The genomic landscape associated with resistance to aromatase inhibitors in breast cancer.

Q2 Agricultural and Biological Sciences Genomics and Informatics Pub Date : 2023-06-01 DOI:10.5808/gi.23012
Kirithika Sadasivam, Jeevitha Priya Manoharan, Hema Palanisamy, Subramanian Vidyalakshmi
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Abstract

Aromatase inhibitors (AI) are drugs that are widely used in treating estrogen receptor (ER)-positive breast cancer patients. Drug resistance is a major obstacle to aromatase inhibition therapy. There are diverse reasons behind acquired AI resistance. This study aims at identifying the plausible cause of acquired AI resistance in patients administered with non-steroidal AIs (anastrozole and letrozole). We used genomic, transcriptomic, epigenetic, and mutation data of breast invasive carcinoma from The Cancer Genomic Atlas database. The data was then separated into sensitive and resistant sets based on patients' responsiveness to the non-steroidal AIs. A sensitive set of 150 patients and a resistant set of 172 patients were included for the study. These data were collectively analyzed to probe into the factors that might be responsible for AI resistance. We identified 17 differentially regulated genes (DEGs) among the two groups. Then, methylation, mutation, miRNA, copy number variation, and pathway analyses were performed for these DEGs. The top mutated genes (FGFR3, CDKN2A, RNF208, MAPK4, MAPK15, HSD3B1, CRYBB2, CDC20B, TP53TG5, and MAPK8IP3) were predicted. We also identified a key miRNA - hsa-mir-1264 regulating the expression of CDC20B. Pathway analysis revealed HSD3B1 to be involved in estrogen biosynthesis. This study reveals the involvement of key genes that might be associated with the development of AI resistance in ER-positive breast cancers and hence may act as a potential prognostic and diagnostic biomarker for these patients.

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乳腺癌中与芳香酶抑制剂耐药性相关的基因组景观。
芳香化酶抑制剂(Aromatase inhibitors, AI)是广泛用于治疗雌激素受体(estrogen receptor, ER)阳性乳腺癌患者的药物。耐药性是芳香酶抑制治疗的主要障碍。获得性人工智能抵抗背后有多种原因。本研究旨在确定服用非甾体类AIs(阿那曲唑和来曲唑)患者获得性AI耐药的合理原因。我们使用了来自癌症基因组图谱数据库的乳腺浸润性癌的基因组学、转录组学、表观遗传学和突变数据。然后根据患者对非甾体类药物的反应性将数据分为敏感组和耐药组。研究包括150名敏感患者和172名耐药患者。对这些数据进行了综合分析,以探讨可能导致人工智能抵抗的因素。我们在两组中鉴定了17个差异调节基因(DEGs)。然后,对这些deg进行甲基化、突变、miRNA、拷贝数变异和途径分析。预测最高突变基因(FGFR3、CDKN2A、RNF208、MAPK4、MAPK15、HSD3B1、CRYBB2、CDC20B、TP53TG5和MAPK8IP3)。我们还发现了一个关键的miRNA - hsa-mir-1264调节CDC20B的表达。通路分析显示HSD3B1参与雌激素的生物合成。这项研究揭示了可能与雌激素受体阳性乳腺癌中AI耐药性发展相关的关键基因的参与,因此可能作为这些患者的潜在预后和诊断生物标志物。
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来源期刊
Genomics and Informatics
Genomics and Informatics Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
12 weeks
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