SMC3 may play an important role in atopic asthma development

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Simple Summary

It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options.

Abstract

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

Low tolerance for transcriptional variation at cohesin genes is accompanied by functional links to disease-relevant pathways

BACKGROUND

The cohesin complex plays an essential role in genome organisation and cell division. A full complement of the cohesin complex and its regulators is important for normal development, since heterozygous mutations in genes encoding these components can be sufficient to produce a disease phenotype. The implication that genes encoding the cohesin subunits or cohesin regulators must be tightly controlled and resistant to variability in expression has not yet been formally tested.

METHODS

Here, we identify spatial-regulatory connections with potential to regulate expression of cohesin loci (Mitotic: SMC1A, SMC3, STAG1, STAG2, RAD21/RAD21-AS; Meiotic: SMC1B, STAG3, REC8, RAD21L1), cohesin-ring support genes (NIPBL, MAU2, WAPL, PDS5A, PDS5B) and CTCF, including linking their expression to that of other genes. We searched the genome-wide association studies (GWAS) catalogue for SNPs mapped or attributed to cohesin genes by GWAS (GWAS-attributed) and the GTEx catalogue for SNPs mapped to cohesin genes by cis-regulatory variants in one or more of 44 tissues across the human body (expression quantitative trail locus-attributed).

RESULTS

Connections that centre on the cohesin ring subunits provide evidence of coordinated regulation that has little tolerance for perturbation. We used the CoDeS3D SNP-gene attribution methodology to identify transcriptional changes across a set of genes coregulated with the cohesin loci that include biological pathways such as extracellular matrix production and proteasome-mediated protein degradation. Remarkably, many of the genes that are coregulated with cohesin loci are themselves intolerant to loss-of-function.

CONCLUSIONS

The results highlight the importance of robust regulation of cohesin genes and implicate novel pathways that may be important in the human cohesinopathy disorders.

T Cell Transcriptomes Describe Patient Subtypes in Systemic Lupus Erythematosus

Background

T cells regulate the adaptive immune response and have altered function in autoimmunity. Systemic Lupus Erythematosus (SLE) has great diversity of presentation and treatment response. Peripheral blood component gene expression affords an efficient platform to investigate SLE immune dysfunction and help guide diagnostic biomarker development for patient stratification.

Methods

Gene expression in peripheral blood T cell samples for 14 SLE patients and 4 controls was analyzed by high depth sequencing. Unbiased clustering of genes and samples revealed novel patterns related to disease etiology. Functional annotation of these genes highlights pathways and protein domains involved in SLE manifestation.

Results

We found transcripts for hundreds of genes consistently altered in SLE T cell samples, for which DAVID analysis highlights induction of pathways related to mitochondria, nucleotide metabolism and DNA replication. Fewer genes had reduced mRNA expression, and these were linked to signaling, splicing and transcriptional activity. Gene signatures associated with the presence of dsDNA antibodies, low complement levels and nephritis were detected. T cell gene expression also indicates the presence of several patient subtypes, such as having only a minimal expression phenotype, male type, or severe with or without induction of genes related to membrane protein production.

Conclusions

Unbiased transcriptome analysis of a peripheral blood component provides insight on autoimmune pathophysiology and patient variability. We present an open source workflow and richly annotated dataset to support investigation of T cell biology, develop biomarkers for patient stratification and perhaps help indicate a source of SLE immune dysfunction.

Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome

Background

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is affected by both environmental and genetic factors. Although the genetic component of PCOS is evident, studies aiming to identify susceptibility genes have shown controversial results. This study conducted a pathway-based analysis using a dataset obtained through a genome-wide association study (GWAS) to elucidate the biological pathways that contribute to PCOS susceptibility and the associated genes.

Methods

We used GWAS data on 636,797 autosomal single nucleotide polymorphisms (SNPs) from 1,221 individuals (432 PCOS patients and 789 controls) for analysis. A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA). Top-ranking pathways or gene sets associated with PCOS were identified, and significant genes within the pathways were analyzed.

Results

The pathway analysis of the GWAS dataset identified significant pathways related to oocyte meiosis and the regulation of insulin secretion by acetylcholine and free fatty acids (all nominal gene-set enrichment analysis (GSEA) P-values < 0.05). In addition, INS, GNAQ, STXBP1, PLCB3, PLCB2, SMC3 and PLCZ1 were significant genes observed within the biological pathways (all gene P-values < 0.05).

Conclusions

By applying MAGENTA pathway analysis to PCOS GWAS data, we identified significant pathways and candidate genes involved in PCOS. Our findings may provide new leads for understanding the mechanisms underlying the development of PCOS.