinical information, and reached a consensus after discussing the cases in which there was disagreement¹⁸.

Functional enrichment analysis of NCOR1-related differentially expressed genes in CcRCC

Entrez IDs were converted to gene symbols using the R package “org.Hs.eg.db”, and functional annotation of differentially expressed genes (DEGs) — including Gene Ontology (GO) enrichment analysis for cellular component (CC), molecular function (MF), and biological process (BP) terms — as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA)^19–22, were conducted with the “ClusterProfiler” R package²³. The analysis utilized the canonical pathways gene set (c2.cp.v7.5.1.symbols.gmt) from the MSigDB database. For GSEA, gene-set permutations (n = 1000) were conducted to calculate the significance. The enrichment score for each gene set was normalized accounting for differences in gene set size. A gene set was considered significantly enriched if its P-value was below 0.05 and its false discovery rate (FDR, calculated using the Benjamini-Hochberg procedure) was below 0.25. The FDR threshold of < 0.25 is recommended by the GSEA methodology for exploratory analyses to reduce the risk of type II errors while still indicating statistical confidence.

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” package²⁴ was used to perform Spearman’s correlation analysis to investigate the association between NCOR1 expression and immune cell infiltration²⁵. 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 χ² 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

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. 1A). 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. 1B, 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. 1D). Compared with adjacent normal kidney tissues, the IHC score of NCOR1 in ccRCC tissues was significantly reduced (P < 0.01, Fig. 1E), 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. 1F). Among these genes, 8 genes were upregulated and 410 genes were downregulated. The heatmap in Fig. 1G illustrates the co-expression of individual genes, highlighting the 10 genes with the most significant differential expression.

Fig. 1.

Expression of NCOR1 in a variety of cancers. (A) TCGA database analysis shows the NCOR1 expression levels in 33 types of cancer tissues and their corresponding adjacent normal tissues. ns, P ≥ 0.05; *P < 0.05; **P < 0.01; ***, P < 0.001. (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 NCOR1 protein expression levels in ccRCC tissues and normal renal tissues as detected by EnVision immunohistochemistry (IHC) technology. (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.

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, 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. 2A). 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, 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. 2B–I). These clusters included cell cycle checkpoint (NES = 3.037, Padj < 0.001, FDR < 0.001), G1 phase and G1-S transition (NES = 3.002, Padj < 0.001, FDR < 0.001), G2-M checkpoints (NES = 2.901, Padj < 0.001, FDR < 0.001), S phase (NES = 2.790, Padj < 0.001, FDR < 0.001), mitotic spindle checkpoint (NES = 2.772, Padj < 0.001, FDR < 0.001), cell cycle (NES = 2.686, Padj < 0.001, FDR < 0.001), G1-S specific transcription (NES = 2.626, Padj < 0.001, FDR < 0.001), and cyclin A/B-regulated G2-M transition (NES = 2.484, Padj < 0.001, FDR < 0.001).

Fig. 2.

Functional enrichment analysis of differentially expressed genes between the NCOR1 expression groups in ccRCC. (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.

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^26,27. 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. 3B), cytotoxic cells (Fig. 3C), Tregs (Fig. 3D), Th17 cells (Fig. 3E), neutrophils (Fig. 3F), and Tcm cells (Fig. 3G) were consistent with the results of the Spearman’s correlation analysis depicted in Fig. 3A. TIGIT and CTLA-4 are significant immune checkpoint proteins associated with immune evasion by tumors²⁸. TP53, a tumor suppressor gene, exhibits low expression in normal cells but high expression in malignant tumors²⁹. 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 tumors³⁰. 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. 4A-D).

Fig. 3.

Correlation analysis of immune cell infiltration and NCOR1 expression in ccRCC. (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.

Fig. 4.

Correlation analysis between the expression levels of NCOR1, TIGIT, CTLA4, TP53 and PTEN in ccRCC. (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.

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.

Fig. 5.

The DNA methylation level of the NCOR1 gene correlates with the prognosis of ccRCC patients.

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

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. 6A). 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. 6B, C).

Fig. 6.

NCOR1 gene alterations are not associated with the survival outcomes in ccRCC. (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.

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. 7A), age (Fig. 7B), T stage (Fig. 7C), pathological stage (Fig. 7D), histological grade (Fig. 7E), OS (Fig. 7F), DSS (Fig. 7G), and progression-free interval (PFI) (Fig. 7H). 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%)

Fig. 7.

NCOR1 expression levels and clinicopathological features of ccRCC patients. (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.

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

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. 8A-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).

Fig. 8.

NCOR1 shows a high prognostic prediction value in ccRCC patients. The 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. P < 0.05 indicates statistical significance. The red and green curves represent high and low NCOR1 expressing ccRCC patients, respectively.

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.

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. 9A). 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. 9B). 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. 9C). 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.

Fig. 9.

