Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer

被引:9
作者
Pliakou, Evangelia [1 ]
Lampropoulou, Dimitra Ioanna [2 ]
Dovrolis, Nikolas [3 ]
Chrysikos, Dimosthenis [4 ]
Filippou, Dimitrios [5 ]
Papadimitriou, Christos [6 ]
Vezakis, Antonios [7 ]
Aravantinos, Gerasimos [1 ]
Gazouli, Maria [8 ]
机构
[1] Gen Oncol Hosp Kifissia Agioi Anargiroi, Dept Med Oncol 2, Nea Kifissia, Athens 14564, Greece
[2] ECONCARE, Athens 11528, Greece
[3] Democritus Univ Thrace, Dept Med, Lab Biol, Alexandroupolis 68100, Greece
[4] Natl & Kapodistrian Univ Athens, Hippoctat Hosp, Med Sch, Dept Propaedeut Surg 1, Athens 11528, Greece
[5] Natl & Kapodistrian Univ Athens, Med Sch, Dept Anat, Athens 11527, Greece
[6] Natl & Kapodistrian Univ Athens, Aretaie Hosp, Med Sch, Dept Surg 2, Athens 11528, Greece
[7] Natl & Kapodistrian Univ Athens, Aretaie Univ Hosp, Med Sch, Dept Surg, Athens 11528, Greece
[8] Natl & Kapodistrian Univ Athens, Med Sch, Dept Basic Med Sci, Lab Biol, Athens 11527, Greece
关键词
microRNAs; colorectal cancer; machine learning; artificial intelligence; irinotecan; resistance; DOWN-REGULATION; GASTRIC-CANCER; CELLS; ANGIOGENESIS; BEVACIZUMAB; MANAGEMENT; ONCOLOGY; SURVIVAL; GROWTH;
D O I
10.3390/ijms24010046
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.
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页数:17
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共 108 条
[1]   Machine learning based refined differential gene expression analysis of pediatric sepsis [J].
Abbas, Mostafa ;
EL-Manzalawy, Yasser .
BMC MEDICAL GENOMICS, 2020, 13 (01)
[2]   Machine learning approaches to drug response prediction: challenges and recent progress [J].
Adam, George ;
Rampasek, Ladislav ;
Safikhani, Zhaleh ;
Smirnov, Petr ;
Haibe-Kains, Benjamin ;
Goldenberg, Anna .
NPJ PRECISION ONCOLOGY, 2020, 4 (01)
[3]   Non-coding RNA networks in cancer [J].
Anastasiadou, Eleni ;
Jacob, Leni S. ;
Slack, Frank J. .
NATURE REVIEWS CANCER, 2018, 18 (01) :5-18
[4]   Gene Ontology: tool for the unification of biology [J].
Ashburner, M ;
Ball, CA ;
Blake, JA ;
Botstein, D ;
Butler, H ;
Cherry, JM ;
Davis, AP ;
Dolinski, K ;
Dwight, SS ;
Eppig, JT ;
Harris, MA ;
Hill, DP ;
Issel-Tarver, L ;
Kasarskis, A ;
Lewis, S ;
Matese, JC ;
Richardson, JE ;
Ringwald, M ;
Rubin, GM ;
Sherlock, G .
NATURE GENETICS, 2000, 25 (01) :25-29
[5]   MicroRNA Methylome Signature and Their Functional Roles in Colorectal Cancer Diagnosis, Prognosis, and Chemoresistance [J].
Baharudin, Rashidah ;
Rus Bakarurraini, Nurul Qistina ;
Ismail, Imilia ;
Lee, Learn-Han ;
Ab Mutalib, Nurul Syakima .
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2022, 23 (13)
[6]   MiRNAs as molecular biomarkers in stage II egyptian colorectal cancer patients [J].
Bahnassy, Abeer A. ;
Salem, Salem E. ;
El-Sayed, Mohammad ;
Khorshid, Ola ;
Abdellateif, Mona S. ;
Youssef, Amira S. ;
Mohanad, Marwa ;
Hussein, Marwa ;
Zekri, Abdel-Rahman N. ;
Ali, Nasr M. .
EXPERIMENTAL AND MOLECULAR PATHOLOGY, 2018, 105 (03) :260-271
[7]   The role of epigenetic therapies in colorectal cancer [J].
Baretti, Marina ;
Azad, Nilofer Saba .
CURRENT PROBLEMS IN CANCER, 2018, 42 (06) :530-547
[8]   Identification of let-7-regulated oncofetal genes [J].
Boyerinas, Benjamin ;
Park, Sun-Mi ;
Shomron, Noam ;
Hedegaard, Mads M. ;
Vinther, Jeppe ;
Andersen, Jens S. ;
Feig, Christine ;
Xu, Jinbo ;
Burg, Christopher B. ;
Peter, Marcus E. .
CANCER RESEARCH, 2008, 68 (08) :2587-2591
[9]   POINTS OF SIGNIFICANCE Statistics versus machine learning [J].
Bzdok, Danilo ;
Altman, Naomi ;
Krzywinski, Martin .
NATURE METHODS, 2018, 15 (04) :232-233
[10]   miRNA-181b increases the sensitivity of pancreatic ductal adenocarcinoma cells to gemcitabine in vitro and in nude mice by targeting BCL-2 [J].
Cai, Baobao ;
An, Yong ;
Lv, Nan ;
Chen, Jianmin ;
Tu, Min ;
Sun, Jie ;
Wu, Pengfei ;
Wei, Jishu ;
Jiang, Kuirong ;
Miao, Yi .
ONCOLOGY REPORTS, 2013, 29 (05) :1769-1776