A novel prognostic and immune-related biomarker KCNC3 in colorectal cancer: a pan-cancer analysis

Introduction

As reported by the American Cancer Society in 2025, cancer remains the second major cause of death, despite a year-by-year decline in mortality (1). Notably, colorectal cancer (CRC) has increasingly occurred in younger people, making it the leading cause of cancer deaths in men aged below 50 years and the second leading cause in women aged below 50 years. The estimated mortality of CRC is not optimistic across multiple countries (2). To date, cancer is still the greatest threat to human life and health in public (3,4).

The tumor immune microenvironment (IME) is a collection of immune infiltrates, including immune cells, stromal cells, and cytokines that permeate and reside between tumor cells (5,6), and it possesses an intimate relation with tumor cells and enhances or inhibits their growth. Pro-tumorigenic factors in the IME are hotspots of current research. For example, vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) secreted by lymphocytes and macrophages in the IME can contribute to tumor angiogenesis by activating multiple pathways downstream [e.g., Raf-mitogen-activated protein kinase kinase-mitogen-activated protein kinase (Raf-MEK-MAPK), and phosphatidylinositol-3 kinase/protein kinase B (PI3K/AKT)] (7), thereby creating necessary conditions for tumor proliferation and invasion. A variety of anti-inflammatory factors suppress the tumor immune response by inhibiting cytotoxic T-cell function and T-cell infiltration (8,9). Cytokines (e.g., transforming growth factor-β (TGF-β), platelet-derived growth factor (PDGF), and epidermal growth factor (EGF)] in the IME achieve epithelial-mesenchymal transition (EMT) in tumor cells by activating transcription factors Snai1, Slug, and Twist1 (10). Some biomarkers can influence the changes in the IME state, and investigating them is clinically important for cancer immunotherapy and targeted therapy in the era of diversified tumor therapies.

Over the years, the mechanisms of cancer have been increasingly revealed as cellular experiments and molecular techniques advance. In this context, voltage-gated potassium (Kv) channels have aroused global attention for their regulatory effects on many biological behaviors of cancer (11). Encoded by the Kv channel subfamily C member 3 (KCNC3), Kv3.3 is a tetrameric channel protein, with six transmembrane fragments and one foldback loop in each subunit (12). Mutations in KCNC3 were initially identified as the cause of spinocerebellar ataxia 13 (13), and current studies on KCNC3 focus primarily on nervous system diseases (12,14,15) and less on tumors. To fill the gap in research, this study was conducted to explore the differential expression, prognostic value, IME signature, and biological functions of KCNC3 in pan-cancer, especially CRC, by a bioinformatics analysis with The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCEL), and Tumor-Immune System Interaction Database (TISIDB) databases, and to validate the findings in clinical samples by immunohistochemistry (IHC). This study demonstrates the prognostic and immune value of KCNC3 across cancers, especially CRC, and its potential as a novel prognostic and immune-related biomarker in CRC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1318/rc).

Methods

Data collection

The data on messenger RNA (mRNA) expression and mutation of 33 cancers in human normal and cancer tissues were obtained from TCGA (https://portal.gdc.cancer.gov/), the clinical and prognostic data were obtained from the University of California, Santa Cruz Xena (http://xena.ucsc.edu/), and the data on pan-cancer cells were obtained from the CCLE (https://portals.broadinstitute.org/ccle/). The association of KCNC3 with immune indicators was investigated in TIMER2 and TISIDB (http://timer.cistrome.org/; http://cis.hku.hk/TISIDB/index.php). The data on drug sensitivity were also downloaded from CellMiner (https://discover.nci.nih.gov/cellminer/home.do).

Differential expression of KCNC3

The differential expression of KCNC3 mRNA was detected in 33 cancers by TCGA. The transcript data were normalized by log₂ (fpkm+1) transform. The KCNC3 expression in pan-cancer (CCLE) was analyzed, and the results were visualized using the R “ggplot2” package.

Clinical and prognostic analyses

The data on clinical stage, overall survival (OS), and progression-free interval (PFI) in TCGA patients were downloaded to further investigate the relation of gene expression with clinical stage and prognosis. The “ggplot2” package was used to draw the box plot for the differential expression of KCNC3 across stages. Patients were categorized into a high expression (Hexp) and a low expression (Lexp) group based on the median of KCNC3 expression, and the two groups underwent Kaplan-Meier (K-M) survival analyses in pan-cancer using the “survival” and “survminer” packages. In addition, the association of gene expression with survival status was explored by univariate Cox analyses.

