Title:Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug
Sensitivity in Hepatocellular Carcinoma
Volume: 19
Author(s): Peng Liu, Yan Zhou, Shun Zeng and Xiangjuan Chen*
Affiliation:
- Department of Obstetrics and Gynecology & Carson International Cancer Research Center, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Xueyuan Road 1098, 518055 Shenzhen, China
Keywords:
Bioinformatic, autophagy-related gene, immune status, drug, hepatocellular carcinoma, neoplasm.
Abstract: Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies
in the world, but molecular complexity and tumor heterogeneity make predictive models
for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important
in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a
novel signature for individual prognosis prediction in patients with HCC.
Methods: Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were
screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were
performed to determine ARGs related to survival in HCC. An autophagy-based signature was
constructed using LASSO, and its correlation with the prognosis and the immune infiltration of
HCC patients was explored.
Results: In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets.
Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory
roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in
the context of cancer and neurological diseases. Based on PPI Network, we identified several
hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD,
SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the
TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival,
respectively. Cox regression analysis showed that the five-gene signature is an independent
and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of
immune cell infiltration and exhibited a strong correlation with the immune microenvironment
and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators
involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a
crucial role in chemotherapy drug sensitivity and resistance.
Conclusion: We introduce here a novel ARG-based predictive feature for HCC patients. Effective
use of this signature will aid in determining a patient's prognosis and may lead to novel approaches
to clinical decision-making and therapy.
