Identification of Potential Biomarkers From Hepatocellular Carcinoma With MT1 Deletion

Bioinformatics combined with network pharmacology and experimental validation to identify key biomarkers of hepatocellular carcinoma and corresponding compounds in Radix Astragali and Pueraria Mirifica

The occurrence and death rates of primary hepatocellular carcinoma (HCC) are increasing, and there remains a shortage of effective oral medications with minimal side effects. We aim to identify potential biomarkers and compounds from Radix Astragali (RA) and Pueraria Mirifica (PM) to treat liver cancer and improve prognosis. Differentially expressed genes (DEGs) associated with HCC were identified by bioinformatics analysis of three datasets, GSE112791, GSE101685, and GSE45114. Using public databases to predict the bioactive components and possible targets of RA and PM. Target crossover from Gene Expression Omnibus (GEO) and public databases were used to identify potential biomarkers for HCC. Subsequently, validation and prognostic value analyses were performed using the Gene Expression Profile Interaction Analysis (GEPIA) platform. The Cytoscape software created a network of "compound targets" to pinpoint compounds linked to the biomarkers. Molecular docking techniques were utilized to validate the connection between these compounds and the identified biomarkers. Ultimately, the HepG2 liver cancer cell line was chosen to assess the inhibitory effect of Hederagenin (HDG) and to confirm the expression of ADH1B through Western blot analysis. In this study, four key biomarkers (NR1I2, ADH1B, NQO1, GHR) were identified. Molecular docking showed that these four core targets could form stable conformations with the corresponding compounds. As the drug concentration decreases, the inhibitory effect on HepG2 diminishes, and the survival rate of HepG2 cells significantly declines following the administration of 100 µmol/L HDG. Compared to the control, the expression of ADH1B protein is significantly increased in HepG2 cells treated with 100 µmol/L HDG. The study identified four key biomarkers (ADH1B, GHR, NQO1, NR1I2) that have prognostic ability for HCC. This study provides biomarkers and potential targeted monomeric medicines for treating HCC.

Screening and molecular docking verification of feature genes related to phospholipid metabolism in hepatocarcinoma caused by hepatitis B

BACKGROUND

The progression of tumours is related to abnormal phospholipid metabolism. This study is anticipated to present a fresh perspective for disease therapy targets of hepatocarcinoma caused by hepatitis B virus in the future by screening feature genes related to phospholipid metabolism.

METHODS

This study analysed GSE121248 to pinpoint differentially expressed genes (DEGs). By examining the overlap between the metabolism-related genes and DEGs, the research focused on the genes involved in phospholipid metabolism. To find feature genes, functional enrichment studies were carried out and a network diagram was proposed. These findings were validated via data base of The Cancer Genome Atlas (TCGA). Further analyses included immune infiltration studies and metabolomics. Finally, the relationships between differentially abundant metabolites and feature genes were confirmed by molecular docking, providing a thorough comprehension of the molecular mechanisms.

RESULTS

The seven genes with the highest degree of connection (PTGS2, IGF1, SPP1, BCHE, NR1I2, NAMPT, and FABP1) were identified as feature genes. In the TCGA database, the seven feature genes also had certain diagnostic efficiency. Immune infiltration analysis revealed that feature genes regulate the infiltration of various immune cells. Metabolomics successfully identified the different metabolites of the phospholipid metabolism pathway between patients and normal individuals. The docking study indicated that different metabolites may play essential roles in causing disease by targeting feature genes.

CONCLUSIONS

In this study, for the first time, it reveals the possible involvement of genes linked to phospholipid metabolism-related genes using bioinformatics analysis. Identifying genes and probable therapeutic targets could provide clues for the further treatment of disease.

Dickkopf-1 is an immune infiltration-related prognostic biomarker of head and neck squamous cell carcinoma

Immunotherapy is currently one of the most viable therapies for head and neck squamous cell carcinoma (HNSCC), characterized by high immune cell infiltration. The Wnt-signaling inhibitor and immune activation mediator, Dickkopf-1 (DKK1), has a strong correlation with tumor growth, tumor microenvironment, and, consequently, disease prognosis. Nevertheless, it is still unclear how DKK1 expression, HNSCC prognosis, and tumor-infiltrating lymphocytes are related. To better understand these associations, we examined how DKK1 expression varies across different tumor and normal tissues. In our study, we investigated the association between DKK1 mRNA expression and clinical outcomes. Next, we assessed the link between DKK1 expression and tumor immune cell infiltration. Additionally, using immunohistochemistry, we evaluated the expression of DKK1 in 15 healthy head and neck tissue samples, and the expression of CD3, CD4, and DKK1 in 27 HNSCC samples. We also explored aberrant DKK1 expression during tumorigenesis. DKK1 expression was remarkably higher in HNSCC tissues than in healthy tissues, and was shown to be associated with tumor stage, grade, lymph node metastasis, histology, and a dismal clinical prognosis in HNSCC. DKK1 expression in HNSCC tissues was inversely correlated with CD3+ (P < 0.0001) and CD4+ (P < 0.0001) immune cell infiltration, while that in immune cells was inversely associated with HNSCC prognosis. These findings offer a bioinformatics perspective on the function of DKK1 in HNSCC immunotherapy and provide justification for clinical research on DKK1-targeted HNSCC treatments. DKK1 is a central target for improving the efficacy of HNSCC immunotherapy.

