dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of co-expression modules Construction of co-expression modules was performed using weighted gene co-expression network analysis (WGCNA;  Langfelder & Horvath, 2008). We kept the upper 75 th quantile of the normalised count data from the alignment to the Emperor penguin genome, resulting in a dataset of 2699 genes. All samples and genes passed the missing data filter implemented using the default parameters in the goodSamplesGenesMS function. Gene co-expression networks were constructed for both species using the blockwiseModules function. In weighted co-expression networks, genes (network nodes) are connected by the pairwise correlation of their expression (network edges) in an adjacency matrix. In WGCNA, the adjacency (correlation) matrix is raised to a power β ≥ 1 (soft thresholding), so that high correlations are accentuated over low correlations  (Zhang & Horvath, 2005).
, 2011). To interpret the potential biological function of the genes within our chosen modules (i. e. , the Emperor penguin specific modules, a. k. a. the least preserved modules in the King penguin network), we performed a GO enrichment analysis. We used the chicken (Gallus gallus) gene sets and related GO terms as a starting point, which were downloaded from Ensembl using biomart v2. 56 in R. Intersecting this database with the Emperor penguin genome annotation (14,572 protein-coding genes), we harnessed 66,367 Biological Process (BP) GO terms related to a total of 9,132 genes (i. e. , 63% of the genes had associated GO terms), which was used as the background for GO enrichment. Analysis was performed using the enricher function in clusterProfiler v4. 8. 2  (Yu et al. , 2012) , using a minimum gene size of 10 and a maximum gene size of 500, and an FDR value ≤ 0.
149) and the Proportion of Variance Explained (propVarExpl = 0. 146) by the ME (Fig. 3C , Table  S10 ). Low meanMAR values indicate that genes from an Emperor penguin module have weak correlations in the King penguin network, even if those genes have a high number of connections (i. e. , high density). propVarExpl is a density statistic that measures how tightly correlated are genes from a module in the test dataset based on the module's eigengene (i. e. , expression variance in that module)  (Langfelder et al. , 2011). Two other muscle modules, yellow and tan, the muscle-liver pink module, and the liver green module also showed low values of meanMAR statistics (Fig. 3C , Table  S10 ). In accordance with low meanMAR values, we also detected a tendency towards lower intramodular connectivity (kIM) values (i.
e. , weaker connections) among genes of the three muscle modules, purple, yellow, and tan, in the King penguin network in comparison to the Emperor penguin network (kIM distribution shifted to the left in the King penguin; Fig. 3D ; further details on kIM in  Langfelder & Horvath, 2008). Another module that showed signals of weak preservation in the King penguin dataset was the brain-related turquoise module, with a propVarExpl = 0. 200 (Fig. 3C , Table  S10 ). S9 ), where B stands for brain, K for kidney, L for liver, M for muscle, and S for skin samples. Filled and empty dots represent Emperor penguin and King penguin samples, respectively. (C) Main summary statistics that indicate the weak preservation of purple, yellow, tan, pink, turquoise, and green Emperor penguin modules in the King penguin network.
In the left panel, the mean Maximum Adjacency Ratio (meanMAR) statistics, in which low meanMAR indicates modules composed of nodes with weak connections (i. e. , coexpression) with neighbour nodes in the King penguin dataset. In the right panel, the Proportion of Variance Explained by the module eigengenes (propVarExpl), in which low propVarExpl indicates low module density in the King penguin dataset, as correlation between gene expression patterns are lower. (D) kIM densities of genes of the least preserved modules (green, pink, tan, yellow, purple, and turquoise) in Emperor penguin (top) and King penguin (bottom) networks. Dashed vertical lines in the Emperor penguin plots indicate the 90 th quantile of kIM distribution threshold used to determine hub genes (Table  S12 ). To investigate the biological meaning of the six candidate Emperor penguin modules: purple (muscle); yellow (muscle); tan (muscle); pink (muscle & liver); green (liver); turquoise (brain), we performed GO term enrichment analysis on all genes identified within each module (see Table  S11  for full results).
