dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Ontology Analysis Gene ontology (GO) analysis was carried using FunNet  12  (http://www. funnet. info/). FunNet integrates experimental gene expression data and knowledge about the biological roles of transcripts available in genomic annotation systems. Briefly, FunNet performs functional profiling of gene expression data to identify a set of significant (ie, overrepresented) biological themes characterizing the analyzed transcripts (ie, cellular processes, pathways, and molecular functions in which the analyzed transcripts are involved). The options provided with this type of analysis are related to the gene annotation systems to be used for functional profiling of transcripts and to the type of gene-enrichment computation performed for GO categories (ie, the 3 main conceptual axes of the GO lattice).
Gene Network Building The gene network was generated through the use of Ingenuity Pathway Analysis (Ingenuity Systems). A data set containing gene identifiers and corresponding expression values was uploaded into in the application. Each identifier was mapped to its corresponding object in the Ingenuity Knowledge Base. The genes, the expression of which was significantly differentially regulated, are called network-eligible molecules and were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base. Networks of network-eligible molecules were then algorithmically generated based on their connectivity.
Because Ki-67 and p-H3 are nuclear antigens and CD31 and caveolin1 are membrane proteins, Ki-67-positive and p-H3-positive nuclei are surrounded with CD31 or caveolin 1 staining. Vinculin antibody allowed the identification of cardiomyocyte outlines, whereas a-smooth muscle actin and CD68 antibodies detected smooth muscle cells and macrophages, respectively. A minimum of 5 fields per section was recorded at 910 and 920 using a Leica camera equipped with a fluorescent Leica DMR (Leica Microsystems). The Ki-67 or p-H3 index is defined as the number of positive Ki-67 or p-H3 nuclei per total nuclei. The numbers of capillaries and positive and total nuclei were determined using ImageJ (version 1. 43) software. The capillary density was defined as the number of capillaries per surface unit.
Statistics The results are reported as meanAESEM. The Shapiro-Wilk and Levene tests were used to analyze normality and variance or homogeneity of residuals, respectively. Statistical analysis was performed by t test or Mann-Whitney test. For aldosterone with or without spironolactone treatment on human umbilical vein ECs, we used a 2-factor ANOVA with repeatedmeasure analysis followed by Tukey's post hoc test. For the thoracic aortic constriction experiment, the Kruskal-Wallis test was performed. The Holm post hoc test was used to adjust for multiple comparisons. Statistical analysis was performed with SigmaPlot (version 11. 0) software. Values of P<0. 05 were considered statistically significant.
Sources of Funding This work was supported by grants from  INSERM (Institut National pour la Sant e et Recherche M edicale) , the  Agence Nationale pour la Recherche  ( ANR005-PCOD005  and  ANR09-BLAN-0156-01 ), the  European Union  ( FP7  funded  Fibrotarget  project  SP7 # 602904 ) and the  CRIT (Centre de Recherche Industrielle et Technique ). Messaoudi received grants from the  Region île de France (CODDIM)  and  Societ e Franc ßaise d'Hypertension Arterielle (SFHTA).
95 Family with sequence similarity 64, member A, [NM_144526] Bub1 Up 0. 0061 2. 96 Budding uninhibited by benzimidazoles 1 homolog (Saccharomyces cerevisiae), TV 2, [NM_009772] Neil3 Up 0. 0027 3. 36 Nei like 3 (Escherichia coli), [NM_146208] Lgr6 Up 0. 0065 6. 28 Adult male aorta and vein cDNA, RIKEN full-length enriched library, clone:A530037C04 product:CDNA FLJ14471 FIS, CLONE MAMMA1001030, WEAKLY SIMILAR TO LUTROPIN-CHORIOGONADOTROPIC HORMONE RECEPTOR homolog [Homo sapiens]. [AK040883] Some genes appear more than 1 time because the different probe corresponding to these genes have been differentially hybridized between aldosterone-treated and control mice. FC indicates absolute fold change; P, corrected P value; Reg, regulation; TV, transcript variant. Accession number: for all the genes, accession number provided is the National Center for Biotechnology Information GenBank accession number, except for Disc1, Ahcyl2, Mid1, and Emid2, which is the Ensembl accession number.
