The Stroma Liquid Biopsy Panel Contains a Stromal-Epithelial Gene Signature Ratio That Is Associated with the Histologic Tumor-Stroma Ratio and Predicts Survival in Colon Cancer

Liquid biopsy has emerged as a novel approach to tumor characterization, offering advantages in sample accessibility and tissue heterogeneity. However, as mutational analysis predominates, the tumor microenvironment has largely remained unacknowledged in liquid biopsy research. The current work provides an explorative transcriptomic characterization of the Stroma Liquid BiopsyTM (SLB) proteomics panel in colon carcinoma by integrating single-cell and bulk transcriptomics data from publicly available repositories. Expression of SLB genes was significantly enriched in tumors with high histologic stromal content in comparison to tumors with low stromal content (median enrichment score 0.308 vs. 0.222, p = 0.036). In addition, we identified stromal-specific and epithelial-specific expression of the SLB genes, that was subsequently integrated into a gene signature ratio. The stromal-epithelial signature ratio was found to have prognostic significance in a discovery cohort of 359 colon adenocarcinoma patients (OS HR 2.581, 95%CI 1.567–4.251, p < 0.001) and a validation cohort of 229 patients (OS HR 2.590, 95%CI 1.659–4.043, p < 0.001). The framework described here provides transcriptomic evidence for the prognostic significance of the SLB panel constituents in colon carcinoma. Plasma protein levels of the SLB panel may reflect histologic intratumoral stromal content, a poor prognostic tumor characteristic, and hence provide valuable prognostic information in liquid biopsy.

Different Glucose Metabolic Features According to Cancer and Immune Cells in the Tumor Microenvironment

BackgroundA close metabolic interaction between cancer and immune cells in the tumor microenvironment (TME) plays a pivotal role in cancer immunity. Herein, we have comprehensively investigated the glucose metabolic features of the TME at the single-cell level to discover feasible metabolic targets for the tumor immune status.MethodsWe examined expression levels of glucose transporters (GLUTs) in various cancer types using The Cancer Genome Atlas (TCGA) data and single-cell RNA-seq (scRNA-seq) datasets of human cancer tissues including melanoma, head and neck, and breast cancer. In addition, scRNA-seq data of immune cells in the TME acquired from human melanoma after immune checkpoint inhibitors were analyzed to investigate the dynamics of glucose metabolic profiles of specific immune cells.ResultsPan-cancer bulk RNA-seq showed that the GLUT3-to-GLUT1 ratio was positively associated with immune cell enrichment score. The scRNA-seq datasets of various human cancer tissues showed that GLUT1 was highly expressed in cancer cells, while GLUT3 was highly expressed in immune cells in TME. The scRNA-seq data obtained from human melanoma tissues pre- and post-immunotherapy showed that glucose metabolism features of myeloid cells, particularly including GLUTs expression, markedly differed according to treatment response.ConclusionsDifferently expressed GLUTs in TME suggest that GLUT could be a good candidate a surrogate of tumor immune metabolic profiles and a target for adjunctive treatments for immunotherapy.

Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell.

Clinics and genetic background of hereditary gingival fibromatosis

Background Hereditary gingival fibromatosis (HGF) is a rare condition characterized by slowly progressive overgrowth of the gingiva. The severity of overgrowth may differ from mild causing phonetic and masticatory issues, to severe resulting in diastemas or malposition of teeth. Both, autosomal-dominant and autosomal-recessive forms of HGF are described. The aim of this review is a clinical overview, as well as a summary and discussion of the involvement of candidate chromosomal regions, pathogenic variants of genes, and candidate genes in the pathogenesis of HGF. The loci related to non-syndromic HGF have been identified on chromosome 2 (GINGF, GINGF3), chromosome 5 (GINGF2), chromosome 11 (GINGF4), and 4 (GINGF5). Of these loci, pathogenic variants of the SOS-1 and REST genes inducing HGF have been identified in the GINGF and the GINGF5, respectively. Furthermore, among the top 10 clusters of genes ranked by enrichment score, ATP binding, and fibronectin encoding genes were proposed as related to HGF. Conclusion The analysis of clinical reports as well as translational genetic studies published since the late’90s indicate the clinical and genetic heterogeneity of non-syndromic HGF and point out the importance of genetic studies and bioinformatics of more numerous unrelated families to identify novel pathogenic variants potentially inducing HGF. This strategy will help to unravel the molecular mechanisms as well as uncover specific targets for novel and less invasive therapies of this rare, orphan condition.