Association of KCNC3 expression with IME signature in pan-cancer

The RNA-seq data were analyzed in pan-cancer by the CIBERSORT to calculate the proportions of 22 immune infiltrating cells, analyze the association of KCNC3 expression with immune cells, and also discuss the disparity in immune cell infiltration in the Hexp and Lexp groups. The immune score, stroma score, and purity were assessed in pan-cancer by the Estimated algorithm. Based on the algorithm of IME signature scores and the related gene sets (16,17), the association of KCNC3 expression with the IME signature score in pan-cancer was analyzed, and the difference was compared between the Hexp and Lexp groups. Finally, the relation between KCNC3 expression profiles and the immune system was summarized in TIMER. The results were analyzed and visualized using the “ggplot2”, “ggpubr”, and “cowplot” packages.

Association of KCNC3 expression with specific genes, microsatellite instability (MSI), and tumor mutation burden (TMB)

The potential relation of KCNC3 expression with specific genes, including critical genes encoding immunomodulatory factors and tumor-associated pathways in TISIDB, was investigated, with a heatmap presented using the “pheatmap” package. The association between KCNC3 and known prognostic markers for CRC was also investigated, with a scatter plot presented using the “ggscatter” function. TMB refers to the total somatic gene coding errors, insertions, base substitutions, or deletions every million bases. This study defined TMB as the number of missense mutation sites/the total length of the protein-coding region. Besides, MSI refers to a pronounced mutational phenotype induced by defective DNA mismatch repair. The data on MSI for each patient were obtained from a previous study (18). The results were analyzed and plotted into a radar diagram using the R “limma”, “Fmsb”, and “dplyr” packages.

Drug sensitivity analysis

The US National Cancer Institute 60 human tumor cell line (NCI-60) is the most popular cancer cell sample cluster for anticancer drug analysis nowadays. The drug sensitivity and RNA-seq data were downloaded from CellMiner, and the association of KCNC3 with the half-maximal inhibitory concentration (IC₅₀) of common antitumor drugs was investigated by Spearman association analyses.

Pathway enrichment analysis

Gene set variation analysis (GSVA), a non-parametric unsupervised method for evaluating gene set enrichment in transcriptomes, was utilized to give an overall score to each C2 gene set downloaded from the MisgDB (v7.0) (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), assessing the potential biological functional changes in the Hexp and Lexp groups. In addition, gene set enrichment analysis (GSEA) was conducted by the “clusterprofiler” and “enrichplot” packages, and the pathway enrichment patterns were ranked and compared between the two groups to detect the possible molecular mechanism of KCNC3 in 33 cancers.

Weighted gene co-expression network analysis (WGCNA)

A WGCNA network was created to identify co-expressed gene modules and explore their association with KCNC3. The top 10,000 genes in terms of the variance in CRC were screened using the R “limma” package, and their co-expression network was created using the “WGCNA” package, with a soft threshold of 8. The module that had the strongest association with KCNC3 was selected, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses by the “Clusterprofilter” package.

Nomogram

A nomogram was created by the R “rms” package with the KCNC3 expression and clinical parameters (age and cancer stage) to predict the 3- and 5-year OS in CRC, and calibration curves were also plotted to assess its accuracy.

IHC staining

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Dalian University Affiliated Xinhua Hospital Ethics Committee (No. 2024-109-01). Informed consent was waived in this retrospective study. Sixty cases of cancer tissues were collected from CRC patients who underwent surgical excision in Dalian University Affiliated Xinhua Hospital, as well as 30 cases of corresponding para-carcinoma normal tissues. The paraffin-embedded tissues were cut into 3 µm-thick sections, deparaffinized, and hydrated, followed by high-pressure antigen retrieval. After endogenous peroxidase was eliminated by 20 min of incubation (3% hydrogen peroxide) at room temperature, the sections underwent 30 min of incubation (5% bovine serum albumin) at room temperature to block the nonspecific binding sites. Then they were incubated with anti-KCNC3 primary antibody (1:100, NOVUS, item No. NBP-81337, rabbit-derived) at 4 ℃ overnight, followed by 30 min of incubation with the secondary antibody (1:50, Immunoway, item No. RS0002, goat anti-rabbit) at room temperature. Finally, the sections were counterstained by hematoxylin after 3,3'-diaminobenzidine (DAB) staining.

