The value of haematological parameters and serum tumour markers for predicting KRAS mutations in 784 Chinese colorectal cancer patients: a retrospective analysis

Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations. The clinical data of patients wi...

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Bibliographic Details
Published in:BMC cancer 2020-11, Vol.20 (1), p.1099-1099, Article 1099
Main Authors: Cao, Yinghao, Gu, Junnan, Yan, Lizhao, Deng, Shenghe, Mao, Fuwei, Cai, Wentai, Li, Hang, Liu, Xinghua, Wang, Jiliang, Wu, Ke, Cai, Kailin
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Asian Continental Ancestry Group - genetics
Biomarkers
Biomarkers, Tumor - blood
Biomarkers, Tumor - genetics
Biopsy
Cancer
Cancer therapies
Care and treatment
Colorectal cancer
Colorectal carcinoma
Colorectal Neoplasms - blood
Colorectal Neoplasms - genetics
Colorectal Neoplasms - pathology
Development and progression
Female
Females
Follow-Up Studies
Gene expression
Gene mutation
Genetic aspects
Haematological parameters
Health aspects
Hematocrit
Hematology
Humans
K-Ras protein
KRAS mutation
Male
Medical prognosis
Metastasis
Middle Aged
Monoclonal antibodies
Mortality
Mutation
Patients
Predictive Value of Tests
Proto-Oncogene Proteins p21(ras) - genetics
Regression analysis
Retrospective Studies
ROC Curve
Serum tumour markers
Tumors
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Summary:Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations. The clinical data of patients with colorectal cancer from January 2014 to December 2018 were retrospectively collected, and the associations between KRAS mutations and other indicators were analysed. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. Univariate and multivariate logistic regression models were applied to identify predictors of KRAS mutations by calculating the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). KRAS mutations were identified in 276 patients (35.2%). ROC analysis revealed that age, CA12-5, AFP, SCC, CA72-4, CA15-3, FERR, CYFRA21-1, MCHC, and tumor location could not predict KRAS mutations (P = 0.154, 0.177, 0.277, 0.350, 0.864, 0.941, 0.066, 0.279, 0.293, and 0.053 respectively), although CEA, CA19-9, NSE and haematological parameter values showed significant predictive value (P = 0.001,
ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-020-07551-4