Abstract
Hepatocellular carcinoma (HCC) is the most widespread malignancy in the universe, with low early diagnosis rates and high mortality. Therefore, early detection and treatment are critical to improving patients' life. Anoikis is one of the modes of cell death, and resistance to anoikis arising by aggressive tumor cells has been considered a pivotal element in cancer proliferation, while rarely have studies focused on the relationship between HCC and anoikis.
Methods
Anoikis-related genes were gathered from the GeneCards and MSigDB, and the R software of "limma” and the WGCNA were employed to select anoikis-related differentially expressed genes (ARDEGs). Patients from three independent cohorts (TCGA-LIHC, ICGC, and GSE14520) were classified by Nonnegative Matrix Factorization (NMF) to analyze the overall survival (OS), copy number variation (CNV), tumor microenvironment (TME), and biological characteristics of different HCC clusters. We then rely on the expression of prognostic anoikis-related differentially expressed genes (PARDEGs) to build the signature by the least absolute shrinkage and selection operator (LASSO) regression analysis, then patients were assigned into two risk groups. The study of enrichment pathways, immune microenvironment, clinicopathologic feature stratification, nomogram, tumor mutation burden (TMB), and drug prediction related to the signature was performed. More importantly, the mRNA level of the critical genes was verified at the HCC tissue level.
Results
HCC patients were randomly segmented into four clusters based on the PARDEGs. The result showed that clusterC2 had the worst survival time and clinical performance. Four PARDEGs, including CD24, SKP2, E2F1, and NDRG1, were selected for conducting a risk model. This risk model was significantly validated by different datasets (TCGA-LIHC, ICGC, and GSE14520) to distinguish the survival status of other HCC patients. Analysis such as the receiver operating characteristic (ROC) analyses, concordance index(C-index), and nomogram indicated that the model had excellent sensitivity and specificity. Drug response and immunotherapy also manifested differently in two risk HCC patients.
Conclusion
A model constructed with four PARDEGs helps to improve the detection rate of early HCC, long-term prognostic stratification of HCC patients, and postoperative personalized monitoring and treatment plan development, reflecting the medical concept of early screening, early diagnosis, early and precise therapy of HCC.