Home > Publications database > A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer. > print

001     287616
005     20250814105224.0
024 7 _ |a 10.1016/j.xcrm.2024.101399
|2 doi
024 7 _ |a pmid:38307032
|2 pmid
024 7 _ |a altmetric:159080180
|2 altmetric
037 _ _ |a DKFZ-2024-00270
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Bao, Xuanwen
|b 0
245 _ _ |a A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.
260 _ _ |a Maryland Heights, MO
|c 2024
|b Elsevier
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1712310317_9969
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2024 Feb 20;5(2):101399
520 _ _ |a Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.
536 _ _ |a 314 - Immunologie und Krebs (POF4-314)
|0 G:(DE-HGF)POF4-314
|c POF4-314
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a FOLR2(+) macrophages
|2 Other
650 _ 7 |a artificial intelligence
|2 Other
650 _ 7 |a colorectal cancer
|2 Other
650 _ 7 |a immuno module
|2 Other
650 _ 7 |a tumor microenvironment
|2 Other
700 1 _ |a Li, Qiong
|b 1
700 1 _ |a Chen, Dong
|b 2
700 1 _ |a Dai, Xiaomeng
|b 3
700 1 _ |a Liu, Chuan
|b 4
700 1 _ |a Tian, Weihong
|b 5
700 1 _ |a Zhang, Hangyu
|b 6
700 1 _ |a Jin, Yuzhi
|b 7
700 1 _ |a Wang, Yin
|b 8
700 1 _ |a Cheng, Jinlin
|b 9
700 1 _ |a Lai, Chunyu
|b 10
700 1 _ |a Ye, Chanqi
|b 11
700 1 _ |a Xin, Shan
|b 12
700 1 _ |a Li, Xin
|0 P:(DE-He78)128e4949aa4cb04fe109c52df0c67732
|b 13
|u dkfz
700 1 _ |a Su, Ge
|b 14
700 1 _ |a Ding, Yongfeng
|b 15
700 1 _ |a Xiong, Yangyang
|b 16
700 1 _ |a Xie, Jindong
|b 17
700 1 _ |a Tano, Vincent
|b 18
700 1 _ |a Wang, Yanfang
|b 19
700 1 _ |a Fu, Wenguang
|b 20
700 1 _ |a Deng, Shuiguang
|b 21
700 1 _ |a Fang, Weijia
|b 22
700 1 _ |a Sheng, Jianpeng
|b 23
700 1 _ |a Ruan, Jian
|b 24
700 1 _ |a Zhao, Peng
|b 25
773 _ _ |a 10.1016/j.xcrm.2024.101399
|g p. 101399 -
|0 PERI:(DE-600)3019420-9
|n 2
|p 101399
|t Cell reports / Medicine
|v 5
|y 2024
|x 2666-3791
856 4 _ |u https://inrepo02.dkfz.de/record/287616/files/1-s2.0-S2666379124000089-main.pdf
856 4 _ |u https://inrepo02.dkfz.de/record/287616/files/1-s2.0-S2666379124000089-main.pdf?subformat=pdfa
|x pdfa
909 C O |p VDB
|o oai:inrepo02.dkfz.de:287616
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 13
|6 P:(DE-He78)128e4949aa4cb04fe109c52df0c67732
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-314
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Immunologie und Krebs
|x 0
914 1 _ |y 2024
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b CELL REP MED : 2022
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2023-04-12T14:51:29Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2023-04-12T14:51:29Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2023-04-12T14:51:29Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-08-22
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-08-22
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b CELL REP MED : 2022
|d 2023-08-22
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-08-22
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-08-22
920 1 _ |0 I:(DE-He78)F180-20160331
|k F180
|l Chronische Entzündung und Krebs
|x 0
920 1 _ |0 I:(DE-He78)D440-20160331
|k D440
|l Chronische Entzündung und Krebs
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)F180-20160331
980 _ _ |a I:(DE-He78)D440-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21