Anatomical Covariance Analysis: Detection of Disrupted Correlation Network Related to Clinical Trait Fatigue in Multiple Sclerosis: A Pilot Study

Background. Fatigue is one of the most distressing symptoms among persons with multiple sclerosis (PwMS). The experience of fatigue is inherently interoceptive, yet no study to date has explicitly investigated the insular cortex (IC) as a primary goal in the experience of fatigue in PwMS. In addition, it is unknown how brain regions such as IC play a role in state or trait fatigue. Objective. Assess the involvement of the IC in trait fatigue and state fatigue in PwMS with and without clinical fatigue. Methods. Trait and state fatigue, cognitive status, and structural MRI were assessed in 27 PwMS. PwMS were stratified into nonclinical fatigue (nF-MS, FSS ≤ 4.0 ) ( n = 10 ) and clinical fatigue (F-MS, FSS ≥ 5.0 ) ( n = 10 ). Voxel-based morphometry analysis (VBM) for the whole sample ( n = 20 ) and for the two groups was performed. Anatomical covariance analysis (ACA) analysis was conducted by selecting different volumes included in the corticostriatal network (CoStN) and analyzing interhemispheric correlations between those volumes to explore the state of the CoStN in both groups. Results. In the VBM analysis, when considering the whole sample of PwMS, higher levels of trait fatigue were negatively associated with grey matter (GM) volume in the left dorsal anterior insula (dAI) ( rho = − 0.647 ; p = 0.002 ; R 2 = 0.369 ). When comparing nF-MS versus F-MS, significant differences were found in the left dAI, where the F-MS group showed less GM volume in the left dAI. In the ACA analysis, the F-MS group showed fewer significant interhemispheric correlations in comparison with the Low-FSS group. Conclusions. The present results provide support to the interoceptive component of self-reported fatigue and suggest that changes in the relationship between the different anatomical regions involved in the CoStN are present even in nonclinical trait fatigue. Those changes might be responsible for the experience of trait fatigue in PwMS. Future studies with larger samples and multimodal MRI acquisitions should be considered to fully understand the changes in the CoStN and the specific role of the IC in trait fatigue.

Gene Co-expression Networks Are Associated with Obesity-Related Traits in Kidney Transplant Recipients

Background Obesity is common among kidney transplant recipients; However biological mediators of obesity are not well understood in this population. Because subcutaneous adipose tissue can be easily obtained during kidney transplant surgery, it provides a unique avenue for studying the mechanisms of obesity for this group. Although differential gene expression patterns were previously profiled for kidney transplant patients, gene co-expression patterns can shed light on gene modules not yet explored on the coordinative behaviors of gene transcription in biological and disease processes from a systems perspective. Methods In this study, we collected 29 demographic and clinical variables and matching microarray expression data for 26 kidney transplant patients. We conducted Weighted Gene Co-expression Network Analysis (WGCNA) for 5,758 genes with the highest average expression levels and related gene co-expression to clinical traits. Results A total of 35 co-expression modules were detected, two of which showed associations with obesity-related traits, mainly at baseline. Gene Ontology (GO) enrichment was found for these two clinical trait-associated modules. One module consisting of 129 genes was enriched for a variety of processes, including cellular homeostasis and immune responses. The other module consisting of 36 genes was enriched for tissue development processes. Conclusions Our study generated gene co-expression modules associated with obesity-related traits in kidney transplant patients and provided new insights regarding the cellular biological processes underlying obesity in this population.

Gene Co-expression Networks Are Associated with Obesity-Related Traits in Kidney Transplant Recipients

Background Obesity is common among kidney transplant recipients; However biological mediators of obesity are not well understood in this population. Because subcutaneous adipose tissue can be easily obtained during kidney transplant surgery, it provides a unique avenue for studying the mechanisms of obesity for this group. Although differential gene expression patterns were previously profiled for kidney transplant patients, gene co-expression patterns can shed light on gene modules not yet explored on the coordinative behaviors of gene transcription in biological and disease processes from a systems perspective. Methods In this study, we collected 29 demographic and clinical variables and matching microarray expression data for 26 kidney transplant patients. We conducted Weighted Gene Co-expression Network Analysis (WGCNA) for 5,758 genes with the highest average expression levels and related gene co-expression to clinical traits. Results A total of 35 co-expression modules were detected, two of which showed associations with obesity-related traits, mainly at baseline. Gene Ontology (GO) enrichment was found for these two clinical trait-associated modules. One module consisting of 129 genes was enriched for a variety of processes, including cellular homeostasis and immune responses. The other module consisting of 36 genes was enriched for tissue development processes. Conclusions Our study generated gene co-expression modules associated with obesity-related traits in kidney transplant patients and provided new insights regarding the cellular biological processes underlying obesity in this population.

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Objective: Invasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction. Methods: Differentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes’ prognostic value. Results: After threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients’ estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA. Conclusion: Our findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.

Gene Co-expression Networks Are Associated with Obesity-Related Traits in Kidney Transplant Recipients

Background: Weight gain is often observed following kidney transplantation. Biological mediators of weight gain are not well understood in this population. Because subcutaneous adipose tissue can be easily obtained during kidney transplant surgery, it provides a unique avenue for studying the mechanisms of obesity for this group.Although differential gene expression patterns were previously profiled for kidney transplant patients, gene co-expression patterns can shed light on gene modules not yet explored on the coordinative behaviors of gene transcription in biological and disease processes. Methods: In this study, we collected 29 demographic and clinical variables and matching microarray expression data for 26 kidney transplant patients. We conducted Weighted Gene Co-expression Network Analysis (WGCNA) for 5,758 genes with the highest average expression levels and related gene co-expression to clinical traits. Results: A total of 35 co-expression modules were detected, seven of which showed association with clinical traits. Gene Ontology (GO) enrichment was found for five clinical trait-associated modules. One module consisting of 129 genes was enriched for metabolic and immune processes. The genes within this module were associated with multiple obesity-related traits, and is consistent with findings of previous studies in other clinically obese populations. Conclusions : Our study generatedgene coexpression modules associated with obesity-related traits in kidney transplant patients and provided new insights regarding the cellular biological processes underlying obesity. Trial Registration: N/A Keywords : co-expression modules, obesity, association, enrichment analysis, gene ontology, kidney transplant