This study therefore represents a first contribution to understanding the role of gene expression in adaptation to the most extreme environmental conditions on Earth for a warm-blooded vertebrate. In summary, through complementary analyses of a multi-tissue dataset, we were able to determine which tissues show the strongest signal of gene expression shifts in the Emperor penguin. We identified a prominent signature of the muscle and liver, and an apparent rewiring in the brain expression network. The lack of a gene expression signature in the skin suggests we should explore the role of coding sequence changes in this tissue. Future research on functions and roles of genes showing signals of divergence, either in expression or in coding sequence, is needed to better understand the initial phases of ecological divergence and rapid adaptation to novel selective constraints.
Acknowledgements This study was supported by the  Institut Polaire Français Paul-Emile Victor (IPEV)  within the framework of the  Program 137-ANTAVIA  (PI: CLB), by the  Centre Scientifique de Monaco  with additional support from the  LIA-647  and  RTPI-NUTRESS (CSM/CNRS-UNISTRA ), by the  Centre National de la Recherche Scientifique (CNRS)  through the  Programme Zone Atelier de Recherches sur l' Environnement Antarctique et Subantarctique (ZATA ). The study was approved by the French ethics committee (last: APAFIS# 29338-2020070210516365 ) and the  French Polar Environmental Committee , and permits to handle animals and access breeding sites were delivered by the " Terres Australes et Antarctiques Françaises" (TAAF). JRP is supported by funding from the  European Union 's  Horizon Europe research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
Data Accessibility and Benefit-Sharing All data pertaining to this study are accessible at the European Nucleotide Archive (ENA) under the study accession PRJEB64484. The Emperor penguin transcriptome can be found under ERS16107788 (HCAY01000001-HCAY01106060). The King penguin transcriptome can be found under the ERS16107787 (HCAX01000001-HCAX01080605). Clean reads from the 3'-QuantSeq mRNA data for each tissue and each species can also be found on the ENA (Study: PRJEB64484) under the sample accessions: ERS16201405 -ERS16201453 (Emperor penguin), and ERS16201454 -ERS16201503 (King penguin). Additional Data including the transcriptome annotations, catalogues of long non-coding RNAs, tissue-enhanced transcripts, and Differentially Expressed Genes (DEGs) per tissue can be found on GitHub at https://github.
Expression quantitative trait loci enrichment to examine tissue-specific regulatory mechanisms of effects For all top findings from "Discovery" and "Validation" analyses we examined whether instruments overlapped with eQTL (P < 5. 0 x 10 -8 ) to identify potential tissue-specific regulatory effects of instruments using data on 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors in the GTEx project V8 and 1,544 RNA-sequencing samples from 13 immune cells of 91 healthy subjects in the Database for Immune Cell Expression (DICE)  [92, 93]. Where there was eQTL overlap, we then used multiple trait colocalisation ("moloc") to evaluate colocalisation across circulating inflammatory marker concentrations, tissue-specific or immune cell-specific gene expression, and cancer risk  [94].
Acknowledgements The authors would like to thank the participants of the individual studies contributing to the  BCAC ,  CIMBA ,  CORECT ,  CCFR ,  ECAC ,  GECCO ,  INTEGRAL-ILCCO ,  OCAC , PANC4, and the  UK Biobank  study. The authors would also like to acknowledge the investigators of these consortia and studies for generating the data used for this analysis. The authors would like to acknowledge the following investigators of the OncoArray and GAME-ON1KG INTEGRAL-ILCCO analyses:  Maria Teresa Landi ,  Victoria Stevens ,  Ying Wang ,  Demetrios Albanes ,  Neil Caporaso ,  Paul Brennan ,  Christopher I. Amos ,  Sanjay Shete ,  Rayjean J. Hung ,  Heike Bickeböller ,  Angela Risch ,  Richard Houlston ,  Stephen Lam ,  Adonina Tardon ,  Chu Chen ,  Stig E. Bojesen ,  Mattias Johansson ,  H-Erich Wichmann ,  David Christiani ,  Gadi Rennert ,  Susanne Arnold ,  John K.
