The COSMIC database (Tate et al., 2019) is a comprehensive platform to explore somatic mutations in human cancers. The latest version was released on 28 May 2021 (v94), which included gene mutations, copy number variations, genomic rearrangements and gene fusions across 1,491,089 cancer samples. As such, genomic alterations of CHAF1A were summarized using COSMIC database.

Oncomine (Rhodes et al., 2007) is a cancer database with genome-wide expression analyses of 715 datasets and a total of 12764 normal and 86733 tumor samples. Through “differential analysis” module, the expression of a single gene could be analyzed across various cancer types compared with corresponding normal samples.

TIMER 2.0 (Li et al., 2016; Li et al., 2017; Li et al., 2020) is a database for comprehensive investigation of immune cells infiltrated in cancer tissues in a large variety of malignant diseases. There are three major modules for analysis of cancer exploration, immune association and immune estimation, including gene expression, gene correlation, immune infiltration in this study.

UALCAN (Chandrashekar et al., 2017; Chen et al., 2019; Chen et al., 2022) is an on-lined data-mining resource to analyze gene expression and protein expression profile across various cancer types based on publicly available cancer OMICS data, including TCGA, CBTTC and CPTAC. It provides patient survival information for lincRNA-coding, miRNA-coding and protein-coding genes at the same time, which could discover candidate proteins that may be used as tumor biomarkers.

The Kaplan-Meier plotter (Győrffy, 2021; Lánczky and Győrffy, 2021) is an on-line platform which contains the expression of 30,000 genes and the survival data over 25,000 patients from 21 cancer types. The correlation of CHAF1A expression (Jetset Best Probe: 214426_x_at) and patients’ survival were investigated by applying a log-rank test.

The cBioPortal (Cerami et al., 2012; Gao et al., 2013) is an open source which provides a comprehensive platform for exploration, interactive visualization and analysis of large-scale cancer genomic datasets for scientific research. The co-expression data of CHAF1A were downloaded in cBioportal database, which was used for further investigation.

OmicShare (Su et al., 2019; Wang et al., 2020) is a platform for comprehensive data analysis. It contains multiple modules for different uses, such as heatmap, Gene Ontology (GO) enrichment, senior bubble plot, pathway enrichment and so on.

GSEA (Mootha et al., 2003; Subramanian et al., 2005) is a bioinformatic software that analyze and determine the statistically significant and concordant differences between two datasets based on a priori defined set of genes. The enrichment of CHAF1A co-expression genes were used to investigate potential functions and mechanisms of CHAF1A. Normalized enrichment score (NES) was calculated, and nominal p-value <0.05 was considered as statistically significant enrichment terms.

GEPIA (Tang et al., 2017) is a platform containing 9,736 tumors samples and 8,587 normal samples based on TCGA and Genotype-Tissue Expression (GTEx) projects. It is applied for the pan-cancer analysis of the RNA-sequencing expression data, including gene expression, gene correlation, survival rate and so on.

Breast cancer tissues and adjacent benign tissues were obtained in Qilu Hospital of Shandong University. The study was approved by the Ethics Committee of School of Basic Medical Sciences of Shandong University. Preparation of paraffin-embedded tissue sections, immunohistochemical and western blot analyses were performed as previously reported (Qu et al., 2018). The anti-human CHAF1A antibody (ab126625, Abcam, Cambridge, UK) was used to detect the expression of CHAF1A.

MDA-MB-231 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured with Dulbecco’s Modified Eagle’s Medium (DMEM, CM15019, Macgene, China) with 10% FBS (S711-001S, Lonsera, Uruguay). Cells were maintained as monolayer cultures at 37°C in a humidified incubator with no CO₂ atmosphere. shRNA of CHAF1A was obtained from GenePharma (Shanghai, China). Cells were transfected with shRNA using polybrene (GenePharma, Shanghai, China) according to the manufacturer’s guidelines.

The CCK8 Cell Counting Kit (Vazyme, Biotech Co., Ltd) assay was performed using the protocol reported (Qin et al., 2021). MDA-MB-231 cells with stable silencing of CHAF1A using shRNA were seeded in 96-well plates. After replaced with fresh culture medium, 10 μL CCK-8 solution was added to each well and incubated at 37 °C for 3 h. The absorbance was determined at 450 nm on microplate absorbance reader (Bio-rad, United States) at 0h, 48h, and 96 h, respectively.

