They are highly stable in bodily fluids which makes them feasible candidates to become minimally invasive biomarkers. In fact, several studies already proposed circulating miRNA-based biomarkers for different types of neoplastic, cardiovascular and degenerative diseases. However, many of these studies rely on small RNA sequencing experiments that are based on different RNA extraction and processing protocols, rendering results incomparable. We generated liqDB, a database for liquid biopsy small RNA sequencing profiles that provides users with meaningful information to guide their small RNA liquid biopsy research and to overcome technical and conceptual problems. By means of a user-friendly web interface, miRNA expression profiles from 1607 manually annotated samples can be queried and explored at different levels. Result pages include downloadable expression matrices, differential expression analysis, most stably expressed miRNAs, cluster analysis and relevant visualizations by means of boxplots and heatmaps. We anticipate that liqDB will be a useful tool in liquid biopsy research as it provides a consistently annotated large compilation of experiments together with tools for reproducible analysis, comparison and hypothesis generation. LiqDB is available at  Despite the well-established usage of blood and urine in disease detection and diagnosis, the term liquid biopsy does not appear in PubMed until 2011 in a work where breast cancer patients response to trastuzumab was monitored using circulating epithelial tumour cells (CETC) , RNA extraction protocol, exosome isolation (yes or no) and RNA library preparation protocol, all of which can have an impact on the miRNA abundance acting as confounding variables for the variable of interest, usually related to the health state.Result Page below for more details). Frequently, users are interested in a particular project focused on a specific cancer or biofluid and this information can be quickly accessed in this way.Browse studies: For each of the 30 SRA projects in the database, results were pre-calculated. Differential expression is calculated whenever possible for the relevant variables . Additionally to the miRNA expression profiles, other results are generated .Search samples: Users can customize a set of samples by selecting query values for 6 variables and a threshold number of miRNA-mapping reads in order to improve confidence in the results. The Search miRNAs: Users interested in one particular miRNA can search it and analyse its expression values as a function of the different variables.Compare two datasets: Similarly to the selection of one set, the user can define two different sets of samples. Additionally to the general output results, differentially expressed microRNAs between the two sets will be calculated.Compare with user data: This tool allows comparison of a selected set of samples from the database to user-provided samples. Users should first profile their sequencing reads input file using sRNAbench from the sRNAtoolbox server . SubsequThe database can be used and queried in five different ways:Overview/query results: The full annotation of all selected samples.miRNA profiles: a sortable and searchable expression matrix with adjusted RPM values (Reads Per Million), a boxplot of the 20 most abundant microRNAs, a pie-chart showing the relative frequency of the 10 most abundant microRNAs, the 20 microRNAs with highest coefficient of variation (CV) and the 20 microRNAs with lowest CV. The CV is calculated as the standard deviation of the adjusted RPM expression values divided by the mean value. It is a standardized measure of the dispersion of RPM values which does not depend on the magnitude and therefore allows to compare the dispersion of highly and lowly abundant microRNAs. The expression of microRNAs with lowest CV is less affected by the different variables in the analysis and could be used as reference microRNAs to normalize or standardize qPCR validation experiments.sRNA types distribution: Proportions of reads assigned to each of the different small RNA types  are shown in a table and the 10 most frequent categories are depicted in boxplots. The category ‘Un-assigned’ contains genome mapped reads which could not be assigned to any annotation.hsa_HR will therefore refer to the relative frequency of reads that map more than 20 times to the human genome. Furthermore, a read can map with the same quality (number of mismatches and length) to different indexes. In this cases, a new category is generated mentioning all genomes separated by ‘-’. For example human_virus-hsawill refer to the number and percentage of reads that map both to the human genome and the virus collection.Species Distribution: A high number of reads that cannot be mapped to the genome can indicate contamination or the presence of genetic material from symbionts or parasites. In order to address this question, liqDB summarizes the mapping to the human genome, virus and bacteria collections showing the relative frequencies of reads assigned to the different species. If a read maps to more than 20 loci in the genome, it will not be used for expression profiling. Those reads get the label ‘HR’ for highly redundant. Download: Expression matrices are available for download as well as a zip file with the complete analysis.Differential Expression: if available, pre-calculated study specific comparisons or generated from user-provided groups are displayed. Differentially expressed microRNAs are calculated using two-sided t-test on Reads Per Million values. Subsequently, p-values are corrected for multiple testing applying the Bonferroni procedure. Boxplots of the most abundant differentially expressed microRNAs are displayed and a link to a heatmap is provided. Files for the complete analysis can be found in the download section. Note that the output for ‘Compare two datasets’ (user selected sets) will always contain one pairwise comparison, while the differential expression section in ‘Browse studies’ might contain several pairwise comparisons if more than two groups exist for one variable .The standard results page includes the sections described below.The microRNA search generates one output page with several boxplots depicting the expression of the microRNAs as a function of the different variables.https://plot.ly) for interactive data visualization. A backend java program connected to the database calculates and prepares the raw results files. Apache2 was chosen as HTTP webserver.All expression values and metadata were uploaded to a MySQL database. The interactive web interface was implemented using the Django framework together with Bootstrap, javascript and the plotly package  repository and publications included in PubMed. The data was downloaded in sra format and converted with analyzer  was usedanalyzer  the collanalyzer . One mismiRNAs: miRBase v22 . We produced a merged annotation using miRBase names adding miRGeneDB sequences if those are not annotated in miRBase. Human and several virus annotations are used for profiling.Base v22  and miRGtRNAs: GtRNAdb, a genomic tRNA database vault-RNA, yRNA and guide RNAs extracted from RefSeq Non-coding RNAs from Ensembl Non-coding RNAs from RNAcentral release 9 RNA sequences of coding genes from Ensembl (Release 91) The genome mapped reads are then assigned successively to several reference libraries in a hierarchical way in the exact order described below. After each step, reads can only be assigned to one library in order to avoid cross-library matches. For example, after mapping miRNA reads, those are removed and cannot be assigned to tRNAs or other small RNA annotations.Please note that no further filtering of the miRBase reference sequences was performed which implies that, very likely, false positive miRNAs exist in the database. An example of this is miR-1246, which turned out to be a U2 small nuclear RNA (RNU2-1) fragment useful to discriminate tumours from controls  and then navigating to the miRNA Profiles tab. In the miRNA profiles table it can be confirmed that liqDB lists the same five miRNAs as the most frequent to make up for 46% of miRNA mapped reads. Small differences are likely due to different miRBase versions as well as the inclusion of miRGeneDB in liqDB. Figure The influence of different library preparation protocols on plasma-derived exosomal RNA frequencies was first studied in 2013 by Huang et al. . These aCompare datasets > Compare two sets of samples and we set the selectors (see Figure However, since many more samples are included in liqDB, we can extend this analysis to a larger query in order to increase confidence in the results. For instance, we can select plasma from all healthy subjects that used miRNeasy RNA extraction and compare the samples that applied Illumina library preparation protocol to those that used NEBnext. To do so, we go to Recently, the influence of sample processing in miRNA sequencing was evaluated on a large scale study among nine different laboratories  confirmiBrowse liqDB > Search samples; then a series of selectors are displayed. For these, we chose serum for Fluid, Both for Sex, True for Healthy Subjects, miRNeasy for RNA extraction Protocol and NEBnext for RNA Library preparation and then clicked Filter. To avoid effect of other confounding factors, we limited the analysis to only one extraction and one library protocol. Subsequently, we kept all samples for analysis by clicking on Proceed with all samples. In the results page, we then navigate to the miRNA Profiles tab where the last graph will show the miRNAs with the lowest variability for the specified query (Figure For some downstream validation experiments such as qRT-PCR, it may be relevant to know which miRNAs are most stably expressed in order to use them as reference. In this case, we queried the database for serum samples using the following workflow: y Figure .Compare Datasets > Compare two sets of samples. Similarly to the previous example, we will find two sets of selectors: one for the first group of our comparison and another for the second (See Figure plasma, Healthy subjects to True, Extraction protocol to miRNeasy and Library protocol to NEBnext; for Group 1 Sex will be male and for Group 2 female. Once in the page of results, we navigated to the Differential Expression tab.In order to compare two sets of samples in the most realistically possible way, potential confounding variables should be controlled for both sets. In this case we analysed differences in plasma between men and women. To do so, we first navigated to The boxplots graph (see Figure Given that miRNAs are highly stable and relatively detectable in most biofluids, it is safe to assume that they will play important roles in the fast growing field of liquid biopsy. Many projects use high-throughput sequencing approaches in the exploratory phase of biomarker discovery making the data publicly available through repositories such as SRA or GEO. In order to structure, organize and unify this vast amount of information into an interactive database, major problems like incomplete or inconsistent sample annotations need to be solved. liqDB is the first database that provides researchers from the liquid biopsy field with browse- and downloadable coherently annotated datasets generated using the same bioinformatics protocol. Furthermore the database allows the comparison to external data therefore enabling the generation and testing of new hypothesis. We also encourage researchers to share their data through SRA and submit the accession to liqDB or just to point out any overlooked SRA projects that might be suitable for inclusion.In the short term, there are three main improvements planned. First, other small RNA derived sequences like isomiRs or fragments of tRNA, yRNA or vault-RNA molecules will be included in liqDB as they all might have biomarker potential . SecondlIn summary, we anticipate that liqDB will be a useful tool for liquid biopsy researchers as it can help to develop standardized and stable protocols opening the door to reproducible reanalysis, realistic comparison and hypothesis generation, important tasks to avoid unnecessary validation of defective biomarker candidates which could prevent the discovery of actually useful biomarkers.


