dataesr/scientific-paragraphs-categorization huggingface dataset:
GJA1 mRNA also weakly negatively correlated with DNA copy number (Figure  3c ), suggesting that Cx43 over-expression in breast cancer is not driven by DNA amplification. To validate our procedure, we used the HSF2 gene, a close neighbor of GJA1 on chr6q22 which shares similar copy number in 99% of TCGA's breast cancer cases. In contrast to what was observed for GJA1, HSF2 mRNA was positively correlated with the copy number of its own gene (Figure  3c ). Moreover, somatic point mutation data showed that, in the TCGA cohort, only three breast cancer patients out of 977 harbored at least one GJA1 mutation, accounting for 0. 31% of the tumors (Figure  3e ). Only one tumor with an extremely high number of total mutations (TCGA-AN-A046) was found to have both a GJA1 mutation and a slightly higher expression of the gene (2.
GJA1 Level Is Dependent on Hormonal Receptor Status Because Cx43 level varies through the mammary gland development and the reproductive cycle, it has been suggested that it could be regulated by hormones, similar to what has been observed in other tissues  [4, [30] [31] [32]. We thus next investigated whether the GJA1 mRNA level was directly linked with hormonal receptors status. Consistent with the subtype-specific expression of Cx43, ERα-or PR-positive breast tumors had a significantly higher expression of GJA1 mRNA compared to ERα-or PR-negative tumors (Figures  4a  and A1-A3 ). Results were similar for all five datasets, except for PR in the NKI dataset where the low number of samples did not allow statistical significance to be reached (Figures 4a, A2a and A3a). However, there were no strong correlations between GJA1 expression and ESR1 mRNA, with total protein level (ERα), or with the activated form of ERα phosphorylated on serine 118 (ERα_pS118) (Figures  4b-e  and A1b,c ).
While only weak correlations were observed in most individual subtypes, a stronger correlation between GJA1 and ESR1 mRNA and protein was observed when the tumors were pooled (Figure  4b-d ). As expected, ESR1 mRNA was better correlated with total ERα (Pearson's rho = 0. 9011, Spearman's rho = 0. 8969) than with ERα_pS118 proteins (Pearson's rho = 0. 5459, Spearman's rho = 0. 6407). Y: GJA1 exprs in tcga (probe A_24_P55287) • • • • • • • • • •• • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • (Subtype • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • (Subtype • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • (Subtype • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • er_status_by_ihc, n = 520) ER- ER+ *** n = 520 Negative Positive -2 0 2 4 6 GJA1 mRNA expression level in hucOrig tcga pr_status_by_ihc GJA1 mRNA expression • • • • • • •• • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • pr_status_by_ihc, n = 520) *** PR- PR+ n = 520 Negative Positive -2 0 2 4 6 GJA1 mRNA expression level in hucOrig tcga her2_status_by_ihc GJA1 mRNA expression • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • her2_status_by_ihc, n = 520) Her2-Her2+ Stronger correlations between PGR mRNA and protein levels and GJA1 mRNA levels were observed, not only in unstratified (pooled) analysis, but also in individual subtypes within most datasets (Figures  4b-e  and A1b,c ).
This association was stronger in cancer samples than in normal breast tissues in all datasets for which normal tissues were available. Similar to ERα, total PR protein was well correlated with PGR mRNA (Pearson' rho = 0. 8593 and Spearman's rho = 0. 8723). Tumors positive for the HER2 receptor by histochemistry (TCGA dataset) did not express significantly different levels of Cx43 mRNA. However, when HER2 status was given by HER2 amplicon probes or HER2 mRNA expression (Vanvliet, NKI and Curtis datasets), HER2+ tumors had a significantly lower level of GJA1. No direct correlation was observed between GJA1 and the HER2 (ERBB2) mRNA (Figures  4a-e  and A4a-d ). A good correlation was observed between HER2 mRNA and total HER2 protein level (Pearson's rho = 0. 8344, Spearman's rho 0. 68634). The correlation between HER2 protein (HER2 and HER2_pY1248 activated form) and GJA1 mRNA was not stronger than that observed for HER2 mRNA (Figures  4b-e  and A3d ).