Abnormal gene expression in cancer represents an under-explored source of cancer markers and therapeutic targets. In order to identify gene expression signatures associated with survival in acute lymphoblastic leukemia (ALL), a strategy was designed to search for aberrant gene activity, which consists of applying several filters to transcriptomic datasets from two pediatric ALL studies. Six genes whose expression in leukemic blasts was associated with prognosis were identified: three genes predicting poor prognosis (AK022211, FASTKD1 and STARD4) and three genes associated with a favorable outcome (CAMSAP1, PCGF6 and SH3RF3). Combining the expression of these 6 genes could successfully predict prognosis not only in the two discovery pediatric ALL studies, but also in two independent validation cohorts of adult patients, one from a publicly available study and one consisting of 62 newly recruited Chinese patients.
The approach is detailed in Material and Methods. The second step consisted of looking for the aberrant expression of these genes in ALL samples. In order to do so, for each gene, a threshold between background levels and expression of the gene had to be established. Following an approach previously used and validated  [1] , based on the fusion of uniformly normalized independent Affymetrix transcriptomic datasets, we considered a signal as positive if it was above a threshold equal to the mean + 3 standard deviations of its expression values in 112 normal somatic adult tissues. We screened transcriptomic data of pediatric ALL patients from two publicly available series, both available on the GEO database under the respective references of GSE11877  [11, 12]  and GSE7440  [13] , that were obtained using the same Affymetrix technology as the normal tissue data.
Respectively 6 and 17 out of the genes negatively or positively associated with prognosis had a logrank test p-value < 0. 05 in both studies. We ranked these genes by increasing p-values and selected the first three of each list (Figure  1  step 3; Supp. Figure  S1 ). These include 3 genes, CAMSAP1, PCGF6 and SH3RF3, whose aberrant expression was significantly associated with a higher probability of long-term survival (called hereafter "positive genes") and another 3 genes, AK022211, FASTKD1 and STARD4, whose expression was associated with shorter survival (named hereafter "negative genes") (Supp. Table  S1 ). Considering the expression of the six genes in large groups of normal tissues, including germline tissues, embryonic stem (ES) cells, placenta and adult somatic tissues, we confirmed that the maximum expression levels of these genes were in germline cells, ES cells and/or placenta, and the lowest expression levels were largely confined to adult somatic tissues (Supp.
Combinations of genes whose expression is associated with significant differences in survival probabilities led to the establishment of a prognosis classification tool for childhood ALL Based on the selected six genes we designed an algorithm to stratify childhood ALL. A detailed presentation of survival probabilities in ALL patients with blasts expressing different combinations of "negative" and "positive" genes is shown in Supp. Figure  S4. In our "learning group" (GSE11877), an optimized separation of patients was obtained as follows: patients with blasts expressing one or more of the "negative genes" associated with none or only one of the "positive genes" had the shortest survival probability (called here P3). In contrast, patients with blasts expressing none of the "negative genes" and at least two of the "positive genes" would fall within a group with the most favorable prognosis (called here P1).
An intermediary group, P2, comprised patients either expressing at least one of the "negative genes" together with at least two "positive genes" or expressing less than two "positive genes" but none of the "negative genes". Since survival probabilities were not significantly different between patients of the P1 and P2 groups, based on the active expression of the 6 genes found in their blasts, the patients were finally assigned to two main groups, P1&2 (high probability of long term survival) and P3 (low survival probability) (Figure  2A  left panel). Hence, this algorithm enabled the identification of 61 cases (29%) of ALL from this series (GSE11877) with highly reduced survival probabilities (Figure  2A , middle panel). The same algorithm used in the second series of ALL patients (GSE7440) confirmed the potential of this combination of genes to identify aggressive ALL, corresponding to 21 cases (35%) in this series of patients (Figure  2A , right panel).
