dataesr/scientific-paragraphs-categorization huggingface dataset:
We analyzed their expression in two well-annotated public CRC datasets from The Cancer Genome Atlas (TCGA, RNAseq dataset) and La Ligue Contre le Cancer (GSE39582, microarray dataset). First, we studied the association of gene expression profiles with CRC initiation by comparing the expression between normal and tumor tissues. Next, we investigated their co-expression and their association with tumor primary sites, lymph node metastases and prognostic capacity. We found that KCa and Ca 2+ channels could be important contributors to CRC initiation and progression. Our results also provide new insights about Ca 2+ remodeling in CRC.
Gene Expression of KCa and Ca 2+ Channels in Normal Mucosa versus CRC Tissues Our first aim was to analyze whether the expression of selected genes varies between normal colorectal mucosa and CRC tissues. Therefore, we compared their expression in 47 pairs of matched normal mucosa and tumor tissues from the TCGA dataset. Interestingly, the expression profile of the 35 genes was sufficient to perfectly discriminate normal samples from tumor ones when performing unsupervised hierarchical clustering (Figure  1A ). Interestingly, five genes were highly deregulated between the two tissues (Figure  1B ): (i) KCNN4 and TRPM2 were upregulated in tumor tissues with a fold-change of 1. 24 and 1. 28 (Table  S2 ), respectively; (ii) while KCNMA1, KCNMB1 and TRPM6 were significantly downregulated in tumor tissues by -1.
66, -1. 73 and -2. 76 fold, respectively (Table  S2 ). To validate these results, we next compared the expression profile of the genes in unmatched normal (n = 19) and tumor (n = 566) tissues from the GSE39582 dataset. The studied genes were again efficient to separate normal and tumor samples into distinct clusters (Figure  1C ). Pairwise analysis showed that five genes were significantly deregulated between normal and tumor samples (Figure  1D ), among which, four genes previously identified in the TCGA dataset: (1) KCNN4 and TRPM2 were again upregulated in tumor tissues with a 1. 02 and 1. 12-fold-change, respectively (Table  S3 ). (2) KCNMA1, TRPM6 and TRPM4 were downregulated with a -1. 13, -3. 34 and -1. 42 fold-change, respectively (Table  S3 ). Indeed, TRPM4, which we found significantly down-regulated in tumor tissues (fold-change < -1) in the microarray dataset, was also down-regulated with a fold-change of -0.
Co-Expression of Ca 2+ and KCa Channels in CRC In order to better understand the relationship between Ca 2+ and KCa channels coding-genes, we analyzed their pairwise co-expressions by performing Pearson's correlation analysis. A global matrix summarizing all pairwise correlations is presented in Supplementary Figure  S1. Hierarchical clustering resulted in two clusters of positively-correlated genes in each dataset (Figure  S1 ), suggesting the existence of possible co-regulation at the transcriptional level by common transcription factors. Since two KCa and two TRPM channels were differentially expressed between normal and tumor tissues, we decided to study more closely the co-expression of members within each of KCa and TRPM channel families (Figure  2 ). We found that the expression of KCNMA1 is positively correlated with KCNN2 and KCNN3 expression (Figure  2A ).
In addition, KCNN4 is negatively correlated with KCNN2 and KCNMA1 (Figure  2A ). Moreover, we found that in both datasets, KCNMA1 expression is positively correlated with the expression of both regulatory subunits KCNMB1 and KCNMB2 (Figure  S1B ). Moreover, TRPM5 expression level is positively correlated with TRPM2 and TRPM4, while TRPM7 is negatively correlated with TRPM2 and TRPM5 in both datasets (Figure  2B ). We also studied the co-expression of the most selective Ca 2+ channels. We found that ORAI2 and ORAI3 are positively correlated (Figure  2C ). TRPC1 is positively correlated with both ORAI2 and ORAI3 and is negatively correlated with ORAI1. No correlation is found between the expression of STIM1 and STIM2 (Figure  S1C ). ORAI2 and ORAI3 are positively correlated (Figure  2C ). TRPC1 is positively correlated with both ORAI2 and ORAI3 and is negatively correlated with ORAI1.
