Anoikis-Related LncRNA Signature Predicts Prognosis and Is Associated With Immune Infiltration in Hepatocellular Carcinoma

Abstract Anoikis is a term used to describe the programmed cell death that takes place when cells become disconnected from the extracellular matrix[1]. Numerous long non-coding RNAs (lncRNAs) have been found to be linked to anoikis resistance in various tumors, including glioma[2], breast cancer[3], and bladder cancer[4]. There is a lack of studies examining the relationship between ARLs and HCC prognosis. Further research is needed to explore this potential link and understand the role of ARLs in HCC progression. Patients and methods: In this study, we acquired 36 genes associated with anoikis through the GO and GSEA databases, and identified 22 differentially expressed lncrnas that were correlated with these genes based on data from The Cancer Genome Atlas (TCGA). Using Cox regression analyses, we created an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which we then validated in both a testing cohort and a combined cohort composed of data from both cohorts. Then, we collected 8 pairs of liver cancer tissues and adjacent tissues from Affiliated Tumor Hospital of Nantong University. Results: Five ARLs were developed as a risk stratification system to categorize patients into low- and high-risk groups. The high-risk group exhibited a notably lower overall survival (OS) rate in comparison to the low-risk group. The model's predictive performance is supported by the receiver operating characteristic curve, calibration of nomogram, clinical correlation analysis, and clinical decision curve. Furthermore, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations indicated significant discrepancies between the high and low-risk groups. IC50 analysis determined that commonly prescribed drugs were more effective for patients in the high-risk group. Lastly, quantitative real-time PCR validated the expression levels of the five lncRNAs in specific drug-resistant cell lines. In conclusion, our ARlncSig model possesses critical predictive value in the prognosis of HCC patients, and it may provide clinical guidance for personalized immunotherapy.