RESUMEN
Hepatocellular carcinoma (HCC) is a challenging disease to evaluate in terms of prognosis, requiring close attention to the prognosis of HCC patients. Exosomes have been shown to play an important role in HCC development and have significant potential in managing HCC patient prognosis, as they are detectable in patients' blood. By using small extracellular vesicular RNA, liquid biopsies can reflect the underlying physiological and pathological status of the originating cells, providing a valuable assessment of human health. No study has explored the diagnostic value of mRNA expression changes in exosomes for liver cancer. The present study investigated establishing a risk prognosis model based on mRNA expression levels in exosomes from blood samples of liver cancer patients and evaluated its diagnostic and prognostic value, providing new targets for liver cancer detection. We obtained mRNA data from HCC patients and normal controls from the TCGA and exoRBase 2.0 databases and established a risk prognostic assessment model using exosomes-related risk genes selected through prognostic analysis and Lasso Cox analysis. The patients were divided into high-risk and low-risk groups based on median risk score values to validate the independence and evaluability of the risk score. The clinical value of the model was further analyzed using a nomograph model, and the efficacy of immunotherapy and cell-origin types of prognostic risk genes were further assessed in the high- and low-risk groups by immune checkpoint and single-cell sequencing. A total of 44 genes were found to be significantly associated with the prognosis of HCC patients. From this group, we selected six genes (CLEC3B, CYP2C9, GNA14, NQO1, NT5DC2, and S100A9) as exosomal risk genes and used them as a basis for the risk prognosis model. The clinical information of HCC patients from the TCGA and ICGC databases demonstrated that the risk prognostic score of the model established in this study was an independent prognostic factor with good robustness. When pathological stage and risk prognostic score were incorporated into the model to predict clinical outcomes, the nomograph model had the best clinical benefit. Furthermore, immune checkpoint assays and single-cell sequencing analysis suggested that exosomal risk genes were derived from different cell types and that immunotherapy in the high-risk groups could be beneficial. Our study demonstrated that the prognostic scoring model based on exosomal mRNA was highly effective. The six genes selected using the scoring model have been previously reported to be associated with the occurrence and development of liver cancer. However, this study is the first to confirm that these related genes existed in the blood exosomes, which could be used for liquid biopsy of patients with liver cancer, thereby avoiding the need for puncture diagnosis. This approach has a high value in clinical application. Through single-cell sequencing, we found that the six genes in the risk model originate from multiple cell types. This finding suggests that the exosomal characteristic molecules secreted by different types of cells in the microenvironment of liver cancer may serve as diagnostic markers.
RESUMEN
Background: Kidney renal papillary cell carcinoma (KIRP) is a rare malignancy with a very poor prognosis. Anoikis is a specific form of apoptosis involved in carcinogenesis, but the role of anoikis in KIRP has not been explored. Methods: Anoikis-related genes (ARGs) were obtained from the GeneCards database and Harmonizome database and were used to identify different subtypes of KIRP and construct a prognostic model of KIRP. In addition, we also explored the immune microenvironment and enrichment pathways among different subtypes by consensus clustering into different subtypes. Drug sensitivity analysis was used to screen for potential drugs. Finally, we verified the mRNA and protein expression of the independent prognostic gene PLK1 in patient tissues and various cells and further verified the changes in relevant prognostic functions after constructing a PLK1 stable knockdown model using ShRNA. Results: We identified 99 differentially expressed anoikis-related genes (DEGs) associated with KIRP survival, and selected 3 genes from them to construct a prognostic model, which can well predict the prognosis of KIRP patients. Consensus clustering divided KIRP into two subtypes, and there was a significant difference in survival rates between the two subtypes. Immune profiling revealed differing immune statuses between the two subtypes, and functional analysis reveals the differential activity of different functions in different subtypes. Drug sensitivity analysis screened out 15 highly sensitive drugs in the high-risk group and 11 highly sensitive drugs in the low-risk group. Univariate and multivariate Cox regression analysis confirmed that PLK1 was an independent prognostic factor in KIRP, and its mRNA and protein expression levels were consistent with gene differential expression levels, both of which were highly expressed in KIRP. Functional verification of PLK1 in KIRP revealed significant results. Specifically, silencing PLK1 inhibited cell proliferation, clonogenicity, and migration, which indicated that PLK1 plays an important role in the proliferation and migration of KIRP. Conclusion: The prognosis model constructed by ARGs in this study can accurately predict the prognosis of KIRP patients. ARGs, especially PLK1, play an important role in the development of KIRP. This research can help doctors provide individualized treatment plans for KIRP patients and provide researchers with new research ideas.