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

Purpose. Treatment outcomes for advanced liver cancer are poor. Immunotherapy is a treatment strategy that has been widely used to treat other cancers. Studies have shown that CD8+ T lymphocytes are essential factors affecting the efficacy of immunotherapy. We used computational biology methods to determine the coexpressed gene network that promotes CD8+ T lymphocyte infiltration. Method. We obtained the liver cancer gene matrix and clinical follow-up information data from TCGA liver hepatocellular carcinoma FPKM. We obtained single nucleotide polymorphism (SNP) data to evaluate the tumor mutation burden. The “estimate” package and the CIBERSORT algorithm were used to evaluate tumor purity and the proportion of CD8+ T lymphocytes in the liver cancer cohort. We used the gene expression matrix of liver cancer and the relative proportion of CD8+ T lymphocytes as input files and performed WGCNA based on this analysis. The weighted coexpression network identified the most CD8+ T lymphocyte-related coexpression modules in liver cancer. Then, we analyzed the biological processes involved in the module. We determined the coexpression module with CD8+ T lymphocyte infiltration in terms of data and function. We then screened the factors in the coexpression module correlated with CD8+ T lymphocyte content greater than 0.4. Finally, the expression levels of these factors were verified at the protein level using immunohistochemistry and single-cell sequencing. Results. We determined the CD8+ T lymphocyte proportions that correlated with coexpression networks. Four coexpressed genes (C1QC, CD3D, GZMA, and PSMB9) were identified as CD8+ T cell coexpression genes that promoted infiltration of CD8+ T cells. Because the factors in the coexpression network often participate in similar biological processes, we found that these factors were most related to antigen processing and presentation of peptide antigen through functional enrichment. In the clinical phenotype analysis, we found that 18 factors can be used as independent prognostic protective factors. We found that these factors were significantly negatively correlated with tumor purity and negatively correlated with M2 macrophages in the immunophenotyping analysis. Using immunohistochemistry and single-cell sequencing analysis, we found that CD3D antibody staining was weaker in tumor tissues than normal tissues and was related to CD8+ T cells. Conclusion. These coexpressed genes were positively related to the high infiltration proportion of CD8+ T lymphocytes in an antigen presentation process. The biological process might provide new directions for patients who are insensitive to immune therapy.

Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

Purpose. To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated. Method. We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model’s essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model. Results. The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk score = 0.25 ∗ NOTCH 3 + 0.008 ∗ DBN 1 − 0.031 ∗ KDELC 2 − 0.032 ∗ STAB 1 . The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability. Conclusion. We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism

ABSTRACTAlcohol exposure triggers changes in gene expression and biological pathways in human brain. We explored alterations in gene expression in the Pre-Frontal Cortex (PFC) of 65 alcoholics and 73 controls of European descent, and identified 129 genes that showed altered expression (FDR < 0.05) in subjects with alcohol dependence. Differentially expressed genes were enriched for pathways related to interferon signaling and Growth Arrest and DNA Damage-inducible 45 (GADD45) signaling. A coexpression module (thistle2) identified by weighted gene co-expression network analysis (WGCNA) was significantly correlated with alcohol dependence, alcohol consumption, and AUDIT scores. Genes in the thistle2 module were enriched with genes related to calcium signaling pathways and showed significant downregulation of these pathways, as well as enrichment for biological processes related to nicotine response and opioid signaling. A second module (brown4) showed significant upregulation of pathways related to immune signaling. Expression quantitative trait loci (eQTLs) for genes in the brown4 module were also enriched for genetic associations with alcohol dependence and alcohol consumption in large genome-wide studies included in the Psychiatric Genetic Consortium and the UK Biobank’s alcohol consumption dataset. By leveraging multi-omics data, this transcriptome analysis has identified genes and biological pathways that could provide insight for identifying therapeutic targets for alcohol dependence.

Systems genetic analysis of brown adipose tissue function

Brown adipose tissue (BAT) has been suggested to play an important role in lipid and glucose metabolism in rodents and possibly also in humans. In the current study, we used genetic and correlation analyses in the BXH/HXB recombinant inbred (RI) strains, derived from Brown Norway (BN) and spontaneously hypertensive rats (SHR), to identify genetic determinants of BAT function. Linkage analyses revealed a quantitative trait locus (QTL) associated with interscapular BAT mass on chromosome 4 and two closely linked QTLs associated with glucose oxidation and glucose incorporation into BAT lipids on chromosome 2. Using weighted gene coexpression network analysis (WGCNA) we identified 1,147 gene coexpression modules in the BAT from BXH/HXB rats and mapped their module eigengene QTLs. Through an unsupervised analysis, we identified modules related to BAT relative mass and function. The Coral4.1 coexpression module is associated with BAT relative mass (includes Cd36 highly connected gene), and the Darkseagreen coexpression module is associated with glucose incorporation into BAT lipids (includes Hiat1, Fmo5, and Sort1 highly connected transcripts). Because multiple statistical criteria were used to identify candidate modules, significance thresholds for individual tests were not adjusted for multiple comparisons across modules. In summary, a systems genetic analysis using genomic and quantitative transcriptomic and physiological information has produced confirmation of several known genetic factors and significant insight into novel genetic components functioning in BAT and possibly contributing to traits characteristic of the metabolic syndrome.

Analysis of microRNA Regulated Seed Biology Networks in Arabidopsis

Seed maturation and embryogenesis in plants are crucial event for food production of all human beings. Delayed seed maturation and abnormal embryo formation of food crops degrade the quality and quantity of food grains. By performing comparative gene analysis of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, using as model plant, here the authors identified a gene coexpression module in preglobular stage. In this module, different genes have been studied which are over-expressed during embryogenesis related with several KEGG metabolic pathways. Analysing the gene cluster evolved from network we concluded that microRNA regulates gene expression of two genes. One of them NRMP6, a metal ion transporter protein gene and second one SKS8, has copper ion binding activity, are regulated by miR167A/B. Since these two genes are also expressed during embryogenesis of other food crops e.g. rice tomato etc, so the microRNAs regulation on gene expression during embryogenesis can be extrapolated for other economically important seeds.