| Literature DB >> 35330456 |
Fatemeh Hajibabaie1, Navid Abedpoor2, Nazanin Assareh1, Mohammad Amin Tabatabaiefar3,4, Laleh Shariati5,6, Ali Zarrabi7.
Abstract
Dysregulated mRNA-miRNA profiles might have the prospective to be used for early diagnosis of gastrointestinal cancers, estimating survival, and predicting response to treatment. Here, a novel biomarker based on miRNAs binding to mRNAs in single nucleotide polymorphism (SNP) sites related to gastrointestinal cancers is introduced that could act as an early diagnosis. The electronic databases used for the recruiting published articles included EMBASE, SCOPUS, Web of Science, and PubMed, based on MESH keywords and PRISMA methodology. Based on the considered criteria, different experimental articles were reviewed, during which 15 studies with the desired criteria were collected. Accordingly, novel biomarkers in prediction, early prognosis, and diagnosis of gastrointestinal cancers were highlighted. Moreover, it was found that 20 SNP sites and 16 miRNAs were involved in gastrointestinal cancers, with altered expression patterns associated with clinicopathological and demographic data. The results of this systematic study revealed that SNPs could affect the binding of miRNAs in the SNP sites that might play a principal role in the progression, invasion, and susceptibility of gastrointestinal cancers. In addition, it was found that the profiles of SNPs and miRNAs could serve as a convenient approach for the prognosis and diagnosis of gastric and colorectal cancers.Entities:
Keywords: biomarkers; colorectal cancer; gastric cancer; miRNA; single nucleotide polymorphism
Year: 2022 PMID: 35330456 PMCID: PMC8954022 DOI: 10.3390/jpm12030456
Source DB: PubMed Journal: J Pers Med ISSN: 2075-4426
Figure 1The flowchart of the systematic review. We indicated the algorithm for identifying relevant papers for inclusion. Based on the comprehensive literature, 15 eligible pieces of evidence were included for the effect of the SNPs in the binding site of miRNAs and the role of miRNAs binding to SNP sites on gastrointestinal cancer susceptibility.
Characteristics of studies that included the feature of patients and miRNA binding to SNP sites in colorectal cancer.
| Population | Region | Variables | Age | Gender Control | Gender Patient | Stage | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ID | Patient | Control | Country | miRNAs | SNP | Gene | Patient | Control | Male | Female | Male | Female | Low | intermediate | high |
| Xie, 2018 | 1147 | 1203 | China | miR-21 | rs6504593 | IGF2BP1 | 60.0 ± 12.6 | 59.9 ± 14.3 | 698 | 505 | 702 | 445 | 85 | 880 | 182 |
| Gu, 2018 | 1147 | 1203 | China | miR-532-5p | rs1590 | TGFBR1 | 60.0 ± 12.6 | 59.9 ± 14.3 | 698 | 505 | 702 | 445 | 85 | 880 | 182 |
| Zhang, 2017 | 200 | 200 | China | miR-193a-3p | rs10862, rs3212986 | MLH3, ERCC1 | 62.18 ± 12.637 | 61.59 ± 13.153 | 89 | 89 | 111 | 111 | ND | ND | ND |
| Shaker, 2016 | 86 | 36 | Egypt | mir-375 | rs4939827 | SMAD-7 | 50.4 ± 12.4 | 46.8 ± 8.7 | 19 | 17 | 59 | 19 | ND | ND | ND |
| Li, 2017 | 1841 | 1837 | China | hsa-miR-185-3p | rs12915554 | GREM1 | 40 ± 24.8 | 40 ± 26.1 | 1026 | 811 | 1025 | 816 | 1841 | ND | ND |
| Wang, 2021 | 507 | 497 | China | miR-143, miR-145 | rs74693964 | KRAS | 62.55 ± 11.88 | 62.75 ±11.99 | 288 | 207 | 329 | 178 | ND | ND | ND |
| Ding, 2015 | 386 | 394 | China | miR-520a | rs141178472 | PIK3CA | 60.1 ± 12.3 | 60.7 ± 12.9 | 229 | 165 | 216 | 170 | 38 | 308 | 40 |
| Kim, 2015 | 831 | - | South Korean | miR-571 | rs12373, rs3757417T | PAUF | 63 ± 20.3 | ND | ND | ND | 55.3 ± 2.4 | 43.2 ± 1.3 | 150 | 665 | 16 |
| Wu, 2015 | 946 | 989 | China | hsa-mir-509-3p | rs13347, rs10836347 | CD44 | 55 ± 24.6 | 58 ± 22.7 | 535 | 454 | 519 | 427 | 84 | 606 | 256 |
Figure 2Differential expressions of pivotal miRNAs that targeted SNP single nucleotide polymorphism sites in candidate genes as biomarkers in prediction, early prognosis, diagnosis, and follow-up in colorectal cancers.
