Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer

Discovering Novel Biomarkers and Potential Therapeutic Targets of Amyotrophic Lateral Sclerosis Through Integrated Machine Learning and Gene Expression Profiling

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficult to understand and its diagnosis and treatment during the early stages difficult to determine. Discovering novel biomarkers in ALS for diagnostic and therapeutic potential has become important. Consequently, bioinformatics and machine learning algorithms are useful for identifying differentially expressed genes (DEGs) and potential biomarkers, as well as understanding the molecular mechanisms and intricacies of diseases such as ALS. To achieve the aim of the present study, six datasets obtained from the Gene Expression Omnibus (GEO) were utilized and analyzed using an integrative bioinformatics and machine learning approach. Log transformation was done during data preprocessing, RMA normalization was performed, and the batch effect was corrected. Differential expression analysis identified 206 DEGs that were significantly associated with different biological processes, including muscle function, energy metabolism, and mitochondrial membrane activity. Functional enrichment analysis highlighted pathways, including those related to prion disease, Parkinson's disease, and ATP synthesis via chemiosmotic coupling. We employed a multi-step machine learning framework incorporating random forest, LASSO regression, and SVM-RFE to identify robust biomarkers. This approach identified three key genes, CHRNA1, DLG5, and PLA2G4C, which could be explored as promising biomarkers for ALS after further validation. The internal validation, including principal component analysis (PCA) and ROC-AUC analysis, demonstrated strong diagnostic potential of these hub genes, achieving an AUC of 0.96. This work highlights the utility of bioinformatics and machine learning in identifying key genes as biomarkers for diagnostic and therapeutic potential in ALS.

DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network

Uncovering key genes that drive diseases and cancers is crucial for advancing understanding and developing targeted therapies. Traditional differential expression analysis often relies on arbitrary cutoffs, missing critical genes with subtle expression changes. Some methods incorporate protein-protein interactions (PPIs) but depend on prior disease knowledge. To address these challenges, we developed DiCE (Differential Centrality-Ensemble), a novel approach that combines differential expression with network centrality analysis, independent of prior disease annotations. DiCE identifies candidate genes, refines them with an information gain filter, and reconstructs a condition-specific weighted PPI network. Using centrality measures, DiCE ranks genes based on expression shifts and network influence. Validated on prostate cancer datasets, DiCE identified genes over-represented in key pathways and cancer fitness genes, significantly correlating with disease-free survival (DFS), despite DFS not being used in selection. DiCE offers a comprehensive, unbiased approach to identifying disease-associated genes, advancing biomarker discovery and therapeutic development.

A structural genomics approach to investigate Dystrophin mutations and their impact on the molecular pathways of Duchenne muscular dystrophy

Background

Dystrophin is a key protein encoded by the DMD gene, serves as a scaffold linking the cytoskeleton to the extracellular matrix that plays a critical role in muscle contraction, relaxation, and structural integrity. Mutations, particularly single-point amino acid substitutions, can lead to dysfunctional Dystrophin, causing muscular dystrophies, with Duchenne muscular dystrophy (DMD) being the most severe form.

Objective

This study aimed to evaluate the effects of 184 single-point amino acid substitutions on the structure and function of Dystrophin using computational approaches.

Methods

Many computational tools were used to predict the impact of amino acid substitutions on protein stability, solubility, and function. Pathogenic potential was assessed using disease phenotype predictors and CADD scores, while allele frequency data from gnomAD contextualized mutation prevalence. Additionally, aggregation propensity, frustration analysis, and post-translational modification sites were analyzed for functional disruptions.

Results

Of the 184 substitutions analyzed, 50 were identified as deleterious, with 41 predicted to be pathogenic. Seventeen mutations were localized in the Calponin-homology (CH) 1 domain, a critical functional region of Dystrophin. Six substitutions (N26H, N26K, G47W, D98G, G109A, and G109R) were predicted to decrease protein solubility and were located in minimally frustrated regions, potentially compromising Dystrophin functionality and contributing to DMD pathogenesis.