MRI features’ predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients.This study aimed to examine multi‐dimensional FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time . Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis.Radiomics features were extracted from segmented lesions of T2‐GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve (AUC) of the receiver operative characteristic (ROC) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect (P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes  showed moderate (0.3 < |r| < 0.5) or high correlation (|r| > 0.5) with image features.The MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.Radiogenomics analysis shows that  Due to the heterogeneous nature of GBM, relatively high age of disease onset, migration of malignant cells to surrounding tissue, the treatment outcome for GBM are highly variable, yielding an average survival rate of 12.6 months [2]. Current clinical practice for treating GBM mostly involves tumour resection and chemotherapy [3].Glioblastoma multiforme (GBM), one of the most invasive and fatal brain tumours that develops from glial cells, can severely affects the central nervous system and general health [1]. The percent 5‐year surviving rate was estimated to be 33.2% between 2008 and 2014 according to statistics from the Surveillance, Epidemiology and End Results (SEER) database and the Centers for Disease Control and Prevention's National Center for Health Statistics , and corresponding genomics data were acquired from the Genomic Data Commons (GDC) Data Portal. A total of 137 patients with MRI data, 129 patients with known genomic data were included in the analysis and 46 patients were the intersection of MRI data set and gene data set. Patient characteristics are summarized in Table MRI data were obtained from the Cancer Imaging Archive (TCIA) (2.2https://www.slicer.org/). All original loaded MRI images of patients were DICOM format. After adding MRI data into 3D slicer, we selected the Segment Editor module to segment the lesion.Lesion segmentation is required before feature extraction. Lesion volumes were manually delineated by an experienced radiologist using 3D slicer  Features, Gray Level Size Zone Matrix (GLSZM) Features, Gray Level Run Length Matrix (GLRLM) Features, Neighboring Gray Tone Difference Matrix (NGTDM) Features, Gray Level Dependence Matrix (GLDM) Features. Detailed number of each feature is listed in Table S1.2.4https://doi.org/10.2307/2699986), implemented by python package sci‐kit learn (https://scikit-learn.org/stable/index.html). In the final prediction model construction, feature with importance value smaller than 0.04 which were treated as not important were excluded. This threshold is chosen after the manually checking of the distribution of feature importance.The MRI dataset was divided into the training and testing sets according to a ratio of 7:3. Four machine learning algorithms including GBDT (Gradient Boosting Decision Tree), logistic regression, support vector machine (SVM) and KNN (k‐nearest neighbours) were tested. These four methods are representative in their own category. Gradient boosting decision tree is a tree‐based ensemble machine learning model which can achieve state‐of‐the‐art accuracy in classification and regression. Logistic regression is a classic probabilistic model. Support vector machine is another widely used model featured by kernel trick [12]. As for k‐nearest neighbours, it is a typical lazy‐learning method and is frequently treated as a benchmark in predictive modelling [13]. Feature importance was computed using GBDT (Confusion matrices and receiver operative characteristic (ROC) were computed to evaluate and compare the performances of all four machine learning models. The model that is most predictive of GBM patients’ survival time is chosen for further radiogenomics analysis.2.5t test are widely used methods to estimate gene variances in practice [14]. The condition we added for screening out DEGs was |log2(fold change)| > 1 and adjusted P < 0.05. And the same DEGs analytical process was applied to Dataset of Gene and Dataset with both MRI and Gene data to obtain DEGs. DEGs are treated as metagenes in our analysis.Differentially expressed genes (DEGs) analysis was performed with R software, using package DESeq2. A gene is declared to be DEGs if a difference or change observed in read counts or expression is statistically significant. Fold change and After screening out DEGs, the number of samples was reduced while individual differences among groups were enhanced. In order to screen for efficiently DEGs, we selected the DEGs from the intersection of the Genetic Dataset and Dataset which contain both MRI and Gene data.2.6To survey the potential correlations between the important image features of the classification model and the efficiently DEGs, we performed Pearson correlation analysis. Statistically, the absolute value of Pearson's correlation coefficient is between 0.3 and 0.5, indicating a moderate correlation and greater than 0.5 indicating a significant correlation. We also filtered out the weak correlation based on Benjamin‐Hochberg adjusted P‐values [15]. For the generalization purpose, we used the Pearson correlation as the final correlation selection metrics.2.7In order to survey the prognostic power of identified metagenes. We used the maximally selected rank statistics [16], implemented by R package maxstat to find the optimal cut point for the risk stratification on the basis of expression value of corresponding metagenes. Afterwards, we used Kaplan‐Meier (KM) estimator to measure the patients’ survival rates in high and low gene expression strata and plotted the aforementioned information by R package survminer.33.1Thresholding based on feature importance (importance index >0.04) resulted in a total of 72 features for constructing the final prediction model. The threshold is chosen after the manually checking of feature importance distribution . Table 3.2Among the four machine learning algorithms, GBDT had the highest accuracy of 0.81 for discriminating patients with short or long survival in testing set, while the accuracy of logistic regression, SVM and KNN is 0.69, 0.76 and 0.79, respectively. Figure 3.3WDR72, C14orf39, TIMP1, CHIT1, ROS1 and EREG were found to have significantly different expression levels among patients with short vs. long survival time . EREG is positively associated with Dependence Non‐Uniformity (gldm‐DNUN), Difference Average (glcm‐DA), Contrast (glcm‐Contrast) and Cluster Prominence (glcm‐CP) and negatively associated with Inverse Difference (glcm‐Id), Zone Variance (glszm‐ZV), LargeArea Emphasis (glszm‐LAE) and Root Mean Squared (firstorder‐RMS). ROS1 gene is negatively associated with Inverse Difference Moment (glcm‐LLH‐Idm). TIMP1 is positively associated with Contrast (glcm‐Contrast), Cluster Prominence (glcm‐CP) and negatively associated with Inverse Difference (glcm‐Id), Zone Variance (glszm‐ZV), LargeArea Emphasis (glszm‐LAE). Correlation thresholding based on Benjamini‐Hochberg adjusted P‐values was show in Figure S1B. The correlations of image features and metagenes are shown in Figure Figure 44.1et al. used deep learning method to correlate radiomics features with survival in GBM [18]. Our study provides additional evidence of using computer assisted learning methods to examine the relevant information contained in image features. Compared to conventional manual analysis approaches, radiomics analysis can have the advantage of providing more efficient and unbiased quantification.Our results indicate that prediction models using radiomics features can discriminate patients with under or over 1‐year survival time, suggesting that MR image features are predictive of survival outcome in GBM. Textual features such as large dependence emphasis and entropy are especially indicative of clinical outcome. Similarly, Gutman et al. showed that contrast‐enhanced tumour volume was strongly correlated with poor survival [17]. Lao 4.2WDR72, C14orf39, TIMP1, CHIT1, ROS1 and EREG) with significantly different levels of expression between short and long survival groups. To reveal the relationship between expression levels of six genes and the prognosis of patients, a survival analysis was performed. In this study, we used Kaplan‐Meier (KM) estimator to measure