Expression data from TCGA database
Data from the colon adenocarcinoma (COAD) and rectal adenocarcinoma(READ) cohort in the TCGA database (https://www.cancer.gov/tcga) were selected to be used in this study. The TCGA database is a publicly funded project aimed at cataloging and discovering major oncogenic genomic alterations in a large cohort of more than 30 human tumors through large-scale genome sequencing. The RNA-seq data of CRC cases (58 normal samples, cases; tumor samples, 640 cases) were downloaded from TCGA database. Based on the median value of KCNC3 expression, the tumor samples were divided into high- and low- expression groups. In this study, the expression of KCNC3 in paired tumor and adjacent samples, as well as non-paired samples, was analyzed using the Wilcoxon signed rank test and the Wilcoxon rank sum test, respectively.
Correlation between KCNC3 and clinical parameters
The tissue microarray used was provided by the Department of Clinical Biobank of the Affiliated Hospital of Nantong University, and was used to determine whether KCNC3 was clinically correlated with colorectal cancer. The expression level of KCNC3 was divided into 107 cases of high-expression colorectal tumors paired with 58 cases of low-expression colorectal tumors were determined using immunohistochemistry. The core of the TMA (The tissue microarray) represents a sample with a diameter of 2 millimeters. We obtained the average of the results of multiple samples obtained from the same patient. Then, the clinicopathological features and the prognosis of the patients were retrospectively analyzed. The patients had not received radiotherapy, chemotherapy, or biological immunotherapy before surgery. This research protocol was approved by the Human Research Ethics Committee of the Affiliated Hospital of Nantong University. Additionally, TIMER was used to analyze the OS (overall survival) and DFS (disease-free survival) in CRC using TCGA data.
Gene set enrichment analysis (GSEA)
The RNA-Sequence data of CRC were downloaded from TCGA database. The data were first used to generate an ordered list of all genes based on their correlation with KCNC3 expression, and then a predefined gene set receives an enrichment score (ES). GSEA was performed using R software (v.3.5.3) to identify signaling pathways that were differentially activated in CRC.
Tissue microarray and immunohistochemical staining
The TMA sections were fixed using formalin and embedded with paraffin, followed by deaffinity and rehydration using alcohol xylene. Then, the TMA sections were heated in a microwave in a sodium citrate buffer (0.01 M, pH 6.0) to repair the antigen. Thereafter, the sections were cultured with 5% BSA to inhibit endogenous peroxidase activity and then incubated with rabbit anti-PD-L1 (13684S, Cell Signaling Technology), anti-CTLA4(NB10064849, NOVUS), CD20 (ab78237, Abcam), anti-PD-1, anti-CD3 (85061S, Cell Signaling Technology), anti-CD4 (ab133616, Abcam), anti-LAG3 (Cell Signaling Technology), and anti-CD66b antibodies (ARG66287, 20210303). The results of the TMA staining were evaluated by a pathologist using a semi-quantitative scoring method. The staining intensity score was multiplied by the percentage of positive staining cells to calculate the total score, which ranged from 0 to 300.
Tumor-infiltrating immune cell profile
Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/) is a general analytic online tool that provides six modules that can be used to explore the relationship between immune infiltrates and the prognosis of different tumors. After submitting “KCNC3” to the Diff Exp Module of TIMER, the expression level of KCNC3 in different cancer types was obtained. After submitting “KCNC3” and selecting the cancer type as COAD and READ in the Gene module, TIMER automatically generated the relationship images between KCNC3 and immune cells. We performed a gene module analysis to assess the association between KCNC3 expression in colon cancer and tumor-infiltrating immune cells, including B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells.
Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), is an analytical method that is applied to estimate member cell types in a mixed cell population using gene expression data[15]. A total of 22 tumor-infiltrating immunological cell types were inferred from the deep transcriptome using CIBERSORTx immune deconvolution software. Meanwhile, based on TCGA database, the tumors were categorized into two groups based on the median values of the densities of each T cell subset in each region. The correlation analysis between the expression of KCNC3 and immunocytes was performed using R software, while the spearman rank correlation coefficient method was used for comparison.
Additionally, we used TISIDB (http://cis.hku.hk/TISIDB/) to analyze the effect of KCNC3 expression on the prognosis of patients with colon cancer and its association with the clinicopathological parameters and immune subtypes of colon cancer.
String analysis
To further explore the function of the biomarkers, the Retrieval of Interacting Genes (STRING) resource was used to perform the protein-protein interaction (PPI) analysis. Proteins on the STRING website (https://string-db.org/) were used to construct the KCNC3 protein-protein interaction (PPI) network, which contained 20 related proteins. The main parameters were as follows: active interaction sources (‘‘experiments”), the minimum required interaction score [‘‘Low confidence (0.150)”], and max number of interactors to show (‘‘no more than 20 interactors”). Subsequently, the PPI network was visualized using Cytoscape software.
Fluorescence-based multiplex immunohistochemistry (mIHC)
mIHC/IF was performed using an Opal Multiplex fIHC kit, and was conducted using the Opal method on the TMAs. The slides were formalin-fixed, paraffin-embedded, deparaffinized, and rehydrated using alcohol and xylene. The TMA sections were heated using a microwave in an AR6 buffer (AR600, AKOYA) to repair the antigen. After the secondary antibody was added, MIHC staining was performed. The antigen was repaired via heat induction and cooling. After subsequent antigen retrieval, the nuclei were counterstained with DAPI at room temperature in the dark for 30 minutes. Then, the slides were scanned using the Vectra 3.0 automated quantitative pathology imaging system to detect and measure the positive rate of the biomarkers. An x 20 Olympus lens was used to capture the core containing both the tumor and stroma.
The following primary antibodies were used in this study: rabbit anti-CD3 (85061S, Cell Signaling Technology), rabbit anti-CD4 (ab133616, Abcam), rabbit anti-CTLA4 (NB10064849, NOVUS), and rabbit anti-KCNC3 (HOOOO3748-M01, NOVUS). The secondary antibody used was Opal™ polymer HRP Ms+Rb (ARH1001EA, Perkin Elmer). Fluoroshield along with DAPI (F6057, Sigma) were used to stain the nuclei and seal the slices.
Statistical analysis
The expression levels of KCNC3 in CRC between tumors and adjacent tissues samples were analyzed using the Student’s tests. The correlation of KCNC3 expression with the protein-coding genes in CRC was determined by conducting a Pearson coefficient analysis, with r and p values, as indicated. Then, the cumulative survival curves were drawn using the Kaplan–Meier method, and a comparison between the survival curves was performed using the log-rank test. The difference was considered significant when p<0.05. Data on the expression levels of KCNC3 in the paired CRC tissues were analyzed using a paired t-test. Histograms were used to present the mean values, and the error bars indicated standard deviation. Data were analyzed using the Student's tests.