Conclusion

This study provides novel insights into the molecular mechanisms of DMD, highlighting specific mutations that disrupt Dystrophin's solubility and function. These findings could inform future therapeutic strategies targeting Dystrophin mutations to address DMD pathogenesis.

Identification of critical biomarkers and immune landscape patterns in glioma based on multi-database

PURPOSE

Glioma is the most prevalent tumor of the central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore the urgent need for early diagnosis and an optimized prognostic approach for glioma. Therefore, the aim of this study was to identify sensitive biomarkers for glioma.

PATIENTS AND METHODS

Differentially expressed genes (DEGs) of glioma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The potential biomarkers were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. The prognostic ability of the potential biomarkers was evaluated by Cox regression and survival curve. CellMiner was used to access the correlation between the expression of potential biomarkers and anticancer drug sensitivity. We then explored the association of potential biomarkers and tumor immune infiltration by single-sample GSEA (ssGSEA) and CIBERSORT. Immune staining in glioma patient samples and cell experiments of potential biomarkers further verified their expression and function.

RESULTS

Ultimately, we identified three potential biomarkers: SLC8A2, ATP2B3, and SRCIN1. These 3 genes were found significantly correlated with clinicopathological features (age, WHO grade, IDH mutation status, 1p19q codeletion status). Furthermore, the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were found to be positively correlated with high expression of these 3 potential biomarkers. Besides, there was a substantial relationship between the sensitivity of anticancer drugs and these biomarkers expression. More importantly, the negative association between the 3 genes with most tumor immune cells was also established. Moreover, the decreased expression of the biomarkers was also verified in glioma patient samples. Finally, we confirmed that these 3 genes might promotes glioma proliferation and migration in vitro.

CONCLUSION

SLC8A2, ATP2B3, and SRCIN1 were identified as underlying biomarkers for glioma associated with prognosis assessments and personal immunotherapy.

Multi-omics and in-silico approach reveals AURKA, AURKB, and RSAD2 as therapeutic biomarkers in OSCC progression

Oral squamous cell carcinoma (OSCC), a prevalent form of head and neck cancer, poses a significant health challenge with limited improvements in patient outcomes over the years. Its development is influenced by a complex interplay of genetic alterations and environmental factors. While progress has been made in understanding the molecular mechanisms driving OSCC, pinpointing critical molecular markers and potential drug candidates has proven elusive. This study uniquely endeavors to conduct a meta-analysis to unveil therapeutic genes responsible for OSCC tumorigenesis. A multi-omics approach identified 951 differentially expressed genes (DEGs) associated with OSCC by analyzing microarray data from the NCBI GEO database. Weighted gene co-expression network analysis (WGCNA) detected a significant hub gene module comprising 805 genes, followed by the construction of protein-protein interaction network resulting in two small clusters of 7 gene-encoded proteins each. These clusters were filtered out based on top 10 significant pathways and gene ontology terms to identify six key target proteins with elevated expression levels, acting as potential therapeutic biomarkers for OSCC. Notably, RSAD2 emerged as a novel biomarker linked to OSCC progression. Furthermore, we identified potential inhibitors targeting AURKA, AURKB, and RSAD2 proteins and validated their interactions through molecular dynamics simulation studies. The simulations confirmed the stability of receptor-ligand complexes, suggesting ZINC03839281, ZINC04026167, and ZINC00718292 compounds hold promise as potential inhibitors for therapeutically targeting AURKA, AURKB, and RSAD2 as significant OSCC biomarkers. We recommend further comprehensive studies, including experimental and preclinical investigations, to validate the effectiveness of these lead compounds for OSCC treatment.

