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. 2022 Jun 3:9:812815.
doi: 10.3389/fmolb.2022.812815. eCollection 2022.

KCNN4 is a Potential Biomarker for Predicting Cancer Prognosis and an Essential Molecule that Remodels Various Components in the Tumor Microenvironment: A Pan-Cancer Study

Shaohua Chen  1   2 , Xiaotao Su  3 , Zengnan Mo  1   2
Affiliations

KCNN4 is a Potential Biomarker for Predicting Cancer Prognosis and an Essential Molecule that Remodels Various Components in the Tumor Microenvironment: A Pan-Cancer Study

Shaohua Chen et al. Front Mol Biosci. .

Abstract

Objectives: Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4) is a member of the KCNN family. Studies have revealed that KCNN4 is implicated in various physiological processes as well as promotes the malignant phenotypes of cancer cells. However, little is known about its associations with survival outcomes across varying cancer types. Methods: Herein, we systematically explored the prognostic value of KCNN4 in the pan-cancer dataset retrieved from multiple databases. Next, we performed correlation analysis of KCNN4 expression with tumor mutational burden (TMB) and microsatellite instability (MSI), and immune checkpoint genes (ICGs) to assess its potential as a predictor of immunotherapy efficacy. Afterwards, patients were divided into increased-risk group and decreased-risk group based on the contrasting survival outcomes in various cancer types. Furthermore, the underlying mechanisms of the distinctive effects were analyzed using ESTIMATE, CIBERSORT algorithms, and Gene Set Enrichment Analysis (GSEA) analysis. Results: KCNN4 expression levels were aberrant in transcriptomic and proteomic levels between cancer and normal control tissues in pan-cancer datasets, further survival analysis elucidated that KCNN4 expression was correlated to multiple survival data, and clinical annotations. Besides, KCNN4 expression was correlated to TMB and MSI levels in 14 types and 12 types of pan-cancers, respectively. Meanwhile, different types of cancer have specific tumor-infiltrating immune cell (TICs) profiles. Conclusions: Our results revealed that KCNN4 could be an essential biomarker for remodeling components in the tumor microenvironment (TME), and a robust indicator for predicting prognosis as well as immunotherapy response in pan-cancer patients.

Keywords: KCNN4; biomarker; pan-cancer; tumor microenvironment; tumor-infiltrating immune cells.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Analytical workflow of KCNN4.
FIGURE 2
FIGURE 2
Expression patterns of KCNN4 in pan-cancer datasets based on TIMER database. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
FIGURE 3
FIGURE 3
Alteration frequency of varying types of KCNN4 mutations.
FIGURE 4
FIGURE 4
Correlation between KCNN4 expression level and survival indicators as determined through Kaplan–Meier curve analysis. (A) Correlation between KCNN4 expression level and OS as determined through Kaplan–Meier curve analysis. (B) Correlation between KCNN4 expression level and DSS as determined by Kaplan–Meier curve analysis. (C) Correlation between KCNN4 expression level and PFI as determined by Kaplan–Meier curve analysis.
FIGURE 5
FIGURE 5
Correlation between KCNN4 expression level and survival indicators as determined through univariate Cox analysis. (A) Univariate Cox analysis of the correlation between KCNN4 expression level and OS. (B) Univariate Cox analysis of the correlation between KCNN4 expression level and DSS. (C) Univariate Cox analysis of the correlation between KCNN4 expression level and PFI.
FIGURE 6
FIGURE 6
Correlation between KCNN4 expression level and clinical annotations. (A) Correlation between KCNN4 expression level and ages in SKCM, UCEC, PRAD, BRCA, LGG, and SARC. (B) Correlation between KCNN4 expression level and gender in BRCA, and LUAD. (C) Correlation between KCNN4 expression level and tumor stages in KIRC, and THCA.
FIGURE 7
FIGURE 7
Correlation of KCNN4 expression with TMB, MSI, and ICGs. (A) Correlation between KCNN4 expression and TMB. (B) Correlation between KCNN4 expression and MSI. (C) Correlation of KCNN4 expression and ICGs.
FIGURE 8
FIGURE 8
Correlation between KCNN4 expression and different components of TME. (A) Correlation between KCNN4 expression and different components of the increased-risk group. (B) Correlation between KCNN4 expression and various components of the decreased-risk group.
FIGURE 9
FIGURE 9
Association of TIC subtype with KCNN4 expression in increased-risk group. (A–K) Association of each TIC subtype with KCNN4 expression in KIRC. (L) Association of each TIC subtype with KCNN4 expression in GBM. (M–U) Association of each TIC subtype with KCNN4 expression in LGG. (V–Y) Association of each TIC subtype with KCNN4 expression in PAAD.
FIGURE 10
FIGURE 10
Association of TIC subtype with KCNN4 expression in decreased-risk group and the association of KCNN4 expression with immune-related genes. (A–E) Association of each TIC subtype with KCNN4 expression in BLCA. (F–M) Association of each TIC subtype with KCNN4 expression in SKCM. (N) Correlation between KCNN4 expression and immune-related genes.
FIGURE 11
FIGURE 11
GSEA analysis of top functional terms associated with KCNN4 expression. (A) GO functional terms of KCNN4 in various cancer types in the increased-risk group and decreased-risk group. (B) KEGG pathway analysis of KCNN4 in various cancer types in the increased-risk group and decreased-risk group.
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