FBXO15 plays a critical suppressive functional role in regulation of breast cancer progression

Zhao, Yi; Shim, Nayeon; Cui, Yan Hong; Kang, Jae Hyeok; Yoo, Kyoungkeun; Kim, Seungmo; Yi, Joo Mi; Kim, Min Jung; Yoon, Jin‐Kook; Lee, Su Jae

doi:10.1038/s41392-021-00605-4

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Then, with a series of molecular structure descriptors as independent variables and the bioactivity value of compounds as dependent variables, a prediction model is constructed to predict and guide the structural optimization of existing active compounds. Zhao et al (2021) . (2021) have shown that FBXO15 plays a critical inhibitory function in regulating breast cancer progression.…”

Section: Introductionmentioning

confidence: 99%

MLP-Based Regression Prediction Model For Compound Bioactivity

Qin

Shi

et al. 2022

Front. Bioeng. Biotechnol.

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The development of breast cancer is closely linked to the estrogen receptor ERα, which is also considered to be an important target for the treatment of breast cancer. Therefore, compounds that can antagonize ERα activity may be drug candidates for the treatment of breast cancer. In drug development, to save manpower and resources, potential active compounds are often screened by establishing compound activity prediction model. For the 1974 compounds collected, the top 20 molecular descriptors that significantly affected the biological activity were screened using LASSO regression models combined with 10-fold cross-validation method. Further, a regression prediction model based on the MLP fully connected neural network was constructed to predict the bioactivity values of 50 new compounds. To measure the validity of the model, the model loss term was specified as the mean squared error (MSE). The results showed that the MLP-based regression prediction model had a loss value of 0.0146 on the validation set. This model is therefore well trained and the prediction strategy used is valid. The methods developed by this paper may provide a reference for the development of anti-breast cancer drugs.

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Section: Introductionmentioning

confidence: 99%

MLP-Based Regression Prediction Model For Compound Bioactivity

Qin

Shi

et al. 2022

Front. Bioeng. Biotechnol.

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Angelic acid triggers ferroptosis in colorectal cancer cells via targeting and impairing NRF2 protein stability

Cao,

Wang,

et al. 2024

J Nat Med

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A Molecular Signature of the Ubiquitin-Proteasome System for Forecasting Prognosis in Thyroid Carcinoma Patients

Zeng,

Geng,

Wan

et al. 2024

JIR

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Background The ubiquitin-proteasome system (UPS) is vital for protein quality control and its dysregulation is linked to diseases, including cancer. Targeting the UPS is becoming a promising approach in cancer therapy. However, the role of UPS modulation in thyroid carcinoma (THCA) remains to be fully elucidated. Methods Initially, we utilized data from The Cancer Genome Atlas (TCGA) database to employ weighted gene co-expression network analysis (WGCNA) with LASSO regression to develop a prognostic model for core UPS genes implicated in THCA. Subsequently, we stratified the THCA training set into two distinct subtypes based on ubiquitin-proteasome system prognostic model score (UPS-PMS) characteristics. Key genes within the model were then subjected to functional analysis, immunotherapy evaluation, and drug sensitivity studies. Results We delineated a prognostic model of the UPS comprising six genes, which we subsequently demonstrated was capable of forecasting patient prognosis. Moreover, our findings indicated a substantial correlation between UPS-PMS and immune microenvironmental factors, notably a negative correlation with myeloid immune cells and a potential influence on the Th1 to Th2 cells ratio. Especially, we observed a significant association between high UPS-PMS and an immunosuppressive microenvironment. Then, we elucidated the biological distinctions among various THCA sample subtypes, highlighting that the cluster_1 subtype is associated with an unfavorable prognosis. Of note, KCNA1 was identified as a pivotal prognostic gene within the UPS-PMS framework. We constructed a three-tiered regulatory network centered on KCNA1-related competing endogenous RNA (ceRNA). Furthermore, our results suggested that KCNA1 has potential as a target for immunotherapeutic strategies. Concurrently, drug sensitivity analyses demonstrated that high KCNA1 expression promoted gemcitabine resistance in patients, while KCNA1 knockdown increased sensitivity to gemcitabine. Conclusion In conclusion, we developed a novel UPS-based prognostic model for THCA, identified key gene KCNA1, and assessed immunotherapy and drug sensitivity, revealing new therapeutic targets.

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FBXO15 plays a critical suppressive functional role in regulation of breast cancer progression

Cited by 3 publications

References 8 publications

MLP-Based Regression Prediction Model For Compound Bioactivity

MLP-Based Regression Prediction Model For Compound Bioactivity

Angelic acid triggers ferroptosis in colorectal cancer cells via targeting and impairing NRF2 protein stability

A Molecular Signature of the Ubiquitin-Proteasome System for Forecasting Prognosis in Thyroid Carcinoma Patients

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