Field ,  Loic Le Marchand ,  Olle Melander ,  Hans Brunnström ,  Geoffrey Liu ,  Angeline Andrew ,  Lambertus A Kiemeney ,  Hongbing Shen ,  Shan Zienolddiny ,  Kjell Grankvist ,  Mikael Johansson ,  M. Dawn Teare ,  Yun-Chul Hong ,  Jian-Min Yuan ,  Philip Lazarus ,  Matthew B Schabath ,  Melinda C. Aldrich. GECCO acknowledgments: ASTERISK: We are very grateful to  Dr. Bruno Buecher  without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. CCFR: The Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and the financial support from the  U. S. National Cancer Institute , without which this important registry would not exist.
Funding JY is supported by a  Cancer Research   UK Population Research Postdoctoral Fellowship  ( C68933/A28534 ). SJL, KKT and RMM are supported by  Cancer Research UK  ( C18281/A29019 ) programme grant (the  Integrative Cancer Epidemiology Programme ) (https://www. cancerresearchuk. org/). KKT is also supported by  Cancer Research UK  ( PPRCPJT\100005 ) and  World Cancer Research Fund  ( IIG_FULL_2020_022 ) grants. RMM is a  National Institute for Health Research Senior Investigator  ( NIHR202411 ). RMM is also supported by the  NIHR Bristol Biomedical Research Centre  which is funded by the  NIHR  ( BRC-1215-20011 ) and is a partnership between  University Hospitals Bristol  and  Weston NHS Foundation Trust  and the  University of Bristol. Department of Health and Social Care  disclaimer: The views expressed are those of the author(s) and not necessarily those of the NHS, the  NIHR  or the  Department of Health and Social Care.
JWR, KB, and GDS are part of the  Medical Research Council Integrative Epidemiology Unit at the University of Bristol  which is supported by the  Medical Research Council  ( MC_UU_00011/1 ,  MC_UU_00011/3 ,  MC_UU_00011/6 , and  MC_UU_00011/4 ) and the  University of Bristol  (https://mrc. ukri. org/; https://www. bristol. ac. uk/). VK is funded by  Academy of Finland  Project  326291  and the  European Union 's  Horizon 2020  grant agreement no. 848158  (EarlyCause). NM is supported by the  French National Cancer Institute  ( INCa SHSESP20 , grant No. 2020-076 ). SSZ is supported by a  National Institute for Health Research Clinical Lectureship  and works in centres supported by  Versus Arthritis  (grant no. 21173 ,  21754  and  21755 ). INTEGRAL-ILCCO is supported by a  National Institutes of Health  grant ( U19 CA203654 ) (https://www.
( United Kingdom). EPICOLON: This work was supported by grants from  Fondo de Investigación Sanitaria/FEDER  ( PI08/0024 ,  PI08/1276 ,  PS09/02368 ,  P111/00219 ,  PI11/00681 ,  PI14/00173 ,  PI14/00230 ,  PI17/00509 ,  17/00878 ,  PI20/00113 ,  PI20/00226 ,  Acción Transversal de Cáncer) ,  Xunta de Galicia  ( PGIDIT07PXIB9101209PR ),  Ministerio de Economia y Competitividad  ( SAF07-64873 ,  SAF 2010-19273 ,  SAF2014-54453R ),  Fundación Científica de la Asociación Española contra el Cáncer  ( GCB13131592CAST ),  Beca Grupo de Trabajo "Oncología" AEG (Asociación Española de Gastroenterología) ,  Fundación Privada Olga Torres ,  FP7 CHIBCHA Consortium ,  Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR ,  Generalitat de Catalunya ,  2014SGR135 ,  2014SGR255 ,  2017SGR21 ,  2017SGR653 ),  Catalan Tumour Bank Network (Pla Director d'Oncologia, Generalitat de Catalunya) ,  PERIS  ( SLT002/16/00398 ,  Generalitat de Catalunya) ,  CERCA Programme (Generalitat de Catalunya)  and  COST Action   BM1206  and  CA17118.
