The role of KIAA1467 in breast cancer: insights from pan-cancer and single-cell sequencing analysis

Abstract

Background

Improving the response rate of single-agent immune checkpoint blockade (ICB) urgently requires the discovery of new therapeutic targets for combinatorial regimens. Analyses of tumor microenvironment (TME)-associated biomarkers have verified that KIAA1467 drives the formation of an immune-excluded, non-inflamed TME in breast cancer (BRCA). This study systematically explores the expression pattern, prognostic value, immune regulatory function, biological effects, and drug resistance relevance of FAM234B (also known as KIAA1467) in BRCA.

Methods

We performed pan-cancer survival analysis using The Cancer Genome Atlas (TCGA) datasets. Multi-omics bioinformatics analyses were conducted to evaluate KIAA1467 expression across malignancies. Single-cell RNA sequencing (scRNA-seq) data from GSE176078 was utilized to localize KIAA1467 expression at the cellular level. Immunohistochemistry and western blot assays validated KIAA1467 expression in BRCA clinical specimens. Correlation analyses were implemented to assess relationships between KIAA1467 expression, clinicopathological features, immune modulators, tumor-infiltrating immune cells, and p53 mutation status. Functional enrichment analysis uncovered relevant signaling pathways. Bioinformatic half maximal inhibitory concentration (IC50) prediction and in vitro cellular experiments were applied to evaluate associations between KIAA1467 and chemotherapeutic drug sensitivity.

Results

TCGA pan-cancer survival analysis demonstrated that elevated KIAA1467 expression significantly predicted shortened overall survival in BRCA and multiple other tumor types. KIAA1467 displayed distinct expression patterns across cancers, with prominent upregulation in BRCA. scRNA-seq confirmed enriched KIAA1467 expression within BRCA cells, and its upregulation in BRCA tissues was further verified by immunohistochemistry and western blot. High KIAA1467 expression was positively correlated with advanced tumor grade and lymphatic metastasis. KIAA1467 showed negative correlations with most immune modulators and core immune checkpoint molecules, as well as tumor-infiltrating immune cells in the TME, implying its potential function in tumor immune evasion. Low KIAA1467 expression was tightly linked to p53 mutations. Enrichment analysis indicated participation of KIAA1467 in epithelial-mesenchymal transition, apoptosis and cell cycle arrest. Furthermore, high KIAA1467 expression corresponded to higher estimated IC50 values of cisplatin, gefitinib, paclitaxel and gemcitabine, consistent with reduced chemosensitivity observed in vitro.

Conclusions

This study reveals the multifaceted oncogenic role of KIAA1467 in BRCA. KIAA1467 participates in remodeling an immunosuppressive TME, correlates with malignant progression and chemoresistance, and may serve as a promising candidate target to optimize ICB-based combination therapy for BRCA. These findings offer new perspectives for the clinical treatment and comprehensive management of BRCA.

Highlight box.

Key findings

• High KIAA1467 predicts poor survival in breast cancer (BRCA) patients.

• KIAA1467 drives immunosuppressive tumor microenvironment in BRCA.

What is known and what is new?

• It is known that EMT, p53 dysfunction and abnormal cell cycle promote BRCA progression and drug resistance.

• This study, through pan-cancer analysis, confirmed that KIAA1467 overexpression links to worse overall survival across tumors. In addition, single-cell sequencing proves KIAA1467 specifically overexpresses in breast malignant cells.

What is the implication, and what should change now?

•Combined detection of KIAA1467 and p53 mutation helps stratify high-risk BRCA subgroups.

• Future work should further dissect the precise molecular crosstalk between KIAA1467 and p53 mutation to overcome drug resistance.

• Exploratory studies should also assess combinatorial regimens of KIAA1467-targeting agents plus p53 modulators to reverse dual immune and chemo resistance.

Introduction

Breast cancer (BRCA) is one of the most common malignant tumors in women worldwide. In China, there are estimated 429,105 new cases of BRCA and 124,002 death cases in 2022 (1). Among all solid tumors, BRCA has good prognosis during post-operative cancer treatment due to the development of chemotherapy, endocrine therapy, and human epidermal growth factor receptor 2 (HER-2) targeted therapy. However, the overall prognosis of patients with advanced BRCA and some highly malignant subtypes, such as triple-negative BRCA, is unfavorable (2). Recent studies focused on the therapeutic targets and related signaling pathways in the pathogenesis and development of BRCA (3).

Immune checkpoint blockade (ICB) is a newly invented immunotherapy and confers benefits for patients with BRCA (4). ICB suppresses tumor growth by re-stimulating tumor-cytotoxic T cells in tumor microenvironment (TME) but not inducing their formation (5). Theoretically, molecules or pathways resulting in a non-inflamed TME will cause resistance to ICB. Although BRCA is not considered a typical immunogenic tumor, recent research revealed that some aggressive triple-negative breast cancers are immunogenic and show resistance to chemotherapy (6). In many clinical trials, programmed cell death ligand 1 (PD-L1) is validated to be associated with the clinical response of ICB (7). Combination of atezolizumab and nab-paclitaxel is recommended as first-line treatment for patients with PD-L1-positive metastatic triple-negative breast cancer (8).

