DiffChIPL: a differential peak analysis method for high-throughput sequencing data with biological replicates based on limma

Comparison of Protein Solubilization and Normalization Methods for Proteomics Analysis of Extracellular Vesicles from Urine

Extracellular vesicles (EVs) play a vital role in numerous biological processes. Proteomic research of EVs is crucial for understanding their functions and potential therapeutic implications. Despite many sample preparation protocols for mass spectrometry-based proteomics of EVs being described, the variability in protein extraction across different protocols has not been extensively investigated. Moreover, given the inherent heterogeneity of EVs, it is vital to conduct a thorough evaluation of normalization methods. Here, we present a comprehensive comparison of three widely used lysis agents─sodium dodecyl sulfate (SDS), urea, and sodium deoxycholate (SDC)─for protein extraction from EVs. We also assess the impact of different normalization strategies on protein quantification, which is crucial for ensuring reliable results. Our results show that method-dependent differences in protein recovery were observed, particularly for membrane-associated proteins. We also find that common normalization strategies, such as urine creatinine and EV markers, did not significantly stabilize protein quantification, indicating that these methods are not universally applicable as normalization standards. Our work thereby provides a reference for the selection of MS sample preparation and normalization strategies for a given EV proteomics project.

Modular-Based Synergetic Mechanisms of Jasminoidin and Ursodeoxycholic Acid in Cerebral Ischemia Therapy

Objectives: Jasminoidin (JA) and ursodeoxycholic acid (UA) have been shown to exert synergistic effects on cerebral ischemia (CI) therapy, but the underlying mechanisms remain to be elucidated. Objective: To elucidate the synergistic mechanisms involved in the combined use of JA and UA (JU) for CI therapy using a driver-induced modular screening (DiMS) strategy. Methods: Network proximity and topology-based approaches were used to identify synergistic modules and driver genes from an anti-ischemic microarray dataset (ArrayExpress, E-TABM-662). A middle cerebral artery occlusion/reperfusion (MCAO/R) model was established in 30 Sprague Dawley rats, divided into sham, vehicle, JA (25 mg/mL), UA (7 mg/mL), and JU (JA:UA = 1:1) groups. After 90 minutes of ischemia, infarct volume and neurological deficit scores were evaluated. Western blotting was performed 24 h after administration to validate key protein changes. Results: Six, eleven, and four drug-responsive On_modules were identified for JA, UA, and JU, respectively. Three synergistic modules (Sy-modules, JU-Mod-7, 8, and 10) and 12 driver genes (e.g., NRF1, FN1, CUL3) were identified, mainly involving the PI3K-Akt and MAPK pathways and regulation of the actin cytoskeleton. JA and UA synergistically reduced infarct volume and neurological deficit score (2.5, p < 0.05) in MCAO/R rats. In vivo studies demonstrated that JU suppressed the expression of CUL3, FN1, and ITGA4, while it increased that of NRF1. Conclusions: JU acts synergistically on CI-reperfusion injury by regulating FN1, CUL3, ITGA4, and NRF1 and inducing the PI3K-Akt, MAPK, and actin cytoskeleton pathways. DiMS provides a new approach to uncover mechanisms of combination therapies.

Identification of novel biomarkers associated with immune infiltration in major depression disorder and atopic dermatitis

Major depression disorder (MDD) and atopic dermatitis (AD) are distinct disorders involving immune inflammatory responses. This study aimed to investigate the comorbid relationship between AD and MDD and to identify possible common mechanisms. We obtained AD and MDD data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and the Genecard database were employed to identify shared genes associated with inflammatory diseases. These shared genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hub genes were selected based on the protein-protein interactions using CytoHubba, and key regulatory genes were identified through enrichment analysis. Subsequently, we conducted immune infiltration and correlation analyses of the shared genes in AD. Finally, we employed three machine learning models to predict the significance of shared genes. A total of 17 shared genes were identified in the AD_Inflammatory_MDD dataset (S100A9, PTGER2, PI3, SNCA, DAB2, PDGFA, FSTL1, CALD1, XK, UTS2, DHRS9, PARD3, NFIB, TMEM158, LIPH, RAB27B, and SH3BRL2). These genes were associated with biological processes such as the regulation of mesenchymal cell proliferation, cellular ketone metabolic processes, and glial cell differentiation. The neuroactive ligand-receptor interaction, IL-17 signaling, and Rap1 signaling pathways were significantly enriched in KEGG analysis. SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as hub genes contributing to comorbid AD and MDD development. The three machine learning models consistently identified SNCA and PARD3 as important biomarkers.SNCA, S100A9, SH3BGRL2, RAB27B, TMEM158, DAB2, FSTL1, CALD1, and XK were identified as significant genes contributing to the development of AD and MDD comorbidities. Immune infiltration analysis showed a notable increase in the infiltration of various subtypes of CD4 + T cells, suggesting a potential association between the development of skin inflammation and the immune response. Across different machine learning models, SNCA and PARD3 consistently emerged as important biomarkers, providing a new direction for clinical diagnosis and treatment.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms

