To identify reliable and robust transcriptomics-based carcinogenesis-related biomarkers
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ABSTRACT:
ORGANISM(S): Mus musculus
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PROVIDER: S-DIXA-051 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:ObjectiveInvasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction.MethodsDifferentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes' prognostic value.ResultsAfter threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients' estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA.ConclusionOur findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.
| S-EPMC7672771 | biostudies-literature
Project description:Vascular endothelial function is challenged during cerebral ischemia and reperfusion. The endothelial responses are involved in inflammatory leukocyte attraction, adhesion and infiltration, blood-brain barrier leakage, and angiogenesis. This study investigated gene expression changes in brain endothelial cells after acute ischemic stroke using transcriptomics and translatomics. We isolated brain endothelial mRNA by: (i) translating ribosome affinity purification, enabling immunoprecipitation of brain endothelial ribosome-attached mRNA for translatome sequencing and (ii) isolating CD31+ endothelial cells by fluorescence-activating cell sorting for classical transcriptomic analysis. Both techniques revealed similar pathways regulated by ischemia but they showed specific differences in some transcripts derived from non-endothelial cells. We defined a gene set characterizing the endothelial response to acute stroke (24h) by selecting the differentially expressed genes common to both techniques, thus corresponding with the translatome and minimizing non-endothelial mRNA contamination. Enriched pathways were related to inflammation and immunoregulation, angiogenesis, extracellular matrix, oxidative stress, and lipid trafficking and storage. We validated, by flow cytometry and immunofluorescence, the protein expression of several genes encoding cell surface proteins. The inflammatory response was associated with the endothelial upregulation of genes related to lipid storage functions and we identified lipid droplet biogenesis in the endothelial cells after ischemia. The study reports a robust translatomic signature of brain endothelial cells after acute stroke and identifies enrichment in novel pathways involved in membrane signaling and lipid storage. Altogether these results highlight the endothelial contribution to the inflammatory response, and identify novel molecules that could be targets to improve vascular function after ischemic stroke.
| S-EPMC10494365 | biostudies-literature
Project description:BACKGROUND: Cancer cachexia is a multi-organ tissue wasting syndrome that contributes to morbidity and mortality in many cancer patients. Skeletal muscle loss represents an established key feature yet there is no molecular understanding of the disease process. In fact, the postulated molecular regulators of cancer cachexia originate largely from pre-clinical models and it is unclear how these translate to the clinical environment. METHODS: Rectus abdominis muscle biopsies were obtained from 65 upper gastrointestinal (UGI) cancer patients during open surgery and RNA profiling was performed on a subset of this cohort (n = 21) using the Affymetrix U133+2 platform. Quantitative analysis revealed a gene signature, which underwent technical validation and independent confirmation in a separate clinical cohort. RESULTS: Quantitative significance analysis of microarrays produced an 83-gene signature that was able to identify patients with greater than 5% weight loss, while this molecular profile was unrelated to markers of systemic inflammation. Selected genes correlating with weight loss were validated using quantitative real-time PCR and independently studied as general cachexia biomarkers in diaphragm and vastus lateralis from a second cohort (n = 13; UGI cancer patients). CaMKIIbeta correlated positively with weight loss in all muscle groups and CaMKII protein levels were elevated in rectus abdominis. TIE1 was also positively associated with weight loss in both rectus abdominis and vastus lateralis muscle groups while other biomarkers demonstrated tissue-specific expression patterns. Candidates selected from the pre-clinical literature, including FOXO protein and ubiquitin E3 ligases, were not related to weight loss in this human clinical study. Furthermore, promoter analysis identified that the 83 weight loss-associated genes had fewer FOXO binding sites than expected by chance. CONCLUSION: We were able to discover and validate new molecular biomarkers of human cancer cachexia. The exercise activated genes CaMKIIbeta and TIE1 related positively to weight-loss across muscle groups, indicating that this cachexia signature is not simply due to patient inactivity. Indeed, excessive CaMKIIbeta activation is a potential mechanism for reduced muscle protein synthesis. Our genomics analysis also supports the view that the available preclinical models do not accurately reflect the molecular characteristics of human muscle from cancer cachexia patients.
| S-EPMC2829926 | biostudies-literature
Project description:The classical factors for predicting prognosis currently cannot meet the developing requirements of individualized and accurate prognostic evaluation in lung adenocarcinoma (LUAD). With the rapid development of high-throughput DNA sequencing technologies, genomic changes have been discovered. These sequencing data provide unprecedented opportunities for identifying cancer molecular subtypes. In this article, we classified LUAD into two distinct molecular subtypes (Cluster 1 and Cluster 2) based on Copy Number Variations (CNVs) and mRNA expression data from the Cancer Genome Atlas (TCGA) based on non-negative matrix factorization. Patients in Cluster 1 had worse outcomes than that in Cluster 2. Molecular features in subtypes were assessed to explain this phenomenon by analyzing differential expression genes expression pattern, which involved in cellular processes and environmental information processing. Analysis of immune cell populations suggested different distributions of CD4+ T cells, CD8+ T cells, and dendritic cells in the two subtypes. Subsequently, two novel genes, TROAP and RASGRF1, were discovered to be prognostic biomarkers in TCGA, which were confirmed in GSE31210 and Tianjin Medical University Cancer Institute and Hospital LUAD cohorts. We further proved their crucial roles in cancers by vitro experiments. TROAP mediates tumor cell proliferation, cycle, invasion, and migration, not apoptosis. RASGRF1 has a significant effect on tumor microenvironment. In conclusion, our study provides a novel insight into molecular classification based on CNVs related genes in LUAD, which may contribute to identify new molecular subtypes and target genes.
| S-EPMC8208901 | biostudies-literature
Project description:We report S-phase cancer associated lncRNAs which were identified using nascent RNA capture assay in HeLa cells. Coupling high throughput RNA sequencing with clinical investigation across TCGA datasets we identified a number of lncRNAs that act as prognostic biomarkers for survival in different cancer types
2018-02-13 | GSE92250 | GEO