No correlation is found between the expression of STIM1 and STIM2 (Figure  S1C ). Heatmap presenting the expression profile of 34 Ca and KCa channels coding-genes between CRC and unmatched normal tissues from GSE39582 dataset; (D) Boxplots of differentially expressed genes between normal and CRC tissues in the GSE39582 dataset. Hierarchical clustering was done using Euclidean distance and average linkage method from both genes and samples. Signed-rank Wilcoxon's test and unpaired t-test were used to perform pairwise comparisons between CRC and normal tissues in the TCGA and GSE39582 datasets, respectively and were followed by BH adjustment, respectively. (***: BH-adjusted p < 0. 001). CRC: colorectal cancer; TCGA: The Cancer Genome Atlas; Ca: calcium; KCa: Ca 2+ -dependent potassium channels.
BH: Benjamini-Hochberg. Cancers 2019, 11, 561 5 of 15 between normal and CRC tissues in the GSE39582 dataset. Hierarchical clustering was done using Euclidean distance and average linkage method from both genes and samples. Signed-rank Wilcoxon's test and unpaired t-test were used to perform pairwise comparisons between CRC and normal tissues in the TCGA and GSE39582 datasets, respectively and were followed by BH adjustment, respectively. (***: BH-adjusted p < 0. 001). CRC: colorectal cancer; TCGA: The Cancer Genome Atlas; Ca: calcium; KCa: Ca 2+ -dependent potassium channels. BH: Benjamini-Hochberg.
Gene Expression Analysis According to Tumor Primary Site, lymph Node Metastases and Tumor Stage. First, we aimed to verify if the expression of Ca 2+ and KCa channel coding-genes was correlated with tumor primary sites. In this way we compared gene expression levels between distal/rectal tumors and proximal tumors. Interestingly, we found that STIM2 and KCNN2 are more expressed in proximal tumors compared to distal tumors, both in the TCGA dataset (Figure  3A  and Table  S4. STIM2: fold-change -0. 22 with p ≤ 0. 001; KCNN2: fold-change -0. 13 with p < 0. 001) and the GSE39582 dataset (Figures  3C  and S5. STIM2: fold-change -0. 31 with p < 0. 001; KCNN2: fold-change -0. 25 with p < 0. 001). On the other hand, the results show that ORAI2 and TRPM6 are less expressed in proximal tumors compared to distal tumors, both, in the TCGA dataset (Figures  3B  and S4.
Gene Expression Analysis According to Tumor Primary Site, Lymph Node Metastases and Tumor Stage First, we aimed to verify if the expression of Ca 2+ and KCa channel coding-genes was correlated with tumor primary sites. In this way we compared gene expression levels between distal/rectal tumors and proximal tumors. Interestingly, we found that STIM2 and KCNN2 are more expressed in proximal tumors compared to distal tumors, both in the TCGA dataset (Figure  3A  and Table  S4. STIM2: fold-change -0. 22 with p ≤ 0. 001; KCNN2: fold-change -0. 13 with p < 0. 001) and the GSE39582 dataset (Figure  3C   S5. ORAI2: fold-change 0. 16 with p < 0. 001; TRPM6: fold-change 0. 44 with p < 0. 001). However, only the TCGA dataset contained rectum tumor samples, in which the expression profiles of KCNN2, STIM2 and TRPM6 followed that of the distal tumor samples (Figure  3A ).
S5. ORAI2: fold-change 0. 16 with p < 0. 001; TRPM6: fold-change 0. 44 with p < 0. 001). However, only the TCGA dataset contained rectum tumor samples, in which the expression profiles of KCNN2, STIM2 and TRPM6 followed that of the distal tumor samples (Figure  3A ). Next, the correlation of Ca 2+ and KCa channels coding-gene expressions with lymph node metastases was studied. We found that, among the studied genes, only ORAI1 is deregulated in tumors with lymph node metastases in both the TCGA and the GSE39582 datasets (Figure  4  and Tables S6,S7. fold-change of -0. 13 and 0. 13 with p = 0. 016 and p = 0. 03, respectively). Afterwards, we investigated the correlation of gene expression with CRC progression. For this purpose, we decided to compare gene expression in stage IV, the most aggressive stage with distant metastases, to that of stages I, II and III combined which are less advanced.