A six-gene classifier has stronger prognostic value than several known ALL prognostic factors By comparing the distribution of our prognosis groups and clinical and laboratory data of pediatric ALL patients from the series GSE11877 we found that P3 ALL were enriched in some, but not all, of the poor prognosis ALL clinical and molecular categories. Interestingly, several of the factors which have been described previously as associated with poor prognosis in ALL patients, including the presence of blasts in the central nervous system (CNS disease), gender, age > 10 years old, or > 50G white blood cells (WBC) at diagnosis (Supp. Figure  S5 ), or even the presence of a MLL translocation (Figure  2B : lower left panel) were not associated with significantly shorter survival in this particular series of patients.
In a univariate approach, we assessed the prognostic value of our new classifying six genes algorithm with respect to that of these factors. Considering each of the following parameters (MLL translocation, MRD at day 29 and CRLF2 overexpression), we found that, within each subgroup of patients which could be defined according to their status, our 6 gene classifier could also separate the patients into two prognosis classes, and therefore enabled refining the prognosis (Figure  2B , 2C and 2D: right panels). For instance, in this series of GSE11877 patients CRLF2 expression level could separate two groups of patients with significantly different survival probabilities (Figure  2D , left lower panel). However, among CRLF2 low expressing patients (n = 160), our classifying genes enabled to isolate a subgroup of 47 patients with a much poorer prognosis, whereas among CRLF2 high expressing patients (n = 47), it could differentiate two groups of higher and lower survival probabilities (n = 33 and n = 14 respectively) but with no statistical significance (Figure  2  D, right panels).
Additionally, deletion or a mutation of the IKZF1 gene, also a factor associated with poor prognosis in many studies, was previously identified in 59 patients of the GSE11877 cohort (Supp. Table  S8  in  [14] ). By classifying these 59 patients into prognosis groups using our six genes, we could clearly isolate a group of patients with good prognosis (35 patients, most remaining alive after 5 years), whereas approximately 40% of the 27 P3 patients survived after 5 years (Figure  2E ). Altogether, these data show that our six-gene classifier increases our ability for outcome prediction, particularly in MLL negative patients, MRD positive cases, CRLF2 positive or IKZF1 deleted or mutated patients. This observation is also illustrated by the forest plot presented Supp. Figure  S6. The specific contribution of our 6-gene classification system as an explanatory variable of survival probability was also tested in a multivariate analysis including all these parameters, which clearly demonstrated that our six gene-prognostic test is the most significant explaining variable (Supp.
The six gene-based algorithm can predict outcome in adult ALL We questioned whether this six-gene classification algorithm could also be applied to predict outcome of ALL in adult patients. This question was addressed by analyzing the predictive power of our six-gene combination using two publicly available datasets of adult ALL. First, we explored the predictive ability of the algorithm using an American publicly available series of 187 B-ALL patients with bone marrow transcriptomic data (GSE34861 on GEO website) and for which survival data were available. By using exactly the same approach and the same six genes, the patients were assigned to two groups corresponding to P1&2 and P3 respectively, and their survival probabilities were compared. As shown in Figure  3A  (lower panel), the survival of the 129 adult P3 patients was significantly shorter than the survival of P1&2 patients, suggesting that the 6-gene classification algorithm is also able to identify aggressive forms of ALL in adult patients.
Moreover, within this cohort of B-ALL, 77 patients were positive for the translocation t(9;22) (BCR. ABL), and those had a shorter survival probability than the remaining 110 BCR. ABL negative patients (although the difference did not reach statistical significance). Interestingly, our 6-gene algorithm was very efficient in identifying aggressive forms of ALL, among the 110 BCR. ABL negative patients (Figure  3B ). Second, using a larger transcriptomic data set from 750 adult ALL patients (of various molecular subtypes) which was part of the American "MILE" study (GEO website: GSE13159), the same 6 gene-based algorithm was used again to classify these adult ALL into P1&2 and P3 groups. No survival data were available for this series of adult ALL patients but useful information was provided about their molecular subtypes (also available from GEO website).
We measured the enrichment of the different molecular subgroups of adult ALL cases in our two prognosis groups P1&2 and P3, obtained with our 6 genes. For this purpose, we took into account the already established survival data of the different subtypes of adult ALL  [15, 16]. As shown in Figure  3C  and 3D , the distribution of the patients into P1&2 and P3 groups respectively co-segregated well with ALL subtypes known to correlate with a better or worse prognosis (also see Supp. Table  S3 ). Indeed, patients with high risk ALL forms, such as those with MLL or BCR. ABL translocations, were preferentially assigned to our "P3" poor prognosis group with the 6 gene classifier, whereas patients with better prognosis forms of ALL (standard or low risk ALL), such as those with ETV6/RUNX1 or hyperdiploid karyotypes, where mostly classified "P1&2".
