Image_2_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.tif

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Table_2_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.docx

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Image_4_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.tif

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Image_3_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.tif

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Table_1_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.docx

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Image_1_Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma.tif

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (pConclusionOur study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.</p

Additional file 1 of Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Additional file 1

Additional file 2 of Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Additional file 2

Additional file 3 of Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Additional file 3

Radiative Cooling and Solar Heating Janus Films for Personal Thermal Management

Hot and cold seasonal temperature fluctuations pose a serious public health threat. Radiative thermal management has been shown to be an effective method for personal thermal management. However, the currently available materials cannot maintain human thermal comfort against the hot and cold seasonal temperature fluctuations, such as heating in cold weather or cooling in hot weather. Here, a Janus film that integrates the two opposite requirements of heating and cooling into one functional dual-mode film is fabricated. In cooling mode, the Al backing and embedded silicon dioxide (SiO2) microparticle can achieve a high solar reflectivity (∼0.85) and high IR emissivity (∼0.95) to induce a temperature drop of ∼2 °C. In contrast, the embedded carbon nanotubes (CNTs) can improve solar absorption (∼0.95) and induce a temperature increase of ∼7 °C. Owing to its radiative cooling and solar heating capability and compatibility with large-scale production, this Janus film is promising to bring new insights into the design of the next-generation functional textiles