Differential gene expression analysis supports dysregulation of mitochondrial activity as a new perspective for glioblastoma's aggressiveness

Brain cancer is considered one of the most aggressive and lethal types of cancer, including primary tumors, being subdivided into milder forms such as low-grade gliomas and glioblastoma, considered the most aggressive form with higher invasion. Among the hallmarks of glioblastoma, the deregulation of mitochondrial metabolism has not yet been fully elucidated. Therefore, the search for mitochondrial biomarkers that can be used as indicators of the progression of this type of cancer is necessary. The aim of this study was to investigate the difference in gene expression between astrocytoma-type gliomas and glioblastomas, and how genes involved in mitochondrial metabolism can influence the proliferative cascade and be associated with tumor invasion. From the differential analysis of glioblastoma expression when compared to the milder form, 11 differentially expressed genes (DEGs) were found in our study, six of which were upregulated (ATP5MGL, C15orf48, MCUB, TERT, AGXT and CYP27B1) and four downregulated (SLC2A4, GK2, SLC25A48, ETNPPL and HMGCS2). To validate the findings, we used other independent bulk RNA-seq datasets and evaluated the number of normalized counts of the DEGs founded. Among these genes, we highlight that none of them had been reported in glioblastoma until this research, and we suggest these genes as possible biomarkers to be further explored, since they are associated with essential pathways for the tumor, such as glucose metabolization, gluconeogenesis, calcium and vitamin D metabolism, tumor progression and activation of the invasion cascade.

Gene Signature for Predicting Metastasis in Prostate Cancer Using Primary Tumor Expression Profiles

Prostate cancer (PCa) is currently the most commonly diagnosed cancer and second leading cause of cancer-related death in men in the United States. The development of metastases is associated with a poor prognosis in PCa patients. Since current clinicopathological classification schemes are unable to accurately prognosticate the risk of metastasis for those diagnosed with localized PCa, there is a pressing need for precise and easily attainable biomarkers of metastatic risk in these patients. Primary tumor samples from 1239 individuals with PCa were divided into development (n=1000) and validation (n=239) cohorts. In the development cohort, we utilized a meta-analysis workflow on retrospective primary tumor gene expression profiles to identify a subset of genes predictive of metastasis. For each gene, we computed Hedges' g effect size and combined their p-values using Fisher's combined probability test. We then adjusted for multiple hypothesis testing using the Benjamini-Hochberg method. Our developed gene signature, termed Meta-Score, achieved a robust performance at predicting metastasis from primary tumor gene expression profiles, with an AUC of 0.72 in the validation cohort. In addition to its robust predictive power, Meta-Score also demonstrated a significant prognostic utility in two independent cohorts. Specifically, patients with a higher risk-score had a significantly worse metastasis-free survival and progression-free survival compared to those with lower score. Multivariate cox proportional hazards model showed that Meta-Score is significantly associated with worse survival even after adjusting for Gleason score. Our findings suggest that our primary tumor transcriptional signature, Meta-Score, could be a valuable tool to assess the risk of metastasis in PCa patients with localized disease, pending validation in large prospective studies.

Author Summary

Metastasis is the leading cause of death in patients diagnosed with prostate cancer (PCa), underscoring the need for reliable prediction tools to forecast the risk of metastasis at an early stage. Here, we utilize the gene expression profiles of 1,000 unique primary tumors from patients with localized PCa to develop a gene signature capable of predicting metastasis. Our signature, termed Meta-Score, comprises forty-five genes that can accurately distinguish primary tumor with high propensity for metastasis across different patient cohorts. Notably, Meta-Score maintained its robust predictive performance in an internal validation cohort of comprising primary tumor samples from 239 patients. In addition to its robust predictive performance, Meta-Score demonstrates a significant association with survival, independent of Gleason score in two independent patient cohorts, underscoring its prognostic utility. Taken together, Meta-Score is a robust risk-stratification tool that can be leveraged to identify patients at high-risk of metastasis and unfavorable survival using their primary tumor gene expression profiles.

Molecular Alterations Associated with Histologically Overt Stromal Response in Patients with Prostate Cancer

