dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression analysis In each cohort of microarray data, a single probe analyzed the SALL4 expression and detected its two isoforms (SALL4A and SALL4B) (Table  S3 ). For genes with several probes, the expression was evaluated from the mean of duplicate probes. SALL4 expression was correlated (Pearson correlation) to the expression of each gene. The resulting Pvalues were adjusted for multiple testing using the approach of Benjamini and Hochberg. The cut-offs were adjusted P-value < 0. 05 and correlation coefficient > 0. 4 for the ICGC and TCGA cohorts, and > 0. 3 for the GSE85916. The intersected genes between these three cohorts were kept to identify the most relevant genes and define the SALL4 signature. The scaled expression of each relevant gene was used for clustering and heatmap visualization, using the R package "PHEATMAP".
Pathway analysis To investigate the association between SALL4 expression and stemness phenotype, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway namely "hsa04550" (Signaling pathways regulating pluripotency of stem cells). KEGG pathway and GeneOntology (GO) Biological Process (BP) enrichment analyses were performed using DAVID software (https://david-d. ncifcrf. gov/). The cut-off was adjusted P-value (FDR) < 0. 01.
Molecular and cancer-associated fibroblasts subtypes of pancreatic cancer: published classifications The molecular subtype labels from the Puleo classification were available in the GSE85916 dataset  [5]. The gene expression signatures for each of the five subtypes were selected from a differential analysis based on a one-versus-all comparison, using the R package "LIMMA"  [24]. The 1000 most differentially expressed genes of each subtype were used as gene signatures. These signatures were used to build independent centroids for each subtype. The subtypes were assigned to the samples of the ICGC and TCGA datasets, by correlating the centroids to the transcriptomic profile of each patient, using Pearson correlation analysis. Then, basal and classical subtypes were called based on gene expression levels of gene sets described in Moffitt et al.
[3]. Consensus clustering was performed based on the correlation distance of the 50 gene signatures. Dendrograms were then cut and branches labeled semiautomatically to assign subtypes to samples consistent with their respective expression patterns. The four pCAFassigner subtypes (A, B, C, and D) were taken from Table  S4  of the article by Neuzillet et al. [25]. The dominant subtype labels were available for 62 samples among the selected samples profiled by whole-transcriptome RNA sequencing in the ICGC database. The gene expression signatures for each CAF subset were identified by the same method as described above and applied to the 259 patients in the microarray set.
Tumor microenvironment profile The MCP-counter algorithm was used to infer the tissue-infiltrating immune and fibroblast proportions of the tumor microenvironment. The MCP-counter consists of eight immune and two stromal cell populations (fibroblasts, endothelial cells) and was constructed from gene expression profiles of these cell types  [26]. Several of the stromal TGF-β response signatures, including fibroblasts (F-TBRS), were identified in cohorts of patients with colorectal cancer  [27, 28]. To evaluate the subtype of myofibroblasts, this F-TBRS signature was applied in pancreatic cancer datasets. Another TGF-β CAF signature was recently published and evaluated in our article  [29]. Then, RNA subtypes clustering was performed based on the correlation distance of 12 gene signatures from different classifications  [26, [29] [30] [31]  and SALL4 signature.
Pancreatic intraepithelial neoplasia (PanIN) dataset PanIN dataset was downloaded previously normalized from the provided GEO entries GSE43288. The R package "LIMMA" was used for differential gene expression (DGE) analysis between PDAC and PanIN, and PDAC and normal pancreas. The criteria for DGEs were log 2 fold-change (log 2 FC) > 1 (or < -1) and adjusted P-value < 0. 05  [24]. The intersected genes between these two DGEs and the SALL4 signature were kept to identify the most relevant genes for clinical application. The expression (scaled and clustered) of each relevant gene was used for heatmap visualization, using the R package "PHEATMAP".
PDAC single-cell RNA-sequencing data processing Data from 24 PDAC patients and 11 control pancreas tissues were obtained from the Genome Sequence Archive under project PRJCA001063 in FASTQ format. Reads were processed using CELL RANGER 3. 1. 0 using default parameters and supplying a custom reference package based on human reference genome GRCh38. Samples of 22 patients and 11 control tissues for which the correct chemistry was detected by CELL RANGER (10x Genomics, Pleasanton, CA, USA) from the sequencing data were used for downstream analysis. Subsequent data analysis was carried out in R 3. 6. 2 and the SEURAT package (v 3. 1. 2). Low-quality cells (< 300 genes/cell, < 3 cells/gene, and > 5% mitochondrial genes) were excluded. To remove noise from droplets containing more than one cell, we focused on cells with at most 5000 measured genes, keeping 72 694 cells for further analyses.