During the implementation of the Human Genome Project, researchers integrated newly discovered macromolecular proteins encoded by large complementary DNA (cDNA) fragments and built a corresponding human unidentified gene-encoded (HUGE) protein database. These macromolecular proteins are named KIAA, and there have been more than 2000 KIAA proteins. KIAA1467, also known as family with sequence similarity 234 member B (FAM234B), is expressed in multiple cell types and organs, especially in brain tissues (9). It is associated with neurodevelopmental disorders (10) and high hyperdiploid acute lymphoblastic leukemia (11) and can predict the unfavorable prognosis in patients with luminal BRCA (12). Until now, no studies have revealed the functions of KIAA1467 in cancer cell biology.In this study, we performed bioinformatics analysis to reveal the prognostic value, expression profile, immune correlation, and pathways of KIAA1467 in BRCA, mainly based on The Cancer Genome Atlas (TCGA) and GSE176078 dataset. Functional experiments were also performed for validation of roles of KIAA1467 in BRCA. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1233/rc).

Methods

Data retrieval and gene expression analysis

The pan-cancer RNA sequencing data, somatic mutation data, and survival information were downloaded from the UCSC Xena data portal (13). RNA-sequencing (RNA-seq) data were log2 transformed. Somatic mutation data were analyzed using VarScan2 and used to calculate tumor mutation burden (TMB). Microsatellite instability (MSI) data were collected from the supplementary files of Bonneville’s study (14). Expression data of KIAA1467 in multiple cancer cell lines were downloaded from the Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle/). We also obtained scRNA-seq data from the GSE176078 database (15) and the immunotherapy-related cohorts from GSE78220 (melanoma), GSE135222 (non-small cell lung cancer), and GSE91061 (melanoma). The Genomics of Drug Sensitivity in Cancer (GDSC) database (16) was used to reveal the association of KIAA1467 and drug resistance in BRCA. Wilcox rank sum test was applied for gene expression comparison in pan-cancer. The results were visualized using the “ggplot2” R package.

Expression data for KIAA1467 in normal tissues were obtained from the BioGPS data portal and the Genotype-Tissue Expression (GTEx) project. Additionally, data on the expression of KIAA1467 in cancer cell lines were sourced from the BioGPS data portal and the Cancer Cell Line Encyclopedia (CCLE) project.

Survival analysis of KIAA1467

Kaplan-Meier survival analysis was used to find different overall survival outcomes in TCGA-BRCA cohort with high and low KIAA1467 expressions. Univariate Cox regression analysis was used to assess the prognosis value of KIAA1467 for OS prediction in pan-cancer. The results were visualized using “survival” and “forestplot” R packages.

Immune-related characteristics analysis of KIAA1467

Correlation between KIAA1467 expression and immune cell infiltration in BRCA using CIBERSORT, MCPcounter, EPIC, estimate, TIMER, and quantiseq algorithms. The “estimate” package was used to calculate the ImmuneScore, StromalScore as well as the ESTIMATEScore and Spearman’s correlation analysis was used to determine the correlation between KIAA1467 expression and ImmuneScore, StromalScore and the ESTIMATEScore. Single-sample gene set enrichment analysis (ssGSEA) algorithm was used to explore the pan-cancer abundance of infiltrating immune cells with the “GSVA” R package. Spearman’s correlation analysis was used to determine the correlation between the expression of KIAA1467 and four immune checkpoints or the correlation between the KIAA1467 expression and infiltration levels of different tumor-infiltrating immune cells (TIICs) in the TME of different cancers.

Gene set variation analysis (GSVA)

GSVA was used for investigating the potential biological pathways in high risk or low risk BRCA subgroups. The gene set “h.all.v7.5.1.symbols” was downloaded from the MSigDB database to enriched the hallmark pathways as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using “GSVA” and “clusterProfiler” R packages.

The scRNA-seq data analysis

The scRNA-Seq data of 26 primary tumor samples from the GSE176078 cohort were read by Seurat R package and Seurat object was generated. Quality control (QC) was conducted by eliminating low-quality cells and doublets by the cell subset at: nCount_RNA <100,000, & sequencing depth >200 & nFeature_RNA <7,000, & percent.mt <20%, & percent.HB <3. The correlation between sequencing depth (nFeature_RNA) and gene expression counts (nCount_RNA) was analyzed with the FeatureScatter function. The NormalizeData method was used for data normalization. The t-distributed stochastic neighbor-embedding (tSNE) and UMAP algorithms were used for dimension optimization. The expression of KIAA1467 in different cell groups was calculated using the Findmarker algorithm. The single-cell RNA-seq clusters were annotated based on Human Primary Cell Atlas data using SingleR (Single-cell Recognition) function (SingleR package). KIAA1467-positive or KIAA1467-negative cells were determined using the gene set enrichment analysis (GSEA) according to different gene expression ranked by fold change (FC) value. The scRNA-Seq samples were integrated and filtered, subject to cell cycle regression and dimensional reduction (Figure S1). The cell qualities were visualized with nFeature_RNA, nCount_RNA, percent.mt, percent.HB and percent.Ribosome. The Pearson correlation coefficient between gene numbers and sequencing depth was 0.9, which indicated a positive correlation (Figure S1A,S1B).

Chromosome copy number variation (CNV) analysis

The inferCNV (V1.6.0) method was used to calculate the chromosomal CNV score of BRCA cells for elucidating the differnt patterns of chromosome CNV in tumor and normal cell clusters. T cells were used as the reference.

