MiRNAs expression in breast cancer tumor tissues with or without axillary lymph nodes metastastsis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE100453 | GEO | 2019/06/30
REPOSITORIES: GEO
Similar Datasets
Project description:Expression data from 4T1 subclones derived from mammary fat pad tumors (MFP), axillary lymph node tumors (AxLN), and axillary lymph node-derived lung metastases (AxLN-LuM). In parallel, expression data, in the same subclones, of tail vein-derived (TV) lung metastases. The mechanism of how lymph node metastases seed distant metastases is unknown. We used the 4T1 breast cancer cell line, which is an immune competent model of triple negative breast cancer and spontaneously metastasizes in balb/c mice. 4T1-GFP/fLuc cells were injected into MFP to form tumors and 4T1-mCherry/rLuc cells were injected into axillary lymph nodes to form tumors and then allowed to metastasize to lung. TV cells were allowed to metastasize in the lung. Cells were harvested at different time intervals after the injection. Tumors were extracted, dissociated, and then expanded in vitro to obtain MFP, AxLN, AxLN-LuM and TV-LuM subclones isolated after different time lags with respect to the injection.
2019-08-20 | GSE136031 | GEO
Project description:Circular RNAs (circRNAs) play critical roles in the initiation and progression of cancer. Emerging evidence shows that circRNAs may function as potential markers for cancer diagnosis and treatment. However, the roles of circRNAs in breast cancer axillary lymph node metastasis (ALNM) remain need to be determined. Transcriptome sequencing was performed to screen the differentially expressed circRNAs in three breast cancer tissues with ALNM and three without ALNM.
2022-03-03 | GSE173766 | GEO
Project description:The combination of serum miRNAs and clinical factors could be a powerful classifier for evaluating axillary lymph node metastasis in breast cancer.
2023-02-13 | GSE110317 | GEO
Project description:The One Step Nucleic Acid Amplification (OSNA) assay is used to identify occult metastases in the axillary Sentinel Lymph Nodes (SLNs) of early stage breast cancer patients by quantification of the expression of cytokeratin19 (CK19) gene. The discarded samples of OSNA assay were explored as a source of information of SLN microenvironment response in patients with early stage Luminal A breast cancer using an RNAseq panel. Funding: project “GenomePT—National Laboratory for Genome Sequencing and Analysis” (POCI-01-0145-FEDER-022184), and project “Central Region Training Project for Personalized/Precision Medicine, with a genomic basis”, financed by the program CENTRO2020 [CENTRO-08-5864-FSE-000039 (PEP IN1194) 07-2021].
2023-01-06 | GSE210006 | GEO
Project description:Identification of axillary lymph node metastasis-related circRNAs in breast cancer using transcriptome sequencing
| PRJNA726984 | ENA
Project description:Background Breast cancer patients who present in the early stage of disease are affected by metastasis to the axillary group of lymph nodes. The first among this group that is affected is called as sentinel lymph node, and metastasis to this lymph node is crucial for the staging of cancer and the quality of surgical intervention. Sentinel Lymph Node Biopsy (SLNB) that is currently used to assess lymph node metastasis is neither sensitive, nor specific, is time-consuming, thereby necessitating the identification of novel biomarkers that can flag sentinel lymph node metastasis. Methods Breast cancer patients were screened, and those with early stage were recruited in the study. Surgical resection of the breast was followed by identification of sentinel lymph nodes by methylene fluorescent technique. Histo-pathology of fresh frozen section biopsy was used as the gold standard to assign the clinical phenotypes of metastatic (SLNM+) and non-metastatic sentinel lymph nodes (SLNM-). Discovery phase of the experiment included isobaric Tags for Relative and Absolute Quantitation (iTRAQ) technique comprising of six comparative experiments coupled with mass spectrometric analysis on Orbitrap Fusion to identify differentially expressed proteins on Proteome Discoverer 2.4. Functional enrichment and pathway analyses of differentially regulated genes was carried out in DAVID functional annotation tool. Validation was done by ELISA and protein concentrations were used to estimate the ROC for computing diagnostic parameters. Results Based on MS/MS spectra there were 2396 unique protein groups and 81 differentially expressed proteins comparing SLNB + and SLNB -. Nineteen proteins up-regulated, and eight proteins that were down regulated in SLNB+ as compared to SLNB-. Bioinformatic analysis showed the implication of extra cellular matrix proteins and ECM-receptor interaction pathways to be implicated in lymph node metastasis. ELISA confirmed the up-regulation of caveolin 1, collagen α-1, desmin, fibrillin-1, and microfibrillar associated glycoprotein 4 in metastatic, as compared to non-metastatic lymph nodes. These proteins are known to be integral in tumorogenesis, cell proliferation, invasion, cell survival and anti-apoptosis. These proteins have 80%-100%, of sensitivity and specificity to differentiate the two clinical phenotypes. Conclusion Identified extra cellular matrix protein biomarkers have requisite diagnostic parameters to be developed as a translational tool to assess the status of sentinel lymph nodes during mastectomy procedure to guide surgical therapy of axillary lymph nodes in early breast oncology.