Subsequently, data were normalized to log(CPM/100 + 1) and scaled regressing out the number of distinct UMIs and the fraction of mitochondrial reads during scaling. The normalization and scaling of the data were performed using the functions NormalizeData (method "LogNormalize") and ScaleData implemented in Seurat. Dimensionality reduction was carried out with the Seurat package  [32]. We identified the 2000 most variable genes and applied principal component analysis to cells in this gene space. Principal components 1-20 were provided as an input for dimensionality reduction via t-Distributed Stochastic Neighbor Embedding (tSNE) with default parameters. Clusters of cells were identified based on a shared-nearest neighbor (SNN) graph between cells and the smart moving (SLM) algorithm (resolution = 0.
Statistical analysis Overall survival (OS) was calculated from the date of cancer diagnosis to the date of death from any cause. Survival data were censored at the last follow-up. OS was estimated using the Kaplan-Meier method and described using median or rate at specific time points with 95% confidence intervals (CI), and compared using the log-rank test. A potential nonlinear relationship between SALL4 expression and OS was investigated using the restricted cubic splines method with graphical evaluation. Various thresholds (median, tertiles, and quartiles) were evaluated to identify different groups according to the SALL4 expression. To give a reasonable spread of risk, we distinguish two prognostic groups according to the maximizing of the log-rank test, which was determined following the Hothorn and Lausen method (R package "MAXSTAT"  [34] ).
The SALL4 gene expression assumed a normal distribution and showed no evident cohort-bias clustering (Fig. S1A ). Besides, the potential linear relationship between SALL4 expression and OS was investigated by the restricted cubic splines method with graphical evaluation in the ICGC and GSE85916 cohorts (Fig. 1A, B ). These observations allowed to divide the expression levels into intervals containing the same number of data (quantile). Therefore, these analyses let us select the upper tertile cut-off (threshold value = 5. 18) to categorize patients into two risk groups, such as one-third of the samples had a high SALL4 expression (SALL4 high ), and 67% had a low SALL4 expression (SALL4 low ) (Fig. S1B ). Median value (interquartile range) and frequency (percentage) were provided for the description of continuous and categorical variables, respectively.
Medians were compared using Student's t-tests or ANOVA, and proportions were compared using chi-square tests (or Fisher's exact test, if appropriate). Pearson's test was applied to determine the correlation between gene or signature expressions. Cox proportional hazard models were performed to estimate hazard ratio and 95% CI for factors associated with OS. The association of the baseline parameters with OS was first assessed using univariate Cox analyses, and then parameters with P values of < 0. 05 were entered into a final multivariable Cox regression model. All analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA), R software version 3. 6. 2 (R Development Core Team, Vienna, Austria; http://www. r-project. org), and GRAPHPAD version 8. 1. 2 (GraphPad Software, San Diego, California, USA).
SALL4 expression is a prognostic biomarker in PDAC Among stemness-related transcription factors, SOX2 and SALL4 were independently associated with a shortened patient survival (Table  S3 ). However, the prognostic relevance of the SALL4 expression in PDAC and the mechanisms governing SALL4 expression in such cancers have so far not been thoroughly investigated. Here we examined how SALL4 expression predicts the OS of patients with localized PDAC. Patients from the ICGC cohort were categorized into two groups according to SALL4 gene expression levels, based on the "maxstat" method (best performing threshold). The median OS was 24. 5 months in the SALL4 low group (95% CI = 18. 0-32. 3 months), and 15. 6 months (95% CI = 13. 4-18. 1 months) in the SALL4 high group (Fig. 1A ). High expression of SALL4 also conferred poor survival outcomes, in a validation cohort (GSE85916; Fig.
1B ). We then identified the upper tertile cut-off as the threshold closest to the maxstat threshold and transposable to other datasets (Supplementary method and Fig. S1 ). This threshold followed previous observations in all solid tumors  [16]  and was applied for downstream analyses. Localized PDAC patients' characteristics are summarized in Table  S4  according to SALL4 expression. SALL4 expression differed only according to the stage (P = 0. 04). We found no association between SALL4 levels and other clinicopathological factors (Table  S4 ). Moreover, in the overall population including patients with metastatic PDAC, increased SALL4 expression was significantly associated with advanced diseases (Fig. 1C ). Thereby, we identified a high SALL4 expression level as a poor prognostic factor for the survival of PDAC patients.