Patient samples and microarray

Six pairs of fresh BRCA tissues and paracarcinoma tissues were taken from Department of Thyroid and Breast Surgery, Affiliated Hospital of Nantong University and immediately placed in liquid nitrogen and preserved in liquid nitrogen for immunohistochemistry (IHC) and western blotting. The tissue microarray was constructed by Affiliated Hospital of Nantong University from samples that were collected from 142 BRCA patients who underwent surgery from 2014 to 2024. Tumour and paired para-tumour samples were collected. The patients were followed up regularly until death or March 21, 2024. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of Affiliated Hospital of Nantong University (No. 2021-L066). All patients were informed with the purpose and design of the study and signed the informed written consents.

Cell culture and transfection

Human BRCA cell lines HCC-1937 and MDA-MB-231 were purchased from National Collection of Authenticated Cell Cultures (Shanghai, China). Cells have been tested for mycoplasma. HCC-1937 cells were cultured in 1640 complete culture (45 mL base culture + 5 mL serum + 500 µL double antibody), and MDA-MB-231 was cultured in DMEM complete culture medium (45 mL base culture + 5 mL serum + 500 µL double antibody) in a 37 ℃ incubator with 5% (v/v) CO₂. The medium is changed every 24 h, and cells underwent passage every 48 h. The shRNA sequences of KIAA1467 and a negative control shRNA (shNC) were synthesized by Guangzhou Funeng Gene Co., Ltd. (Guangzhou, China). The sequences of these shRNA are as follows. The transfection operation was carried out using Lipofectamine™3000 (Thermo Fisher Scientific), following the manufacturer’s instructions. After the transfection was completed, the HCC-1937 and MDA-MB-231 cells were cultured for 48 hours before proceeding with the subsequent experiments.

KIAA1467-shRNAsequences: 5'-GCTCCATTGTTTGGAGTTACC-3'
KIAA1467-sh-NCRNAsequences: 5'-GCTTCGCGCCGTAGTCTTA-3'

EdU staining

The EdU staining assay was used to assess cell proliferation. Cells at a density of 2×10⁴ cells/500 µL culture medium were seeded into the 24-well plate, incubated overnight, and fixed with 4% paraformaldehyde at room temperature for 20 min. An EdU Assay/EdU Staining Proliferation Kit (iFluor 488) (ab219801) was used according to the protocols. Images were photographed using a fluorescence microscope (Olympus, Japan).

Western blotting

Western blotting was conducted according to standard procedures. The primary antibodies included anti-E-cadherin (ab40772), anti-N-cadherin (ab76011), anti-Vimentin (ab92547), anti-KIAA1467 (orb450626), anti-p53 (ab26), anti-AIF (ab32516), anti-Parkin (ab15494), and anti-GAPDH (60004-1-lg). Anti-KIAA1467 was purchased from Biorbyt (Cambridge, UK), and anti-GAPDH was purchased from proteintech (Wuhan, China). Other primary antibodies and the secondary antibody anti-IgG were purchased from Abcam (Shanghai, China). A ChemiDoc Touch Imaging System (Bio-Rad, USA) was used to capture and analyze protein bands.

IHC

IHC was performed according to a standard protocol. In brief, BRCA slides were deparaffinized with xylene and ethanol and treated with 3% H₂O₂ for 10 minutes to block endogenous peroxidase. Subsequently, slides were incubated in retrieval buffer, boiled for 4 minutes, washed with PBS 3 times, and blocked with 5% normal serum. Incubation of primary antibody against KIAA1467 was performed at 4 ℃ overnight, and then samples were treated with HRP solution (31490, Pierce, USA) for 60 minutes. After staining with DAB Peroxidase Substrate (ab64238, Abcam), an EnVision Detection System (K5007, Agilent Technologies, USA) was used to detect the antigen levels.

Statistical analysis

Data from functional assays were analyzed with GraphPad Prism, and R programming language was used for bioinformatics analysis. Student’s two-tailed independent t-test (for data conformed to normal distribution) and Mann-Whitney U test (for data not conformed to normal distribution) were used for comparisons between two groups. Spearman correlation analysis was used for determining the correlations of KIAA1467 and immunoregulators or immune cells. Kaplan-Meier method followed by log-rank tests were used for plotting survival curves. All functional assays were performed independently in thrice, and data derived from functional assays are shown as the mean ± standard deviation. P value <0.05 was used to indicate statistical significance.

