000277451 001__ 277451 000277451 005__ 20240229155017.0 000277451 0247_ $$2doi$$a10.1007/s12672-023-00742-y 000277451 0247_ $$2pmid$$apmid:37428291 000277451 0247_ $$2ISSN$$a1868-8497 000277451 0247_ $$2ISSN$$a1868-8500 000277451 0247_ $$2ISSN$$a2730-6011 000277451 0247_ $$2altmetric$$aaltmetric:151205487 000277451 037__ $$aDKFZ-2023-01381 000277451 041__ $$aEnglish 000277451 082__ $$a610 000277451 1001_ $$aTao, Zhimin$$b0 000277451 245__ $$aConstruction of a novel nomogram based on competing endogenous RNAs and tumor-infiltrating immune cells for prognosis prediction in elderly patients with colorectal cancer. 000277451 260__ $$a[New York]$$bSpringer$$c2023 000277451 3367_ $$2DRIVER$$aarticle 000277451 3367_ $$2DataCite$$aOutput Types/Journal article 000277451 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1689086011_25397 000277451 3367_ $$2BibTeX$$aARTICLE 000277451 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000277451 3367_ $$00$$2EndNote$$aJournal Article 000277451 520__ $$aCompetitive endogenous RNAs (ceRNAs) and tumor-infiltrating immune cells play essential roles in colorectal cancer (CRC) tumorigenesis. However, their prognostic role in elderly patients with CRC is unclear. Gene expression profiles and clinical information for elderly patients with CRC were downloaded from The Cancer Genome Atlas. Univariate, LASSO, and multivariate Cox regression analyses were utilized for screening key ceRNAs and prevent overfitting. A total of 265 elderly patients with CRC were included. We constructed a novel ceRNA network consisting of 17 lncRNAs, 35 miRNAs, and 5 mRNAs. We established three prognosis predictive nomograms based on four key ceRNAs (ceRNA nomogram), five key immune cells (immune cell nomogram), and their combination (ceRNA-immune cell nomogram). Among them, the ceRNA-immune cell nomogram had the best accuracy. Furthermore, the areas under the curve of the ceRNA-immune cell nomogram were also significantly greater than the TNM stage at 1 (0.818 vs. 0.693), 3 (0.865 vs. 0.674), and 5 (0.832 vs. 0.627) years. Co-expression analysis revealed that CBX6 was positively correlated with activated dendritic cells (R = 0.45, p < 0.01), whereas negatively correlated with activated mast cells (R =- 0.43, p < 0.01). In conclusion, our study constructed three nomograms to predict prognosis in elderly patients with CRC, among which the ceRNA-immune cell nomogram had the best prediction accuracy. We inferred that the mechanism underlying the regulation of activated dendritic cells and mast cells by CBX6 might play a crucial role in tumor development and prognosis of elderly patients with CRC. 000277451 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0 000277451 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de 000277451 650_7 $$2Other$$aColorectal cancer 000277451 650_7 $$2Other$$aCompeting endogenous RNAs 000277451 650_7 $$2Other$$aElderly patient 000277451 650_7 $$2Other$$aImmune cell 000277451 650_7 $$2Other$$aNomogram 000277451 650_7 $$2Other$$aPrognosis 000277451 7001_ $$aLi, Bo$$b1 000277451 7001_ $$aKang, Chunyan$$b2 000277451 7001_ $$aWang, Wei$$b3 000277451 7001_ $$0P:(DE-He78)a92f91afa83da73641a4f3abec1d3c6d$$aLi, Xianzhe$$b4$$udkfz 000277451 7001_ $$aDu, Yaowu$$b5 000277451 773__ $$0PERI:(DE-600)3059869-2$$a10.1007/s12672-023-00742-y$$gVol. 14, no. 1, p. 125$$n1$$p125$$tDiscover oncology$$v14$$x1868-8497$$y2023 000277451 909CO $$ooai:inrepo02.dkfz.de:277451$$pVDB 000277451 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)a92f91afa83da73641a4f3abec1d3c6d$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ 000277451 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0 000277451 9141_ $$y2023 000277451 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2022-11-29 000277451 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-29 000277451 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-29 000277451 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2021-07-21T15:23:25Z 000277451 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-29 000277451 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-29 000277451 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bDISCOV ONCOL : 2022$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-08-25 000277451 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-04-12T15:13:12Z 000277451 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-04-12T15:13:12Z 000277451 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2023-04-12T15:13:12Z 000277451 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-08-25 000277451 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0 000277451 980__ $$ajournal 000277451 980__ $$aVDB 000277451 980__ $$aI:(DE-He78)C070-20160331 000277451 980__ $$aUNRESTRICTED