Clear cell renal cell carcinoma (ccRCC) is the most aggressive and deadly cancer of the urinary system and is regulated by multiple signaling pathways. However, the specific molecular mechanisms underlying ccRCC have not been fully studied or demonstrated. This study aimed to elucidate the function of lysosomal-associated transmembrane protein 5 (LAPTM5) in ccRCC cell lines and animal models and determine the potential underlying mechanisms. Our results demonstrated that LAPTM5 expression in patients with ccRCC was significantly higher in the tumor group than that in the adjacent nontumor group. Moreover, LAPTM5 promoted proliferation, migration, and invasion of ccRCC cells through the gain and loss of the function of LAPTM5 in 786-0 and Caki-1 cell lines. Similar results regarding LAPTM5 overexpression were obtained in BALB/c nude mice. In addition, LAPTM5 activated the Jun N-terminal kinase (JNK)/p38 signaling cascade by interacting with Ras-related C3 botulinum toxin substrate 1 (RAC1). Treatment with an RAC1 inhibitor eliminated the effects of LAPTM5 in ccRCC. In conclusion, these results indicate that LAPTM5 may be a new therapeutic target for ccRCC via activation of the RAC1-JNK/p38 axis.

Prostate cancer (PCa) is a malignant tumor of the male reproductive system, and its incidence has increased significantly in recent years. This study aimed to further identify candidate biomarkers with prognostic and diagnostic significance by integrating gene expression and DNA methylation data from PCa patients through association analysis. To this end, this paper proposes a sparse partial least squares regression algorithm based on hypergraph regularization (HR-SPLS) by integrating and clustering two kinds of data. Next, module 2, with the most significant weight, was selected for further analysis according to the weight of each module related to DNA methylation and mRNAs. Based on the DNA methylation sites in module 2, this paper uses multiple machine learning methods to construct a PCa diagnosis-related model of 10-DNA methylation sites. The results of ROC analysis showed that the DNA methylation-related diagnostic model we constructed could diagnose PCa patients with high accuracy. Subsequently, based on the mRNAs in module 2, we constructed a prognostic model for 7-mRNAs (MYH11, ACTG2, DDR2, CDC42EP3, MARCKSL1, LMOD1, and MYLK) using multivariate Cox regression analysis. The prognostic model could predict the disease free survival of PCa patients with moderate to high accuracy (AUC=0.761). In addition, GSEA and immune analysis indicated that the prognosis of patients in the risk group might be related to immune cell infiltration. Our findings may provide new methods and insights for identifying disease-related biomarkers by integrating DNA methylation and gene expression data.