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At least 631 records · Page 35Linked to original sources
PubMed2001

[Transcriptome analysis in cancerology: bioinformatics aspects].

Recent technological advances (e.g. various DNA arrays and chips) allow the measurement of expression level (mRNA abundance) for thousand of genes simultaneously, over multiple conditions or time. Initially developed and tested on model systems such as yeast or in vitro cell line cultures, these techniques have recently begun to be applied to the analysis of human cancers. Initial results are promising, and large-scale gene expression profiling is now expected to become a clinical tool for better tumour identification, prognosis, and optimal treatment design. It is thus important that clinicians become familiar with the theoretical principles underlying the interpretation of gene expression profiles as used in three different contexts: gene discovery, tumour class prediction, and molecular diagnosis. This is the purpose of the present article.

Forecasting↗
PubMed2001

[Molecular typing of breast cancer: transcriptomics and DNA microarrays].

Breast cancer is the most frequent and deadly cancer of women. Its great heterogeneity makes prognosis and response to current treatments highly variable and difficult to predict. Mammary oncogenesis remains poorly understood. These issues should benefit from recent development of techniques capable of large-scale molecular analyses. The use of cDNA array techniques allows for the simultaneous analysis of the mRNA expression levels of thousands of genes in mammary tumor cell lines and breast tumors. Expression profiles will help classify tumors and provide new prognostic tools and potential therapeutic targets. They will also boost our knowledge of the molecular events responsible for the development and progression of this cancer.

Breast Neoplasms↗
PubMed2003

Transcriptome analysis of HER2 reveals a molecular connection to fatty acid synthesis.

HER2 (erbB2/neu) is a member of the erbB family of receptor tyrosine kinases and is involved in regulating the growth of several types of human carcinomas. HER2 represents a successful therapeutic target of the biotechnology era as exemplified by the drug Herceptin (trastuzumab), which has clinical activity in a subset of breast cancer patients. Using DNA microarrays, we identified a cohort of genes that are differentially regulated by HER2 in breast epithelial cells. One of the HER2-regulated genes discovered was fatty acid synthase (FAS), which has been shown to be overexpressed in breast cancer as well as other cancers. FAS is implicated in tumorigenesis through its role in cell proliferation and membrane lipid incorporation of neoplastic cells. Here, we demonstrate that HER2-mediated induction of FAS is inhibitable by Herceptin and tyrosine kinase inhibitors of HER2. Through a phosphatidylinositol 3'-kinase-dependent pathway, HER2 stimulates the FAS promoter and ultimately mediates increased fatty acid synthesis. Interestingly, pharmacological inhibition of FAS preferentially induced apoptosis of HER2-overexpressing breast epithelial cells relative to matched vector control cells. These studies characterize a molecular connection between two genes individually implicated in tumorigenesis but never linked together.

Antibodies, Monoclonal↗
PubMed2003

Relevance network between chemosensitivity and transcriptome in human hepatoma cells.

Generally, hepatoma is not a chemosensitive tumor, and the mechanism of resistance to anticancer drugs is not fully elucidated. We aimed to comprehensively evaluate the relationship between chemosensitivity and gene expression profile in human hepatoma cells, by using microarray analysis, and analyze the data by constructing relevance networks. In eight hepatoma cell lines (HLE, HLF, Huh7, Hep3B, PLC/PRF/5, SK-Hep1, Huh6, and HepG2), the baseline expression levels of 2300 genes were measured by cDNA microarray. The concentrations of eight anticancer drugs (nimustine, mitomycin C, cisplatin, carboplatin, doxorubicin, epirubicin, mitoxantrone, and 5-fluorouracil) needed for 50% growth inhibition were examined and used as a measure of chemosensitivity. These data were combined and comprehensive pair-wise correlations between gene expression levels and the 50% growth inhibition values were calculated. Significant correlations with significance were used to construct networks of similarity. Fifty-two relations, including 42 genes, were selected. Among them, nearly 20% were various types of transporters, and most of them negatively correlated with chemosensitivity. Transporter associated with antigen processing 1 was associated with resistance to mitoxantrone, consistent with previous reports. Other transporters were not reported previously to associate with chemosensitivity. Resistance to doxorubicin and its analogue, epirubicin, were positively correlated with topoisomerase II beta expression, whereas it negatively correlated with expression of carboxypeptidases A3 and Z. Response to nimustine was associated with expression of superoxide dismutase 2. Relevance networks identified several negative correlations between gene expression and resistance, which were missed by hierarchical clustering. Our results suggested the necessity of systematically evaluating the transporting systems that may play a major role in resistance in hepatoma. This may provide useful information to modify anticancer drug action in hepatoma.

