GALNT6, GALNT14, and Gal-3 in association with GDF-15 promotes drug resistance and stemness of breast cancer via ß-catenin axis.

N-acetylgalactosaminyltransferases (GALNTs) are a polypeptide responsible for aberrant glycosylation in breast cancer (BC), but the mechanism is unclear. In this study, expression levels of GALNT6, GALNT14, and Gal-3 were assessed in BC, and their association with GDF-15, ß-catenin, stemness (SOX2 and OCT4), and drug resistance marker (ABCC5) was evaluated. Gene expression of GALNT6, GALNT14, Gal-3, GDF-15, OCT4, SOX2, ABCC5, and ß-catenin in tumor and adjacent non-tumor tissues (n = 30) was determined. The same was compared with GEO-microarray datasets. A significant increase in the expression of candidate genes was observed in BC tumor compared to adjacent non-tumor tissue; and in pre-therapeutic patients compared to post-therapeutic. GALNT6, GALNT14, Gal-3, and GDF-15 showed positive association with ß-catenin, SOX2, OCT4, and ABCC5 and were significantly associated with poor Overall Survival. Our findings were also validated via in silico analysis. Our study suggests that GALNT6, GALNT14, and Gal-3 in association with GDF-15 promote stemness and intrinsic drug resistance in BC, possibly by ß-catenin signaling pathway.

Integrated Assessment of GFAP and UCH-L1 for their utility in severity assessment and outcome prediction in Traumatic Brain Injury.

OBJECTIVES: This study aimed to explore the potential of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) as biomarkers for diagnosis and prognosis in mild and severe TBI cases, including TBI-related deaths. METHODS: This prospective cohort study includes 40 cases each of mild, severe, fatal TBI cases, and 40 healthy controls. Serum samples were collected from live patients at 8 and 20 h post injury for UCH-L1 and GFAP respectively, and from deceased patients within 6 h of death. RESULTS: Elevated levels of both GFAP and UCH-L1 were observed in patients with severe and fatal TBI cases. These biomarkers exhibited promising potential for predicting various Glasgow Outcome Scale Extended (GOSE) categories. Combining GFAP and UCH-L1 yielded higher predictive accuracy both for diagnosis and prognosis in TBI cases. The study additionally established specific cut-off levels for GFAP and UCH-L1 stratified according to the severity and prognosis. CONCLUSION: GFAP and UCH-L1 individually demonstrated moderate to good discrimination capacity in predicting TBI severity and functional outcomes. However, combining these biomarkers is recommended for improved diagnostic and prognostic utility. This precision tool can enhance patient care, enabling tailored treatment plans, ultimately reducing morbidity and mortality rates in TBI cases.

GALNT14 in association with GDF-15 promotes stemness and drug resistance through ß-catenin signalling pathway in breast cancer.

BACKGROUND: Altered glycosylation plays a role in carcinogenesis. GALNT14 promotes cancer stem-like properties and drug resistance. GDF-15 is known to induces drug resistance and stemness markers for maintenance of breast cancer (BC) stem-like cell state. Currently there is lack of data on association of GDF-15 and GALNTs. In this study, the expression and interaction of GALNT14 and GDF-15 with stemness (OCT4 and SOX2) and drug resistance (ABCC5) markers were evaluated in BC. METHODS: We investigated tumour tissue from 30 BC patients and adjacent non-tumour tissues. Expression of serum GALNT14 from BC patients and matched healthy controls was evaluated. Expression of GALNT14, GDF-15, OCT4, SOX2, ABCC5, and ß-catenin in BC tissue was determined by RT-PCR. Knockdown of GALNT14 and GDF-15 in the MCF-7 cell line was done through siRNA, gene expression and protein expression of ß-catenin by western blot were determined. RESULTS: A significant increase in the expression of GALNT14, GDF-15, OCT4, SOX2, ABCC5, and ß-catenin was observed in BC tumour tissues compared to adjacent non-tumour tissues. The serum level of GALNT14 was significantly high in BC patients (80.7 ± 65.3 pg/ml) compared to healthy controls (12.2 ± 9.12 pg/ml) (p < 0.000). To further analyse the signalling pathway involved in BC stemness and drug resistance, GALNT14 and GDF-15 were knocked down in the MCF-7 cell line, and it was observed that after knockdown, the expression level of OCT4, SOX2, ABCC5, and ß-catenin was decreased, and co-knockdown with GALNT14 and GDF-15 further decreased the expression of genes. CONCLUSION: It can be concluded that GALNT14, in association with GDF-15, promotes stemness and intrinsic drug resistance in BC, possibly through the ß-catenin signalling pathway.