An IHC score (percentage of positive cells × intensity of staining) was assigned for subsequent statistical analyses. Percentage of positive cells: <25% as 1, 25–50% as 2, 51–75% as 3, and >75% as 4; intensity of staining: negative (no staining) as 0, weakly positive (light yellow) as 1, positive (brown yellow) as 2, and strongly positive (dark brown) as 3. Two independent pathologists assessed under a light microscope (400×), and the results were averaged.

Statistical analysis

R4.3.2 was utilized for statistical analyses. The data were compared between the two groups by the Wilcoxon signed-rank test, and the association of KCNC3 expression with the clinical stage was analyzed by the Kruskal-Wallis test. The significance of the difference in the survival curves was detected by the log-rank test. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. The association of KCNC3 with immune cells, immune indicators [TMB, MSI, immune checkpoints (ICs), and chemokines], and drug sensitivity was revealed by Spearman association analyses. The nomogram was created by multivariate Cox regression analyses. P<0.05 (*, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001) was deemed statistical significance.

Results

KCNC3 expression in pan-cancer

To explore the KCNC3 expression in 33 cancers and corresponding para-carcinoma tissues, pan-cancer samples were included from TCGA (Table S1). KCNC3 mRNA was significantly higher in a variety of cancers (BLCA, CESC, CHOL, COADREAD, ESCA, HNSC, LIHC, PCPG, and STAD) than in para-carcinoma tissues (Figure 1A). Moreover, the data on the relative expression of KCNC3 across tumor cell lines were also acquired from the CCLE (Figure 1B). KCNC3 mRNA had no high expression in cancer and normal tissues, but a significant difference was still observed.

Figure 1 Expression of KCNC3 in pan-cancer and its association with clinical stage. (A) Expression differences of KCNC3 mRNA in 33 human tumors and their corresponding normal tissues in the TCGA database. (B) Expression of KCNC3 mRNA in 30 human cancer cells in the CCEL database. (C-F) There was a significant association between KCNC3 and the stages of the four tumor types: ESCA, KIRP, TGCT, and UVM. **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant. CCEL, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas.

KCNC3 expression correlated with clinical stage and prognosis

The difference in the KCNC3 expression pattern was analyzed across clinical stages of pan-cancer. The results showed that the KCNC3 mRNA expression was significantly different across the clinical stages of four cancers (ESCA, KIRP, TGCT, and UVM) (Figure 1C-1F). The KCNC3 expression in different clinical stages of other cancers is displayed in Figure S1. The KCNC3 mRNA expression exhibited an overall increasing trend as the stage increased, with a significant difference between lower (I/II) and higher stages (III/IV). In addition, survival status was assessed in pan-cancer in the Hexp and Lexp groups. The OS displayed great differences in seven cancers, with the greatest difference in COCDREAD (P=0.01), and the high KCNC3 expression correlated with unfavorable OS in COADREAD, GBM, KIRP, PRAD, and LAML (Figure 2A-2E). Meanwhile, the high KCNC3 expression also correlated with better OS in PAAD and LUAD (Figure 2F,2G). The OS in other cancers is shown in Figure S2. To exclude bias resulting from non-tumor factors, PFI was also used as a survival indicator. Up-regulation of KCNC3 predicted shorter PFI in COADREAD, PRAD, and STAD (Figure 2H-2J) and longer PFI in HNSC, LUAD, and PAAD (Figure 2K-2M). The PFI in other cancers is shown in Figure S3. Besides, KCNC3 was identified as a risk factor for OS in five cancers (COADREAD, KIRP, LAML, PCPG, and PRAD) and a protective factor for OS in three cancers (LGG, PAAD, and SKCM) (Figure 2N). Similarly, KCNC3 was identified as a risk factor for PFI in five cancers (COADREAD, ESCA, KIRP, PRAD, and STAD) and a protective factor for PFI in three cancers (LGG, LUAD, and PAAD) (Figure 2O). To sum up, KCNC3 exhibited unique value in predicting the CRC prognosis.

