RESUMEN
Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, proteinâprotein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.
Asunto(s)
Proteínas Bacterianas , Transformación Celular Neoplásica , Biología Computacional , Gonorrea , Neisseria gonorrhoeae , Mapas de Interacción de Proteínas , Humanos , Gonorrea/microbiología , Gonorrea/genética , Neisseria gonorrhoeae/genética , Neisseria gonorrhoeae/patogenicidad , Mapas de Interacción de Proteínas/genética , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Transformación Celular Neoplásica/genética , Genes Esenciales , Virulencia/genética , Inflamación/genética , Factores de Virulencia/genética , Interacciones Huésped-Patógeno/genética , MultiómicaRESUMEN
BACKGROUND: We characterized age at diagnosis and estimated sex differences for lung cancer and its histological subtypes among individuals who never smoke. METHODS: We analyzed the distribution of age at lung cancer diagnosis in 33,793 individuals across 8 cohort studies and two national registries from East Asia, the United States (US) and the United Kingdom (UK). Student's t-tests were used to assess the study population differences (Δ years) in age at diagnosis comparing females and males who never smoke across subgroups defined by race/ethnicity, geographic location, and histological subtypes. RESULTS: We found that among Chinese individuals diagnosed with lung cancer who never smoke, females were diagnosed with lung cancer younger than males in the Taiwan Cancer Registry (n = 29,832) (Δ years = -2.2 (95% confidence interval (CI):-2.5, -1.9), in Shanghai (n = 1049) (Δ years = -1.6 (95% CI:-2.9, -0.3), and in Sutter Health and Kaiser Permanente Hawai'i in the US (n = 82) (Δ years = -11.3 (95% CI: -17.7, -4.9). While there was a suggestion of similar patterns in African American and non-Hispanic White individuals. the estimated differences were not consistent across studies and were not statistically significant. CONCLUSIONS: We found evidence of sex differences for age at lung cancer diagnosis among individuals who never smoke.