Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

Erythropoietin induces tumour progression and CD39 expression on immune cells in a preclinical model of triple-negative breast cancer

The adverse effects observed in some cancer patients treated with erythropoiesis-stimulating agents such as erythropoietin (EPO) might be due to the latter's well-known immunosuppressive functions. Here, we used a mouse model of syngeneic triple-negative breast cancer to explore EPO's immunomodulatory role in a tumour setting. Our results showed that EPO treatment promotes tumour growth, exacerbates the 'immune desert', and results in a 'cold tumour'. EPO treatment changed the immune cell distribution in peripheral blood, secondary lymphoid organs, and the tumour microenvironment (TME). Our in-depth analysis showed that EPO mainly impacts CD4 T cells by accelerating their activation in the spleen and thus their subsequent exhaustion in the TME. This process is accompanied by a general elevation of CD39 expression by several immune cells (notably CD4 T cells in the tumour and spleen), which promotes an immunosuppressive TME. Lastly, we identified a highly immunosuppressive CD39+ regulatory T cell population (ICOS+, CTLA4+, Ki67+) as a potential biomarker of the risk of EPO-induced tumour progression. EPO displays pleiotropic immunosuppressive functions and enhances mammary tumour progression in mice.

Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma

Background

Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management.

Methods

HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically.

Results

A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors.

Conclusions

The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

Identification of diagnostic immune-related gene biomarkers for predicting heart failure after acute myocardial infarction

Post-myocardial infarction heart failure (HF) is a major public health concern. Previous studies have reported the critical role of immune response in HF pathogenesis. However, limited studies have reported predictive immune-associated biomarkers for HF. So we attempted to identify potential immune-related indicators for HF early diagnosis and therapy guidance. This study identified two potential immune-related hub genes (IRHGs), namely CXCR5 and FOS, using bioinformatic approaches. The expression levels of CXCR5 and FOS and their ability to predict long-term HF were analyzed. Functional enrichment analysis revealed that the hub genes were enriched in immune system processes, including the interleukin-17 and nuclear factor-kappa B signaling pathways, which are involved in the pathogenesis of HF. Quantitative real-time polymerase chain reaction revealed that the Fos mRNA levels, but not the Cxcr5 mRNA levels, were downregulated in the mice of the HF group. This study successfully identified two IRHGs that were significantly and differentially expressed in the HF group and could predict long-term HF, providing novel insights for future studies on HF and developing novel therapeutic targets for HF.

Erythropoietin receptor is a risk factor for prognosis: A potential biomarker in lung adenocarcinoma

Lung cancer has the highest mortality rate of all cancers, and LUAD's survival rate is particularly poor. Erythropoietin receptor (EPOR) can be detected in lung adenocarcinoma (LUAD), however, the expression levels and prognostic value of EPOR in LUAD are still unclear. In our study, clinicopathological data of 92 LUAD patients between January 2008 and June 2016, multiple bioinformatics databases and immunohistochemistry were used to explore the EPOR expression, the mutant genes affecting EPOR expression, and the correlation of EPOR expression with oxidative stress - related genes, prognosis, immune microenvironment. All statistical analyses were performed in the R version 4.1.1. The study found that EPOR expression might be down-regulated at the mRNA levels and significantly up-regulated at the protein levels in LUAD, which indicates that the mRNA and protein levels of EPOR are inconsistent. The muTarget showed that the expression of EPOR was significantly different between the mutant group and the wild group of 15 genes, including DDX60L and C1orf168. Importantly, we found that EPOR was associated with VEGF and HIF family members, and had significant positive correlation with oxidative stress - related genes such as CCS, EPX and TXNRD2. This suggests that EPOR may be involved in the regulation of oxidative stress. The Kaplan-Meier Plotter and PrognoScan databases consistently concluded that EPOR was associated with prognosis in LUAD patients. Our clinicopathological data showed that high EPOR expression was associated with poorer overall survival (29.5 vs 46 months) and had a good predictive ability for 4-year and 5-year survival probability. EPOR is expected to be a potential new prognostic marker for LUAD.

Characterization of stem cell subtypes and prognostic signature in hepatocellular carcinoma

BACKGROUND

Cancer stem cells (CSCs) were linked to cancer aggressiveness and poor prognosis in patients with hepatocellular carcinoma (HCC).