Figure 2 Prognostic value of KCNC3 in pan-cancer. (A-G) OS curves of COADREAD, GBM, KIRP, PRAD, LAML, PAAD, and LUAD. (H-M) PFI curves of COADREAD, PRAD, STAD, HNSC, LUAD, and PAAD. (N) Forest plot of hazard ratios for the association between KCNC3 expression and OS in pan-cancer. (O) Forest plot of hazard ratios for the association between KCNC3 expression and PFI in pan-cancer. CI, confidence interval; Hexp, high expression; Lexp, low expression; OS, overall survival; PFI, progression-free interval.

KCNC3 expression closely correlated with IME in pan-cancer

After the value of KCNC3 in prognosis was determined, the association of KCNC3 expression with the IME was investigated. First, the association of KCNC3 expression in pan-cancer with 22 immune cells in TCGA was analyzed by the CIBERSORT (Figure 3A). The results revealed that the KCNC3 expression was highly associated with regulatory T cells (Tregs) in six cancers, with macrophages M2 in nine cancers, and with T cells CD4 memory activated in 15 cancers. To increase credibility, the difference in immune cells was also compared between the Hexp and Lexp groups in pan-cancer (Figure S4), especially in CRC. T cells CD4 memory resting and activated, macrophages M1 and M2, Tregs, and Neutrophils showed great differences between the two groups in CRC (Figure 3B). Besides, the immune score, stroma score, and purity in pan-cancer were assessed for their association with KCNC3 using the Estimated algorithm (Figure 3C). The KCNC3 expression was highly associated with the immune score in 19 cancers and with the stroma score in 13 cancers. Notably, KCNC3 had close associations with the immune score, stroma score, and purity in 11 cancers (ACC, COADREAD, LGG, LIHC, MESO, PAAD, PCPG, SARC, SKCM, TGCT, and YHCA). Next, we assessed the IME signature score in pan-cancer. The KCNC3 expression was significantly associated with several IME signature scores in pan-cancer (Figure 3D). Similarly, the difference in the IME signature score was compared in the Hexp and Lexp groups in pan-cancer (Figure S5), especially in CRC. TMEscore, CD_8_T_effecto, Immune_Checkpoin, TMEscoreA, TMEscoreB, Nucleotide_excision_repair, Mismatch_Repair, Pan_F_TBR, DNA_replication, EMT1, EMT2, and EMT3 scores all showed great differences between the two groups in CRC (Figure 3E). Finally, the relation of KCNC3 with the immune system was analyzed by six algorithms in TIMER (Figure 3F). Similarly, a considerable association was present between the KCNC3 expression in pan-cancer and the immune system.

Figure 3 Analysis of KCNC3 immune microenvironment in pan-cancer. (A) Association between KCNC3 expression and infiltration of 22 immune cells. (B) Differences in infiltration of 22 immune cells between Hexp and Lexp groups in COADREAD. (C) Association between pan-cancer KCNC3 expression and immune score, stromal score and tumor purity under the Estimated score. (D) Association between KCNC3 expression and tumor microenvironment feature score in pan-cancer. (E) Differences in tumor microenvironment feature scores between Hexp and Lexp groups in COADREAD. (F) Association between KCNC3 and immune system in pan-cancer under 6 algorithms in the TIMER database. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant; ?, value of 0, unable to be statistically analyzed. Hexp, high expression; Lexp, low expression.

Co-expression of KCNC3 with critical regulatory genes

The co-expression of KCNC3 with critical genes encoding immunomodulatory factors in TISIDB, including chemokines and their receptors, ICs, immunostimulatory and immunosuppressive factors, and major histocompatibility complex (MHC), was investigated (Figure 4A-4F). In addition, critical genes of tumor-associated pathways, such as cell hypoxia, DNA repair, ferroptosis, pyroptosis, autophagy, TGF_BETA_SIGNALING, and TNFA_SIGNALING_VIA_NFKB, were also incorporated into the analysis (Figure 4G-4M). The results revealed significant co-expression of KCNC3 with the critical genes of the above pathways in most of the cancers, especially positive co-expression in COADREAD, SKCM, PRAD, and LAML.

