## 1 Introduction  
Kidney cancer is one of the deadliest diseases and unfortunately it is hard to detect early through normal clinical means [1]. Despite being one of the top-ten killer-cancers, there is a lack of research effort on kidney cancer. It has been overshadowed by other cancer types in the medical community, which has hindered the development of new techniques to detect and treat it. For decades, patients with kidney cancer have had limited options of treatment beyond surgery, and in most cases, life expectancy is less than one year. Thus, it is crucial to detect the disease early. Besides traditional clinical techniques, the study of various biomarkers brings researchers closer to understanding the onset of kidney cancer, making accurate diagnoses, and removing the ambiguity surrounding this disease. However, even with all the genomic understanding and technological progress, there are many unclear answers and undiscovered paths of research. Researchers need to explore new techniques to detect this disease and diagnose both the stage and sub-type accurately, thus assisting physicians in prescribing the right treatment options to each distinguished case while reducing the undesired harmful side-effects of drugs and increasing the survival rate of patients. Recently, various efforts have been developed to differentiate among sub-types of kidney cancer. One of these promising paths is the analysis of the genetic information of the patient. Clinical diagnosis through miRNA expression has enticed a significant amount of researchers, especially since the technological revolution of miRNA information extraction during the last two decades. One of the foremost sources of data for genetic information of various cancers is furnished by The Cancer Genome Atlas (TCGA), which is a product of the collaboration between the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). This platform has generated comprehensive multi-dimensional maps of the key genomic changes in the 33 types of cancers [2], including kidney cancer. In this study, we focus on kidney cancer sub-type detection and classification in an effort to assist researchers in medicine to address the key points of kidney subtypes and their characteristics. Many studies have shown strong relationships between cancer types and variations in miRNA regulation. In this study, we utilize the variation in miRNAs due to the difference in miRNA up-regulation and down-regulation among the kidney cancer subtypes and consider it as the main attribute to perform this differentiation. With the proposed method, we were able to select high weighted features that led us to obtain 35 miRNAs, which were extracted from a total of 1881 identified by the TCGA dataset for kidney cancer. By performing a deep training procedure, we were able to distinguish the five kidney cancer sub-types in the TCGA and TARGET projects dispensed by the TCGA repository. More specifically, we use Neighborhood Component Analysis (NCA) [3] as a feature selection algorithm to find the most discriminative miRNAs for each subtype. The selected miRNAs are fed to a classifier to determine the exact kidney subtype. The adopted classification method is a type of Recurrent Neural Network, Long Short-Term Memory (LSTM) [4; 5], which is a machine learning tool that deals with sequential and non-sequential data signals. To assess the performance of the proposed method, we adopted the Data Analysis Protocol and the Matthews Correlation Coefficient [6]. It has to be stressed, however, that the effectiveness of the selected miRNA subset for diagnosing specific subtypes of kidney needs to be investigated clinically. The rest of the paper is organized in the following manner: Section 2 discusses the relationship between miRNA-sequence and cancer in general and kidney cancer in particular. Section 3 gives a summary of the tools used in this study, i.e., the Neighborhood Component Analysis procedure and the Long Short-Term Memory algorithm.