Performance comparison between multi-level gene expression data in cancer subgroup classification.

Cancer is a serious disease that can affect various parts of the body such as breast, colon, lung or stomach. Each of these cancers has their own treatment dependent historical subgroups. Hence, the correct identification of cancer subgroup has almost same importance as the timely diagnosis of cancer. This is still a challenging task and a system with highest accuracy is essential. Current researches are moving towards analyzing the gene expression data of cancer patients for various purposes including biomarker identification and studying differently expressed genes, using gene expression data measured in a single level (selected from different gene levels including genome, transcriptome or translation). However, previous studies showed that information carried by one level of gene expression is not similar to another level. This shows the importance of integrating multi-level omics data in these studies. Hence, this study uses tumor gene expression data measured from various levels of gene along with the integration of those data in the subgroup classification of nine different cancers. This is a comprehensive analysis where four different gene expression data such as transcriptome, miRNA, methylation and proteome are used in this subgrouping and the performances between models are compared to reveal the best model.

Macrophage migration inhibitory factor: A noval biomarker upregulates in myasthenia gravis and correlates with disease severity and relapse.

OBJECTIVE: To explore the relationship between macrophage migration inhibitory factor (MIF) and disease severity and relapse in patients with myasthenia gravis (MG). METHODS: 145 MG patients including 79 new-onset patients, 30 remission patients and 36 relapse patients were enrolled in this study. The detailed characteristics of all enrolled MG patients were routinely recorded, including gender, age, type, MGFA classification, antibody, thymic status, clinical score, treatment, MGFA-PIS and B cell subsets (memory B cells, plasmablast cells and plasma cells) detected by flow cytometry. Serum MIF levels were measured by enzyme-linked immunosorbent assay (ELISA) kit. The correlation of MIF levels with clinical subtypes, disease severity and B cell subsets were investigated. Moreover, logistic regression analysis was applied to assess the factors affecting relapse of generalized MG (GMG). RESULTS: Serum MIF levels were higher in new-onset MG patients than those in controls and were positively associated with QMG score, MGFA classification and memory B cells. Subgroup analysis revealed that MIF levels were increased in GMG patients than in ocular MG (OMG), as well as elevated in MGFA III/IV compared with MGFA I/II. With the remission of the disease, the expression of serum MIF decreased. The multivariate logistic regression models indicated that high MIF and thymoma was a risk factor for relapse of GMG, and rituximab could prevent disease relapse. CONCLUSIONS: MIF can be used as a novel biomarker to reflect disease severity and predict disease relapse in MG patients.

MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches.

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.

Circulating miRNAs drive personalized medicine based on subgroup classification in myasthenia gravis patients.

Myasthenia gravis (MG) is a classic autoimmune neuromuscular disease with strong clinical heterogeneity. The concept of subgroup classification was proposed to guide the precise treatment of MG. Subgroups based on serum antibodies and clinical features include ocular MG, early-onset MG with AchR antibodies, late-onset MG with AchR antibodies, thymoma-associated MG, MuSK-associated MG, LRP4-associated MG, and seronegative MG. However, reliable objective biomarkers are still needed to reflect the individualized response to therapy. MicroRNAs (miRNAs) are small non-coding RNA molecules which can specifically bind to target genes and regulate gene expression at the post-transcriptional level, and then influence celluar biological processes. MiRNAs play an important role in the pathogenesis of autoimmune diseases, including MG. Several studies on circulating miRNAs in MG have been reported. However, there is rare systematic review to summarize the differences of these miRNAs in different subgroups of MG. Here, we summarize the potential role of circulating miRNAs in different subgroups of MG to promote personalized medicine.

Electroglottographic and Acoustic Parametrization of Phonatory Quality Provide Voice Profiles of Pathological Speakers.

OBJECTIVES: The present study firstly tries to find subgroups of pathological male and female phonation using data from a number of 534 pathological speakers. Secondly, this subgroup classification provides a basis for achieving voice profiles of pathological phonatory quality. METHODS: Using complementarily orientated electroglottographic and acoustic parametrization of phonatory quality, sustained vowel productions of 267 male and 267 female speakers were considered. RESULTS: In a first step, a clustering technique differentiates three subgroups within each gender on the basis of the EGG- and three subgroups on the basis of the acoustic parameters. In a second step, this subgroup definition allows one to present voice profiles of pathological speakers by combining the parameter means of the electroglottographically determined subgroups with those of the acoustically determined subgroups. CONCLUSIONS: The presented voice profiles provide a finer reference basis for the classification of different pathological phonation types as well as for the evaluation of shifts in individual phonatory behavior due to therapy or spontaneous recovery.

Subgroup Identification with Gene Expression Profiles of Adipose Tissue in Patients with Coronary Artery Disease.

Among many diseases, coronary artery disease (CAD) is the primary cause of mortality and morbidity worldwide. With the aim of revealing the underlying genetic characteristics of the CAD subtypes, we recruited patients with CAD and categorized them into subgroups according to the transcriptome expression profiles of the adipose tissue.With the removal of the batch effect, consensus clustering was employed to determine the subgroup numbers. Subgroup-specific genes were determined to conduct analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) revealed the subgroup-specific WGCNA modules. Moreover, gene set enrichment analysis (GSEA) was conducted. Overrepresentation enrichment analysis (OEA) of subgroup-specific signatures was also conducted to reveal the significant gene module associated with the corresponding clinical characteristics.After the removal of the batch effect, 77 CAD objects were divided into three subgroups. It was observed that the patients in subgroup III tended to be fat. After analyzing the dominant pathways of each subgroup, we discovered that the protein digestion and absorption pathway was specifically upregulated in subgroup I, which might result from the lowest proportion of the epicardial adipose tissue (EAT) sample. Moreover, subgroup II patients had genetic characteristics of high expression of complement and coagulation cascades and TNF signaling pathway. Furthermore, Th17 cell differentiation was significantly upregulated in subgroup III, indicating that Th17 cell differentiation is related to the clinical characteristics of body mass index (BMI).In conclusion, the genetic classification of CAD subjects indicated that subjects from different subgroups may exhibit specific gene expression patterns, suggesting that more personalized treatment should be applied to patients in each subgroup.