Figure 4 Co-expression of KCNC3 and key genes. (A-F) Association of KCNC3 with key genes encoding chemokines and their receptors, immune checkpoints, immunostimulatory factors, immunosuppressive factors, and MHC. (G-M) Association of KCNC3 with key genes in tumor-related pathways such as hypoxia, DNA repair, ferroptosis, pyroptosis, autophagy, TGF_BETA_SIGNALING, TNFA_SIGNALING_VIA_NFKB. *, P<0.05; **, P<0.01; ***, P<0.001. MHC, major histocompatibility complex.

Association of KCNC3 expression with MSI and TMB

MSI and TMB are novel markers associated with the immunotherapy response (19-21). The relation of KCNC3 with MSI and TMB in pan-cancer was investigated. The results revealed great associations of the KCNC3 expression with TMB in several cancers, especially significant positive associations in COADREAD, HNSC, PCPG, and THYM (Figure 5A). Similarly, significant positive associations were detected between KCNC3 and MSI in LGG, PRAD, BLCA, THCA, LUAD, LUSC, and ACC (Figure 5B). To sum up, up-regulation of KCNC3 often predicts higher TMB and MSI in various cancers.

Figure 5 Radar chart of the association between KCNC3 and TMB and MSI in pan-cancer. (A) Association between KCNC3 and TMB in pan-cancer. (B) Association between KCNC3 and MSI in pan-cancer. *, P<0.05; **, P<0.01; ***, P<0.001. MSI, microsatellite instability; TMB, tumor mutation burden.

KCNC3 as a predictor for drug sensitivity

The association of KCNC3 expression with chemotherapeutic drug sensitivity was explored in CellMiner. KCNC3 was associated with tolerance to various antitumor drugs. Specifically, KCNC3 had significant positive associations with the tolerance to fulvestrant, acetalax, and SR16157, and significant negative associations with the tolerance to epothilone B, staurosporine, and asparaginase (Figure 6).

Figure 6 Dot plot of KCNC3 expression and drug sensitivity.

Pathway enrichment analysis of KCNC3

To deeply explore the underlying molecular mechanism of KCNC3 in pan-cancer, the two groups underwent the GSVA, and the score was compared (Figure S6). The angiogenesis pathway score was significantly higher in the Hexp group in 16 cancers, which may offer sufficient nutrients for tumor proliferation and growth. In addition, the GSEA using the c2.cp.kegg.v7.4 dataset revealed the top five differential pathways between the two groups (Figure S7). Cell cycle, endocytosis, and focal adhesion pathways were enriched in the Hexp group in many cancers, and we also surprisingly found the enrichment of tumor-associated pathways in this group. KEGG_PATHWAYS_IN_CANCER was enriched in the Hexp group in COADREAD, DLBC, ESCA, KIRP, KICH, LIHC, PRAD, OV, STAD, TGCT, THYM, and UCS, and KEGG_JAK_STAT_SIGNALING_PATHWAY, KEGG_MAPK_SIGNALING_PATHWAY, and KEGG_WNT_SIGNALING_PATHWAY that facilitated tumor proliferation were also obviously enriched in the Hexp group in more than half of cancers. In particular, both GSEA and GSVA confirmed that multiple signaling pathways, including but not limited to ANGIOGENESIS, TNFA_SIGNALING_VIA_NFKB, EPITHELIAL_MESENCHYMAL_TRANSITION, TGF_BETA_SIGNALING, and KEGG_MAPK_SIGNALING_PATHWAY that promoted tumor growth, were enriched in the Hexp group in COADREAD (Figure 7A,7B).

Figure 7 GSVA, GSEA, and WGCNA of colorectal cancer. (A) GSVA of colorectal cancer. (B) Top 5 KCNC3-related pathways enriched by GSEA in COADREAD. (C) Scale independence and mean connectivity diagram of WGCNA analysis. (D) Cluster dendrogram diagram of WCGNA. (E) Association between KCNC3 expression and different modules. (F) GO analysis results of magenta module genes. (G) KEGG analysis results of magenta module genes. BP, biological process; CC, cellular component; GO, Gene Ontology; GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; WGCNA, weighted gene co-expression network analysis.