the patients’ survival rates in high and low gene expression [19]. Figure P < 0.05). The C‐index of the six genes  is 0.59, 0.55, 0.47, 0.46, 0.55, 0.45, respectively. EGFR has long been identified as an important therapeutic target for the treatment of GBM, and in patients with low overall survival time, elevated levels of EREG expression has been found. [20]. EREG can initiate the signalling cascade, and in gastric, EREG is up‐regulate [21]. Previous studies have shown the Epiregulin (EGFR) ligands have the effect of stabilizing receptors, affecting breast cancer cells associated with differentiation function [22]. Altered TIMP‐1 expression has been identified as a biomarker in GBM, with decreased TIMP‐1 linking to longer survival in GBM [23]. ROS1, which belongs to one subfamily of kinase insulin receptor genes, is a proto‐oncogene, highly expressed in a variety of tumour cells. This gene is often altered in lung cancer, of which the effects on the progression of GBM are remains to be eliminated [24].We identified six genes  are provided with genomics data as well. For future analysis, larger patient sample size with both imaging and genomics data may be better to detect more correlating genes. In addition to FLAIR data, additional sequences and imaging modalities can be combined for multimodal analysis, which can provide comparison results about different methods.We selected 72 features to construct the prediction model. More advanced dimensionality reduction method can be implemented for potential improvements of dimensionality reduction and improving classification performances.Our study validates the method of radiogenomics analysis to study the correlations among gene variables, imaging features and survival outcome in GBM. Our findings provide useful information for further examination of corresponding genes, which may potentially serve as biomarkers for GMB diagnosis and treatment indicators.The authors declare that they have no conflict of interest with the contents of this article.XL performed the research and wrote the paper, BC designed the research study and wrote the paper, YL(Luo) and WS analysed the data, YL(Li) contributed essential reagents and analysed the data. Click here for additional data file. Click here for additional data file.


CYTOR as a poor prognosis factor in gliomas with IDH mutation. Open chromatin assay points to differential accessibility of non-coding RNAs as an important source of epigenetic heterogeneity within individual tumors and between molecular subgroups. Rare populations of nuclei that resemble either IDH mutant molecular group co-exist within IDHmut-noncodel and IDHmut-codel groups, and along with non-coding RNAs may be an important issue to consider for future studies, as they may help guide predict treatment response and relapse.The presence of genome-wide DNA hypermethylation is a hallmark of lower grade gliomas (LGG) with isocitrate dehydrogenase (IDH) mutations. Further molecular classification of IDH mutant gliomas is defined by the presence (IDHmut-codel) or absence (IDHmut-noncodel) of hemizygous codeletion of chromosome arms 1p and 19q. Despite the DNA hypermethylation seen in bulk tumors, intra-tumoral heterogeneity at the epigenetic level has not been thoroughly analyzed. To address this question, we performed the first epigenetic profiling of single cells in a cohort of 5 gliomas with IDH1 mutation using single nucleus Assay for Transposase-Accessible Chromatin with high-throughput sequencing (snATAC-seq). Using the Fluidigm HT IFC microfluidics platform, we generated chromatin accessibility maps from 336 individual nuclei, and identified variable promoter accessibility of non-coding RNAs in LGGs. Interestingly, local chromatin structures of several non-coding RNAs are significant factors that contribute to heterogeneity, and show increased promoter accessibility in IDHmut-noncodel samples. As an example for clinical significance of this result, we identify shiny.turcanlab.org.A web-based explorer for the data is available at  Isocitrate dehydrogenase 1 (IDH1), and to a lesser extent, its mitochondrial homolog, IDH2, are mutated in a majority of adult lower grade gliomas (LGGs) . IDH proCYTOR as a poor prognosis factor in gliomas with IDH mutation. Overall, our results point to differential accessibility of non-coding RNAs as an important source of epigenetic heterogeneity within individual tumors and between molecular subgroups. The molecules identified are promising targets for future molecular research.Using the Fluidigm microfluidics platform, we established a biologically-relevant analysis to overcome the technical limitation and high background noise associated with snATAC-seq. We identified heterogeneity in promoter accessibility within and between IDHmut-codel and IDHmut-noncodel samples. Interestingly, our results indicate differential accessibility of non-coding RNAs such as the CYTOR locus that exhibits a profound increase in promoter accessibility within IDHmut-noncodel tumors. Furthermore, we identify Our cohort was primarily composed of WHO grade II gliomas, with the exception of one WHO grade III IDHmut-noncodel glioma, and all tumors harbored an IDH1 R132 mutation (Table We applied a pipeline (HOMER), commonly used for peak calling from ChIP-seq data, to call peaks in our snATAC-seq datasets on both pseudo-bulk and single nuclei level. Peaks called from pseudo-bulk profiles of the snATAC-seq data closely resembled bulk ATAC-seq data obtained from IDH mutant TCGA LGG samples Fig. a 6]. Fo. Fo6]. FNext, to reduce noise, we decided to limit our analysis to accessible peaks identified from bulk LGGs. To achieve this, we overlapped the peaks from our snATAC-seq data with the peaks called from bulk ATAC-seq of TCGA LGG samples  Additioa. ApproxTo determine whether 1p/19q codeletion can be detected from the snATAC-seq data, we inferred large-scale copy number alterations in these chromosomes by averaging ATAC-seq coverage for each nuclei. This analysis indicated decreased coverage for both 1p and 19q arms relative to 1q and 19p in IDHmut-codel tumors, revealing molecular evidence in the snATAC-seq data for the codeletion pattern in these samples Fig. c.To determine whether chromatin accessibility within transcription factor (TF) binding sites differ, we applied chromVAR to identify highly variable TF motifs  Additiod. A heatWe identified IDHmut-noncodel and IDHmut-codel specific peaks near the promoters of several non-coding RNAs. These included LINC01193, CCT8L2 and MIR4436A, that exhibited increased chromatin accessibility in IDHmut-noncodel samples, and MALAT1 with increased chromatin accessibility in IDHmut-codel samples  arrays as a part of the DNA-methylation based classification efforts of central nervous system tumor entities . The metCYTOR expression was significantly higher in IDHmut-noncodel samples compared to IDHmut-codel samples in both datasets . We identified the maximal cut-off point and classified each tumor by high- or low- CYTOR expression. Comparison of IDHmut-noncodel and IDHmut-codel samples revealed that high CYTOR expression was associated with poor overall survival prognosis in both CGGA and TCGA data sets , which includes two datasets with 693 (CGGA-1) and 325 (CGGA-2) samples, respectively. We restricted our analyses to IDH mutant gliomas (WHO Grade II-IV) within the CGGA data, which included 258 CGGA-1 samples and 152 CGGA-2 samples. ets Fig. a. OverlaEts activity leads to a block in glioma initiation [Gliomas with IDH mutation exhibit global DNA hypermethylation. While previous studies have investigated heterogeneity at the transcriptional level, it is unclear whether these tumors harbor epigenetic heterogeneity at the individual cell level. To our knowledge, our study is the first description of chromatin accessibility of gliomas with IDH mutation using single nucleus ATAC-seq. Using conservative cutoff values, and by relying on overlapping peaks from bulk ATAC-seq from IDH mutant glioma patients samples, we were able to reduce noise and constrain our analysis to biologically significant regions. We observed differential accessibility of several transcription factor binding sites, within and between IDHmut-codel and IDHmut-noncodel samples. Accessibility of DNA binding sites for JUN or FOS were highly variable, and interestingly were accessible to a much