Aggressive pediatric ALL are characterized by a transcriptomic profile, which is partially overlapping with adult ALL Our ability to specifically predict outcome in childhood ALL, independently from other prognostic parameters (in GSE11877), enabled us to carry out a supervised transcriptomic analysis looking for genes differentially expressed between the 61 ALL cases associated with an inferior outcome (P3) and the other cases of ALL with better prognosis (P1&2). This analysis, performed considering all the human genes represented on the 54K Affymetrix microarray, showed that 1572 genes were down regulated and 1726 up regulated in the ALLs associated with poor outcome (corrected Mann-Whitney p < 0. 01). This expression signature characteristic of P3 ALL forms is represented in Figure  4A  demonstrating striking differences between P1&2 and P3 patients for these genes.
RT-qPCR based detection of our six prognostic genes predicts response to induction treatment and disease free survival probability in a prospective cohort of 62 Chinese adult ALL patients Finally, using RT-qPCR we validated the use of our 6 genes to help predict the response to the conventional induction therapy (Vincristine/Daunorubicin/ Cyclophosphamide/Prednisone/L-Asparaginase administered over a 4 weeks period) in a series of 62 adult ALL patients (clinical data detailed in Supp. Table  S7 ). RNA was prepared from the bone marrow cells of these patients at diagnosis and the expression of the six diagnostic genes was detected by RT-qPCR (primers listed in Supp. Table  S8 ). Following exactly the same algorithm, these patients were assigned to either group P1&2 or P3 (Supp. Table  S7 ).
Interestingly, a follow-up of the disease free survival (DFS) of these patients demonstrates that our six genes test can efficiently identify patients with a significantly poorer outcome (Figure  5B ). As expected, the P3 group is associated with the most aggressive molecular subtypes (B-ALL with BCR. ABL and T-ALL). However, as observed with the GSE34861 published study, the six gene based classification was also particularly informative in the case of the B-ALL patients without the BCR. ABL translocation, since, for these patients, belonging to the "P3" group, means a significantly poorer prognostic than belonging to the P1&2 group (Figure  5C ).
Although the functional data available for these genes are scarce, a few hypotheses can be made. Except CAMSAP1, our selected genes showed active expression levels in at least one specific hematopoietic cell type/stage. Indeed AK022211, FASTKD1 and PCGF6 are preferentially expressed expression in early precursors of hematopoietic cells (CD34 positive cells from cord blood and/or bone marrow), whereas STARD4 is active in blood B-lymphocytes and SH3RF3 in bone marrow neutrophils. Therefore, in theory, the abnormally high levels of expression that we observe could be due either to an abnormal activation of a gene or an abnormal increase in the number of cells normally expressing it. For instance, a high expression level of the genes corresponding to a particular cell type, i. e. CD34+, could correspond either to an increase in the proportions of this cell type or to a gene activation.
Meta-analysis of available transcriptomic data All the analysed data were available from the GEO website (http://www. ncbi. nlm. nih. gov/geo/) in Affymetrix technology (54K: Affymetrix Human Genome U133 Plus 2. 0 Array). The following datasets were used. -Expression in normal human tissue samples: GSE3526 for non-germline adult tissues, GSE7434, GSE9994 and GSE18809 for placenta, GSE9440 for ES, and GSE15431, GSE6872 and GSE6969 for male germ cells. -Expression in normal hematopoietic cells: GSE11092 and GSE19735 for Cord blood cells, GSE3526 for total bone marrow, lymph nodes, spleen and tonsils, GSE12662 for CD34 positive cells, promyelocytes and neutrophils (PMN) of bone marrow, GSE9119 for resting and activated blood B cells, and GSE13159 for 74 samples of non-leukemic bone marrows from adult patients, which included healthy bone marrow specimens and nonleukemia conditions, such as megaloblastic anemia, hemolysis, iron deficiency, or idiopathic thrombocytopenic purpura  [24].
