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Liquid biomarkers associate with TGF-beta Type I receptor and hypoxia in kidney cancer
No full textClear 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
Atmospheric N2O product from Svartberget (150.0 m)
No full textAtmospheric N2O concentrations, both ICOS and non-/pre-ICOS data, delivered by the Atmospheric Thematic Center
Larmanou, E., Marklund, P. (2025). Atmospheric N2O product from Svartberget (150.0 m), 2025-03-27–2025-03-31, European ObsPack, https://hdl.handle.net/11676/CZuR-DZy5e36asglPjHt404
Atmospheric CO2 product from Norunda (32.0 m)
No full textAtmospheric CO2 concentrations, both ICOS and non-/pre-ICOS data, delivered by the Atmospheric Thematic Center
Lehner, I., Molder, M. (2025). Atmospheric CO2 product from Norunda (32.0 m), 2017-01-31–2025-03-31, European ObsPack, https://hdl.handle.net/11676/edCK-tgHQgBO0JngdyNLPEN
Phenocam - Region Of Interest (ROI) Time Series from Abisko Scientific Research Station, Abisko Observatory
No full textDaily aggregated time series containing solar-weighted mean vegetation indices and RGB channel values per Region of Interest, temporal data, solar metrics, and processing statistics for each daily composite.
Abisko Scientific Research Station (2025). Phenocam - Region Of Interest (ROI) Time Series from Abisko Scientific Research Station, Abisko Observatory, 2022-06-30–2025-05-26 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/mb01Qsj7sRkJZiQsFzaUJgV
Chemical variables - stream from Tarfalajokk, Water sample B
No full textManual grab samples from the stream for chemical analysis are taken on a biweekly basis during ice-free conditions, and on a monthly basis in the presence of stream ice.
Tarfala Research Station (2025). Chemical variables - stream from Tarfalajokk, Water sample B, 2018-06-22–2018-09-15 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/k064iK3IOW6BikSKHoFM8di
Chemical variables - stream from Tarfalajokk, Water sample C
No full textManual grab samples from the stream for chemical analysis are taken on a biweekly basis during ice-free conditions, and on a monthly basis in the presence of stream ice.
Tarfala Research Station (2025). Chemical variables - stream from Tarfalajokk, Water sample C, 2018-06-22–2018-09-15 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/L1dd2_Dcawspu-EhXtcEYHs
Meteorological data from Latnjajaure, Field Station AWS
No full textAutomatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled.
Abisko Scientific Research Station (2025). Meteorological data from Latnjajaure, Field Station AWS, 2021 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/s_ExU2062XdDJMKGmAS1KLG
Meteorological data from Almbergasjön, limnic profile
No full textAutomatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled.
Abisko Scientific Research Station (2025). Meteorological data from Almbergasjön, limnic profile, 2018-06-28–2018-09-26 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/TIRzt7Ss3NcPyghuoWiX0RD
RNA-sequencing data from: The AML cellular state space unveils NPM1 immune evasion subtypes with distinct clinical outcomes, and: The complement receptor C3AR constitutes a novel therapeutic target in NPM1-mutated AML
No full textThis dataset contains bulk RNA-sequencing (RNA-seq) gene expression data from from 120 AML-samples from the subtypes NPM1 (n=33), AML-MR (n=30), TP53 (n=18), PML::RARA (n=8), CBFB::MYH11 (n=8), AML without class defining mutations (n=8), RUNX1::RUNX1T1 (n=3), KMT2A fusion genes (n=3), AML meeting the criteria for two subtypes (n=2), DEK-NUP214 (n=2), GATA2::MECOM (n=1), and bialleleic CEBPA mutation (n=1). The single cell libraries were constructed from bone marrow (n=102) or peripheral blood (n=18) using the TruSeq RNA Library Prep Kit v2 (Illumina) and sequenced on a NextSeq 500. Reads were aligned against human reference genome hg19 and read counts were determined using RSEM v1.2.30 (https://github.com/deweylab/RSEM) with gencode v19 as gene reference. Data is available as fpkm-values as determined by RSEM. Raw sequencing reads (fastq) are available at the European Genome-Phenome Archive (EGA) under accession ID EGAD50000001576: https://ega-archive.org/datasets/EGAD50000001576
Meteorological data from Storglaciären
No full textAutomatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled.
Tarfala Research Station (2025). Meteorological data from Storglaciären, 2014-04-09–2014-09-13 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/jj0ige2TRDfdGmoDOM_eRRb