Construction and Validation of Two Hepatocellular Carcinoma-Progression Prognostic Scores Based on Gene Set Variation Analysis

Abstract

Background: Liver hepatocellular carcinoma (LIHC) remains a global health challenge with a low early diagnosis rate and high mortality. Therefore, finding new biomarkers for diagnosis and prognosis is still one of the current research priorities.Methods: Based on the variation of gene expression patterns in different stages, the LIHC-development genes (LDGs) were identified by differential expression analysis. Then, prognosis-related LDGs were screened out to construct the LIHC-unfavorable gene set (LUGs) and LIHC-favorable gene set (LFGs). Gene set variation analysis (GSVA) was conducted to build prognostic scoring models based on the LUGs and LFGs. ROC curve analysis and univariate and multivariate Cox regression analysis were carried out to verify the diagnostic and prognostic utility of the two GSVA scores in two independent datasets. Additionally, the key LCGs were identified by the intersection analysis of the PPI network and univariate Cox regression and further evaluated their performance in expression level and prognosis prediction. Single-sample GSEA (ssGSEA) was performed to understand the correlation between the two GSVA enrichment scores and immune activity.Result: With the development of LIHC, 83 LDGs were gradually upregulated and 247 LDGs were gradually downregulated. Combining with LIHC survival analysis, 31 LUGs and 32 LFGs were identified and used to establish the LIHC-unfavorable GSVA score (LUG score) and LIHC-favorable GSVA score (LFG score). ROC curve analysis and univariate/multivariate Cox regression analysis suggested the LUG score and LFG score could be great indicators for the early diagnosis and prognosis prediction. Four genes (ESR1, EHHADH, CYP3A4, and ACADL) were considered as the key LCGs and closely related to good prognosis. The frequency of TP53 mutation and copy number variation (CNV) were high in some LCGs. Low-LFG score patients have active metabolic activity and a more robust immune response. The high-LFG score patients characterized immune activation with the higher infiltration abundance of type I T helper cells, DC, eosinophils, and neutrophils, while the high-LUG score patients characterized immunosuppression with the higher infiltration abundance of type II T helper cells, TRegs, and iDC. The high- and low-LFG score groups differed significantly in immunotherapy response scores, immune checkpoints expression, and IC50 values of common drugs.Conclusion: Overall, the LIHC-progression characteristic genes can be great diagnostic and prognostic signatures and the two GSVA score systems may become promising indices for guiding the tumor treatment of LIHC patients.

Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set

“…Moreover, ACADL has been linked to the advancement of esophageal squamous cell carcinoma and the negative prognosis of affected individuals ( Yu et al, 2018 ). ACADL was identified as a critical gene in the development of liver hepatocellular carcinoma and is strongly associated with favorable prognosis ( He et al, 2022 ). The crucial factor determining the anti-atherogenic phenotype of dKO mice was found to be the dysregulation of ACADL ( Längst et al, 2022 ).…”

Dupilumab Therapy Modulates Circulating Inflammatory Mediators in Patients with Prurigo Nodularis

“…GSVA was conducted using the GSVA package (version 1.50.0), and gene sets were obtained from the Gene Set Enrichment Analysis Molecular Signatures Database. Similar to the analysis of differentially expressed proteins, statistical differences in GSVA enrichment scores between groups were obtained through moderated pairwise 2-tailed t -tests using limma ( He et al, 2022 ). Itch correlation analysis involved a correlation test for repeated measures between Worst Itch Numeric Rating Scale and cytokine plasma expression levels for a total of 9 sample points (3 baseline, 3 after treatment, and 3 healthy controls) using the rmcorr package (version 0.6.0).…”

Machine Learning-Based Identification of B4GALNT1 as a Key Player in Hepatocellular Carcinoma: A Comprehensive Bioinformatics and Structural Analysis

“…In LIHC Supplementary Figure S4(B), no immune cells showed any significant survival analysis accept when cumulative survival analysis was done which in turns demonstrated that immunological infiltrates were strongly associated (p < 0.05) with B4GALNT1. At last, GISTIC 2.0 was used to characterise somatic copy number variations such as deep deletion (-2), arm-level deletion (-1), diploid/normal (0), arm-level gain (1), and strong amplification (2). In LIHC Supplementary Figure S4(C…”

“…The global prevalence of LIHC is rising. LIHC is common in people with cirrhosis and chronic liver disease [2]. LIHC ranks as the sixth most prevalent cancer worldwide and holds the position of the third primary factor leading to cancer-related deaths.…”