-Expression in two children ALL series: GSE11877 (207 cases;  [12] ) and GSE7440 (59 cases;  [13] ). It is of note that all 207 patients included in the GSE11877 study were high-risk B-precursor ALL patients, and were treated uniformly with a modified augmented Berlin-Frankfurt-Munster (BFM) regimen  [12]. -Expression in a large adult ALL series (MILES study, GSE13159; n = 2096 cases; 750 cases of ALL bone marrows) (  [24, 25] ). All the above transcriptomic data were obtained with in Affymetrix Human Genome U133 Plus 2. 0 Array technology. -Expression in another series of adult ALL (GSE34861)  [26, 27]. Since the data from this series were obtained in a different technology (GPL15088: NimbleGen Human Expression Array), the normalized data were used, and the controls consisted in the three nonleukemic samples from the same study.
Firstly, the pattern of expression of all human genes was assessed in normal tissues. The aim was to define a list of genes never expressed in normal bone marrow. A strategy similar to that used in  [1]  was applied here, but with a particular aim at defining genes not expressed in bone marrow tissue. We considered the transcriptomic data from various normal human tissues samples available online from publicly accessible databases (data GEO references detailed above). All these data were available on the GEO database (http://www. ncbi. nlm. nih. gov/geo/). For the Affymetrix dat, the CEL files were downloaded and the data were normalized using R software (version 2. 10. 0) with the RMA algorithm for summarization and quantile normalization. These normalized expression values were then used to establish a list of genes with a predominant expression in one tissue and, for each of these genes, to identify the group of tissues in which the maximum expression was found according to the following method.
In order to detect aberrant gene expression, we defined a threshold value for each gene. Considering each one of the germline/ES/placenta genes, this threshold value was defined according to the distribution of expression signal values in a subset of 112 normal adult somatic tissues (from GSE3526). This threshold corresponded to the mean signal in the 112 control tissues + 3 standard deviations. After calculating the threshold value of each gene, we tested for their expression in each of the 74 samples of non-leukemic bone marrows (from GSE13159), and all genes with an expression signal above the threshold, even sporadically in a few of the non-leukemic bone marrows, were filtered out. This step enabled us to ensure that the expression of each gene was always low in non-leukemic bone marrows.
These same threshold values were then used to discriminate between expression and no expression of the genes in blast from blood or bone marrow of patients. Importantly the data of the adult and children ALL series, respectively referenced as GSE13159 and GSE11877, were obtained in the same Affymetrix technology and the corresponding. CEL files were downloaded and normalized together with the normal adult somatic tissue samples. For the data of GSE 34861, the threshold values were calculated by a similar approach but using the nonleukemic samples from this same study.
Assessment of prognostic values of the abnormal expression of germline/ES genes in the bone marrow of children ALL patients Each of the germline/ES genes found expressed in at least 10% of the children ALL patients of both series GSE11877 and GSE7440 was tested for its prognostic potential in a univariate analysis, using R software (version 2. 10. 0). The overall survival probabilities were compared between the patients whose leukemic blasts expressed the gene (signal over the threshold established as described above) and those whose blasts did not. A logrank Mantel-Cox test allowed comparing the two survival curves. For each gene, hazard ratios (HR) for the association with the overall survival probability were calculated independently in both settings. Out of the 2119 genes, 416 were consistently associated with shorter survival in both studies (HR > 1), whereas 693 were consistently associated with longer survival in both studies (HR < 1).
Respectively 6 and 17 out of the genes negatively or positively associated with prognosis had a logrank test p-value < 0. 05 in both studies. The first three of each list were retained. These genes were then tested in combinations in univariate and multivariate approaches in the two childhood ALL series (GSE11877 and GSE7440). We first ranked the patients according to the total number of 3 aberrantly expressed genes (detected in their bone marrow blasts or peripheral blood) associated with good prognosis and compared their survival probabilities. Supp. Figure  S4A  (left panel) shows an increase of the survival probability with increasing numbers of expressed genes. The patients were classified into two subgroups defined according to the number of expressions of these 3 genes positively correlated with prognosis (Supp.