BackgroundResearch on glucosamine shows anti-inflammatory and anti-cancer benefits with a minimal adverse effects. We aimed to explore the relationship between use of glucosamine and risk of lung cancer and lung cancer mortality based on data from the large-scale nationwide prospective UK Biobank cohort study.MethodsParticipants were enrolled between the year 2006 and 2010 and followed up to 2020. Cox proportion hazards model were used to assess the relationship between glucosamine use and risk of lung cancer and lung cancer mortality. Subgroup analyses and sensitivity analyses were performed to explore the potential effect modifications and the robustness of main findings.ResultsA total of 439,393 participants (mean age: 56 years; 53% females) with a mean follow-up of 11 years were included for analyses. There were 82,603 (18.80%) participants reporting regular use of glucosamine at baseline. During follow-up, there were 1,971 (0.45%) lung cancer events documented. Glucosamine use was significantly associated with a decreased risk of lung cancer (hazard ratio=0.84, 95% CI: 0.75–0.92, p<0.001) and lung cancer mortality (hazard ratio=0.88, 95% CI: 0.81–0.96, p=0.002) in fully-adjusted models. A stronger association between glucosamine use and decreased lung cancer risk was observed in participants with a family history of lung cancer when compared to those without a family history.ConclusionRegular use of glucosamine was significantly related with decreased risk of lung cancer and lung cancer mortality, based on data from this nationwide prospective cohort study.
Background As a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as a selection bias towards covariates with many possible split points. Conditional inference forests (CIF) methodology is known to reduce the selection bias via a two-step split procedure implementing hypothesis tests as it separates the variable selection and splitting, but its computation costs too much time. Random forests with maximally selected rank statistics (MSR-RF) methodology proposed recently seems to be a great improvement on RSF and CIF. Methods In this paper we used simulation study and real data application to compare prediction performances and variable selection performances among three survival forests methods, including RSF, CIF and MSR-RF. To evaluate the performance of variable selection, we combined all simulations to calculate the frequency of ranking top of the variable importance measures of the correct variables, where higher frequency means better selection ability. We used Integrated Brier Score (IBS) and c-index to measure the prediction accuracy of all three methods. The smaller IBS value, the greater the prediction. Results Simulations show that three forests methods differ slightly in prediction performance. MSR-RF and RSF might perform better than CIF when there are only continuous or binary variables in the datasets. For variable selection performance, When there are multiple categorical variables in the datasets, the selection frequency of RSF seems to be lowest in most cases. MSR-RF and CIF have higher selection rates, and CIF perform well especially with the interaction term. The fact that correlation degree of the variables has little effect on the selection frequency indicates that three forest methods can handle data with correlation. When there are only continuous variables in the datasets, MSR-RF perform better. When there are only binary variables in the datasets, RSF and MSR-RF have more advantages than CIF. When the variable dimension increases, MSR-RF and RSF seem to be more robustthan CIF Conclusions All three methods show advantages in prediction performances and variable selection performances under different situations. The recent proposed methodology MSR-RF possess practical value and is well worth popularizing. It is important to identify the appropriate method in real use according to the research aim and the nature of covariates.
Background: High mobility group box (HMGB) family protein Ixr1 has been shown to be involved in DNA damage repair, however, its role and mechanism remain largely unclear.Methods:Genes ofS. cerevisiaewere deleted or tagged with myc, GFP, or mcherry using the lithium acetate method. Sensitivity of strains to hydroxyurea (HU), methyl methanesulfonate (MMS), camptothe-cin (CPT), 4-nitroquinoline N-oxide (4-NQ), or Zeocin was tested. Distribution of GFP or mcherry fusion proteins was visualized with laser scanning confocal microscopy. RNA-seq was used to determine differential gene expression between mutant and control strains. Results: Ixr1 deletion (ixr1) mutant strain was sensitive to HU. Additionally, phosphorylation of effector of DNA damage checkpoint kinase Rad53 was lower in ixr1 than WT. Deletion of DNA damage checkpoint mediators ixr1 Rad9 was more sensitive to HU than ixr1 or Rad9, and ixr1 mrc1 had similar sensitivity to HU as mrc1 but stronger than ixr1. Deletion of ribonucleotide reductase inhibitors sml1 or crt10 didnt reduce the sensitivity of ixr1 induced by HU. Repli-cation fork nuclease exo1 ixr1 or helicase sgs1 ixr1 double deletions were more sensitive to HU than single deletion. In addition, laser scanning confocal microscopy imaging indicated that in response to HU, Ixr1 may be in the same pathway as Mrc1, possibly downstream. Gene Ontol-ogy enrichment analysis of differentially expressed genes (DEGs) between ixr1 and wildtype, untreated and treated with HU, confirmed that Ixr1 plays an important role in regulating the transcription of genes related to DNA replication or DNA damage repair. We also found that, re-gardless of HU exposure, Ixr1 localized to the nucleus and may bind DNA through its two HMG-boxes. Conclusion: Ixr1 participates in the DNA replication stress response through a DNA damage checkpoint pathway mediated by Mrc1, and regulates expression of genes related to DNA damage repair.
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