Table_1_Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis.xlsx

BackgroundPatients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis.MethodsThe disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein–protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD.ResultsThis study identified 495 risk genes for DKD complicated with IBD. After constructing a protein–protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C–C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product–receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway.ConclusionThere is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.</p

DataSheet1_Serious Adverse Events Reporting in Phase III Randomized Clinical Trials of Colorectal Cancer Treatments: A Systematic Analysis.docx

Objective: The occurrence, development, and prognosis of serious adverse events (SAEs) associated with anticancer drugs in clinical trials have important guiding significance for real-world clinical applications. However, to date, there have been no studies investigating SAEs reporting in randomized clinical trials of colorectal cancer treatments. This article systematically reviewed the SAEs reporting of phase III randomized clinical trials of colorectal cancer treatments and analyzed the influencing factors.Methods: We reviewed all articles about phase III randomized clinical trials of colorectal cancer treatments published in the PubMed, Embase, Medline, and New England Journal of Medicine databases from January 1, 1993, to December 31, 2018, and searched the registration information of clinical trials via the internet sites such as “clinicaltrials.gov”. We analyzed the correlation between the reported proportion (RP) of SAEs in the literature and nine elements, including the clinical trial sponsor and the publication time. Chi-square tests and binary logistic regression were used to identify the factors associated with improved SAEs reports. This study was registered on PROSPERO.Results: Of 1560 articles identified, 160 were eligible, with an RP of SAEs of 25.5% (41/160). In forty-one publications reporting SAEs, only 14.6% (6/41) described the pattern of SAEs in detail. In clinical trials sponsored by pharmaceutical companies, the RP of SAEs was significantly higher than that in those sponsored by investigators (57.6 versus 20.7%, p Conclusion: Although the RP of SAEs increased over time, SAEs reporting in clinical trials needs to be further improved. The performance, outcomes and prognosis of SAEs should be reported in detail to guide clinical practice in the real world.</p

Data_Sheet_1_Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.docx

ObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.Materials and methodsGene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.ResultsA total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.ConclusionDUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.</p

Additional file 1 of Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis

Additional file 1

Supplementary Data from Genetic Aberrations in the CDK4 Pathway Are Associated with Innate Resistance to PD-1 Blockade in Chinese Patients with Non-Cutaneous Melanoma

Supplementary Data Supplementary Table S1.Clinical pathological parameters of enrolled patients Supplementary Table S2. The sample information of QuantiGenePlex RNA assay Supplementary Table S3. Correlation of CDK4 pathway aberrations withPD-L1 protein expression in melanoma patients.</p

Figure S2 from Genetic Aberrations in the CDK4 Pathway Are Associated with Innate Resistance to PD-1 Blockade in Chinese Patients with Non-Cutaneous Melanoma

Supplementary Figure S2. The PD-1 staining of C57BL/6-hPD-1 mice.</p

Figure S3 from Genetic Aberrations in the CDK4 Pathway Are Associated with Innate Resistance to PD-1 Blockade in Chinese Patients with Non-Cutaneous Melanoma

Supplementary Figure S3. CDK4/6 inhibitor treatment positively regulates PD-L1 protein expression and influences immune gene expression.</p

Figure S1 from Genetic Aberrations in the CDK4 Pathway Are Associated with Innate Resistance to PD-1 Blockade in Chinese Patients with Non-Cutaneous Melanoma

Supplementary Figure S1. The flow chart describing the total population of the study.</p