lower extent in IDHmut-codel tumors. Activity of AP-1, the transcriptional activator composed of members of the Jun and Fos families are regulated by MAPK signaling, and control proliferation and apoptosis . Sequencitiation , 14.CYTOR could be of importance in the pathogenesis of IDH mutant tumors, as high CYTOR expression is associated with poor overall survival. Given our small sample size, we were unable to determine whether chromatin accessibility of CYTOR varied by grade in IDHmut-codel samples. However, our integrative data indicates that a subset of IDHmut-codel tumors also harbors an open chromatin for CYTOR and having an open promoter might be indicative of a malignant subpopulation associated with progression within Grade II IDHmut-codel gliomas. Therefore, it may be possible to target such existing programs at an early stage of tumor development, that could offer a therapeutic benefit for patients. However, future studies are needed to elucidate such interactions during malignant progression.Our data show that majority of IDHmut-codel nuclei, and only a subset of IDHmut-noncodel nuclei harbor an open chromatin within the promoter of OLIG2. Expression of OLIG2 is restricted to the central nervous system and determines oligodendrocyte and astrocyte fate determination in the developing brain , 50, 51.A limitation of scATAC-seq studies is due to sparsity of data, it is challenging to determine cell identity. Given that IDHmut-codel samples harbor chromosome arm deletions, we were able to infer the presence of 1p/19q deletions in a large majority of our snATAC-seq data. In addition, tumor-associated microglia and macrophages (TAMs) are the most abundant cell types that infiltrate gliomas . HoweverAll tumors were obtained from patients following surgical resection at the Department of Neurosurgery at the University Hospital Heidelberg, Germany. Use of patient material was approved by the Institutional Review Board at the Medical Faculty of Heidelberg. Informed consent was obtained from all patients included in the study. Each sample was examined histologically for sufficient tumor cell content of at least 60% and diagnosed by a neuropathologist. The pathologic characteristics of the tumor samples are summarized in Additional file Following surgical resection, fresh tumor samples were separated into single cell suspensions via mechanical and collagenase enzymatic dissociation (StemCell Technologies- Canada). Debris was removed using tubes with strainer cap . The cells were snap frozen in a stem cell freezing media  and stored in liquid nitrogen until diagnosis was confirmed.Nuclei were obtained using Nuclei EZ Prep . The single nucleus ATAC-Seq protocol performed on HT IFC microfluidics system  and HiSeq 2000 sequencing technologies  with an adapted protocol  was sequenced with 75 bp single reads, while the rest of the samples were sequenced with 50 bp single reads. Duplicate nuclei in C1–800 chip were estimated with visual microscopic analysis, and quality control (QC) was checked at each step. We tested 3 IDHmut-noncodel and 2 IDHmut-codel patient samples. After data processing, 145 cells from IDHmut-noncodel, and 191 cells from IDHmut-codel samples passed our cutoffs with selective peak-calling.http://broadinstitute.github.io/picard) but failed to reduce biases due to leakages. We have chosen to remove all reads that exist in more than one well before alignment using custom python3.7 script (described later). Noticeably, fastq files were inflated with PCR amplicons and some read sequences sustained mutations after PCR amplification and sequencing. A small percentage in total, this kept introducing bias to the final result. Ultimately, we deduplicated fastq files using clumpify (sourceforge.net/projects/bbmap/) with default dedupe parameters which removed most of the duplicates and the mutant amplicons. Then, we applied the custom python3.7 script to remove the leaky reads among the cells. The script reads each individual fastq file, iterates through each of the other files in the sample/HT-IFC chip, then removes any duplicated read. The script removed the exact sequence of DNA that exist in two or more wells. Remaining mutant sequences were not addressed due to unreasonable computational costs. The amount of leakages is reduced by 98%  in R v3.61 base. The clusters were not perfect, so we improved manually the borders of the clusters  for the gene in the specific group/clusterx+: Total number of nuclei with peak(s) for the geneY-: Number of nuclei with no peaks for the gene in the specific group/clusterx-: Total number of nuclei with no peaks for the geneYATAC-seq coverage and CNV. We used a method similar to Satpathy et al. [w0 was computed aswi represent the GC-content matching windows. Telomeric and centromeric regions with low mapping percentage were manually excluded from the plots based on the mapping profiles over the single-cells.y et al. . ChromosWe used chromVAR v1.6.0 to infer transcription factor accessibility in our snATAC-seq. Counts were obtained with getCounts function using peaks from bulk ATAC-seq TCGA for LGG (peak-names start with LGG), a CG bias was corrected, and peaks filtered with min_depth = 500 and min_in_peaks = 0.013. A total 280 cells passed the filtering process, which were matched to JasperMotifs  and genocran.r-project.org) was developed with ggplot2 library  and tested on amazon cloud service (AWS-EC2) with 1GB of RAM and 1 core on Ubuntu 18 server instance, and a duplicate copy was tested on a similar server from hostinger.com to check platform compatibility. To visualize the data from selective peak-calling, we produced an explorer based on R/ shiny package 1.3.2. The clustered data, the code and a manual are submitted at github.com/RuslanAlali/SHINY_scATAC-Seq. Data can be visualized at shiny.turcanlab.org.We used the peaks we called and selected against bulk TCGA ATAC-Seq peaks as starting point to produce a web-based explorer of the data. Gene annotation were used from TCGA publication and all peaks related to one gene were collapsed after t-SNE clustering. A SHINY-R3.6 app  was used for data analysis. The minfi methylation pipeline was used to extract and analyze data from idat files, and normalization was carried out using functional normalization method . DiffereCYTOR versus all other genes. Genes with correlation coefficients > 0.5 were considered to be positively correlated with CYTOR. To determine the functional enrichment of genes correlating with CYTOR, WebGestalt was used to perform over-representation using the following functional databases: Gene Ontology databases , and Pathway  [CGGA RNA-seq datasets were used to determine the Pearson’s correlation coefficient for EACTOME) .CYTOR expression to dichotomize patients into low and high expression groups [P-value less than 0.05 was considered statistically significant.All statistical analyses were performed using the R software (v3.6.0). RNA-seq data from the CGGA cohort was downloaded, and processed in R. The maxstat R package was used to determine the optimal cut-off points for n groups . The surn groups , 38. StaCalled peaks from Homer pipeline have been deposited in the Gene Expression Omnibus under accession number GSE137266.In conclusion, although we obtained a limited number of nuclei from five samples, scATAC-seq is shown to be scalable to scRNA-seq level with thousands of cells . By inteAdditional file 1: TableS1. Statistics on snATAC-seq data.Additional file 2: Figure S1. The effect of removal of leaky reads from Oligo1. Figure S2. Different clustering approaches on snATAC-seq data. Figure S3. Cluster identification. Figure S4. chromVAR results on all 5 samples. Figure S5. Overview of snATAC workflow. Figure S6. Prognostic significance of CYTOR in IDH mutant gliomas.Additional file 3: Table S2. chromVAR analysis of snATAC-seq data indicates variability scores for 386 transcription factor motifs.Additional file 4: Table S3. Genes that correlate with specific open chromatin in IDH mutant LGGs.Additional file 5: Table S4. Regions correlating with CYTOR across all samples.Additional file 6: Table S5. Differentially methylated probes between IDH-codel and IDH-intact samples .Additional file 7: Table S6. Genes correlating with CYTOR in the CGGA cohorts.Additional file 8: Table S7. Overrepresented gene sets in the 101 genes correlating with CYTOR.


