A pathology atlas of the human cancer transcriptome

By Mathias Uhlen, Cheng Zhang, Sunjae Lee, E. Sjöstedt, Linn Fagerberg, G. Bidkhori, Rui Benfeitas, M. Arif, Zhengtao Liu, F. Edfors and 17 more. Science, 2017

3,007 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e244706a168828e3eb8db361a10d210cf6cc018c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Yuan Zhou
2. Juan Wang
3. Qinghua Cui
4. Zhan Tong
5. Xing Chen

In Molecular Biology

1. Young M. Park
2. Yuan Zhou
3. Juan Wang
4. Zemin Zhang
5. Xiaochen Bo
6. Michael Hucka
7. Qinghua Cui
8. Nicola Buso

Citations in the corpus

• CCRK is a novel signalling hub exploitable in cancer immunotherapy (2018, positive): “In terms of myeloid cells, there was direct evidence that inhibition of CDK4 in mouse bone marrow-derived dendritic cells (BMDCs) decreased c-Jun/AP-1 signal transduction and, IL-6 and IL-12 production, which impaired the antigen presentation ability of BMDCs (Vanden Bush & Bishop, 2011).”
• Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types (2019, methodology): “Prognostic genes were identified using the Human Pathology Atlas [29].”
• TransmiR v2.0: an updated transcription factor-microRNA regulation database (2018, methodology): “The TFs were annotated with the following four items: Entrez gene ID, Ensembl gene ID, gene associated diseases (from DisGeNET database (22)), and cancer prognostic association data (from Human Protein Atlas database (23)).”
• Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts (2020, background): “The new category sources are Tempus xT(8), a panel of actionable cancer therapy target genes, a list of the top priority genes of the Illuminating the Druggable Genome (IDG)(9) Initiative, the Human Protein Atlas(10), Oncomine(11), a clinical cancer biomarker assay, and Pharos(12), which hosts data ”
• Changes in Protein Structural Motifs upon Post-Translational Modification in Kidney Cancer (2021, background): “27 for dead patients) is favorable in patients with renal carcinoma [71].”
• GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis (2019, background): “P rognostic genes in different cancer types have been identified recently (21).”
• BioModels—15 years of sharing computational models in life science (2019, methodology): “Path2Models, a large collection of over 140 000 models auto-generated from pathway resources, including KEGG, BioCarta, MetaCyc and SABIO-RK, was deposited into BioModels (14), as well as another collection of 6750 patient-specific genome-scale metabolic models (15) representing tumour growth in 17 ”
• The human protein atlas: A spatial map of the human proteome (2018, methodology): “In version 17 of the HPA, the Cancer Atlas changed its name to Pathology Atlas together with a major re-design and release of new data, showing the association of all human genes with clinical outcome.(6) In the Pathology Atlas, a systems level approach was used to analyze the human genome with resp”