Screening Hub Genes of Hepatocellular Carcinoma Based on Public Databases

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.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

The clinical significance of integrin subunit alpha V in cancers: from small cell lung carcinoma to pan-cancer

BACKGROUND

Little is known about the relationship between integrin subunit alpha V (ITGAV) and cancers, including small cell lung cancer (SCLC).

METHODS

Using large sample size from multiple sources, the clinical roles of ITGAV expression in SCLC were explored using differential expression analysis, receiver operating characteristic curves, Kaplan-Meier curves, etc. RESULTS: Decreased mRNA (SMD = - 1.05) and increased protein levels of ITGAV were detected in SCLC (n = 865). Transcription factors-ZEB2, IK2F1, and EGR2-may regulate ITGAV expression in SCLC, as they had ChIP-Seq (chromatin immunoprecipitation followed by sequencing) peaks upstream of the transcription start site of ITGAV. ITGAV expression made it feasible to distinguish SCLC from non-SCLC (AUC = 0.88, sensitivity = 0.78, specificity = 0.84), and represented a risk role in the prognosis of SCLC (p < 0.05). ITGAV may play a role in cancers by influencing several immunity-related signaling pathways and immune cells. Further, the extensive pan-cancer analysis verified the differential expression of ITGAV and its clinical significance in multiple cancers.

CONCLUSION

ITGAV served as a potential marker for prognosis and identification of cancers including SCLC.