Xg expression in Ewing's sarcoma is of prognostic value and contributes to tumor invasiveness

In Silico Identification of Key Genes and Immune Infiltration Characteristics in Epicardial Adipose Tissue from Patients with Coronary Artery Disease

Background

The present study is aimed at identifying the differentially expressed genes (DEGs) and relevant biological processes and pathways associated with epicardial adipose tissue (EAT) from patients with coronary artery disease (CAD). We also explored potential biomarkers using two machine-learning algorithms and calculated the immune cell infiltration in EAT.

Materials and Methods

Three datasets (GSE120774, GSE64554, and GSE24425) were obtained from the Gene Expression Omnibus (GEO) database. The GSE120774 dataset was used to evaluate DEGs between EAT of CAD patients and the control group. Functional enrichment analyses were conducted to study associated biological functions and mechanisms using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). After this, the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were performed to identify the feature genes related to CAD. The expression level of the feature genes was validated in GSE64554 and GSE24425. Finally, we calculated the immune cell infiltration and evaluated the correlation between the feature genes and immune cells using CIBERSORT.

Results

We identified a total of 130 upregulated and 107 downregulated genes in GSE120774. Functional enrichment analysis revealed that DEGs are associated with several pathways, including the calcium signaling pathway, complement and coagulation cascades, ferroptosis, fluid shear stress and atherosclerosis, lipid and atherosclerosis, and regulation of lipolysis in adipocytes. TCF21, CDH19, XG, and NNAT were identified as feature genes and validated in the GSE64554 and GSE24425 datasets. Immune cell infiltration analysis showed plasma cells are significantly more numerous in EAT than in the control group (p = 0.001), whereas macrophage M0 (p = 0.024) and resting mast cells (p = 0.036) were significantly less numerous. TCF21, CDH19, XG, and NNAT were correlated with immune cells, including plasma cells, M0 macrophages, and resting mast cells.

Conclusion

TCF21, CDH19, XG, and NNAT might serve as feature genes for CAD, providing new insights for future research on the pathogenesis of cardiovascular diseases.

Regulation of Metastasis in Ewing Sarcoma

Ewing sarcoma (EwS) is a type of bone and soft tissue tumor in children and adolescents. Over 85% of cases are caused by the expression of fusion protein EWSR1-FLI1 generated by chromosome translocation. Acting as a potent chimeric oncoprotein, EWSR1-FLI1 binds to chromatin, changes the epigenetic states, and thus alters the expression of a large set of genes. Several studies have revealed that the expression level of EWSR1-FLI1 is variable and dynamic within and across different EwS cell lines and primary tumors, leading to tumoral heterogeneity. Cells with high EWSR1-FLI1 expression (EWSR1-FLI1-high) proliferate in an exponential manner, whereas cells with low EWSR1-FLI1 expression (EWSR1-FLI1-low) tend to have a strong propensity to migrate, invade, and metastasize. Metastasis is the leading cause of cancer-related deaths. The continuous evolution of EwS research has revealed some of the molecular underpinnings of this dissemination process. In this review, we discuss the molecular signatures that contribute to metastasis.

Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).

Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.

Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4⁺ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.

Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.

The molecular genetic background leading to the formation of the human erythroid-specific Xga/CD99 blood groups

The Xg^a and CD99 antigens of the human Xg blood group system show a unique and sex-specific phenotypic relationship. The phenotypic relationship is believed to result from transcriptional coregulation of the XG and CD99 genes, which span the pseudoautosomal boundary of the X and Y chromosomes. However, the molecular genetic background responsible for these blood groups has remained undetermined. During the present investigation, we initially conducted a pilot study aimed at individuals with different Xg^a/CD99 phenotypes; this used targeted next-generation sequencing of the genomic areas relevant to XG and CD99 This was followed by a large-scale association study that demonstrated a definite association between a single nucleotide polymorphism (SNP) rs311103 and the Xg^a/CD99 blood groups. The G and C genotypes of SNP rs311103 were associated with the Xg(a+)/CD99H and Xg(a-)/CD99L phenotypes, respectively. The rs311103 genomic region with the G genotype was found to have stronger transcription-enhancing activity by reporter assay, and this occurred specifically with erythroid-lineage cells. Such activity was absent when the same region with the C genotype was investigated. In silico analysis of the polymorphic rs311103 genomic regions revealed that a binding motif for members of the GATA transcription factor family was present in the rs311103[G] region. Follow-up investigations showed that the erythroid GATA1 factor is able to bind specifically to the rs311103[G] region and markedly stimulates the transcriptional activity of the rs311103[G] segment. The present findings identify the genetic basis of the erythroid-specific Xg^a/CD99 blood group phenotypes and reveal the molecular background of their formation.

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

Background

Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage.

Results

In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study.

Conclusion

The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0452-9) contains supplementary material, which is available to authorized users.