2022-05-01 | PXD027668 | Pride
Project description:ObjectiveFat-infiltrated axillary lymph nodes (LNs) are unique sites for ectopic fat deposition. Early studies showed a strong correlation between fatty LNs and obesity-related diseases. Confirming this correlation requires large-scale studies, hindered by scarce labeled data. With the long-term goal of developing a rapid and generalizable tool to aid data labeling, we developed an automated deep learning (DL)-based pipeline to classify the status of fatty LNs on screening mammograms.MethodsOur internal data set included 886 mammograms from a tertiary academic medical institution, with a binary status of the fat-infiltrated LNs based on the size and morphology of the largest visible axillary LN. A two-stage DL model training and fine-tuning pipeline was developed to classify the fat-infiltrated LN status using the internal training and development data set. The model was evaluated on a held-out internal test set and a subset of the Digital Database for Screening Mammography.ResultsOur model achieved 0.97 (95% CI: 0.94-0.99) accuracy and 1.00 (95% CI: 1.00-1.00) area under the receiver operator characteristic curve on 264 internal testing mammograms, and 0.82 (95% CI: 0.77-0.86) accuracy and 0.87 (95% CI: 0.82-0.91) area under the receiver operator characteristic curve on 70 external testing mammograms.ConclusionThis study confirmed the feasibility of using a DL model for fat-infiltrated LN classification. The model provides a practical tool to identify fatty LNs on mammograms and to allow for future large-scale studies to evaluate the role of fatty LNs as an imaging biomarker of obesity-associated pathologies.Advances in knowledgeOur study is the first to classify fatty LNs using an automated DL approach.
| S-EPMC10607412 | biostudies-literature
Project description:We have established a highly sensitive and quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) method to detect axillary lymph node metastases of breast cancer. Amplifying cytokeratin 19 (CK19) mRNA transcripts using real-time TaqMan PCR made it possible to quantify axillary metastatic burden. Metastases in 358 axillary lymph nodes obtained from 23 breast cancers of 22 patients were investigated by conventional haematoxylin and eosin (H&E) staining, immunohistochemical staining and quantitative RT-PCR assay. The detection rates of axillary lymph node metastasis using H&E staining, immunohistochemistry and RT-PCR assay were 4.5, 5.9 and 13.1%, respectively. RT-PCR assay was the most sensitive of these three methods for detecting lymph node metastases. Cytokeratin 19 mRNA expression values of both histologically and immunohistochemically positive lymph nodes were significantly higher than the values for lymph nodes judged to be negative by both histological and immunohistochemical methods (P<0.0001), and those of histologically negative, but immunohistochemically positive lymph nodes were significantly higher than the values for lymph nodes judged to be negative by both histological and immunohistochemical methods (P<0.0001). Furthermore, metastatic rates of sentinel nodes were higher than the rates of nonsentinel lymph nodes as measured by all three methods. These results indicate that quantitative RT-PCR assay is a sensitive and reliable method for detecting lymph node metastasis. Furthermore, quantification of metastases in sentinel lymph nodes by quantitative RT-PCR assay may be useful to assess the entire axillary burden of breast cancer patients.
| S-EPMC2394408 | biostudies-other
Project description:Objective:Biopsy markers are often placed into biopsy-proven metastatic axillary lymph nodes to ensure later accurate node excision. Ultrasound is the preferred imaging modality in the axilla. However, sonographic identification of biopsy markers after neoadjuvant therapy can be challenging. This is due to poor conspicuity relative to surrounding parenchymal interfaces, treatment-related alteration of malignant morphology during neoadjuvant chemotherapy, or extrusion of the marker from the target. To the authors' knowledge, the literature provides no recommendations for ultrasound scanning parameters that improve the detection of biopsy markers. The purpose of this manuscript is 3-fold: (1) To determine scanning parameters that improve sonographic conspicuity of biopsy markers in a phantom and cadaver model; (2) to implement these scanning parameters in the clinical setting; and (3) to provide strategies that might increase the likelihood of successful ultrasound detection of biopsy markers in breast imaging practices. Materials and Methods:An ex vivo study was performed using a phantom designed to simulate the heterogeneity of normal mammary or axillary soft tissues. A selection of available biopsy markers was deployed into this phantom and ultrasound (GE LOGIQ E9) was performed. Scanning parameters were adjusted to optimize marker conspicuity. For the cadaver study, the biopsy markers were deployed using ultrasound guidance into axillary lymph nodes of a female cadaver. Adjustments in transducer frequency, dynamic range, cross-beam (spatial compound imaging), beam steering, speckle reduction imaging, harmonic imaging, colorization, and speed of sound were evaluated. Settings that improved marker detection were used clinically for a year. Results:Sonographic scanning settings that improved biopsy marker conspicuity included increasing transducer frequency, decreasing dynamic range, setting cross-beam to medium hybrid, turning on beam steering, and setting speckle reduction imaging in the mid-range. There was no appreciable improvement with harmonic imaging, colorization, or speed of sound. Conclusion:On a currently available clinical ultrasound scanning system, ultrasound scanning parameters can be adjusted to improve the conspicuity of biopsy markers. Overall, optimization requires a balance between techniques that clinically increase contrast (dynamic range, harmonic imaging, and steering) and those that minimize graininess (spatial compound imaging, speckle reduction imaging, and steering). Additional scanning and procedural strategies have been provided to improve the confidence of sonographic detection of biopsy markers closely associated with the intended target.
| S-EPMC7193150 | biostudies-literature
Project description:ObjectivesTo determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging.MethodsA total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status-related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in the training and validation subgroups were then evaluated and compared.ResultRadiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts' prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930, and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized ≤ 3cm and tumor sized>3 cm, respectively.ConclusionThe radiomics model demonstrated a good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.
| S-EPMC10641324 | biostudies-literature