All of these analyses were taken with p < 0.05 as the significance threshold, unless specificlly mentioned. ANOVA was applied to study the expression of CHAF1A in GEPIA database. Log-rank p-value was used for Kaplan-Meier Plotter analysis, and nominal p-value was used for GSEA analysis. Students’ t-test was used to analyze the data in Oncomine, UALCAN and CCK8. Spearman’s Correlation was used for selection of co-expression genes in cBioportal database and TIMER database. A statistic significant correlation between candidate gene(s) and immune cell(s) was considered if |Rho| > 0.1.

In order to identify the contribution of CHAF1A gene in human cancers, COSMIC (v94 GRCh38) (Tate et al., 2019) was applied for genomic alteration assessment. In the latest released version of COSMIC, CHAF1A was tested in 39,615 cancer samples across 40 different types of cancer. The total mutation frequency of CHAF1A was 1.105%, while missense mutation counted for 0.792% and the rest were nonsense and synonymous mutations (Table 1). Compared with mutation, Insertion and Deletion were rarely found in cancers. Only one Frameshift Insertion (0.003%), three Inframe Deletion (0.008%) and five Frameshift Deletion (0.012%) were found in cancers (Table 1). The total frequency of copy number alterations was 0.089% in cancers, including 0.008% for copy number gain while 0.081% for copy number loss (Table 1). As shown in Supplementary Table S1, one copy number gain was found in malignancies of central nervous system, haematopoietic and lymphoid, and lung, respectively. Copy number loss was found in 12 malignancies, with the highest rate of 1.154% in upper aerodigestive tract. Taken together, the data indicated that no major alterations in either sequence or copy number of the CHAF1A gene were responsible for cancer development.

Using TIMER 2.0 database (Li et al., 2017; Li et al., 2020), the gene expression of CHAF1A was investigated in various cancer types. Among the 22 paired tumor and corresponding normal samples, the expression of CHAF1A was significantly elevated in 20 types of malignancies compared with normal tissues (Figure 1). In consistent with the result from TIMER 2.0 database, it was also found abnormally high expression of CHAF1A in 36 study cohorts covering 17 types of cancers in Oncomine database (Rhodes et al., 2007) (Supplementary Table S2). In both two databases, breast cancer cohort contains the greatest amount of patient samples. The patient number included in breast cancer cohorts is more than other types of cancer, and with statistical significance as shown in Figure 1 and Supplementary Table S2. In addition, breast cancer is a leading cause of death in female. Thereby, we focused on breast cancer in the further investigation.

To further investigate the clinical significance and application of CHAF1A in breast cancer, the correlation between the expression of CHAF1A and breast cancer phenotype in UALCAN database was analyzed (Chandrashekar et al., 2017; Chen et al., 2019; Chen et al., 2022). Both mRNA expression and protein expression of CHAF1A were significantly elevated in breast cancer samples compared with normal samples (Figures 2A, B). Furthermore, among the subtypes of breast cancer, the expression of CHAF1A was significantly higher in triple negative breast cancer (TNBC) compared to either luminal or HER2 positive breast cancers in both mRNA level and protein level (Figures 2C, D). In addition, the expression of CHAF1A was higher in TP53 mutant tissues compared to TP53 wild type tissues in breast cancer (Figure 2E).

Then, we determined the potential effect of elevated CHAF1A expression on patients’ outcome. Elevated expression of CHAF1A was significantly correlated with shorter overall survival (OS) (Figure 2F), shorter recurrence free survival (RFS) (Figure 2G), and shorter distant metastasis free survival (DMFS) (Figure 2H) of breast cancer patients obtained from Kaplan-Meier plotter (Győrffy, 2021) survival analysis. The HR was 1.33, 1.25, and 1.48, respectively. As such, the data suggest that elevated expression of CHAF1A is a prognostic biomarker of poor patients’ outcome in breast cancer. The mechanisms underlying these events need to be further investigated.