CD99: A Cell Surface Protein with an Oncojanus Role in Tumors

The cell surface molecule CD99 has gained interest because of its involvement in regulating cell differentiation and adhesion/migration of immune and tumor cells. However, the molecule plays an intriguing and dual role in different cell types. In particular, it acts as a requirement for cell malignancy or as an oncosuppressor in tumors. In addition, the gene encodes for two different isoforms, which also act in opposition inside the same cell. This review highlights key studies focusing on the dual role of CD99 and its isoforms and discusses major critical issues, challenges, and strategies for overcoming those challenges. The review specifically underscores the properties that make the molecule an attractive therapeutic target and identifies new relationships and areas of study that may be exploited. The elucidation of the spatial and temporal control of the expression of CD99 in normal and tumor cells is required to obtain a full appreciation of this molecule and its signaling.

CD99 isoforms regulate CD1a expression in human monocyte-derived DCs through ATF-2/CREB-1 phosphorylation

CD1a expression is considered one of the major characteristics qualifying in vitro human dendritic cells (DCs) during their generation process. Here, we report that CD1A transcription is regulated by a mechanism involving the long and short isoforms of CD99. Using a lentiviral construct encoding for a CD99 short hairpin RNA, we were able to inhibit CD99 expression in human primary DCs. In such cells, CD1a membrane expression increased and CD1A transcripts were much higher in abundance compared to cells expressing CD99 long form (CD99LF). We also show that CD1A transcription is accompanied by a switch in expression from CD99LF to expression at comparable levels of both CD99 isoforms during immature DCs generation in vitro. We demonstrate that CD99LF maintains a lower level of CD1A transcription by up-regulating the phosphorylated form of the ATF-2 transcription factor and that CD99 short form (SF) is required to counteract this regulatory mechanism. Elucidation of the molecular mechanisms related to CD99 alternative splicing will be very helpful to better understand the transcriptional regulatory mechanism of CD1a molecules during DCs differentiation and its involvement in the immune response.

Comparison of Latino and non-Latino patients with Ewing sarcoma

BACKGROUND

Ewing sarcoma (ES) is a malignancy of bone and soft tissue in children and adults. Previous registry-based studies indicate that Latino patients with ES have inferior outcomes compared to non-Latino patients, though an etiology for this difference could not be identified. To explore possible differences that might underlie this disparity, we conducted a retrospective study to compare clinical characteristics, tumor features, healthcare access, and treatment outcomes between Latino and non-Latino patients with ES.

METHODS

Primary data for 218 ES patients treated at two academic medical centers between 1980 and 2010 were collected. Categorical data were compared using Fisher exact tests; Wilcoxon rank-sum tests were used for continuous variables. Survival was estimated using Kaplan-Meier analysis and compared using log-rank testing.

RESULTS

Latino patients were diagnosed at a younger age (P = 0.014). All other clinical and histological data were similar between groups, including radiologic and histologic response to neoadjuvant chemotherapy. Latino patients had lower socioeconomic status (P = 0.001), were less likely to have insurance (P = 0.001), and were more likely to present to the emergency room at onset of symptoms (P = 0.031) rather than to primary care physicians. Five-year event free survival (EFS) and overall survival (OS) were similar between Latino and non-Latino patients (EFS: 60.5% vs. 50.9% P = 0.37; OS: 77.6% vs. 68.6% P = 0.54).

CONCLUSION

Latino patients with ES present at a younger age, and have evidence of impaired access to healthcare. Response to initial therapy appears similar between Latino and non-Latino patients.

Biomarkers in Ewing Sarcoma: The Promise and Challenge of Personalized Medicine. A Report from the Children's Oncology Group

A goal of the COG Ewing Sarcoma (ES) Biology Committee is enabling identification of reliable biomarkers that can predict treatment response and outcome through the use of prospectively collected tissues and correlative studies in concert with COG therapeutic studies. In this report, we aim to provide a concise review of the most well-characterized prognostic biomarkers in ES, and to provide recommendations concerning design and implementation of future biomarker studies. Of particular interest and potentially high clinical relevance are studies of cell-cycle proteins, sub-clinical disease, and copy number alterations. We discuss findings of particular interest from recent biomarker studies and examine factors important to the success of identifying and validating clinically relevant biomarkers in ES. A number of promising biomarkers have demonstrated prognostic significance in numerous retrospective studies and now need to be validated prospectively in larger cohorts of equivalently treated patients. The eventual goal of refining the discovery and use of clinically relevant biomarkers is the development of patient specific ES therapeutic modalities.

