DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method.

Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.

Protein acetylation regulates xylose metabolism during adaptation of Saccharomyces cerevisiae.

BACKGROUND: As the second most abundant polysaccharide in nature, hemicellulose can be degraded to xylose as the feedstock for bioconversion to fuels and chemicals. To enhance xylose conversion, the engineered Saccharomyces cerevisiae with xylose metabolic pathway is usually adapted with xylose as the carbon source in the laboratory. However, the mechanism under the adaptation phenomena of the engineered strain is still unclear. RESULTS: In this study, xylose-utilizing S. cerevisiae was constructed and used for the adaptation study. It was found that xylose consumption rate increased 1.24-fold in the second incubation of the yYST12 strain in synthetic complete-xylose medium compared with the first incubation. The study figured out that it was observed at the single-cell level that the stagnation time for xylose utilization was reduced after adaptation with xylose medium in the microfluidic device. Such transient memory of xylose metabolism after adaptation with xylose medium, named "xylose consumption memory", was observed in the strains with both xylose isomerase pathway and xylose reductase and xylitol dehydrogenase pathways. In further, the proteomic acetylation of the strains before and after adaptation was investigated, and it was revealed that H4K5 was one of the most differential acetylation sites related to xylose consumption memory of engineered S. cerevisiae. We tested 8 genes encoding acetylase or deacetylase, and it was found that the knockout of the GCN5 and HPA2 encoding acetylases enhanced the xylose consumption memory. CONCLUSIONS: The behavior of xylose consumption memory in engineered S. cerevisiae can be successfully induced with xylose in the adaptation. H4K5Ac and two genes of GCN5 and HPA2 are related to xylose consumption memory of engineered S. cerevisiae during adaptation. This study provides valuable insights into the xylose adaptation of engineered S. cerevisiae.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy.

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important 'hub' genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Multiple genome pattern analysis and signature gene identification for the Caucasian lung adenocarcinoma patients with different tobacco exposure patterns.

BACKGROUND: When considering therapies for lung adenocarcinoma (LUAD) patients, the carcinogenic mechanisms of smokers are believed to differ from those who have never smoked. The rising trend in the proportion of nonsmokers in LUAD urgently requires the understanding of such differences at a molecular level for the development of precision medicine. METHODS: Three independent LUAD tumor sample sets-TCGA, SPORE and EDRN-were used. Genome patterns of expression (GE), copy number variation (CNV) and methylation (ME) were reviewed to discover the differences between them for both smokers and nonsmokers. Tobacco-related signature genes distinguishing these two groups of LUAD were identified using the GE, ME and CNV values of the whole genome. To do this, a novel iterative multi-step selection method based on the partial least squares (PLS) algorithm was proposed to overcome the high variable dimension and high noise inherent in the data. This method can thoroughly evaluate the importance of genes according to their statistical differences, biological functions and contributions to the tobacco exposure classification model. The kernel partial least squares (KPLS) method was used to further optimize the accuracies of the classification models. RESULTS: Forty-three, forty-eight and seventy-five genes were identified as GE, ME and CNV signatures, respectively, to distinguish smokers from nonsmokers. Using only the gene expression values of these 43 GE signature genes, ME values of the 48 ME signature genes or copy numbers of the 75 CNV signature genes, the accuracies of TCGA training and SPORE/EDRN independent validation datasets all exceed 76%. More importantly, the focal amplicon in Telomerase Reverse Transcriptase in nonsmokers, the broad deletion in ChrY in male nonsmokers and the greater amplification of MDM2 in female nonsmokers may explain why nonsmokers of both genders tend to suffer LUAD. These pattern analysis results may have clear biological interpretation in the molecular mechanism of tumorigenesis. Meanwhile, the identified signature genes may serve as potential drug targets for the precision medicine of LUAD.

Epigenome-Wide Tobacco-Related Methylation Signature Identification and Their Multilevel Regulatory Network Inference for Lung Adenocarcinoma.