CIBERehd  is funded by the  Instituto de Salud Carlos  III. ESTHER/VERDI. This work was supported by grants from the Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid. Harvard cohorts : HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, and R35 CA197735), NHS by the National Institutes of Health (P01 CA087969, UM1 CA186107, R01 CA137178, R01 CA151993, and R35 CA197735), and PHS by the National Institutes of Health (R01 CA042182). Hawaii Adenoma Study: NCI grants R01 CA072520. HCES-CRC: the Hwasun Cancer Epidemiology Study-Colon and Rectum Cancer (HCES-CRC; grants from Chonnam National University Hwasun Hospital, HCRI15011-1). IWHS: This study was supported by NIH grants CA107333 (R01 grant awarded to P.
J. Limburg) and HHSN261201000032C (N01 contract awarded to the University of Iowa). Japan Public Health Center-based Prospective Study was supported by the National Cancer Center Research and Development Fund (23-A-31[toku], 26-A-2, 29-A-4, 2020-J-4) (since 2011) and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan (from 1989 to 2010). Kentucky: This work was supported by the following grant support: Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8); NCI R01CA136726. LCCS: The Leeds Colorectal Cancer Study was funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167). MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria.
Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. BMLynch was supported by MCRF18005 from the Victorian Cancer Agency. MEC: National Institutes of Health (R37 CA054281, P01 CA033619, and R01 CA063464). MECC: This work was supported by the National Institutes of Health, U. S. Department of Health and Human Services (R01 CA081488, R01 CA197350, U19 CA148107, R01 CA242218, and a generous gift from Daniel and Maryann Fong. MSKCC: The work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the National Institutes of Health (grant numbers R01 CA189184, P30 CA076292), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation.
Funding was provided to Michael O. Woods by the Canadian Cancer Society Research Institute. NSHDS: The research was supported by Biobank Sweden through funding from the Swedish Research Council (VR 2017-00650, VR 2017-01737), the Swedish Cancer Society (CAN 2017/581), Region Västerbotten (VLL-841671, VLL-833291), Knut and Alice Wallenberg Foundation (VLL-765961), and the Lion's Cancer Research Foundation (several grants) and Insamlingsstiftelsen, both at Umeå University. OSUMC: OCCPI funding was provided by Pelotonia and HNPCC funding was provided by the NCI (CA016058 and CA067941). PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Funding was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438.
outcomes evaluated. Cancer Center Support Grant P30 CA015704. Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685. ASTERISK: a Hospital Clinical Research Program (PHRC-BRD09/C) from the University Hospital Center of Nantes (CHU de Nantes) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC). The ATBC Study is supported by the Intramural Research Program of the U. S. National Cancer Institute, National Institutes of Health, Department of Health and Human Services. CLUE II funding was from the National Cancer Institute (U01 CA086308, Early Detection Research Network; P30 CA006973), National Institute on Aging (U01 AG018033), and the American Institute for Cancer Research.
The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. Maryland Cancer Registry (MCR) Cancer data was provided by the Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health, with funding from the State of Maryland and the Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data is also supported by the Cooperative Agreement NU58DP006333, funded by the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services.
Background: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. Objective: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of realworld data. Design, setting, and participants: From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146).
PIONEER is a European network of excellence for big data in PCa that is part of the Innovative Medicine Initiative's (IMI's) ''Big Data for Better Outcomes'' program  [14]. The overall aim of PIONEER is to improve PCa care across Europe through the application of big data analytics. Through a broad stakeholder prioritization exercise including healthcare professionals, pharmaceutical companies, and PCa patients, the PIONEER consortium identified this PCa knowledge gap as a priority for all stakeholders. The aim of this study is to describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of realworld data.
