Identification of CD8+ T Cell-Related Genes: Correlations with Immune Phenotypes and Outcomes of Liver Cancer

Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq

CD8+ T lymphocytes are critical in the immune response against neoplasms, yet the prognostic relevance of CD8+ T cell-associated genes in hepatocellular carcinoma (HCC) is not fully understood. We sourced single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data for HCC from the GSE98638 dataset and The Cancer Genome Atlas (TCGA) repository. We utilized Weighted Gene Correlation Network Analysis (WGCNA) to identify CD8+ T cell-related genes. A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis. The Immunophenotypic Score (IPS) was utilized to evaluate the potential of immunotherapeutic interventions in the categorized cohorts. Validation of the expression of CD8+ T cell-associated risk genes was performed using quantitative reverse transcription PCR (qRT-PCR). Integrating scRNA-seq with RNA-seq data, we identified five CD8+ T cell-related signature genes: IKBKE, ATP1B3, MSC, ADA, and BATF. Notably, HCC patients in the high-risk group had markedly decreased overall survival. Elevated infiltration levels of CD8+ T cells, B cells, and macrophages were observed in the high-risk group. Moreover, there was a positive correlation between the risk score and immune checkpoints (ICPs), including PDCD1, CD274, and CTLA4. Patients within the high-risk group subject to PD1 and CTLA4 blockade exhibited higher IPS levels. Additionally, the expression of the five risk genes was upregulated in HCC cell lines and tissues compared to normal cells and tissues. Our findings establish a prognostic signature based on CD8+ T cells, offering a potent predictive model for clinical outcomes and responsiveness to immunotherapy in HCC patients.

Hypomethylation of CD3D promoter induces immune cell infiltration and supports malignant phenotypes in uveal melanoma

Alterations in DNA methylation in malignant diseases have been heralded as promising targets for diagnostic, prognostic, and predictive values. This study was based on epigenetic alterations and immune cell infiltration analysis to investigate the mechanism of CD3D methylation in uveal melanoma (UM). Bioinformatics analysis was performed on transcriptome data, 450 K methylation data, and clinical information of UM patients from the TCGA database. Stromal and immune cell infiltration was evaluated by calculating the StromalScore and ImmuneScore of UM samples. UM samples were divided into high and low StromalScore and ImmuneScore groups, followed by differential and enrichment analyses. PPI network construction and correlation analysis was used to identify the core prognosis-related genes. The bioinformatics analysis results were confirmed in UM cell experiments. StromalScore and ImmuneScore were significantly associated with the prognosis of UM patients. CD3D, IRF1, CCL3, and FN1 were identified as core genes driven by methylation that affected the prognosis of UM patients. CD3D expression showed the highest correlation with its methylation and was closely related to the four key immune cells in UM development. CD3D was hypomethylated and abundantly expressed in UM cells, while silencing of CD3D inhibited the proliferation, migration, and invasion of UM cells in vitro. In summary, this study identifies hypomethylation of CD3D promoter in UM, which was associated with immune cell infiltration of UM.

Clinical-mediated discovery of pyroptosis in CD8+ T cell and NK cell reveals melanoma heterogeneity by single-cell and bulk sequence

Histologically, melanoma tissues had fewer positive cells percentage of pyroptosis-related genes (PRGs), GZMA, GSDMB, NLRP1, IL18, and CHMP4A in epidermal than in normal skin. Pyroptosis, a new frontier in cancer, affects the tumor microenvironment and tumor immunotherapy. Nevertheless, the role of pyroptosis remains controversial, which reason is partly due to the heterogeneity of the cellular composition in melanoma. In this study, we present a comprehensive analysis of the single-cell transcriptome landscape of pyroptosis in melanoma specimens. Our findings reveal dysregulation in the expression of PRGs, particularly in immune cells, such as CD8+ cells (representing CD8+ T cells) and CD57+ cells (representing NK cells). Additionally, the immunohistochemical and multiplex immunofluorescence staining experiments results further confirmed GZMA+ cells and GSDMB+ cells were predominantly expressed in immune cells, especially in CD8 + T cells and NK cells. Melanoma specimens secreted a minimal presence of GZMA+ merged CD8+ T cells (0.11%) and GSDMB+ merged CD57+ cells (0.08%), compared to the control groups exhibiting proportions of 4.02% and 0.62%, respectively. The aforementioned findings indicate that a reduced presence of immune cells within tumors may play a role in diminishing the ability of pyroptosis, consequently posing a potential risk to the anti-melanoma properties. To quantify clinical relevance, we constructed a prognostic risk model and an individualized nomogram (C-index=0.58, P = 0.002), suggesting a potential role of PRGs in malignant melanoma prevention. In conclusion, our integrated single-cell and bulk RNA-seq analysis identified immune cell clusters and immune gene modules with experiment validation, contributing to our better understanding of pyroptosis in melanoma.

LATPS, a novel prognostic signature based on tumor microenvironment of lung adenocarcinoma to better predict survival and immunotherapy response

Background

Clinically, only a minority of patients benefit from immunotherapy and few efficient biomarkers have been identified to distinguish patients who would respond to immunotherapy. The tumor microenvironment (TME) is reported to contribute to immunotherapy response, but details remain unknown. We aimed to construct a prognostic model based on the TME of lung adenocarcinoma (LUAD) to predict the prognosis and immunotherapy efficacy.