Results

Pan-cancer expression of KIAA1467 and it’s prognostic significance in BRCA

Based on a comprehensive analysis of expression data from TCGA, KIAA1467 showed high expression levels in most cancers, including bladder carcinoma (BLCA) and BRCA, compared with normal tissues (Figure 1A,1B). This gene was also expressed in various cancer cell lines, including the BRCA cell line MCF-7, according to expression data screened from the BioGPS and CCLE databases (Figure 1B,1C). The boxplot revealed that the expression levels of KIAA1467 were significantly higher in BRCA tissues (N=1,069) compared with normal tissues (N=113, Figure 1D). As expected, KIAA1467 emerged as a potential prognostic biomarker in BRCA (Figure 1E), although its prognostic value varied among different cancers. Kaplan-Meier analysis further indicated that high expression of KIAA1467 was associated with an unfavorable prognosis in BRCA (Figure 1F). We conducted further analysis based on different molecular subtypes of BRCA and found that the expression pattern of KIAA1467 in BRCA exhibited significant molecular subtype heterogeneity. In the TCGA-BRCA overall cohort, KIAA1467 was significantly elevated compared to adjacent tissues, and patients with high expression showed a trend of poorer overall survival. However, stratified analysis revealed that this overall increase was mainly driven by the HR+/HER2− and HER2+ subtypes (Figure S2A). At the same time, we also conducted a prognostic analysis. The high expression of KIAA1467 was nominally associated with poorer OS in the overall BRCA cohort, but this association did not reach significance after multiple test corrections; within each molecular subtype, this association was also not statistically significant (Figure S2B). This might be related to the reduced sample size and number of death events within the subtypes, and it also suggests that KIAA1467 is more likely to be an expression feature related to the molecular status of BRCA rather than a robust single-gene prognostic marker. GSVA was performed to assess the TCGA-derived hallmark pathway (Figure 2A) and KEGG pathway (Figure 2B) in the high-risk or low-risk group of BRCA patients. We found that immune-related pathways were highly enriched in the high-risk group. KIAA1467 was significantly associated with apoptosis, p53 pathway, and cell cycle. We also explored the potential functions of KIAA1467 in BRCA. The ssGSEA results showed that high KIAA1467 expression was associated with EMT, and low KIAA1467 expression was associated with MSI (Figure S3A). According to the ssGSEA analysis results (Figure S3B), upregulated KIAA1467 was associated with biosynthesis of unsaturated fatty acids, butanoate metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Hedgehog signaling pathway, and histidine metabolism, and downregulated KIAA1467 was associated with Allograft rejection, asthma, autoimmune thyroid disease, graft-versus-host disease, and primary immunodeficiency.

Figure 1.

Pan-cancer expression and prognostic significance of KIAA1467 and its expression in BRCA. (A) The expression pattern of KIAA1467 of pan-cancers in TCGA. The asterisks indicated a significant statistical P value calculated with Mann-Whitney U test. *, P<0.05; **, P<0.01; ***, P<0.001. (B,C) The expression of KIAA1467 in cancer cell lines in BioGPS and CCLE. (D) RT-qPCR estimation on KIAA1467 mRNA levels in BRCA tissues (N=1,069) and normal tissues based on TCGA data (N=113). (E) Forest plot of the IRS RNA-expression profiles in univariate Cox analysis. (F) Pan-cancer overall survival analysis of patients with differential expression of KIAA1467 using a univariate Cox regression model. Hazard ratio >1 revealed KIAA1467 as a risk factor and that <1 indicated KIAA1467 as a protective factor. Data were retrieved from TCGA. BRCA, breast cancer; CCLE, Cancer Cell Line Encyclopedia; RT-qPCR, reverse transcription quantitative polymerase chain reaction; TCGA, The Cancer Genome Atlas.

Figure 2.

Functional roles of KIAA1467 in BRCA based on TCGA. (A) TCGA-based GSVA analysis for KIAA1467-related hallmarks in BRCA. (B) TCGA-based KEGG pathway analysis for KIAA1467-related hallmarks in BRCA. *, P<0.05; **, P<0.01; ***, P<0.001. BRCA, breast cancer; GSVA, gene set variation analysis; TCGA, The Cancer Genome Atlas.

Pan-cancer immunological characteristics of KIAA1467

An analysis of TCGA data illustrated in Figure 3A indicates a prevalent negative correlation between KIAA1467 and most immunomodulators in BRCA. To quantitatively evaluate the association between KIAA1467 and major immune-checkpoint molecules in BRCA, we generated scatter plots using TCGA-BRCA primary tumor samples and performed Spearman correlation analysis. KIAA1467 expression was negatively correlated with CD274, CTLA4, PDCD1, and LAG3 (Figure 3B). Using the ssGSEA algorithm, it was found that KIAA1467 generally inversely correlated with TIICs across various cancers, with exceptions including central memory CD8⁺ T cells, gamma delta T cells, immature dendritic cells, and mast cells in BRCA (Figure 3C). Furthermore, KIAA1467 showed a negative association with TMB and MSI in several cancers, including BRCA, which suggests its role in reflecting the immunogenicity of these cancers (Figure S4A). Alterations in the copy number of KIAA1467, either deletions or gains, were found to decrease its mRNA levels (Figure S4B). The top 30 mutational genes in groups with high and low KIAA1467 expression are presented in Figure S4C, where notably, low KIAA1467 expression closely associates with p53 mutations. We have stratified by TP53-mutant and TP53-wild-type, and separately calculated the Spearman correlation between KIAA1467 and the ssGSEA immune cell infiltration score. The results showed that in the wild-type samples, most immune cells showed a stronger negative correlation; in the mutant samples, Th1, NK, CD8 T cells, activated dendritic cells, cytotoxic cells, etc. still showed a significant negative correlation, but mast cells and Th2 cells showed a positive correlation in the mutant samples (Figure S5).

Figure 3.