Prostate cancer has substantial heterogeneity in clinical outcomes and therapeutic responses, posing challenges in predicting disease progression and tailoring treatment strategies. Recent studies have highlighted the potential prognostic value of evaluating the tumor microenvironment, including the presence of a histologically overt stromal response (HOST-response) characterized by peri-glandular stromal changes and architectural distortions. This retrospective study examined patient records from The Cancer Genome Atlas database to identify genomic alterations associated with the HOST-response in prostate cancer. Among 348 patients who underwent radical prostatectomy, 160 (45.98%) were identified as having a HOST-response. A gene expression analysis revealed 1263 genes with significantly higher expression in patients with a HOST-response. A protein-protein interaction network analysis identified seven hub genes (KIF2C, CENPA, CDC20, UBE2C, ESPL1, KIF23, and PLK1) highly interconnected in the network. A functional enrichment analysis revealed alterations in the cell division, cytoskeletal organization, cytokinesis, and interleukin-16 signaling pathways in patients with a HOST-response, suggesting dysregulated proliferation and inflammation. The distinct molecular signature associated with the HOST-response provides insights into the tumor-stroma interactions driving adverse outcomes and potential targets for tailored therapeutic interventions in this subset of patients with prostate cancer.

Discovering Promising Biomarkers and Therapeutic Targets for Duchenne Muscular Dystrophy: a Multiomics Meta-Analysis Approach

Duchenne muscular dystrophy (DMD) is a genetic disorder that causes muscle weakness and degeneration. In this study, we identified potential biomarkers and drug targets for DMD through a comprehensive meta-analysis of mRNA profiles. We conducted an in-depth analysis of three microarray datasets from the GEO database, utilizing the Affymetrix platform. A rigorous data pre-processing pipeline encompassed background correction, normalization, log2 transformation and probe-to-gene symbol mapping. Robust multi-array average method followed by Limma package in R was employed to ensure differential expression analysis within individual datasets, yielding gene-specific p-values. We identified 63 genes exhibiting statistically significant differential expression across the three datasets (p < 0.05) and an absolute log fold change > 1.5. Functional enrichment analyses of these differentially expressed genes were done, followed by pathway analyses. Our results suggested pertinent biological processes, molecular functions and cellular components associated with DMD. Finally, eight hub genes-COL6A3, COL1A1, COL3A1, COL1A2, POSTN, TIMP1, THBS2 and SPP1-were pinpointed as central players in the network. Two differentially expressed genes with substantial absolute log-fold changes, namely, DMD, downregulated and MYH3, upregulated, were identified as potential therapeutic candidates. In light of these findings, our work contributes not only to understanding DMD at the molecular level but also presents potential targets for therapeutic strategies. Finally, our study facilitates the development of therapeutic interventions that can effectively control and mitigate the impact of DMD.

The combination therapy using tyrosine kinase receptors inhibitors and repurposed drugs to target patient-derived glioblastoma stem cells

The lesson from many studies investigating the efficacy of targeted therapy in glioblastoma (GBM) showed that a future perspective should be focused on combining multiple target treatments. Our research aimed to assess the efficacy of drug combinations against glioblastoma stem cells (GSCs). Patient-derived cells U3042, U3009, and U3039 were obtained from the Human Glioblastoma Cell Culture resource. Additionally, the study was conducted on a GBM commercial U251 cell line. Gene expression analysis related to receptor tyrosine kinases (RTKs), stem cell markers and genes associated with significant molecular targets was performed, and selected proteins encoded by these genes were assessed using the immunofluorescence and flow cytometry methods. The cytotoxicity studies were preceded by analyzing the expression of specific proteins that serve as targets for selected drugs. The cytotoxicity study using the MTS assay was conducted to evaluate the effects of selected drugs/candidates in monotherapy and combinations. The most cytotoxic compounds for U3042 cells were Disulfiram combined with Copper gluconate (DSF/Cu), Dacomitinib, and Foretinib with IC50 values of 52.37 nM, 4.38 µM, and 4.54 µM after 24 h incubation, respectively. Interactions were assessed using SynergyFinder Plus software. The analysis enabled the identification of the most effective drug combinations against patient-derived GSCs. Our findings indicate that the most promising drug combinations are Dacomitinib and Foretinib, Dacomitinib and DSF/Cu, and Foretinib and AZD3759. Since most tested combinations have not been previously examined against glioblastoma stem-like cells, these results can shed new light on designing the therapeutic approach to target the GSC population.

Histologically Overt Stromal Response and the Risk of Progression after Radical Prostatectomy for Prostate Cancer

PURPOSE

Given the variable clinical course of prostate cancer and the limitations of current prognostic factors, this study was conducted to investigate the impact of a histologically overt stromal response (HOST-response) to prostate cancer on clinical outcomes after radical prostatectomy.

