KCNN4 is a diagnostic and prognostic biomarker that promotes papillary thyroid cancer progression

Wen, Jialiang; Lin, Bangyi; Lin, Lizhi; Chen, Yizuo; Wang, Ouchen

doi:10.18632/aging.103710

Cited by 25 publications

(21 citation statements)

References 60 publications

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“…The protein encoded by KCNN4 is part of a potentially heterotetrameric voltageindependent potassium channel that is activated by intracellular calcium and may be part of the predominant calcium-activated potassium channel in T-lymphocytes. KCNN4 was highly expressed in various cancers, including thyroid cancer (18), colorectal cancer (19), and hepatocellular carcinoma (20). Moreover, KCNN4 was strongly implicated in tumor invasion, metastasis, and epithelial-mesenchymal transition (EMT) (20,21).…”

Section: Introductionmentioning

confidence: 99%

KCNN4 is a potential prognostic marker and critical factor affecting the immune status of the tumor microenvironment in kidney renal clear cell carcinoma

Chen¹,

Wang²,

Su³

et al. 2021

Transl Androl Urol

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Background: The tumor microenvironment (TME) has emerged as a crucial factor in cancer development and progression. Recent findings have indicated that tumor-infiltrating immune cells (TICs) in the TME may predict cancer prognosis and response to treatment. Herein, we sought to identify critical modulators of the kidney renal clear cell carcinoma (KIRC) TME.Methods: KIRC datasets from The Cancer Genome Atlas (TCGA) were analyzed using the ESTIMATE algorithm to determine the ImmuneScore and StromalScore. By profiling the differentially expressed genes (DEGs) in the ImmuneScore and StromalScore, we finally identified the immune-and stromal-related DEGs of the cases, through which we then performed intersection analysis to determine the immunerelated genes (IRGs). Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify critical IRGs and construct a prognostic model. The CIBERSORT algorithm was used to calculate the relative content of 22 immune cell types. Finally, the datasets from the Gene Expression Omnibus (GEO) database were analyzed to validate results from the above analyses.Experimental validation was used on KIRC tissues by quantitative polymerase chain reaction (qPCR) and western blot.Results: We found that the ImmuneScore was negatively correlated with patients' prognosis. Intersection analysis of the ImmuneScore and StromalScore identified 118 IRGs that were enriched in immune-related functions. Following IRGs screening by Cox and LASSO regression analyses, six genes were identified and used to construct a KIRC prognostic model. Intersection analysis of these six genes and protein-protein interaction (PPI) were performed and obtained the most critical gene: Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4). Further analysis showed that KCNN4 expression was higher in tumor samples relative to normal controls, and was negatively correlated with prognosis. CIBERSORT analysis revealed significant correlation between KCNN4 expression and multiple types of TICs, demonstrating that KCNN4 may affect KIRC prognosis by influencing the TME immune status. Ultimately, the GEO datasets and validation experiments confirmed that KCNN4 was highly expressed in tumor tissues compared to the corresponding normal tissues.

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Section: Introductionmentioning

confidence: 99%

KCNN4 is a potential prognostic marker and critical factor affecting the immune status of the tumor microenvironment in kidney renal clear cell carcinoma

Chen¹,

Wang²,

Su³

et al. 2021

Transl Androl Urol

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show abstract

“…Another gene identified from this study that is implicated in thyroid cancer is Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4), a known oncogene, very recently was reported to be upregulated in PTC and was proposed as a diagnostic and prognostic marker for PTC. 42 Apart from thyroid cancer, differential expression of KCNN4 in various cancers was indicated either in poor prognosis, drug resistance and/or poor survival. 43–45 In the present study, KCNN4 was occurring in both the datasets with high frequency and showed approximately 2 fold change in expression.…”

Section: Discussionmentioning

confidence: 99%

In silico Analysis of Publicly Available Transcriptomics Data Identifies Putative Prognostic and Therapeutic Molecular Targets for Papillary Thyroid Carcinoma

Almansoori

Bhamidimarri

Bendardaf

et al. 2022

IJGM

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Background Thyroid cancer is the most common endocrine malignancy. However, the molecular mechanism involved in its pathogenesis is not well characterized. Purpose The objective of this study is to identify key cellular pathways and differentially expressed genes along the thyroid cancer pathogenesis sequence as well as to identify potential prognostic and therapeutic targets. Methods Publicly available transcriptomics data comprising a total of 95 samples consisting of 41 normal, 28 non-aggressive and 26 metastatic papillary thyroid carcinoma (PTC) cases were used. Transcriptomics data were normalized and filtered identifying 9394 differentially expressed genes. The genes identified were subjected to pathway analysis using absGSEA identifying PTC related pathways. Three of the genes identified were validated on 508 thyroid cancer biopsies using RNAseq and TNMplot. Results Pathway analysis revealed a total of 2193 differential pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. Pathways for non-aggressive PTC include calcium and potassium ion transport, hormone signaling, protein tyrosine phosphatase activity and protein tyrosine kinase activity. Metastatic pathways include growth, apoptosis, activation of MAPK and regulation of serine threonine kinase activity. Genes for non-aggressive are KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B and metastatic PTC are EGFR, PTK2B, KCNN4 and BCL2. Three of the genes identified were validated using clinical biopsies showing significant overexpression in aggressive compared to non-aggressive PTC; EGFR (p < 0.05), KCNN4 (p < 0.001) and PTK2B (p < 0.001). DrugBank database search identified several FDA approved drug targets including anti-EGFR Vandetanib used to treat thyroid cancer in addition to others that may prove useful in treating PTC. Conclusion Transcriptomics analysis identified putative prognostic targets including EGFR, PTK2B, BCL2, KCNQ1, KCNN4 and CACNA1D. EGFR, PTK2B and KCN44 were validated using thyroid cancer clinical biopsies. The drug search identified FDA approved drugs including Vandetanib in addition to others that may prove useful in treating the disease.

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“…The same findings were also demonstrated in papillary thyroid cancer where K ca 3.1 mRNA levels are higher than in normal thyroid tissues. The high expression is associated with a larger tumor size, lymph node metastasis and advanced disease stage [21]. In human prostate cancer K ca 3.1 channels are expressed gradedependently [22].…”

Section: K Ca 31 Expression In Cancermentioning

confidence: 99%

Targeting Kca3.1 Channels in Cancer

Todesca

Maskri

Brömmel

et al. 2021

Cell Physiol Biochem

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The Kca3.1 channels, previously designated as IK1 or SK4 channels and encoded by the KCNN4 gene, are activated by a rise of the intracellular Ca2+ concentration. These K+ channels are widely expressed in many organs and involved in many pathologies. In particular, Kca3.1 channels have been studied intensively in the context of cancer. They are not only a marker and a valid prognostic tool for cancer patients, but have an important share in driving cancer progression. Their function is required for many characteristic features of the aggressive cancer cell behavior such as migration, invasion and metastasis as well as proliferation and therapy resistance. In the context of cancer, another property of Kca3.1 is now emerging. These channels can be a target for novel small molecule-based imaging probes, as it has been validated in case of fluorescently labeled senicapoc-derivatives. The aim of this review is (i) to give an overview on the role of Kca3.1 channels in cancer progression and in shaping the cancer microenvironment, (ii) discuss the potential of using Kca3.1 targeting drugs for cancer imaging, (iii) and highlight the possibility of combining molecular dynamics simulations to image inhibitor binding to Kca3.1 channels in order to provide a deeper understanding of Kca3.1 channel pharmacology. Alltogether, Kca3.1 is an attractive therapeutic target so that senicapoc, originally developed for the treatment of sickle cell anemia, should be repurposed for the treatment of cancer patients.

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KCNN4 is a diagnostic and prognostic biomarker that promotes papillary thyroid cancer progression

Cited by 25 publications

References 60 publications

KCNN4 is a potential prognostic marker and critical factor affecting the immune status of the tumor microenvironment in kidney renal clear cell carcinoma

KCNN4 is a potential prognostic marker and critical factor affecting the immune status of the tumor microenvironment in kidney renal clear cell carcinoma

In silico Analysis of Publicly Available Transcriptomics Data Identifies Putative Prognostic and Therapeutic Molecular Targets for Papillary Thyroid Carcinoma

Targeting Kca3.1 Channels in Cancer

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