The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing . It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary.Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays.We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays.We set up a novel solution for differential gene expression analysis of individual samples and applying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.By Whole-genome sequencing of cancer samples has enabled the identification and detailed description of complex structural variants (cSVs) with chromothripsis being their prime example . ChromotIn contrast to the concept of gradual accumulation of chromosomal defects in the cancer genome, it has been assumed that chromothripsis arises in a single catastrophic event. The most widely accepted explanation of chromothripsis origin is based on aberrant mitosis, which is accompanied by physical separation of certain chromosomes in nuclear structures called micronuclei . AnotherNext-generation sequencing (NGS) serves as a powerful tool for describing any abnormalities occurring in the genome and has also been instrumental in discovering and describing cSVs. However, in many NGS experiments, consequent bioinformatic analysis remains challenging. Publicly available tools are often developed for a specific purpose with limitations in different experimental settings and do not take into account all genomic rearrangements that might be present in a sample. Moreover, when attempting to describe the impact of cSVs on cell phenotype, each case should be considered independently. The reason for such an approach is that, although there are a limited number of processes causing cSVs among individual cases, they affect different genomic loci with different impact. Thus, we developed a statistical approach for differential gene expression analysis from total RNA-Seq data, which is based on the comparison of individual cases within a tested dataset. Applying the method on a set of ten CLL samples, our strategy provided highly convincing gene candidates. In the next step, we focused on the identification of fusion genes and their stemming breakpoints from total RNA-Seq data. Due to the high rate of FP results of different methods, we developed a pipeline combining available analytic tools to maximize TP rate in dataset. Results were then cross-checked with data from genomic arrays in order to assess the sensitivity and specificity of our method.® Stranded Total RNA kit (Illumina) with Ribo-Zero ribosomal RNA depletion and sequenced using an Illumina HiSeq 2500 machine producing 125bp long pair-end reads. Sequencing read quality was evaluated in FastQC software. Adapter sequences were trimmed from raw reads using Trimmomatic software (® Human Transcriptome Arrays 2.0 (Thermo Fisher Scientific) to complement expression data from the RNA-Seq experiment. These data were then analyzed by the Transcriptome Analysis Console (Thermo Fisher Scientific) by comparing each consecutive sample to all other samples using a one-way ANOVA statistical test to identify a unique expression pattern in each sample. Identified fusion gene junctions and their respective sequencing reads were visualized in the Integrative Genomic Viewer  with cSVs that were previously detected using genomic CytoScan™ HD Arrays and analyzed with Chromosome Analysis Suite (Thermo Fisher Scientific). These cases were identified during the long-term clinical research at the University Hospital Brno and were classified as chromothripsis as they showed clustered copy number alterations on a limited number of chromosomes. Genomic breakpoint localization was extracted for all detected copy number variants and losses of heterozygosity. Total RNA-Seq libraries were prepared with TruSeqsoftware   approach that allows obtaining a gene expression profile of individual samples.Gene expression analysis from RNA-Seq data is based on counting reads covering gene regions (defined by reference relative GTF file) and statistical analysis of these read counts. In the first step, we used an htseq-count script , which e package . The funt-test was applied to assess the p.value to every single gene outlier in a given sample according to the gene expression in the rest of the samples. All gene candidates with significant p.value that can be specified manually (default 0.05) were considered as differentially expressed. Graphical representation of PComp algorithm is depicted in For the read count (expression) table resulting from RNA-Seq data processing steps, linear regression model with confidence bands containing expression values for all possible sample combinations  was applied to identify gene outliers in particular sample. Only genes constantly lying outside these bands in all possible sample combination were stored. In the next step, one sample limma (limma we compared gene expression in a single sample (group 1) with all other samples (group 2) in all possible combinations and correlated the results to the PComp output.RNA-Seq data were also subjected to a differential gene expression analysis using widely used and well-established algorithm limma  to gain Based on a literature search , four stFor efficient fusion gene detection, we developed an in-house meta-caller, which combines results from the selected tools into a consensus call. Each piece of software applies various metrics enabling classification of fusions to confidence subsets. In the first meta-caller step, detected fusions were filtered in individual callers as follows: for EricScript fusions with an Eric score (ES) of over 0.90, for JAFFA only fusions with the “HighConfidence” tag, and for FusionCatcher only high confidence fusions. In the consequent consensus call only fusions that passed at least two callers were kept which allowed increasing TP fusion rate and removal of FP fusions arising as specific artifacts of an individual caller algorithm . The whole fusion gene detection pipeline is schematically visualized in the Finally, to estimate TP and FP rates of the individual callers and the meta-caller, coordinates of fusions detected in RNA-Seq data were compared with the coordinates of genomic breakpoints detected by the genomic arrays. The interval of ± 100 kb around the breakpoints was applied in order to adjust to the array resolution and to the fact that the breakpoints can be located in introns, whereas fusions in RNA-Seq data appear in exon-exon boundaries. All potentially TP fusions identified by this approach were inspected visually in the array results.https://github.com/Hynst/WTS_cSVs_analysis), where the source codes are accessible for download. No installation is needed, as all dependencies  are installed automatically to the R environment.Algorithms and procedures of our novel solution were implemented in the R programming language. The meta-caller and the tool for differential expression are both freely available at GitHub  for each pair of samples. All genes lying outside the confidence bands were suggested as potentially differentially expressed candidates. We repeated this procedure for each sample pair combination and obtained nine sets of potential candidates per sample. Genes occurring in all nine sets were considered as differentially expressed for a given sample. By applying one sample t-test we assessed the differential expression of a given gene in a given sample according to the rest of the samples. We considered all gene candidates in particular sample with a p < 0.001 by one sample t-test to be differentially expressed. Using this approach, we found 15,200 differentially expressed genes in total  using transcriptomics arrays and extracted only annotated gene transcripts which resulted in 6,678 deregulated genes. We created an overlap between PComp and transcriptomic array results and found 1,808 significantly upregulated and 236 significantly downregulated gene candidates . We alsolimma. When we overlapped array data with limma results we found 1168 upregulated and 414 downregulated genes .Similarly, in RNA-Seq data we identified 14,803 deregulated genes using ed genes . We compapproach , where 2In RNA-Seq data, we pre-filtered results of the individual pieces of software in the first meta-caller step to create a fusion gene subset with high confidence. No explicit pre-filtering of lowly expressed fusions was done in order to allow for the capture of lowly expressed fusion genes . In the next step of the meta-caller, results obtained by ≥2 callers were combined into a consensus call. Following these steps, our pipeline was capable of identifying 40 fusion genes which were potentially TP with high confidence across the ten samples . FurtherConsidering candidates that were not supported by genomic arrays (21 of 40 features), potential fusions ZMYM5-PSPC1, HACL1-COLQ, MFSD7-ATP5I, and CYTIP-ERMN, occurring recurrently in six, three, three and two instances, respectively, were considered FP because they represented neighboring genes (read-through). Similarly, YWHAZ-ZNF706, LINCPINT-MKLN1, and NFATC3-PLA2G15 were also considered FP due to their adjacent localization in the genome, although not observed recurrently. Altogether, 17 of 21 fusions were considered FP. The remaining four fusions were not confirmed by the independent method; in all of them, at least one fusion partner appeared recurrently in the data, however, with a different partner. All of the four fusions could still represent TP resulting from balanced translocations or inversions that were not detectable by genomic arrays.To assess the ability of the meta-caller to increase TP rate, we inspected overlap between individual callers and genomic arrays . The metSeveral mechanisms of the influence of cSVs on cancer development have been suggested. NGS techniques provide an opportunity to unravel them by focusing on changes occurring at both the DNA and the RNA level. However, there still is a need for designing efficient pipelines and algorithms for consequent bioinformatic analyses. In this article, we introduce our approach for RNA-Seq data analysis in cases with cSVs. We tested the proposed workflow on a set of ten CLL patients with chromothripsis to validate the efficiency of our method.t-test, is the output of the analysis. PComp provides solid results comparable with transcriptomic array analysis and in our comparison performed better than limma tool, which is used widely for RNA-Seq data analysis. Although in our experimental setup we expected unique expression profiles for individual samples of our sample set, it is possible to specify the number of samples that can share expression similarities within the tool. We believe that the PComp approach is versatile and applicable to various RNA-Seq experimental designs and biological questions where a single sample expression profile is desired. Last but not least, the PComp tool is freely available on GitHub and easy to use by the potential users on their own data.Although cSVs reminiscent of chromothripsis likely share a common mechanism of origin across cSV cases, they appear in various genomic loci and, consequently, they lead to diverse expression profiles which makes it very difficult to select an appropriate biological group as a control. Unfortunately, the majority of conventional tools for differential gene expression  are deside novo proteins with aberrant structure and function. Aberrant protein products of fusion genes can influence important biological processes through direct or indirect regulation of expression of various genes which can eventually be reflected in the overall expression profile. Effective screening for such events in various diagnoses could help with identifying specific, potentially targetable disease markers . In our test dataset, the PComp tool outperformed well-established 10.7717/peerj.7071/supp-1Table S1The table depicts comparison of numbers of genes identified as differentially expressed using PComp approach for RNA-Seq data analysis and one-way ANOVA statistical test for transcriptomic array. The concordance was calculated as percentage of genes assigned concordantly between the methods.Click here for additional data file.10.7717/peerj.7071/supp-2Table S2limma analysis was applied to identify deregulated genes. Numbers in parentheses represent deregulated genes including also unannotated transcripts.For RNA-Seq data, Click here for additional data file.10.7717/peerj.7071/supp-3Table S3Overall 40 fusion genes in 8 out of 10 tested samples were identified using consensus meta-caller step. True/false positive gene fusions were assessed manually based on visual inspection of genomic array data .Click here for additional data file.10.7717/peerj.7071/supp-4Table S4Overview of 29 TP fusion genes identified by all used callers, i.e., EricScript (eric), FusionCatcher (FC) and JAFFA. Fusion genes identified by meta-caller had to be present in the results of ≥2 callers.Click here for additional data file.10.7717/peerj.7071/supp-5Supplemental Information 1The file contains the description of procedure used for RNA expression analysis using transcriptomic arrays.Click here for additional data file.10.7717/peerj.7071/supp-6Supplemental Information 2The file contains the description of procedure used for cytogenomic analysis using cytogenetic arrays.Click here for additional data file.


Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry.n = 58), stored at room temperature for 2–23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses.True archival FFPE breast cancer cases (We report a single 10 μm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases.Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance. Within oncology, the ability to rapidly characterize a whole tumor transcriptome has resulted in the increasing stratification of patient cohorts by molecular subtype. These data inform disease diagnosis, selection of precision therapies, and prognosis. To date, the efforts to build molecular profiles of common malignancies, including The Cancer Genome Atlas (TCGA), utilized fresh tumor specimens, which provide high quality RNA analyses . HoweverDespite the advantages of FFPE tissues, uncertainty about the fidelity of FFPE RNA remains a serious limitation. FFPE tissue processing and sample storage are known to result in highly degraded RNA, which limits gene detection and introduces sequencing artifacts , 8. NoneIn this report, we define an effective FFPE-RNA-seq pipeline, identifying several key steps related to tissue selection, RNA isolation, library selection and data analysis. Using this optimized pipeline, we utilize true archival FFPE breast cancer (BrCa) tissues to distinguish between ER+ and ER- breast cancer cases, and confirm gene expression fidelity by comparing FFPE data to publicly available databases obtained from fresh tissues. Using novel analytical methodologies beyond single-gene comparisons, we identify key molecular pathways that distinguish ER+ and ER- breast cancers with high confidence. Further, in a proof-of-principle application, we identify the transcriptional regulator KDM4B to associate positively with active ER signaling and provide prognostic significance for patient outcomes.The research was conducted on archived FFPE tissue samples collected under IRB-approved protocols at University of Colorado and Kaiser Permanente Center for Health Research. These tissue archives are comprised of clinical samples obtained from women with invasive cancer who were receiving standard of care. For this study, breast specimens from premenopausal women aged 20–45 years were obtained under IRB approvals . All cases were de-identified to the research team at all points and therefore this study was considered exempt for participation consent by the participating IRBs.n = 58) had been stored at room temperature between 2 and 23 years before RNA isolation was performed. For all cases, multiple H&E slides were reviewed from each case by a pathologist and sections with tumor were selected for inclusion in the study. Adjacent serial unstained sections were then submitted for RNA extraction.The obtained archival FFPE breast cancer (BrCa) tissues  according to the manufacturer’s protocol, using 1–4 sections (10–40 μm) per case depending on assay. RNA yield and quality were determined by UV absorption on a NanoDrop 1000 spectrophotometer and fragment size was analyzed using the RNA 6000 Nano assay  run on the 2100 Bioanalyzer. DV200 values representing the percentage of RNA fragments above 200 nucleotides in length were determined according to Agilent and Illumina recommended protocols . To deten = 3) and ER- (n = 3), were generated from FFPE RNA with DV200 values ranging from 27 to 44%. Libraries were quantified as described and pooled at 3 libraries per lane. Sequencing was performed on a Hi-Seq 2500 using a 100 cycle, single read protocol with a depth of approximately 90 million reads per sample. Following initial sequencing, 3 of the 6 libraries were repooled and independently sequenced. Base call files were converted to fastq format using Bcl2Fastq (Illumina).Two library preparation methods were tested. An input of 75 ng of total FFPE RNA was used with the TruSeq RNA Access Library Prep Kit  and an input of 150 ng of total FFPE RNA was used with the Ovation Human FFPE RNA-seq Library System . Libraries were prepared in triplicate from two FFPE RNA samples according to manufacturer instructions  were selected from Varley et al.   using STAR (version 2.5.2b) . The STASTAR read counts were used as input into DESeq2 . Genes wCounts output by STAR were normalized using a variety of different methods. Genes with Access-identified probes and counts per million (cpm) greater than 1 in 50% of samples from at least one library preparation method  were selected for visualizations.ER+ vs. ER-: Genes with Access-identified probes and counts per million (cpm) greater than 1 in 50% of either FFPE or fresh-frozen samples  were selected for analysis. Differential expression analysis was performed using DESeq2  with ER p-values. Filtered genes were plotted using ‘pheatmap’ [Euclidean distance, using ‘dist’ in R, was calculated based on the VST gene expressions to produce an aggregate sample-to-sample distance matrix. To assess global similarity of FFPE and fresh-frozen gene expression profiles, pairwise Pearson correlations between all samples were visualized using the ‘pheatmap’ R package . Dendrogheatmap’  and sampTumor biologic subtypes  were predicted using the PAM50 prediction parameters as determined by Parker et al. .We subset FFPE samples for protein-coding genes with a cpm greater than 1 in at least 3 cases, resulting in 13,807 genes. Fresh samples were filtered for these exact genes. All data were combined together and run through DESeq2 with ER status as the variable of interest with a log 2 fold-change threshold of 1, as well as kept separately  and subjected to the same analysis. In addition, all ER+ cases were grouped together and DESeq2 was performed with sample type as the variable of interest, with the same done for ER- cases.To assess the ability of previously reported cancer gene sets to distinguished cohorts, transformed counts were subset for all matching genes from the Oncotype DX, MammaPrint, and PAM50 gene sets. Gene expression data were z-score transformed for each gene for visualization purposes. Dendrograms were produced using complete clustering of the pairwise Pearson correlation values. Pearson correlation was calculated on raw values for the Oncotype DX and MammaPrint subsets, and z-score transformed values for the PAM50 subset, in accordance with the methodology of each gene set’s construction. For PAM50, subtypes as well as proliferation, ER, and HER2 scores were generated using the original prediction parameters determined by Parker et al. .p ≤ 0.05, Fisher’s exact test using all input genes  as background results in p < 2.2e-16) differentially expressed genes between ER+ (red) and ER- (blue) samples from FFPE or fresh specimens  as well as custom gene sets (described below) that were significantly enriched between ER+ and ER- cases or between sample storage methods. When testing for differences between ER status, FFPE and fresh-frozen samples were individually divided by ER phenotype and queried against reference sets. When testing for differences between sample storage methods, all samples were divided by storage method and queried against reference sets. Custom gene sets were created by determining genes that were unique or commonly identified as statistically significant   compiledThe single-sample-based regulon activities were inferred by function ‘viper’, which is an extension of MARINa  and tranp-values displayed determined by log-rank analysis.The key genes/transcription factors determined from BrCa regulon analyses were selected as genes of interest for determination of gene expression or regulon activity correlation with survival outcome. Kaplan-Meier plots were produced by downloading TCGA outcomes, cohort metadata and gene expression data through the UCSC-Xena Functional Genomics Browser  tool, whWhile FFPE tissues are more abundant than fresh tumor tissue, these archival tissues still represent a limited resource that require judicious utilization. Thus, we initially set out to determine the minimum amount of FFPE breast tissue that will yield the quality and quantity of RNA needed for successful sequencing. Many protocols developed for extracting RNA from FFPE blocks suggest utilizing 40 micrometers (μm) of tissue, an amount that is 10 times greater than what is required for immunohistochemistry (IHC) evaluation. We first examined the influence of the number of 10 μm sections on overall RNA yield. 