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Record W2187129674 · doi:10.18632/oncoscience.2

MicroRNA in drug resistance

2014· article· en· W2187129674 on OpenAlexaff
Haoran Li, Burton B. Yang

Bibliographic record

VenueOncoscience · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsmicroRNADrugDrug resistanceComputational biologyMedicinePharmacologyBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) is one of the leading causes of cancer mortality worldwide. Over the last three decades, considerable progress has been made on the treatment of CRC. For example, it has been estimated that over 80% of patients diagnosed with CRC after 2010 received chemotherapy [1]. Despite considerable success however, resistance to chemotherapeutic treatment has emerged as an obstacle to effective treatment. Thus, there is a growing need in identifying critical molecular biomarkers for predicting both the clinical outcome of chemotherapy as well as patients at risk of developing drug resistance. Recently, it has become clear that multiple drug resistance (MDR) arises as a consequence of an accumulation of genetic and epigenetic alternations. Among them, the microRNA (miRNA) network has been identified as a master regulator of MDR. Currently, several laboratories are exploring how microRNAs manipulate drug resistance to cause CRC tumor relapse through epigenetic modulations. Recent research from our lab, published in Oncotarget (Fang et al., Oncotarget, 2014, Advance Publications) [2], uncovered the underlying mechanism responsible for acquired drug resistance and distant metastasis in CRC patients. Tumor samples of patients undergoing neoadjuvant chemotherapy were collected and microarrays were conducted by an independent group. Corresponding clinical outcomes were recorded according to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1), and samples they were categorized into either chemosensitive or chemoresistant groups. By comparing miRNA expression patterns between these two groups, it was shown that microRNA-17 (miR-17) was consistently elevated in the chemoresistant group. We hypothesized that miR-17 might be a predictive factor of chemotherapeutic response in colorectal cancer. We found that high levels of miR-17 expression was closely correlated with worsened long-term survival in 81 patients receiving chemotherapy (5.26 vs. 7.29 yrs). To confirm the role of miR-17 in inducing MDR, we stably transfected the CRC cell lines, COLO205 and SW480 with a miR-17 overexpression plasmid. Indeed, MDR was correlated with miR-17 expression in a dose-dependent manner. Less drug-induced apoptosis was noted in CRC cells which highly expressed miR-17. In addition, knocking down miR-17 by antisense oligo was found to sensitize cells to cytotoxic agents' treatment. It has long been thought that resistance to chemotherapy can be divided into two categories: innate and acquired [3]. Now this theory is facing challenges with the emergence of new evidence. In this paper, we reported that the levels of miR-17 were dramatically increased under chemotherapeutic agents' treatment. We believe that such a change confers an increased capacity for tumor cells to survive in stressed condition. Thus miR-17 over-expressing tumor cells have an innate survival advantage. Following the selective pressure of chemotherapy, this eventually leads to the accumulation of miR-17 expressing tumor cells in the surviving fraction. We found that high expression of miR-17 completely knocked down its target tumor suppressor PTEN, leading to excessive activation of downstream components such as AKT and HIF-1α. HIF-1α is capable of promoting miR-17 transcription, which in turn increases miR-17 accumulation [4]. Given this positive feedback loop, miR-17 concentration is continually increased after drug treatment, leading to a subsequent decrease in PTEN expression. These findings, in line with clinical data, provided direct insight into how miR-17 confers a poor prognosis by affecting tumor sensitivity to chemotherapy. Adding another layer of complexity however, single microRNAs can also act pleiotropically to target several pathways. Our understanding of these microRNA networks will help us bridge the link between innate and acquired drug resistance, and illuminate how this transition can occur at the post-transcriptional level. There is now growing evidence that miR-17 plays a key role in determining the prognosis of colorectal cancer. Yu and colleagues reported that high miR-17 expression was associated with reduced overall survival in 96 colon cancer patients [5]. These findings were in agreement with results published by Ma et al [6]. Transfecting miR-17 oligo mimics into LoVo cells, the authors showed that miR-17 regulated CRC tumorigenesis by targeting P130. This study was an important contribution to our understanding of miRNAs in colorectal cancer. To further reveal the crosstalk between the microRNA network and tumor microenvironment, we generated stably transfected cell lines and showed temporal changes in miR-17 expression in CRC cells exposed to chemotherapy. With extensive investigation, PTEN/PI3K/AKT/HIF-1alpha cascade has been shown as one of the most crucial pathways which are responsible for therapy response and oxidative stress [7]. Therefore, it is with great value to decipher how epigenetic alterations of PTEN pathway can result in chemotherapeutic drug resistance. Figure the proposed functions of microRNA in regulation of chemotherapy resistance Given its inherent genomic instability, CRC is able to maintain growth and proliferation through cross-talk between several signaling pathways. This level of complexity makes it more challenging to successfully treat patients. In recognition of the important role of microRNAs in drug resistance, future research should be focused on therapeutic strategies that target this dysfunctional network.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.235
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2014
Admission routes1
Has abstractyes

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