https://researchdata.se/sv/catalogue Search results sv BackgroundEndoplasmic reticulum (ER) stress and its adaptive signaling through the unfolded protein response (UPR) are increasingly implicated in driving tumor progression and reshaping the tumor microenvironment in hepatocellular carcinoma (HCC). Among the UPR sensors, PERK (EIF2AK3) is thought to play a central role in enabling tumor cell survival under stress and promoting stromal activation, fibrosis, and inflammatory signaling. Additionally, ER stress–regulated secreted factors such as GP73 (GOLM1) and extracellular GRP78 may mediate communication between malignant cells and surrounding stromal populations, contributing to a pro-tumorigenic microenvironment.ObjectiveThe overall objective of both projects is to investigate how modulation of ER stress and UPR signaling influences HCC development, tumor progression, and tumor–stroma interactions. Specifically, the first project aims to determine whether pharmacological inhibition of PERK using a selective small-molecule inhibitor (AMG-PERK 44) can suppress ER stress-driven tumor growth and stromal activation, and to define the molecular mechanisms underlying ER stress-dependent communication between malignant cells and hepatic stellate cells, with a focus on GP73- and GRP78-mediated signaling. The second project seeks to evaluate whether alleviation of ER stress using the chemical chaperone tauroursodeoxycholic acid (TUDCA) can reduce early tumorigenesis, fibrosis, inflammation, and malignant phenotypes. Together, these studies aim to characterize ER stress-regulated transcriptional and cellular programs and to assess the therapeutic and preventive potential of targeting ER stress pathways in HCC. Approach - Model Systems: Project 1 (AMG-PERK inhibitor): Chemically induced mouse model of HCC, in vitro HCC cell lines, and patient-derived organoids were used to assess the impact of PERK inhibition across multiple biological contexts. Project 2 (TUDCA inhibitor): Chemically induced mouse model of HCC and in vitro HCC cell lines were used to evaluate the effects of ER stress inhibition on tumor development, fibrosis, inflammation, and tumor progression. - Pharmacologic Intervention: Project 1: Use of the selective PERK inhibitor AMG-PERK 44 to assess its effects on PERK signaling, tumor behavior, and tumor–stroma interactions. Project 2: Treatment with tauroursodeoxycholic acid (TUDCA), a liver-derived bile acid conjugate known to reduce ER stress signaling, to alleviate ER stress and limit early hepatocarcinogenesis. - Mechanistic Studies: Project 1: Investigate the role of GP73 as a mediator of ER stress–dependent tumor–stroma communication, and examine its interaction with extracellular GRP78 and downstream PERK–CHOP signaling in hepatic stellate cells. Project 2: Assess how TUDCA modulates UPR sensors and downstream fibrogenic, proinflammatory, and EMT pathways in liver tissue and HCC cell lines. Molecular and Cellular Profiling: Project 1: Single-cell RNA sequencing, bulk transcriptomics, and immunohistochemistry to identify cell populations expressing PERK, GP73, and GRP78 (BiP), and to characterize ER stress–associated transcriptional programs, including proliferation, EMT, and inflammation. Project 2: Gene expression analyses and immunohistochemistry to measure UPR sensor expression, EMT markers, fibrosis, and inflammatory markers in liver tissue and HCC cell lines following exposure to TUDCA treatment. Thu, 05 Mar 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30846731 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30846731 Uppsala universitet Jaafar Khaled Maria Kopsida Tania Payo Serafin Sofi Sennefelt Nyman Fredrik Rorsman Charlotte Ebeling Barbier Hans Lennernäs Markus Sjöblom Femke Heindryckx Clear cell renal cell carcinoma (ccRCC) is an aggressive kidney cancer subtype frequently associated with poor prognosis. Most ccRCC cases are asymptomatic in early stages and symptomatic mostly in advanced stages. Furthermore, the heterogeneity of ccRCC presents a challenge to design new treatments. In this study, using proximity extension assay (PEA), we analyzed blood samples from 134 patients with ccRCC and from 111 age- and gender-matched healthy donors. We identified a panel of seven proteins (ANXA1, ESM1, FGFBP1, MDK, METAP2, SDC1, and TFPI2) that are associated with clinicopathological parameters and patient survival. These biomarkers can differentiate patients with ccRCC from the control individuals with high diagnostic sensitivity and specificity. Moreover, by studying protein expression in solid tumors from the same ccRCC patients, we revealed associations between the panel biomarkers and proteins in the TGF-β and VHL-HIF signaling pathways. We found that most tumor promoting biomarkers were positively associated with TGF-β signaling and HIF-2α, and negatively associated with pVHL and HIF-1α. We also found that most tumor suppressing biomarkers were positively associated with pVHL and HIF-1α and negatively associated with TGF-β signaling and HIF-2α. For ccRCC patients, the blood protein biomarkers that were connected to poor prognosis and TGF-β/HIF-2α signaling, as identified in this study, are potentially important assets in personalized medicine. We used an Olink panel to measure protein levels in clear cell renal cell carcinoma (ccRCC) patients (N=134) and healthy controls (N=111). 