METHODS

We integrated two external HCC cohorts to develop the stem cell subtypes according to unsupervised clustering with 26 stem cell gene sets. Between the subtypes, differences in prognosis, clinical characteristics, recognized HCC subtypes, metabolic profile, immune-related features, somatic mutation, and drug sensitivity were examined. The prognostic signature was created, and validated by numerous cohorts, and used to assess the efficacy of immunotherapy and transcatheter arterial chemoembolization (TACE) treatment. The nomogram was developed based on the signature and clinical features. We further examined the function of KIF20A in HCC and proved that KIF20A had the potential to regulate the stemness of HCC cells through western blot.

RESULTS

Low stem cell patterns, a good prognosis, positive clinical features, specific molecular subtypes, low metastatic characteristics, and an abundance of metabolic and immunological aspects were associated with Cluster 1, whereas Cluster 2 was the reverse. Chemotherapy and immunotherapy were more effective in Cluster 1. Cluster 1 and CTNNB1 and ALB mutation were more closely. Additionally, the prognosis, immunotherapeutic, and TACE therapy responses were all worse in the high-risk group. The nomogram could predict the survival probability of HCC patients. KIF20A was discovered to be overexpressed in HCC and was revealed to be connected to the stemness of the HepG2 cell line.

CONCLUSIONS

Two stem cell subgroups with different prognoses, metabolic, and immunological characteristics in HCC patients were identified. We also created a 7-gene prognostic signature and a nomogram to estimate the survival probability. The function of KIF20A in HCC stemness was initially examined.

A prognostic risk model, tumor immune environment modulation, and drug prediction of ferroptosis and amino acid metabolism-related genes in hepatocellular carcinoma

The prognosis of hepatocellular carcinoma (HCC) is challenging due to its heterogeneity. Ferroptosis and amino acid metabolism have been shown to be closely related to HCC. We obtained HCC-related expression data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We then crossed differentially expressed genes (DEGs), amino acid metabolism genes, and ferroptosis-related genes (FRGs) to obtain amino acid metabolism-ferroptosis-related differentially expressed genes (AAM-FR DEGs). Moreover, we developed a prognostic model using Cox analysis, followed by a correlation analysis of risk scores with clinical characteristics. We also performed an immune microenvironment analysis and drug sensitivity analysis. Finally, the expression levels of model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical assays. We found that the 18 AAM-FR DEGs were mainly enriched to the alpha-amino acid metabolic process and amino acid biosynthesis pathways. Cox analysis identified CBS, GPT2, SUV39H1, and TXNRD1 as prognostic biomarkers for the risk model construction. Our results showed that the risk scores differed between pathology stage, pathology T stage, and HBV, and the number of HCC patients in the two groups. In addition, the expression of PD-L1 and CTLA-4 was high in the high-risk group, and the half-maximal inhibitory concentration (IC50) of sorafenib also differed between the two groups. Finally, the experimental validation demonstrated that the expression of biomarkers was consistent with the study analysis. Therefore, in this study, we constructed and validated a prognostic model (CBS, GPT2, SUV39H1, and TXNRD1) related to ferroptosis and amino acid metabolism and examined their prognostic value for HCC.

An anoikis-based signature for predicting prognosis in hepatocellular carcinoma with machine learning

Background: Hepatocellular carcinoma (HCC) is a common malignancy with high mortality worldwide. Despite advancements in diagnosis and treatment in recent years, there is still an urgent unmet need to explore the underlying mechanisms and novel prognostic markers. Anoikis has received considerable attention because of its involvement in the progression of human malignancies. However, the potential mechanism of anoikis-related genes (ANRGs) involvement in HCC progression remains unclear. Methods: We use comprehensive bioinformatics analyses to determine the expression profile of ANRGs and their prognostic implications in HCC. Next, a risk score model was established by least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Then, the prognostic value of the risk score in HCC and its correlation with clinical characteristics of HCC patients were further explored. Additionally, machine learning was utilized to identify the outstanding ANRGs to the risk score. Finally, the protein expression of DAP3 was examined on a tissue microarray (TMA), and the potential mechanisms of DAP3 in HCC was explored. Results: ANRGs were dysregulated in HCC, with a low frequency of somatic mutations and associated with prognosis of HCC patients. Then, nine ANRGs were selected to construct a risk score signature based on the LASSO model. The signature presented a strong ability of risk stratification and prediction for overall survival in HCC patients.Additionally, high risk scores were closely correlated with unfavorable clinical features such as advanced pathological stage, poor histological differentiation and vascular invasion. Moreover, The XGBoost algorithm verified that DAP3 was an important risk score contributor. Further immunohistochemistry determined the elevated expression of DAP3 in HCC tissues compared with nontumor tissues. Finally, functional analyses showed that DAP3 may promote HCC progression through multiple cancer-related pathways and suppress immune infiltration. Conclusion: In conclusion, the anoikis-based signature can be utilized as a novel prognostic biomarker for HCC, and DAP3 may play an important role in the development and progression of HCC.