WGCNA of CRC

A WGCNA network was further created with the KCNC3 expression in CRC to explore the KCNC3 co-expression network in CRC, with a soft threshold of 8 (Figure 7C). Eleven gene modules were detected based on the topological overlap matrix (TOM) (Figure 7D). Furthermore, in terms of the module-trait association, the magenta module showed the closest association with the KCNC3 expression (Cor =0.28, P=1e−13) (Figure 7E). Then the magenta module genes were extracted for pathway analyses. The GO analysis revealed that the module genes were enriched primarily in basement membrane, collagen-containing extracellular matrix, and endoplasmic reticulum lumen pathways (Figure 7F), and they were also enriched mainly in PI3K/AKT, Cytoskeleton in muscle cells, and ECM-receptor interaction pathway (Figure 7G).

Construction of a nomogram for CRC and association of KCNC3 with other prognostic markers

The nomogram prediction model for CRC was established based on the KCNC3 expression, sex, age, and tumor stage (Figure 8A). The logistic regression analysis showed that the clinical indicators and the distribution of KCNC3 expression in CRC samples contributed differently to the overall scoring. The calibration curve was also plotted for the nomogram for 1-, 3-, and 5-year OS, and the model fit was good (Figure 8B). Additionally, four biomarkers known to be associated with poor CRC prognosis were selected to investigate their associations with KCNC3 (22-26). The results showed that KCNC3 exhibited significant positive co-expression with all four biomarkers (Figure 8C-8F).

Figure 8 Construction of nomogram for colorectal cancer and association of KCNC3 with other prognostic markers. (A) Construction of nomogram for prediction of 1-year, 3-year and 5-year OS of colorectal cancer based on KCNC3. (B) Calibration curve of the nomogram. (C-F) Associations of KCNC3 with FGFR1, SPHK1, CD163, and CPLX1. OS, overall survival.

KCNC3 expression in CRC samples and validation of its prognostic value

To validate the prognostic value of KCNC3 in CRC, IHC staining was performed on CRC and para-carcinoma normal tissues. The voltage-gated potassium channel KCNC3 expression was mainly present in the cytoplasm and membrane of CRC cells, and it was significantly higher in CRC tissues (Figure 9A,9B). Based on the median IHC score, 60 patients were assigned to the Hexp and Lexp groups (30 cases each). As shown in the K-M curves, the high voltage-gated potassium channel KCNC3 expression correlated with unfavorable prognosis (Figure 9C), further suggesting at the protein level that KCNC3 can act as a prognostic marker for CRC.

Figure 9 IHC experiment and prognostic validation. (A) Comparison of IHC staining of voltage-gated potassium channel KCNC3 in colorectal cancer tissues and para-carcinoma normal tissues. (B) Box plot of the difference in IHC scores between colorectal cancer tissues and para-carcinoma normal tissues. (C) OS curves in the Hexp and Lexp groups. ***, P<0.001. Hexp, high expression; IHC, immunohistochemistry; Lexp, low expression; OS, overall survival.

Discussion

With the in-depth study on the molecular mechanisms of cancer, aberrant expression of the Kv family has been identified as one of the contributors to tumor proliferation (11). Research suggests that Kv channels, as important proteins for stabilizing cell membrane potential and keeping cellular homeostasis, are aberrantly expressed in various cancers (27), which accelerate the cell cycle transition and cancer cell proliferation by regulating cell cycle proteins and their dependent kinases (28). They can also upregulate the expression of vimentin to enable tumor cells to undergo EMT and acquire migratory capability (29,30). In addition, Kv channels participate in activating extracellular signal-regulated kinase (ERK) and PI3K/AKT pathways in tumors, leading to tumor progression (29,31,32). Most of the Kv channels may exert a “permissive effect” as “regulators” of the proliferation of a variety of cancer cells (33). Pharmacological inhibition of Kv channels can greatly restrain the growth of cancer cells (34). Based on the above theories, the differential expression, prognostic value, drug sensitivity characteristics, and possible pathways regulated by KCNC3 in pan-cancer were investigated, and its immune value was assessed in pan-cancer by analyzing the IME and its co-expression with critical genes in this study. Finally, the KCNC3 expression and prognostic value were validated in CRC.