GJA1 mRNA Is Dysregulated at the Early Stages of Breast Cancer and Is Reduced with Grade When Tumors Are Pooled To reconcile evidence supporting both tumor-suppressive and -promoting roles, it has been suggested that Cx43 function could depend on tissue type or evolve with tumor stage  [11]. We therefore investigated whether GJA1 expression in primary breast tumor changed with stage and grade at the mRNA level in breast cancer. Since grade/stage are strongly associated with subtype, we first stratified our cohorts by intrinsic subtype. We used the Curtis dataset, as GJA1 expression was available for invasive tumors (stages 0 to IV) and "normal" adjacent tissue for numerous samples. A significant dysregulation of GJA1 expression occurred at the early stages in all breast cancer subtypes, although both over-expression and downregulation could be observed (Figures  5  and A5 ).
Most of the GJA1 over-expressing luminal tumors were found to be of low stage (0-II). However, a reduction was observed in early stage basal-like and Her2e tumors (Figures  5  and A5 ). A significant increase in GJA1 was also observed in the invasive stage I compared with stage 0 in all subtypes (Figure  5 ). We then investigated whether or not the expression of GJA1 could be linked to tumors' grade. Our analysis revealed that GJA1 mRNA expression was significantly decreased with grade when all tumors were pooled, but not when they were stratified by intrinsic subtype (Figures  6  and A6 ). A significant decrease in GJA1 with grades in LumB tumors could be observed only in the Vanvliet's dataset but not in other datasets analyzed (Figures  6  and A6 ). Interestingly, basal and Her2e tumors, which express a low level of GJA1 (Figure  2 ), account for an important proportion of grade 3 tumors, thus reducing the mean GJA1 expression for this grade (Figure  6 ).
Conversely, basal-like and Her2e tumors followed an opposite trend (hazard ratio > 1), with high expression of GJA1 strongly associated with a worse prognosis in the Her2e subtype (Figures  7  and A7a,b ). Since the aggregation of several datasets in BreastMark and KMPlotter platforms could lead to artifacts in survival analysis, we went further by performing our own survival analysis for each subtypes for either aggregated (Figure  7 ) or individual datasets (Figures  A9  and A12 ) following the determination of the best cutoff either by the receiver operating characteristic (ROC) curve (Figure  A8a-c ) or by the smallest p value of the log rank test for different thresholds (10-90) (Figure  A10 ). ROC curves have shown that the highest area Under the curve (AUC) for GJA1 was obtained when tumors were pooled (Figure  A8a ) and GJA1 was then ranked, at worst, in the eleven first percentiles when compared to all the probes present in the five datasets (Figure  A8c ).
Using this method, we showed that GJA1 mRNA increases in a subset of ERα-and PR-positive tumors and in the luminal subtypes, which are largely ERα-and PR-positive. These results were not surprising as much evidence supports a link between Cx43 and hormones in breast tissue  [32]  and in other tissues  [40] [41] [42] [43]. GJA1 is also expressed at lower levels when HER2 status is positive and within the Her2e breast cancer subtype, except in the TCGA dataset. In an early study, it was reported that Cx43 gap junctions were dramatically reduced in breast tumors, and that this reduction was considered to occur regardless of ERα, PR or HER2 status  [12]. More recent studies have reported that Cx43 protein expression correlated positively with PR and ERα status  [44, 45]  and negatively with HER2 protein expression  [45].
Gene Expression We used 4K samples over different expression platforms. Vanvliet used Affymetrix Human Genome U133A (data processed with Robust Multi-Array Average (RMA))  [50]. Curtis discovery and Curtis validation used the Illumina HT-12 v3 platform (expression given as a Log2 intensity level)  [47]. The Cancer Genome Atlas (TCGA) used a custom Agilent G4502A 244K array (expression given as Log2 Lowess normalized ratio)  [51]. NKI used a Hu25K Agilent platform (samples were hybridized against a pool of equal amount of RNA from each patient and gene expression is given as a log10 of intensity ratio)  [52]. Normalized signal per probe or probe set mRNA expression was downloaded for tumor samples for all five datasets. Breast cancer intrinsic subtype was assigned to each sample with the Pam50 molecular subtyping algorithm using the R genefu package  [29].
Survival for each case was determined as in  [27]. ERα, PR and HER2 status provided in the original publication of the dataset was used. For TCGA, ER, PR and HER2 status was obtained by immunohistochemistry (IHC). For NKI, ER and PR status was determined by IHC and the sample was considered positive if at least 10% of the cells were positive. For Vanvliet, ER and PR status was determined with the Bioconductor package ROCR based on the expression of the probe 205225_at and validated with IHC when available. NKI and Vanvliet HER2 status was determined using the probes of the HER2 amplicon genes. For Curtis datasets, ER, PR and HER2 status was based on mRNA expression. In the TCGA dataset, the level of some proteins has been investigated with reverse phase protein assay (RPPA). Data were available for total ERα, PR and HER2 as well as for the phosphorylated forms of ERα (pS118) and HER2 (pY1248) that are at least partially indicative of the activation status  [46, 53].
