Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer

Wang, Jiao; Zhang, Xiaocui; Zheng, Fei; Yang, Qing; Bi, Fangfang

doi:10.1186/s13048-023-01247-6

Cited by 4 publications

(6 citation statements)

References 45 publications

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“…Our prior bioinformatics analysis suggested ULK1 as a prognostic risk factor for patients in the TCGA-OV dataset, with its high expression correlating to shortened OS periods [ 12 ]. To examine ULK1 expression in EOC, we initially analyzed the protein expression of ULK1 in 35 primary EOC tissues and 26 normal ovarian tissues using western blot assays, and the mRNA expression in 67 primary EOC tissues and 50 normal ovarian tissues via RT-qPCR assays.…”

Section: Resultsmentioning

confidence: 99%

“…Total RNA extraction was performed using TRIzol reagent (Takara Bio, Kusatsu, Japan), with RNA concentration and purity assessed using a NanoDrop 2000 system (Thermo Scientific). The reverse transcription of RNA samples and the qPCR analysis were carried out in accordance with our previously documented methodology [ 12 ]. The 2 −∆∆Ct method was employed for gene expression analysis, using ACTB as the internal reference.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, RM2Target ( http://rm2target.canceromics.org/ ) [ 29 ] facilitated the investigation of the correlation between the expressions of IGF2BP3 and ULK1 in TCGA cancers, including the associations between IGF2BP3 and ULK1. The data of mutations were downloaded and visualized as previously described [ 12 ]. The ggplot2 package in R software was used to present the expression distribution of m6A-related genes.…”

Section: Methodsmentioning

confidence: 99%

“…Our findings indicated that 4.36% of the samples had gene mutations. Additionally, a significant prevalence of copy number variations (CNV) was observed in mitophagy-related genes [ 12 ]. This suggests that alterations in mitophagy may play a role in the onset and progression of OV.…”

Section: Introductionmentioning

confidence: 99%

“…ULK1 has been extensively documented in various human diseases, including cancer [ 13 ]. Our previous bioinformatics analysis revealed that ULK1 is a significant prognostic factor in OV, with high expression levels correlating with a decreased overall survival (OS) period [ 12 ]. However, the specific role and molecular regulatory mechanism of ULK1 in the progression of EOC remain unclear.…”

Section: Introductionmentioning

confidence: 99%

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Regulation of ULK1 by WTAP/IGF2BP3 axis enhances mitophagy and progression in epithelial ovarian cancer

Wang,

Zheng,

Wang

et al. 2024

Cell Death Dis

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There is a pressing need for innovative therapeutic strategies for patients with epithelial ovarian cancer (EOC). Previous studies have shown that UNC-51-like kinase 1 (ULK1), a serine/threonine kinase, is crucial in regulating cellular autophagy and mitophagy across various tumor types. However, the clinical implications, biological functions, and potential mechanisms of ULK1 in EOC remain poorly understood. This study demonstrates that ULK1 expression is upregulated in EOC tissue samples and EOC cell lines, with increased ULK1 expression correlating with poor prognosis. Functionally, overexpressed ULK1 enhances the proliferation and migration abilities of EOC cells both in vitro and in vivo. Mechanistically, ULK1 was identified as an m6A target of WTAP. WTAP-mediated m6A modification of ULK1 enhanced its mRNA stability in an IGF2BP3-dependent manner, leading to elevated ULK1 expression and enhanced mitophagy in EOC. In summary, our research reveals that the WTAP/IGF2BP3-ULK1 axis significantly influences protective mitophagy in EOC, contributing to its progression. Therefore, the regulatory mechanisms and biological function of ULK1 identify it as a potential molecular target for therapeutic intervention in EOC.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regulation of ULK1 by WTAP/IGF2BP3 axis enhances mitophagy and progression in epithelial ovarian cancer

Wang,

Zheng,

Wang

et al. 2024

Cell Death Dis

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Genetically predicted gynecologic surgery and risk of breast cancer in European population

Feng,

Gong

2023

Preprint

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Purpose We conducted Mendelian randomization (MR) analyses to investigate the associations between gynecologic surgery and risk of breast cancer. Methods We performed MR using publicly accessible genome-wide association study (GWAS) datasets. Our primary analytical method was the inverse variance weighted (IVW) method. Additionally, we utilized complementary methods, including MR-Egger regression, weighted median, and MR-PRESSO. To assess potential horizontal pleiotropy, we conducted the MR-Egger intercept test and employed the global test for pleiotropic residuals and outliers through the MR-Presso method. We also assessed instrumental heterogeneity using the Cochran Q statistic. In addition, we conducted analyses of risk factors to further explore possible mediator relationships for causal inference. Results The initial findings from the IVW analysis reveal a statistically significant relationship between hysterectomy and ER- breast cancer (OR(Odds Ratio) = 82.73, 95% Confidence Interval (CI) = 1.45–4.73 × 103, p = 0.032). These results were consistent with those obtained from MR-Egger regression, where bilateral ovariectomy demonstrates a statistically significant link with ER + breast cancer (OR = 6.42 × 10− 2, 95% CI = 6.08 × 10− 3 − 0.68, p = 0.022). Importantly, the robustness of these findings was confirmed in all sensitivity analyses. Conclusion Our study reveals an association between gynecological surgery and breast cancer and menorrhagia and irregular menstruation is a potential mediator.

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Identification of platinum-resistance related small GTPase binding signatures to predict the prognosis of ovarian cancer by machine learning and integrated bioinformatic analyses

Zhong,

Zhu,

Wang

et al. 2024

Preprint

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Background High grade serous ovarian cancer (HGSOC) had high lethality due to its high relapse rate and acquired drug resistance. The tumor immune microenvironment (TIME) of HGSOC was heterogeneous, and mostly immune cold. We aimed to build the bridge between platinum-resistance related signatures and patient overall survival (OS). Methods The RNA sequencing data from GSE160626 was used for extraction of platinum-resistance related genes. The TCGA-OV cohort were fitted into 101 kinds of machine learning methods, and the validation cohort included GSE9899, GSE63885 and GSE26193. Numerous methods including the Cindex, receiver operating characteristic curve (ROC), univariate and multivariate Cox regression, and the decision curve analysis (DCA) were applied to detect the performances of platinum-resistance related risk score (PRRS) and a PRRS based nomogram. The single-cell RNA sequencing data and Spatial Transcriptomics data were used to determine the risky cell types correlated with our PRRS. Results Based on platinum-resistance related genes, we conducted consensus clustering and defined a platinum-resistance resembling cluster, which had significantly shorter OS. And with DEGs related to small GTPase between two clusters, we established a PRRS and a PRRS based nomogram, which had excellent performances in predicting OS of serous ovarian cancer patients. We further determined SPP1 + M2-like Macrophages were risky factors correlated with the PRRS, and determined ABCA1 and NDRG1 as the hub genes related to patient OS. Conclusion Small GTPase was a dominant feature of platinum-resistance resembling clusters. PRRS had terrific predicting value and correlated with SPP1 + M2-like Macrophages.

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Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer

Cited by 4 publications

References 45 publications

Regulation of ULK1 by WTAP/IGF2BP3 axis enhances mitophagy and progression in epithelial ovarian cancer

Regulation of ULK1 by WTAP/IGF2BP3 axis enhances mitophagy and progression in epithelial ovarian cancer

Genetically predicted gynecologic surgery and risk of breast cancer in European population

Identification of platinum-resistance related small GTPase binding signatures to predict the prognosis of ovarian cancer by machine learning and integrated bioinformatic analyses

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