Atorvastatin lowers breast cancer risk by reversing an early tumorigenic signature.

Breast cancer remains a significant health challenge with complex molecular mechanisms. While many studies have explored genetic markers in breast carcinogenesis, few have studied the potential impact of pharmacological interventions such as Atorvastatin on its genetic landscape. This study aimed to elucidate the molecular distinctions between normal and tumor-adjacent tissues in breast cancer and to investigate the potential protective role of atorvastatin, primarily known for its lipid-lowering effects, against breast cancer. Searching the Gene Expression Omnibus database identified two datasets, GSE9574 and GSE20437, comparing normal breast tissues with tumor-adjacent samples, which were merged, and one dataset, GSE63427, comparing paired pre- and post-treated patients with atorvastatin. Post-ComBat application showed merged datasets' consistency, revealing 116 DEGs between normal and tumor-adjacent tissues. Although initial GSE63427 data analysis suggested a minimal impact of atorvastatin, 105 DEGs post-treatment were discovered. Thirteen genes emerged as key players, both affected by Atorvastatin and dysregulated in tumor-adjacent tissues. Pathway analysis spotlighted the significance of these genes in processes like inflammation, oxidative stress, apoptosis, and cell cycle control. Moreover, there was a noticeable interaction between these genes and the immunological microenvironment in tumor-adjacent tissues, with Atorvastatin potentially altering the suppressive immune landscape to favor anti-tumor immunity. Survival analysis further highlighted the prognostic potential of the 13-gene panel, with 12 genes associated with improved survival outcomes. The 13-gene signature offers promising insights into breast cancer's molecular mechanisms and atorvastatin's potential therapeutic role. The preliminary findings advocate for an in-depth exploration of atorvastatin's impact on.

Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Transgenerational Effect of Maternal Pre-Pregnancy Obesity.

Background: Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin- umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018). Results: Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. Comprehensive functional analysis showed hypermethylation in promoters of genes involved in cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, the CpG sites associated with maternal obesity from these pathways also predicted highly accurately (average AUC = 0.8687) between cancer vs. normal tissues in 14 cancer types in The Cancer Genome Atlas (TCGA). Conclusions: This study revealed the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation. Moreover, these maternal obesity epigenetic markers in uHSCs may predispose offspring to higher cancer risks.

An integrated organoid omics map extends modeling potential of kidney disease.

Kidney organoids are a promising model to study kidney disease, but their use is constrained by limited knowledge of their functional protein expression profile. Here, we define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increase deposition of extracellular matrix but decrease expression of glomerular proteins. Single cell transcriptome integration reveals that most proteome changes localize to podocytes, tubular and stromal cells. TNFα treatment of organoids results in 322 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 322 proteins is significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression is increased in both human tubular and organoid kidney cell populations, highlighting the potential for organoids to advance biomarker development. By integrating kidney organoid omic layers, incorporating a disease-relevant cytokine stressor and comparing with human data, we provide crucial evidence for the functional relevance of the kidney organoid model to human kidney disease.

Integrative Metatranscriptomic Analysis Reveals Disease-specific Microbiome-host Interactions in Oral Squamous Cell Carcinoma.

Studies on the microbiome of oral squamous cell carcinoma (OSCC) have been limited to 16S rRNA gene sequencing. Here, laser microdissection coupled with brute-force, deep metatranscriptome sequencing was employed to simultaneously characterize the microbiome and host transcriptomes and predict their interaction in OSCC. The analysis involved 20 HPV16/18-negative OSCC tumor/adjacent normal tissue pairs (TT and ANT) along with deep tongue scrapings from 20 matched healthy controls (HC). Standard bioinformatic tools coupled with in-house algorithms were used to map, analyze, and integrate microbial and host data. Host transcriptome analysis identified enrichment of known cancer-related gene sets, not only in TT versus ANT and HC, but also in the ANT versus HC contrast, consistent with field cancerization. Microbial analysis identified a low abundance yet transcriptionally active, unique multi-kingdom microbiome in OSCC tissues predominated by bacteria and bacteriophages. HC showed a different taxonomic profile yet shared major microbial enzyme classes and pathways with TT/ANT, consistent with functional redundancy. Key taxa enriched in TT/ANT compared with HC were Cutibacterium acnes, Malassezia restricta, Human Herpes Virus 6B, and bacteriophage Yuavirus. Functionally, hyaluronate lyase was overexpressed by C. acnes in TT/ANT. Microbiome-host data integration revealed that OSCC-enriched taxa were associated with upregulation of proliferation-related pathways. In a preliminary in vitro validation experiment, infection of SCC25 oral cancer cells with C. acnes resulted in upregulation of MYC expression. The study provides a new insight into potential mechanisms by which the microbiome can contribute to oral carcinogenesis, which can be validated in future experimental studies. Significance: Studies have shown that a distinct microbiome is associated with OSCC, but how the microbiome functions within the tumor interacts with the host cells remains unclear. By simultaneously characterizing the microbial and host transcriptomes in OSCC and control tissues, the study provides novel insights into microbiome-host interactions in OSCC which can be validated in future mechanistic studies.

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis.

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer.

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised k-means and partition around medoids (pam) to cluster metabolomics data from two data sets. The first comprised 271 BC tumors (data set 1) that were estrogen receptor (ER) positive (ER+, n = 204) or negative (ER-, n = 67) with 162 identified and validated metabolites. The second data set contained 67 BC samples (data set 2; ER+, n = 33; ER-, n = 34) and 352 known metabolites. Significance Analysis of Microarrays (SAM) was used to identify the most significant metabolites among these clusters, which were then reassigned into new clusters using prediction analysis of microarrays (PAM). Generally, metabolome-defined BC subtypes identified from either data set 1 or data set 2 were different from the well-known receptor- or transcriptome-defined subtypes. Metabolomics-directed clustering of data set 2 identified distinctive BC tumors characterized by metabolome profiles that associated with DNA methylation (p-value = 0.000 048, χ2 test). Pathway analysis of cluster metabolites revealed that nitrogen metabolism and aminoacyl-tRNA biosynthesis were highly related to BC subtyping. The pipeline may be run from GitHub: https://github.com/FADHLyemen/Metabolomics_signature. Our proposed bioinformatics pipeline analyzed metabolomics data from BC tumors, revealing clusters characterized by unique metabolic signatures that may potentially stratify BC patients and tailor precision treatment.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.