Unfortunately, none of the genes were concomitantly deregulated in the two datasets (Tables  S8, S9 ). Next, the correlation of Ca 2+ and KCa channels coding-gene expressions with lymph node metastases was studied. We found that, among the studied genes, only ORAI1 is deregulated in tumors with lymph node metastases in both the TCGA and the GSE39582 datasets (Figure  4  and Tables  S6  and S7. fold-change of -0. 13 and 0. 13 with p = 0. 016 and p = 0. 03, respectively). Afterwards, we investigated the correlation of gene expression with CRC progression. For this purpose, we decided to compare gene expression in stage IV, the most aggressive stage with distant metastases, to that of stages I, II and III combined which are less advanced. Unfortunately, none of the genes were concomitantly deregulated in the two datasets (Tables  S8  and S9 ).
ORAI3 and TRPC1 Expression Predict Poor Prognosis of CRC Patients Since the expression of the selected genes did not correlate with tumor stage, we questioned whether they could harbor some prognostic information. Therefore, we checked the genes' prognostic values on overall survival (OS) in both datasets, in addition to event-free survival (EFS) and relapse-free survival (RFS) for either TCGA or GSE39582 datasets, respectively. Univariate Cox regression analysis showed that ORAI3 and TRPC1 are the only genes that are concomitantly prognostic factors in both datasets (Tables  1  and S10 ,S11). In fact, ORAI3 correlated with a poor outcome for both OS and EFS in the TCGA dataset (Table  1. OS HR = 1. 72 with p = 0. 003 and EFS HR = 1. 6 with p = 0. 001; and Figure  5A. OS Log-Rank p = 0. 002 and EFS Log-Rank p = 0.
001) whereas it only correlated with RFS in the GSE39582 dataset (Table  1. RFS HR = 1. 6 with p = 0. 001; and Figure  5B. RFS Log-Rank p = 0. 02). Likewise, TRPC1 was correlated with poor outcome for OS and EFS in the TCGA dataset (Table  1. OS HR = 1. 44 with p = 0. 042 and EFS HR = 1. 39 with p = 0. 025; and Figure  5C. OS Log-Rank p = 0. 04 and EFS Log-Rank p = 0. 02), whereas it was only correlated with RFS in the GSE38582 dataset (Table  1. RFS HR = 1. 43 with p = 0. 05; and Figure  5D. RFS Log-Rank p = 0. 05).
ORAI3 and TRPC1 Expression Predict Poor Prognosis of CRC Patients Since the expression of the selected genes did not correlate with tumor stage, we questioned whether they could harbor some prognostic information. Therefore, we checked the genes' prognostic values on overall survival (OS) in both datasets, in addition to event-free survival (EFS) and relapse-free survival (RFS) for either TCGA or GSE39582 datasets, respectively. Univariate Cox regression analysis showed that ORAI3 and TRPC1 are the only genes that are concomitantly prognostic factors in both datasets (Table  1  and Tables  S10  and S11 ). In fact, ORAI3 correlated with a poor outcome for both OS and EFS in the TCGA dataset (Table  1. OS HR = 1. 72 with p = 0. 003 and EFS HR = 1. 6 with p = 0. 001; and Figure  5A. OS Log-Rank p = 0.
002 and EFS Log-Rank p = 0. 001) whereas it only correlated with RFS in the GSE39582 dataset (Table  1. RFS HR = 1. 6 with p = 0. 001; and Figure  5B. RFS Log-Rank p = 0. 02). Likewise, TRPC1 was correlated with poor outcome for OS and EFS in the TCGA dataset (Table  1. OS HR = 1. 44 with p = 0. 042 and EFS HR = 1. 39 with p = 0. 025; and Figure  5C. OS Log-Rank p = 0. 04 and EFS Log-Rank p = 0. 02), whereas it was only correlated with RFS in the GSE38582 dataset (Table  1. RFS HR = 1. 43 with p = 0. 05; and Figure  5D. RFS Log-Rank p = 0. 05).