The effect of KIAA1467 on immunological status in pan-cancers. (A) Correlation between KIAA1467 and 122 immunomodulators (chemokines, receptors, MHC, and immunostimulators) in pan cancers based on TCGA. (B) Scatter plots showing the correlations between KIAA1467 and four immune-checkpoint molecules (CD274, CTLA4, PDCD1, and LAG3) in TCGA-BRCA primary tumor samples. Spearman correlation coefficients and P values are shown in each panel. (C) Pancancer correlation between KIAA1467 and 28 tumor-associated immune cells calculated with the ssGSEA algorithm based on TCGA. The color indicates the correlation coefficient, which was calculated using spearman correlation analysis. BRCA, breast cancer; MHC, major histocompatibility complex; TCGA, The Cancer Genome Atlas.

Immunological landscape associated with KIAA1467 in BRCA

KIAA1467 has negative correlation with many immunomodulators in BRCA (Figure 4A). Downregulated major histocompatibility complex (MHC) molecules in the high-KIAA1467 group suggested the reduced capacity of antigen presentation and processing by KIAA1467 (17). Many chemokines and receptors that showed downregulation in high-KIAA1467 group can promote the recruitment of effector TIICs. Thus, correlation of KIAA1467 and infiltration levels of TIICs in BRCA were assessed using seven independent algorithms (Figures S6-S9). KIAA1467 showed negative correlation with various immunomodulators, effector genes of TIICs, and immune checkpoints in GSE78220 (Figure 4B-4D) and IMvigor210 cohorts (Figure S10). Moreover, we identified that high KIAA1467 group showed lower infiltration levels of plasma cells, CD8⁺ T cells, activated memory CD4⁺ T cells, follicular helper T cells, Tregs, monocytes, M1 macrophages, and activated dendritic cells, and higher proportion of M2 macrophages, resting dendritic cells and resting mast cells according to the CIBERSORT (Figure S11A). Similarly, the ESTIMATE score showed that the stromal, immune and ESTIMATE scores were higher in the low KIAA1467 group relative to the high KIAA1467 group, which indicated the KIAA1467 expression was negatively correlated with tumor purity and immune cell infiltration in BRCA (Figure S11B,S11C). The results of ssGSEA also demonstrated that high KIAA1467 showed lower infiltration levels of B cells, CD4⁺ T cells, CD8⁺ T cells, natural killer cells and others, indicating the association of KIAA1467 with an immunosuppressive TME in BRCA patients (Figure S11D). Then we explored the expression levels of immune checkpoint (ICP) in the high or low KIAA1467 group, and patients with high KIAA1467 expression showed low levels of CD274 and CTLA4, and higher levels of CD44 (Figure S11E). Next, we explored the association between risk score and immune cell infiltration in BRCA using CIBERSORT, MCPcounter, EPIC, estimate, TIMER, and quantiseq algorithms, and the result was shown in a heatmap (Figure S12). The low-risk score group showed higher levels of immune cell infiltration compared with the high-risk score group in BRCA, which indicated the immunosuppressive environment in the high-risk group. Additionally, we explored the response of patients in high- or low-KIAA1467 groups to the immunotherapy using the submap algorithm. The results indicated that BRCA patients with low KIAA1467 expression were predicted with higher possibility to response to anti-PD-1 therapy (Figure S10E).

Figure 4.

Association of KIAA1467 expression and immunological status in BRCA in TCGA and GSE78220 cohort. (A) Correlation between KIAA1467 and 122 immunomodulators in BRCA based on TCGA. (B) Expression of CD8 T cell and NK cell effector gene in high KIAA1467 expression and low KIAA1467 expression groups in BRCA. (C) Expression of inhibitory immune checkpoint in high KIAA1467 expression and low KIAA1467 expression groups in BRCA. (D) Relationship between KIAA1467 expression and enrichment scores of immunotherapy predicted signatures in BRCA. *, P<0.05; **, P<0.01; ***, P<0.001. BRCA, breast cancer; TCGA, The Cancer Genome Atlas.

KIAA1467 associated with drug resistance

According to GDSC analysis results, high expression of KIAA1467 was associated with high estimated half maximal inhibitory concentration (IC50) of Cisplatin, Gefitinib, Paclitaxel, and Gemcitabine (Figure S13A-S13D). To explore potential drugs with high sensitivity in BRCA, we analyzed the PPS score, and calculated the correlation coefficient between AUC value and PPS score based on the pharmacogenomics profile databases CTRP and PRISM. The results showed that the drug sensitivity was negatively correlated with the PPS score, and high expression of KIAA1467 was related to lower AUC value compare with the low KIAA1467 group, which indicated that BRCA patients with high KIAA1467 expression were predicted to be less sensitive to commonly used chemotherapeutic drugs (Figure S14A,S14B).

Evaluation of chromosome CNV in BRCA

As shown in Figure 5A, 5 clusters with similar CNV were identified by consensus clustering. For identifying the malignant cells in BRCA, we inferred CNV and the CNV scores of each cluster. Based on the scRNA-Seq data, we explored the CNV in BRCA and normal samples. The heatmap showed that BRCA showed evident CNV amplification (red color) or CNV depletion (blue color), and cells in the normal samples showed lower CNV score and rare CNV (Figure 5B). Furthermore, T cells and epithelial cells were used as reference cells and their CNV pattern was shown in the upper panel of the heatmap, the lower panel of the heatmap showed the results for target cells, which indicated the different CNV patterns compared with the reference cells (Figure 5C). The top 10 mutational genes in the KIAA1467 groups, and an overview of the mutation profiles in BRCA are summarized in Figure S15A.