Formalin Fixation, Delay to Fixation, and Time in Fixative Adversely Impact Copy Number Variation Analysis by aCGH

Although molecular profiling of DNA isolated from formalin-fixed, paraffin-embedded (FFPE) tumor specimens has become more common in recent years, it remains unclear how discrete FFPE processing variables may affect detection of copy number variation (CNV). To better understand such effects, array comparative genomic hybridization (aCGH) profiles of FFPE renal cell carcinoma specimens that experienced different delays to fixation (DTFs; 1, 2, 3, and 12 hours) and times in fixative (TIFs; 6, 12, 23, and 72 hours) were compared to snap-frozen tumor and blood specimens from the same patients. A greater number of regions containing CNVs relative to commercial reference DNA were detected in DNA from FFPE tumor specimens than snap-frozen tumor specimens even though they originated from the same tumor blocks. Extended DTF and TIF affected the number of DNA segments with a copy number status that differed between FFPE and frozen tumor specimens; a DTF ≥3 hours led to more segments, while a TIF of 72 hours led to fewer segments. Importantly, effects were not random as a higher guanine-cytosine (GC) content and/or a higher percentage of repeats were observed among stable regions. While limiting aCGH analysis to FFPE specimens with a DTF <3 hours and a TIF <72 hours may circumvent some effects, results from FFPE specimens should be validated against fresh or frozen specimens whenever possible.

A Multi-Omics Analysis of NASH-Related Prognostic Biomarkers Associated with Drug Sensitivity and Immune Infiltration in Hepatocellular Carcinoma

Background: Nonalcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is becoming a major health-related problem. The exploration of NASH-related prognostic biomarkers and therapeutic targets is necessary. Methods: Data were downloaded from the GEO database. The "glmnet" package was used to identify differentially expressed genes (DEGs). The prognostic model was constructed by the univariate Cox and LASSO regression analyses. Validation of the expression and prognosis by immunohistochemistry (IHC) in vitro. Drug sensitivity and immune cell infiltration were analyzed by CTR-DB and ImmuCellAI. Results: We constructed a prognostic model that identified the NASH-related gene set (DLAT, IDH3B, and MAP3K4), which was validated in a real-world cohort. Next, seven prognostic transcription factors (TFs) were identified. The prognostic ceRNA network included three mRNAs, four miRNAs, and seven lncRNAs. Finally, we found that the gene set was associated with drug response which was validated in six clinical trial cohorts. Moreover, the expression level of the gene set was inversely correlated with CD8 T cell infiltration in HCC. Conclusions: We established a NASH-related prognostic model. Upstream transcriptome analysis and the ceRNA network provided clues for mechanism exploration. The mutant profile, drug sensitivity, and immune infiltration analysis further guided precise diagnosis and treatment strategies.

EIF2S2 is a novel independent prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly malignant disease with poor prognosis. It is urgent to find effective biomarkers. Eukaryotic Translation Initiation Factor 2 Subunit Beta (EIF2S2) is a subunit of heterotrimeric G protein EIF2, and its function is still unclear. We studied the role of EIF2S2 in the malignant progression of liver cancer and its relationship with immune infiltration.

Methods: Download the RNA expression and clinical information of EIF2S2 from the Cancer Genome Atlas (TCGA) database, analyze the relationship between the expression of EIF2S2 and the prognosis and clinicopathological characteristics of HCC, analyze the differential genes by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and tumor related immune infiltrating cells. The Protein expression level of EIF2S2 was obtained from Human Protein Atlas (HPA) databases. The relationship between EIF2S2 expression and immune infiltrates in HCC was analyzed on TIMER 2.0. The data processing analysis based on R language. Drug Sensitivity data from Genomics of Drug Sensitivity in Cancer (GDSC).

Results: EIF2S2 is highly expressed in HCC patients and is associated with poor prognosis. The expression of EIF2S2 was also correlated with age, clinical stage and pathological grade. Univariate and multivariate COX regression analysis showed that EIF2S2 was an independent risk factor for survival. The receiver operating characteristic (ROC) curve of EIF2S2 also confirmed the diagnostic value of EIF2S2 in HCC patients. Through GO and KEGG enrichment analysis, EIF2S2 expression was found to be closely related to some immune pathways. The expression of EIF2S2 was correlated with memory B cell, plasma B cell, CD8+ T cell, CD4+ resting memory T cell and the expression of some immune checkpoints, such as PDCD1, TIGIT and CTLA-4. It is also more sensitive to paclitaxel, sunitinib and other drugs.

Conclusion: This study shows that EIF2S2 can be used as a prognostic factor for HCC, which is closely related to immune infiltration and immune checkpoints, and may play a potential regulatory role in predicting drug sensitivity.