Phase I/II study of vinorelbine and exisulind as first-line treatment of advanced non-small cell lung cancer in patients at least 70 years old: a wisconsin oncology network study

Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis

Background

As yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.

Materials and methods

The gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package "WGCNA". Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using "Limma" R package. The "randomForest" packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by "ConsensusClusterPlus"R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package "pheatmap". Diagnostic parameter capability was evaluated by ROC curve. The"XSum"packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.

Results

Via WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The"XSum"algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of -7.400 kcal/mol, -7.191 kcal/mol and -6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.

Conclusion

Using WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.

Genome-wide RNA-sequencing dataset reveals the prognostic value and potential molecular mechanisms of lncRNA in non-homologous end joining pathway 1 in early stage Pancreatic Ductal Adenocarcinoma

Objective: Our current study is to explore the prognostic value and molecular mechanisms underlying the role of lncRNA in non-homologous end joining pathway 1 (LINP1) in early stage pancreatic ductal adenocarcinoma (PDAC).

Methods: Genome-wide RNA-seq datasets of 112 early stage PDAC patients were got from The Cancer Genome Atlas and analyzed using multiple online tools.

Results: Overall survival in high LINP1 expression patients was shorter than those with low expression (high-LINP1 vs. low-LINP1=481 vs. 592 days, log-rank P=0.0432). The multivariate Cox proportional hazard regression model suggested that high-LINP1 patients had a markedly higher risk of death than low-LINP1 patients (adjusted P=0.004, hazard ratio=2.214, 95% confidence interval=1.283-3.820). Analysis of genome-wide co-expressed genes, screening of differentially expressed genes, and gene set enrichment analysis indicated that LINP1 may be involved in the regulation of cell proliferation-, cell adhesion- and cell cycle-related biological processes in PDAC. Six small-molecule compounds including STOCK1N-35874, fenofibrate, exisulind, NU-1025, vinburnine, and doxylamine were identified as potential LINP1-targeted drugs for the treatment of PDAC.

Conclusions: Our study indicated that LINP1 may serve as a prognostic biomarker of early stage PDAC. Analysis of genome-wide datasets led to the elucidation of the underlying mechanisms and identified six potential targeted drugs for the treatment of early PDAC.