Development and validation of a novel mitophagy-related gene prognostic signature for glioblastoma multiforme

Wang, Jinghua; Qiu, Xinqi; Huang, Jiayu; Zhuo, Zewei; Chen, Hao; Zeng, Ruijie; Wu, Huihuan; Guo, Kehang; Yang, Qi; Ye, Huiling; Huang, Wei; Luo, Yujun

doi:10.1186/s12885-022-09707-w

Cited by 12 publications

(9 citation statements)

References 46 publications

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“…Recently, several studies have constructed risk prediction models for different cancers, including pancreatic cancer [25], glioblastoma [26], hepatocellular cancer [27], and lung cancer [28], based on MRGs. The prognostic model constructed based on these 12 MRGs had better predictive accuracy and robustness than the other models, and patients at high risk had worse prognoses in both the training and validation cohorts.…”

Section: Discussionmentioning

confidence: 99%

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma

Wang,

Liu,

et al. 2024

Biomolecules

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Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.

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Section: Discussionmentioning

confidence: 99%

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma

Wang,

Liu,

et al. 2024

Biomolecules

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“…4 ERGs were eventually selected to calculate the risk score. The formula for performing risk score [ 19 , 20 ] is presented below: where the expr is an expression of the ERGs, and the coef is the corresponding regression coefficient calculated by multi-variant Cox regression analysis.…”

Section: Methodsmentioning

confidence: 99%

Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

Zhang,

Zhang

et al. 2023

BMC Med Genomics

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Background Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated. Method The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted. Result An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies. Conclusion We established an ER Stress-based risk signature that could effectively predict EC patients’ prognosis and their immune correlation.

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“…The chip platform for the GSE22138 dataset (sample size:63) was the [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, as shown in Table 1 . The set of mitophagy-related genes was obtained from Wang et al, (2022) .…”

Section: Methodsmentioning

confidence: 99%

“…Various studies have shown that mitophagy pathways are intimately associated with the metabolic rearrangement of cancer cells to meet the high bioenergetic needs of tumors ( Vara-Perez et al, 2019 ). Although definitive functional changes and molecular mechanisms of mitophagy have not been completely elucidated, the expression levels of some mitophagy-related genes could serve as biomarkers for the diagnosis, prognosis, or therapy of some cancers ( Wang et al, 2021 ; Wang et al, 2022 ). A recent paper revealed that mitophagy is closely related to the clinical prognosis of patients with UM, and indicated that mitophagy-related biomarkers ( PGAM5, SQSTM1, ATG9A , and GABARAPL1 ) are survival-related genes of UM patients ( Liu et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Integrative analyses of a mitophagy-related gene signature for predicting prognosis in patients with uveal melanoma

et al. 2022

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We aimed to create a mitophagy-related risk model via data mining of gene expression profiles to predict prognosis in uveal melanoma (UM) and develop a novel method for improving the prediction of clinical outcomes. Together with clinical information, RNA-seq and microarray data were gathered from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ConsensusClusterPlus was used to detect mitophagy-related subgroups. The genes involved with mitophagy, and the UM prognosis were discovered using univariate Cox regression analysis. In an outside population, a mitophagy risk sign was constructed and verified using least absolute shrinkage and selection operator (LASSO) regression. Data from both survival studies and receiver operating characteristic (ROC) curve analyses were used to evaluate model performance, a bootstrap method was used test the model. Functional enrichment and immune infiltration were examined. A risk model was developed using six mitophagy-related genes (ATG12, CSNK2B, MTERF3, TOMM5, TOMM40, and TOMM70), and patients with UM were divided into low- and high-risk subgroups. Patients in the high-risk group had a lower chance of living longer than those in the low-risk group (p < 0.001). The ROC test indicated the accuracy of the signature. Moreover, prognostic nomograms and calibration plots, which included mitophagy signals, were produced with high predictive performance, and the risk model was strongly associated with the control of immune infiltration. Furthermore, functional enrichment analysis demonstrated that several mitophagy subtypes may be implicated in cancer, mitochondrial metabolism, and immunological control signaling pathways. The mitophagy-related risk model we developed may be used to anticipate the clinical outcomes of UM and highlight the involvement of mitophagy-related genes as prospective therapeutic options in UM. Furthermore, our study emphasizes the essential role of mitophagy in UM.

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Development and validation of a novel mitophagy-related gene prognostic signature for glioblastoma multiforme

Cited by 12 publications

References 46 publications

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma

Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

Integrative analyses of a mitophagy-related gene signature for predicting prognosis in patients with uveal melanoma

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