Objective

Abdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.

Methods

Sourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes.

Results

We discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells.

Conclusion

Three feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

BACKGROUND

Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration.

METHODS

PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149.

RESULTS

Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI.

CONCLUSIONS

This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

CBFA2T3-GLIS2 mediates transcriptional regulation of developmental pathways through a gene regulatory network

CBFA2T3-GLIS2 is a fusion oncogene found in pediatric acute megakaryoblastic leukemia that is associated with a poor prognosis. We establish a model of CBFA2T3-GLIS2 driven acute megakaryoblastic leukemia that allows the distinction of fusion specific changes from those that reflect the megakaryoblast lineage of this leukemia. Using this model, we map fusion genome wide binding that in turn imparts the characteristic transcriptional signature. A network of transcription factor genes bound and upregulated by the fusion are found to have downstream effects that result in dysregulated signaling of developmental pathways including NOTCH, Hedgehog, TGFβ, and WNT. Transcriptional regulation is mediated by homo-dimerization and binding of the ETO transcription factor through the nervy homology region 2. Loss of nerve homology region 2 abrogated the development of leukemia, leading to downregulation of JAK/STAT, Hedgehog, and NOTCH transcriptional signatures. These data contribute to the understanding of CBFA2T3-GLIS2 mediated leukemogenesis and identify potential therapeutic vulnerabilities for future studies.

MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Multi-omics and single-cell sequencing analyses reveal the potential significance of circadian pathways in cancer therapy

BACKGROUND

Circadian rhythm disturbance is an independent risk factor for cancer. However, few studies have been reported on circadian rhythm related genes (CRGs) in cancer, so it is important to further explore the impact of CRGs in pan-cancer.

RESEARCH DESIGN AND METHODS

The Cancer Genome Atlas database was used to collect cancer-related data such as copy number variation, single nucleotide variants, methylation, and survival differences. Immunohistochemistry (IHC) was used to verify the expression of circadian rhythm hub genes. The circadian pathway scores (CRS) were calculated using single-sample gene enrichment analysis. TIMER and GEPIA databases were used for immune-cell integration and assessment. Single-cell sequencing data was used to evaluate the abundance of CRS in tumor microenvironment cells.

RESULTS

In this study, we found that the expression of circadian pathway varies between tumors. CSNK1E was significantly up-regulated in most tumors and CRY2 was significantly down-regulated in most tumors. The protein interaction network suggested CRY2 as the core gene and IHC verified its significant low expression in KIRC. In addition, CRGs were found to be protective factors in most tumors and have the potential to act as specific immune markers in different tumors. CRS was significantly lower in abundance in most tumors. CRS was significantly associated with overall survival in tumor patients and associated with the expression of many immune cells in the tumor immune microenvironment. CRS is significantly associated with tumor mutational burden and microsatellite instability scores in most tumors and may serve as a potential immunotherapeutic marker.

CONCLUSIONS

The circadian rhythm pathway may be a breakthrough point in regulating the tumor microenvironment meanwhile a suitable immunotherapy method in the future.

Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients

BACKGROUND

Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction.

METHODS

Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments.

RESULTS

Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation.

CONCLUSION

This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

BACKGROUND

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

METHODS

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

RESULTS

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

CONCLUSIONS

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Background

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).

Methods

We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.

Results

We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.

Conclusion

Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.