Dataset Information

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

ABSTRACT: Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein-protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

SUBMITTER: Janyasupab P

PROVIDER: S-EPMC11352152 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

Janyasupab Panisa P Singhanat Kodchanan K Warnnissorn Malee M Thuwajit Peti P Suratanee Apichat A Plaimas Kitiporn K Thuwajit Chanitra C

Biomolecules 20240724 8

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven ...[more]

PMID: 39199284

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Project description:Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The "limma" package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell-substrate adhesion cytokine-cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.

Project description:PurposeInflammatory breast cancer (IBC) is characterized by numerous tumor emboli especially within dermal lymphatics. The explanation remains a mystery.MethodsThis study combines experimental studies with two different IBC xenografts with image algorithmic studies utilizing human tissue microarrays (TMAs) of IBC vs non-IBC cases to support a novel hypothesis to explain IBC's sina qua non signature of florid lymphovascular emboli.ResultsIn the human TMAs, compared to tumor features like nuclear grade (size), mitosis and Ki-67 immunoreactivity which show that IBC is only modestly more proliferative with larger nuclei than non-IBC, what really sets IBC apart is the markedly greater number of tumor emboli and distinctly smaller emboli whose numbers indicate geometric or exponential differences between IBC and non-IBC. In the experimental xenograft studies, Mary-X gives rise to tight spheroids in vitro which exhibit dynamic budding into smaller daughter spheroids whereas Karen-X exhibits only loose non-budding aggregates. Furthermore Mary-X emboli also bud dramatically into smaller daughter emboli in vivo. The mechanism that regulates this involves the generation of E-cad/NTF1, a calpain-mediated cleavage 100 kDa product of 120 kDa full length membrane E-cadherin. Inhibiting this calpain-mediated cleavage of E-cadherin by blocking either the calpain site of cleavage (SC) or the site of binding (SB) with specific decapeptides that both penetrate the cell membrane and mimic either the cleavage site or the binding site on E-cadherin, inhibits the generation of E-cad/NTF1 in a dose-dependent manner, reduces spheroid compactness and decreases budding.ConclusionSince E-cad/NFT1 retains the p120ctn binding site but loses the α-and β-catenin sites, promoting its 360° distribution around the cell's membrane, the vacilating levels of this molecule trigger budding of both the spheroids as well as the emboli. Recurrent and geometric budding of parental emboli into daughter emboli then would account for the plethora of emboli seen in IBC.

Dataset Information

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

Publications

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis.

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