The nomogram model with NCOR1 shows a superior diagnostic and prognostic performance in ccRCC. (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.

Prognostic value of NCOR1 in a clinicopathological subgroup of patients with CcRCC

Table 5; 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. 10A). Furthermore, high NCOR1 expression was linked to favorable DSS (Fig. 10B) and PFI (Fig. 10C), 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.

Fig. 10.

Prognostic 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. (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.

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 prognosis³¹. 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 diagnosis³². 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 diagnosis³³. 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 regulation³⁴, Bcl-2 family members, which are associated with apoptosis pathways³⁵, and VEGF, which plays a role in angiogenesis³⁶. 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 phase³⁷. 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 degradation³⁸.

Previous research has indicated that kidney cancer patients often experience immune suppression during tumor progression, which may be ascribed to the high immunogenicity of ccRCC³⁹, 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 cells⁴⁰. 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 cells⁴¹. The Tregs/Th17 cell ratio is associated with various immune disorders and cancers⁴². Our results suggest that decreased NCOR1 expression significantly facilitates immune evasion in ccRCC cells, thereby promoting the growth and progression of ccRCC.

The present study suggested that low NCOR1 expression might activate cell cycle progression by GSEA analysis. Notably, cell cycle-related biomarkers not only directly drive tumor proliferation, but recent studies have also revealed extensive interactions with the tumor immune microenvironment, which is particularly significant in ccRCC^14,43. For example, hyperactivation of key cell cycle regulators such as Cyclin D1/CDK4/6, in addition to promoting the G1/S phase transition, can shape the inhibitory immune microenvironment by regulating the expression of immune checkpoint molecules, such as PD-L1⁴⁴, or by influencing the release of chemokine profiles from tumor cells, which may help the tumor cells escape from immune surveillance^45,46. Similarly, deletion or inactivation of classical tumor suppressors, such as p53 and PTEN, leads to cell cycle checkpoint failure, which is often accompanied by immunosuppression such as inhibition of the interferon signaling pathway, attenuated presentation of tumor antigens, and aggregation of regulatory T cells (Tregs)^47–49. The down-regulation of NCOR1 expression found in this study was associated with cell cycle activation, while its association with immune-infiltrating cells, such as Tregs, Th17 cells, and neutrophils, suggests that NCOR1 may be located at the crossroads of nodes linking cell cycle regulation and immune microenvironment remodeling. This provides new ideas to further explore the complete mechanism by which NCOR1 affects ccRCC progression - i.e., whether it does so simultaneously by driving cycle progression and suppressing anti-tumor immunity.

CTLA-4 is a protein that is crucial in tumor immune evasion²⁸, and CTLA-4 inhibitors can substantially improve the OS rate of patients with melanoma and advanced liver cancer^50–52. TP53 mutations are associated with poor prognosis in various human cancers²⁹, as they suppress anti-tumor immunity and diminish the efficacy of tumor immunotherapy^53,54. 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 responses⁵⁵. 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 cancer⁵⁶, lung cancer⁵⁷, and thyroid cancer⁵⁸. 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 cancer⁵⁹, prostate cancer¹¹, and lung cancer¹². 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.

The present study has some limitations. First, although we revealed significant associations of low NCOR1 expression with cell cycle activation, immune microenvironment alterations, and poor prognosis by bioinformatics analysis and validated them at the tissue level using IHC, current conclusions regarding the specific molecular functional mechanisms of NCOR1 in ccRCC are mainly based on database correlation analysis. For example, the GSEA-suggested enrichment of cell cycle pathways, as well as the correlation with specific immune cell infiltration and checkpoint molecule expression, still need to be verified by direct in vitro and in vivo functional experiments (e.g., knockdown/overexpression, cell cycle assays, co-culture models, and animal experiments) to verify whether and how NCOR1 regulates these processes. Second, this study relied on retrospective data from a public database (TCGA), and although the sample size was large, there is a lack of independent prospective cohorts to further validate the diagnostic or prognostic value of NCOR1. Future studies are needed to clarify the downstream signaling pathways by which NCOR1 affects ccRCC cell proliferation and immune escape through functional experiments, and to collect multicenter clinical samples and serum/urine samples to explore the potential of NCOR1 as a non-invasive liquid biopsy marker in order to advance its translation to clinical applications.

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

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

DEGs

Differentially expressed genes

GO

Gene ontology

CC

Cellular component

MF

Molecular function

BP

Biological process

KEGG

Kyoto Encyclopedia of Genes and Genomes

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

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.

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), 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), Inner Mongolia Medical University Affiliated Hospital Talent Training Project - Sailing Series (Grant No. HH202405), 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), and the Trinity College Students Innovation and Entrepreneurship Cultivation Project of Inner Mongolia Medical University (Grant No. SWYT2020008).

Data availability

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), https://www.kegg.jp/kegg/kegg1.html/(KEGG database).

Declarations

Competing interests

The authors declare no competing interests.

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.