1. Introduction

Kidney cancer, also known as renal cancer, is a formidable health challenge with significant morbidity and mortality worldwide [1]. It originates in the kidneys, organs essential for filtering waste from the blood and producing urine. Among its types, renal cell carcinoma (RCC) is the most common, accounting for approximately 90% of cases [2]. Risk factors include smoking, obesity, hypertension, and certain genetic conditions, underscoring the complex interplay between lifestyle, environment, and genetics in its etiology [3]. Recent years have witnessed remarkable advancements in understanding the molecular and genetic landscapes of kidney cancer. These insights have paved the way for targeted therapies and immunotherapies, revolutionizing the treatment paradigm and offering new hope for patients [4]. Despite these advances, kidney cancer remains a critical public health issue, with ongoing research focused on unraveling its complexities to improve prevention, diagnosis, and therapy [5].

Histones are fundamental proteins around which DNA is wrapped, forming the basic unit of DNA packaging in eukaryotic cells known as nucleosomes [6]. This structural role is pivotal not only for compacting DNA but also for regulating gene expression. Alterations in histone modification patterns can profoundly influence chromatin structure and function, leading to aberrant gene expression and, consequently, various diseases, including cancer [7]. Kidney cancer, a major health concern worldwide, has been increasingly studied in the context of histone modifications, revealing a complex relationship between epigenetic alterations and tumorigenesis [8].

RCC, the most prevalent form of kidney cancer, exhibits distinct histone modification patterns that contribute to its development, progression, and response to therapy [9]. These modifications include methylation, acetylation, and phosphorylation, among others, which can either activate oncogenes or silence tumor suppressor genes [10]. The dysregulation of histone-modifying enzymes, such as histone deacetylases (HDACs) and histone methyltransferases (HMTs), has been identified in RCC, suggesting their potential as therapeutic targets [11].

The interplay between histone modifications and kidney cancer opens new avenues for research and therapy. Understanding how these epigenetic alterations influence renal tumorigenesis can lead to the development of novel diagnostic biomarkers and targeted treatments. For instance, inhibitors targeting HDACs have shown promise in preclinical models of RCC, offering insights into overcoming resistance to conventional therapies.

2. Method

3. Results

3.1. Elucidation of HRG Dysregulation and Prognostic Significance in ccRCC: A TCGA Data-Driven Approach

Our investigation into ccRCC through TCGA has specifically focused on the expression and prognostic relevance of HRGs. The volcanic plot (Figure 1(a)) pinpointed a subset of these genes, which were notably dysregulated, as evidenced by significant log2 fold changes juxtaposed against their statistical significance, drawing attention to histone modifiers like PRDM16 and UHRF1. The intersection of DEHRGs with those holding prognostic value was depicted in a Venn diagram (Figure 1(b)), identifying 31 key genes with potential dual roles in ccRCC pathogenesis and clinical prognosis. A detailed network of protein–protein interactions among HRGs (Figure 1(c)) demonstrated a dense and intricate network, indicative of a robust epigenetic regulatory mechanism potentially disrupted in ccRCC. The heatmap in Figure 1(d) presented a striking visual contrast in the expression levels of these genes between normal and tumor samples, underscoring the distinct epigenetic landscapes that define ccRCC. Notably, network analysis (Figure 1(e)) revealed that several HRGs act as either risk factors or favorable prognostic indicators. The network highlighted significant positive and negative correlations with patient outcomes, emphasizing the influence of epigenetic modulation on ccRCC progression. Chromosomal mapping of CNVs (Figure 1(f)) brought to light the genomic alterations affecting HRGs, indicating possible areas of epigenetic deregulation through gains and losses in gene copy numbers. The bar chart quantifying CNVs (Figure 1(g)) provided evidence of the extent of genomic instability affecting these epigenetic regulators in ccRCC. Mutation spectrum analysis (Figure 1(h)) cataloged the mutational burden borne by HRGs, further illustrating the genetic diversity and complexity within ccRCC and potentially linking these mutations to aberrant epigenetic regulation.