Upregulated ORAI3/ORAI1 Ratio Predicts Poor Prognosis of CRC Patients. Since ORAI1 expression decreased in lymph node metastatic tumors and high ORAI3 expression was a predictor of poor prognosis in both datasets, we investigated the possibility of a functional shift in the Ca 2+ entry from SOCE (ORAI1-dependent) to Store-independent Ca 2+ channel entry SICE (ORAI3-dependent) and its association with a poor outcome, as described in prostate and breast cancers  [23, 24]. Therefore, we investigated whether the ratio of ORAI3/ORAI1 is correlated with the survival of CRC patients. Indeed, Pearson's correlation showed that ORAI1 and ORAI3 expression levels are poorly correlated in both datasets (Figures  2C  and S1D. R = 0. 07 with p = 0. 095 for the TCGA dataset and R = 0. 09 with p = 0. 032 for GSE39582 dataset).
However, despite the lack of apparent correlation, we found that a high ORAI3/ORAI1 ratio correlated with a poor outcome for OS and EFS in the TCGA dataset (Table  2. OS HR = 1. 45 with p = 0. 037 and EFS HR = 1. 54 with p = 0. 003; and Figure  6A. OS Log-Rank p = 0. 04 and EFS Log-Rank p = 0. 003). In addition, this ratio also correlated with a poor outcome for OS and RFS in the GSE39582 dataset (Table  2. OS HR = 1. 47 with p = 0. 009 and RFS HR = 1. 69 with p = 0. 001; and Figure  6B. OS Log-Rank p = 0. 009 and RFS Log-Rank p < 0. 001).
Upregulated ORAI3/ORAI1 Ratio Predicts Poor Prognosis of CRC Patients Since ORAI1 expression decreased in lymph node metastatic tumors and high ORAI3 expression was a predictor of poor prognosis in both datasets, we investigated the possibility of a functional shift in the Ca 2+ entry from SOCE (ORAI1-dependent) to Store-independent Ca 2+ channel entry SICE (ORAI3-dependent) and its association with a poor outcome, as described in prostate and breast cancers  [23, 24]. Therefore, we investigated whether the ratio of ORAI3/ORAI1 is correlated with the survival of CRC patients. Indeed, Pearson's correlation showed that ORAI1 and ORAI3 expression levels are poorly correlated in both datasets (Figure  2C  and Figure  S1D. R = 0. 07 with p = 0. 095 for the TCGA dataset and R = 0. 09 with p = 0.
032 for GSE39582 dataset). However, despite the lack of apparent correlation, we found that a high ORAI3/ORAI1 ratio correlated with a poor outcome for OS and EFS in the TCGA dataset (Table  2. OS HR = 1. 45 with p = 0. 037 and EFS HR = 1. 54 with p = 0. 003; and Figure  6A. OS Log-Rank p = 0. 04 and EFS Log-Rank p = 0. 003). In addition, this ratio also correlated with a poor outcome for OS and RFS in the GSE39582 dataset (Table  2. OS HR = 1. 47 with p = 0. 009 and RFS HR = 1. 69 with p = 0. 001; and Figure  6B. OS Log-Rank p = 0. 009 and RFS Log-Rank p < 0. 001). Next, we investigated the correlation of ORAI3/ORAI1 ratio with CRC progression by studying its variation according to tumor stages. Interestingly, although the genes individually did not correlate with tumor progression (Tables  S8  and S9 ), the ORAI3/ORAI1 Ratio was significantly increased in stages III and IV compared to Stage I in both TCGA and GSE39582 datasets (Figure  6C ).
Taken together, those results indicate that a Ca 2+ influx switch could exist between ORAI3 and ORAI1 expressions in favor of the former during the progression of CRC, explaining its association with a poor outcome. Next, we investigated the correlation of ORAI3/ORAI1 ratio with CRC progression by studying its variation according to tumor stages. Interestingly, although the genes individually did not correlate with tumor progression (Tables  S8, S9 ), the ORAI3/ORAI1 Ratio was significantly increased in stages III and IV compared to Stage I in both TCGA and GSE39582 datasets (Figure  6C ). Taken together, those results indicate that a Ca 2+ influx switch could exist between ORAI3 and ORAI1 expressions in favor of the former during the progression of CRC, explaining its association with a poor outcome.