92 oncology-related protein levels are measured across all samples (Supplementary Data 1), and the dataset is corrected for patient age (Supplementary Data 2). 80 proteins are significantly altered in ccRCC patients compared to controls (Supplementary Data 3). Using the top 50 most significantly altered proteins, we trained a random forest (RF) model, with cross-validation (Supplementary Data 4 and 5). The top seven significantly altered proteins are sufficient to perfectly (AUC=1) classify patients and healthy controls (Supplementary Data 6). We further trained an elastic-net penalized logistic regression (ENLR) model using the top seven proteins, which also resulted in a perfect classifier. Use of random sets of seven proteins and their combinations are not as significant (Supplementary Data 7-10). We explored the correlations between transforming growth factor-β (TGF-β), VHL, and hypoxia signaling pathway protein expressions (TGFBR1-Full length receptor (FL), TGFBR1-intracellular domain (ICD), HIF-1A, HIF-2A, pVHL, pSMAD2/3) in solid tumors and the plasma protein levels from the same cohort (Supplementary Data 11-12). The names and accession numbers (UniProt) for the Olink proteins are listed in Supplementary Data 13. Levels of the TGFB and VHL pathway proteins in solid tumors are given in Supplementary Data 14 and the antibodies used in immunoblotting (IB) are listed in Supplementary Data 15. Lists of protein names measured in solid tumor samples vs protein names measured in plasma are in Supplementary Data 16. Mon, 18 Aug 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28711088 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28711088 Umeå universitet Cemal Erdem Pramod Mallikarjuna Ruben Beorlegui Anders Larsson Börje Ljungberg Masood Kamali-Moghaddam Maréne Landström Data Set Description The dataset consists of whole genome (WGS) and whole exome sequencing (WES) data from 82 biobanked central nervous system (CNS) tumors and patient-matched peripheral blood-derived DNA from 79 affected pediatric patients. The data was generated as part of a study conducted by Díaz de Ståhl, T. et. al., mansucript to be sumitted. The sequence data presented in this publication contains sensitive information and cannot be openly shared. However, the authors intend to share the data for use in research projects after a medicolegal review and controlled access through FEGA Sweden, which is a national node of the Federated European Genome-phenome Archive (FEGA). FEGA Sweden will be hosted by the National Bioinformatics Infrastructure Sweden (NBIS) at SciLifeLab, and the datasets will be findable through the European Genome-phenome Archive web portal. The dataset comprises WGS from 137 samples (70 tumors and 67 blood samples) and WES from 24 samples (12 tumors and 12 blood samples) from several diagnoses, including medulloblastomas, ependymomas, glioblastomas/primitive neuroectodermal tumors/embryonal tumors, pilocytic astrocytomas, astrocytoma/glioma, atypical teratoid rhabdoid tumors, oligodendrogliomas, meningiomas, craniopharyngiomas, pineoblastoma, ganglioglioma, pituitary adenoma, choroid plexus tumor, and schwannoma. The tumor DNA was extracted from fresh frozen tissue, and patient-matched normal DNA was extracted from peripheral blood cells.  The WGS and WES libraries and associated next-generation sequencing (NGS) were conducted at the Genomic Production Center, SciLifeLab, Stockholm, Sweden. The WGS libraries were prepared using the TruSeq PCR-free (126 samples) or TruSeq Nano, NeoPrep (11 samples) DNA sample preparation kits, followed by paired-end 150 bp read length sequencing on a HiSeq X (Illumina Inc.) instrument. The WES libraries were prepared using the Agilent SureSelect Human All Exon V5 (22 samples) or the Twist Human Core Exome (2 samples) DNA sample preparation kits, followed by paired-end 100 bp read length sequencing on a HiSeq 2500 (Illumina Inc.) instrument. Terms for access Access to the pediatric cancer dataset will only be granted to authorized research projects who meet specific ethical and legal criteria and have a clear need for the data. These criteria include adherence to ethical guidelines, such as an ethical permit and obedience to the General Data Protection Regulation (GDPR) It also includes the submission of a research proposal outlining their objectives, methods, and commitment to upholding the highest ethical and legal standards. The protected pediatric dataset is exclusively available for research projects that require access to pediatric data sets and cannot be achieved using adult data. These projects aim to deepen our understanding of the causes and mechanisms of pediatric cancer, identify new therapeutic targets, and develop more effective treatments for affected children. Our goal is to facilitate meaningful research that contributes to better outcomes for pediatric cancer patients in the future. Thu, 16 Mar 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22249933 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22249933 Karolinska Institutet Teresita Díaz de Ståhl Alia Shamikh Markus Mayrhofer Szilvester Juhos Elisa Basmaci Gabriela Prochazka Maxime Garcia Praveen Raj Somarajan Katarzyna Zielinska-Chomej Karim Katkhada Jenny von Salomé Christopher Illies Ingrid Ora Peter Siesjö Per-Erik Sandström Jakob Stenman Magnus Sabel Bengt Gustavsson Per Kogner Susan Pfeifer Gustaf Ljungman Johanna Sandgren Monica Nistér