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Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multiomics Analysis.

Overview of attention for article published in Cancer Immunology Research, May 2022
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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17 X users

Citations

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23 Dimensions

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27 Mendeley
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Title
Molecular Subgroups of Intrahepatic Cholangiocarcinoma Discovered by Single-Cell RNA Sequencing-Assisted Multiomics Analysis.
Published in
Cancer Immunology Research, May 2022
DOI 10.1158/2326-6066.cir-21-1101
Pubmed ID
Authors

Xuanwen Bao, Qiong Li, Jinzhang Chen, Diyu Chen, Chanqi Ye, Xiaomeng Dai, Yanfang Wang, Xin Li, Xiaoxiang Rong, Fei Cheng, Ming Jiang, Zheng Zhu, Yongfeng Ding, Rui Sun, Chuan Liu, Lingling Huang, Yuzhi Jin, Bin Li, Juan Lu, Wei Wu, Yixuan Guo, Wenguang Fu, Sarah Raye Langley, Vincent Tano, Weijia Fang, Tiannan Guo, Jianpeng Sheng, Peng Zhao, Jian Ruan

Abstract

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but highly aggressive tumor type that responds poorly to chemotherapy and immunotherapy. Comprehensive molecular characterization of ICC is essential for the development of novel therapeutics. Here, we constructed two independent cohorts from two clinic centers. A comprehensive multi-omics analysis of ICC via proteomic, whole-exome sequencing (WES), and single-cell RNA sequencing (scRNA-seq) was performed. Novel ICC tumor subtypes were derived in the training cohort (n=110) using proteomic signatures and their associated activated pathways, which was further validated in a validation cohort (n=41). Three molecular subtypes, chromatin remodeling, metabolism, and chronic inflammation, with distinct prognoses in ICC were identified. The chronic inflammation subtype associated with a poor prognosis. Our random forest algorithm revealed that mutation of lysine methyltransferase 2D (KMT2D) frequently occurred in the metabolism subtype and associated with lower inflammatory activity. scRNA-seq further identified an APOE+C1QB+ macrophage subtype, which showed the capacity to reshape the chronic inflammation subtype and contribute to a poor prognosis in ICC. Altogether, with single-cell transcriptome-assisted multi-omics analysis, we identified novel molecular subtypes of ICC and validated APOE+C1QB+ tumor-associated macrophages (TAMs) as potential immunotherapy targets against ICC.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 11%
Researcher 3 11%
Student > Doctoral Student 2 7%
Other 2 7%
Student > Master 2 7%
Other 5 19%
Unknown 10 37%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 30%
Medicine and Dentistry 4 15%
Agricultural and Biological Sciences 4 15%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Unspecified 1 4%
Other 0 0%
Unknown 9 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2022.
All research outputs
#3,882,957
of 24,351,425 outputs
Outputs from Cancer Immunology Research
#432
of 1,467 outputs
Outputs of similar age
#78,971
of 429,867 outputs
Outputs of similar age from Cancer Immunology Research
#14
of 36 outputs
Altmetric has tracked 24,351,425 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 429,867 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.