1, 2, 3 or 4 10 μm serial sections were used to isolate RNA from 5 separate FFPE breast cancer cases , sections were quantified for epithelial tumor content. A clear positive relationship emerged Fig. e, with cGiven the highly fragmented and chemically modified properties of the input RNA from FFPE sources, we next evaluated the impact of library preparation on resulting gene expression profiles. We tested Illumina-Access and NuGEN–Ovation platforms, both useful library preparation methods for FFPE samples, which differ in how RNA is enriched and amplified. The Access kit is based upon biased, selective hybridization and enrichment of RNA using bead-conjugated oligomers designed for 19,000+ genes with > 10 different probes per gene. In contrast, the Ovation platform adopts an unbiased targeting approach utilizing both Poly A and random primer hybridization, resulting in a product with reduced ribosomal content and a more highly diverse RNA transcript library.To compare these two library preparation platforms, two separate ER+ breast cancer FFPE biological samples S1 and S2), each with a DV200 value > 30% were run in triplicate using both library preparation platforms  . For a gene to be differentially expressed between groups, both the change in the magnitude and the degree of variability in gene expression need to be considered. Our experimental design of 2 independent samples assayed in triplicate is well suited for determining relative library variability. By plotting the coefficient of variation for each gene from one platform compared to the other, we observed that the Access platform displayed reduced variability with 11.1% of all genes examined with a coefficient of variation (CV) of greater than 20%   utilizinWe next employed the common strategy of determining the overlap of individual genes that are significantly differentially expressed between two groups, in this case between FFPE and fresh frozen samples. We first compared gene lists of the differentially expressed genes between the ER+ and ER- samples from both FFPE and fresh frozen samples and observed 278 common statistically significant, differentially expressed genes upregulated in ER+ samples in both FFPE and fresh specimens Fig. b. Thus, To resolve the discordance between results from curated GSEA pathway analyses Fig. a and sinWe also observed genes uniquely expressed in FFPE tissues Fig. d and theWe next considered newer computational approaches employing unbiased, in-silico constructed gene association networks known as regulons , 25. RegTo further illustrate regulon conservation between fresh and FFPE samples, we plotted the rank of the activity score for all regulons (~ 5000) in FFPE by their rank in fresh Fig. b. In thip < .0001) between ER+ and ER- cohorts  ER status, identifying 478 ER+ and 153 ER- cases. Next, for the four most up-regulated and four most down-regulated FFPE regulons  or progesterone receptor , was observed to be largely dependent upon the age of the block, but not in a predictably linear fashion. Rather than age alone, our data may reflect shifts in tissue collection or preservation practices, which warrants further investigation. In addition to providing optimized recommendations for the amount of tissue required and block age, we also describe the impact of library selection on expression results. In agreement with previous studies, our study reveals that library preparation introduces the greatest level of variation, greater than that observed between FFPE and fresh tissue cohorts. Previously, others have demonstrated that variance in ribosomal depletion strategies, or selection of either Poly A and/or targeted capture oligo bead hybridization libraries, all differentially change count and relative abundance results that impact evaluation of global gene expression concordance , 51. BasWe have also shown and validated by comparison to fresh tissues, to our knowledge, the first successful application of RNA-seq regulon analysis for FFPE specimens. Evaluation of regulon activity can overcome many of the barriers imposed by analysis of single-gene expression levels, which can be significantly impacted by tissue preservation approaches. This observation is in line with more recent analytic approaches such as relative gene ranking  or reiteIn sum, our FFPE RNA-seq pipeline supports the approach of utilizing clinically-annotated FFPE cancer tissues with outcomes data to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease and the molecular mechanisms of treatment resistance. Additionally, investigation of archival FFPE tissues with treatment and outcomes data through global RNA-seq profiling could be utilized to enhance the performance of prognostic gene panels, increasing their utility for patient treatment stratification. Further, in the exploding era of bioinformatics and 'omic analytics, new tools such as CITE-Seq and cellular deconvolution  will furAdditional file 1: Figure S1. Repeatability of RNA assessment. RNA isolated from 6 FFPE samples were assessed at separate times (run) to evaluate variance in determination of a) yield from Nanodrop and Bioanalyzer instruments and b) RNA quality by evaluation of DV200 values. Each separate specimen is identified by a different colored symbol with values corresponding to matched samples across runs connected by the line. c) Plots of unique reads as % of total reads and d) overall number of unique reads in Access (Acc) vs. Ovation (Ova) RNA library preparation kits for sample 1 (orange) and sample 2 (green). Limma normalization of gene expression in Access (Acc) vs. Ovation (Ova) libraries for both sample 1  and sample 2 , results in e) linear relationship across the range with raw values and f) count bias toward the Acc library.Additional file 2: Figure S2. Extended evaluation of global gene expression reveals high concordance between FFPE and fresh samples. a) Sequence alignment parameters of clipping  and allowed nucleotide mismatches (mm) of 3–15 were evaluated for impact of aligned read percentage for the 6 FFPE samples in comparison to soft-clipping utilization in fresh  samples. b) Global linear correlation evaluation of FFPE compared to fresh samples based upon the average gene expression determined in Fresh plotted by the average gene expression determined in FFPE in ER+ (left) and ER-(right) cases. The best fit linear line is depicted in red from which r values were derived.Additional file 3: Figure S3. FFPE compared to fresh samples utilizing the PAM50 genes and GSEA. a) Gene expression values from genes included in the PAM50 gene panel  and n = 20 fresh cases (right panel) specimens. The scores reflect similar performance based upon ER subtype , independent of tissue processing. b) Plot of adjusted p-value and log 2 fold change comparing all genes between FFPE and fresh specimens, regardless of ER status. Shading highlights genes distinctly unique to each group. c) GSEA enrichment profiles comparing all FFPE samples (red) to all fresh samples (blue), demonstrating trending enrichment in UV response and P53 gene sets (left two panels) in FFPE samples while no enrichment is observed in E2F and Estrogen Response (right two) gene sets for either sample type.nel Fig.  were utiAdditional file 4: Figure S4. Validation of ER+ vs. ER- regulon results from FFPE tissues. From the TCGA cohort, RNA expression data from 478 ER+ (blue) and 153 ER– negative (red) primary breast cancer tumors from women under the age of 76 were evaluated for a) single-gene expression of the most differentially active regulon factors from Fig. p < .0001, Welch’s unpaired, two tailed, t-test) and b) plotted for single-sample gene expression by regulon activity score. Dashed lines represent median values based upon the entire 897 TCGA breast cancer cohort used for identifying high and low expression (Table 5: Table S1) for separation of 5 year Overall Survival (O.S.) probability post-diagnosis (Dx) in the 897 TCGA primary female breast cancer cohort under the age of 76. Distribution of this score c) was plotted for all cases  or TCGA-annotated ER+  or ER–  cases. Dotted line depicts threshold value for Hi vs. Lo designation based upon whole cohort distribution. d) Overall Survival (O.S.) probability is depicted for all the included TCGA breast cancer cases based upon Hi (n = 473) vs. Lo (n = 424) optimized ER signature score, revealing highly statistically significant differences  with a Hazard Ratio (HR) favoring 5 year survival of ER signature high patients. e) 5 year Overall Survival probability of ER+ (n = 478) breast cancer patientson Table . OptimalAdditional file 5: Table S1. ER Regulon Activity Signature determined 5 year Overall Survival in TCGA breast cancer cohort or ER+ only cases. Single sample regulon activity values were z-score transformed based upon data from all 897 female primary breast cancer patients under the age of 76 obtained from the TCGA. Z-score regulon activity values were then selected and combined based upon the significance in impacting survival as determined in Supplemental Table 1. The indicated 3 ER activity regulon signature values were then used to stratify to samples to either high (Hi) or low (Lo) groups based upon the distribution of the whole cohort. 5 year overall survival distribution was determined and Hazard Ratio (HR) expressed as risk comparing the high (Hi) expression/activity to low (Lo) group for all TCGA cases  or for just IHC-annotated ER+ cases . Log-rank p-values are displayed and the final frequency of survival at 5 years post diagnosis indicated for the high and low groups (Hi/Lo) for all cases and ER+ only cases separately.