Application of genomic selection and experimental techniques to predict cell death and immunotherapeutic efficacy of ferroptosis-related CXCL2 in hepatocellular carcinoma

Since most hepatocellular carcinoma (HCC) patients are diagnosed at advanced stages, there is no effective treatment to improve patient survival. Ferroptosis, a regulated cell death driven by iron accumulation and lipid peroxidation, has been reported to play an important role in tumorigenesis. However, the detailed mechanism and biological function of ferroptosis are still incompletely understood in HCC patients. In this study, we analyzed genomic profiles of three HCC datasets, GSE6764, GSE14520, and GSE14323. Venn diagrams were implemented to visualize the overlapping genes between differentially expressed genes and ferroptosis-related gene set. Then, one up-regulated gene, ACSL4, and five down-regulated genes, STEAP3, MT1G, GCH1, HAMP, and CXCL2, were screened. Based on the survival analysis performed by Kaplan-Meier plotter database, ferroptosis-related gene CXCL2 was demonstrated positively-correlated with the patients’ prognosis. Moreover, CXCL2 overexpression significantly inhibited cell growth and improved cellular ROS, Fe²⁺ and MDA levels in HCC cells Huh7 and MHCC97H, suggesting the roles of CXCL2 in inducing ferroptotic cell death. In addition, aberrantly expressed CXCL2 was negatively associated with malignancy clinical features, such as nodal metastasis and higher grades. The ssGSEA enrichment analysis revealed that CXCL2 co-expressed molecules were mainly involved in inflammation and immune-related pathways, such as acute inflammatory response, humoral immune response, adaptive immune response. TISIDB algorithm indicated the positive correlation between CXCL2 expression and tumor-infiltrating immune cells, including neutrophils and macrophages. Additionally, we also found that CXCL2 was positively correlated with immune infiltration score, and HCC patients with higher score harbored better prognosis. Together, these findings suggested that CXCL2 may enhance ferroptosis sensitivity and regulate immune microenvironment in HCC, and serve as a promising prognosis biomarker for HCC patients.

Predicting potential biomarkers and immune infiltration characteristics in heart failure

BACKGROUND

Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF). The purpose of this study is used by bioinformatics analysis to determine the potential diagnostic markers of heart failure and establish an applicable model to predict the association between heart failure and immune cell infiltration.

METHODS

Firstly, gene expression profiles of dilated heart disease GSE3585 and GSE120895 were obtained in Gene Expression Omnibus (GEO) database. This study then selected differentially expressed genes (DEGs) in 54 patients with HF and 13 healthy controls. In this study, biomarkers were identified using Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Additionally, we evaluated the prognostic discrimination performance by the receiver operating characteristic (ROC) curve. Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for analyzing immune cell infiltration in HF tissues. Lastly, immune biomarkers were correlated with each other.

RESULT

After 24 DEGs were analyzed using a combinatorial model of LASSO regression and SVM-RFE analysis, four key genes were obtained, namely NSG1, NPPB, PHLDA1, and SERPINE2.The area under the curve (AUC) of these four genes were greater than 0.8. Subsequently, using CIBERPORT, we also found that compared with normal people, the proportion of M1 macrophages and activated mast cells in heart failure tissues decreased. In addition, correlation analysis showed that NPPB, PHLDA1 and SERPINE2 were associated with immune cell infiltration.

CONCLUSION

NSG1, NPPB, PHLDA1 and SERPINE2 were identified as potential biomarkers of heart failure. It reveals the comprehensive role of relevant central genes in immune infiltration, which provides a new research idea for the treatment and early detection in heart failure.