Figure 5.

Chromosome copy number variation in BRCA. (A) CNV score of different clusters of BRCA samples with inferCNV algorithm. (B) Heatmap of the CNV profile for BRCA cells and normal cells of each cell cluster. (C) InferCNV plot identified malignant cells from normal cells. BRCA, breast cancer; CNV, copy number variation.

Single-cell transcriptomics indicate the role of KIAA1467 in TME

Initially, we accessed a single-cell dataset comprising 26 primary tumors from three major clinical subtypes of BRCA, including 11 ER⁺, 5 HER2⁺, and 10 TNBC. We also performed bulk RNA-Seq on a total of 24 of these matched samples. Following QC, normalization, and preliminary dimensionality reduction, we obtain 99684 cells from the following screening. Principal component analysis (PCA) was used for cell cycle regression, and the results showed that the cells were differentiated by stages (Figure S1D). Integrate all the single-cell data of the patients, use different colors to distinguish each Patient sample, to visually observe whether there are obvious clusters in the cell transcriptomes of different patients and the degree of cell heterogeneity among patients (Figure S1E). Then tSNE algorithms and uniform manifold approximation and projection (UMAP) were employed to categorize the cells into 26 subpopulations (Figure 6A and Figure S15B). Subsequently, the cells were categorized into 11 major cell types including T cells, epithelial cells, macrophages, fibroblasts, SMC, plasma cells, endothelial cells, B cells, monocytes, NK cells and PDC (Figure 6B and Figure S15C). The markers and immune markers in the 26 clusters were explored and their expression was also visualized (Figure 6C and Figure S15D). Additionally, based on CNV characteristics, the 11 major cell clusters encompassed 1,069 tumor and 113 normal cells. Meanwhile we can clearly see that malignant cells are mainly expressed in epithelial cells (Figure 6D and Figure S15E). We then examined KIAA1467 expression in breast cancer. We found that epithelial cells showed high expression of KIAA1467 (Figure 6E,6F). We further performed scRNA analysis based on the GSE176078 database and found that KIAA1467 exhibited obviously high expression in malignant cells than other types of cells in BRCA (Figure 6G,6H). For example, we can see that cluster 0 is highly expressed in IL7R and CD34E, which is consistent with Figure 5B, and cluster 0 belongs to T_cells cluster.

Figure 6.

scRNA-seq analysis of main cell types in BRCA tumor tissues. (A) tSNE was used to cluster the cells from 26 primary breast tumors analyzed by scRNA-seq analysis in the GSE176078 cohort. (B) tSNE was used to cluster the cells based on cell lineage. (C) Dot plot of immune markers of the 26 clusters. (D) Use tSNE to identify malignant cells and normal cells. (E,F) Dot plot of the KIAA1467 expression in different cell clusters. (G,H) Expression of KIAA1467 in BRCA tissues and normal tissues based on GSE176078 database. BRCA, breast cancer; scRNA-seq, single-cell RNA sequencing; pDC, plasmacytoid dendritic cell; SMC, smooth muscle cell; tSNE, t-distributed stochastic neighbor-embedding.

We further analyzed the metabolic pathway activity of KIAA1467 and found that the positive and negative expressions of KIAA1467 were completely different in the metabolism-related pathways. Moreover, positivity significantly activated malignant pathways such as oxidative phosphorylation, which have been proven to be associated with malignant expression (Figure 7A). The butterfly plot in Figure 7B revealed the GSE176078 dataset-derived hallmarks with association of KIAA1467. As is shown in the picture, KIAA1467 is significantly positively correlated with protein secretion, androgen response, bile acid metabolism and pancreas beta cell. On the other hand, it is negatively correlated with KRAS signaling, P53 pathway and apoptosis. For the comparison of incoming and outgoing signals in BRCA, we found that KIAA1467 was associated with SEMA3 and FGF pathways in the outgoing signaling pattern and GRN and EGF pathways in the incoming signaling pattern in cell communication (Figure 7C). GSVA showed that pathways related to early or late estrogen response, metabolism, peroxisome, PI3K-Akt signaling, P53 pathway, TGF-β signaling and others were enriched in the KIAA1467-positive BRCA cells (Figure S16). Based on the cellchat analysis of the scRNA-seq dataset GSE176078, in which cells were clustered into 13 cell groups such as fibroblasts, macrophages, endothelial cells, KIAA1467⁺ malignant cells and others, the number of interactions and interaction weights/strength between cells were visualized (Figure S17A,S17B). The expression pattern of MIF, CD74, CXCR4, and CD44 in different cell clusters was shown in Figure S17C, and we found that KIAA1467⁺ malignant cells showed higher MIF, CD74 and CD44 expression and rare CXCR4 expression. The ligand-receptor association between cells was shown in Figure S17D.

Figure 7.

Functional roles of KIAA1467 in BRCA. (A) GSE176078 dataset-based single gene GSEA analysis shows KIAA1467-related metabolic pathway. The “Value” represents the pathway activity. (B) The butterfly plot reveals the KIAA1467-related hallmarks in BRCA based on GSE176078 dataset. (C) Pathway enrichment of different cell types in BRCA based on GSE176078 dataset. BRCA, breast cancer; GSEA, gene set enrichment analysis.