Co-expression gene networks currently have been frequently used for exploration for the functional roles of the target genes (Villa-Vialaneix et al., 2013). In cBioPortal database (Cerami et al., 2012; Gao et al., 2013), the list of co-expression genes of CHAF1A in breast invasive carcinoma were downloaded, including three cohorts of TCGA data: Nature 2021, Cell 2015, and Firehose Legacy (Network, 2012; Ciriello et al., 2015). In order to get more precision co-expression networks, the genes shown consistent positive correlation score and consistent negative correlation score in all three cohorts were selected for further investigation.

The KEGG pathway annotation and enrichment of co-expression genes were generated with OmicShare online platform (Su et al., 2019; Wang et al., 2020). Figure 3 displayed the comprehensive functions and mechanisms of CHAF1A with statistical significance. The major functional roles of CHAF1A and its co-expressed genes focused on metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems and human diseases (Figure 3A). Especially, the effect on metabolism (amino acid metabolism and energy metabolism), immune system, and drug resistance are more closely connected with potential response to cancer treatment. Among the top 20 KEGG pathway enrichment (Figure 3B), the co-expression network was covered cancer metabolism, stemness, microRNA, cell cycle, RNA splicing, TGF-β pathway, apoptosis and DNA repair.

In addition, the pathways involved in cancer hallmarks were investigated using GSEA (Mootha et al., 2003; Subramanian et al., 2005). As shown in Figure 3C, the co-expression network in TCGA breast cancer cohorts was significantly positively enriched in E2F targets pathway, G2M checkpoint pathway, mitotic spindle pathway, DNA repair pathway, MYC target pathway, MTORC1 signaling pathway, and glycolysis pathway. The results from GSEA analysis were specific to signaling pathways, while still consistent with that from OmicShare KEGG pathway annotation and enrichment. Interestingly, cancer metabolism was of the most outstanding generated by various analyses, which suggested that CHAF1A might be critical to breast cancer development and progression, thus might provide novel therapeutic target for the treatment of breast cancer.

Since the co-expression network analysis indicated that immune system might be involved (Figure 3A), we looked deep into the tumor-infiltrating immune cells of breast cancer samples to investigate the correlation between CHAF1A and immune response in TIMER 2.0 database (Li et al., 2016; Li et al., 2017; Li et al., 2020). In breast cancer, it was found that the expression of CHAF1A was negatively correlated with CD8⁺ T cell, but positively correlated with regulatory T cell (Treg) and MDSC (Myeloid-derived suppressor cell) (Figure 4A).

Furthermore, we checked the correlation between CHAF1A and immune checkpoints in TIMER 2.0 database. The expression of CHAF1A was positively correlated with that of both CD274 and CTLA4 (Figure 4B). This correlation between CHAF1A and either CD274 or CTLA4 was also confirmed by GEPIA database (Figure 4C). Thereby, the elevated expression of CHAF1A might be related with suppressive tumor microenvironment resulting in poor survival outcome. However, these patients might response to immunotherapy.

In order to explore whether elevated CHAF1A expression detected in breast cancer patients’ samples could be used to predict the sensitivity of the patients to therapeutic treatment, the correlations between the oncolytic response of patients and elevated CHAF1A expression were determined in Kaplan-Meier plotter (Győrffy, 2021).

It was shown that the breast cancer patients with elevated expression of CHAF1A showed significantly shorter recurrence free survival whether receiving endocrine therapy or chemotherapy (Figure 5A). Since the mechanisms of endocrine therapy and chemotherapy are different, the breast cancer patients were divided into two different groups, one group received endocrine therapy and the other one received chemotherapy. Patients with elevated expression of CHAF1A showed significantly shorter recurrence free survival in each group (Figures 5B,C). As such, breast cancer patients with higher expression of CHAF1A might progress into treatment resistance to endocrine therapy and chemotherapy in shorter time.

Clinical tissues were used to validate the expression of CHAF1A in patients. The expression of CHAF1A is much higher in breast cancer tissues compared with benign ones as shown by the IHC images (Figure 6A). To further investigate the function of CHAF1A in breast cancer, shRNA targeting CHAF1A was used to treat MDA-MB-231 cells. The expression of CHAF1A was significantly downregulated as shown in Figure 6B. Moreover, cell proliferation was inhibited when CHAF1A was silencing in MDA-MB-231 cells (Figure 6C), indicating that CHAF1A played important role in breast cancer cell growth. As such, CHAF1A may be a potential therapeutic target of breast cancer.