DNA Alteration Copy number alterations (CNAs) were measured in the TCGA dataset with Affymetrix 6. 0 single nucleotide polymorphism (SNP) arrays and segmented using Circular Binary Segmentation (labeled here as Relative linear copy number values)  [51]. Data were further processed by TCGA using GISTIC 2. 0 to assign the Putative copy number calls per gene (-2: Homozygous deletion, -1: Hemizygous deletion, 0: Neutral/no change, 1: Gain, 2: High level amplification)  [51]. Mutations were detected using whole-exome sequencing after controlling for germline and normal adjacent tissue mutations  [51]. Linear and called CNA data as well as mutation data for the TCGA dataset were retrieved using R via cBioportal  [55, 56]. A total of 977 patients had data for mutations  [51].
Survival Analysis Survival data was available for all five datasets. The log-rank test was used to estimate significance and hazard ratios (95% CI) were computed via Cox regression using survival package  [57]. ROC curves were computed using the pROC package  [58]. Kaplan-Meier plots were used to visualize the data. The best cutoff to determine tumors expressing high or low levels of Cx43 was selected using either the ROC curves or based on the smallest p value of the log rank test computed for each threshold between 10 and 90. For aggregated datasets analysis, each cohort was first split into groups based on the selected threshold and datasets were pooled only after splitting. In addition, survival analyses were computed using the BreastMark and KMPlotter web platforms that use several well-known dataset  [35, 59].
Statistical Analysis All statistical analyses were carried out with R version 3. 4. 3 [60]. For two class comparisons, (cancer vs. normal tissues; positive vs. negative hormonal status) the Wilcoxon-Mann-Witney test was used. When more than two classes were compared, the Kruskall-Wallis test was used followed by the Dunn post-hoc test to assess the statistical significance for each pair of samples (to compare subtypes). For stages and grades, differential gene expression was assessed using Limma package  [61]. A Benjamini-Hochberg correction was applied to adjust the p values for multiple testing. Because each subtype had a different number of patients, when correlation tests were performed between the expression of GJA1 and the expression of other genes, a non-parametric bootstrap procedure was used for each subtype to derive the mean correlation coefficient and a percent confidence interval.
12 GJA1 mRNA expression level in hucOrig curtis. validation Her2. Expr GJA1 mRNA expression • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Her2. Expr, n = Figure A9. Survival analysis of GJA1 in individual subtypes of breast cancer with cutoff selected according to the ROC curve. Kaplan-Meyer plots show survival curves for patients with breast tumors expressing either high (blue) or low (red) levels of GJA1 mRNA in pooled tumors or in individual intrinsic subtypes. TCGA, NKI, Vanvliet and both Curtis datasets were used for the analysis. The best cutoff was determined from ROC curves as in Figure  A8.
(d) Contingency table and barplot showing the distribution of copy number alteration (CNA) by subtype. Due to the small number of samples in -2 and 2 CNA, Fisher's exact test was applied on -1, 0 and 1 CNA. (e) Total number and GJA1 mutations observed in the 3 cases out of 988 patients. All data from the TCGA dataset. p value: * <0. 05; ** <0. 01; *** <0. 001; NS Not statistically significant.
GJA1 mRNA level in breast cancer clinical samples is dependent on breast cancer subtype in four datasets. GJA1 mRNA level in tumors stratified by subtype in Vanvliet, NKI, Curtis Discovery and Curtis Validation datasets. In the legend, "Breast" indicates adjacent normal breast tissue. p value: * <0. 05; ** <0. 01; *** <0. 001. in breast cancer clinical samples is higher in ER positive tumors in four datasets. A, GJA1 mRNA level in breast tumors stratified by estrogen receptor status determined as described in Material and Methods. p value: * < 0. 05; ** < 0. 01; *** < 0. 001; B, Expression of GJA1 vs estrogen receptor alpha (ESR1) mRNA in each subtype and in normal breast tissue; C, Bootsrtapped correlations between ESR1 and GJA1 mRNA level either in pooled BC tumors or in individual BC molecular subtypes and in normal breast tissue; Data from Vanvliet, NKI, Curtis Discovery and Curtis Validation dataset.