Identification of cell surface proteins as potential immunotherapy targets in 12 pediatric cancers

Technological advances now allow us to rapidly produce CARs and other antibody-derived therapeutics targeting cell surface receptors. To maximize the potential of these new technologies, relevant extracellular targets must be identified. The Pediatric Oncology Branch of the NCI curates a freely accessible database of gene expression data for both pediatric cancers and normal tissues, through which we have defined discrete sets of over-expressed transcripts in 12 pediatric cancer subtypes as compared to normal tissues. We coupled gene expression profiles to current annotation databases (i.e., Affymetrix, Gene Ontology, Entrez Gene), in order to categorize transcripts by their sub-cellular location. In this manner we generated a list of potential immune targets expressed on the cell surface, ranked by their difference from normal tissue. Global differences from normal between each of the pediatric tumor types studied varied, indicating that some malignancies expressed transcript sets that were more highly diverged from normal tissues than others. The validity of our approach is seen by our findings for pre-B cell ALL, where targets currently in clinical trials were top-ranked hits (CD19, CD22). For some cancers, reagents already in development could potentially be applied to a new disease class, as exemplified by CD30 expression on sarcomas. Moreover, several potential new targets shared among several pediatric solid tumors are herein identified, such as MCAM (MUC18), metadherin (MTDH), and glypican-2 (GPC2). These targets have been identified at the mRNA level and are yet to be validated at the protein level. The safety of targeting these antigens has yet to be demonstrated and therefore the identified transcripts should be considered preliminary candidates for new CAR and therapeutic antibody targets. Prospective candidate targets will be evaluated by proteomic analysis including Westerns and immunohistochemistry of normal and tumor tissues.

Wnt5a promotes ewing sarcoma cell migration through upregulating CXCR4 expression

Background

As one of the malignant tumors most often affecting children and young adults, Ewing sarcoma (ES) is characterized by early metastasis contributing to unfavorable prognosis. However, the molecular mechanisms responsible for ES metastasis remain poorly understood. In this study, we aimed to explore whether Wnt5a, a putative pro-metastatic factor, plays a role in ES metastasis.

Methods

Expression of Wnt5a and CXCR4 was determined by real-time PCR or Western blot in 15 ES specimens and 4 ES cell lines, A-673, RD-ES, SK-N-MC and SK-ES-1. Expression of Wnt antagonists, SFRP1, SFRP2 and SFRP5, and some components in noncanonical Wnt pathway (p-JNK, p-cJUN and p-PKC) was also analyzed in this study. Methylation status of SFRP1, SFRP2 and SFRP5 was detected by Methylation-specific PCR (MSP). Wnt5a shRNA and pcDNA3.1 SFRP5 vector were used to abrogate Wnt5a expression and overexpress SFRP5 in ES cells, respectively.

Results

Wnt5a expression was positively correlated with CXCR4 expression in ES specimens. Levels of both Wnt5a mRNA and CXCR4 mRNA were significantly higher in specimens from ES patients with metastasis at diagnosis compared with specimens from those without metastasis. Recombinant Wnt5a enhanced CXCR4 expression in ES cells, which was accompanied by increased ES cell migration, whereas Wnt5a shRNA has opposite effects. SFRP5 was methylated and silenced in ES cells, and both recombinant SFRP5 and pcDNA3.1 SFRP5 vector suppressed CXCR4 expression as well as ES cell migration. Wnt5a shRNA and recombinant SFRP5 inhibited phosphorylation of JNK and cJUN, and JNK inhibitor also reduced CXCR4 expression and cell migration in ES cells.

Conclusions

Wnt5a increases ES cell migration via upregulating CXCR4 expression in the absence of Wnt antagonist SFRP5, suggesting that Wnt5a overexpression and SFRP5 deficiency may jointly promote ES metastasis.

The clinical use of biomarkers as prognostic factors in Ewing sarcoma

Ewing Sarcoma is the second most common primary bone sarcoma with 900 new diagnoses per year in Europe (EU27). It has a poor survival rate in the face of metastatic disease, with no more than 10% survival of the 35% who develop recurrence. Despite the remaining majority having localised disease, approximately 30% still relapse and die despite salvage therapies. Prognostic factors may identify patients at higher risk that might require differential therapeutic interventions. Aside from phenotypic features, quantitative biomarkers based on biological measurements may help identify tumours that are more aggressive. We audited the research which has been done to identify prognostic biomarkers for Ewing sarcoma in the past 15 years. We identified 86 articles were identified using defined search criteria. A total of 11,625 patients were reported, although this number reflects reanalysis of several cohorts. For phenotypic markers, independent reports suggest that tumour size > 8 cm and the presence of metastasis appeared strong predictors of negative outcome. Good histological response (necrosis > 90%) after treatment appeared a significant predictor for a positive outcome. However, data proposing biological biomarkers for practical clinical use remain un-validated with only one secondary report published. Our recommendation is that we can stratify patients according to their stage and using the phenotypic features of metastases, tumour size and histological response. For biological biomarkers, we suggest a number of validating studies including markers for 9p21 locus, heat shock proteins, telomerase related markers, interleukins, tumour necrosis factors, VEGF pathway, lymphocyte count, and a number of other markers including Ki-67.