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

Pathological imaging-assisted cancer gene-environment interaction analysis

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.

A predictive molecular signature consisting of lncRNAs associated with cellular senescence for the prognosis of lung adenocarcinoma

The role of long noncoding RNAs (lncRNAs) has been verified by more and more researches in recent years. However, there are few reports on cellular senescence-associated lncRNAs in lung adenocarcinoma (LUAD). Therefore, to explore the prognostic effect of lncRNAs in LUAD, 279 cellular senescence-related genes, survival information and clinicopathologic parameters were derived from the CellAge database and The Cancer Genome Atlas (TCGA) database. Then, we constructed a novel cellular senescence-associated lncRNAs predictive signature (CS-ALPS) consisting of 6 lncRNAS (AC026355.1, AL365181.2, AF131215.5, C20orf197, GAS6-AS1, GSEC). According to the median of the risk score, 480 samples were divided into high-risk and low-risk groups. Furthermore, the clinicopathological and biological functions, immune characteristics and common drug sensitivity were analyzed between two risk groups. In conclusion, the CS-ALPS can independently forecast the prognosis of LUAD, which reveals the potential molecular mechanism of cellular senescence-associated lncRNAs, and provides appropriate strategies for the clinical treatment of patients with LUAD.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

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

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.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Prognostic Value and Potential Mechanism of MTFR2 in Lung Adenocarcinoma

Mitochondrial fission regulator 2 (MTFR2) belongs to the MTFR1 family, which plays a crucial role in regulating oxidative phosphorylation. Recent studies indicate that it also participates in cancer carcinogenesis and development; however, the clinical significance of MTFR2 in lung adenocarcinoma has not been fully confirmed. Our current study investigated the relationships between clinical characteristics and MTFR2 expression based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GSE31210) dataset, and clinical histopathological sample cohort. In addition, Kaplan–Meier and Cox regression analyses were additionally performed to evaluate the association between MTFR2 expression and patient survival. Gene set enrichment analysis (GESA) was conducted to spot possible pathways associated with MTFR2. Moreover, a single-sample GESA (ssGESA) was performed to evaluate the association between MTFR2 expression and immune cell infiltration. Cell colony formation assay, CCK-8 assay, cell cycle assay, and transwell assay were performed to verify the cell proliferation, migration, and invasion abilities after interfering with MTFR2 in lung cancer cells. Western blot assay was applied to identify the underlying protein levels. The results indicated that the elevated MTFR2 expression in lung adenocarcinoma samples correlated with T stage (P < 0.001), N stage (P = 0.005), M stage (P = 0.015), pathological stage (P = 0.002), and TP53 status (P < 0.001). Patients with a higher MTFR2 expression correlated with poorer overall survival (P < 0.01) and progression-free survival (P = 0.002). Knockdown of MTFR2 inhibited cell proliferation, migration, and invasion via AKT-cyclin D1 signaling and EMT pathways. Moreover, MTFR2 expression significantly positively correlated with Th2 cells (P < 0.001). Taken together, MTFR2 could serve as a novel prognostic indicator and therapeutic target for lung adenocarcinoma.

Association of High Expression of Mitochondrial Fission Regulator 2 with Poor Survival of Patients with Esophageal Squamous Cell Carcinoma

Mitochondrial fission regulator 2 (MTFR2) is associated with mitochondrial fission, while few studies have assessed the associations between MTFR2 expression and clinical characteristics or prognosis of esophageal squamous cell carcinoma (ESCC). In this study, we compared the expression of MTFR2 in 6 ESCC tumors and relative normal tissues by immunohistochemistry (IHC). To assess the effect of MTFR2 expression on clinicopathologic characteristics and survival, 115 paraffin embedded ESCC tissue samples were assessed by IHC staining. Furthermore, the association between clinicopathological properties and MTFR2 expression in patients with ESCC was examined. The survival analysis was performed using the Cox regression models. We found that MTFR2 expression was significantly increased in ESCC tumors compared with normal esophageal epithelial cells. IHC analysis of 115 paraffin embedded ESCC tumor specimens of the patients showed that the expression of MTFR2 was significantly associated with clinical stage (P < 0.001), tumor classification (P < 0.001), histological grade (P < 0.001), and other clinicopathological characteristics. Both univariate and multivariate analyses showed that MTFR2 expression was inversely correlated with the survival of ESCC patients. In conclusion, the expression of MTFR2 is significantly associated with clinicopathologic characteristics and prognosis of ESCC. Thus, MTFR2 expression could serve as a potentially important prognostic biomarker and clinical target for patients with ESCC.

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

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.