3.2. Cluster-Based Stratification of ccRCC Patients Using HRG Expression Profiles: A Prognostic and Pathway Analysis Approach

Subsequent analysis further stratified ccRCC patients using expression profiles of HRGs, as illustrated in Figure 2. The consensus matrix for k = 2 (Figure 2(a)) indicated two distinct clusters, A and B, suggesting robust partitioning of the patient cohort based on gene expression similarity. The consensus CDF graph (Figure 2(b)) supported this two-cluster solution as an optimal stratification method, showing a marked inflection corresponding to the chosen number of clusters. PCA (Figure 2(c)) visualized the segregation of the cohort into clusters A and B, with clear differentiation between the two groups along the principal components, accounting for a significant portion of the data variance. Survival analysis, depicted by Kaplan–Meier curves (Figure 2(d)), demonstrated a significant difference in survival probability between the clusters (p < 0.001), with Cluster A associated with a better prognosis compared to Cluster B, suggesting that HRG expression can stratify patients by survival outcomes. The gene expression heatmap (Figure 2(e)) presented the expression patterns of HRGs across various stages, grades, and demographic factors, with the cluster assignment denoted by the colored bar. The differential expression observed within these clusters emphasizes the potential of HRGs as biomarkers for tumor classification and prognosis. An integrated heatmap and pathway enrichment analysis (Figure 2(f)) correlated gene expression data with KEGG pathways. The hierarchical clustering on the left side of the heatmap grouped samples by expression patterns, while the right side displayed pathway enrichment scores, revealing metabolic and biosynthetic pathways potentially affected by HRG dysregulation in ccRCC.

3.3. Differential Immune Landscape and MFs in ccRCC Based on HRG Expression Clusters

In our extended analysis depicted in Figure 3, the immune landscape associated with the HRG clusters A and B in ccRCC was dissected. We observed significant differences in immune cell infiltration levels between the two clusters (Figure 3(a)), with Cluster B generally exhibiting higher infiltration rates of various immune cell types. This suggests a more immunologically active microenvironment in Cluster B. The expression of major histocompatibility complex (MHC) molecules and immune checkpoint genes (Figure 3(b)) differed between the clusters, pointing toward a differential capacity for antigen presentation and immune evasion, which could have implications for immunotherapy responsiveness. In the comparative gene expression analysis (Figure 3(c)), genes implicated in immune modulation and tumor progression showed distinct expression patterns between clusters A and B, marked by statistical significance. This reinforces the notion of a unique immune and molecular signature associated with each HRG cluster. Further dissecting the tumor microenvironment, violin plots (Figure 3(d)) revealed significant disparities in immune and stromal scores, as well as in ESTIMATE scores and tumor purity between the clusters, illustrating the varying degrees of immune and stromal cell infiltration that contribute to the ccRCC tumor milieu. The network plots for functional and pathway enrichment analysis (Figure 3(e)) encapsulated the diversity in biological processes (BPs), MFs, cellular components (CCs), and KEGG pathways enriched within each cluster. The enriched functions in Cluster B, for example, included several pathways pertinent to immune response, metabolism, and potentially oncogenic processes, reflecting a more complex biological behavior influenced by HRG expression profiles.

3.4. Prognostic Stratification of ccRCC Using HRG Expression: A Risk Assessment and Survival Analysis

A multivariable Cox regression model revealed several genes with significant coefficients, indicating their potential as prognostic markers (Figure 4(a)). Kaplan–Meier analysis stratified patients into high- and low-risk groups, demonstrating a stark contrast in survival outcomes, with the high-risk group exhibiting substantially reduced survival (p < 0.0001) (Figure 4(b)). PCA underscored the robustness of this risk stratification, as patients grouped distinctly into high- and low-risk categories along the principal component axes (Figure 4(c)). The diagnostic utility of the histone-related risk score was confirmed via ROC curve analysis, showing excellent predictive performance (Figure 4(d)). The risk score distribution across patients was shown above a survival scatter plot, indicating a correlation between higher risk scores, increased mortality, and HRG expression patterns (Figure 4(e)). The distribution of patient demographics and clinical features further contextualized the risk profiles, as depicted in the accompanying bar plot (Figure 4(f)). Detailed analyses of age, gender, grade, and stage distributions within each risk category provided additional clinical insights, with higher proportions of advanced stage and grade observed in the high-risk group (Figures 4(g), 4(h), 4(i), and 4(j)).