As a common cancer type in women, cervical cancer remains one of the leading causes of cancer-associated mortalities word wide. Recent evidence has demonstrated the regulatory role of a large number of long non-coding RNAs (lncRNAs) in cervical cancer. Here, we aimed to identify new biomarkers that related with the recurrence through comprehensive bioinformatics analysis.Firstly, we collected online lncRNA expression data of cervical cancer patients which were divided into training, validation, and test set. Then we developed a nine-lncRNA signature from training set by conducting LASSO Cox regression model along with 10-fold cross validation. The prognostic value of this risk score was validated in all the three sets using Kaplan–Meier analysis, C-index, time-dependent ROC curves and dynamic AUC. Biological function of these lncRNAs in cervical cancer cells were evaluated by performing gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathways analysis.According to the results, a higher predict accuracy was observed in the nine-lncRNA signature than that of FIGO stage in all the three sets. Stratified analysis also demonstrated that the nine-lncRNA signature can predict the recurrence of cervical cancer within FIGO stage. The potential mechanisms underlying the nine-lncRNAs from the signature were also identified according to the gene enrichment analysis.In the present article, we provided a reliable prognostic tool to facilitate the individual management of patients with cervical cancer after treatment. As the second most common cause of female cancer-associated mortalities worldwide, cervical cancer ranks the fourth most frequently diagnosed cancer . PreviouPrevious studies based on the gene expression data have identified a series of gene signature which was used as recurrence predictive model. As the RNA transcripts longer than 200 nucleotides, lncRNAs lack the ability of generating protein . HoweverThe Gene Expression Omnibus is an international public repository that archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomic data sets . The Cann = 150) and an internal validation set (n = 150). RNA sequencing (RNA-seq) and corresponding clinical data  were downloaded from the publicly available TCGA database. After excluded those without complete clinical and survival information, a number of 49 patients with cervical cancer were enrolled into the external test set. Each lncRNA expression level was determined by the value of Reads Per Kilobase of exon model per Million mapped reads (RPKM) ” of GSE44001 datasets from GEO which has the microarray data of 300 patients with cervical cancer and then processed these data sets using R to generate the lncRNA expression matrix. The clinical information  of 300 patients was also extracted and patients were randomly assigned to a training set (s (RPKM) , 2019b.n-fold = 10 by using R package “glmnet” (i).Data preprocessing was performed according to our previous published study ,c, 2019a“glmnet” . The valThe resulting score allowed the division of patients into two classes, namely the high-risk group and low risk group based on the median risk score value.Firstly, Kaplan–Meier and Chi-square analysis was performed using Graphpad prism. C-index was calculated using R with Package “survival” . After t1 signaling pathways analysis. The enrichment analysis results, including enrichment score, the count of genes enriched in the terms and false discovery rate were shown as pictures which plotted using R with Package “ggplot2”  and internal validation cohort (n = 150). LASSO Cox regression model along with 10-fold cross validation was performed to analyze the expression data in the training cohort  curves and dynamic AUC to assess the predictive accuracy of lncRNA signature, FIGO stage and a new variable combined both. Firstly, we plotted the time-dependent ROC curves and calculated the corresponding AUC on the 12th month of follow up. As shown in Subsequently, the dynamic area under curves (dynamic -AUC) at continuous time was calculated and depicted as the line chart. Results showed that the lncRNA signature combined with FIGO stage has a higher dynamic AUC level than either alone. The lncRNA alone also showed high accuracy with the dynamic AUC estimates exceeding 0.75 which was much better than FIGO stage alone. Similarly, the nine-lncRNA signature showed better predict accuracy than FIGO stage in the GEO validation set and TCGA test set as shown in Furthermore, we compared the recurrence predict performance of the nine-lncRNA signature with a 12-mRNA signature which was established in a previously published article . ResultsIn order to further describe the function of lncRNAs in the signature, we performed the function annotation and enrichment analysis. Studies on lncRNA have demonstrated that lncRNAs usually act as competing endogenous RNAs (ceRNAs) which modulate the gene expression and maintain the functional balance of various gene networks . Hence, Moreover, genes, which was considered as related with the lncRNAs in the signature, were put into GO biological process and KEGG signaling pathways analysis. As shown in Cervical cancer is a common gynecological malignancy with high mortality and its incidence of the younger trend in recent years. Due to the advancements in systemic chemotherapy and radiotherapy, the incidence of cervical cancer and mortality have dropped significantly. However, its recurrence rate and mortality in developing countries still remains high . ConsideThe microarray data we downloaded from GEO belongs to high-throughput biological data. In order to solve the common problem “curse-of-dimensionality”  in high-throughput biological data, LASSO Cox regression model along with 10-fold cross validation was applied to analyze the expression data in the training cohort. As we have previously depicted, LASSO regression is good at handling high dimensional regression variables with no prior feature selection step by shrinking all regression coefficients toward zero, and thus forcing many regression variables to be exactly zero ,b. It haIn evaluating the predict accuracy of the nine-lncRNA signature, we first plotted the Kaplan–Meier curves and calculated the C-index of the signature. According to the results, significant difference was observed between the survive time along with status of the two groups separated by the same criteria in all three sets. Besides, further study showed that the nine-lncRNA signature has predict efficiency within different FIGO stage. Similarly, the C-index of lncRNA signature was higher than FIGO and a new variable combing both has the highest value than either alone. Moreover, we calculated the AUC for time specific ROC curves at continuous time point and dynamic AUC line was plotted to depict the temporal changes in accuracy. Results showed that the nine-lncRNA signature has a higher accuracy than FIGO stage alone. The new variable combined both nine-lncRNA signature and FIGO stage has better predict capacity for recurrence than either alone.To gain more insights into the modulatory roles of the lncRNAs in the signature, we performed functional enrichment and annotation analysis for nine-lncRNAs in the signature. Results showed that lncRNAs in the signature might regulate biological processes, such as RNA splicing, cell proliferation and cell cycle, and pathways such as RNA transport and cell adhesion molecules. These data indicated the potential regulatory mechanism of lncRNAs in the signature.Moreover, we also explored the regulatory mechanism of nine-lncRNAs in the signature by searching the published article. Among the nine-lncRNAs, lncRNA ATXN8OS participate in spinocerebellar ataxia by affecting the localization and activity of splicing factors and mutations in the ATXN8OS are associated with the amyotrophic lateral sclerosis . DysreguIn summary, we conducted comprehensive comparative analysis of lncRNA expression pattern and constructed a nine-lncRNA signature that can be applied to predict disease free survival in cervical cancer. Gene annotation and functional enrichment analysis further revealed the underlying mechanisms whereby lncRNAs in the signature exerts their biological roles in tumor progression. Although further study are still needed to confirm the established signature, our study here still provide valuable indication for both the basic research and clinical treatment of cervical cancer.The datasets generated for this study can be found in GEO (GSE44001) and TCGA.YM contributed to the study design, data profiling, and manuscript draft. YZ and LD prepared the figures and tables. JD and XL performed the language editing. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.