FKBP4 is a malignant indicator in luminal A subtype of breast cancer

Xiong, Hanchu; Chen, Zihan; Zheng, Wenwen; Sun, Jing; Fu, Qingshuang; Teng, Renli; Chen, Jida; Xie, Shuduo; Wang, Linbo; Yu, Xiaofang; Zhou, Jichun

doi:10.7150/jca.40982

Cited by 24 publications

(27 citation statements)

References 33 publications

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“…FKBP4 acts as a specific positive regulator of androgen receptors, glucocorticoid receptors, and progesterone receptors function through the interaction of the proline-rich loop with the ligand-binding domain of the steroid hormone receptors (Guy et al, 2015). FKBP4 showed significantly increased reactivity in primary breast cancer and carcinoma in situ compared with healthy controls (Desmetz et al, 2009) and might be putative prediction markers in discriminating malignant (Xiong et al, 2020) and drug-resistant of breast cancers (Ostrow et al, 2009;Yang W.S. et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome Based Estrogen Related Genes Biomarkers for Diagnosis and Prognosis in Non-small Cell Lung Cancer

Jia

Xie

et al. 2021

Front. Genet.

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BackgroundLung cancer is the tumor with the highest morbidity and mortality, and has become a global public health problem. The incidence of lung cancer in men has declined in some countries and regions, while the incidence of lung cancer in women has been slowly increasing. Therefore, the aim is to explore whether estrogen-related genes are associated with the incidence and prognosis of lung cancer.MethodsWe obtained all estrogen receptor genes and estrogen signaling pathway genes in The Cancer Genome Atlas (TCGA), and then compared the expression of each gene in tumor tissues and adjacent normal tissues for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) separately. Survival analysis was performed of the differentially expressed genes in LUAD and LUSC patients separately. The diagnostic and prognostic values of the candidate genes were validated in the Gene Expression Omnibus (GEO) datasets.ResultsWe found 5 estrogen receptor genes and 66 estrogen pathway genes in TCGA. A total of 50 genes were differently expressed between tumor tissues and adjacent normal tissues and 6 of the 50 genes were related to the prognosis of LUAD in TCGA. 56 genes were differently expressed between tumor tissues and adjacent normal tissues and none of the 56 genes was related to the prognosis of LUSC in TCGA. GEO datasets validated that the 6 genes (SHC1, FKBP4, NRAS, PRKCD, KRAS, ADCY9) had different expression between tumor tissues and adjacent normal tissues in LUAD, and 3 genes (FKBP4, KRAS, ADCY9) were related to the prognosis of LUAD.ConclusionsThe expressions of FKBP4 and ADCY9 are related to the pathogenesis and prognosis of LUAD. FKBP4 and ADCY9 may serve as biomarkers in LUAD screening and prognosis prediction in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Transcriptome Based Estrogen Related Genes Biomarkers for Diagnosis and Prognosis in Non-small Cell Lung Cancer

Jia

Xie

et al. 2021

Front. Genet.

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“…Moreover, TRPC1 interacts with several proteins that are implicated in the regulation of intracellular and ER Ca 2+ concentrations, such as ORAI channels (80,81), the SERCA pump (82,83), PMCA (84), IP 3 R (82, 85), NCS-1 (86), and FKBP4 (87). Since Ca 2+ signals are crucial for a myriad of cellular processes, it is not surprising that all these TRPC1associated proteins have been proposed to play a role in breast cancer progression (88)(89)(90)(91)(92)(93).…”

Section: Trp Channels and Their Interactors In Breast Cancer Trpc1mentioning

confidence: 99%

TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer

et al. 2021

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Breast cancer is one of the most frequent cancer types worldwide and the first cause of cancer-related deaths in women. Although significant therapeutic advances have been achieved with drugs such as tamoxifen and trastuzumab, breast cancer still caused 627,000 deaths in 2018. Since cancer is a multifactorial disease, it has become necessary to develop new molecular therapies that can target several relevant cellular processes at once. Ion channels are versatile regulators of several physiological- and pathophysiological-related mechanisms, including cancer-relevant processes such as tumor progression, apoptosis inhibition, proliferation, migration, invasion, and chemoresistance. Ion channels are the main regulators of cellular functions, conducting ions selectively through a pore-forming structure located in the plasma membrane, protein–protein interactions one of their main regulatory mechanisms. Among the different ion channel families, the Transient Receptor Potential (TRP) family stands out in the context of breast cancer since several members have been proposed as prognostic markers in this pathology. However, only a few approaches exist to block their specific activity during tumoral progress. In this article, we describe several TRP channels that have been involved in breast cancer progress with a particular focus on their binding partners that have also been described as drivers of breast cancer progression. Here, we propose disrupting these interactions as attractive and potential new therapeutic targets for treating this neoplastic disease.

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“…The cluster ‘BREAST-CANCER’ ( Figure 5 e) presents the most prevalent type of cancer affecting approximately 12.5% of women worldwide [ 53 , 54 ]. The cluster ‘OVEREXPRESSION’ and ‘METASTASIS’ highlights the high number of studies using data mining to understand the association of overexpression of molecules (e.g., MUC1 [ 54 ], TRIM29 [ 55 ], FKBP4 [ 56 ], etc.) with breast cancer metastasis.…”

Section: Science Mapping Analysis Of Data Mining In Healthcarementioning

confidence: 99%

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

Kolling

Furstenau

Sott

et al. 2021

IJERPH

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In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes (‘NEURAL-NETWORKS’, ‘CANCER’, ‘ELETRONIC-HEALTH-RECORDS’, ‘DIABETES-MELLITUS’, ‘ALZHEIMER’S-DISEASE’, ‘BREAST-CANCER’, ‘DEPRESSION’, and ‘RANDOM-FOREST’) are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, i) practices and techniques related to data mining in healthcare, and ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field’s evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.

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FKBP4 is a malignant indicator in luminal A subtype of breast cancer

Cited by 24 publications

References 33 publications

Transcriptome Based Estrogen Related Genes Biomarkers for Diagnosis and Prognosis in Non-small Cell Lung Cancer

Transcriptome Based Estrogen Related Genes Biomarkers for Diagnosis and Prognosis in Non-small Cell Lung Cancer

TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

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