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

KCNN4 is significantly upregulated in PTC

To analyze the expression of the four KCNN family members in PTC, we downloaded transcriptome sequencing data and microarray data from The Cancer Genome Atlas (TCGA) and GSE58689, respectively. Only KCNN4 was significantly overexpressed in tumor tissues compared to nontumorous thyroid tissues (Figure 1A). Furthermore, we mined the differentially expressed genes between PTC tissues and adjacent normal tissues in the GSE3678 and GSE9115 datasets. After data normalization, 90 and 100 differentially expressed genes were extracted from GSE3678 and GSE9115, respectively (adjusted p-value < 0.05 and log2|fold change| ≥ 2). As shown in the heatmap (Figure 1B), KCNN4 was markedly upregulated in PTC tissues among the top 50 significantly differentially expressed genes in both datasets, based on the adjusted p-value (GSE3678: KCNN4, log2|fold change|=2.30, false discovery rate [FDR]=8.40×10^-5; GSE9115: KCNN4, log2|fold change|=3.73, FDR=0.01). Next, the Tumor IMmune Estimation Resource (TIMER) database was used to perform a pan-cancer analysis of KCNN4 expression. The results indicated that KCNN4 was upregulated in various cancers in TCGA (Figure 1C).

Figure 1. KCNN4 was differentially expressed in PTC and most other cancers. (A) The expression of the KCNN family in PTC was assessed using data from TCGA and GSE58689. (B) Heatmap of the top 50 significantly differentially expressed genes in GSE3678 and GSE9115, based on the adjusted p-value. (C) KCNN4 was overexpressed in multiple cancers in TCGA. The statistical significance of differential expression was evaluated using the Wilcoxon test. *p<0.05, **p<0.01, ***p<0.001. ACC: Adrenocortical carcinoma; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute myeloid leukemia; LGG: Brain lower grade glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular germ cell tumor; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma.

The mRNA levels of KCNN4 in TC tissues and adjacent nontumorous tissues in the aforementioned databases are shown in Figure 2A–2D (GSE58689: PTC tissues 7.21 ± 1.24, Normal tissues 4.25 ± 0.74, p<0.0001; GSE3678: PTC tissues 270.8 ± 123.5, Normal tissues 52.50 ± 19.48, p<0.01; GSE9155: PTC tissues 2.92 ± 1.50, Normal tissues -0.70 ± 1.11, p<0.01; TCGA: PTC tissues 48.82 ± 51.50, Normal tissues 4.44 ± 6.13, p<0.0001). We also performed RNA sequencing on 70 pairs of PTC tissues and matched adjacent nontumorous tissues, and observed the same trend (Figure 2E; PTC tissues 1868 ± 1127, Normal tissues 197.3 ± 245.3, p<0.0001). Furthermore, we used quantitative real-time PCR (qRT-PCR) to analyze KCNN4 expression in 42 pairs of PTC tissues and matched normal thyroid tissues (validated cohort). As expected, KCNN4 mRNA levels were significantly greater in PTC tissues than in normal thyroid tissues (Figure 2F; PTC tissues 12.59 ± 7.26, Normal tissues 1.90 ± 3.44, p<0.0001). These findings demonstrated that KCNN4 was overexpressed in PTC tissues.

Figure 2. KCNN4 was overexpressed in PTC. (A–D) KCNN4 mRNA levels were greater in PTC tissues than in normal thyroid tissues in GSE58689, GSE3678, GSE9115 and TCGA. (E) KCNN4 was upregulated in 70 PTC tissues compared with matched adjacent nontumorous thyroid tissues from our RNA sequencing dataset. (F) The overexpression of KCNN4 in PTC was verified in our validated cohort using qRT-PCR. Statistical analyses were performed as follows: (A, C, D): Mann-Whitney test; (B): Paired t-test; (E, F): Wilcoxon test. **p<0.01, ****p<0.0001.

KCNN4 overexpression is associated with a range of clinicopathological factors in PTC

Next, we evaluated the correlation between KCNN4 expression and clinical features of PTC in TCGA and our validated cohort. Patients were split into two groups based on the median KCNN4 level. In the cohort from TCGA, higher KCNN4 expression was associated with a higher T stage (p=0.001), a greater incidence of LNM (p<0.0001), a more advanced disease stage (p=0.001) and a higher frequency of the classical type (p<0.0001) (Table 1). In our validated cohort, KCNN4 expression also correlated significantly with the tumor size (p=0.028), LNM (p<0.030) and disease stage (p=0.009) (Table 2).

Then we evaluated KCNN4 expression in PTC patients with different tumor grades, stages, subtypes and mutations in TCGA. As illustrated in Figure 3A–3C, most patients with higher T stages, N stages and disease stages had higher mRNA levels of KCNN4. In a subtype analysis, KCNN4 levels were significantly greater in the classical and columnar variant subtypes than in the follicular subtype (Figure 3D). We also divided patients according to their driver mutation status, and found that KCNN4 expression was significantly greater in the BRAF mutation group (Figure 3E, p<0.0001), the RAS wild-type group (Figure 3F, p<0.0001) and the RET fusion group (Figure 3G, p<0.0001) than in their respective counterpart groups. KCNN4 expression was also significantly greater in the BRAF-like group than in the RAS-like group (Figure 3H; BRAF-like: 68.63 ± 51.57, RAS-like: 4.36 ± 14.22, p<0.0001). Thus, KCNN4 expression was associated with many clinicopathological features of PTC.

Figure 3. The relationship between KCNN4 expression and clinicopathological characteristics of PTC. (A–C) KCNN4 expression varied according to the T stage, N stage and tumor stage. Higher KCNN4 expression tended to be associated with worse clinicopathological features. (D) The expression of KCNN4 in different subtypes of PTC. KCNN4 was significantly upregulated in the columnar variant subtype and the classical subtype compared to the follicular subtype. (E) KCNN4 expression was significantly higher in the BRAF mutation group than in the BRAF wild-type group. (F) KCNN4 expression was significantly higher in the RAS wild-type group than in the RAS mutation group. (G) KCNN4 expression was significantly higher in the RET fusion group than in the RET wild-type group. (H) KCNN4 expression was higher in the BRAF-like group than in the RAS-like group. Statistical analyses were performed as follows: A-D: Kruskal-Wallis test; E-H: Mann-Whitney test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

The upregulation of KCNN4 increases the risk of LNM in PTC

To examine whether high KCNN4 expression is a major risk factor for LNM in PTC, we constructed regression models using data from TCGA (Table 3). Univariate logistic regression demonstrated that high KCNN4 expression (odds ratio [OR]=3.665, 95% confidence interval [CI]: 2.483-5.409, p<0.001), female (OR=0.640, 95% CI: 0.422-0.972, p=0.036), disease stage III/IV (OR=3.524, 95% CI: 2.336-5.316, p<0.001), T stage III/IV (OR=2.688, 95% CI: 1.830-3.970, p<0.001) and classical type (OR=2.370, 95% CI: 1.535-3.660, p<0.001) were associated with the risk of LNM. Multivariate logistic regression indicated that high KCNN4 expression (OR=2.914, 95% CI: 1.925-4.411, p<0.001), disease stage III/IV (OR=2.708, 95% CI: 1.656-4.428, p<0.001), T stage III/IV (OR=1.703, 95% CI: 1.054-2.753, p=0.030) and classical type (OR=2.506, 95% CI: 1.528-4.110, p<0.001) were associated with the risk of LNM, whereas gender was excluded from the model (OR=0.770, 95% CI: 0.482-1.229, p=0.274). These data indicated that elevated KCNN4 expression increased the risk of LNM.

The diagnostic and prognostic value of KCNN4 in PTC patients

Next, we used receiver operating characteristic (ROC) curves to examine the diagnostic significance of KCNN4 expression in TCGA, GSE58689 and our validated cohort. KCNN4 expression distinguished between PTC tissues and normal tissues with an area under the ROC curve (AUC) of 81.4% (95% CI: 77.6-85.2%, p<0.0001) in TCGA, 93.5% (95% CI: 88.7-98.3%, p=0.024) in our validated cohort and 97.7% (95% CI: 94.4-100%, p<0.0001) in GSE58689 (Figure 4A). In TCGA, KCNN4 also had diagnostic value for the T stage, with an AUC of 61.1% (95% CI: 55.9-66.3%, p<0.0001) (Figure 4B). For LNM, the AUC was 71.0% (95% CI: 66.2-75.8%, p<0.0001) in TCGA and 76.7% (95% CI: 61.7-91.7%, p=0.003) in our validated cohort (Figure 4C). The AUC for distinguishing tumor stage (I + II) / (III + IV) was 61.2% (95% CI: 56.0-66.5%, p<0.0001) in TCGA and 74.2% (95% CI: 59.4-89.1%, p=0.011) in our validated cohort (Figure 4D). These findings suggested that KCNN4 expression may have diagnostic value for PTC patients.

Figure 4. The upregulation of KCNN4 served as a prognostic and diagnostic biomarker in PTC. (A) ROC curve analysis demonstrated that KCNN4 could distinguish PTC from nontumorous tissues in TCGA, GSE58689 and our validated cohort. (B) Diagnostic value of KCNN4 expression for the T stage in TCGA. (C, D) ROC curve analysis depicting KCNN4 expression against the N stage and tumor stage in TCGA and our validated cohort. (E, F) Kaplan-Meier analyses of disease-free survival based on low and high KCNN4 expression in PTC patients and the BRAF-like group from TCGA. (G) KCNN4 expression was associated with immune infiltration in TCGA. THCA: Thyroid carcinoma; Cor: Correlation.

To investigate the prognostic value of KCNN4, we performed a disease-free survival analysis using Kaplan-Meier curves from the Gene Expression Profiling Interactive Analysis (GEPIA) server. Higher KCNN4 expression was associated with a greater risk of relapse or death (hazard ratio [HR]=1.7, p=0.078), especially in the BRAF-like subgroup (HR=2.3, p=0.047) (Figure 4E, 4F). Thus, KCNN4 overexpression may predict a worse prognosis in PTC patients.

KCNN4 overexpression is associated with immune cell infiltration

Previous studies have indicated that KCNN4 can alter antigen presentation and various immune cell functions [30, 31]. Using TCGA data from the TIMER online tool, we explored the association between KCNN4 expression and immune cell infiltration in PTC. KCNN4 expression correlated positively with the levels of infiltrating B cells (Correlation=0.218, p<0.0001), CD8+ T cells (Correlation=0.213, p<0.0001), CD4+ T cells (Correlation=0.284, p<0.0001), neutrophils (Correlation=0.485, p<0.0001) and dendrites (Correlation=0.504, p<0.0001) (Figure 4G).

KCNN4 is overexpressed in PTC cell lines and promotes PTC cell proliferation

We then examined the mRNA levels of KCNN4 in PTC and normal thyroid cell lines using qRT-PCR. KCNN4 expression was markedly higher in TPC-1 (4.83 ± 0.375, p<0.0001), KTC-1 (4.65 ± 0.327, p<0.0001) and BCPAP (1.30 ± 0.168, p<0.01) cells than in normal HTORI-3 cells (0.198 ± 0.025) (Figure 5A). Next, we used small interfering RNA (siRNA) to knock down KCNN4 (Si-KCNN4) in PTC cells. The siRNA reduced KCNN4 mRNA levels by > 50% in all three PTC cell lines (Figure 5B, p<0.01), and also significantly reduced KCNN4 protein levels (Figure 5C).

Figure 5. KCNN4 was upregulated in PTC cell lines and promoted their proliferation in vitro. (A) The relative expression of KCNN4 in PTC cell lines (compared with GAPDH). KCNN4 was upregulated in three PTC cell lines (KTC-1, TPC-1 and BCPAP) compared to the normal thyroid cell line HTORI-3. (B) KCNN4 expression was lower in the Si-KCNN4 group than in the Si-NC group in the three PTC cell lines. (C) KCNN4 protein expression was lower in the Si-KCNN4 group than in the Si-NC group. (D, E) CCK-8 assays and colony formation assays in the three PTC cell lines. Two-way analysis of variance was used to analyze the CCK-8 assay data, and Student’s t-test was used for the others. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Next, we assessed the effects of KCNN4 on PTC cell proliferation. A Cell Counting Kit-8 (CCK-8) assay demonstrated that knocking down KCNN4 reduced the proliferative capacity of PTC cells, particularly in the last 24 hours of measurement (Figure 5D, p<0.0001). Additionally, a colony formation assay indicated that the downregulation of KCNN4 attenuated the proliferative abilities of TPC-1 cells (negative control siRNA [Si-NC]: 114.3 ± 8.5 colonies, Si-KCNN4: 51.7 ± 10.4 colonies, p<0.01), KTC-1 cells (Si-NC: 165.0 ± 9.6 colonies, Si-KCNN4: 55.0 ± 8.9 colonies, p<0.001) and BCPAP cells (Si-NC: 163.7 ± 7.0 colonies, Si-KCNN4: 73.7 ± 7.4 colonies, p<0.001) (Figure 5E). Thus, the downregulation of KCNN4 inhibited the proliferation of PTC cell lines.

KCNN4 knockdown reduces the migration and invasion capacities of PTC cells

Since KCNN4 expression correlated with LNM in PTC, we performed Transwell migration assays, wound healing assays and Matrigel invasion assays to assess the impact of KCNN4 on PTC cell migration and invasion. The migration and invasion abilities of TPC-1, KTC-1 and BCPAP cells transfected with Si-KCNN4 were significantly lower than those of cells transfected with Si-NC (Figure 6A, 6B). The number of migratory cells was as follows - TPC-1: Si-NC 213.7 ± 11.4, Si-KCNN4 81.7 ± 7.4, p<0.0001; KTC-1: Si-NC 250.0 ± 9.0, Si-KCNN4 59.7± 6.8, p<0.0001; BCPAP: Si-NC 122.7 ± 11.1, Si-KCNN4 65.7 ± 7.1, p<0.01. The number of invading cells was - TPC-1: Si-NC 141.0 ± 11.1, Si-KCNN4 42.3 ± 7.8, p<0.001; KTC-1: Si-NC 193.0 ± 19.1, Si-KCNN4 32.7 ± 8.3, p<0.001; BCPAP: Si-NC 114.3 ± 8.5, Si-KCNN4 16.3 ± 7.5, p<0.001. In the wound healing assay, 24 hours after the scratch, the migration rate was reduced to less than 40% of the control value when KCNN4 was downregulated (Figure 6C). These data indicated that KCNN4 may promote tumor metastasis in PTC.

Figure 6. Downregulation of KCNN4 inhibited the migration and invasion of PTC cell lines. (A, B) Transwell migration and Matrigel invasion assays in the KCNN4-knockdown group and the corresponding control group in three PTC cell lines. The quantitative results of the migration and invasion assays were determined from five random fields. (C) A wound-healing assay indicated that downregulating KCNN4 reduced the migration abilities of PTC cell lines. The data came from at least two independent experiments. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 in comparison with the Si-NC group using Student’s t-test.

KCNN4 suppresses apoptosis and induces the epithelial-mesenchymal transition (EMT) in thyroid cancer cell lines

Single Gene Set Enrichment Analysis (GSEA) was used to identify genes that may be regulated by KCNN4 in PTC. Based on the median level of KCNN4, PTC patients from TCGA were divided into two groups. We discovered that genes involved in the EMT (FDR=0.006, Enrichment score=0.6437) and apoptosis (FDR=0.0056, Enrichment score=0.5776) were associated with KCNN4 expression (Figure 7A, 7B). Therefore, we used flow cytometry to detect apoptosis in PTC cell lines after the downregulation of KCNN4. The apoptosis rate, calculated based on the total percentage of cells in the second and third quadrants, was higher in the KCNN4-knockdown group than in the control group in each cell line (TPC-1: Si-NC 0.76 ± 0.34, Si-KCNN4 2.66 ± 0.49, p<0.05; KTC-1: Si-NC 2.14 ± 0.20, Si-KCNN4 4.05± 0.07, p<0.01; BCPAP: Si-NC 4.29 ± 0.40, Si-KCNN4 5.92 ± 0.12, p<0.05) (Figure 7C).

Figure 7. The downregulation of KCNN4 prevented the EMT while promoting apoptosis in PTC cell lines. (A, B) Single GSEA based on data from TCGA demonstrated that KCNN4 was associated with the EMT and apoptosis. (C) Silencing KCNN4 promoted apoptosis in PTC cell lines. (D, E) Silencing KCNN4 upregulated E-cadherin and downregulated N-cadherin, Vimentin and Slug at the mRNA and protein levels. (F) qRT-PCR demonstrated that downregulating KCNN4 enhanced Bax expression but reduced Bcl-2 expression. (G) Western blotting assays revealed that downregulating KCNN4 increased Bax expression and reduced Bcl-2 and Bcl-xl expression at the protein level. *p<0.05 and **p<0.01 in comparison with the Si-NC group using Student’s t-test.

Next, we quantified the mRNA and protein levels of several representative markers of the EMT and apoptosis in PTC cells. E-cadherin is an epithelial marker that suppresses tumor invasion, and its expression is negatively regulated by transcription factors such as Slug and Snail1 [32, 33]. Mesenchymal markers including Vimentin and N-cadherin are indicators of the EMT and cancer metastasis [34, 35]. The switch from E-cadherin to N-cadherin is an important hallmark of EMT induction [36]. We found that the downregulation of KCNN4 reduced N-cadherin, Vimentin and Slug expression while elevating E-cadherin expression (Figure 7D, 7E).

The Bcl-2 gene family, which participates in mitochondrion-induced cellular apoptosis, includes both anti-apoptotic (Bcl-2 and Bcl-xl) and pro-apoptotic genes (Bax). Bax exerts pro-apoptotic effects by sequestering Bcl-2 or Bcl-xl in the form of a heterodimer [37]. In our study, the downregulation of KCNN4 enhanced Bax expression and reduced Bcl-2 and Bcl-xl expression (Figure 7F, 7G). These results demonstrated that KCNN4 promotes the EMT and hinders apoptosis in PTC.

Bioinformatics

Public microarray datasets (GSE58689 [56], GSE9115 [57], GSE3678) including 44 PTC tissues and 29 nontumorous tissues were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Transcriptome sequencing data from 502 PTC samples and 58 normal thyroid tissues, along with the corresponding clinical data, were obtained from the thyroid cancer database of TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers/thyroid). R software (https://bioconductor.org/biocLite.R) was used to preprocess the data and investigate the differentially expressed genes. Differentially expressed genes in the microarray datasets were identified using the Limma package, with screening conditions of an adjusted p-value < 0.05 and a log2|fold change| ≥ 2. The heatmap was clustered using a Pearson correlation metric.

Pan-cancer analysis and immune infiltration evaluation were performed using the TIMER database (https://cistrome.shinyapps.io/timer/) [58]. Kaplan-Meier plots for disease-free survival were downloaded from the online database GEPIA (http://gepia.cancer-pku.cn) [59]. Single GSEA between the low and high KCNN4 expression groups in TCGA was conducted using GSEA software (GSEA v3.0, http://www.broadinstitute.org/gsea) [60].

Tissue samples

Forty-two matched pairs of PTC tissues and adjacent nontumorous tissues (patient age range: 16-72 years; male/female: 1/2) were dissected in the Department of Thyroid and Breast Surgery at The First Affiliated Hospital of Wenzhou Medical University (Wenzhou, Zhejiang, China) in 2018. After excision, the samples were instantly plunged into liquid nitrogen and frozen at -80 °C for long-term storage. Each specimen was histologically analyzed by three pathologists. Written informed consent was obtained from each patient. The clinical data were collected legally under protocols developed by the Committee for Medical Research Ethics of the First Affiliated Hospital of Wenzhou Medical University.

RNA sequencing data from 70 pairs of PTC tissues and matched adjacent nontumorous tissues were derived from our unpublished data.

Cell culture

Human PTC cell lines (TPC-1, KTC-1 and BCPAP) were provided by Prof. Mingzhao Xing of the Johns Hopkins University School of Medicine (Baltimore, MD, USA). The normal human thyroid cell line (HTORI-3) was purchased from the Cell Bank of the Shanghai Chinese Academy of Sciences (Shanghai, China). The research resource identification number is CVCL_6298 for TPC-1, CVCL_6300 for KTC-1, CVCL_0153 for BCPAP and CVCL_4W02 for HTORI-3. The cells were cultured in RPMI 1640 medium containing 10% fetal bovine serum (Gibco, Invitrogen, Carlsbad, CA, USA) and 1% penicillin/streptomycin (Solarbio, Beijing, China) at 37 °C under 5% CO₂.

Cell transfection

SiRNAs (si-KCNN4 and si-NC) were designed and supplied by Gene Pharma (Shanghai, China). Cells were seeded in six-well plates one day before transfection (TPC-1, BCPAP: 60,000 cells per well; KTC-1: 80,000 cells per well). The siRNA was introduced into the cells using Lipofectamine RNAiMAX (Invitrogen, Grand Island, NY, USA) (siRNA:iMAX = 7.5 uL:3 uL). After seven hours, the medium was replaced. The cells were used after 48 hours for further experiments. The sequence of the KCNN4-targeted siRNA was as follows: KCNN4-homo-1481 sense 5’- GGGAACAAGUGAACUCCAUTT-3’/antisense 5’-AUGGAGUUCACUUGUUCCCTT-3’.

RNA isolation, reverse transcription and qRT-PCR

Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), and reverse transcription was performed with a ReverTra Ace qPCR RT Kit (Toyobo, Osaka, Japan). The qRT-PCR was performed using a SYBR Premix Ex Taq II kit (RR820A, TaKaRa, Dalian, China) on an Applied Biosystems 7500 Real-Time PCR System. The mRNA expression relative to GAPDH expression was calculated using the 2^−ΔΔCt method. All measurements were performed according to standard protocols. The primers used in this study are shown in Supplementary Table 1 (Generay, Shanghai, China; Sangon, Shanghai, China).

Cell proliferation assays

Cell viability was measured with a CCK-8 assay and a colony formation assay. TPC-1 cells (1,250 per well), KTC-1 cells (1,500 per well) and BCPAP cells (1,500 per well) were plated into 96-well plates. The cells were incubated with the CCK-8 reagent (Beyotime Biotechnology, Shanghai, China) for three hours (10 uL per well). Detection was performed at an absorbance of 450 nm on four consecutive days. For the colony formation assay, cells were seeded in six-well plates in the same numbers described above. At least five days later (> 50 cells/colony and > 30 colonies), colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Photos were taken under bright light. The experiments were carried out three times.

Migration and invasion analysis

Cells were collected 48 hours after transfection and seeded (35,000 cells per well) into the upper chamber of a Transwell plate (#3422, Corning, NY, USA) with serum-free medium. The lower chamber was filled with 600 ul of medium containing 10% fetal bovine serum. After 22 hours, the cells that had migrated to the lower side of the membrane were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Cells in five random visual fields from each chamber were imaged and counted under a microscope at ×20 magnification. A Matrigel invasion chamber (#354480, Corning Biocoat, Corning) was used with the same protocol to evaluate cellular invasion.

For the scratch wound assay, transfected cells were cultured under serum-free conditions at a density of 2×10⁵ cells per well in 24-well plates. A pipette tip was used to scratch the middle of each plate. Five fixed points were imaged at ×5 magnification before and after scratching. The migration rate (%) was calculated as (original wound area – 24-hour wound area) / original wound area × 100%. All experiments were performed at least three times.

Flow cytometry analysis

Cell apoptosis was detected with an Annexin V-fluorescein isothiocyanate (FITC) apoptosis kit (#556547; Becton, Dickinson and Company, Franklin Lakes, NJ, USA) according to the manufacturer’s instructions. Cells were collected and washed three times with phosphate-buffered saline. Next, 500 uL of 1× binding buffer was used to resuspend the cells, and the cell suspension was dyed successively with Annexin V-FITC for 15 min and propidium iodide (PI) for 5 min in the dark. A flow cytometer (BD Biosciences Accuri C6; Becton, Dickinson and Company) and FlowJo software (FlowJo, Ashland, OR, USA) were used to analyze the cells. The Annexin V- / PI+, Annexin V+ / PI+, Annexin V+ / PI- and Annexin V- / PI- populations corresponded to necrotic cells (quadrant 1), late apoptotic cells (quadrant 2), early apoptotic cells (quadrant 3) and viable cells (quadrant 4), respectively.

Western blotting

Total protein was extracted using radioimmunoprecipitation assay buffer (Solarbio) and protease inhibitors (Solarbio), and was measured with a bicinchoninic acid assay (Thermo Scientific, USA). The protein lysates were mixed with loading buffer and separated by 8-10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (BioRad, Berkeley, CA, USA) at 80-120 V, and transferred to polyvinylidene difluoride membranes (EMD Millipore, Billerica, MA, USA) at 300 mA. After being blocked with 5% non-fat dried milk for two hours, the membranes were incubated with primary antibodies at 4 °C overnight. The antibodies used in this research are shown in Supplementary Table 2. Next, the blots were incubated with horseradish peroxidase-conjugated anti-rabbit or anti-mouse IgG (1:5000) secondary antibodies (Abcam, Cambridge, UK) at room temperature for two hours. Finally, proteins were visualized using an enhanced chemiluminescence (Thermo Scientific) detection system, and the images were analyzed with ImageJ software (NIH, Bethesda, MD, USA). Every experiment was repeated independently at least twice.

Statistical analysis

Statistical analyses were performed using SPSS 22.0 software (IBM SPSS Inc, Chicago, IL, USA) and GraphPad Prism 8 software (GraphPad, San Diego, CA, USA). ROC curves were applied to evaluate the diagnostic efficacy of KCNN4. Kaplan-Meier curves were used to evaluate the impact of KCNN4 expression on disease-free survival. Clinicopathological parameters were assessed using the χ2 test. The Shapiro-Wilk normality test (α = 0.05) was used to evaluate the data distribution. Normally distributed data were compared using Student’s t-test, while non-normally distributed data were analyzed using non-parametric tests (the Kruskal-Wallis test, Mann-Whitney test or Wilcoxon rank-sum test). Two-way analysis of variance was used for the CCK-8 assay. The results are presented as the mean ± standard deviation, and p-values < 0.05 were considered to be significant.