S2A ). Of note, the tumor mutation burden was similar between the SALL4 high and SALL4 low groups (Fig. S2B ). The distribution of highly altered genes in PDAC such as KRAS, TP53, and CDKN2A was also similar in PDAC subsets defined by SALL4 expression level in the validation cohorts (GSE85915 and TCGA; Table  S5 ). These results confirmed that genomic alteration patterns were not different between SALL4 high and SALL4 low expression in PDAC.
SALL4 expression and its transcriptional signature are correlated with stroma remodeling The transcriptomic analyses allowed us to investigate the gene expression profile associated with SALL4 high level in PDAC. We identified the list of genes correlated with SALL4 expression in each cohort (ICGC, TCGA, and GSE859216). Genes with the highest correlation levels were selected and reported in Table  S6. Among all genes correlated with SALL4, 24 genes enriched in the SALL4 high PDAC subset had expression overlapping in the three cohorts (Fig. 2A ) and were selected for further explorations. The involved genes included ANTXR1, CASC15, COL1A1, COL5A2, COL8A1, COL11A1, DCBLD1, DLG4, ITGA11, KANK4, LRRC15, MEIS3, MMP11, MMP14, NOTCH3, NOX4, NUAK1, PPEF1P PFIBP1, PTK7, SERPINH1, SOX11, SPOCD1, and Fig.
1. SALL4 expression is a prognostic biomarker in pancreatic ductal adenocarcinoma. (A) Kaplan-Meier curves of overall survival for 257 patients with localized pancreatic carcinoma in the ICGC cohort. Groups were split by high (red) or low (blue) levels of SALL4 expression (best cutoff) and compared using the log-rank test. Hazard ratio modelization with 95% confidence intervals by restricted cubic spline according to SALL4 expression. (B) Kaplan-Meier curves of overall survival for 288 patients with localized pancreatic carcinoma in the GSE85916 cohort. Groups were split by high (red) or low (blue) levels of SALL4 expression (best cutoff) and compared using the log-rank test. Hazard ratio modelization with 95% confidence intervals by restricted cubic spline according to SALL4 expression. (C) Boxplots and barplots comparing the distribution of the SALL4 expression in different tumor stages in the ICGC cohort, including advanced stages (stages III and IV, n = 13 available patients).
(D) Boxplots and barplots comparing the distribution of the SALL4 expression in basal/classical and activated/normal stroma subtypes from the Moffit classification  [3]  and Puleo classification  [5]  for 259 patients with localized pancreatic carcinoma in the ICGC cohort. Boxplots show the median and interquartile range. Medians were compared using Student's t-tests. UNC5B. The heatmap displayed that this signature composed of these 24 intersected genes was relevant to distinguishing the SALL4 high and SALL4 low patients (Fig. 2A ; Fig. S4A ). The prognostic value of SALL4related 24-gene signature was also investigated. Patients with high level of 24-gene signature showed a poor prognosis (median OS = 15. 6 months [95% CI = 13. 9-17. 1 months] and 20. 5 months [95% CI = 16. 9-24. 1 months], in ICGC and GSE85916 cohorts, respectively; Fig.
3C ). This association was confirmed in the TCGA and GSE859216 cohorts (Fig. S4C ). The distribution of SALL4 expression was then assessed at the single cell level by analyzing tumor and stromal cells from patients with PDAC using in situ hybridization (RNAscope). The results revealed that SALL4 was expressed in both tumor cells and CAF. Of note, the expression of SALL4 was higher in CAF than tumor cells (P = 0. 008; Fig. 3D ). Moreover, SALL4 is required for maintaining embryonic stem cell pluripotency and self-renewal. Indeed,  Zhang et al. [9]  have highlighted a reduction of other stemness-related transcription factors OCT4, SOX2, and NANOG in a condition of SALL4 knockdown. Our findings confirmed that SALL4 expression was correlated with signaling pathways regulating pluripotency of stem cells, surrogated by transcription factors such as OCT3/4 and SOX2 (Fig.