Tobacco exposure is one of the major risks for the initiation and progress of lung cancer. The exact corresponding mechanisms, however, are mainly unknown. Recently, a growing body of evidence has been collected supporting the involvement of DNA methylation in the regulation of gene expression in cancer cells. The identification of tobacco-related signature methylation probes and the analysis of their regulatory networks at different molecular levels may be of a great help for understanding tobacco-related tumorigenesis. Three independent lung adenocarcinoma (LUAD) datasets were used to train and validate the tobacco exposure pattern classification model. A deep selecting method was proposed and used to identify methylation signature probes from hundreds of thousands of the whole epigenome probes. Then, BIMC (biweight midcorrelation coefficient) algorithm, SRC (Spearman's rank correlation) analysis, and shortest path tracing method were explored to identify associated genes at gene regulation level and protein-protein interaction level, respectively. Afterwards, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and GO (Gene Ontology) enrichment analysis were used to analyze their molecular functions and associated pathways. 105 probes were identified as tobacco-related DNA methylation signatures. They belong to 95 genes which are involved in hsa04512, hsa04151, and other important pathways. At gene regulation level, 33 genes are uncovered to be highly related to signature probes by both BIMC and SRC methods. Among them, FARSB and other eight genes were uncovered as Hub genes in the gene regulatory network. Meanwhile, the PPI network about these 33 genes showed that MAGOH, FYN, and other five genes were the most connected core genes among them. These analysis results may provide clues for a clear biological interpretation in the molecular mechanism of tumorigenesis. Moreover, the identified signature probes may serve as potential drug targets for the precision medicine of LUAD.

Severe mental disorders following anti-retroviral treatment in a patient on peritoneal dialysis: A case report and literature review.

BACKGROUND: Antiviral drugs are widely used in populations with viral infection caused by immunologic inadequacy. Because these drugs are mainly metabolized by the kidneys, patients with renal failure undergoing renal replacement therapy are prone to drug adverse effects and poisoning. Severe neurotoxicity caused by antiviral drugs is a rare but life-threatening complication. CASE SUMMARY: This study reported one male patient on peritoneal dialysis who suffered from severe mental disorders after receiving an overdose of acyclovir and valacyclovir for the treatment of herpes zoster. The literature review suggested that hemodialysis is better than peritoneal dialysis to clear acyclovir from the circulation. The patient died after his consciousness deteriorated despite peritoneal dialysis and continuous blood purification. CONCLUSION: This case emphasizes cautiousness when using anti-retroviral drugs in patients with uremia. Hemodialysis is optimal method to remove the drugs.

Identification of eight key miRNAs associated with renal cell carcinoma: A meta-analysis.

Renal cell carcinoma (RCC) is the most common renal carcinoma in the human kidney. To date, to the best of our knowledge, there are no biomarkers for the early monitoring and diagnosis of RCC patients. The present study aimed to develop deeper insight into the molecular mechanisms of microRNAs (miRNAs/miRs) in the regulation of RCC development and to reveal candidate miRNA biomarkers in human RCC. A meta-analysis was used to integrate the published and independent RCC miRNA expression profiling investigations that compared the miRNA expression profiles in RCC samples with control samples. The meta-signature miRNA target genes were then predicted in TargetScan. The predicted targets were further analyzed using Gene Ontology and pathway enrichment analysis with the Database for Annotation, Visualization and Integrated Discovery online tool, and then the transcription factors of meta-signature miRNA target genes were identified in Tfacts. A total of 7 publicly available and independent RCC miRNA expression profiling datasets were collected, and 2 upregulated (hsa-miR-155-5p and hsa-miR-210-5p) and 6 downregulated (hsa-miR-138-5p, hsa-miR-141-5p, hsa-miR-200c-5p, hsa-miR-362-5p, hsa-miR-363-5p and hsa-miR-429) meta-signature miRNAs in renal carcinoma were identified. The targeted gene enrichment analysis indicated that the meta-signature miRNAs may influence several pathways that participate in cancerogenesis, including the 'rap1 signaling pathway', 'renal cell carcinoma' and 'microRNAs in cancer'. Overall, the present meta-analysis identified 2 upregulated and 6 downregulated meta-signature miRNAs from 7 renal carcinoma datasets, the dysregulated miRNAs that may contribute to kidney carcinoma development. This research may reveal candidate miRNA biomarkers in human RCC.