In-Silico Analysis of Differentially Expressed Genes and Their Regulating microRNA Involved in Lymph Node Metastasis in Invasive Breast Carcinoma.

Axillary nodal metastasis is related to poor prognosis in breast cancer (BC). Key candidate genes in BC lymph node metastasis have been identified from Gene Expression Omnibus datasets and explored through functional enrichment database for annotation, visualization and integrated discovery (DAVID) , protein-protein interaction by Search Tool for the Retrieval of Interacting Genes and proteins (STRING), network visualization (Cytoscape), survival analysis (GEPIA, KM Plotter), and target prediction (miRNet). A total of 102 overlapping differentially expressed genes were found. In-silico survival and expression analyses revealed six candidate hub genes, Desmocollin 3 (DSC3), KRT5, KRT6B, KRT17, KRT81, and SERPINB5, to be significantly associated with nodal metastasis and overall survival, and 83 MicroRNA (miRNAs), which may be potential diagnostic markers and therapeutic targets in BC patients.

Critical appraisal of epigenetic regulation of galectins in cancer.

Galectins are defined as the glycan-binding protein containing either one or two carbohydrate-binding domains and participate in various biological functions such as developmental processes, vascularisation programs, cell migration, and immune-regulation and apoptosis. Galectins are also linked to many diseases, including cancer. They are widely spread in extracellular and intracellular spaces, and their altered expression in cancer leads to tumor progression, metastasis, angiogenesis and stemness through different signalling pathways. Promoter methylation, microRNA, and histone modification constitute the epigenetic changes that regulate galectin activity in cancer. Our review discusses the concept of epigenetics in cancer and how the aforementioned factors i.e., promoter methylation, histone modification, change in miRNAs expression affect the glycomic changes in malignancies.

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics.

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Acute kidney injury in COVID 19 - an update on pathophysiology and management modalities.

Acute kidney injury (AKI), characterised by fluid imbalance and overload, is prevalent in severe disease phenotypes of coronavirus disease 2019 (COVID-19). The elderly immunocompromised patients with pre-existing comorbidities being more risk-prone to severe COVID-19, the importance of early diagnosis and intervention in AKI is imperative. Histopathological examination of COVID-19 patients with AKI reveals viral invasion of the renal parenchyma and evidence of AKI. The definitive treatment for AKI includes renal replacement therapy and renal transplant. Immunosuppressant regimens and its interactions with COVID-19 have to be further explored to devise effective treatment strategies in COVID-19 transplant patients. Other supportive strategies for AKI patients include hemodynamic monitoring and maintenance of fluid balance. Antiviral drugs should be meticulously monitored in the management of these high-risk patients. We have focussed on the development of renal injury provoked by the SARS-CoV-2, the varying clinical characteristics, and employment of different management strategies, including renal replacement therapy, alongside the emerging cytokine lowering approaches.

The Differentially Expressed Genes Responsible for the Development of T Helper 9 Cells From T Helper 2 Cells in Various Disease States: Immuno-Interactomics Study.

