Introduction

Kidney cancer is one of the most prevalent types of cancer in both males and females. The number of patients with kidney cancer has increased in the past two decades, comprising up to 2–3% of all new occurrences of cancer [1]. Renal cell carcinoma (RCC), is the most frequent type of kidney cancer, accounting for up to 85% of all cases [2]. RCC affects approximately 400000 individuals annually worldwide [3]. It mostly affects males over the age of 60 [4]. Among different pathological subtypes of RCC, kidney renal clear cell carcinoma (KIRC) is the most common subtype, comprising 75% of all renal cell cancer cases [5]. Surgical resection is often the only effective treatment option for KIRC, since it is generally resistant to chemotherapy and radiotherapy [6]. However, even with early surgical intervention, 30% of patients with localized tumors will subsequently show metastasis [7]. Therefore, early identification of KIRC patients with high metastasis risk could be useful for a more accurate prediction of clinical outcome. Furthermore, effective tumor immunotherapy biomarkers will be advantageous to improve the response rate. In addition, determining patient subgroups that could benefit from certain targeted therapies requires urgent investigation of the factors involved in the carcinogenesis and progression of KIRC.

Moreover, omics approaches, encompassing genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the management of different cancer types as well as RCC [8]. Early detection and diagnosis benefit from omics-derived biomarkers, enabling timely intervention for improved outcomes [9]. Fujiwara et al.'s study underscores the significance of omics studies in cancer management, with a focus on early detection and diagnosis through omics-derived biomarkers. They highlighted that various biomolecules, including germline DNA polymorphisms, transcriptomic dysregulations, circulating molecules, and gut microbiota, contribute to predicting hepatocellular carcinoma (HCC) risk, enhancing the potential for accurate early diagnosis and timely intervention [10]. Furthermore, the study by Qu et al. highlights the crucial role of omics studies in managing ccRCC. Through a comprehensive proteogenomic analysis, the study reveals metabolic dysregulation, an amplified immune response, and molecular subtypes in ccRCC [11]. The identification of potential markers and drug targets emphasizes how omics approaches provide insights into disease complexity, enabling personalized treatment strategies and improved patient outcomes. In sum, omics transforms RCC management, driving us toward personalized therapeutic approaches, early interventions, and improved patient outcomes [12].

Major cellular functions, such as cell differentiation, critical cellular signaling pathways, and cell metabolism are partly regulated at the post-transcriptional level through biochemical modifications of RNA [13]. To date, more than 170 post-transcriptional biochemical modifications of RNA have been reported in noncoding RNAs and mRNAs, generating functional differences [14]. The most common types of such alterations are N1-methyladenosine (m1A) [15], N6-methyladenosine (m6A) [16], pseudouridine (Ψ) [17], and 5-methylcytosine (m5C) [18] modifications. m1A modification is a type of dynamic reversible methylation at the N1 position of adenosine in mammalian cells, contributing to RNA secondary structure stabilization and alteration in protein − RNA binding interactions [55].

In summation, our findings seamlessly align with the narrative of RCC as an immune-rich tumor type, characterized by complex interactions between immune cells, metabolic pathways, and the tumor microenvironment. This intricate interplay not only shapes disease progression, but also holds the potential to guide the development of targeted therapeutic strategies.

While our study provides valuable insights into the role of m1A RNA modification patterns in KIRC, there are certain limitations that should be acknowledged. First, our study is based on the retrospective data analysis, which might introduce biases and confounding factors inherent to this type of research. Moreover, the interplay between m1A regulators and other molecular pathways in the complex tumor microenvironment warrants deeper investigation. In addition, our study focused on a specific set of m1A-related regulatory genes; exploring a broader set of regulators and their interactions could provide a more comprehensive understanding. Furthermore, the immune landscape and tumor microenvironment are highly dynamic and subject to variation over time, which might affect the stability of identified patterns and correlations. Last but not least, while our findings provide potential avenues for prognostic prediction and personalized treatment strategies, validation on larger and independent cohorts is essential for robust clinical translation.

Conclusion

In summary, the present research identifies m1A regulators in KIRC across numerous aspects and substantiates their significance in determining prognosis and immune performance. To our knowledge, the present work is the first to report the complex functions and wide-ranging interconnections of ten different types of m1A-related RNA modifications in KIRC. We identified three different RNA modification patterns, their underlying biological pathways, their correlations with clinicopathological features, and their potential prognostic values in the KIRC patients. This work emphasizes the importance of ten different RNA modifications in KIRC and provides a novel insight for future research, herein.