A novel prognostic target-gene signature and nomogram based on an integrated bioinformatics analysis in hepatocellular carcinoma

There is currently no effective solution to the problem of poor prognosis and recurrence of HCC. The technology of immunotherapy and prognosis of genetic material has made continuous progress in recent years. In the study, a 5-gene signature was established for the prognosis of HCC through biological information, and the immune infiltration of HCC patients was studied. After studied HCC patients’ immune infiltration, the paper screened the differential target genes of miR-126-3p in HCC downloaded from TCGA database, and uses WGCNA method to select the modular genes highly relevant to M2 macrophage. Then we use LASSO and COX regression analysis technology to establish the 5-gene signature. The nomogram is established by combining the prognostic score and clinical phenotype. Cibersort was empolyed to observe the immune infiltration in HCC patients. We revealed the biological pathways of HCC-related genes through GSEA and Metascape. The bioinformatics analysis of 2495 differential target genes finally constructed a 5-gene signature with a reliable prognostic ability (CDCA8, SLC41A3, PPM1G, TCOF1, GRPEL2). The combination of prognostic score and AJCC_Stage resulted in a more reliable prognosis ability. At the same time, 10 immune cells that are differentially expressed in HCC patients were also found. 8 GSEA pathways related to the prognosis were found. In the study, a reliable 5-gene signature was established based on the differential target gene of miR-126-3p to study the immune infiltration in HCC patients. It provides help for HCC-related prognosis research and immunotherapy.     

A novel prognostic gene signature,nomogram and immune landscape based on tanshinone IIA drug targets for hepatocellular carcinoma: Comprehensive bioinformatics analysis and <i>in vitro</i> experiments

Background: Tanshinone IIA, one of the main ingredients of Danshen, is used to treat hepatocellular carcinoma (HCC). However, potential targets of the molecule in the therapy of HCC are unknown. Methods: In this study, we collected the tanshinone IIA targets from public databases for investigation. We screened differentially expressed genes (DEGs) across HCC and normal tissues using mRNA expression profiles from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression models were used to identify and construct the prognostic gene signature. Results: Finally, we discovered common genes across tanshinone IIA targets and HCC DEGs. We reported Fatty acid binding protein-6 (FABP6), Polo-like Kinase 1 (PLK1), deoxythymidylate kinase (DTYMK), Uridine Cytidine Kinase 2 (UCK2), Enhancer of Zeste Homolog 2 (EZH2), and Cytochrome P450 2C9 (CYP2C9) as components of a gene signature. The six-gene signature’s prognostic ability was evaluated using the Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC), multivariate Cox regression analysis, and the nomogram. The mRNA level and protein expression of UCK2 were experimentally validated after treatment with different concentrations of tanshinone IIA in HEPG2 cells. CIBERSORTx, TIMER2.0, and GEPIA2 tools were employed to explore the relationship between the prognostic signature and immune cell infiltration. Conclusion: We established a six-gene signature as a reliable model with significant therapeutic possibility for prognosis and overall survival estimation in HCC patients, which might also benefit medical decision-making for appropriate treatment.     

SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast cells showed significant differences in penetration in the high and low SPP1 expression groups. Next, we used the Weighted Gene Co-Expression Network and Least Absolute Shrinkage Sum Selection Operator algorithms to construct a risk score for the 9-immune-related genes signature. The risk score showed a good ability to identify high and low-risk patients and improved survival prediction. We also used multivariate Cox regression to validate that risk score was significantly correlated with SPP1 and overall survival. Lastly, the Back-Propagation Neural Network confirmed the reliability of the results of multiple algorithms. In conclusion, the findings suggest that SPP1 is an independent marker of HCC survival and immunotherapy.     

Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer

Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance. And we developed a nomogram by combing the risk score with multiple clinical characteristics. CIBERSORT and TIMER algorithms confirmed that there are significant differences in tumor-infiltrating immune cells in different risk groups. In addition, gene set enrichment analysis shows 6 pathways that differ between high- and low-risk group. The immune-related gene signature effectively predicts the survival and immune infiltration of breast cancer patients and is expected to provide more effective immunotherapy targets for the prognosis prediction of breast cancer.     

<i>In Silico</i> analysis and linking of metabolism-related genes with the immune landscape in head and neck squamous cell carcinoma

Metabolic reprogramming and immunologic suppression are two critical characteristics promoting the progression of head and neck squamous cell carcinoma (HNSCC). The integrative analysis of all the metabolism-related genes (MRGs) in HNSCC is lacking and the interaction between the metabolism and the immune characteristics also requires more exploration to uncover the potential mechanisms. Therefore, this study was designed to establish a prognostic signature based on all the MRGs in HNSCC. Genes of HNSCC samples were available from the TCGA and GEO databases while the MRGs were retrieved from a previous study. Ultimately 4 prognostic MRGs were selected to construct a model possessing robust prognostic value and accuracy in TCGA cohorts. The favorable reproducibility of this model was confirmed in validation cohorts from GEO databases. The risk score calculated by this model was an independent prognostic factor that further classified these HNSCC patients into high-/low-risk groups. GSEA analyses and somatic mutations indicated the low-risk group could activate several anti-tumor pathways and possessed lower TP53 mutation. The results of ESTIMATE, single-sample GSEA, CIBERSORT, and some immune-related molecules analyses suggested the low-risk group exhibited lower metabolic activities and higher immune characteristics. The Spearman correlation test implied most metabolic pathways with tumor-promoting function were negatively correlated with the immune activity, indicating a plausible approach of combining the anti-metabolism and the immunotherapy drugs in the high-risk group to enhance therapeutic effects than applied separately. In conclusion, this prognostic signature linking MRGs with the immune landscape could promote the individualized treatment for HNSCC patients.