Clear cell renal cell carcinoma (ccRCC), represents the predominant subset of renal cell carcinoma (RCC) [1]. Epidemiological studies predict a substantial increase in the incidence of RCC in the coming years [2]. Although surgery is an effective treatment for localized ccRCC, recurrence and metastasis occur in approximately 30 % of cases following surgical intervention [3]. The TNM staging system serves as the primary method for risk stratification in ccRCC patients. However, due to tumor heterogeneity, the TNM system alone is insufficient for accurately predicting prognosis and guiding treatment decisions in ccRCC patients [4]. The development of a more accurate and robust prognostic stratification system is crucial for improving patient outcomes in ccRCC. Advancements in multi-omics sequencing and computational algorithms have enabled the creation of precise risk prediction models based on key molecular markers, facilitating accurate prognosis assessment and treatment guidance.

Hypertension is known to be an important factor in the development of RCC [5,6]. Elevated systolic and diastolic blood pressures are associated with an increased incidence of RCC, with hypertensive individuals exhibiting a two-to threefold higher risk [7]. Additionally, the relationship between hypertension and RCC has been examined using Mendelian randomization, which produced similar results [5,8]. The mechanisms underlying hypertension-induced carcinogenesis are complex and multifaceted. Hypertension is hypothesized to facilitate tumor progression through chronic inflammation, hypoxia, endothelial dysfunction, and oxidative stress [9,10]. Despite these insights, research on this topic remains limited, highlighting the need for further investigation.

However, there is limited research on whether hypertension is a risk factor for ccRCC. However, research investigating the role of hypertension as a risk factor for ccRCC remains limited. We hypothesized that hypertension contributes to ccRCC risk and employed Mendelian randomization (MR) to elucidate this causal relationship. Additionally, we proposed that hypertension influences ccRCC progression via hypertension-related genes (HRGs) and may modulate responses to immunotherapy.

This study investigated HRGs in ccRCC using single-cell and multi-omics data. Hypertension-related signatures (HRS) for ccRCC were constructed using machine learning, and their potential utility in immunotherapy was assessed. Finally, the role of the identified genes in ccRCC was analyzed to provide evidence clarifying the relationship between hypertension and ccRCC.