4A ). Similarly, a positive correlation was ascertained between SALL4 and the TGF-β CAF signature  [29]  (Fig. 4B ). These observations were also validated in the TCGA and GSE859216 cohorts (Fig. S3E ). Interestingly, both F-TBRS and the TGF-β CAF signatures were not significantly associated with OS in univariate analysis (Table  S7 ) suggesting that the prognostic role of the SALL4 high PDAC subset does not solely rely on the presence of TGF-β-driven CAF phenotypes. Likewise, the SALL4-related 24gene signature, stemness, and TGF-β/CAF molecular networks as well as low expression of immune-related genes contribute to defining the SALL4 molecular network in ICGC, TCGA, and GSE85916 cohorts (Fig. S4D ). Based on scRNA-seq performed in cells sorted from PDAC derived from 33 patients  [33] , Dominguez et al.
SALL4-related stromal signature allows discriminating invasive from pre-invasive pancreatic lesions and PDAC patients' clinical outcomes Next, we determined the SALL4 transcriptomic activity in the different stages of PDAC carcinogenesis. Data from the GSE43288 cohort were examined to identify genes selectively expressed in pancreatic carcinoma by comparison to preinvasive pancreatic lesions (PanIN) or normal pancreatic tissue. A total of 366 genes were overexpressed in PDAC compared to PanIN samples, and 424 genes were overexpressed in Fig. 4. SALL4 expression identifies a subtype of activated CAF in pancreatic ductal adenocarcinoma. (A) Scatter plot and boxplots comparing the distribution of the F-TBRS signature  [27]  to the SALL4 expression in the ICGC cohort. (B) Scatter plot and boxplots comparing the distribution of the TGF-β CAF signature  (Dominguez et al.
Data are displayed as mean AE standard deviation. One representative experiment is shown (n = 3). (G) Size of the spheroids with Panc-1 and MRC5 with or without SALL4 transduction (mean of triplicates). One representative experiment is shown (n = 2). PDAC and not in normal pancreatic tissues. Altogether, seven genes belonged to these two genes lists and the SALL4 signature: COL1A1, COL5A2, COL11A1, MMP11, NUAK1, PTK7, and SERPINH1 (Fig. 5A ), pointing out that this SALL4-related 7-gene signature discriminated the invasive stage of PDAC compared to PanIN stage and normal pancreatic tissue. Most of these selected genes were highly correlated, especially PTK7 and SERPINH1, and observed in a closed pattern, in the three main cohorts (ICGC, TCGA, and GSE859216; Fig. S6A ). The prognostic value of SALL4-related 7-gene signature was also investigated in ICGC and GSE85916 cohorts.
Patients with high level of 7-gene signature showed a poor prognosis (median OS = 15. 8 months [95% CI = 13. 6-20. 0 months] and 21. 9 months [95% CI = 19. 1-27. 5 months], in ICGC and GSE85916 cohorts, respectively; Fig. S6B ). To validate the potential clinical relevance of this stromal signature, we examined the SALL4-related signatures in samples derived from a case-control cohort of patients discriminated by their PDAC-related specific survival using NanoString RNA quantification. Messenger RNA (mRNA) belonging to the SALL4-related signatures was enriched in patients with short-term survival (Fig. S6C ). The expression of SALL4 in stromal cells was investigated using in situ hybridization (RNAscope). SALL4 mRNA was identified in fibroblasts from PDAC but not PanIN or normal pancreatic tissues (Fig.
Discussion We examined the potential clinical impact of stemnessrelated transcription factors in PDAC databases. Consistent with results observed in other tumor types, our study pointed out a prognostic value of SALL4 expression in PDAC. Gene expression analyses revealed that the SALL4-related molecular network defines a PDAC subset enriched in cancer stem cell functions and stromal organization. These findings underscore a direct role of TGF-β1 in the regulation of SALL4 expression in stromal cells. SALL4 expressing fibroblasts then support the organization of a specific microenvironment promoting in vitro colony formation and invasive features in PDAC patients. One strength here is the characterization of a 24gene signature able to predict PDAC OS in different cohorts. The SALL4-related stromal network might also be of interest in discriminating stromal components associated with pre-invasive and invasive pancreatic carcinomas.
