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Al B. Benson III, MD, FACP, FASCO
Al B. Benson III, MD, FACP, FASCO
Posted: Friday, September 15, 2023
Autophagy-associated effects are known to be closely related to the development of hepatocellular cancer in the Asian population; however, the specific genes involved are not well identified. In an article published in BMC Genomics, Yusong Zhang, PhD, of the Second Affiliated Hospital of Soochow University, Jiangsu, China, and colleagues discussed the construction of their prognostic model, based on autophagy-related genes in Asian patients with this type of liver cancer. Their findings revealed 13 different autophagy-related genes that appeared to be associated with prognosis in this study population; the investigators believe these genes have the potential to become new therapeutic targets for hepatocellular carcinoma.
“Autophagy is the process of transporting damaged, degenerated, or senescent proteins and organelles from cells to lysosomes for digestion and degradation…[and] plays a double-edged role in tumors…. [O]nce a tumor is formed, cellular autophagy provides rich nutrition to cancer cells and promotes tumor growth,” stated Dr. Zhang and colleagues.
Transcriptome sequencing data and clinical information from 161 Asian patients with hepatocellular carcinoma from The Cancer Genome Atlas database were the focus of this study. A total of 206 autophagy-related genes were then used to perform differential and Cox regression analyses to construct a risk score model. The accuracy of the model was then validated using the Kaplan-Meier survival curve, receiver operating characteristic curve, and univariate and multivariate Cox independent prognostic analyses.
The univariate and multivariate Cox analyses revealed 13 autophagy-related genes that the investigators reported were significantly associated with hepatocellular carcinoma prognosis. Additionally, the survival curves showed that the survival rate of the group with low-risk scores was significantly higher than that of the high-risk group (P < .001). The receiver operating characteristic curves further demonstrated the predictive ability of the model (AUC = .877).
Disclosure: The study authors reported no conflicts of interest.