The results showed that the KCNC3 expression was significantly higher in a variety of cancer cells than in para-carcinoma tissues in TCGA and CCLE. Aberrantly expressed potassium channel proteins can regulate the cell cycle, facilitate EMT, and activate tumor-associated pathways (29-32), and minor changes at the transcriptome level may cause alterations in protein levels and tumor biological behaviors. We performed IHC on pathological sections of CRC patients to confirm the difference in the expression of voltage-gated potassium channel KCNC3. Expectedly, the voltage-gated potassium channel KCNC3 expression was significantly higher in CRC tissues.

The KCNC3 expression was associated with the clinical stage of various cancers, with a significant difference between lower (I/II) and higher stages (III/IV). The KCNC3 expression also rose with the tumor progression, indirectly corroborating that KCNC3 may mediate tumor migration and proliferation, resulting in tumor deterioration.

The prognostic value of KCNC3 was evaluated using K-M curves and univariate Cox analyses. The high KCNC3 expression was linked to an unfavorable prognosis in at least five cancers. In particular, the high KCNC3 expression corresponded to poorer OS and PFI in CRC and thus could serve as a risk factor for unfavorable OS and PFI in CRC. Given the superior prognostic value of KCNC3 in CRC, our findings were also validated in clinical patients. Consistent results with the TCGA were obtained that high IHC scores were associated with poor OS in CRC. It further suggested the prognostic value of KCNC3 in CRC and its potential as a novel biomarker for CRC prognosis.

As mentioned earlier, tumor proliferation, migration, and apoptosis are inextricably linked to the IME state (35,36). The tumor IME was assessed using several methods. First, the immune cell infiltration was evaluated in pan-cancer, and an association of KCNC3 with a variety of immune cells was found. Specifically, Tregs and macrophages M2 usually played as “traitors” in the human body, which prevented CD8⁺ T cells from “attacking” tumor cells by secreting inhibitory cytokines, making tumor cells “escape” from the immune response and await an opportunity for proliferation (37-40). Moreover, the KCNC3 expression had positive associations with the infiltration of macrophages M2 and Tregs, and the Hexp group had significantly greater infiltration of Tregs and macrophages M2, especially in CRC, indicating that KCNC3 may mediate the tumor proliferation by enhancing the infiltration of Tregs and macrophages M2. KCNC3 also closely correlated with the IME signature scores in pan-cancer, including EMT and Pan_F_TBRs related to tumor invasion (16), TMEscoreB related to resistance to immunotherapy (17), and Immune_Checkpoin related to immune escape (41-44). Notably, all these IME signature scores were highly positively associated with KCNC3 in CRC, and they were also significantly higher in the Hexp group, which may explain the worse prognosis in patients with high KCNC3 expression. Finally, the great association of KCNC3 with the immune score, stroma score, and purity was also determined in pan-cancer using the ESTIMATE. To sum up, KCNC3 may alter the tumor IME by multiple pathways, especially in CRC, and the high expression of KCNC3 may mediate the pro-tumorigenic transformation of the IME in CRC.

As immune checkpoint blockers (ICBs) against cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) emerge (45,46), cancer immunotherapy has been gradually developed. Different from traditional chemoradiotherapy and surgery, however, immunotherapy achieves significant effects only on a few patients, and most patients are still non-responsive to ICBs (47). Therefore, identifying the patients who are more responsive to ICBs followed by targeted immunotherapy is important (48). A large number of retrospective studies have identified MSI and TMB as predictors for the immunotherapy effect, and the higher the TMB and MSI, the more neoantigens exposed, yielding better effects of immunotherapy (20,49). In this study, the KCNC3 expression had significant positive associations with the TMB or MSI in a variety of cancers (COADREAD, HNSC, THYM, and ACC). In addition to ICs, chemokines and their receptors, and MHC are also potential targets for immunotherapy (50-52). This study revealed significant co-expression of KCNC3 with the critical genes encoding ICs, chemokines and their receptors, and MHC in various cancers, especially positive co-expression in CRC. It suggests that the high KCNC3 expression may indicate exposure to more immunotherapy targets in CRC, producing better efficacy. In addition, KCNC3 also displayed significant co-expression with the critical genes in immunostimulatory and immunosuppressive factors, cell hypoxia, DNA repair, ferroptosis, pyroptosis, autophagy, TGF-β pathway, and nuclear factor kappa B/tumor necrosis factor-α (NF-κB/TNFA) pathway. Therefore, KCNC3 is expected to act as a new predictor for the immunotherapy effect and a new target for tumor therapy.