3.5. Development and Validation of a Nomogram Integrating HRG Risk Score With Clinical Factors for Predicting Survival in ccRCC

The forest plots in Figures 5(a) and 5(b) reveal that grade and stage, along with the risk score, are significant prognostic factors, whereas gender is not a significant factor in this context. The nomogram in Figure 5(c) provides clinicians with a quantifiable tool to predict survival, combining key variables such as age, grade, stage, and the HRG risk score into a comprehensive model. Each variable is proportionally weighted, allowing for an individualized risk assessment. Calibration plots (Figures 5(d), 5(e), and 5(f)) demonstrate the nomogram’s predictive performance, showing concordance between the predicted and observed outcomes at 1, 3, and 5 years, suggesting its reliability in clinical prognostication. The decision curve analyses in Figures 5(g), 5(h), and 5(i) underscore the clinical benefit of the nomogram. These analyses indicate that using the nomogram to guide decision making confers a greater net benefit across a range of threshold probabilities compared to treating all or no patients as high risk.

3.6. Survival Prognostication in ccRCC: Integration of HRG Expression and Clinicopathological Factors

Kaplan–Meier analysis (Figure 6(a)) reveals that patients with a high-risk gene expression profile exhibit significantly reduced survival rates, underscoring the prognostic importance of epigenetic modifications in ccRCC. PCA clustering (Figure 6(b)) confirms the ability of these gene expression profiles to stratify patients into discrete risk categories, suggesting potential underlying biological differences. The ROC analysis (Figure 6(c)) validates the histone-related risk score as an effective tool for predicting survival, with robust AUC values. The risk score distribution (Figure 6(d)) further associates higher scores with decreased survival, and the heatmap shows the gene expression intensities across the patient cohort, aligning with risk and survival status. Multivariable Cox regression (Figures 6(e) and 6(f)) identifies the T, N, and M stages, along with the risk score, as significant independent predictors of survival, demonstrating the combinatorial impact of tumor pathology and gene expression on patient outcomes. Subgroup analyses (Figures 6(g), 6(h), 6(i), 6(j), 6(k), and 6(l)) explore the relationship between the risk score and clinical variables such as age, sex, and TNM classification, highlighting how these factors are variably associated with the risk of poor outcomes in ccRCC.

3.7. Implications of HRG Expression Risk Scores on Survival and Immunotherapy Response in ccRCC

The risk stratification is significantly correlated with both overall and disease-free survival (Figures 7(a), 7(b), 7(c), and 7(d)), indicating that patients in the high-risk group are more likely to have adverse outcomes. Moreover, expression levels of immune checkpoint markers such as PD-1, PD-L1, and CTLA-4 are elevated in the high-risk group (Figures 7(e), 7(f), and 7(g)), implying a potential link to immune escape mechanisms that may contribute to the observed survival differences. TIDE scores, predictive of immune evasion, were found to be higher in the high-risk group (Figure 7(h)), reinforcing the notion that patients with higher histone-related risk scores might be less likely to benefit from immune-mediated tumor clearance. Analysis of external validation cohorts revealed that the high-risk group had a lower proportion of response to therapy (Figures 7(i) and 7(k)), while risk scores were also significantly associated with treatment response (Figures 7(j) and 7(l)), suggesting that the histone-related risk score may serve as a predictive marker for immunotherapy efficacy.