Baseline characteristics Overall, 527 311 men with PCa (cohort 1) across eight datasets including records from >100 000,000 adult individuals were identified. Among these, 123 146 were managed with conservative treatment and included in cohort 3. Table  1  summarizes clinical characteristics of men in cohort 3. The median age at diagnosis ranged from 63 to 73 yr across the eight datasets evaluated. Comorbidity prevalence was heterogeneous among men included in cohort 3. The most common comorbidities were hypertension (35-73%), obe-sity (9. 2-54%), and type 2 diabetes (11-28%). Overall, hypertension was the most common comorbidity in all age groups (Supplementary Table  2 ). Moreover, the prevalence of all comorbidities increased with age, except anxiety and obesity, which instead had a negative trend.
PSA values were reported by four datasets (CUIMC, IQVIA OncoEMR, MAITT, and TMC), and the median PSA varied between 3. 9 and 7. 6 ng/ml across datasets. Of men, <12% had a positive family history for PCa. Information on disease characteristics were available only for those in the MAITT dataset, where the rates of cT2, grade group 1, and high-risk PCa, according to the EAU risk classification, were 9%, 7%, and 12%, respectively.
Outcomes Table  2  depicts the number of patients who experienced symptomatic progression, ED visits, and hospitalization, and received palliative and curative-intent treatments. Table  3  describes the number of patients who did not experience the event at different time points for all datasets. Figure  1  shows the free-to-outcome probability. The free-to-   curative and palliative treatment probabilities decreased during follow-up in all analyzed databases, with the highest rate of change during the first 2-4 yr (up to 38%) and 12-16 yr (up to 42%) for curative and palliative treatment, respectively. The decrease in the free-to-event probability was more consistent for the other outcomes. The rate of PCa-related symptomatic progression ranged between 2. 6% and 6. 2% during the 1st year after diagnosis, 1.
4% and 8. 6% during the 2nd year, and 2. 1% and 5. 4% thereafter (Table  4 ). The rate of palliative treatment ranged between 4. 1% and 12% during the 1st year of follow-up, 1. 9% and 3. 7% during the 2nd year, and 3. 1% and 14% thereafter. The most common events were hospitalization and ED visit, with large differences between the year of follow-up and database. The percentage of patients hospitalized varied between 12% and 25% and between 12% and 25% during the 1st and the 2nd years of follow-up, respectively. The probability of ED visit ranged between 10% and 14% in the 1st year of follow-up and between 8. 2% and 14% in the 2nd year of follow-up. The proportion of men who underwent curative treatment after the first 12 mo from diagnosis decreased during the follow-up in all datasets (from 1.
4-19% to 0. 3-4. 5%). Owing to the lack of information on disease characteristics in most of the available data sources, we had appropriate data to perform subgroup analyses only for patients stratified according to chronic obstructive pulmonary dis-ease (COPD), type 2 diabetes mellitus (T2DM), obesity, and age. Using Kaplan-Meier analyses, PCa patients with COPD (Supplementary Fig. 1B ) and T2DM (Supplementary Fig. 1C ) had the highest probability of ED visit and hospitalization, whereas obese men had the highest probability of curative treatment and symptomatic progression in one dataset (CUIMC) and of being hospitalized in the MarketScan, Optum, and PharMetricsPlus datasets (Supplementary Fig. 1A ). On the contrary, none of these comorbidities were associated with overall survival (Supplementary Fig.
In this context, Lu-Yao et al. [8]  showed how the risk of treatment varied according to patient age, with lower 15-yr risk of curative treatment for patients 75 yr old than for patients aged 65-74 yr. On the contrary, they reported the highest 15-yr risk of ADT in the oldest population. Finally, we evaluated how long-term outcomes of PCa patients on conservative management vary, according to prespecified individual patients' characteristics. We found that the oldest patients had the highest risks of ED visit, hospitalization, and symptomatic progression, and the worst overall survival. Likewise, our data show that patients with COPD and T2DM had the highest risks of hospitalization and ED visit. It is likely that these results may be explained by the well-known association of these pathologies with the highest risk of complications  [21, 22].