Discussion Several studies investigated the role of ion channels in cancer, providing evidence of their important roles in cell proliferation, migration, apoptosis and differentiation, and thus the designation "onco-channels"  [25, 26]. In this study, we investigated the transcriptional expression profile of Ca 2+ and KCa channels in CRC by exploring two expression datasets: RNAseq (TCGA) and microarray (GSE39582). Those two datasets were selected because they are both large (n > 500), diverse (contain normal and CRC samples of various stages) and annotated for patients' survival beside other clinical information. Although many genes gave significant results in one of the two datasets individually, we only focused our analysis on robust results that were constantly significant in both datasets.
The Villalobos group found that normal mucosa cells showed an I CRAC current mediated by ORAI1. Conversely, colon carcinoma cells showed mixed currents composed of enhanced I CRAC besides a nonselective Ca 2+ current mediated by TRPC1  [17]. Moreover, the expression of TRPC1, ORAI1, ORAI2, ORAI3 and STIM1 was increased in tumor cells in contrary to STIM2 protein which was found to be depleted  [17]. Our study showed that TRPC1 is negatively correlated with ORAI1 and positively correlated with ORAI2 and ORAI3, and that TRPC1 expression predicted a poor prognosis in CRC. This result suggests a SOCE remodeling during CRC progression and could be the subject of further analysis. Our results show that ORAI1 decreased with lymph node metastases, one of the first signs of metastatic spread. Also, ORAI3 predicted a poor prognosis for RFS in GSE39582 but not for OS, and ORAI1 predicted poor prognosis in TCGA but not in GSE39582 (Table  S10 ).
However, by inspecting the ratio of ORAI3/ORAI1, we found that a high ORAI3/ORAI1 ratio predicted a poor prognosis for EFS and OS in TCGA, as well as for RFS and EFS in GSE39582, with a higher HR than that of ORAI1 or ORAI3 separately. Thus, the ORAI3/ORAI1 ratio is a better predictor of survival than ORAI3 alone. This finding is consistent with two other studies that described a change in Ca 2+ influx from ORAI1 to ORAI3 in cancer. In prostate cancer, it was suggested that ORAI1 is implicated in a dynamic machinery of Ca 2+ remodeling between ORAI1 and ORAI3  [23]. It was postulated that ORAI3 increases limits SOCE and enhances ORAI1-ORAI3 ARC Ca 2+ entry  [23]. Moreover, high ORAI3/ORAI1 ratio was shown to correlate with poor prognosis in breast cancer  [24]. However, this ratio should not undermine the pro-tumor role of ORAI1.
Public Datasets Two CRC datasets, available from public databases, were used in this paper: an RNAseq dataset (TCGA, n = 656 including 47 normal samples) and an expression microarray dataset (GSE39582, n = 585 including 19 normal samples)  [43]. The two datasets were selected because of their large size, diversity and the availability of clinical annotations. For the RNAseq dataset, Fragment Per Kilobase Million (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal. gdc. cancer. gov/repository) [44]  and were log2 transformed after the addition of one (log2(x + 1)) prior to analysis. Clinical annotations were acquired from cbioportal for Cancer Genomics website (http://cbioportal. org) using the the Cancer Genomics Data Server package (cgdsr) in R environment  [45, 46].
GSE39582 microarray dataset (RMA-normalized, Affimetrix Human Genome U133 Plus 2. 0 Array) was downloaded from the Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/geo/). For each gene, the probeset with the average expression was used. Thirty-five genes related to Ca 2+ and KCa channels were selected for analysis when available (TRPV1 was missing from the microarray dataset). Gene names and functions are summarized in Supplementary Table  S1.
Statistical Analysis Heatmaps Boxplots and correlation plots were generated in R using ComplexHeatmap  [47] , ggpubr (https://CRAN. R-project. org/package=ggpubr) and corrplot (https://github. com/taiyun/corrplot) packages, respectively. Hierarchical clustering was done using Euclidean distance and average linkage method for both genes and samples. Pearson's test was used for gene correlation analysis. Wilcoxon's signed-rank test was used to compare matched normal and tumor tissues in the TCGA dataset. A student's t-test was used for the other pairwise comparisons in the paper. The Benjamini and Hochberg method was used to adjust for multiple comparisons  [48] , and adjusted p-values below 0. 05 were considered significant.
