Abstract

Simple SummaryWe systematically reviewed the recent scientific publications describing the role of microRNAs in the regulation of drug resistance in colon cancer. To clarify the intricate web of resulting genetic and biochemical interactions, we used a machine learning approach aimed at creating: (i) networks of validated miRNA/target interactions involved in drug resistances and (ii) drug-centric networks, from which we identified the major clusters of proteins affected by drugs used in the treatment of colon cancer. Finally, to facilitate a high-level interpretation of these molecular interactions, we determined the cellular pathways related with drug resistance and regulated by the miRNAs in colon cancer.Drug resistance is one of the major forces driving a poor prognosis during the treatment and progression of human colon carcinomas. The molecular mechanisms that regulate the diverse processes underlying drug resistance are still under debate. MicroRNAs (miRNAs) are a subgroup of non-coding RNAs increasingly found to be associated with the regulation of tumorigenesis and drug resistance. We performed a systematic review of the articles concerning miRNAs and drug resistance in human colon cancer published from 2013 onwards in journals with an impact factor of 5 or higher. First, we built a network with the most studied miRNAs and targets (as nodes) while the drug resistance/s are indicated by the connections (edges); then, we discussed the most relevant miRNA/targets interactions regulated by drugs according to the network topology and statistics. Finally, we considered the drugs as nodes in the network, to allow an alternative point of view that could flow through the treatment options and the associated molecular pathways. A small number of microRNAs and proteins appeared as critically involved in the most common drugs used for the treatment of patients with colon cancer. In particular, the family of miR-200, miR34a, miR-155 and miR-17 appear as the most relevant microRNAs. Thus, regulating these miRNAs could be useful for interfering with some drug resistance mechanisms in colorectal carcinoma.

Highlights

  • Simple Summary: We systematically reviewed the recent scientific publications describing the role of microRNAs in the regulation of drug resistance in colon cancer

  • We dissected the relations between long non-coding RNAs, or microRNAs, and drug resistance in various types of carcinomas [1]; successively, we focused on non-coding RNAs and their targets in breast cancer [2]

  • MiR-200c and miR-181a are the most-connected miRNAs participating in this network, and both inhibit catenin beta 1 (CTNNB1) expression, a key target associated with three different drug resistances (Figure 2)

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Summary

The Curated Networks of MiRNAs and Their Targets in Colon Cancer Drug

We dissected the relations between long non-coding RNAs (lncRNAs), or microRNAs (miRNAs), and drug resistance in various types of carcinomas [1]; successively, we focused on non-coding RNAs and their targets in breast cancer [2]. MiR-200c and miR-181a are the most-connected miRNAs participating in this network, and both inhibit catenin beta 1 (CTNNB1) expression, a key target associated with three different drug resistances (Figure 2). MiR-204 and miR-155 were both downregulated in tumor-associated macrophages (TAMs), due to the inhibitory role of the activated interleukin 6 (IL6)/signal transducer and activator of the transcription 3 (STAT3) pathway, with a consequent upregulation of CCAAT enhancer-binding protein beta (CEBPB), IL6 receptor (IL6R), ABCB1 (by miR-155), RAB22A (by miR-204) and the shared BCL2 target [14] This molecular mechanism, possibly involving exosomes and validated by a coculture of TAMs and CRC cells in vitro, conferred. Notch and WNT signaling are over-represented here, together with angiogenesis (FDR < 0.05) (Table S4)

The MiR-514b and MiR128 Activities Converge on CDH1
MiR-195
MiR-15b
Unconfirmed Associations of MiRNAs with Drug Resistance in CRC
Irinotecan Resistance
Cetuximab Resistance
Oxaliplatin Resistance
Findings
Conclusions
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