METHODS

This retrospective analysis utilized The Cancer Genome Atlas (TCGA) to evaluate data from individuals with a confirmed diagnosis of prostate cancer who underwent radical prostatectomy and had available pathology slides. These slides were assessed for the presence of a HOST-response, similar to desmoplasia. The primary endpoint was progression-free survival (PFS). A multivariable competing risk regression analysis was used to assess whether a significant association existed between HOST-response and PFS, adjusting for known prostate cancer prognostic factors.

RESULTS

Among the 348 patients analyzed, 166 (47.70%) demonstrated a HOST-response. After a median follow-up of 37.87 months (IQR: 21.20, 65.50), the presence of a HOST-response was significantly associated with a shorter PFS (SDHR, 2.10; 95% CI, 1.26 to 3.50; p = 0.004), after adjusting for covariates.

CONCLUSIONS

HOST-response in prostate cancer patients treated with radical prostatectomy is significantly associated with reduced PFS, suggesting a potential benefit from adjuvant therapy and highlighting the need for further investigation in a prospective randomized clinical trial.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

Integrative multiomics and weighted network approach reveals the prognostic role of RPS7 in lung squamous cell carcinoma pathogenesis

Lung cancer is one of the most commonly occurring malignant cancers with the highest rate of mortality globally. Difference between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) and their treatment strategies according to genetic markers may be helpful in reducing the cancer progression and increasing the overall survival (OS) in patients. LUSC is known for comparatively less typical onco-drivers, target therapy resistance, marked genomic complexity, and a reasonably higher mutation rate. The mRNA-seq data and clinical information of LUAD and LUSC cohorts from UCSC Xena comprising 437 and 379 patient samples were extracted. Differential expression and weighted network analyses revealed 47 and 18 hub differentially expressed genes (DEGs) corresponding to LUAD and LUSC cohorts. These hub DEGs were further subjected to protein-protein interaction network (PPIN) and OS analyses. Lower mRNA expression levels of both RPS15A and RPS7 worsened the OS of LUSC patients. Additionally, both these prognostic biomarkers were validated via external sources such as UALCAN, cBioPortal, TIMER, and HPA. RPS7 had higher mutation frequency compared to RPS15A and showed significant negative correlations with infiltrating levels of CD4+ T cells, CD8+ T cells, neutrophils, and macrophages. Our findings provided novel insights into biomarker discovery and the critical role of ribosomal biogenesis especially smaller ribosomal subunit in pathogenesis of LUSC.

Exploring the role of miR-200 family in regulating CX3CR1 and CXCR1 in lung adenocarcinoma tumor microenvironment: implications for therapeutic intervention

Lung adenocarcinoma (LUAD) is the most common malignant subtype of lung cancer (LC). miR-200 family is one of the prime miR regulators of epithelial-mesenchymal transition (EMT) and worst overall survival (OS) in LC patients. The study aimed to identify and validate the key differentially expressed immune-related genes (DEIRGs) regulated by miR-200 family which may serve for therapeutic aspects in LUAD tumor microenvironment (TME) by affecting cancer progression, invasion, and metastasis. The study identified differentially expressed miRNAs (DEMs) in LUAD, consisting of hsa-miR-200a-3p and hsa-miR-141-5p, respectively. Two highest-degree subnetwork motifs identified from 3-node miRNA FFL were: (i) miR-200a-3p-CX3CR1-SPIB and (ii) miR-141-5p-CXCR1-TBX21. TIMER analysis showed that the expression levels of CX3CR1 and CXCR1 were significantly positively correlated with infiltrating levels of M0-M2 macrophages and natural killer T (NKT) cells. The OS of LUAD patients was significantly affected by lower expression levels of hsa-miR-200a-3p, CX3CR1 and SPIB. These DEIRGs were validated using the human protein atlas (HPA) web server. Further, we validated the regulatory role of hsa-miR-200a-3p in an in-vitro indirect co-culture model using conditioned media from M0, M1 and M2 polarized macrophages (THP-1) and LUAD cell lines (A549 and H1299 cells). The results pointed out the essential role of hsa-miR-200a-3p regulated CX3CL1 and CX3CR1 expression in progression of LC TME. Thus, the study augments a comprehensive understanding and new strategies for LUAD treatment where miR-200 family regulated immune-related genes, especially chemokine receptors, which regulate the metastasis and invasion of LUAD, leading to the worst associated OS.