Acknowledgments: We gratefully acknowledge financial support: MB was supported by  Alexander Graham Bell Canada Graduate Scholarships-Doctoral  and Master's Programs from the  Natural Sciences and Engineering Research Council of Canada (NSERC) , by the  Fonds de Recherche du Québec-Nature et Technologies (FRQNT)  and by the  Fonds de Recherche du Québec-Santé (FRQS). IP is funded by the  Quebec Breast Cancer Foundation , by the  FRQS  and the  NSERC. MTH is funded by  NSERC  and the  Canadian Institutes of Health Research (CIHR).
TimeTimeTimeTimeTimeFigure 7. 7 Figure 7. GJA1 is associated with a diverging outcome depending on breast cancer subtype. The Kaplan-Meyer plots show survival curves for patients with breast tumors expressing either high (blue) or low (red) levels of GJA1 mRNA in pooled tumors or in individual intrinsic subtypes. TCGA, NKI, Vanvliet and both Curtis datasets were aggregated for the analysis. The best cutoff was determined as the percentile lending the lowest log rank test p value (FigureA12) and was 53 in Pooled tumors, 35 in luminal A (LumA) tumors, 68 in luminal B (LumB), 13 in Basal and 18 in Her2e tumors.
curtis. discovery, Her2, GJA1, ILMN_1727087, n=, 279 Pooled LumA LumB Basal Her2e GJA1 mRNA (log2 intensity) n = 279 * *** Breast LumA LumB Basal Her2e Breast 1 2 3 Breast 1 2 3 Breast 1 2 3 Breast 1 2 3 Breast 1 Grade 2 3 Tumour grade Figure 6. GJA1 mRNA level is downregulated with grade in clinical samples only in pooled tumors but not in individual subtypes. GJA1 mRNA level for each tumor grade in the Curtis Discovery dataset, either in pooled breast tumors or stratified by intrinsic subtype. In the legend, "Breast" indicates adjacent normal breast tissue. p value: * <0. 05; ** <0. 01; *** <0. 001.
One-dimensional polyacrylamide gel electrophoresis (1D-PAGE) Twenty micrograms of each fraction (of 10 µl in case of IEF fractionation) were boiled in Laemmli buffer (62. 5 mM Tris/HCl, pH 6. 8; 10% glycerol; 2% SDS; 5% ß-mercaptoethanol; 0. 02% bromophenol blue) and separated electrophoretically on 7. 5% or on any kD Mini-PROTEAN TGX Stain-Free™ (SF) Precast Gels (25 min, 300 V). The SF imaging was performed with ChemiDoc MP Imager and Image Lab 4. 0. 1 software (Bio-Rad); a 5-min activation time was used for the whole protein pattern imaging.
1% formic acid), peptides were eluted from the analytical column by a two-steps linear gradient (4-20% acetonitrile/H 2 O; 0. 1 % formic acid for 90 min and 20-45% acetonitrile/H 2 O; 0. 1 % formic acid during 30 min for the LTQ-Orbitrap Velos and 4-25% acetonitrile/H2O; 0. 1% formic acid for 40 min and 25-50% acetonitrile/H2O; 0. 1% formic acid during 10 min for the Q-Exactive). For peptide ionisation in the nanospray source, spray voltage was set between 1. 4 and 1. 5 kV and the capillary temperature at 275 °C. The LTQ-Orbitrap-Velos was set up in data dependent mode to switch consistently between MS and MS/MS. MS spectra were acquired with the Orbitrap in the range of m/z 400-1700 at a FWHM resolution of 60 000 measured at 400 m/z and an automatic gain control (AGC) target value of 1e6.
Protein identification The acquired raw LC Orbitrap MS data were processed using Proteome Discoverer 1. 4. 1. 14 (Thermo Fisher Scientific). This software was used to search data via in-house Mascot server (version 2. 3. 0; Matrix Science, London, U. K. ) and SEQUEST HT against the Uniprot mouse reference proteome database (version 09. 08. 2013, 50,850 entries). The following parameters were used for searches: (i) trypsin; (ii) two missed cleavages were allowed; (iii) monoisotopic precursor tolerance of 8 ppm, followed by 0. 8 Da for fragment ions from MS/MS; and (iv) cysteine carbamidomethylation (+57. 0215) and methionine oxidation (+15. 995) as variable modifications. The false discovery rate (FDR) was processed the built-in Percolator and was set to 5%  (57). Protein identification from ßelimination experiments was performed similarly as above for data processing with the exception of the following modifications.