S1 : Symbols, names and functions of the selected genes, Table  S2 : Comparison of gene expression levels in normal mucosa versus tumor samples in TCGA dataset, Table  S3 : Comparison of gene expression levels in normal mucosa versus tumor samples in GSE39582, Table  S4 : Comparison of gene expression levels between proximal and distal/rectal tumors in TCGA dataset, Table  S5 : Comparison of gene expression levels between proximal and distal tumors in GSE39582 dataset, Table  S6 : Comparison of gene expression levels between N0 and N+ (lymph node metastatic) tumors in TCGA dataset, Table  S7 : Comparison of gene expression levels between N0 and N+ (lymph node metastatic) tumors in GSE39582 dataset, Table  S8 : Comparison of gene expression levels between stage IV and stages I + II + III in TCGA dataset, Table  S9 : Comparison of gene expression levels between stage IV and stages I + II + III in GSE39582 dataset, Table  S10 : Univariate analysis of OS and EFS in TCGA dataset, Table  S11 : Univariate analysis of OS and RFS in GSE39582 dataset.
and Figure S5. STIM2: fold-change -0. 31 with p < 0. 001; KCNN2: fold-change -0. 25 with p < 0. 001). On the other hand, the results show that ORAI2 and TRPM6 are less expressed in proximal tumors compared to distal tumors, both, in the TCGA dataset (Figure 3B and Figure S4. ORAI2: fold-change 0. 23 with p < 0. 001; TRPM6: fold-change 0. 31 with p < 0. 001) and GSE39582 dataset (Figure 3D and Table.
The following are available online at http://www. mdpi. com/2072-6694/11/4/561/s1, Figure S1: Pearson's correlation of gene expression in TCGA and GSE39582 datasets. (A) Pearson's correlation heatmaps of Ca 2+ and KCa channels family coding genes; (B) Scatter plots of KCNMA1 and KCNMB subunits correlation; (C) Scatter plots of STIM1 and STIM2 correlation; (D) Scatter plots of ORAI1 and ORAI3 correlation, Table.
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-6694/11/4/561/s1, Figure S1: Pearson's correlation of gene expression in TCGA and GSE39582 datasets. (A) Pearson's correlation heatmaps of Ca 2+ and KCa channels family coding genes; (B) Scatter plots of KCNMA1 and KCNMB subunits correlation; (C) Scatter plots of STIM1 and STIM2 correlation; (D) Scatter plots of ORAI1 and ORAI3 correlation, Table.
Data collection and structure determination The MoPrP(89-230)•Nb484 complex crystal diffracted to 2. 1 Å resolution and a complete dataset was collected at X06DA beamline at SLS, Paul Scherer institute, Switzerland as described previously  [44]. The second crystal diffracted to 1. 2 Å resolution and a complete dataset was collected at X06DA beamline at SLS, Paul Scherer institute. Both Mouse PrP structures data were processed using XDS  [50]. The structures of the different MoPrP•Nb484 complexes were determined by molecular replacement (PHASER)  [51]  using the HuPrP•Nb484 crystal structure (PDB entry 4KML)  [12]. Nb484 was processed using iMOSFLM as described previously  [49]. The Nb484 structure was solved by molecular replacement using PDB entry 1OL0 as search model. The data collection details and refinement statics are shown in Table  1.
Phospholipid classes of marine polar lipid extract and of soya lecithin were characterized by proton nuclear magnetic resonance ( 1 H NMR) according to Cansell et al. (24)  (Table  1 ). The glyceride structure of the marine oil mixture and of the marine PL was enzymatically determined according to methods described below. In the marine TG (oil mixture), EPA was mainly esterified in the sn-1/3 positions of marine TG (26% in the sn-2 position) whereas DHA was mainly located in the sn-2 position (50%). In marine PL, both EPA and DHA were essentially distributed in the sn-2 position (>74%) (see Supplementary data 1).