BACKGROUND: T helper (Th) 9 cells are a novel subset of Th cells that develop independently from Th2 cells and are characterized by the secretion of interleukin (IL)-9. Studies have suggested the involvement of Th9 cells in variable diseases such as allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, and pulmonary hypoplasia), metabolic diseases (eg, acute leukemia, myelocytic leukemia, breast cancer, lung cancer, melanoma, pancreatic cancer), neuropsychiatric disorders (eg, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus, systemic scleroderma, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, atopic dermatitis, eczema), and infectious diseases (eg, tuberculosis, hepatitis). However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases. OBJECTIVE: This study aims to identify significant differentially altered genes in the conversion of Th2 to Th9 cells, and their regulating microRNAs (miRs) from publicly available Gene Expression Omnibus data sets of the mouse model using in silico analysis to unravel various pathogenic pathways involved in disease processes. METHODS: Using differentially expressed genes (DEGs) identified from 2 publicly available data sets (GSE99166 and GSE123501) we performed functional enrichment and network analyses to identify pathways, protein-protein interactions, miR-messenger RNA associations, and disease-gene associations related to significant differentially altered genes implicated in the conversion of Th2 to Th9 cells. RESULTS: We extracted 260 common downregulated, 236 common upregulated, and 634 common DEGs from the expression profiles of data sets GSE99166 and GSE123501. Codifferentially expressed ILs, cytokines, receptors, and transcription factors (TFs) were enriched in 7 crucial Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology. We constructed the protein-protein interaction network and predicted the top regulatory miRs involved in the Th2 to Th9 differentiation pathways. We also identified various metabolic, allergic and pulmonary, neuropsychiatric, autoimmune, and infectious diseases as well as carcinomas where the differentiation of Th2 to Th9 may play a crucial role. CONCLUSIONS: This study identified hitherto unexplored possible associations between Th9 and disease states. Some important ILs, including CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9; receptors, including IL-12RB1, IL-4RA (interleukin 9 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte-associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196); and TFs, including FOXP3 (forkhead box P3), IRF8 (interferon regulatory factor 8), FOXP2 (forkhead box P2), RORA (RAR-related orphan receptor alpha), AHR (aryl-hydrocarbon receptor), MAF (avian musculoaponeurotic fibrosarcoma oncogene homolog), SMAD6 (SMAD family member 6), JUN (Jun proto-oncogene), JAK2 (Janus kinase 2), EP300 (E1A binding protein p300), ATF6 (activating transcription factor 6), BTAF1 (B-TFIID TATA-box binding protein associated factor 1), BAFT (basic leucine zipper transcription factor), NOTCH1 (neurogenic locus notch homolog protein 1), GATA3 (GATA binding protein 3), SATB1 (special AT-rich sequence binding protein 1), BMP7 (bone morphogenetic protein 7), and PPARG (peroxisome proliferator-activated receptor gamma, were able to identify significant differentially altered genes in the conversion of Th2 to Th9 cells. We identified some common miRs that could target the DEGs. The scarcity of studies on the role of Th9 in metabolic diseases highlights the lacunae in this field. Our study provides the rationale for exploring the role of Th9 in various metabolic disorders such as diabetes mellitus, diabetic nephropathy, hypertensive disease, ischemic stroke, steatohepatitis, liver fibrosis, obesity, adenocarcinoma, glioblastoma and glioma, malignant neoplasm of stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, prostate carcinoma, and stomach carcinoma.

In silico analysis of differentially expressed-aberrantly methylated genes in breast cancer for prognostic and therapeutic targets.

Breast cancer (BC) is the leading cause of death among women across the globe. Abnormal gene expression plays a crucial role in tumour progression, carcinogenesis and metastasis of BC. The alteration of gene expression may be through aberrant gene methylation. In the present study, differentially expressed genes which may be regulated by DNA methylation and their pathways associated with BC have been identified. Expression microarray datasets GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724 and one DNA methylation profile dataset GSE20713 were downloaded from Gene Expression Omnibus database (GEO). Differentially expressed-aberrantly methylated genes were identified using online Venn diagram tool. Based on fold change expression of differentially expressed-aberrantly methylated genes were chosen through heat map. Protein-protein interaction (PPI) network of the hub genes was constructed by Search Tool for the Retrieval of Interacting Genes (STRING). Gene expression and DNA methylation level of the hub genes were validated through UALCAN. Overall survival analysis of the hub genes was analysed through Kaplan-Meier plotter database for BC. A total of 72 upregulated-hypomethylated genes and 92 downregulated-hypermethylated genes were obtained from GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724, and GSE20713 datasets by GEO2R and Venn diagram tool. PPI network of the upregulated-hypomethylated hub genes (MRGBP, MANF, ARF3, HIST1H3D, GSK3B, HJURP, GPSM2, MATN3, KDELR2, CEP55, GSPT1, COL11A1, and COL1A1) and downregulated-hypermethylated hub genes were constructed (APOD, DMD, RBPMS, NR3C2, HOXA9, AMKY2, KCTD9, and EDN1). All the differentially expressed hub genes expression was validated in UALCAN database. 4 in 13 upregulated-hypomethylated and 5 in 8 downregulated-hypermethylated hub genes to be significantly hypomethylated or hypermethylated in BC were confirmed using UALCAN database (p < 0.05). MANF, HIST1H3D, HJURP, GSK3B, GPSM2, MATN3, KDELR2, CEP55, COL1A1, APOD, RBPMS, NR3C2, HOXA9, ANKMY2, and EDN1 were significantly (p < 0.05) associated with poor overall survival (OS). The identified aberrantly methylated-differentially expressed genes and their related pathways and function in BC can serve as novel diagnostic and prognostic biomarkers and therapeutic targets.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 4 Given name: [Jeewan Ram] Last name [Vishnoi]. Also, kindly confirm the details in the metadata are correct.It is correct.

