For each facial segment (organized by level and cluster), this dataset contains the principal component analysis information for each vertex within that segment. In conjunction with the ExploringDataFeatures.m script, these principal component constructs can be used to project a given landmark shape into the principal component shape space.

results of Interproscan functional classification of the the ERP016024 dataset (as part of the EBI Metagenomics pipeline [Mitchell et al., Nucl. Acid Res. 2016] v3.0) were analysed using PCA and linear discriminant analysis."ERP016024_IPR_abundances_v3.0_PCA_subsistencestrategy_ populations.pdf":biplots of PCA based on InterPro functional terms relative abundance, showing the distribution of samples by groups of subsistence strategy (HG: hunter-gatheres, TF: traditional farmers, WC: Western controls) or populations (Aeta, Agta, Batak, Zambal, Casigurani, Tagbanua, Western controls) along the 6 first PC axis."ERP016024_IPR_abundances_v3.0.tsv_LDA_*vs*":tables summarize the results of a LDA with one discriminant function, i.e. separating pair of groups (HG vs TF, TF vs WC, WC vs HG). The top-ranking discriminating functional terms are reported.Scripts used to analyse these results can be found on GitHub: https://github.com/flass/microbiomes/tree/master/scripts/interproThis work was published in the journal Molecular Ecology doi: 10.1111/mec.1443

Although radiotherapy is greatly successful in the treatment of prostate cancer (PCa), radioresistance is still a major challenge in the treatment. To our knowledge, this study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify the differentially expressed lncRNAs and protein-coding genes (PCGs) between complete response (CR) and non-complete response (non-CR) groups in radiotherapy. Statistical methods were applied to identify the correlation between lncRNAs and radioresponse as well as lncRNAs and PCGs. The correlation between PCGs and radioresponse was analyzed using weighted gene co-expression network analysis (WGCNA). The three online databases were used to predict the potential target miRNAs of lncRNAs and the miRNAs that might regulate PCGs. RT-qPCR was utilized to detect the expression of lncRNAs and PCGs in our PCa patients. A total of 65 differentially expressed lncRNAs and 468 differentially expressed PCGs were found between the two groups of PCa. After the chi-square test, LINC01600 was selected to be highly correlated with radioresponse from the 65 differentially expressed lncRNAs. Pearson correlation analysis found 558 PCGs co-expressed with LINC01600. WGCNA identified the darkred module associated with radioresponse in PCa. After taking the intersection of the darkred module of WGCNA, differentially expressed PCGs between the two groups of PCa, and the PCGs co-expressed with LINC01600, three PCGs, that is, JUND, ZFP36, and ATF3 were identified as the potential target PCGs of LINC01600. More importantly, we detected the expression of LINC01600 and three PCGs using our PCa patients, and finally verified that LINC01600 and JUND were differentially expressed between CR and non-CR groups, excluding ZFP36 and ATF3. Meantime, the potential regulation ability of LINC01600 for JUND in PCa cell lines was initially explored. In addition, we constructed the competing endogenous RNA (ceRNA) network of LINC01600—miRNA—JUND. In conclusion, we initially reveal the association of LINC01600 with radioresponse in PCa and identify its potential target PCGs for further basic and clinical research.

Although radiotherapy is greatly successful in the treatment of prostate cancer (PCa), radioresistance is still a major challenge in the treatment. To our knowledge, this study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify the differentially expressed lncRNAs and protein-coding genes (PCGs) between complete response (CR) and non-complete response (non-CR) groups in radiotherapy. Statistical methods were applied to identify the correlation between lncRNAs and radioresponse as well as lncRNAs and PCGs. The correlation between PCGs and radioresponse was analyzed using weighted gene co-expression network analysis (WGCNA). The three online databases were used to predict the potential target miRNAs of lncRNAs and the miRNAs that might regulate PCGs. RT-qPCR was utilized to detect the expression of lncRNAs and PCGs in our PCa patients. A total of 65 differentially expressed lncRNAs and 468 differentially expressed PCGs were found between the two groups of PCa. After the chi-square test, LINC01600 was selected to be highly correlated with radioresponse from the 65 differentially expressed lncRNAs. Pearson correlation analysis found 558 PCGs co-expressed with LINC01600. WGCNA identified the darkred module associated with radioresponse in PCa. After taking the intersection of the darkred module of WGCNA, differentially expressed PCGs between the two groups of PCa, and the PCGs co-expressed with LINC01600, three PCGs, that is, JUND, ZFP36, and ATF3 were identified as the potential target PCGs of LINC01600. More importantly, we detected the expression of LINC01600 and three PCGs using our PCa patients, and finally verified that LINC01600 and JUND were differentially expressed between CR and non-CR groups, excluding ZFP36 and ATF3. Meantime, the potential regulation ability of LINC01600 for JUND in PCa cell lines was initially explored. In addition, we constructed the competing endogenous RNA (ceRNA) network of LINC01600—miRNA—JUND. In conclusion, we initially reveal the association of LINC01600 with radioresponse in PCa and identify its potential target PCGs for further basic and clinical research.