Validation of the expression profile, clinical significance, and functional roles of KIAA1467 in BRCA

To understand the expression of KIAA1467 in fresh tissues, we detected the abundance of KIAA1467 in 6 pairs of BRCA and adjacent tissue using Western blot (Figure 8A). The results showed that the expression level of KIAA1467 was elevated in BRCA tissues compared to adjacent tissues, with approximately 3–4 times higher expression (Figure 8B). We also stained the collected tissue microarrays using IHC, which indicated higher expression of KIAA1467 in BRCA, while in benign cases, KIAA1467 was either lowly expressed or almost not expressed (Figure 8C). We used immunoblotting to detect KIAA1467 protein expression in BRCA cell lines MDA-MB-231, MCF-7, HCC-1937, and normal breast epithelial cells MCF-10A. The results showed relatively high expression of KIAA1467 in BRCA cells and low expression in normal breast cells, consistent with the results in BRCA tissues.

Figure 8.

Cell function experiment of KIAA1467. (A) Western blot staining of KIAA1467 in BRCA and adjacent tissues. (B) Abundance of KIAA1467 in BRCA and adjacent tissues. (C) IHC staining of KIAA1467 in BRCA and adjacent tissues. (D) The expression of KIAA1467 in breast epithelial cells MCF-10A and breast cancer cells MCF-7, HCC-1937, MDA-MB-231 was quantified by WB. WB results and quantification map of KIAA1467 expression in control group, KIAA1467-sh-ncrNA group and KIAA1467-shRNA group. (E) The OD values of transfection group and control group were detected by CCK-8 method at the same time point for 5 days. (F) The difference of cell number between the two groups was analyzed by clonal formation and quantified in histogram. (G) The effect of KIAA1467 on the proliferation capacity of breast cancer cells was detected by EdU experiment, and the results were quantitatively analyzed by counting bar =100 μm. (H) The migration and invasion ability of MDA-MB-231 was detected after transfection with KIAA1467 interference vector and the corresponding quantization graph bar =100 μm. The culture plate was stained with 0.1% crystal violet. (I) Detection of migration and invasion ability of HCC-1937 after transfection with KIAA1467 interfering vector and corresponding quantization map bar =100 μm. *, P<0.05 **, P<0.01. All experiments were performed in triplicate to ensure reproducibility. BRCA, breast cancer; CCK-8, Cell Counting Kit-8; EdU, 5-ethynyl-2’-deoxyuridine; IHC, immunohistochemistry; OD, optical density; shNC, negative control shRNA; WB, western blot.

In order to explore the impact of KIAA1467 expression on the biological behavior of BRCA cells, KIAA1467 interference plasmids and control plasmids were transfected into HCC-1937 and MDA-MB-231 cells. The results showed that protein expression of KIAA1467 was significantly inhibited in both cell lines following KIAA1467-shRNA transfection (Figure 8D). CCK-8 assay, colony formation assay, and EdU assay were conducted to analyze the effects of KIAA1467 on cell proliferation. The CCK-8 assay demonstrated a significant decrease in proliferation ability in HCC-1937 and MDA-MB-231 cells with inhibited KIAA1467 expression compared to controls (Figure 8E). Additionally, the colony formation assay indicated that downregulation of KIAA1467 could inhibit cell clone formation (Figure 8F). Similarly, the EdU assay revealed a reduction in cell proliferation after KIAA1467 knockdown (Figure 8G). Independent transwell experiments showed that migration and invasion abilities of the cells significantly decreased post KIAA1467 knockdown (Figure 8H,8I).

Discussion

Based on TCGA and GSE176078 dataset, we performed bioinformatics analysis and revealed the significant upregulation of KIAA1467 in BRCA and its good performance in predicting the unfavorable prognosis of BRCA patients, which was further validated in our collected BRCA cohort. We also revealed that KIAA1467 was positively correlated with the high pathological grade of tumor and the high degree of lymphatic metastasis in BRCA.

Various immune cells infiltrate the breast TME, including T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, macrophages, and regulatory T cells. BRCA cells employ several strategies to evade immune surveillance, including downregulation of MHC molecules, overexpression of immune checkpoint molecules like PD-L1, secretion of immunosuppressive cytokines like TGF-β, and recruitment of immunosuppressive cell populations. These mechanisms inhibit anti-tumor immune responses and promote tumor growth (18). Immune checkpoint pathways, such as PD-1 and CTLA-4 pathways, play critical roles in regulating T cell activation and tolerance. Dysregulation of these pathways in BRCA can lead to immune evasion and resistance to immunotherapy (19). In this study, we revealed the significant correlation of KIAA1467 and immunomudulators including chemokines, receptors, MHC, and immunostimulators and demonstrated the association of KIAA1467 and TILS in BRCA. Within malignant cells, high KIAA1467 may transcriptionally suppresses MHC antigen presentation machinery and chemokine secretion, blocking the recruitment of cytotoxic CD8⁺ T cells, NK cells and dendritic cells. KIAA1467-high expression malignant cells reprogram infiltrating immune cells: promoting M2 polarization of macrophages, impairing dendritic cell maturation, enriching immunosuppressive Tregs and inducing CD8⁺ T cell dysfunction. Furthermore, our study revealed that expression of KIAA1467 was mutually exclusive of four immune checkpoints including CD274, CTLA-4, PDCD1, and LAG-3 in BRCA.

