MétaCan
Menu
Back to cohort
Record W2013421664 · doi:10.4161/cc.25305

MicroRNA-regulated stress response in cancer and its clinical implications

2013· editorial· en· W2013421664 on OpenAlexafffund
Haoran Li, Burton B. Yang

Bibliographic record

VenueCell Cycle · 2013
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsHealth Sciences CentreSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersHeart and Stroke Foundation of Canada
KeywordsBiologymicroRNAAngiogenesisCarcinogenesisContext (archaeology)Cancer researchCancerCell cycleCancer cellCell biologyStem cellImmunologyGenetics

Abstract

fetched live from OpenAlex

During the process of tumorigenesis, the microenvironments of cancer cells are constantly undergoing changes and remodeling. Such changes and remodeling can lead to shortage of blood supply, reactive immune responses and damage to cellular components (Fig. 1). Any environmental fluctuations leading to deviations from physiological homeostasis are considered as stress to cancer cells. It is well known that strategies taken to modulate stress signaling are critical to tumor development. This also marks a distinction of malignant cells from normal ones. Currently, evolving evidence suggests that microRNAs play key roles in stress response mediation.1 MicroRNAs have been shown to exert diverse functions in cancer cell proliferation, cell cycle progression, invasion and angiogenesis.2-4 Notably, microRNAs regulate cellular metabolism in a cell-specific and context-dependent manner. Figure 1. By targeting multiple pathways, microRNA networks such as miR-17-associated pathway play a key role in regulating stress response of cancer cells. These stresses may come from chemotherapy treatment, lack of blood supply, cell apoptosis ... Recent research from our lab has gained insight into this issue, using glioblastoma cells as a model to test the context-dependent functions of microRNA. Glioblastoma is characterized by aggressive angiogenesis and the generation of tumor stem-like cells (TSCs), making it an optimal candidate to test TSC-related phenotypes. Initially, we found that microRNA miR-378 accumulated in glioblastoma U87 cells when deprived of serum in vitro, and miR-378, in turn, contributed to tumor angiogenesis in vivo.5 This led us to further examine alterations to the microRNA network when cancer cells were starved. Under these conditions, we found a group of microRNAs upregulated therein, including miR-17.6 MiR-17 has a controversial role in different cancers: it can function either as an oncomir or as a tumor-suppressor depending on the tumor type. Glioblastoma cells overexpressing miR-17 appeared “highly adaptive” as compared with the other cancerous cells. Under favorable conditions, the proliferative capacity of miR-17-expressing cells decreased. By reducing their metabolic rate, such growth retardation could protect them from serum-starvation. As a result, these cells showed increased survival under serum-free conditions. Moreover, miR-17-expressing cells became more resistant to treatments with cytotoxic reagents, since most chemotherapeutic drugs function by diminishing highly proliferative tumor cells. These effects appeared to be the consequence of miR-17 targeting MDM2 and PTEN. Through the negative regulation of p53, MDM2 acts as an oncogene, and suppression of MDM2 resulted in reduced proliferation. However, PTEN is a tumor suppressor gene that dominates the PTEN/Akt/HIF-1alpha pathway. Downregulating PTEN caused activation of HIF-1alpha, which contributed to tumor survival and angiogenesis. Interestingly, HIF-1alpha expression was only stabilized under stressed conditions and acted as a sensor to detect environmental fluctuation. Activation of HIF-1alpha in response to chemotherapy not only prolonged glioblastoma cell survival, but also accelerated the transformation of TSCs. Tumor stem-like cells have been identified as one of the most important causes of tumor recurrence. It is believed that a subpopulation of cancer cells is capable of preserving their tumorigenicity after cytotoxic chemotherapy. But how these cells remain undamaged after treatment is not readily explained by current theory. The glioblastoma TSCs show characteristic overexpression of miR-17, which restricts cell growth to an indolent pattern. Interestingly however, these cells were more able to resist drug treatment and generate secondary colonies. Particularly under stressed conditions, TSCs were enriched in glioblastoma cells overexpressing miR-17. These cells showed a greater ability to induce angiogenesis when the nutritional supply was decreased, which may contribute to evasion of traditional chemotherapeutic treatment. Thus, the effects of miR-17 in glioblastoma are 2-fold. First, by shifting the metabolic requirements during periods of tumor growth, these malignant cells can evade traditional chemotherapy regimens. Second, increased angiogenic capacity allows these cancer cells to rapidly regrow through increased tumor vascularization. The realization that microRNAs play a dual role in glioblastoma cells will provide a new perspective to our understanding of stress responses in cancer. To adapt to fluctuations in environmental stress, microRNA networks can balance signaling by targeting both positive and negative regulators of tumor progression.7 Any changes leading to imbalanced signaling might trigger the responses of microRNAs accordingly. The heterogeneity of cancer lies not only in its genetic diversity, but also in its wide array of modifications at the post-transcriptional level, indeed posing a new challenge in developing cancer therapeutics. For example, traditional therapy may fail to eliminate miR-17-overexpressing cancer cells, which are inherently resistant to current drug treatments. Targeted therapy might also lead to acquired drug resistance if microRNA altered the therapeutic target secondary to survival pressure. Anti-angiogenic therapy is commonly used in treating advanced glioblastoma patients, but drug resistance has been observed frequently. The finding that miR-17 contributes to glioblastoma angiogenesis by over-inducing expression of VEGF provides another possible mechanism of resistance to anti-angiogenic therapy. Subpopulations of cancer cells with stem cell-like properties have been shown to give rise to secondary tumors following traditional chemotherapy. Understanding the contributions of miRNA to these phenotypes will be valuable. For example, glioblastoma patients with high levels of miR-17 might benefit more from surgical resection, instead of chemotherapy. To address microRNA-induced drug resistance, our lab has developed an anti-miRNA sponge that can efficiently decrease specific microRNA activity in vitro and in vivo.2 The ability of microRNAs to mediate TSC functioning highlights the importance of understanding microRNA networks in cancer progression. Much work needs to be done to uncover the intricacies of miRNA functioning. Understanding the mechanism may provide unprecedented opportunity in targeted cancer therapy.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.988

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 integrity0.0010.000
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 head0.012
GPT teacher head0.315
Teacher spread0.302 · 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 designNot applicable
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

Citations5
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCell CycleSame topicMicroRNA in disease regulationFrench-language works237,207