Expression of several metabolism-related transcripts in NHDF cells grown in HGm and adapted to LGm or OXPHOSm.

Kmer-based methods are becoming increasingly high-utility for various bioinformatics research projects. Here we provide intermediate data files for the kmer-based methods in our manuscript.

PCA and correlation clustering analysis of RNA-Seq data.

Results from guppy software:, based on the output of Phylosift (Darling et al., PeerJ 2014), a method that extracts reads matching marker genes from WGS metagenomes and use them for phylogenetic placement on the Tree of Life to estimate the abundance of taxa present in a microbiome, GUPPY produces a matrix of edge differences of placement probability mass, i.e. "the differences between proportions of phylogenetic placements on either side of each internal edge of the reference phylogenetic tree" (Matsen & Evans, PLOS One 2013). This can be used to compute phylogenetic diversity within samples (aplha diversity) and represent diversity between samples (beta diversity) using edge principal component analysis (edgePCA).Comparison of results involving 24 original samples from the Philippines and meta-analyses with extended datasets including 7 or 9 individuals from the USA are also provided in the .datasets. foldersScripts used to analyse these results can be found on GitHub: https://github.com/flass/microbiomes/tree/master/scripts/phylosiftThis work was published in the journal Molecular Ecology doi: 10.1111/mec.1443

A simple PCA plot done with ~1400 23andMe data sets found on openSNP + the HapMap data. A list of ~100 Ancestry Informative Markers, from http://www.biomedcentral.com/1471-2156/10/39, was used for the PCA. Data mangling was done using PLINK. PCA was run with smartpca, without any optimizing for outliers. Plotting done with R + ggplot2.

HapMap population keys can be found here: https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html

Additional file 1: Figure S1. PCA plots of three datasets before and after batch correction. Figure S2. The PCA plot of immune cells between PAH and control in GSE117261 dataset. Figure S3. Heatmap of 17 feature genes in GSE113439 and GSE53408 datasets. Figure S4. The ROC of 17 genes in GSE117261. Table S1. Details of the DEGs in the dataset GSE117261. Table S2. Identification of seventeen characteristic genes of PAH using LASSO regression algorithm. Table S3. The genes in the dark olive green module by WGCNA. Table S4. The genes in the dark green module by WGCNA.

PCa patients from TCGA database clinicopathological features.

Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa.Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis.Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis.Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.

Docetaxel is a major treatment for advanced prostate cancer (PCa); however, its resistance compromises clinical effectiveness. Estrogen receptor-related receptor alpha (ERRα) belongs to an orphan nuclear receptor superfamily and was recently found to be closely involved in cancer. In the present study, we found that ERRα was involved in docetaxel resistance in PCa. Overexpression of ERRα conferred docetaxel resistance in PCa cell lines, and cells with ERRα downregulation were more sensitive to docetaxel. Among the drug resistance-related genes, ABCC4 demonstrated synchronous expression after ERRα manipulation in cells. Moreover, both ERRα and ABCC4 were overexpressed in the docetaxel-resistant cell, which could be reversed by ERRα knockdown. The knockdown of ERRα also reversed the reduced drug accumulation in the docetaxel-resistant cell. We also demonstrated for the first time that ABCC4 was a direct target of ERRα as determined by the CHIP and luciferase assays. Bioinformatics analysis revealed high expression of ERRα and ABCC4 in PCa patients, and a number of potential ERRα/ABCC4 targets were predicted. In conclusion, our study demonstrated a critical role for ERRα in docetaxel resistance by directly targeting ABCC4 and stressed the importance of ERRα as a potential therapeutic target for drug-resistant PCa.

