BACKGROUND: Musculoskeletal pain (MSKP) is the most reported symptom during treatment with aromatase inhibitors (AIs) for breast cancer. The mechanisms underlying MSKP are multidimensional and not well understood. The goals of this biological pathway analysis were to (1) gain an understanding of the genetic variation and biological mechanisms underlying MSKP with AI therapy and (2) identify plausible biological pathways and candidate genes for future investigation. METHOD: Genes associated with MSKP during AI therapy or genes involved in drug metabolism of and response to AIs were identified from the literature. Studies published through February 2019 were queried in PubMed®. The genes identified from the literature were entered into QIAGEN's Ingenuity® Pathway Analysis (IPA) software to generate canonical pathways, upstream regulators, and networks through a core analysis. RESULTS: The 17 genes identified were ABCB1, ABCG1, CYP17A1, CYP19A1, CYP27B1, CYP2A6, CYP3A4, CYP3A5, ESR1, OATP1B1, OPG, RANKL, SLCO3A1, TCL1A, UGT2A1, UGT2B17, and VDR. These genes are involved in encoding bone-remodeling regulators, drug-metabolizing enzymes (cytochrome P450 family, UDP-glucuronosyltransferases family), or drug transporters (ATP-binding cassette transporters, organic anion transporters). Multiple plausible biological pathways (e.g., nicotine degradation, melatonin degradation) and candidate genes (e.g., NFKB, HSP90, AKT, ERK1/2, FOXA2) are proposed for future investigation based on the IPA results. CONCLUSION: Multiple genes and molecular-level etiologies may contribute to MSKP with AI therapy in women with breast cancer. Our innovative combination of gene identification from the literature plus biological pathway analysis allowed for the emergence of novel candidate genes and biological pathways for future investigations.
BACKGROUND:Musculoskeletal pain (MSKP) is the most reported symptom during treatment with aromatase inhibitors (AIs) for breast cancer. The mechanisms underlying MSKP are multidimensional and not well understood. The goals of this biological pathway analysis were to (1) gain an understanding of the genetic variation and biological mechanisms underlying MSKP with AI therapy and (2) identify plausible biological pathways and candidate genes for future investigation. METHOD: Genes associated with MSKP during AI therapy or genes involved in drug metabolism of and response to AIs were identified from the literature. Studies published through February 2019 were queried in PubMed®. The genes identified from the literature were entered into QIAGEN's Ingenuity® Pathway Analysis (IPA) software to generate canonical pathways, upstream regulators, and networks through a core analysis. RESULTS: The 17 genes identified were ABCB1, ABCG1, CYP17A1, CYP19A1, CYP27B1, CYP2A6, CYP3A4, CYP3A5, ESR1, OATP1B1, OPG, RANKL, SLCO3A1, TCL1A, UGT2A1, UGT2B17, and VDR. These genes are involved in encoding bone-remodeling regulators, drug-metabolizing enzymes (cytochrome P450 family, UDP-glucuronosyltransferases family), or drug transporters (ATP-binding cassette transporters, organic anion transporters). Multiple plausible biological pathways (e.g., nicotine degradation, melatonin degradation) and candidate genes (e.g., NFKB, HSP90, AKT, ERK1/2, FOXA2) are proposed for future investigation based on the IPA results. CONCLUSION: Multiple genes and molecular-level etiologies may contribute to MSKP with AI therapy in women with breast cancer. Our innovative combination of gene identification from the literature plus biological pathway analysis allowed for the emergence of novel candidate genes and biological pathways for future investigations.
Authors: Lindsey A Torre; Freddie Bray; Rebecca L Siegel; Jacques Ferlay; Joannie Lortet-Tieulent; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2015-02-04 Impact factor: 508.702
Authors: T Kameda; H Mano; T Yuasa; Y Mori; K Miyazawa; M Shiokawa; Y Nakamaru; E Hiroi; K Hiura; A Kameda; N N Yang; Y Hakeda; M Kumegawa Journal: J Exp Med Date: 1997-08-18 Impact factor: 14.307
Authors: Tao Wang; Yu-Yan Huang; Xian-Liang Liu; Alex Molassiotis; Li-Qun Yao; Si-Lin Zheng; Jing-Yu Benjamin Tan; Hou-Qiang Huang Journal: Support Care Cancer Date: 2022-09-06 Impact factor: 3.359