Pharmacological features, health benefits and clinical implications of honokiol

Honokiol (HNK) is a natural polyphenolic compound extracted from the bark and leaves of Magnolia grandiflora. It has been traditionally used as a medicinal compound to treat inflammatory diseases. HNK possesses numerous health benefits with a minimal level of toxicity. It can cross the blood-brain barrier and blood-cerebrospinal fluid, thus having significant bioavailability in the neurological tissues. HNK is a promising bioactive compound possesses neuroprotective, antimicrobial, anti-tumorigenic, anti-spasmodic, antidepressant, analgesic, and antithrombotic features . HNK can prevent the growth of several cancer types and haematological malignancies. Recent studies suggested its role in COVID-19 therapy. It binds effectively with several molecular targets, including apoptotic factors, chemokines, transcription factors, cell surface adhesion molecules, and kinases. HNK has excellent pharmacological features and a wide range of chemotherapeutic effects, and thus, researchers have increased interest in improving the therapeutic implications of HNK to the clinic as a novel agent. This review focused on the therapeutic implications of HNK, highlighting clinical and pharmacological features and the underlying mechanism of action.Communicated by Ramaswamy H. Sarma.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

Major regulators of the multi-step metastatic process are potential therapeutic targets for breast cancer management

Metastasis is a multi-step process that leads to the dissemination of tumor cells to new sites and, consequently, to multi-organ neoplasia. Although most lethal breast cancer cases are related to metastasis occurrence, little is known about the dysregulation of each step, and clinicians still lack reliable therapeutic targets for metastasis impairment. To fill these gaps, we constructed and analyzed gene regulatory networks for each metastasis step (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis). Through topological analysis, we identified E2F1, EGR1, EZH2, JUN, TP63, and miR-200c-3p as general hub-regulators, FLI1 for cell-adhesion loss specifically, and TRIM28, TCF3, and miR-429 for angiogenesis. Applying the FANMOD algorithm, we identified 60 coherent feed-forward loops regulating metastasis-related genes associated with distant metastasis-free survival prediction. miR-139-5p, miR-200c-3p, miR-454-3p, and miR-1301-3p, among others, were the FFL's mediators. The expression of the regulators and mediators was observed to impact overall survival and to go along with metastasis occurrence. Lastly, we selected 12 key regulators and observed that they are potential therapeutic targets for canonical and candidate antineoplastics and immunomodulatory drugs, like trastuzumab, goserelin, and calcitriol. Our results highlight the relevance of miRNAs in mediating feed-forward loops and regulating the expression of metastasis-related genes. Altogether, our results contribute to understanding the multi-step metastasis complexity and identifying novel therapeutic targets and drugs for breast cancer management.

The role and regulation of Maf proteins in cancer

The Maf proteins (Mafs) belong to basic leucine zipper transcription factors and are members of the activator protein-1 (AP-1) superfamily. There are two subgroups of Mafs: large Mafs and small Mafs, which are involved in a wide range of biological processes, such as the cell cycle, proliferation, oxidative stress, and inflammation. Therefore, dysregulation of Mafs can affect cell fate and is closely associated with diverse diseases. Accumulating evidence has established both large and small Mafs as mediators of tumor development. In this review, we first briefly describe the structure and physiological functions of Mafs. Then we summarize the upstream regulatory mechanisms that control the expression and activity of Mafs. Furthermore, we discuss recent studies on the critical role of Mafs in cancer progression, including cancer proliferation, apoptosis, metastasis, tumor/stroma interaction and angiogenesis. We also review the clinical implications of Mafs, namely their potential possibilities and limitations as biomarkers and therapeutic targets in cancer.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.