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS.Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response.Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI).Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

Prostate cancer (PCa) is a common lethal malignancy in men. RNA binding proteins (RBPs) have been proven to regulate the biological processes of various tumors, but their roles in PCa remain less defined. In the present study, we used bioinformatics analysis to identify RBP genes with prognostic and diagnostic values. A total of 59 differentially expressed RBPs in PCa were obtained, comprising 28 upregulated and 31 downregulated RBP genes, which may play important roles in PCa. Functional enrichment analyses showed that these RBPs were mainly involved in mRNA processing, RNA splicing, and regulation of RNA splicing. Additionally, we identified nine RBP genes (EXO1, PABPC1L, REXO2, MBNL2, MSI1, CTU1, MAEL, YBX2, and ESRP2) and their prognostic values by a protein–protein interaction network and Cox regression analyses. The expression of these nine RBPs was validated using immunohistochemical staining between the tumor and normal samples. Further, the associations between the expression of these nine RBPs and pathological T staging, Gleason score, and lymph node metastasis were evaluated. Moreover, these nine RBP genes showed good diagnostic values and could categorize the PCa patients into two clusters with different malignant phenotypes. Finally, we constructed a prognostic model based on these nine RBP genes and validated them using three external datasets. The model showed good efficiency in predicting patient survival and was independent of other clinical factors. Therefore, our model could be used as a supplement for clinical factors to predict patient prognosis and thereby improve patient survival.

ObjectiveTo explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.MethodsWe downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.ResultsThe data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.ConclusionThe m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in prostate cancer (PCa) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in PCa and construct a prognostic signature to improve treatment decision making and clinical outcomes. Five prognosis-related costimulatory molecule genes (RELT, TNFRSF25, EDA2R, TNFSF18, and TNFSF10) were identified, and a prognostic signature was constructed based on these five genes. This signature was an independent prognostic factor according to multivariate Cox regression analysis; it could stratify PCa patients into two subgroups with different prognoses and was highly associated with clinical features. The prognostic significance of the signature was well validated in four different independent external datasets. Moreover, patients identified as high risk based on our prognostic signature exhibited a high mutation frequency, a high level of immune cell infiltration and an immunosuppressive microenvironment. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for PCa patients.

PurposeProstate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa are related to the abnormal regulation of various signalling pathways. An increasing number of studies have suggested that mRNAs, miRNAs, lncRNAs, and TFs could play important roles in various biological processes that are associated with cancer pathogenesis. This study aims to reveal functional genes and investigate the underlying molecular mechanisms of PCa with bioinformatics.MethodsOriginal gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the online tool GEO2R based on the R software limma package. Interactions between differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of miRNAs, lncRNAs and mRNAs was selected to construct mRNA-miRNA-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. Protein-protein interaction (PPI) networks were constructed, and transcription factors were annotated. Expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis.ResultsThe results demonstrate that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue.ConclusionBased on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs established a new prostate cancer network. In addition, we conducted functional module analysis within the network. In conclusion, this study provides new insight for exploration of the molecular mechanisms of PCa and valuable clues for further research into the process of tumourigenesis and its development in PCa.

Prostate cancer (PCa) is one of the leading causes of death in men worldwide. The molecular features, associated with the onset and progression of the disease, are under vigorous investigation. Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources for large-scale studies; however, their application in proteomics is limited due to protein cross-linking. In this study, the adjustment of a protocol for the proteomic analysis of FFPE tissues was performed which was followed by a pilot application on FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data for the investigation of PCa. For the optimization, FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization methods (beads, sonication, boiling) and buffers (SDS based and urea–thiourea based). The proteome extraction efficacy was then evaluated based on protein identifications and reproducibility using SDS electrophoresis and high resolution LC-MS/MS analysis. Comparison between the FFPE and matched fresh frozen (FF) tissues, using an optimized protocol involving protein extraction with an SDS-based buffer following beads homogenization and boiling, showed a substantial overlap in protein identifications with a strong correlation in relative abundances (rs = 0.819, p < 0.001). Next, FFPE tissues (3 sections, 15 μm each per sample) from 10 patients with PCa corresponding to tumor (GS = 6 or GS ≥ 8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis. Proteins significantly deregulated between PCa GS ≥ 8 and PCa GS = 6 represented extracellular matrix organization, gluconeogenesis, and phosphorylation pathways. Proteins deregulated between cancerous and adjacent benign tissues, reflected increased translation, peptide synthesis, and protein metabolism in the former, which is consistent with the literature. In conclusion, the results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material.

An outputs files from the 5.PLINK bash script needed for the R script 6.PCA with SNPs.R