Zoledronic Acid Abrogates Restraint Stress-Induced Macrophage Infiltration, PDGF-AA Expression, and Ovarian Cancer Growth

Colon-Echevarria, Claudia B.; Ortiz, Tatiana; Maldonado, Lizette; Hidalgo-Vargas, Melanie J.; Pérez‐Morales, Jaileene; Aquino-Acevedo, Alexandra N.; Herrera-Noriega, Roberto; Bonilla-Claudio, Margarita; Castro, Eida; Armaiz-Pena, Guillermo N.

doi:10.3390/cancers12092671

Cited by 15 publications

(12 citation statements)

References 46 publications

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“…Macrophages are also important components of TME, with which tumors enhance cell proliferation, angiogenesis, and metastasis (DeNardo and Ruffell, 2019). Prostate cancer patients with a higher score of depression revealed higher CD68 + tumor-associated macrophage (TAM) infiltration (Cheng et al, 2019), and daily restraint stress increased infiltration of CD68+ macrophages in rodent models of ovarian cancer as well (Colon-Echevarria et al, 2020). Moreover, β2-AR activation promoted macrophages to polarize to immunosuppressive M2 subtype in a rodent breast cancer model (Qin et al, 2015).…”

Section: Immune Cellsmentioning

confidence: 99%

“…Elevated PGE2 secretion was detected in epinephrine-treated ex vivo human breast and colon cancer explant and mammary tumors of chronic stressexposed mice due to activation (Muthuswamy et al, 2017). Additionally, the cytokine analyses in a stressed rodent ovarian cancer model revealed up-regulation of a large scale of cytokines, including platelet-derived growth factor AA (PDGF-AA), epithelial cell-derived neutrophil-activating peptide (ENA-78), angiogenin, VEGF, granulocytemacrophage colony-stimulating factor (GM-CSF), IL-5, Lipocalin-2, macrophage migration inhibitory factor (MIF), and transferrin receptor (TfR) (Colon-Echevarria et al, 2020).…”

Section: Cytokinesmentioning

confidence: 99%

“…Additionally, a variety of other drugs may also reverse the unfavorable TME alterations in terms of chronic stress. Zoledronic acid, an anti-cancer adjuvant drug, is proficient in abrogating stress-induced macrophage infiltration, and PDGF-AA expression in a rodent ovarian cancer model (Colon-Echevarria et al, 2020). Antidepressants, such as fluoxetine and sertraline, can also alleviate chronic stress and have the potential in associating with cancer treatments, which still need further clinical confirmation (Di Rosso et al, 2018).…”

Section: Othersmentioning

confidence: 99%

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Chronic Stress: Impacts on Tumor Microenvironment and Implications for Anti-Cancer Treatments

Tian

Liu

Cao

et al. 2021

Front. Cell Dev. Biol.

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Chronic stress is common among cancer patients due to the psychological, operative, or pharmaceutical stressors at the time of diagnosis or during the treatment of cancers. The continuous activations of the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS), as results of chronic stress, have been demonstrated to take part in several cancer-promoting processes, such as tumorigenesis, progression, metastasis, and multi-drug resistance, by altering the tumor microenvironment (TME). Stressed TME is generally characterized by the increased proportion of cancer-promoting cells and cytokines, the reduction and malfunction of immune-supportive cells and cytokines, augmented angiogenesis, enhanced epithelial-mesenchymal transition, and damaged extracellular matrix. For the negative effects that these alterations can cause in terms of the efficacies of anti-cancer treatments and prognosis of patients, supplementary pharmacological or psychotherapeutic strategies targeting HPA, SNS, or psychological stress may be effective in improving the prognosis of cancer patients. Here, we review the characteristics and mechanisms of TME alterations under chronic stress, their influences on anti-cancer therapies, and accessory interventions and therapies for stressed cancer patients.

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Section: Immune Cellsmentioning

confidence: 99%

Section: Cytokinesmentioning

confidence: 99%

Section: Othersmentioning

confidence: 99%

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Chronic Stress: Impacts on Tumor Microenvironment and Implications for Anti-Cancer Treatments

Tian

Liu

Cao

et al. 2021

Front. Cell Dev. Biol.

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“…IL-8 can polarize macrophages toward the CD163 + M2 phenotype, which may contribute to poor survival in ovarian cancer (Ning et al, 2018). At the same time, stress hormones can also directly bind to β2-adrenergic receptors on the surface of macrophages (Sloan et al, 2010;Allen et al, 2018;Colon-Echevarria et al, 2020). Ultimately, this will exacerbate the infiltration of TAMs (Figure 4).…”

Section: Tumor Immune Microenvironmentmentioning

confidence: 99%

Stress Hormones: Emerging Targets in Gynecological Cancers

Chen

Qiu²,

Gao

et al. 2021

Front. Cell Dev. Biol.

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In the past decade, several discoveries have documented the existence of innervation in ovarian cancer and cervical cancer. Notably, various neurotransmitters released by the activation of the sympathetic nervous system can promote the proliferation and metastasis of tumor cells and regulate immune cells in the tumor microenvironment. Therefore, a better understanding of the mechanisms involving neurotransmitters in the occurrence and development of gynecological cancers will be beneficial for exploring the feasibility of using inexpensive β-blockers and dopamine agonists in the clinical treatment of gynecological cancers. Additionally, this article provides some new insights into targeting tumor innervation and neurotransmitters in the tumor microenvironment.

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“…It has been proved, due to the amendment of the p53 gene, cells affected due to neurodegenerative diseases persuades ovarian epithelial cancer [ 21 ]. A study [ 22 ] presents data which shows, emission of incendiary proteins in ovarian cancer cell were prompted by stress hormones.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

et al. 2021

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Background In this research, an astute system has been developed by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress. Results For functioning factors and subfactors, several machine learning models like Logistics Regression, Random Forest, AdaBoost, Naïve Bayes, Neural Network, kNN, CN2 rule Inducer, Decision Tree, Quadratic Classifier were compared with standard metrics e.g., F1, AUC, CA. For certainty info gain, gain ratio, gini index were revealed for both cervical and ovarian cancer. Attributes were ranked using different feature selection evaluators. Then the most significant analysis was made with the significant factors. Factors like children, age of first intercourse, age of husband, Pap test, age are the most significant factors of cervical cancer. On the other hand, genital area infection, pregnancy problems, use of drugs, abortion, and the number of children are important factors of ovarian cancer. Conclusion Resulting factors were merged, categorized, weighted according to their significance level. The categorized factors were indexed using ranker algorithm which provides them a weightage value. An algorithm has been formulated afterward which can be used to predict the risk level of cervical and ovarian cancer in relation to women's mental health. The research will have a great impact on the low incoming country like Bangladesh as most women in low incoming nations were unaware of it. As these two can be described as the most sensitive cancers to women, the development of the application from algorithm will also help to reduce women’s mental stress. More data and parameters will be added in future for research in this perspective.

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Zoledronic Acid Abrogates Restraint Stress-Induced Macrophage Infiltration, PDGF-AA Expression, and Ovarian Cancer Growth

Cited by 15 publications

References 46 publications

Chronic Stress: Impacts on Tumor Microenvironment and Implications for Anti-Cancer Treatments

Chronic Stress: Impacts on Tumor Microenvironment and Implications for Anti-Cancer Treatments

Stress Hormones: Emerging Targets in Gynecological Cancers

Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective

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