The pathway enrichment analysis showed that epithelial-mesenchymal transition, apoptosis, and cell cycle arrest. We performed knockdown assays using sh-KIAA1467 to assess its contribution in BRCA cell biology. The results found that KIAA1467 silencing using sh-KIAA1467 reduced proliferation, induced apoptosis, arrested cell cycle progression, suppressed epithelial-mesenchymal transition, and activated the p53 pathway in BRCA cells. Dysregulation of the p53 pathway can lead to impaired apoptosis, allowing the survival of damaged cells, which can contribute to tumorigenesis (20). EMT is a biological process where epithelial cells lose their characteristics and gain mesenchymal properties, leading to increased motility and invasiveness. p53 has been shown to regulate EMT by modulating the expression of genes involved in cell adhesion, migration, and invasion. Loss of functional p53 can promote EMT, facilitating tumor metastasis in BRCA (21). P53 plays a pivotal role in cell cycle regulation, particularly in response to DNA damage. Activation of p53 leads to the transcriptional activation of downstream target genes involved in cell cycle arrest, such as p21, resulting in cell cycle arrest at the G1/S and G2/M checkpoints. Dysfunctional p53 can abrogate this cell cycle arrest, leading to uncontrolled cell proliferation and tumor progression (22).

The interplay between p53 and immune regulation in BRCA intricately shapes tumor progression and response to therapy within the TME. P53 contributes to tumor immunosurveillance by modulating the expression of genes involved in antigen presentation and immune recognition, impacting immune cell recognition of tumor cells. P53 regulates inflammatory pathways, influencing the recruitment and activation of immune cells within the TME (23). P53 may regulate immune checkpoint molecules such as PD-L1 and CTLA-4, impacting tumor immune evasion mechanisms (24). Wild-type p53 enhances cancer cell sensitivity to immune-mediated cytotoxicity, while mutant p53 or loss of p53 function may confer resistance to immune attack (25). Understanding the p53-immune regulation axis provides insights for novel therapeutic strategies, such as targeting immune checkpoints in combination with p53-based therapies, to improve treatment outcomes in BRCA.

Finally, our mutational analysis revealed that low-level expression of KIAA1467 was associated with the wild-type p53, while mutant p53 tumors exhibited an increase in KIAA1467. Wild-type p53 inhibits KIAA1467 through transcriptional repression; whereas mutant p53 eliminates this inhibitory effect, leading to excessive expression of KIAA1467 in BRCA cells. Single-cell analysis confirmed that KIAA1467 is mainly expressed in malignant epithelial cells. The high expression of KIAA1467 inhibits MHC antigen presentation, downregulates multiple chemokines to recruit effector immune cells, reduces the infiltration of CD8⁺ T cells, NK cells, and M1-type macrophages, and promotes the accumulation of immunosuppressive M2-type macrophages, ultimately constructing a non-inflammatory immune cold TME. The CellChat ligand-receptor analysis further confirmed that KIAA1467-positive tumor cells interact with immune cells through the MIF-CD74 axis, enhancing the ability of immune escape. GDSC analysis indicated that high expression of KIAA1467 significantly increased the IC50 values of cisplatin, paclitaxel, gefitinib, and gemcitabine. The p53 pathway influences cisplatin sensitivity by mediating DNA damage response and apoptosis induction. Wild-type p53 enhances cisplatin-induced DNA damage and apoptosis, leading to increased sensitivity to the drug. However, mutations or loss of p53 function can confer resistance to cisplatin by impairing DNA damage response and apoptosis (26). Wild-type p53 can enhance gefitinib-induced apoptosis, whereas mutant p53 may promote resistance by deregulating EGFR signaling or inhibiting apoptosis (27). Wild-type p53 enhances paclitaxel-induced apoptosis and mitotic arrest, whereas dysfunctional p53 may confer resistance by altering microtubule stability or apoptotic signaling (28). Functional p53 enhances gemcitabine-induced DNA damage and apoptosis, while mutant p53 or loss of p53 function may confer resistance by impairing these processes (29). It can be inferred thatKIAA1467 inhibits p53-mediated DNA damage repair and apoptosis, thereby leading to drug resistance to chemotherapy.

Conclusions

KIAA1467 emerges as a multifunctional player in BRCA, exerting significant influence on tumor progression, immune evasion, and response to chemotherapy. Its overexpression correlates with adverse prognostic outcomes, advanced tumor characteristics, and immune suppression in BRCA patients. Bioinformatics analyses underscore its role in promoting proliferation, inhibiting apoptosis, inducing EMT, and mediating drug resistance, implicating it as a potential therapeutic target. Moreover, its association with immune checkpoint expression and tumor-infiltrating immune cells suggests its involvement in immune evasion mechanisms. Further research into KIAA1467’s precise molecular mechanisms and clinical implications is warranted to exploit its therapeutic potential and improve patient outcomes in BRCA.

Supplementary

The article’s supplementary files as

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 Medical Ethics Committee of Affiliated Hospital of Nantong University (No. 2021-L066). All patients were informed with the purpose and design of the study and signed the informed written consents.

Data Sharing Statement

Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1233/dss

tcr-15-08-596-dss.pdf ^{(67KB, pdf)}

DOI: 10.21037/tcr-2026-1233