A Novel Classification of Glioma Subgroup, Which Is Highly Correlated With the Clinical Characteristics and Tumor Tissue Characteristics, Based on the Expression Levels of Gß and Gγ Genes.

PURPOSE: Glioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a reference standard for subgroup classification. Glioma has been diagnosed based on histopathology, grade, and molecular markers including IDH mutation, chromosome 1p/19q loss, and H3K27M mutation. This subgroup classification cannot fully meet the current needs of clinicians and researchers. We, therefore, present a new subgroup classification for glioma based on the expression levels of Gß and Gγ genes to complement studies on glioma and Gßγ subunits, and to support clinicians to assess a patient's tumor status. METHODS: Glioma samples retrieved from the CGGA database and the TCGA database. We clustered the gliomas into different groups by using expression values of Gß and Gγ genes extracted from RNA sequencing data. The Kaplan-Meier method with a two-sided log-rank test was adopted to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was referred to in order to investigate the prognostic role of each Gß and Gγ genes. KEGG and ssGSEA analysis were applied to identify highly activated pathways. The "estimate" package, "GSVA" package, and the online analytical tools CIBERSORTx were employed to evaluate immune cell infiltration in glioma samples. RESULTS: Three subgroups were identified. Each subgroup had its own specific pathway activation pattern and other biological characteristics. High M2 cell infiltration was observed in the GNB2 subgroup. Different subgroups displayed different sensitivities to chemotherapeutics. GNB2 subgroup predicted poor survival in patients with gliomas, especially in patients with LGG with mutation IDH and non-codeleted 1p19q. CONCLUSION: The subgroup classification we proposed has great application value. It can be used to select chemotherapeutic drugs and the prognosis of patients with target gliomas. The unique relationships between subgroups and tumor-related pathways are worthy of further investigation to identify therapeutic Gßγ heterodimer targets.

Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets.

As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques-e.g., NanoString, microarrays, or DNA methylation assays-which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning-based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries.

Prevalence and Genomic Diversity of Feline Leukemia Virus in Privately Owned and Shelter Cats in Aburrá Valley, Colombia.

The feline leukemia virus (FeLV) belongs to the family Retroviridae; it is the first feline retrovirus discovered and one of the agents that has a great impact on cats' health and the ecology of the feline population worldwide. It is associated with the occurrence of several syndromes of fatal diseases, including the development of lymphomas. Studies on FeLV have been reported in Colombia, and most of them have been approached from a clinical point of view. However, only a few studies have focused on the prevalence of the infection, while none have clarified which variant or FeLV viral subgroup is presently circulating in our country. Therefore, the present study investigated the prevalence of the infection associated with the molecular characterization of FeLV present in cats in Aburrá Valley, Colombia. The sampling of privately owned and shelter cats was performed in female (n = 54) and male (n = 46) felines; most of them were seemingly healthy according to the owner's report, with nonspecific clinical history. Immunoassay confirmed that 59.44% (95% confidence interval (CI) = 49.81-69.06%) of felines were FeLV seropositive. The molecular testing of felines using reverse transcription-polymerase chain reaction and sequencing showed that 30% (30/100) of felines were positive, and the most prevalent subgroup in the Aburrá Valley was FeLV-A. In conclusion, the frequency of leukemia virus, as revealed by molecular and serological tests, is one of the highest reported frequencies to date, and a high molecular variation is shown in the Colombian population. More studies on the behaviour of the virus in feline populations in Columbia are warranted to determine its prevalence throughout the country.

Whole Genome DNA Methylation Analysis of Human Glioblastoma Using Illumina BeadArrays.

In this chapter, we describe the use of Illumina® Infinium® HD Assay in conjunction with Illumina's EPIC Methylation 8-sample array platform to obtain glioblastoma molecular profiles. The procedure spans four days, and can be performed by a single laboratory technician. Starting with as little as 250 ng of DNA input, this method allows the flexibility to begin with DNA derived from either formalin-fixed, paraffin-embedded (FFPE) or fresh tissue and is compatible with an Illumina iScan or HiScan system.

A Retrospective Examination of Feline Leukemia Subgroup Characterization: Viral Interference Assays to Deep Sequencing.

Feline leukemia virus (FeLV) was the first feline retrovirus discovered, and is associated with multiple fatal disease syndromes in cats, including lymphoma. The original research conducted on FeLV employed classical virological techniques. As methods have evolved to allow FeLV genetic characterization, investigators have continued to unravel the molecular pathology associated with this fascinating agent. In this review, we discuss how FeLV classification, transmission, and disease-inducing potential have been defined sequentially by viral interference assays, Sanger sequencing, PCR, and next-generation sequencing. In particular, we highlight the influences of endogenous FeLV and host genetics that represent FeLV research opportunities on the near horizon.