Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma

Background

Dysregulation of lipid metabolism is common in cancer. However, the molecular mechanism underlying lipid metabolism in esophageal squamous cell carcinoma (ESCC) and its effect on patient prognosis are not well understood. The objective of our study was to construct a lipid metabolism-related prognostic model to improve prognosis prediction in ESCC.

Methods

We downloaded the mRNA expression profiles and corresponding survival data of patients with ESCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We performed differential expression analysis to identify differentially expressed lipid metabolism-related genes (DELMGs). We used Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to establish a risk model in the GEO cohort and used data of patients with ESCC from the TCGA cohort for validation. We also explored the relationship between the risk model and the immune microenvironment via infiltrated immune cells and immune checkpoints.

Results

The result showed that 132 unique DELMGs distinguished patients with ESCC from the controls. We identified four genes (ACAA1, ACOT11, B4GALNT1, and DDHD1) as prognostic gene expression signatures to construct a risk model. Patients were classified into high- and low-risk groups as per the signature-based risk score. We used the receiver operating characteristic (ROC) curve and the Kaplan-Meier (KM) survival analysis to validate the predictive performance of the 4-gene signature in both the training and validation sets. Infiltrated immune cells and immune checkpoints indicated a difference in the immune status between the two risk groups.

Conclusion

The results of our study indicated that a prognostic model based on the 4-gene signature related to lipid metabolism was useful for the prediction of prognosis in patients with ESCC.

Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning

OBJECTIVE

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood.

METHODS

In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes.

RESULTS

Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma.

CONCLUSIONS

Our study provides further directions for studying the molecular mechanism of asthma.

Transmembrane protein 147, as a potential Sorafenib target, could expedite cell cycle process and confer adverse prognosis in hepatocellular carcinoma

TMEM147 was identified as a core component of ribosome-bound translocon complex at ER/NE. So far, sparse studies reported its expression profiling and oncological implications in hepatocellular carcinoma (HCC) patients. Here we inspected TMEM147 expression levels in HCC cohorts from public databases and tumor tissues. TMEM147 was augmented at transcriptional levels (p < 0.001) and protein levels in HCC patients. A series of bioinformatics tools implemented in R studio were orchestrated in TCGA-LIHC to evaluate the prognostic significance, compile relevant gene clusters, and explore the oncological functions and therapy responses. It is suggested that TMEM147 could predict poor clinical outcomes effectively and independently (p < 0.001, HR = 2.231 for overall survival (OS) vs. p = 0.04, HR = 2.296 for disease-specific survival), and was related to risk factors including advanced histologic tumor grade (p < 0.001), AFP level (p < 0.001) and vascular invasion (p  = 0.007). Functional enrichment analyses indicated that TMEM147 was involved in cell cycle, WNT/MAPK signaling pathways and ferroptosis. Expression profiling in HCC cell lines, mouse model, and a clinical trial revealed that TMEM147 was a considerable target and marker for adjuvant therapy in vitro and in vivo. Subsequentially, in vitro wet-lab experimentation authenticated that TMEM147 would be downregulated by Sorafenib administration in hepatoma cells. Lentivirus-mediated overexpression of TMEM147 could promote cell cycle progression from S phase into G2/M phase, and enhance cell proliferation, thus attenuating drug efficacy and sensitivity of Sorafenib. Further explorations into TMEM147 may inspire a fresh perspective to predict clinical outcomes and improve therapeutic efficacy for HCC patients.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Clinical value of identifying genes that inhibit hepatocellular carcinomas

INTRODUCTION

Primary liver cancer is a major health problem being the sixth most frequent cancer in the world and the fourth most frequent cause of cancer-related death in the world. The most common histological type of liver cancer is hepatocellular carcinoma (HCC, 75-80%).

AREAS COVERED

Based on primary literature, this review provides an updated analysis of studies of genetic characterization of HCC at the level of gene mutation profiling, copy number alterations and gene expression, with definition of molecular subgroups and identification of some molecular biomarkers and therapeutic targets.