Characteristics of studies that included the feature of patients and miRNAs binding to SNP sites in Gastric cancer.
| Population | Region | Variables | Age | Gender Control | Gender Patient | Stage | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ID | Patient | Control | Country | miRNAs | SNP | Gene | Patient | Control | Male | Female | Male | Female | Low | intermediate | high |
| Dong, 2017 | 500 | 500 | China | hsa-miR-10a-3p | rs3748067 | IL17A | 57.93 ± 11.88 | 57.27 ± 12.15 | 124 | 376 | 124 | 376 | ND | ND | ND |
| Lin, 2012 | 500 | 502 | China | miR-181a | rs12537 | MTMR3 | 60 ± 13 | 60 ± 15 | 319 | 183 | 314 | 186 | ND | ND | ND |
| Shi, 2017 | 851 | 799 | China | miR-204 miR-211 | rs3202538 | ErbB3 | ≤50 | >50 | 319 | 480 | 318 | 533 | 214 | 294 | 343 |
| Wang, 2016 | 819 | 765 | China | MiR-502-5p | Rs56288038 | IRF-1 | ≤50 | >50 | 319 | 501 | 318 | 480 | 214 | 294 | 311 |
| Chen, 2015 | 500 | 500 | China | miR-197 | rs2472188 | IL1-F5 | 58.03 ± 11.89 | 57.24 ± 12.15 | 376 | 376 | 124 | 124 | ND | ND | ND |
| Song, 2014 | 753 | 949 | China | miR-148a | rs6976789, rs2235749 | SCRN1 | 65 ± 19 | 64 ± 18 | 628 | 321 | 512 | 241 | 222 | 431 | 12 |
Figure 3Differential expressions of pivotal miRNAs that targeted SNP sites in candidate genes as biomarkers in prediction, early prognosis, diagnosis, and follow-up in gastric cancers.
Evaluated risk of bias and quality assessment of included evidence in colorectal and gastric cancer.
| Study ID | Selection | Comparability | Exposure | Score | Quality Assessment | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Xie, 2018 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 8 | 5 |
| Gu, 2018 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 | 5 |
| Zhang, 2017 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 7 | 4 |
| Shaker, 2016 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 | 5 |
| Li, 2017 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 7 | 4 |
| Wang, 2021 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 7 | 4 |
| Wu, 2015 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 | 5 |
| Kim, 2015 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 6 | 3 |
| Ding, 2015 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 8 | 5 |
| Shi, 2017 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 5 |
| Dong, 2017 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 | 4 |
| Wang, 2016 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 7 | 4 |
| Chen, 2015 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 7 | 4 |
| Peng Song, 2014 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 5 | |
| Yong Lin, 2012 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 7 | 4 |
The summary of expression pattern alteration miRNAs in binding to SNP sites is based on the inclusion criteria as biomarkers in prediction, early prognosis, diagnosis, and follow-up in gastrointestinal cancers.
| Study ID | Genes | SNPs | miRNAs | Expression miRNAs Pattern in Colorectal Cancer | Expression miRNAs Pattern in Gastric Cancer | Biomarker Categories | Significant Scores |
|---|---|---|---|---|---|---|---|
| Ding, 2015 | PIK3CA | rs141178472-T allele | miR-520a | ↑ | ND | Prognosis | 95% CI: 1.716 (1.084–2.716), |
| Kim, 2015 | PAUF | rs12373-A allele | miR-571 | ↑ | ND | Prognosis | 95% CI: 1.59 (1.21–2.08), |
| Wu, 2015 | CD44 | rs13347-CT and TT alleles | miR-509-3p | ↓ | ND | Prognosis | 95% CI: 1.79 (1.50–2.17), |
| Xie, 2018 | IGF2BP1 | rs1049109 T allele and rs6504593 | miR-21 | ↓ | ND | Diagnosis | 95% CI: 1.23 (1.07–1.41), |
| Gu, 2018 | TGFBR1 | rs1590 GT and GG alleles | miR532-5p | ↓ | ND | Prognosis | 95% CI: 0.82 (0.68–0.97) |
| Zhang, 2017 | MLH3 | rs108621 CC and TC alleles | miR-193a-3p | ↑ | ND | Prognosis | 95% CI: 6.237 (1.298–29.966), |
| Zhang, 2017 | ERCC1 | rs3212986 AA alleles | - | ND | ND | Prognosis | 95% CI: 4.043 (1.261–12.968), |
| Shaker, 2016 | SMAD7 | rs4939827 T allele | miR-375 | ↓ | ND | Diagnosis | |
| Li, 2017 | GREM1 | rs12915554 C allele | miR-185-3p | ↓ | ND | Prognosis | 95%CI: 1.43 (1.04–1.95), |
| Wang, 2021 | KRAS | rs74693964 C and T alleles | miR-145 | ↓ | ND | Prognosis | 95% CI: 1.901 (0.943–3.835), |
| Dong, 2017 | IL23R | rs10889677 CC | - | ND | - | Diagnosis | 95% CI: 2.22 (1.27–3.87), |
| IL17A | rs3748067 T, CT and CT + TT alleles | miR-10a-3p | ND | ↑ | Diagnosis | 95% CI: 0.58 (0.43, 0.77), | |
| Lin, 2012 | MTMR3 | rs12537 CT and TT alleles | miR-181a | ND | ↑ | Prognosis | 95% CI: 1.72 (1.36–2.16), |
| Shi, 2017 | ERBB3 | rs3202538 GT and TT alleles | miR-204 | ND | ↓ | Diagnosis | 95% CI: 1.89 (1.48–2.01), |
| Shi, 2017 | ERBB3 | rs3202538 GT and TT alleles | miR-211 | ND | ↓ | Diagnosis | 95% CI: 4.32 (1.34–1.88), |
| Wang, 2016 | IRF-1 | rs56288038 C and G alleles | miR-502-5p | ND | ↑ | Diagnosis | 95% CI: 3.96 (1.52– 2.11), |
| Chen, 2015 | IL-1F5 | rs2472188 GC and GC + CC alleles | miR-197 | ND | ↑ | Prognosis | 95% CI: 1.51 (1.15,1.99), |
| Chen, 2015 | IL-1F5 | rs2515401 C.T. alleles | miR-197 | ND | ↑ | Prognosis | 95% CI: 1.36 (1.04,1.76), |
| Song, 2014 | SCRN1 | rs6976789 allele | miR-148a | ND | ↓ | Prognosis | 95% CI: 2.47, (1.21–5.05), |