HDAC10 Inhibits Cervical Cancer Progression through Downregulating the HDAC10-microRNA-223-EPB41L3 Axis

Gu, Yuan; Zhou, Guannan; Yao, Li; He, Hong Yu; Ding, Jing; Hua, Keqin

doi:10.1155/2022/8092751

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…CCND2 [246], VCAM1 [190], PDGFB (platelet derived growth factor subunit B) [221], PTCH1 [263], FOXQ1 [210], IGF2BP1 [324], ACTC1 [359], EPB41L3 [315], DAPK1 [379], hsa-mir-17-5p [1663], TCF3 [1664], RNF2 [1665], CLOCK (clock circadian regulator) [1666], SMARCA4 [1667] and TRIM28 [1668] provided a clear picture of the prognosis of patients with ovarian cancer. CCND2 [511], VCAM1 [478], PTCH1 [523], FOXQ1 [492], IGF2BP1 [568], EPB41L3 [560], DAPK1 [609], hsa-mir-17-5p [1669], TCF3 [1670] and TRIM28 [1671] as a biomarkers for cervical cancer. CCND2 [814], VCAM1 [615], PDGFB (platelet derived growth factor subunit B) [795], PRKCB (protein kinase C beta) [780], ACTC1 [924], DAPK1 [947], hsa-mir-17-5p [1672], hsa-mir-2110 [1673], TCF3 [1674] and SMARCA4 [1675] are associated with developing cardiovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…In colorectal cancer, ABHD5 suppresses the SRM-dependent spermidine production in tumor-associated macrophages and potentiates tumor growth ( 25 ). HDAC10 , a histone deacetylase, has been reported to be correlated with tumor progression and poor prognosis in multiple cancers, including ovarian, lung, and colon cancer ( 26 - 28 ). AGMAT , AOC1 , and ODC1 have been reported to be oncogenes in several types of tumor ( 29 - 32 ), promoting tumor progression and metastasis.…”

Section: Discussionmentioning

confidence: 99%

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer

Li,

Kong,

Liu

et al. 2023

Transl Cancer Res

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Background Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the “limma” package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients’ outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism–based targeted therapies for KIRC.

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“…[57][58][59] EPB41L3 is a tumor suppressor involved in apoptosis and cell cycle regulation. [60][61][62][63] Decreased expression in DCIS was observed for ATP6V0A1, which plays a role in pH homeostasis and tumor cell invasion. [64][65][66] ACAP1, which is associated with cell proliferation, migration, and immune infiltration in tumors.…”

Section: Loss Of Basal Membrane Stability Inflammatory Processes and ...mentioning

confidence: 99%

Clinical proteomics reveals vulnerabilities in non-invasive breast ductal carcinoma and drives personalized treatment strategies

Mitsa

Florianova

Lafleur

et al. 2023

Preprint

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Ductal carcinoma in situ (DCIS) is the most common type (80%) of noninvasive breast lesions. The lack of validated prognostic markers, limited patient numbers and variable tissue quality significantly impact diagnosis, risk stratification, patient enrolment, and results of clinical studies. We performed label-free quantitative proteomics on 50 clinical formalin-fixed, paraffin embedded biopsies, validating 22 putative biomarkers from independent genetic studies. Our comprehensive proteomic phenotyping reveals more than 380 differentially expressed proteins and metabolic vulnerabilities, that can inform new therapeutic strategies for DCIS and IDC. Due to the readily druggable nature of proteins and metabolites, this study is of high interest for clinical research and pharmaceutical industry. To further evaluate our findings, and to promote the clinical translation of our study, we developed a highly multiplexed targeted proteomics assay for 90 proteins associated with cancer metabolism, RNA regulation and signature cancer pathways, such as Pi3K/AKT/mTOR and EGFR/RAS/RAF.

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HDAC10 Inhibits Cervical Cancer Progression through Downregulating the HDAC10-microRNA-223-EPB41L3 Axis

Cited by 4 publications

References 42 publications

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer

Clinical proteomics reveals vulnerabilities in non-invasive breast ductal carcinoma and drives personalized treatment strategies

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