No abstract

Low expressions of PRKACB are related to the occurrence of various human malignancies. However, the prognostic value of PRKACB expression in colorectal cancer (CRC) patients remains controversial. In this analysis, PRKACB expression in CRC tumors was evaluated across the GEO, TCGA, and Oncomine databases, and a PRKACB survival analysis was performed based on the TCGA profile. We detected PRKACB in 7 GEO series (GSE110225, GSE32323, GSE44076, GSE9348, GSE41328, GSE21510, GSE68468) and TCGA spectra (all P <0.05). A meta-analysis performed in the Oncomine database revealed that PRKACB was significantly up-regulated in neoplastic tissues compared to normal tissues (all P <0.05). A Kaplan-Meier analysis demonstrated that lower PRKACB expression in tumors was significantly associated with poorer overall survival (OS) in patients with CRC (P <0.05). A subgroup analysis showed that low expression of PRKACB correlated with poor 1-, 3-, and 5-year OS (all P <0.05). Furthermore, in males (P = 0.0083), whites (P = 0.0463), and non-mucinous adenocarcinoma patients (P = 0.0108), the down-regulation of PRKACB expression was more significant for the OS prognostic value. Conclusion: PRKACB is down-regulated in tumors and associated with worsening OS in CRC patients.

Autophagy plays a complex role in tumors, sometimes promoting cancer cell survival and sometimes inducing apoptosis, and its role in the colorectal tumor microenvironment is controversial. The purpose of this study was to investigate the prognostic value of autophagy-related genes (ARGs) in colorectal cancer. We identified 37 differentially expressed autophagy-related genes by collecting TCGA colorectal tumor transcriptome data. A single-factor COX regression equation was used to identify 11 key prognostic genes, and a prognostic risk prediction model was constructed based on multifactor COX analysis. We classified patients into high and low risk groups according to prognostic risk parameters (p &lt;0.001) and determined the prognostic value they possessed by survival analysis and the receiver operating characteristic (ROC) curve in the training and test sets of internal tests. In a multifactorial independent prognostic analysis, this risk value could be used as an independent prognostic indicator (HR=1.167, 95% CI=1.078-1.264, P&lt;0.001) and was a robust predictor without any staging interference. To make it more applicable to clinical procedures, we constructed nomogram based on risk parameters and parameters of key clinical characteristics. The area under ROC curve for 3-year and 5-year survival rates were 0.735 and 0.718, respectively. These will better enable us to monitor patient prognosis, thus improve patient outcomes.