Antineoplastic Agents↗
PubMed2026

Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.

Studying brain gene expression in Alzheimer's Disease (AD) remains difficult as postmortem brain is difficult to access, cannot be used to guide donor treatment, may be confounded by environmental factors before and after death, and is difficult to link to early AD states or disease progression. To circumvent these limitations, several studies have tested blood transcriptome biomarkers for AD. However, gene-expression levels in the blood have limited correlation with those in the brain. To evaluate the potential of monitoring Alzheimer's progression with peripheral data, we used transcriptome-imputation to identify brain-region-specific AD-associated gene-expression differences in cohorts with blood-based transcriptome data. This approach provides a high-resolution image of AD-associated molecular differences in the brains of individuals actively living with disease. We analyzed eight AD studies (777 AD cases, 779 cognitively unimpaired controls), imputing transcriptomes in 10 brain regions via the Brain Gene Expression and Network Imputation Engine (BrainGENIE). Hundreds of differentially expressed genes (DEGs) associated with AD were identified in nine brain regions, with anterior cingulate cortex and amygdala showing the most differential expression. AD-associated genes were enriched in pathways such as proteostasis, mitochondrial dysfunction, and immune activation. We observed significant yet moderate concordance between imputed AD-associated changes and those directly measured in the dorsolateral prefrontal cortex and cerebellum. These transcriptomic changes can guide future in vitro studies focused on pathogenesis or be targets of novel therapeutic development. In conclusion, we demonstrated the scope and utility of brain expression imputation from the peripheral transcriptome, laying the groundwork for biomarker discovery and prospective AD studies.

Alzheimer Disease↗
PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2025

Multiomics Analysis Reveals Therapeutic Targets for Chronic Kidney Disease With Sarcopenia.

BACKGROUND: The presence of sarcopenia in patients with chronic kidney disease (CKD) is associated with poor prognosis. The mechanism underlying CKD-induced muscle wasting has not yet been fully explored. This study investigates the influence of renal secretions on muscles using multiomics sequencing. METHODS: The kidney transcriptome analysis by RNA-seq and protein profiling by tandem mass tag (TMT), serum TMT and muscle TMT were performed in CKD established using 0.2% adenine and control mice. Spp1 recombinant protein was used to study its effect on myotube atrophy in&#xa0;vitro. In animal experiments on CKD, pharmacological inhibition of Spp1 was used to explore the role of Spp1 in skeletal muscle wasting. Transcriptome analysis was performed to identify differentially expressed genes (DEGs) in the gastrocnemius muscle following Spp1 pharmacological inhibition. RESULTS: In the renal transcriptome and TMT, 503 and 377 proteins/genes respectively were co-upregulated and co-downregulated. In the serum TMT of CKD and normal control (NC) mice, 22 upregulated and 7 downregulated differentially expressed proteins (DEPs) showed the same expression patterns as those in the kidney transcriptome and TMT analysis. Based on bioinformatics analysis and reported studies, we selected Spp1 for further validation. Spp1 recombinant protein was added to C2C12 myotubes in&#xa0;vitro, and the results indicated that Spp1 significantly increased the protein levels of the muscle atrophy marker (Murf-1) and promoted the smaller myotubes (all p&#x2009;<&#x2009;0.05). Compared with NC mice, Spp1 mRNA and protein levels were significantly upregulated in the kidneys of CKD mice, and the serum concentration of Spp1 was also markedly increased (all p&#x2009;<&#x2009;0.05). In animal experiments, pharmacological inhibition of Spp1 increased the weights of gastrocnemius and tibialis anterior muscles (p&#x2009;<&#x2009;0.05) and improved muscle atrophy phenotype. Transcriptome analysis showed that DEGs in the gastrocnemius muscle following Spp1 pharmacological inhibition were enriched in protein digestion and absorption, glucagon signalling pathway, apelin signalling pathway and ECM-receptor interaction pathway. CONCLUSIONS: Our study is the first to establish a regulatory network of kidney-muscle crosstalk to explore the potential mechanism of CKD-related sarcopenia. Employing multiomics analysis, cellular assessment and animal experiments, we have identified that Spp1 could potentialy serve as a promising therapeutic target for CKD patients with sarcopenia.

Sarcopenia↗
PubMed2026

Thyroid hormone deprivation creates an immunological signature in the mouse liver, involving Kupffer cell presentation as the mouse ages.