Data were presented for the non-fractionated C2C12 extract (WE, whole extract), the non-fractionated cytosol-enriched extract (CYT), and the fractionated cytosol-enriched extract (CYT-AS-IEF combined fractions, corresponding to fCYT fraction). Based on these data, we determined the efficiency factor as the ratio of identified peptides/proteins after extensive fractionation (fCYT fraction) comparing with the non-fractionated cytosol-enriched extract (CYT). We firstly analysed the non-linked peptides to order to identify the O-GlcNAcylated proteins linked to agarose beads, independently of their O-GlcNAc sites. In the non-fractionated whole extract, 554 proteins were identified, while 449 were identified in the cytosol-enriched fraction. Once the fractionation protocol applied, the number of identifications was increased by a factor 3 comparing with CYT fraction since 1362 proteins were identified in fCYT fraction.
4 compared with non-fractionated cytosol-enriched fraction. It should be mentioned that among the peptides identified in the beta-eliminated peptides fractions, some of them were not dehydrated, suggesting that some peptides remained aspecifically retained on agarose beads despite the pre-clearing step with agarose beads to avoid the non-specific retention of proteins on agarose beads, and despite the extensive washing steps applied to abrogate protein-protein interaction and so to eliminate the proteins which were not covalently linked to agarose beads. All data files corresponding to the identification of proteins and the peptides sequenced were presented as supplemental data (Supplemented Table  II  to VII, for WE_BE, WE_NR, CYT_BE, CYT_NR, fCYT_BE and fCYT_NR, respectively); all mass spectrometry data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (58, 63)  with the dataset identifier PXD004860.
Statistical analysis The survey was a descriptive study, and no formal statistical hypotheses were tested. All data were analyzed separately for each country according to a pre-specified analysis plan and pooled analyses across all countries were also conducted. The analyses were performed using SAS statistical software, version 9. 4. Continuous variables were summarized in the form of mean and standard deviation (SD) and median and interquartile range (IQR); both parametric and non-parametric approaches were used because the mean value may be sensitive to a few outlying observations due to the small sample size. For categorical variables, the number and percentage of respondents was calculated for each category. Free-text fields were reviewed and categorized.
Subjects and methods This study is part of the dsd-LIFE study  (23). Patients, aged 16 years and older with a medically confirmed clinical and/or genetic diagnosis of DSD, were recruited from 14 sites, including Berlin, Munich, Lubeck and Munster (Germany); Paris, Lyon, Montpellier and Toulouse (France); Amsterdam and Nijmegen (The Netherlands); Lodz and Warsaw (Poland); Stockholm (Sweden) and Birmingham (UK). Control data were obtained from Eurostat (total n > 200,000; females n > 100,000; males n > 100,000, year 2014) (http://ec. europa. eu/eurostat/ web/health/overview), except in five conditions where no data were available, i. e. , psychiatric disorders, suicide attempts, hypertension, dyslipidaemia and autoimmune disorders (only thyroid disorders) where controls were obtained from Swedish CAH studies with similar age and gender distribution as the dsd-LIFE study (total n = 58,800; females n = 33,500; males n = 25,300)  (16, 18, 24).
Study protocol The complete study protocol has been published in detail previously by Röhle et al. (23). In summary, subjects underwent a medical examination, including questions about the medical history and answered a patient reported outcome questionnaire. The participation rate was 36. 1% of those contacted, the genetic diagnosis rate was 78. 6% of the total and 55. 6% of those with non-sex chromosome DSD, the patient reported outcome questionnaire response rate was 95. 5%, medical history was supplied by 99. 5% and examination was done in 89. 2%. Data were entered anonymously into a database. Healthy lifestyle, age at diagnosis, cardiovascular and metabolic risk factors and comorbidity were evaluated. A healthy lifestyle was defined as never smoked in combination with sport activities ≥2 h/week.
Statistical analysis Mean ± s. d. or median (range) is reported for continuous variables, and absolute and relative frequencies for categorical outcomes. All proportions were calculated discounting missing values. Continuous variables were compared using t-tests, and categorical parameters were compared using chi-squared tests or Fisher exact tests, whichever most appropriate. Logistic regression models were used to explore the associations between different outcomes and age at diagnosis and healthy lifestyle. When odds ratios (ORs) were calculated, 95% confidence intervals (CIs) were reported. Due to the exploratory nature of dsd-LIFE, no adjustments for multiple comparisons were done. R (version 3. 2. 2) and SAS (version 9. 4) were used for all statistical analyses.