Targeting CRL4 suppresses chemoresistant ovarian cancer growth by inducing mitophagy

Meng, Yan; Qiu, Lei; Zeng, Xinyi; Hu, Xiaoyan; Zhang, Yaguang; Wan, Xia; Mao, Xiaobing; Wu, Jian; Xu, Yongfeng; Xiong, Qunli; Chen, Zhuo; Zhang, Bo; Han, Junhong

doi:10.1038/s41392-022-01253-y

Cited by 35 publications

(20 citation statements)

References 81 publications

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“…Lysosome can initiate calcium signaling through the TRPML-1/TFEB pathway and regulate ovarian cancer cell drug resistance ( 65 ). The mitochondrial autophagy-associated PINK1/parkin pathway, BNIP3 molecules, and mitochondrial Ca 2+ absorption promotion in the MAM might all be used to manipulate ovarian cancer cell metastasis and drug resistance ( 68 , 70 , 71 ).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, BNIP3 is hypothesized to be an important target in ovarian cancer metastasis. Depletion of the well-defined E3 ubiquitin ligase CRL4 CUL4A/DDB1 upregulates phosphorylation of 5’ AMP-activated protein kinase (AMPK)α Thr172 and MFF Ser172/Ser146 to enhance mitochondrial fission, which in turn recruits DRP1 into mitochondria; its absence stimulates mitochondrial autophagy via the PINK1/parkin pathway, degrading dysfunctional and fragmented mitochondria and manipulating ovarian cancer cell chemoresistance ( 68 ).…”

Section: Mechanisms Of Cellular Organelles In Ovarian Cancer Pathogen...mentioning

confidence: 99%

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Independent organelle and organelle—organelle interactions: essential mechanisms for malignant gynecological cancer cell survival

Shen,

Chen,

et al. 2024

Front. Immunol.

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Different eukaryotic cell organelles (e.g., mitochondria, endoplasmic reticulum, lysosome) are involved in various cancer processes, by dominating specific cellular activities. Organelles cooperate, such as through contact points, in complex biological activities that help the cell regulate energy metabolism, signal transduction, and membrane dynamics, which influence survival process. Herein, we review the current studies of mechanisms by which mitochondria, endoplasmic reticulum, and lysosome are related to the three major malignant gynecological cancers, and their possible therapeutic interventions and drug targets. We also discuss the similarities and differences of independent organelle and organelle–organelle interactions, and their applications to the respective gynecological cancers; mitochondrial dynamics and energy metabolism, endoplasmic reticulum dysfunction, lysosomal regulation and autophagy, organelle interactions, and organelle regulatory mechanisms of cell death play crucial roles in cancer tumorigenesis, progression, and response to therapy. Finally, we discuss the value of organelle research, its current problems, and its future directions.

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Section: Discussionmentioning

confidence: 99%

Section: Mechanisms Of Cellular Organelles In Ovarian Cancer Pathogen...mentioning

confidence: 99%

Independent organelle and organelle—organelle interactions: essential mechanisms for malignant gynecological cancer cell survival

Shen,

Chen,

et al. 2024

Front. Immunol.

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“…Cells can remove incomplete or damaged mitochondria through the mechanism of autophagy selectively and the process is called mitophagy [ 3 ]. The body can maintain the integrity of mitochondrial function through mitophagy, so as to achieve the purpose of delaying aging and treating diseases [ 3 , 4 ]. In recent years, mitophagy is found to contribute to OC progression [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer

Zheng,

Jiang,

Lin

et al. 2024

BMC Women's Health

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Background Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC. Methods The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on. Results We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs. Conclusions Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.

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“…Furthermore, MAPK1/3 kinase-dependent ULK1 degradation can lead to the attenuation of mitophagy, promoting bone metastasis in breast cancer ( 17 ). In ovarian cancer, inducing mitophagy can inhibit the progression of chemotherapy-resistant ovarian cancer ( 18 ). Therefore, research on the development of new tumor treatment modalities for mitophagy is gaining increasing popularity ( 19 - 21 ).…”

Section: Introductionmentioning

confidence: 99%

Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma

Yin,

Xiao,

et al. 2024

Transl Cancer Res

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Background Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that accounts for a large proportion of kidney cancer, It is prone to recurrence and metastasis, and has a high mortality rate. Although mitophagy is important for metastasis and the recurrence of various tumors, its effect on renal clear cell carcinoma is poorly understood. Methods Mitophagy-related genes were obtained through the GeneCards database. We normalised the data from different sources by removing the batch effect. Next, we conducted a preliminary screening of mitophagy-related genes and obtained prognosis-related genes from differentially expressed genes. We constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) regression with data from The Cancer Genome Atlas (TCGA) and GSE29609 datasets and validated it internally. International Cancer Genome Consortium (ICGC) and E-MTAB-1980 cohorts also provided double external validation. In addition, we combined multi-omics and single-cell data to comprehensively analyse mitophagy-related gene model signature (MRGMS). Combined with the mitophagy-related gene model (MRGM) score, we constructed a nomogram. Finally, we performed pathway enrichment analysis using a variety of methods. Results Multiomics and single-cell data analysis showed that the MRGMS is important for patients with ccRCC and is expected to become a new biomarker. The construction of a nomogram was conducive to accurately predicting patient survival. Conclusions Mitophagy-related genes are important for predicting the prognosis of ccRCC and are conducive to the development of more personalised treatment plans for patients.

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Targeting CRL4 suppresses chemoresistant ovarian cancer growth by inducing mitophagy

Cited by 35 publications

References 81 publications

Independent organelle and organelle—organelle interactions: essential mechanisms for malignant gynecological cancer cell survival

Independent organelle and organelle—organelle interactions: essential mechanisms for malignant gynecological cancer cell survival

Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer

Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma

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