ABSTRACT
OBJECTIVE: The 5-year survival rate of early-stage non-small cell lung cancer (NSCLC) is still not optimistic. We aimed to construct prognostic tools using clinicopathological (CP) and serum 8-miRNA panel to predict the risk of overall survival (OS) in early-stage NSCLC. MATERIALS AND METHODS: A total of 799 patients with early-stage NSCLC, treated between April 2008 and September 2019, were included in this study. A sub-group of patients with serum samples, 280, were analyzed for miRNA profiling. The primary endpoint of the study was OS. The CP panel for prognosis was developed using multivariate and forward stepwise selection analyses. The serum 8-miRNA panel was developed using the miRNAs that were significant for prognosis, screened using real-time quantitative PCR (qPCR) followed by differential, univariate and Cox regression analyses. The combined model was developed using CP panel and serum 8-miRNA panel. The predictive performance of the panels and the combined model was evaluated using the area under curve (AUC) values of receiver operating characteristics (ROC) curves and Kaplan-Meier survival analysis. RESULT: The prognostic panels and the combined model (comprising CP panel and serum 8-miRNA panel) was used to classify the patients into high-risk and low-risk groups. The OS rates of these two groups were significantly different (P<0.05). The two panels had higher AUC than the two guidelines, and the combined model had the highest AUC. The AUC of the combined model (AUC=0.788; 95 %CI 0.706-0.871) was better than that of the National Comprehensive Cancer Network (NCCN) guideline (AUC=0.601; 95 %CI 0.505-0.697) and Chinese Society of Clinical Oncology (CSCO) guideline (AUC=0.614; 95 %CI 0.520-0.708). CONCLUSION: The combined model based on CP panel and serum 8-miRNA panel allows better prognostic risk stratification of patients with early-stage NSCLC to predict risk of OS.
ABSTRACT
BACKGROUND: Cancer-derived exosomes contribute significantly in intracellular communication, particularly during tumorigenesis. Here, we aimed to identify two immune-related ovarian cancer-derived exosomes (IOCEs) subgroups in ovarian cancer (OC) and establish a prognostic model for OC patients based on immune-related IOCEs. METHODS: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq data, as well as clinical and prognostic information. Consensus clustering analysis was performed to identify two IOCEs-associated subgroups. Kaplan-Meier analysis was used to compare the overall survival (OS) between IOCEs-high and IOCEs-low subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the mechanisms and biological effects of differentially expressed genes (DEGs) between the two subtypes. Besides, an IOCE-related prognostic model of OC was constructed by Lasso regression analysis, and the signature was validated using GSE140082 as the validation set. RESULTS: In total, we obtained 21 differentially expressed IOCEs in OC, and identified two IOCE-associated subgroups by consensus clustering. IOCE-low subgroup showed a favorable prognosis while IOCE-high subgroup had a higher level of immune cell infiltration and immune response. GSEA showed that pathways in cancer and immune response were mainly enriched in IOCE-high subgroup. Thus, IOCE-high subgroup may benefit more in immunotherapy treatment. In addition, we constructed a risk model based on nine IOCE-associated genes (CLDN4, AKT2, CSPG5, ALDOC, LTA4H, PSMA2, PSMA5, TCIRG1, ANO6). CONCLUSION: We developed a novel stratification system for OV based on IOCE signature, which could be used to estimate the prognosis as well as immunotherapy for OC patient.