Multi-omics integration and interactomics reveals molecular networks and regulators of the beneficial effect of yoga and exercise.

Background: Yoga is a multifaceted spiritual tool that helps in maintaining health, peace of mind, and positive thoughts. In the context of asana, yoga is similar to physical exercise. This study aims to construct a molecular network to find hub genes that play important roles in physical exercise and yoga. Methodology: We combined differentially expressed genes (DEGs) in yoga and exercise using computational bioinformatics from publicly available gene expression omnibus (GEO) datasets and identified the codifferentially expressed mRNAs with GEO2R. The co-DEGs were divided into four different groups and each group was subjected to protein-protein interaction (PPI) network, pathways analysis, and gene ontology. Results: Our study identified immunological modulation as a dominant target of differential expression in yoga and exercise. Yoga predominantly modulated genes affecting the Th1 and NK cells, whereas Cytokines, Macrophage activation, and oxidative stress were affected by exercise. We also observed that while yoga regulated genes for two main physiological functions of the body, namely Circadian Rhythm (BHLHE40) and immunity (LBP, T-box transcription factor 21, CEACAM1), exercise-regulated genes involved in apoptosis (BAG3, protein kinase C alpha), angiogenesis, and cellular adhesion (EPH receptor A1). Conclusion: The dissimilarity in the genetic expression patterns in Yoga and exercise highlights the discrete effect of each in biological systems. The integration and convergences of multi-omics signals can provide deeper and comprehensive insights into the various biological mechanisms through which yoga and exercise exert their beneficial effects and opens up potential newer research areas.

Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions. OBJECTIVE: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis. METHODS: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM. RESULTS: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance. CONCLUSIONS: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.

A Combined Analysis of Serum Growth Differentiation Factor-15 and Cancer Antigen 15-3 Enhances the Diagnostic Efficiency in Breast Cancer.

INTRODUCTION: Existing diagnostic biomarkers of breast cancer (BC) are limited by poor sensitivity. In this study, we evaluated the role of serum GDF-15 in early BC diagnosis, independently and in combination with CA15-3, a known blood biomarker of BC. MATERIAL AND METHODS: A total of 113 diagnosed, pre-therapy BC patients and 54 healthy controls were recruited. Clinical characteristics, TNM staging, and hormone receptor status of the patients were recorded. Serum GDF-15 and serum CA15-3 were measured by sandwich ELISA and chemiluminescence assay, respectively. RESULTS: The serum GDF-15 levels were significantly (p<0.001) elevated in BC patients compared to healthy controls and in patients with larger tumor size, advanced disease stage, and distant metastasis. ROC analysis revealed that at the cut-off of 525.77 pg/mL, GDF-15 had greater sensitivity than CA15-3. GDF-15 and CA15-3 performed better in combination than individually, with the combined test having an AUC of 0.85 and sensitivity and specificity of 0.63 and 0.98, respectively.Further, serum GDF-15 had a better predictive ability for early-stage BC compared to CA15-3. GDF-15 could independently diagnose BC patients after adjusting for age. CONCLUSION: We conclude that serum GDF-15 is a promising, robust marker for detecting early-stage BC. However, larger prospective studies are necessary to validate this claim.