3.8. Deciphering the Impact of HRG Expression on Immune Landscape Dynamics in ccRCC: A Comprehensive Risk Score Analysis

Specifically, Figure 8(a) maps out the association between the risk score and the infiltration levels of various immune cells, uncovering a nuanced relationship that suggests the risk score’s potential as a predictor of the immune landscape’s complexity and diversity in ccRCC. In Figure 8(b), the focus shifts to the expression levels of pivotal immune checkpoint genes, including PD-1, PD-L1, and CTLA-4, against the backdrop of the risk score. This analysis highlights the profound impact HRG dysregulation may have on the mechanisms of immune evasion, pointing to a direct link between epigenetic alterations and immune responsiveness. Lastly, Figure 8(c) introduces an innovative correlation scoring approach to quantify the immune-related functional capabilities of the tumor, offering a unique lens through which the influence of HRG expression on the immune system’s operational dynamics can be assessed. Together, these findings underscore the pivotal role of epigenetic modulation in orchestrating the immune landscape of ccRCC, advocating for the integration of the HRG expression risk score into the prognostic and therapeutic stratification of patients.

3.9. Correlational Analysis of HRG Expression Risk Scores With Drug Sensitivity in ccRCC

In Figure 9, we present a comprehensive analysis of the correlation between HRG expression risk scores and drug sensitivity in ccRCC. Figures 9(a), 9(b), 9(c), 9(d) showcase boxplots correlating low and high-risk categories with the sensitivity to various drugs, as indicated by the half-maximal inhibitory concentration (IC50) values. Figure 9(a) reveals a statistically significant difference (p < 2.22e − 16) in the sensitivity to sunitinib, with the low-risk group exhibiting increased sensitivity. Conversely, Figures 9(b), 9(c), 9(d) illustrate a nonstatistically significant trend toward differential drug sensitivity for axitinib, pazopanib, and sorafenib, although a potential inclination for the high-risk group to be less sensitive can be observed. Figures 9(e), 9(f), 9(g), 9(h) further delineate these relationships via scatter plots with regression lines, showing a strong negative correlation between risk scores and sunitinib sensitivity (Figure 9(e)), as evidenced by a correlation coefficient (R² = 0.5) with high statistical significance (p < 2.22e − 16). The correlations for axitinib (Figure 9(f)), pazopanib (Figure 9(g)), and sorafenib (Figure 9(h)) are weaker, as indicated by their respective R² values, yet they suggest a possible trend that higher risk scores may be associated with decreased sensitivity to these treatments. Collectively, these results highlight the potential of HRG expression risk scores to serve as predictive markers for drug response in ccRCC, warranting further investigation into their role in personalized treatment strategies.

3.10. Evaluating the Prognostic Landscape: The Interplay of HRG Mutations, TMB, and Immune Profiles in ccRCC

Figure 10 presents a multifaceted evaluation of ccRCC, correlating HRG expression risk with genetic alterations, TMB, and immune profiles. Figure 10(a) demonstrates a significant difference in TMB between low-risk and high-risk groups, with the high-risk group exhibiting a higher TMB (p = 0.022). This relationship is further substantiated in Figure 10(b), which illustrates a positive correlation between the risk score and TMB (R² = 0.14, p = 0.01). Survival analysis in Figure 10(c) shows a distinct separation in survival probabilities between high and low TMB groups within high-risk patients, indicating that TMB may modulate survival outcomes in the context of high HRG expression risk (p = 0.001). This trend persists in Figure 10(d), which stratifies the survival analysis by both TMB and risk score, revealing a complex interaction where high TMB correlates with poorer survival outcomes within the high-risk group (p < 0.001). Figures 10(e) and 10(f) detail the mutational landscape in ccRCC, with oncoprints displaying the distribution and frequency of mutations across HRGs in low and high-risk groups. These oncoprints show that certain key genes, such as VHL and PBRM1, harbor mutations in a significant proportion of samples, hinting at their potential involvement in the disease pathology and progression. Additionally, immune and stromal scores, quantified using ESTIMATE, and tumor purity are assessed in Figures 10(g), 10(h), 10(i), 10(j). The high-risk group is characterized by lower immune and stromal scores, and a higher ESTIMATE score, indicating a less immune-infiltrated and more stroma-rich tumor microenvironment, which is also reflected in the higher tumor purity observed in the high-risk group. These findings suggest that the HRG expression risk score is not only a reflection of the genetic instability but also a potential indicator of the tumor microenvironment’s composition, which could have significant implications for patient prognosis and treatment strategies.