PURPOSE: Aging is associated with an increased prevalence of chronic liver diseases suggesting impaired immune and metabolic function. In addition, thyroid hormone (TH) impacts liver physiology and TH deprivation or excess negatively affect organ maintenance. However, whether age-dependent consequences of TH alterations are reflected in a liver-specific adaptation is unknown so far. The present study aimed to characterize the impact of TH deprivation or excess on the liver transcriptome during aging. METHODS: Five- and 21-month-old male C57BL/6 mice were exposed either to chronic TH deprivation or to chronic TH excess and compared to control treatment by microarray-based liver transcriptome analysis. RESULTS: Significant roles of both TH state and age became obvious: Bioinformatic analysis of the liver transcriptome data revealed an age-dependent immune signature by chronic TH deprivation, an age-dependent immune and metabolic signature independent of exogenous TH modulation, as well as an age-dependent metabolic signature by chronic TH excess. Published data of single cell transcriptomic atlas characterizing aging tissues in the mouse were compared with our data and revealed Kupffer cell presentation in the immunological signature by TH deprivation during aging. Literature data for four prominent differentially expressed genes, namely C1qb, C3ar1, Ctss, and Msr1, revealed that the complement system, extracellular matrix remodelling, as well as the proinflammatory phenotype of Kupffer cells are altered by TH deprivation during aging. CONCLUSION: In conclusion, our study illuminates the interplay between TH deprivation, aging, and liver transcriptome signatures, highlighting potential implications for immune function and tissue maintenance, particularly through the modulation of Kupffer cell presentation.

Animals↗
PubMed2026

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.

Humans↗
PubMed2002

Human prostate epithelial cell-type cDNA libraries and prostate expression patterns.

BACKGROUND: Transcriptome analysis is a powerful approach to uncovering genes responsible for diseases such as prostate cancer. Ideally, one would like to compare the transcriptomes of a cancer cell and its normal counterpart for differences. METHODS: Prostate luminal and basal epithelial cell types were isolated and cell-type-specific cDNA libraries were constructed. Sequence analysis of cDNA clones generated 505 luminal cell genes and 560 basal cell genes. These sequences were deposited in a public database for expression analysis. RESULTS: From these sequences, 119 unique luminal expressed sequence tags (ESTs) were extracted and assembled into a luminal-cell transcriptome set, while 154 basal ESTs were extracted and assembled into a basal-cell set. Interlibrary comparison was performed to determine representation of these sequences in cDNA libraries constructed from prostate tumors, PIN, cell lines. CONCLUSIONS: Our analysis showed that a significant number of epithelial cell genes were not represented in the various transcriptomes of prostate tissues, suggesting that they might be underrepresented in libraries generated from tissue containing multiple cell types. Although both luminal and basal cell types are epithelial, their transcriptomes are more divergent from each other than expected, underscoring their functional difference (secretory vs. nonsecretory). Tumor tissues show different expression of luminal and basal genes, with perhaps a trend towards expression of basal genes in advanced diseases.

DNA, Complementary↗
PubMed2026

Proteomic analysis of pancreatic endocrine cells by mechanistic single-cell isolation identifies membrane pathways.

To better understand diabetes and normoglycemia, pancreatic islet biology requires a precise molecular understanding of islet cell types at both the transcriptomic and proteomic levels. While transcriptomic analyses are well established, comprehensive proteomic characterization has been lacking, limiting our knowledge of islet molecular complexity. Here we introduce a nonenzymatic, mechanistic single-cell isolation technology using laser microdissection (LMD7), facilitating proteomic and transcriptomic analysis of physically isolated &#x3b1;-, &#x3b2;- and &#x3b4;-cells from fresh-frozen, unfixed pancreatic tissue. This mechanistic approach avoids enzymatic digestion and chemical fixation, preserving the cells' native molecular state before processing. Given the limited existing proteomic data, we supplemented our findings with transcriptomic analysis generated using the same method and compared our results with data from enzymatically isolated cells, obtained by fluorescence-activated cell sorting and compiled by others. Our analysis revealed that enzymatic digestion alters gene expression patterns, particularly those of membrane-associated proteins, underscoring the impact of isolation techniques on biological outcomes. We identified cell-type-specific proteins typically underrepresented in pancreatic single-cell transcriptomic datasets. &#x3b2;-cells exhibited enrichment in vesicle trafficking proteins, &#x3b1;-cells displayed distinct calcium-dependent action potential machinery and &#x3b4;-cells showed elevated expression of focal adhesion-related proteins. In addition, we report an inverse molecular relationship between &#x3b2;- and &#x3b4;-cells, potentially driven by transcriptional regulators such as Mlxipl. By establishing robust molecular profiles directly from intact pancreatic tissue, this work provides a reference point for future pathological comparisons, offering a framework to investigate how diabetes and other endocrine disorders reshape islet cell biology.

Journal Article↗
PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir&#x2003;e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