Methods

We integrated computational algorithms to describe the immune infiltrative landscape of LUAD patients. With the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, we developed a LUAD tumor microenvironment prognostic signature (LATPS). Subsequently, the immune characteristics and the benefit of immunotherapy in LATPS-defined subgroups were analyzed. RNA sequencing of tumor samples from 28 lung cancer patients treated with anti-PD-1 therapy was conducted to verify the predictive value of the LATPS.

Results

We constructed the LATPS grounded on four genes, including UBE2T, KRT6A, IRX2, and CD3D. The LATPS-low subgroup had a better overall survival (OS) and tended to have a hot immune phenotype, which was characterized by an elevated abundance of immune cell infiltration and increased activity of immune-related pathways. Additionally, tumor immune dysfunction and exclusion (TIDE) score was markedly decreased in the LATPS-low subgroup, indicating an enhanced opportunity to benefit from immunotherapy. Survival analysis in 28 advanced lung cancer patients treated with an anti-PD-1 regimen at Nanfang hospital revealed that the LATPS-low subgroup had better immunotherapy benefit.

Conclusion

LATPS is an effective predictor to distinguish survival, immune characteristics, and immunotherapy benefit in LUAD patients.

Single-Cell Sequencing Identifies the Heterogeneity of CD8+ T Cells and Novel Biomarker Genes in Hepatocellular Carcinoma

CD8+ T cells are required for the establishment of antitumor immunity, and their substantial infiltration is associated with a good prognosis. However, CD8+ T cell subsets in the tumor microenvironment may play distinct roles in tumor progression, prognosis, and immunotherapy. In this study, we used the scRNA-seq data of hepatocellular carcinoma (HCC) to reveal the heterogeneity of different CD8+ T cell subsets. The scRNA-seq data set GSE149614 was obtained from the GEO database, and the transcriptome and sample phenotypic data of TCGA-LIHC were obtained from the TCGA database. CD8+ T cell subtypes and metabolic gene sets were obtained from published reports. The data processing and analysis of CD8+ T cell groups was performed by R language. The PPI network was constructed to obtain the hub genes, and the KM survival curve of the hub genes was further plotted to determine the hub genes with differences in survival. CD8+ T cells in HCC were divided into 7 subsets, and the cytotoxic CD8 T cells 4 subset showed considerable differences between the TP53-mutant and nonmutant groups, as well as between different degrees of cirrhosis, HCC grades, stages, ages, and body weights. Cytotoxic CD8 T cells 4 differential genes were analyzed by TCGA-LIHC data and single-cell sequencing data set. 10 hub genes were found: FGA, ApoA1, ApoH, AHSG, FGB, HP, TTR, TF, HPX, and APOC3. Different subsets of CD8+ T cells were found to contribute to heterogeneous prognosis and pathway activity in HCC. Alterations in the cytotoxic and immune checkpoint gene expression during CD8+ T cell differentiation were also identified. We found that cytotoxic CD8 T cells 4 is closely associated with survival and prognosis of HCC and identified four differential genes that can be used as biological markers for survival, prognosis, and clinically relevant characteristics of HCC. Results of this study could help finding targets for immunotherapy of HCC and aid in the accelerated development of immunotherapy for HCC.

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.

Screening and analysis of RNAs associated with activated memory CD4 and CD8 T cells in liver cancer

Background

Liver cancer is one of the most common malignant tumors in the world. T cell-mediated antitumor immune response is the basis of liver cancer immunotherapy.

Objective

To screen and analyze the RNAs associated with activated memory CD4 T cells and CD8 T cells in liver cancer.

Methods

ESTIMATE was used to calculate the stromal and immune scores of tumor samples, which were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) in high and low stromal and immune scores were screened, followed by functional enrichment of overlapped DEGs. We then conducted a survival analysis to identify immune-related prognostic indicators and constructed protein-protein interaction (PPI) networks and ceRNA networks. Finally, chemical small-molecule–target interaction pairs associated with liver cancer were screened.

Results

A total of 55,955 stromal-related DEGs and 1811 immune-related DEGs were obtained. The 1238 overlapped DEGs were enriched in 1457 biological process terms and 74 KEGG pathways. In addition, a total of 120 activated memory CD4 T cell-related genes and 309 CD8 T cell-related genes were identified. The survival analysis revealed that upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer. EOMES was regulated by has-miR-23b-3p and has-miR-23b-3p was regulated by lncRNA AC104820.2 in the ceRNA network of activated memory CD4 T cell-related genes. In addition, EOMES was regulated by has-miR-23a-3p and has-miR-23a-3p was regulated by lncRNA AC000476.1 in the ceRNA network of CD8 T cells.

Conclusion

T cell-related RNAs EOMES, CST7, CD5L, has-miR-23b-3p, and has-miR-23a-3p may be associated with the prognosis of liver cancer. And the molecular characteristics of these T cell-related genes were plotted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02461-6.

A total of 309 CD8 T cell-related genes and 120 activated memory CD4 T cell-related genes were screened in liver cancer tumor samples.

Forty-four chemical small-molecule–target interaction pairs associated with activated memory CD4 T cells and 276 pairs associated with CD8 T cells were screened.

Upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer.