KCNJ11 as a prognostic and therapeutic target in colon adenocarcinoma

Introduction

Colorectal cancer (CRC), encompassing colon and rectum adenocarcinoma (READ), constitutes a global health crisis as the third most prevalent malignancy and second leading cause of cancer mortality worldwide (1). With >1.9 million new cases and 904,000 deaths annually (2), its metastatic form maintains a dismal 14% 5-year survival rate (3) due to diagnostic challenges, therapeutic limitations in advanced stages, and molecular heterogeneity hindering targeted therapy development (4). Ion channels have recently emerged as pivotal oncogenic regulators, where potassium channel dysregulation influences tumor proliferation, invasion, and treatment resistance through altered membrane potential and calcium signaling (5,6). This channel protein forms an octameric complex with the sulfonylurea receptor (SUR1), creating a functional adenosine triphosphate sensitive potassium (KATP) channel that couples cellular metabolism to membrane excitability (7), with mutations implicated in neonatal diabetes and MODY13 (8). Critically, despite established roles in metabolic homeostasis and emerging links between ion dysregulation and cancer progression, KCNJ11’s function in CRC pathogenesis remains undefined. Preliminary evidence suggests potassium channels may modulate cancer cell survival (9), yet no comprehensive study has addressed two fundamental questions: (I) How is KCNJ11 expressed in colon adenocarcinoma (COAD) and what is its clinical prognostic value? (II) Could KCNJ11 serve as a viable therapeutic target?

Given the profound metabolic reprogramming and ion homeostasis alterations in the CRC microenvironment, we hypothesize that KCNJ11 is dysregulated in COAD, drives aggressive tumor phenotypes, and correlates with poor clinical outcomes. To test this, we will first establish KCNJ11’s expression profile and prognostic significance through clinical cohort analysis, evaluating associations with tumor stage, differentiation status, and metastatic potential. Subsequently, therapeutic validation experiments will assess KCNJ11-targeting strategies. This integrated approach aims to position KCNJ11 as both a novel prognostic biomarker and a promising therapeutic target for precision intervention in COAD, thereby advancing our understanding of ion channels in CRC progression. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-513/rc).

Methods

Data acquisition and processing

Expression profiling and corresponding clinical survival data for COAD were obtained from The Cancer Genome Atlas (TCGA) database as the training cohort. The GSE71187 dataset from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) was utilized as the validation cohort. KCNJ11 expression across various malignancies was analyzed using the TIMER2.0 database. For colorectal adenocarcinoma-specific analysis, TCGA and Genotype-Tissue Expression (GTEx) databases were integrated, and differential expression of KCNJ11 between tumor and adjacent normal tissues was evaluated using the limma package in R. The analysis was conducted with thresholds of |logfold change (FC)| >1 and P<0.05, with Benjamini-Hochberg method applied for multiple testing correction to ensure statistical reliability (10). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Inclusion and exclusion criteria

Inclusion: (I) patients with histologically confirmed COAD; (II) availability of complete clinical records (including TNM staging, treatment history, and follow-up data).

Exclusion: (I) cases with incomplete clinical documentation; (II) non-adenocarcinoma pathological subtypes; (III) prior history of other malignancies.

Prognostic and diagnostic evaluation

Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic potential and prognostic significance of KCNJ11 in the TCGA-COAD dataset, with subsequent validation in the GSE71187 dataset.

Enrichment analysis

Samples were stratified into high-risk and low-risk groups based on the median expression level of KCNJ11. To elucidate the biological functions and pathways associated with KCNJ11, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Furthermore, gene set enrichment analysis (GSEA) was performed using the GSEA package to identify functionally enriched gene sets associated with KCNJ11 expression.

Immune cell infiltration and drug sensitivity analysis

The relative abundance of 22 infiltrating immune cell types was quantified using the CIBERSORT algorithm, enabling a comprehensive assessment of the tumor immune microenvironment (11). Pearson correlation analysis was subsequently employed to evaluate the interrelationships among these immune cell populations. Differential expression of immune checkpoint molecules was compared between the high-risk and low-risk groups to explore potential immune evasion mechanisms. Additionally, drug sensitivity profiles were predicted using the Genomics of Drug Sensitivity in Cancer (GDSC) database, providing insights into therapeutic sensitivity associated with KCNJ11 expression levels.

Single-cell analysis

We utilized the “Seurat” package (Version 4) to analyze the GSE132465 dataset, aiming to explore the immune microenvironment heterogeneity in colon cancer. Data processing included quality control, dataset integration, normalization, and clustering. Cells were retained based on the following criteria: nFeature_RNA >200 & nFeature_RNA <6,000, percent.mt <20, and nCount_RNA >1,000. Clustering was performed using the FindClusters function, and differential gene expression analysis was conducted with the FindAllMarkers function to identify characteristic gene expression patterns across distinct cell clusters. Visualization of cellular distribution and functional characteristics was achieved through t-SNE plots and bubble charts, providing critical insights into the tumor microenvironment and establishing a robust analytical framework for further biological investigations.

Cell culture

The normal intestinal epithelial cell line NCM460 and colon cancer cell lines HCT116 and LOVO were obtained from the Cell Bank of the Chinese Academy of Sciences. Cells were maintained in a humidified incubator at 37 ℃ with 5% CO2. The culture media used were RPMI-1640 for NCM460, McCOY’s 5A for HCT116, and F-12K for LOVO, all supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin to maintain optimal growth conditions.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted using the RNAeasy kit (GOONIE, 400-100, China). Subsequently, reverse transcription was performed using the reverse transcription kit (GOONIE, 500-101, China) and SYBR qPCR premix (GOONIE, 500-102, China) according to the manufacturer’s instructions. GAPDH was used as an internal control, and relative mRNA expression levels were calculated using the 2^−ΔΔCT method.

KCNJ11: Forward primer: 5′-GAAGAGTCTGGTGGGGAGTT-3′; Reverse primer: 5′-CAGGCGTGTCAGCACGTAT-3′.

GAPDH: Forward primer: 5′-CTGACTTCAACAGCGACACC-3′; Reverse primer: 5′-GTGGTCCAGGGGTCTTACTC-3′.

Statistical analysis

All data analyses and graphs were performed using R software (version 4.3.3). A P value <0.05 was considered statistically significant.

Results

Expression of KCNJ11 in pan-cancer and COAD

A comprehensive analysis of KCNJ11 expression across 33 cancer types revealed elevated expression levels in breast cancer, cholangiocarcinoma, colon cancer, kidney chromophobe, liver hepatocellular carcinoma, pheochromocytoma, prostate adenocarcinoma, rectal adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, and uterine corpus endometrial carcinoma. In contrast, significantly lower expression levels were observed in bladder urothelial carcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney renal clear cell carcinoma, and lung squamous cell carcinoma (Figure 1A). Further investigation into KCNJ11 expression in COAD demonstrated marked upregulation in tumor tissues compared to pericarcinous tissue (Figure 1B). This finding was also validated in the TCGA and GTEx databases (Figure 1C). Using paired-sample analysis, KCNJ11 still showed high expression in COAD (Figure 1D).

Figure 1 mRNA level expression of KCNJ11. (A) Expression levels of KCNJ11 in pan-cancer. (B) Differential expression of KCNJ11 in unpaired samples from the TCGA database. (C) Differential expression of KCNJ11 in TCGA and GTEx datasets. (D) Differential expression of KCNJ11 in paired samples from the TCGA database. *, P<0.05; **, P<0.01; ***, P<0.001. GTEx, Genotype-Tissue Expression; TCGA, The Cancer Genome Atlas; TPM, transcripts per kilobase million.

Diagnostic and prognostic value of KCNJ11 in COAD

The KM survival curves indicated that patients with high KCNJ11 expression in the TCGA database had significantly lower survival rates compared to those with low expression, a finding that was also confirmed in the GSE71187 dataset (Figure 2A). The ROC curve analysis demonstrated that the area under the curve (AUC) for KCNJ11 in the TCGA database was 0.866, while in the GSE71187 dataset, it was 0.877 (Figure 2B).

Figure 2 Diagnostic and prognostic value of KCNJ11 in COAD. (A) KM survival curves stratified by KCNJ11 expression levels in the TCGA database and GSE71187 set. (B) ROC curve analysis of KCNJ11’s diagnostic performance in the TCGA database and GSE71187 set. AUC, area under the curve; CI, confidence interval; COAD, colon adenocarcinoma; FPR, false positive rate; HR, hazard ratio; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TPR, true positive rate.

Enrichment analysis based on KCNJ11 expression

We divided the samples into high-risk and low-risk groups based on KCNJ11 expression (Figure 3A) and conducted functional enrichment analysis. GSEA revealed significant enrichment of the MYC inhibition pathway in COAD, suggesting the role of MYC-related pathways in KCNJ11-mediated COAD progression (Figure 3B). Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated significant enrichment of the high-risk KCNJ11 group in muscle cell contraction (Figure 3C), neuroactive ligand-receptor interactions, and cyclic adenosine monophosphate (cAMP) signaling pathways (Figure 3D). Furthermore, hallmark pathway analysis identified the activation of gene sets associated with E2F targets, MYC targets, and glycolysis (Figure 3E), corroborating the findings from GSEA.

Figure 3 Enrichment analysis of KCNJ11 in COAD. (A) Volcano plot depicting differentially expressed genes between high and low KCNJ11 expression groups. (B) GSEA analysis of the MYC pathway based on KCNJ11 expression levels. (C) GO analysis based on the expression level of KCNJ11. (D) KEGG pathway analysis based on the expression level of KCNJ11. (E) Hallmark pathway analysis based on the expression level of KCNJ11. COAD, colon adenocarcinoma; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.

KCNJ11 immunological and drug sensitivity analysis

We analyzed the immune infiltration of patients in high-risk and low-risk groups using the CIBERSORT algorithm. The results indicated an increased infiltration of M0 macrophages in the high-risk group (Figure 4A). Furthermore, KCNJ11 expression was negatively correlated with the immune checkpoint analyses of CD274 (R=−0.19, P=1.3e−05) and HAVCR2 (R=−0.12, P=0.005), while it showed a positive correlation with PDCD1 (R=0.14, P=0.001) (Figure 4B). Using the half-maximal inhibitory concentration (IC50) values as a measure of drug sensitivity, we found that AZD7762 (P=0.02), GDC0810 (P=0.02), GSK591 (P=0.03), IWP-2 (P=0.01), and Wee1 inhibitors (P=0.01) (Figure 4C) had lower IC50 values in the high-risk group, suggesting that these drugs may be more effective for patients in this group.

Figure 4 Immune and drug sensitivity analysis based on KCNJ11 expression. (A) Immune cell infiltration profiles assessed using CIBERSORT analysis. (B) Expression of CD274, HAVCR2 and PDCD1 in high and low KCNJ11 expression group. (C) Drug sensitivity analysis, highlighting differential responses to therapeutic agents based on KCNJ11 expression levels. ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

Single-cell analysis of COAD

After quality control, dataset integration, and normalization, we identified 2,000 highly variable genes, with a volcano plot displaying the top 10 genes (Figure 5A). We manually annotated the cell clusters into 12 major cell types (Figure 5B). The expression levels of the top 5 marker genes for each cell type were visualized (Figure 5C). Our analysis revealed that macrophages in the tumor microenvironment exhibited different infiltration patterns between the high-risk and low-risk KCNJ11 groups (Figure 5D), with KCNJ11 being highly expressed in macrophages within the tumor group (Figure 5E). Based on these findings, we focused on macrophages for pathway enrichment analysis, which indicated that macrophages could activate the ribosome pathway while inhibiting cytokine interactions and chemokine signaling pathways (Figure 5F).

Figure 5 Single-cell analysis related to KCNJ11. (A) Volcano plot of highly variable genes from GSE132465. (B) Single-cell annotation for normal and tumor groups. (C) Bubble plot of the top 5 marker genes for each cell type. (D) Comparison of cell subtypes between normal and tumor groups. (E) Expression of KCNJ11 in macrophages. (F) KEGG analysis related to macrophages. cDC, conventional dendritic cell; KEGG, Kyoto Encyclopedia of Genes and Genomes; NK, natural killer; pDC, plasmacytoid dendritic cell.

In vitro experimental validation of KCNJ11

To validate KCNJ11 expression levels, we compared the mRNA levels of KCNJ11 in two COAD cell lines (HCT116 and LOVO) with those in the normal intestinal epithelial cell line NCM460. The results showed that the expression levels of KCNJ11 in HCT116 and LOVO were significantly higher than those in NCM460, with statistical significance (Figure 6).

Figure 6 Validation of KCNJ11 expression levels in cell lines.

Discussion

This study establishes KCNJ11 as a clinically significant biomarker and therapeutic target in COAD. Our analysis demonstrates consistent upregulation of KCNJ11 in COAD tissues compared to normal controls, correlating with reduced overall survival. These findings align with emerging evidence implicating potassium channels in tumor pathogenesis across cancer types (12,13).

A key contribution lies in elucidating KCNJ11’s dual regulatory mechanisms. We identified significant negative correlations with immune checkpoint molecules programmed death-ligand 1 (PD-L1) and TIM-3, alongside positive association with CTLA-4. Furthermore, KCNJ11 expression strongly correlates with macrophage infiltration, particularly M0 subtype, potentially mediated through chemokine pathways involving CCL2 and CCL5 (14,15).

This immune modulation converges with metabolic reprogramming, where pathway analyses reveal KCNJ11’s association with activated glycolysis and MYC signaling (16,17). Tumor-derived lactate from enhanced glycolysis may further promote M2 macrophage polarization, establishing a pro-tumorigenic feedback loop (16-18).

Single-cell profiling provides additional mechanistic insight, showing altered macrophage transcriptional programs in high-KCNJ11 environments characterized by enhanced ribosome biogenesis and suppressed cytokine signaling (19). This metabolic-immune crosstalk extends to therapeutic implications, where drug sensitivity analysis indicates heightened responsiveness to selected kinase inhibitors and DNA damage response agents in KCNJ11-high tumors (20,21).

While this study comprehensively maps KCNJ11’s multifaceted roles through bioinformatics approaches, certain limitations warrant acknowledgment. Prospective clinical validation is needed to confirm prognostic utility. The precise mechanisms underlying KCNJ11-mediated macrophage polarization require experimental verification using in vitro and in vivo models. Similarly, the molecular basis for differential drug sensitivity merits deeper investigation.

Collectively, our findings position KCNJ11 as a central regulator bridging metabolic adaptation and immune evasion in COAD, offering new avenues for biomarker-driven therapeutic strategies

Conclusions

This study establishes KCNJ11 as an independent prognostic biomarker in COAD, demonstrating its novel function in driving tumor progression through coordinated activation of the MYC pathway and macrophage infiltration. The significant associations between KCNJ11 expression and immune checkpoint modulation, combined with its influence on chemotherapeutic sensitivity, position this ion channel as a promising multi-target therapeutic node. Future investigations should prioritize mechanistic dissection of KCNJ11-mediated immunometabolic crosstalk and translational validation of KCNJ11-targeted strategies in preclinical models.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-513/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-513/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-513/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-513/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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