EXPERT OPINION

A detailed and comprehensive study of the genetic abnormalities characterizing different HCC subsets represents a fundamental tool for a better understanding of the disease heterogeneity and for the identification of subgroups of patients responding or resistant to targeted treatments and for the discovery of new therapeutic targets. It is expected that a comprehensive characterization of these tumors may provide a fundamental contribution to improve the survival of a subset of HCC patients. Immunotherapy represents a new fundamental strategy for the treatment of HCC.

Chromatin regulators-related lncRNA signature predicting the prognosis of kidney renal clear cell carcinoma and its relationship with immune microenvironment: A study based on bioinformatics and experimental validation

Background: Kidney Renal Clear cell carcinoma (KIRC) is a major concern in the urinary system. A lot of researches were focused on Chromatin Regulators (CRs) in tumors. In this study, CRs-related lncRNAs (CRlncRNAs) were investigated for their potential impact on the prognosis of KIRC and the immune microenvironment.

Methods: The TCGA database was used to obtain transcriptome and related clinical information. CRs were obtained from previous studies, whereas CRlncRNAs were obtained by differential and correlation analysis. We screened the lncRNAs for the signature construction using regression analysis and LASSO regression analysis. The effectiveness of the signature was evaluated using the Kaplan-Meier (K-M) curve and Receiver Operating Characteristic curve (ROC). Additionally, we examined the associations between the signature and Tumor Microenvironment (TME), and the efficacy of drug therapy. Finally, we further verified whether these lncRNAs could affect the biological function of KIRC cells by functional experiments such as CCK8 and transwell assay.

Results: A signature consisting of 8 CRlncRNAs was constructed to predict the prognosis of KIRC. Quantitative Real-Time PCR verified the expression of 8 lncRNAs at the cell line and tissue level. The signature was found to be an independent prognostic indicator for KIRC in regression analysis. This signature was found to predict Overall Survival (OS) better for patients in the subgroups of age, gender, grade, stage, M, N0, and T. Furthermore, a significant correlation was found between riskScore and immune cell infiltration and immune checkpoint. Finally, we discovered several drugs with different IC50 values in different risk groups using drug sensitivity analysis. And functional experiments showed that Z97200.1 could affect the proliferation, migration and invasion of KIRC cells.

Conclusion: Overall, the signature comprised of these 8 lncRNAs were reliable prognostic biomarkers for KIRC. Moreover, the signature had significant potential for assessing the immunological landscape of tumors and providing individualized treatment.

Identification and Validation of a Novel Immune Infiltration-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma by Machine-Learning Strategies

Objective

To investigate the diagnostic gene biomarkers for hepatocellular carcinoma (HCC) and identify the immune cell infiltration characteristics in this pathology.

Methods

Five gene expression datasets were obtained through Gene Expression Omnibus (GEO) portal. After batch effect removal, differentially expressed genes (DEGs) were conducted between 209 HCC and 146 control tissues and functional correlation analyses were performed. Two machine learning algorithms were used to develop diagnostic signatures. The discriminatory ability of the gene signature was measured by AUC. The expression levels and diagnostic value of the identified biomarkers in HCC were further validated in three independent external cohorts. CIBERSORT algorithm was adopted to explore the immune infiltration of HCC. A correlation analysis was carried out between these diagnostic signatures and immune cells.

Results

A total of 375 DEGs were identified. GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 were identified as the early diagnostic signatures of HCC and were all validated in external cohorts. The corresponding results of AUC presented excellent discriminatory ability of these feature genes. The immune cell infiltration analysis showed that multiple immune cells associated with these biomarkers may be involved in the development of HCC.

Conclusion

This study indicates that GPC3, ACSM3, SPINK1, COL15A1, TP53I3, RRAGD, and CLDN10 are potential biomarkers associated with immune infiltration in HCC. Combining these genes can be used for early detection of HCC and evaluating immune cell infiltration. Further studies are needed to explore their roles underlying the occurrence of HCC.