Integrated nomogram based on five stage-related genes and TNM stage to predict 1-year recurrence in hepatocellular carcinoma.
1-year recurrence
Hepatocellular carcinoma
Nomogram
Risk score model
TNM stage
Journal
Cancer cell international
ISSN: 1475-2867
Titre abrégé: Cancer Cell Int
Pays: England
ID NLM: 101139795
Informations de publication
Date de publication:
2020
2020
Historique:
received:
03
03
2020
accepted:
16
04
2020
entrez:
6
5
2020
pubmed:
6
5
2020
medline:
6
5
2020
Statut:
epublish
Résumé
The primary tumor, regional lymph nodes and distant metastasis (TNM) stage is an independent risk factor for 1-year hepatocellular carcinoma (HCC) recurrence but has insufficient predictive efficiency. We attempt to develop and validate a nomogram to predict 1-year recurrence in HCC and improve the predictive efficiency of the TNM stage. A total of 541 HCC patients were enrolled in the study. The risk score (RS) model was established with the logistic least absolute shrinkage and selector operation algorithm. The predictive nomogram was further validated in the internal testing cohort and external validation cohort. The area under the receiver operating characteristic curves (AUCs), decision curves and clinical impact curves were used to evaluate the predictive accuracy and clinical value of the nomogram. In the training cohort, we identified a RS model consisting of five stage-related genes (NUP62, EHMT2, RANBP1, MSH6 and FHL2) for recurrence at 1 year. The 1-year disease-free survival of patients was worse in the high-risk group than in the low-risk group ( A RS model consisting of five stage-related genes was successfully identified for predicting 1-year HCC recurrence. Then, a novel nomogram based on the RS model and TNM stage to predict 1-year HCC recurrence was also developed and validated.
Sections du résumé
BACKGROUND
BACKGROUND
The primary tumor, regional lymph nodes and distant metastasis (TNM) stage is an independent risk factor for 1-year hepatocellular carcinoma (HCC) recurrence but has insufficient predictive efficiency. We attempt to develop and validate a nomogram to predict 1-year recurrence in HCC and improve the predictive efficiency of the TNM stage.
METHODS
METHODS
A total of 541 HCC patients were enrolled in the study. The risk score (RS) model was established with the logistic least absolute shrinkage and selector operation algorithm. The predictive nomogram was further validated in the internal testing cohort and external validation cohort. The area under the receiver operating characteristic curves (AUCs), decision curves and clinical impact curves were used to evaluate the predictive accuracy and clinical value of the nomogram.
RESULTS
RESULTS
In the training cohort, we identified a RS model consisting of five stage-related genes (NUP62, EHMT2, RANBP1, MSH6 and FHL2) for recurrence at 1 year. The 1-year disease-free survival of patients was worse in the high-risk group than in the low-risk group (
CONCLUSIONS
CONCLUSIONS
A RS model consisting of five stage-related genes was successfully identified for predicting 1-year HCC recurrence. Then, a novel nomogram based on the RS model and TNM stage to predict 1-year HCC recurrence was also developed and validated.
Identifiants
pubmed: 32368186
doi: 10.1186/s12935-020-01216-9
pii: 1216
pmc: PMC7189530
doi:
Types de publication
Journal Article
Langues
eng
Pagination
140Informations de copyright
© The Author(s) 2020.
Déclaration de conflit d'intérêts
Competing interestsThe authors declare that they have no competing interests.
Références
Lancet. 2018 Mar 31;391(10127):1301-1314
pubmed: 29307467
J Transl Med. 2019 Jun 10;17(1):193
pubmed: 31182111
J Am Coll Surg. 2006 Feb;202(2):275-83
pubmed: 16427553
EMBO Rep. 2018 Jan;19(1):73-88
pubmed: 29217659
Cancer Manag Res. 2018 Sep 20;10:3707-3715
pubmed: 30288102
Gut. 2014 May;63(5):844-55
pubmed: 24531850
Surg Oncol. 2016 Mar;25(1):24-9
pubmed: 26979637
Ann Surg Oncol. 2009 Apr;16(4):792-4
pubmed: 19190964
Ann Surg. 2015 May;261(5):947-55
pubmed: 25010665
J Stat Softw. 2010;33(1):1-22
pubmed: 20808728
World J Gastroenterol. 2014 May 28;20(20):5935-50
pubmed: 24876717
BMC Med Inform Decis Mak. 2008 Nov 26;8:53
pubmed: 19036144
BMJ. 2015 Jan 07;350:g7594
pubmed: 25569120
Cancer Imaging. 2019 May 14;19(1):22
pubmed: 31088553
World J Gastroenterol. 2015 Jan 28;21(4):1207-15
pubmed: 25632194
J Surg Oncol. 2009 Nov 1;100(6):488-93
pubmed: 19653238
Cell Death Dis. 2018 Aug 28;9(9):856
pubmed: 30154409
Med Decis Making. 2006 Nov-Dec;26(6):565-74
pubmed: 17099194
Sci Rep. 2017 Apr 07;7:46238
pubmed: 28387323
J Surg Oncol. 2019 Jun;119(8):1161-1169
pubmed: 30919992
Neural Netw. 2010 Mar;23(2):257-64
pubmed: 19604671
Hepatol Res. 2018 Mar;48(4):313-321
pubmed: 28984009
Mol Cell Biol. 2017 May 2;37(10):
pubmed: 28223370
HPB (Oxford). 2015 May;17(5):422-7
pubmed: 25421805
J Biopharm Stat. 2011 Nov;21(6):1206-31
pubmed: 22023687
J Thorac Oncol. 2011 Apr;6(4):757-61
pubmed: 21325975
Hepatol Int. 2019 Sep;13(5):618-630
pubmed: 31321712
Hum Pathol. 2018 Feb;72:117-126
pubmed: 29133140
Oncogene. 2013 Sep 19;32(38):4572-8
pubmed: 23108393
J R Stat Soc Series B Stat Methodol. 2012 Mar;74(2):245-266
pubmed: 25506256
Abdom Radiol (NY). 2017 Jun;42(6):1695-1704
pubmed: 28180924
Hepatobiliary Surg Nutr. 2018 Oct;7(5):320-330
pubmed: 30498708
CA Cancer J Clin. 2018 Nov;68(6):394-424
pubmed: 30207593
Ophthalmology. 2015 Jul;122(7):1512-6
pubmed: 25972255
J Clin Oncol. 2016 Jul 20;34(21):2534-40
pubmed: 27247223
Cancer. 2000 Aug 1;89(3):500-7
pubmed: 10931448