A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Zhao, Yingan; Zhang, Yingjue; Dai, Chen; Hong, Kai; Guo, Yangyang

doi:10.18632/aging.204209

Cited by 11 publications

(5 citation statements)

References 60 publications

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“…Mitophagy as a selective degradation process, is critical to keeping mitochondria healthy, producing ATP, and maintaining neuronal activity and survival by removing impaired mitochondria. MRGs have been previously reported as prognostic or diagnostic markers for various tumors, including pancreatic cancer ( Zhuo et al, 2022 ), breast cancer ( Zhao et al, 2022 ), and liver cancer ( Chen et al, 2021 ; Xu et al, 2022 ), whereas only a limited number of studies have examined the usefulness of them as AD biomarkers. Pakpian et al (2020) recently reported that alterations in mitochondrial dynamic-related genes in the peripheral blood may be useful for diagnosing AD.…”

Section: Discussionmentioning

confidence: 99%

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Zhao,

Wu,

Zhao

et al. 2023

Front. Neurosci.

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BackgroundAlzheimer’s disease (AD), a neurodegenerative disorder with progressive symptoms, seriously endangers human health worldwide. AD diagnosis and treatment are challenging, but molecular biomarkers show diagnostic potential. This study aimed to investigate AD biomarkers in the peripheral blood.MethodUtilizing three microarray datasets, we systematically analyzed the differences in expression and predictive value of mitophagy-related hub genes (MRHGs) in the peripheral blood mononuclear cells of patients with AD to identify potential diagnostic biomarkers. Subsequently, a protein–protein interaction network was constructed to identify hub genes, and functional enrichment analyses were performed. Using consistent clustering analysis, AD subtypes with significant differences were determined. Finally, infiltration patterns of immune cells in AD subtypes and the relationship between MRHGs and immune cells were investigated by two algorithms, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).ResultsOur study identified 53 AD- and mitophagy-related differentially expressed genes and six MRHGs, which may be potential biomarkers for diagnosing AD. Functional analysis revealed that six MRHGs significantly affected biologically relevant functions and signaling pathways such as IL-4 Signaling Pathway, RUNX3 Regulates Notch Signaling Pathway, IL-1 and Megakaryocytes in Obesity Pathway, and Overview of Leukocyteintrinsic Hippo Pathway. Furthermore, CIBERSORT and ssGSEA algorithms were used for all AD samples to analyze the abundance of infiltrating immune cells in the two disease subtypes. The results showed that these subtypes were significantly related to immune cell types such as activated mast cells, regulatory T cells, M0 macrophages, and neutrophils. Moreover, specific MRHGs were significantly correlated with immune cell levels.ConclusionOur findings suggest that MRHGs may contribute to the development and prognosis of AD. The six identified MRHGs could be used as valuable diagnostic biomarkers for further research on AD. This study may provide new promising diagnostic and therapeutic targets in the peripheral blood of patients with AD.

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Section: Discussionmentioning

confidence: 99%

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Zhao,

Wu,

Zhao

et al. 2023

Front. Neurosci.

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“…Consequently, predicting a patient's prognosis in PDAC using these markers is challenging. Additionally, in the early diagnosis of PDAC, the detection of single tumor markers, such as CEA, CA199, and CA125, is difficult to reach the ideal level of sensitivity and specificity (21)(22)(23)(24)(25)(26) which could anticipate the OS duration of PDAC patients (27). As per the expression of immune-related genes such as MMP14 and INHBA, Xu et al developed a predictive risk model to assess PADC patients' prognoses and identify PDAC therapy opportunities (28).…”

Section: Discussionmentioning

confidence: 99%

“…A univariate Cox regression analysis was conducted to determine DEGs associated with prognosis. Then, using multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses by "caret", "glmnet" and "survival" packages, thress genes were selected and integrated into the prognostic signature (15). The median risk score was used to classify individuals with PDAC into two categories (low-and high-risk categories).…”

Section: Development and Verification Of The Urgs Prognostic Signaturementioning

confidence: 99%

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Guo¹,

Wu²,

Cen³

et al. 2023

Front. Immunol.

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BackgroundPatients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients’ prognoses.MethodsThe URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.ResultsThe URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.ConclusionThe URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

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“…eg. db" and "DOSE" to conduct gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment (9,10).…”

Section: Differential Expression Of Genes Screening Weighted Correlat...mentioning

confidence: 99%

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics

et al. 2023

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BackgroundRecently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis.MethodsUsing the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis.ResultsFour candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers.ConclusionDOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.

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A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Cited by 11 publications

References 60 publications

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer’s disease and their immune cell infiltration correlation

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics

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