StarD13 differentially regulates migration and invasion in prostate cancer cells

Jaafar, Leila; Fakhoury, Isabelle; Saab, Sahar; El-Hajjar, Layal; Abou-Kheir, Wassim; El‐Sibai, Mirvat

doi:10.1007/s13577-020-00479-8

Cited by 6 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have demonstrated that STARD13 acts as a crucial tumor suppressor gene in cancer, such as prostate cancer [26], colon cancer [27], breast cancer [28], and lung cancer [29]. A recent study [16] indicated that STARD13 could enhance 5-FU sensitivity by suppressing cancer stemness in hepatocellular carcinoma cells via attenuating YAP transcriptional activity.…”

Section: Discussionmentioning

confidence: 99%

Circ_0003570 Suppresses the progression of hepatocellular carcinoma through miR-182-5p/STARD13 regulatory axis

et al. 2022

View full text Add to dashboard Cite

Background Emerging evidence have revealed that circRNAs exert important biological effects in the development and progression of various diseases, including cancer. Our study aimed to elaborated the biological effects of hsa-circ_0003570 in hepatocellular carcinoma (HCC) development at the molecular level. Results The results of functional experiments showed that knockdown of circ_0003570 induced HCC cell growth, migration and invasion, whereas overexpression of circ_0003570 presented the opposite effects. In vivo experiments, xenograft tumors grown from circ-overexpressed cells had smaller tumor volume and weight than the control group. Further investigations suggested that circ_0003570 may function as a competing endogenous RNA via competitively binding miR-182-5p and thereby regulating the repression of downstream target gene STARD13, which were demonstrated by dual luciferase reporter assay and functional rescued experiments. Conclusions Taken together, circ_0003570 suppresses the development of HCC by modulating miR-182-5p/STARD13 axis.

show abstract

Section: Discussionmentioning

confidence: 99%

Circ_0003570 Suppresses the progression of hepatocellular carcinoma through miR-182-5p/STARD13 regulatory axis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Owing to the heterogeneity of PCa tumors and the personalization of patients [ 8 ], conventional risk stratification methods and models cannot stratify the detailed risk groups of patients and predict survival. Owing to the rapid development and advancement of molecular biology [ 9 , 10 ], the emergence of novel molecular biomarkers has provided a new approach for risk stratification and model construction for PCa patients, such as the androgen receptor (AR), prostate-specific membrane antigen (PSMA), and other molecular chaperones [ 11 ]. Additionally, there are many commercially available scoring panels, such as the Decipher score, Oncotype DX, Prolaris, and Stockholm 3 (STHLM3) model [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing

Lyu

Gao

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Background: Prostate cancer is a significant clinical issue, particularly for high Gleason score (GS) malignancy patients. Our study aimed to engineer and validate a risk model based on the profiles of high-GS PCa patients for early identification and the prediction of prognosis. Methods: We conducted differential gene expression analysis on patient samples from The Cancer Genome Atlas (TCGA) and enriched our understanding of gene functions. Using the least absolute selection and shrinkage operator (LASSO) regression, we established a risk model and validated it using an independent dataset from the International Cancer Genome Consortium (ICGC). Clinical variables were incorporated into a nomogram to predict overall survival (OS), and machine learning was used to explore the risk factor characteristics’ impact on PCa prognosis. Our prognostic model was confirmed using various databases, including single-cell RNA-sequencing datasets (scRNA-seq), the Cancer Cell Line Encyclopedia (CCLE), PCa cell lines, and tumor tissues. Results: We identified 83 differentially expressed genes (DEGs). Furthermore, WASIR1, KRTAP5-1, TLX1, KIF4A, and IQGAP3 were determined to be significant risk factors for OS and progression-free survival (PFS). Based on these five risk factors, we developed a risk model and nomogram for predicting OS and PFS, with a C-index of 0.823 (95% CI, 0.766–0.881) and a 10-year area under the curve (AUC) value of 0.788 (95% CI, 0.633–0.943). Additionally, the 3-year AUC was 0.759 when validating using ICGC. KRTAP5-1 and WASIR1 were found to be the most influential prognosis factors when using the optimized machine learning model. Finally, the established model was interrelated with immune cell infiltration, and the signals were found to be differentially expressed in PCa cells when using scRNA-seq datasets and tissues. Conclusions: We engineered an original and novel prognostic model based on five gene signatures through TCGA and machine learning, providing new insights into the risk of scarification and survival prediction for PCa patients in clinical practice.

show abstract

Loss of STARD13 contributes to aggressive phenotype transformation and poor prognosis in papillary thyroid carcinoma

Zeng

Liang

et al. 2023

Endocrine

View full text Add to dashboard Cite

StarD13 differentially regulates migration and invasion in prostate cancer cells

Cited by 6 publications

References 53 publications

Circ_0003570 Suppresses the progression of hepatocellular carcinoma through miR-182-5p/STARD13 regulatory axis

Circ_0003570 Suppresses the progression of hepatocellular carcinoma through miR-182-5p/STARD13 regulatory axis

Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing

Loss of STARD13 contributes to aggressive phenotype transformation and poor prognosis in papillary thyroid carcinoma

Contact Info

Product

Resources

About