After integrating normal and tumor samples of tissues and removing batch effects, we used the FindMarkers function in Seurat to calculate up-regulated and down-regulated rsRNA in tumors. Thresholds were set for changes in logFC over a 1-fold range with p-values less than 0.05, and a volcano plot of differential rsRNA across tissues was drawn. Selecting the top5 differential rsRNA to draw a heat map can observe obvious differential expression. In addition to clustering heatmaps, we also used unsupervised clustering methods to perform dimensionality reduction visualization using t-SNE and uMAP. The rsRNA used were the remaining rsRNA after filtering out low expression levels. It can be seen that rsRNA can divide tissue samples of two states into two distinct sets. The results of pathway enrichment analysis showed that these abnormally expressed rsRNA in the tumor state were indeed related to the occurrence and development of tumors.The differentially expressed rsRNA in this tumor tissue calculated based on the above parameters can be used for functional pathway prediction.