MUC16 promotes triple-negative breast cancer lung metastasis by modulating RNA-binding protein ELAVL1/HUR

Chaudhary, Sanjib; Appadurai, Muthamil Iniyan; Maurya, Shailendra Kumar; Nallasamy, Palanisamy; Marimuthu, Saravanakumar; Shah, Ashu; Atri, Pranita; Ramakanth, Chirravuri Venkata; Lele, Subodh M.; Seshacharyulu, Parthasarathy; Ponnusamy, Moorthy P.; Nasser, Mohd W.; Ganti, Apar Kishor; Batra, Surinder K.; Lakshmanan, Imayavaramban

doi:10.1186/s13058-023-01630-7

Cited by 10 publications

(7 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the overexpressing of GOLM1 markedly promotes the metastasis of breast cancer cells in vivo [ 35 ]. ELAVL1 (EP: 0.0016, t -test p -value: 0.805) was found to be modulated by MUC16, which promotes triple-negative breast cancer lung metastasis [ 36 ]. UBP1 (EP: 0.0034, t -test p -value: 0.546) consists of the CP2 transcription factor with TFCP2, which is known to be essential to the EMT process [ 37 ].…”

Section: Results and Evaluationsmentioning

confidence: 99%

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Jung,

Yoo

2023

Genes

View full text Add to dashboard Cite

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.

show abstract

Section: Results and Evaluationsmentioning

confidence: 99%

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Jung,

Yoo

2023

Genes

View full text Add to dashboard Cite

show abstract

“…CA125 is best known as a biomarker for monitoring epithelial ovarian cancer [ 59 ]. In addition, CA125 is a repeating peptide epitope of the mucin MUC16, which promotes breast cancer cell proliferation and metastasis [ 60 , 61 ]. An increase in the concentration of CA125 is an indicator of disease recurrence [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based models for the prediction of breast cancer recurrence risk

Zuo,

Yang,

Jin

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.

show abstract

“…HuR is a key gatekeeper of liver homeostasis and prevents hepatocellular carcinoma and non-alcoholic fatty liver disease-related fibrosis, suggesting that the HuR-dependent network could be used therapeutically. However, whether HuR is a friend or foe in the field of human cancer remains a matter of debate, even if previous evidence shows that targeting the inhibition of HuR presents beneficial effects against glioma [ 26 ], prostate cancer [ 27 ], breast cancer [ 28 ] and pancreatic cancer [ 29 ]. Regarding the central nervous system, HuR is highly expressed in the neocortex during developmental stages, and its absence destabilizes the laminar structure of the neocortex, indicating that HuR mRNA metabolism promotes the cell motility of migrating mouse neurons [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Cyclosporin A-mediated translocation of HuR improves MTX-induced cognitive impairment in a mouse model via NCOA4-mediated ferritinophagy

Ding,

Xiang,

Jia

et al. 2023

Aging

View full text Add to dashboard Cite

Chemotherapy-induced cognitive impairment (CICI) is a subject that requires critical solutions in neuroscience and oncology. However, its potential mechanism of action remains ambiguous. The aim of this study was to investigate the vital role of HuR in the neuroprotection of cyclosporin A (CsA) during methotrexate (MTX)-induced cognitive impairment. A series of Hu-antigen R (HuR) gain and loss experiments were used to examine cyclosporin A (CsA)-mediated translocation of HuR’s ability to improve MTX-induced cognitive impairment through NCOA4-mediated ferritinophagy in vitro and in vivo . Obtained results show that the administration of CsA alleviated MTX-induced cognitive impairment in mice. The presence of MTX promoted the shuttling of HuR from the cytoplasm to the nucleus, whereas treatment with CsA increased cytoplasmic HuR expression levels and the levels of ferritinophagy-related proteins, such as NCOA4 and LC3II, compared to the MTX group. However, applying KH-3, an inhibitor of HuR, reversed CsA’s impact on the expression of ferritinophagy-related proteins in the hippocampus and in vitro . Also, treatment with CsA attenuated microglial activation by altering Iba-1 expression and decreased TNF-α and IL-1β levels in mice hippocampi. Moreover, KH-3 neutralized CsA’s effects on the expression of both Iba-1 and HuR in vivo and in vitro . In summary, CsA was confirmed to have a neuroprotective role in CICI. Its possible underlying mechanisms may be involved in the translocation of HuR. Mediating the translocation of HuR during CICI could mitigate neruoinflammation and neuronal apoptosis via NCOA4-mediated ferritinophagy and, thus, alleviate cognitive impairment in mice with CICI.

show abstract

MUC16 promotes triple-negative breast cancer lung metastasis by modulating RNA-binding protein ELAVL1/HUR

Cited by 10 publications

References 56 publications

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Machine learning-based models for the prediction of breast cancer recurrence risk

Cyclosporin A-mediated translocation of HuR improves MTX-induced cognitive impairment in a mouse model via NCOA4-mediated ferritinophagy

Contact Info

Product

Resources

About