Although fibroblasts play a critical role in tissue homeostasis and disease processes, understanding their function has been hampered by their inherent heterogeneity and a lack of robust markers. Underlined by bioinformatic analyses, the occurrence of a stromal signature characterized the SAL-L4 high PDAC subset which might account for the unfavorable prognosis, in ICGC and GSE85916 cohorts. Unfortunately, TCGA survival features were not applied due to discordant data with the literature and pancreatic cancer statistics  [55, 56]. Knudsen et al. [57]  have identified three PDAC stromal compartments according to morphological characteristics, including the "immature" stroma with highly cellular and collagen-poor stroma, associated with shorter OS. Other classifications showed the poor prognostic value of CAF, specifically stroma enriched in αSMA+ fibroblasts compared to stroma enriched in collagen  [58, 59].
These will then become suitable for ensemble asteroseismology in the future, once dedicated high-cadence high-precision monitoring for their members has become available. In terms of impact, Gaia DR3 (and later data releases) may lead to the discovery of tens of thousands of new IHM pulsating dwarfs, delivering the frequencies and amplitudes of their dominant (non)radial oscillation mode(s), aside from a high-precision parallax. Here, we assess Gaia's performance on this front, offering as well an observational census of the instability strips along the upper main sequence for samples of new pulsating IHM dwarfs with M ≥ 1. 3 M. The borders of those instability strips depend on the physics of internal rotation, gravitational settling, radiative levitation, shear mixing, and so on, as well as on the bulk metallicity and possible binarity at birth.
Given the complex (often non-linear) interplay of these astrophysical phenomena during the evolution and the initial conditions at birth, samples of thousands of (non-)radial pulsators are required to interpret the borders of the instability strips in terms of the excitation physics  (Townsend 2005; Szewczuk & Daszyńska-Daszkiewicz 2017)  and transport processes  (Aerts et al. 2019). This paper is one among several publications led by the Gaia Data Processing Analysis Consortium (DPAC) accompanying the Gaia DR3. We present the Gaia photometric dataset of variable IHM dwarfs in Sect. 2 and develop our strategies to detect IHM main sequence pulsators in Sect. 3, distinguishing non-radial gravity-mode (g mode) pulsators from (non-)radial pressure-mode (p mode) pulsators. We compare the samples of IHM dwarf pulsators with those in the literature in Sect.
The dataset Within the Gaia Data Processing and Analysis Consortium (DPAC), the CU7 coordination unit responsible for analysing variable stars processed a grand total of 1 840 651 642 sources, of which 25% of the most variable sources were selected for further investigation. This variability was tested in the time domain rather than the Fourier domain for computational reasons, as described in  Eyer et al. (2023). These variable sources were then further characterised in the Fourier domain and classified by multiple classifiers each with their own setup  Rimoldini et al. (2023). We also refer to the documentation provided by  Rimoldini (2022)  for further processing details. For this paper, we started with those 450 605 variable sources from the Gaia DR3 table vari_classifier_result with at least 40 photometric measurements in the Gaia G-band, and which the CU7 classification team classified as either β Cep, δ Sct, SX Phe, SPB, or γ Dor star.
No constraints were put on the apparent G magnitude. To this sample we added the 54 476 IHM main sequence pulsator candidates from the Gaia DR3 table vari_ms_oscillator in Gaia DR3, which used a different set of classification criteria. We filtered out any duplicates common to both samples to end up with a sample of 460 519 candidate main sequence variables with at least 40 Gaia G data points. Both classifications mentioned here are based on a list of attributes that includes the main frequency ν 1 found in the generalised Lomb-Scargle periodogram  (Zechmeister & Kürster 2009). This main frequency optimises the fit of the following A36, page 3 of 22 model to the data: G n = C + A sin(2πν 1 t n + ϕ), (1) where G n is the observed Gaia G magnitude at time t n , C is a constant term, and A and ϕ are the amplitude and phase of the sine wave, respectively.
We see high peaks around harmonics of 4 d -1 , which is the spin frequency of the spacecraft (cf. Gaia Collaboration 2016). The strong wings around these peaks suggest that sources in a fairly broad frequency range are affected. For these stars, the Fourier analysis therefore extracted the signature of an instrumental variation rather than an astrophysical variation. Holl et al. (2023)  and  Distefano et al. (2023)  also observed instrumental periodic variations in the photometric data of Gaia DR3, be it while focusing on a different frequency range (ν < 0. 5 d -1 ) from that relevant for this article. Although these authors were not able to clean the affected time series from instrumental variations, they were able to efficiently filter out the impacted sources. Their filtering method proved not as efficient for our sources, casting doubt on whether the instrumental frequencies observed at very low frequencies are of the same origin as those we observe at high frequencies.