Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2020 Oct 30;40(10):BSR20200576.
doi: 10.1042/BSR20200576.

Association of UGT1A1*6 polymorphism with irinotecan-based chemotherapy reaction in colorectal cancer patients: a systematic review and a meta-analysis

Xiaoyun Zhu #  1 , Ruchao Ma #  2 , Xin Ma  1 , Gang Yang  1
Affiliations
Meta-Analysis

Association of UGT1A1*6 polymorphism with irinotecan-based chemotherapy reaction in colorectal cancer patients: a systematic review and a meta-analysis

Xiaoyun Zhu et al. Biosci Rep. .

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related deaths across the world. Irinotecan (IRI) is commonly used to treat CRC, and IRI-based chemotherapy is linked with adverse reaction and the efficacy of the treatment regimen. The gene UGT1A1 plays a central role in the IRI metabolic pathway. A polymorphism UGT1A1*6 has been widely researched which may be related to response of IRI-based chemotherapy in CRC. All relevant studies were strictly searched from PubMed, Embase, Cochrane Library and Web of Science databases to explore the associations between UGT1A1*6 and response of IRI-based chemotherapy with CRC. Nine articles comprising 1652 patients were included in the final combination. Meta-analysis showed G allele or GG had a lower risk of severe late-onset diarrhea compared with A/AA in allele model and homozygote model (G vs. A: OR = 0.53, 95% CI: 0.28-0.99, P=0.05; GG vs. AA: OR = 0.48, 95% CI: 0.23-0.99, P=0.05), no significant association was observed in other models. In addition, a significant association between UGT1A1*6 and neutropenia was observed in all models (G vs. A: OR = 0.57, 95% CI: 0.46-0.71, P=0.00; GG vs. AA: OR = 0.28, 95% CI: 0.17-0.45, P=0.01; GA vs. AA: OR = 0.42, 95% CI: 0.26-0.70, P=0.00; GG+GA vs. AA: OR = 0.32, 95% CI: 0.20-0.52, P=0.00; GG vs. AA+GA: OR = 0.40, 95% CI: 0.22-0.71, P=0.00), whereas, no relationship was found between UGT1A1*6 and clinical response among the different genotypes. UGT1A1*6 may be considered as a biomarker for IRI-based chemotherapy in CRC.

Keywords: UGT1A1; colorectal cancer; irinotecan; meta-analysis; response; rs4148323.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. Flow diagram of the study selection process
Figure 2
Figure 2. Forests for UGT1A1*6 polymorphism and IRI-based chemotherapy RR
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 3
Figure 3. Forests for UGT1A1*6 polymorphism and IRI-based chemotherapy DCR
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 4.1
Figure 4.1. Forests for UGT1A1*6 polymorphism and IRI-induced severe late-onset diarrhea
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 4.2
Figure 4.2. Forests for UGT1A1*6 polymorphism and IRI-induced severe late-onset diarrhea
(A) Represents sensitive analysis in allele model; (B) represents the results of removing heterogeneity in allele model; (C) represents sensitive analysis in recessive model; (D) represents the results of removing heterogeneity in recessive model.
Figure 5.1
Figure 5.1. Forests for UGT1A1*6 polymorphism and IRI-induced severe neutropenia
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 5.2
Figure 5.2. Forests for UGT1A1*6 polymorphism and IRI-induced severe neutropenia
(A) Represents sensitive analysis in recessive model; (B) represents the results of removing heterogeneity in recessive model.
Figure 6
Figure 6. Forests for Begg’s test for RR
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 7
Figure 7. Forests for Begg’s test for DCR
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 8
Figure 8. Forests for Begg’s test for IRI-induced severe late-onset diarrhea
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
Figure 9
Figure 9. Forests for Begg’s test for IRI-induced severe neutropenia
(A) Represents allele model (G vs. A); (B) represents homozygote model (GG vs. AA); (C) represents heterozygote model (GA vs. AA); (D) represents dominant model (GG+GA vs. AA); (E) represents recessive model (GG vs. GA+AA).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Thanikachalam K. and Khan G. (2019) Colorectal cancer and nutrition. Nutrients 11, 164 10.3390/nu11010164 - DOI - PMC - PubMed
    1. Pellino G., Gallo G., Pallante P., Capasso R., De Stefano A., Maretto I. et al. . (2018) Noninvasive biomarkers of colorectal cancer: role in diagnosis and personalised treatment perspectives. Gastroenterol. Res. Pract. 2018, 2397863. - PMC - PubMed
    1. Abdel Ghafar M.T., Gharib F., Abdel-Salam S., Elkhouly R.A., Elshora A., Shalaby K.H. et al. . (2020) Role of serum Metadherin mRNA expression in the diagnosis and prediction of survival in patients with colorectal cancer. Mol. Biol. Rep. 47, 2509–2519 10.1007/s11033-020-05334-5 - DOI - PubMed
    1. Rapado-Gonzalez O., Alvarez-Castro A., Lopez-Lopez R., Iglesias-Canle J., Suarez-Cunqueiro M.M. and Muinelo-Romay L. (2019) Circulating microRNAs as promising biomarkers in colorectal cancer. Cancers 11, 898 10.3390/cancers11070898 - DOI - PMC - PubMed
    1. Cremolini C., Rossini D., Dell’Aquila E., Lonardi S., Conca E., Del Re M. et al. . (2019) Rechallenge for patients with RAS and BRAF wild-type metastatic colorectal cancer with acquired resistance to first-line cetuximab and irinotecan: a phase 2 single-arm clinical trial. JAMA Oncol. 5, 343–350 10.1001/jamaoncol.2018.5080 - DOI - PMC - PubMed

Publication types