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BACKGROUND: The development and progression of colon cancer are significantly affected by the tumor microenvironment, which has attracted much attention. The goal of our study was primarily to find out all possible tumor microenvironment-related genes in colon cancer. METHOD: This study quantified the immune and stromal landscape using the ESTIMATION algorithm using the gene expression matrix obtained from the UCSC Xena database. Dysregulated genes were harvested using the limma R package, and relevant pathways and biofunctions were identified using enrichment analysis. A least absolute shrinkage and selection operator (LASSO) regression was used to select the pivotal genes from the DEGs. Then, survival analysis was performed to determine the hub genes and a prognostic model was constructed by these hub genes with (or) TNM stage. Besides, associations between hub gene expressions and immune cell infiltration were assessed. RESULTS: A total of 725 DEGs were identified. Most of the results of the enrichment analysis were immune-related items. 13 genes were selected as the hub genes and a moderate-to-strong positive correlation between most hub genes and several immune cells were observed. Besides, the prognostic value of the hub genes were comparable to TNM staging. CONCLUSIONS: Our study provides a better understanding of how interactions between the 13 immune-prognostic hub genes and immune cells in the tumor microenvironment affect biological processes in colon cancer. These genes exhibit an equivalent ability to TNM staging in prognosis prediction. They are particularly expected to become novel prognostic biomarkers and targets of immunotherapies for colon cancer.
Assuntos
Algoritmos , Neoplasias do Colo/genética , Expressão Gênica , Microambiente Tumoral/genética , Fatores Etários , Idoso , Neoplasias do Colo/mortalidade , Neoplasias do Colo/fisiopatologia , Bases de Dados Genéticas , Feminino , Marcadores Genéticos/genética , Humanos , Imunidade Celular , Estimativa de Kaplan-Meier , Masculino , Estadiamento de Neoplasias , Prognóstico , Modelos de Riscos Proporcionais , Transcriptoma , Microambiente Tumoral/imunologiaRESUMO
The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies' income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1% higher social inclusion is associated with a 0.153%-0.166% more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.
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Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).
Assuntos
Diabetes Mellitus , Saúde Global , Teorema de Bayes , China/epidemiologia , Diabetes Mellitus/epidemiologia , Humanos , México/epidemiologia , Prevalência , Análise Espacial , Fatores de TempoRESUMO
The aim of this study was to determine the biomechanical parameters that explain ventral start performance in swimming. For this purpose, 13 elite swimmers performed different variants of the ventral start technique. Two-dimensional video analyses of the aerial and underwater phases were used to assess 16 kinematic parameters from the starting signal to 5â m, and an instrumented starting block was used to assess kinetic data. A Lasso regression was used to reduce the number of parameters, providing the main determinants to starting performance, revealing different combinations of key determinants, depending on the variant (r² ≥ 0.90), with flight distance being the most relevant to all variants (r ≤ -0.80; p < .001). Also, special attention should be given to the total horizontal impulse in the grab start (r = -0.79; p < .001) and to the back foot action in the track and kick starts (r ≤ 0.61; p < .001). In addition, we provide two equations that could be easily used to predict starting performance by assessing block time and flight time (r² = 0.66) or block time and flight distance (r² = 0.83). These data provide relevant contributions to the further understanding of the biomechanics of swimming starts as well as insights for performance analysis and targeted interventions to improve athlete performance.