Pathogenic neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. For this method RNA was extracted from lymphocytes, followed by targeted RNAseq. Next, an in-house developed tool (QURNAs) was used to calculate the enrichment score (ERS) for each splicing event. This method was thoroughly tested using two different patient cohorts with known pathogenic splice-variants in NF1. In both cohorts all 56 normal reference transcript exon splice junctions, 24 previously described and 45 novel non-reference splicing events were detected. Additionally, all expected pathogenic splice-variants were detected. Eleven patients with NF1 symptoms were subsequently tested, three of which have a known NF1 DNA variant with a putative effect on RNA splicing. This effect could be confirmed for all 3. The other eight patients were previously without any molecular confirmation of their NF1-diagnosis. A deep-intronic pathogenic splice variant could now be identified for two of them (25%). These results suggest that targeted RNAseq can be successfully used to detect pathogenic RNA splicing variants in NF1.

Identification of molecular subtyping system and four-gene prognostic signature with immune-related genes for uveal melanoma

Immunotherapy is the most promising treatment for uveal melanoma patients with metastasis. Tumor microenvironment plays an essential role in tumor progression and greatly affects the efficacy of immunotherapy. This research constructed an immune-related subtyping system and discovered immune prognostic genes to further understand the immune mechanism in uveal melanoma. Immune-related genes were determined from literature. Gene expression profiles of uveal melanoma were clustered using consensus clustering based on immune-related genes. Subtypes were further divided by applying immune landscape, and weighted correlation network analysis was performed to construct immune gene modules. Univariate Cox regression analysis was conducted to generate a prognostic model. Enriched immune cells were determined after gene set enrichment analysis. Three major immune subtypes (IS1, IS2, and IS3) were identified, and IS2 could be further divided into IS2A and IS2B. The subtypes were closely associated with uveal melanoma prognosis. IS3 group had the most favorable prognosis and was sensitive to PD-1 inhibitor. Immune genes in IS1 group showed an overall higher expression than IS3 group. Six immune gene modules were identified, and the enrichment score of immune genes varied within immune subtypes. Four immune prognostic genes ( IL32, IRF1, SNX20, and VAV1) were found to be closely related to survival. This novel immune subtyping system and immune landscape provide a new understanding of immunotherapy in uveal melanoma. The four prognostic genes can predict prognosis of uveal melanoma patients and contribute to new development of targeted drugs.

The Role of Ion Channel-Related Genes in Autism Spectrum Disorder: A Study Using Next-Generation Sequencing

The clinical heterogeneity of autism spectrum disorder (ASD) is closely associated with the diversity of genes related to ASD pathogenesis. With their low effect size, it has been hard to define the role of common variants of genes in ASD phenotype. In this study, we reviewed genetic results and clinical scores widely used for ASD diagnosis to investigate the role of genes in ASD phenotype considering their functions in molecular pathways. Genetic data from next-generation sequencing (NGS) were collected from 94 participants with ASD. We analyzed enrichment of cellular processes and gene ontology using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We compared clinical characteristics according to genetic functional characteristics. We found 266 genes containing nonsense, frame shift, missense, and splice site mutations. Results from DAVID revealed significant enrichment for “ion channel” with an enrichment score of 8.84. Moreover, ASD participants carrying mutations in ion channel-related genes showed higher total IQ (p = 0.013) and lower repetitive, restricted behavior (RRB)-related scores (p = 0.003) and mannerism subscale of social responsiveness scale scores, compared to other participants. Individuals with variants in ion channel genes showed lower RRB scores, suggesting that ion channel genes might be relatively less associated with RRB pathogenesis. These results contribute to understanding of the role of common variants in ASD and could be important in the development of precision medicine of ASD.

VCAM1 expression in the myocardium is associated with the risk of heart failure and immune cell infiltration in myocardium

Ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) are the two most common etiologies of heart failure (HF). Both forms share common characteristics including ventricle dilation in the final stage. Immune mechanisms in HF are increasingly highlighted and have been implicated in the pathogeneses of IHD and DCM. A better understanding of adhesion molecule expression and correlated immune cell infiltration could enhance disease detection and improve therapeutic targets. This study was performed to explore the common mechanisms underlying IHD and DCM. After searching the Gene Expression Omnibus database, we selected the GSE42955, GSE76701, GSE5406, GSE133054 and GSE57338 datasets for different expressed gene (DEGs) selection and new cohort establishment. We use xcell to calculate immune infiltration degree, ssGSEA and GSEA to calculate the pathway and biological enrichment score, consensus cluster to identify the m6A modification pattern, and LASSO regression to make risk predicting model and use new combined cohort to validate the results. The screening stage revealed that vascular cell adhesion molecule 1 (VCAM1) play pivotal roles in regulating DEGs. Subsequent analyses revealed that VCAM1 was differentially expressed in the myocardium and involved in regulating immune cell infiltration. We also found that dysregulated VCAM1 expression was associated with a higher risk of HF by constructing a clinical risk-predicting model. Besides, we also find a connection among the m6A RNA modification ,expression of VCAM1 and immune regulation. Those connection can be linked by the Wnt pathway enrichment alternation. Collectively, our results suggest that VCAM-1 have the potential to be used as a biomarker or therapy target for HF and the m6A modification pattern is associated with the VCAM1 expression and immune regulation.

Effects of Radix Scrophulariae on Hyperthyroidism Assessed by Metabonomics and Network Pharmacology

Radix Scrophulariae (Chinese name: Xuanshen), a traditional Chinese herb, is used for the treatment of hyperthyroidism, and in this study, its mechanisms were evaluated by metabonomics and system pharmacology. To study the anti-hyperthyroidism effects of R. Scrophulariae, a male SD rat (180–220 g) model of hyperthyroidism induced by Euthyrox was used. Thirty rats were randomly distributed into three groups: the Model group, the R. Scrophulariae treatment group (RS group) and the healthy Control group. Using the UHPLC/Q-TOF-MS metabolomics approach, 44 metabolites were found to be profoundly altered in the model group, and the levels of these biomarkers were significantly decreased after treatment with R. Scrophulariae. Forty-four metabolites and 13 signaling pathways related to R. Scrophulariae, including the biosynthesis of unsaturated fatty acids, primary bile acid biosynthesis and sphingolipid metabolism, were explored, and linoleic acid metabolism and sphingolipid metabolism were identified as the most relevant metabolic pathways. In addition, the system pharmacology paradigm revealed that R. Scrophulariae contains 83 active ingredients and is related to 795 genes, and 804 disease genes are related to hyperthyroidism. The construction of the R. Scrophulariaceae-chemical composition-target-hyperthyroidism network identified a total of 112 intersection genes. The enriched gene targets were analyzed, and five pathways were found to be enriched. Among them pathways, the HIF signaling pathway had the highest enrichment score, which indicated that this pathway might be the main signaling pathway related to the treatment of hyperthyroidism by R. Scrophulariae.The integrated approach involving metabolomics and network pharmacology revealed that R. Scrophulariae might play a role in the treatment of hyperthyroidism by regulating the “IL6-APOA1-cholesterol” pathway and disturbing the HIF signaling pathway. The results demonstrate that the combination of metabolomics and network pharmacology could be used to reflect the effects of R. Scrophulariae on the biological network and metabolic state of hyperthyroidism and to evaluate the drug efficacy of R. Scrophulariaceae and its related mechanisms.

Multiomics Analysis Reveals the Prognostic Non-tumor Cell Landscape in Glioblastoma Niches

A comprehensive characterization of non-tumor cells in the niches of primary glioblastoma is not fully established yet. This study aims to present an overview of non-malignant cells in the complex microenvironment of glioblastoma with detailed characterizations of their prognostic effects. We curate 540 gene signatures covering a total of 64 non-tumor cell types. Cell type-specific expression patterns are interrogated by normalized enrichment score across four large gene expression profiling cohorts of glioblastoma with a total number of 967 cases. The glioblastoma multiforms (GBMs) in each cohort are hierarchically clustered into negative or positive immune response classes with significantly different overall survival. Our results show that astrocytes, macrophages, monocytes, NKTs, and MSC are risk factors, while CD8 T cells, CD8 naive T cells, and plasma cells are protective factors. Moreover, we find that the immune system and organogenesis are uniformly enriched in negative immune response clusters, in contrast to the enrichment of nervous system in positive immune response clusters. Mesenchymal differentiation is also observed in the negative immune response clusters. High enrichment status of macrophages in negative immune response clusters is independently validated by analyzing scRNA-seq data from eight high-grade gliomas, revealing that negative immune response samples comprised 46.63 to 55.12% of macrophages, whereas positive immune response samples comprised only 1.70 to 8.12%, with IHC staining of samples from six short-term and six long-term survivors of GBMs confirming the results.