Evaluation of clinical value and potential mechanism of MTFR2 in lung adenocarcinoma via bioinformatics

Chen, Cheng; Tang, Yang; Qu, Wendong; Han, Xu; Zuo, Jiebin; Cai, Qingyong; Xu, Gang; Song, Yimin; Ke, Xixian

doi:10.1186/s12885-021-08378-3

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Mutations in mitochondria DNA (mDNA) have been identified in several tumor types (Larman et al, 2012;Bonora et al, 2021;Kopinski et al, 2021). For example, mitochondrial fission regulator 2 (MTFR2) was a biomarker for diagnosis and poor prognosis in LUAD (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

FDX1 can Impact the Prognosis and Mediate the Metabolism of Lung Adenocarcinoma

et al. 2021

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Background: Lung cancer has emerged as one of the most common cancers in recent years. The mitochondrial electron transport chain (ETC) is closely connected with metabolic pathways and inflammatory response. However, the influence of ETC-associated genes on the tumor immune response and the pathogenesis of lung cancer is not clear and needs further exploration.Methods: The RNA-sequencing transcriptome and clinical characteristic data of LUAD were downloaded from the Cancer Genome Atlas (TCGA) database. The LASSO algorithm was used to build the risk signature, and the prediction model was evaluated by the survival analysis and receiver operating characteristic curve. We explored the function of FDX1 through flow cytometry, molecular biological methods, and liquid chromatography–tandem mass spectrometry/mass spectrometry (LC–MS/MS).Results: 12 genes (FDX1, FDX2, LOXL2, ASPH, GLRX2, ALDH2, CYCS, AKR1A1, MAOB, RDH16, CYBB, and CYB5A) were selected to build the risk signature, and the risk score was calculated with the coefficients from the LASSO algorithm. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves of the dataset were 0.7, 0.674, and 0.692, respectively. Univariate Cox analysis and multivariate Cox regression analysis indicated that the risk signature is an independent risk factor for LUAD patients. Among these genes, we focused on the FDX1 gene, and we found that knockdown of FDX1 neither inhibited tumor cell growth nor did it induce apoptosis or abnormal cell cycle distribution. But FDX1 could promote the ATP production. Furthermore, our study showed that FDX1 was closely related to the glucose metabolism, fatty acid oxidation, and amino acid metabolism.Conclusion: Collectively, this study provides new clues about carcinogenesis induced by ETC-associated genes in LUAD and paves the way for finding potential targets of LUAD.

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Section: Introductionmentioning

confidence: 99%

FDX1 can Impact the Prognosis and Mediate the Metabolism of Lung Adenocarcinoma

et al. 2021

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“…Overexpression of CDK1 in lung cancer reduces chemosensitivity and is related to the lower survival rate of patients [ 33 – 35 ]. According to the literature, direct inhibition of CDK kinase activity is the basic strategy for developing effective cell cycle inhibitors [ 36 ]. Based on the results of this study, we speculate that CDK1 has excellent value in the survival and prognosis of lung cancer patients and may provide some possibilities for targeted drug delivery of lung cancer chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Cai

Xie

Liu

et al. 2023

BioMed Research International

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Objective. To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods. Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan–Meier online plotter tool. Results. CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion. CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

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“…Similar to some published studies, we take a looser definition for E variables. All four have been suggested as associated with lung cancer survival and have relatively low missingness (Chen et al, 2021). The pipeline for extracting pathological imaging features is sketched in Web them into a tiff format for the OpenSlide Python library.…”

Section: Analysis Of Tcga Data On Lung Adenocarcinoma (Luad)mentioning

confidence: 99%

Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis

Fang

Zhang

et al. 2023

Biometrics

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Gene–environment (G–E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main‐effect‐only analysis, G–E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the “main effects, interactions” variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G–E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a “byproduct” of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G–E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G–E interaction analysis leads to different findings with competitive prediction performance and stability.

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Evaluation of clinical value and potential mechanism of MTFR2 in lung adenocarcinoma via bioinformatics

Cited by 10 publications

References 33 publications

FDX1 can Impact the Prognosis and Mediate the Metabolism of Lung Adenocarcinoma

FDX1 can Impact the Prognosis and Mediate the Metabolism of Lung Adenocarcinoma

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis

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