Bioinformatics Analysis of Prognostic Tumor Microenvironment-Related Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Alterations of the microenvironment of hepatocellular carcinoma in different unfolded protein response activity states

BACKGROUND

The unfolded protein response (UPR) is an adaptive and cytoprotective sensing-signaling network. Numerous studies have indicated the crucial role of UPR in the anti-tumor drug resistance and the modification of tumor microenvironment (TME). The aim of this study is to analyze the alterations of microenvironment and key regulatory genes in hepatocellular carcinoma (HCC) with high UPR activity.

METHODS

We profiled differentially expressed genes (DEGs) by UPR activity, and the biological functions of DEGs and the alterations of signaling pathways were explored. The Immune/Stromal scores and relative abundance of infiltrating cells of HCC tissues with RNA sequencing data downloaded from The Cancer Genome Atlas (TCGA) were calculated by the xCell and ESTIMATE algorithm. The correlations between the prognostic UPR-related genes with the microenvironment scores and infiltrating cells were analyzed using R package "corrplot".

RESULTS

Our results demonstrated that UPR-related genes mainly involved in immune-related signaling pathways. Microenvironment analysis revealed that HCC tissues with higher UPR activity had lower Stromal scores and the relative abundance of various infiltrating cells including hematopoietic stem cells (HSC), lymphatic endothelial cells (LECs), microvascular endothelial cells, endothelial cells (ECs) and adipocytes decreased most significantly. Kaplan-Meier survival analysis indicated that the decline of Stromal scores and corresponding infiltrating stromal cells would result in worse prognosis. The expression levels of CLEC3B, RAMP3, GPR182 and DNASE1L3 were significantly positively correlated with Stromal scores and various infiltrating stromal cells, and down-regulation of these genes were also associated with worse prognosis of HCC.

CONCLUSIONS

HCC with high UPR activity had lower Stromal scores and worse prognosis. Down-regulated genes CLEC3B, RAMP3, GPR182 and DNASE1L3 may play an important regulatory role in the modification of microenvironment of HCC with high UPR activity.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.

METHODS

This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.

RESULTS

The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.

CONCLUSION

In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.

Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.

Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.

Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

DNA Methylation Profiling of Human Hepatocarcinogenesis

BACKGROUND AND AIMS

Mutations in TERT (telomerase reverse transcriptase) promoter are established gatekeepers in early hepatocarcinogenesis, but little is known about other molecular alterations driving this process. Epigenetic deregulation is a critical event in early malignancies. Thus, we aimed to (1) analyze DNA methylation changes during the transition from preneoplastic lesions to early HCC (eHCC) and identify candidate epigenetic gatekeepers, and to (2) assess the prognostic potential of methylation changes in cirrhotic tissue.

APPROACH AND RESULTS

Methylome profiling was performed using Illumina HumanMethylation450 (485,000 cytosine-phosphateguanine, 96% of known cytosine-phosphateguanine islands), with data available for a total of 390 samples: 16 healthy liver, 139 cirrhotic tissue, 8 dysplastic nodules, and 227 HCC samples, including 40 eHCC below 2cm. A phylo-epigenetic tree derived from the Euclidean distances between differentially DNA-methylated sites (n = 421,997) revealed a gradient of methylation changes spanning healthy liver, cirrhotic tissue, dysplastic nodules, and HCC with closest proximity of dysplasia to HCC. Focusing on promoter regions, we identified epigenetic gatekeeper candidates with an increasing proportion of hypermethylated samples (beta value > 0.5) from cirrhotic tissue (<1%), to dysplastic nodules (≥25%), to eHCC (≥50%), and confirmed inverse correlation between DNA methylation and gene expression for TSPYL5 (testis-specific Y-encoded-like protein 5), KCNA3 (potassium voltage-gated channel, shaker-related subfamily, member 3), LDHB (lactate dehydrogenase B), and SPINT2 (serine peptidase inhibitor, Kunitz type 2) (all P < 0.001). Unsupervised clustering of genome-wide methylation profiles of cirrhotic tissue identified two clusters, M1 and M2, with 42% and 58% of patients, respectively, which correlates with survival (P < 0.05), independent of etiology.

CONCLUSIONS

Genome-wide DNA-methylation profiles accurately discriminate the different histological stages of human hepatocarcinogenesis. We report on epigenetic gatekeepers in the transition between dysplastic nodules and eHCC. DNA-methylation changes in cirrhotic tissue correlate with clinical outcomes.

Obesity-induced changes in human islet G protein-coupled receptor expression: Implications for metabolic regulation

G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that are the targets for many different classes of pharmacotherapy. The islets of Langerhans are central to appropriate glucose homeostasis through their secretion of insulin, and islet function can be modified by ligands acting at the large number of GPCRs that islets express. The human islet GPCRome is not a static entity, but one that is altered under pathophysiological conditions and, in this review, we have compared expression of GPCR mRNAs in human islets obtained from normal weight range donors, and those with a weight range classified as obese. We have also considered the likely outcomes on islet function that the altered GPCR expression status confers and the possible impact that adipokines, secreted from expanded fat depots, could have at those GPCRs showing altered expression in obesity.

Identification and immunoprofiling of key prognostic genes in the tumor microenvironment of hepatocellular carcinoma

Tumor microenvironment (TME) is involved in the occurrence and development of hepatocellular carcinoma (HCC), and immune cells in the TME have been implicated in its progression and treatment. However, the association of genes involved in the TME with HCC prognosis remains unclear. Thus, in this study, we obtained transcriptomic and clinicopathological data of patients with HCC from The Cancer Genome Atlas to identify key genes in TME associated with HCC prognosis. Stromal and immune cell scores were calculated using the ESTIMATE method, and differentially expressed genes (DEGs) were determined. We identified 830 DEGs, which were further subjected to survival analyses and functional enrichment analysis. Next, we identified prognostic TME-associated DEGs, established a protein-protein interaction (PPI) network, and performed Cox analysis.Consequently, four key prognostic genes (CXCL5, CXCL8, IL18RAP, and TREM2) associated with TME, were identified, in which CXCL5 and IL18RAP may be potential independent prognostic factors. Age, clinical stage, N stage, and risk score were also determined as significant prognostic variables. CIBERSORT was used to predict the constitution and relative content of the immune cells, wherein M0 macrophages were the most closely related to the key genes. In conclusion, CXCL5, CXCL8, IL18RAP, and TREM2 were associated with HCC prognosis and were important for immune cell invasion into the TME. Additionally, IL18RAP expression may contribute toward favorable prognosis in patients with HCC. Consequently, these genes may serve as potential biomarkers and immunotherapeutic targets for HCC.

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.

Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).

Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.

Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.