Interplay between HMGA and TP53 in cell cycle control along tumor progression

Costa, Nathalia Meireles Da; Palumbo, Antônio; Martino, Marco De; Pinto, Luís Felipe Ribeiro; Nasciutti, Luiz Eurico

doi:10.1007/s00018-020-03634-4

Cited by 11 publications

(6 citation statements)

References 207 publications

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“…Furthermore, we found that the TP53 mutation rate (54%) was the highest in the cohort. TP53 is a driver gene of LUAD revealed by large-scale genomic studies, and TP53 is one of the most common somatic mutations regulating cell cycle and apoptosis (31,32). Amplification of TP53 made p53 activation upon transcriptionally downregulated genes for many central cell cycle proteins which achieved cell cycle arrest (33).…”

Section: Discussionmentioning

confidence: 99%

Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma

Huang

Guo

et al. 2021

Front. Immunol.

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BackgroundLung adenocarcinoma (LUAD) contains a variety of genomic and epigenomic abnormalities; the effective tumor markers related to these abnormalities need to be further explored.MethodsClustering analysis was performed based on DNA methylation (MET), DNA copy number variation (CNV), and mRNA expression data, and the differences in survival and tumor immune microenvironment (TIME) between subtypes were compared. Further, we evaluated the signatures in terms of both prognostic value and immunological characteristics.ResultsThere was a positive correlation between MET and CNV in LUAD. Integrative analysis of multi-omics data from 443 samples determined molecular subtypes, iC1 and iC2. The fractions of CD8+ T cells and activated CD4+ T cells were higher, the fraction of Tregs was lower, and the expression level of programmed death-ligand 1 (PD-L1) was higher in iC2 with a poor prognosis showing a higher TIDE score. We selected PTTG1, SLC2A1, and FAM83A as signatures of molecular subtypes to build a prognostic risk model and divided patients into high-risk group and low-risk group representing poor prognosis and good prognosis, respectively, which were validated in 180 patients with LUAD. Further, the low-risk group with lower TIDE score had more infiltrating immune cells. In 100 patients with LUAD, the high-risk group with an immunosuppressive state had a higher expression of PD-L1 and lower counts of CD8+ T cells and dendritic cells.ConclusionsThese findings demonstrated that combined multi-omics data could determine molecular subtypes with significant differences of prognosis and TIME in LUAD and suggested potent utility of the signatures to guide immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma

Huang

Guo

et al. 2021

Front. Immunol.

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“…Striated muscles, which are affected in pulmonary arterial hypertension, are associated with exercise intolerance in these patients ( 37 ). The TP53 encoding protein (p53) is a transcription factor involved in DNA repair, cell cycle arrest, and apoptosis ( 38 ). Dysregulation of p53 in pulmonary artery smooth muscle cells (PASMCs) plays an important role in vascular remodeling, a key process contributing to the progression of CTEPH ( 39 , 40 ).…”

Section: Discussionmentioning

confidence: 99%

Single-cell RNA-sequencing and microarray analyses to explore the pathological mechanisms of chronic thromboembolic pulmonary hypertension

Miao

Dong

Gong

et al. 2022

Front. Cardiovasc. Med.

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ObjectiveThe present study aimed to explore the pathological mechanisms of chronic thromboembolic pulmonary hypertension (CTEPH) using a gene chip array and single-cell RNA-sequencing (scRNA-seq).Materials and methodsThe mRNA expression profile GSE130391 was downloaded from the Gene Expression Omnibus database. The peripheral blood samples of five CTEPH patients and five healthy controls were used to prepare the Affymetrix microRNA (miRNA) chip and the Agilent circular RNA (circRNA) chip. The pulmonary endarterectomized tissues from five CTEPH patients were analyzed by scRNA-seq. Cells were clustered and annotated, followed by the identification of highly expressed genes. The gene chip data were used to identify disease-related mRNAs and differentially expressed miRNAs and circRNAs. The protein–protein interaction (PPI) network and the circRNA–miRNA–mRNA network were constructed for each cell type.ResultsA total of 11 cell types were identified. Intersection analysis of highly expressed genes in each cell type and differentially expressed mRNAs were performed to obtain disease-related genes in each cell type. TP53, ICAM1, APP, ITGB2, MYC, and ZYX showed the highest degree of connectivity in the PPI network of different types of cells. In addition, the circRNA–miRNA–mRNA network for each cell type was constructed.ConclusionFor the first time, the key mRNAs, miRNAs, and circRNAs, as well as their possible regulatory relationships, during the progression of CTEPH were analyzed using both gene chip and scRNA-seq data. These findings may contribute to a better understanding of the pathological mechanisms of CTEPH.

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“…In KEGG and GSEA analyses, a variety of signaling pathways were enriched. The P53 signaling pathway, cell cycle, Ras signaling pathway, and VEGF signaling pathway have been proven to play key roles in the progression of many tumors ( Diez et al, 2011 ; Sohn et al, 2018 ; Meireles Da Costa et al, 2020 ). In general, the functions and pathways enriched in this study might also promote HNSCC metastasis.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Validation of a Comprehensive Prognostic Model for Patients With HNSCC Metastasis

Shen

et al. 2021

Front. Genet.

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ObjectiveTo identify biomarkers related to head and neck squamous cell carcinoma (HNSCC) metastasis and establish a prognostic model for patients with HNSCC.MethodsHNSCC mRNA expression data of metastasis and non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. After screening the differentially expressed genes (DEGs) in the two datasets, a prognostic model, including clinical factors and biomarkers, was established, and verified in 36 samples of HNSCC by quantitative real-time transcription (qRT)-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene sets enrichment analysis (GSEA) were consulted to explore the functions of the DEGs.ResultsIn total, 108 DEGs were identified. GSEA, GO, and KEGG analyses showed that these DEGs were mainly involved in the proliferation and metastasis of HNSCC. Six genes that were significantly related to metastasis, immune cell infiltration and prognosis were further identified to construct a prognostic gene signature. The reliability of the gene signature was verified in 36 samples of HNSCC. A prognostic model, including tumor stage, risk level, and a nomogram for prediction were further established. Receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), C-index, and calibration plots showed that the model and nomogram perform well.ConclusionWe constructed a six-gene signature and a nomogram with high performance in predicting the prognosis of patients with HNSCC metastasis.

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Interplay between HMGA and TP53 in cell cycle control along tumor progression

Cited by 11 publications

References 207 publications

Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma

Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma

Single-cell RNA-sequencing and microarray analyses to explore the pathological mechanisms of chronic thromboembolic pulmonary hypertension

Establishment and Validation of a Comprehensive Prognostic Model for Patients With HNSCC Metastasis

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