3.11. HRG Dysregulation in ccRCC: Expression Analysis and Clinical Correlation

The comprehensive analysis of HRG expression in ccRCC compared to adjacent normal renal tissue revealed significant dysregulation of key genes implicated in epigenetic modification. The boxplot series in Figure 11 (top row) (Figures 11(a), 11(b), 11(c), 11(d), 11(e)), and 11(f) illustrates marked upregulation of HJURP, KAT2A, PER2, PRDM16, PRDT1, and TWIST1 in tumor tissues. Each gene demonstrated statistically significant higher expression levels in tumor samples compared to normal (p < 0.0001), underscoring their potential involvement in oncogenesis and tumor progression in ccRCC. In a paired differential expression analysis (bottom Figure 11 bottom row (g–l)), we quantitatively compared the expression of these genes between matched normal and tumor tissues from the same patients. HJURP showed the most pronounced increase in expression in tumor tissue (p = 3.5e − 10), followed by PER2 (p = 1.4e − 10), PRDM16 (p = 3.9e − 13), KAT2A (1.4e-10), PRDT1 (p = 1.2e − 05), and TWIST1, which, while exhibiting less variability, still showed significant upregulation in tumor tissue (p = 0.012).

4. Discussion

In our comprehensive investigation of ccRCC utilizing TCGA data, we have elucidated the significant role of HRG dysregulation in the pathogenesis and prognosis of ccRCC. Our findings underscore the complexity of epigenetic modulation in cancer and its profound impact on tumor behavior and patient outcomes. The differential expression analysis of HRGs revealed a distinct pattern of dysregulation, with key genes such as PRDM16 and UHRF1 emerging as potential epigenetic modifiers in ccRCC. This dysregulation points to the disruption of epigenetic landscapes, which likely contributes to tumor development and progression. Our study adds to the growing body of evidence that aberrant histone modification plays a critical role in cancer, aligning with previous research highlighting its impact in various malignancies. Cluster-based stratification, utilizing HRG expression profiles, identified two distinct patient groups with significantly different survival outcomes. This stratification not only provides insight into the heterogeneity of ccRCC but also underscores the potential of epigenetic markers in improving prognostic accuracy. The association between HRG expression patterns and patient survival further reinforces the need for personalized medicine approaches in ccRCC treatment strategies. Our analysis of the immune landscape revealed significant differences in immune cell infiltration and immune checkpoint gene expression between the clusters. These findings suggest that HRG dysregulation could influence the tumor microenvironment, potentially affecting the response to immunotherapy. The varying immune landscapes between patient clusters highlight the intricate interplay between the epigenome and the immune system in ccRCC, paving the way for future investigations into epigenetic-based immunotherapeutic approaches. The development and validation of a HRG expression risk score further demonstrated its utility in prognostic stratification. This risk score, in conjunction with clinical factors, was integrated into a nomogram that reliably predicts survival outcomes, offering a valuable tool for clinical decision making. Moreover, the correlation between HRG expression risk scores and drug sensitivity underscores the potential of these scores in guiding personalized treatment plans, particularly in the selection of targeted therapies. Notably, our study revealed a complex relationship between HRG mutations, TMB, and the immune profiles of ccRCC patients. The higher TMB observed in the high-risk group, coupled with a distinct immune and stromal landscape, suggests that patients with higher HRG expression risk scores may exhibit a more aggressive disease phenotype and a different response to treatment.

5. Conclusion

In conclusion, our findings highlight the critical role of HRG dysregulation in ccRCC and its potential as a prognostic marker and therapeutic target. The integration of epigenetic information with traditional clinical factors holds promise for enhancing patient stratification, prognostication, and personalized treatment strategies. Future studies should focus on unraveling the mechanistic links between histone modifications and tumor biology, which could open new avenues for therapeutic intervention in ccRCC and other cancers characterized by epigenetic dysregulation.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This work was sponsored by the corresponding author.