The association of IC₅₀ with KCNC3 was analyzed, and it was confirmed that the IC₅₀ of three chemotherapeutic drugs (SR16157, fulvestrant, and acetalax) rose with the increase in the KCNC3 expression. These three drugs have great potential for treating breast cancer. In particular, fulvestrant has been widely applied clinically as a standard treatment for patients with breast cancer that progressed or relapsed following anti-estrogen therapy or postmenopausal patients with estrogen receptor-positive locally advanced or metastatic breast cancer; it can also significantly improve patient survival rates when combined with other drugs (such as capivasertib and anastrozole) (53-56). KCNC3 may contribute to resistance to the aforementioned chemotherapeutic drugs for breast cancer, making it a promising potential target for reversing drug resistance in breast cancer.

The GSVA revealed that in pan-cancer, the high expression of KCNC3 was associated with angiogenesis, EMT, TGF-β, and JAK-STAT signaling pathways that contributed to tumor proliferation (57-60). In contrast, the low expression of KCNC3 had associations with DNA repair, G2/M checkpoint, and p53 pathway. A similar conclusion was made by the GSEA that the angiogenesis, MAPK, and TGF-β pathways were enriched primarily in the Hexp group. In summary, KCNC3 may be involved as a critical regulator in the aberrant activation of multiple tumor-associated pathways.

Since KCNC3 demonstrated higher prognostic and immune value in CRC than in other cancers, this study focused on CRC. WGCNA was conducted to cluster the co-expressed genes into different modules in CRC, and the module most closely correlated with KCNC3 was screened, followed by KEGG and GO analyses on the module genes. The GO analysis revealed that the module genes participated in the formation of the extracellular matrix and structure, metabolism of collagen, and adherence plaques. The KEGG analysis revealed that the module was linked to ECM-receptor interaction, Proteoglycans in cancer, and PI3K-AKT. In CRC, KCNC3 may regulate the above pathways by co-expression with these genes, leading to tumor progression (60-62). In addition, a nomogram was created for the 1-, 3-, and 5-year OS in CRC, with KCNC3 incorporated as a risk factor, and the calibration curve plotted. The KCNC3 expression contributed to the accuracy of prognostic prediction in CRC. Meanwhile, KCNC3 exhibited significant positive associations with the expressions of FGFR1, SPHK1, CD163, and CPLX1. Elevated expressions of these genes have been verified to correlate with CRC proliferation and unfavorable prognosis. Highly expressed KCNC3 may interact synergistically with these genes in CRC, contributing to CRC progression. Notably, our findings are highly consistent with a recently published study (63), jointly validating that KCNC3 can predict the prognosis in CRC. Our study innovatively extended the research range of KCNC3 to pan-cancer, and elucidated its clinical significance and potential biological roles in pan-cancer. However, due to limitations of conditions, the sample size was too small, so the clinical utility of KCNC3 as a prognostic marker for CRC remains to be validated by larger clinical cohorts and longer follow-up periods. In the future, more patients should be included, and in vivo and in vitro experiments of KCNC3 overexpression or knockdown should be refined to enhance the credibility of the clinical translational significance of KCNC3.

Conclusions

The bioinformatics analysis verified the aberrant expression of KCNC3 in pan-cancer and its ability to influence the prognosis of cancers. The high expression of KCNC3 facilitates the pro-tumorigenic transformation of the IME in CRC. KCNC3 is implicated in regulating immune indicators, such as ICs, chemokines and their receptors, MHC, TMB, and MSI, and also correlates with the sensitivity to multiple drugs. In addition, the obvious up-regulation of voltage-gated potassium channel KCNC3 in CRC and its association with poor prognosis were specifically confirmed by samples and clinical data of CRC patients. Nowadays, drugs targeting Kv channels have not been used for the clinical treatment of tumors yet (11,64), but we believe that KCNC3 is expected to become a promising target for antitumor therapy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1318/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1318/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1318/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1318/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Dalian University Affiliated Xinhua Hospital Ethics Committee (No. 2024-109-01). Informed consent was waived in this retrospective study.

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