H2AC14 is a novel and reliable prognostic marker for CRC.

1. Introduction
In recent years, colorectal cancer (CRC) has emerged as one of the most prevalent malignancies of the digestive tract globally. According to data compiled by the International Agency for Research on Cancer, CRC ranks 2nd in mortality and 3rd in incidence among all cancers. In China, newly diagnosed CRC cases account for 15.2% of all cancer cases, with a mortality rate of 11.5%.[1,2] The disease has profoundly impacted lifestyle and living conditions. Over the past decade, despite increased dietary demands, nutritional risk factors are often overlooked. Notably, CRC typically lacks obvious clinical symptoms in its early stages, leading to diagnosis at advanced stages (III or IV), which significantly reduces the clinical cure rate.[3–5] This delayed detection is a major contributor to the high mortality associated with CRC. Early-stage CRC may present with gastrointestinal symptoms such as diarrhea and abdominal pain; however, most patients resort to self-medication with anti-inflammatory drugs rather than seeking medical examination, thereby masking disease progression.[6] Currently, the primary diagnostic methods for CRC in clinical settings include endoscopy and colonoscopy. However, patient compliance with these invasive procedures is low, and they carry the risk of secondary harm to patient prognosis. Consequently, there is an urgent need for suitable biomarkers to track disease progression and predict survival outcomes.[7]
The nucleosome, the fundamental unit of chromatin, is formed by the wrapping of human genomic DNA around histone proteins. Chromatin plays a critical role in regulating gene expression, DNA replication and repair, and the transmission of genetic information. Structural abnormalities or genetic mutations in chromatin can lead to severe consequences, with cancer being one of the most significant outcomes.[8,9] The core octamer structure of the nucleosome consists of dimers of histones H2A, H2B, H3, and H4, wrapped by approximately 147 base pairs of DNA. This structure is essential for transcriptional control and DNA protection. Dysregulation of chromatin can lead to various diseases, including tumors. Variants of the histone family, frequently produced during functional processes, influence transcription, cell division, DNA repair, differentiation, and other cellular activities. Given the critical role of chromatin in cancer, we aim to explore the mechanisms of histone proteins, the building blocks of chromatin, in cancer development. Our experiments focus on predicting the role of the histone family in cancer through bioinformatics analysis.
H2A variant, located at the entry and exit points of DNA packaging, plays a significant role in gene expression and other processes.[10] H2AC14, located within the histone gene cluster on chromosome 6p22-p21.3, is an important histone modification involving acetylation of lysine 14 on histone H2. This modification is critical for regulating gene expression, DNA repair, and cell cycle progression. The levels of H2A and K14 acetylation are dynamically regulated by acetyltransferases and deacetylases, influencing various cellular functions.[11,12] The H2A family includes variant forms such as H2A.x, H2A.Z, and H2A.B, which collectively maintain chromatin structure, regulate gene expression, and facilitate DNA repair, making the study of H2A family members highly significant. H2AC14 is a member of this extensive family.[13] The transcript of H2AC14 lacks a poly-A tail but contains a palindromic termination element, underscoring its importance in gene transcription. Errors in gene transcription can lead to various diseases, including tumors. However, the role of H2AC14 in CRC remains unexplored. Therefore, our study aims to elucidate the role of H2AC14 in CRC and confirm its clinical significance.

2. Materials and methods

2.1. Differentially expressed H2AC14 at the transcriptional level
RNA sequencing data, comprising 41 pairs of matched normal and malignant colon tissues, 480 CRC tissues, and 41 normal tissue samples in transcripts per million format, were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov). Considering the inherent characteristics of the data format, suitable statistical methodologies were applied, and the results were graphically represented using the ggplot2 package.

2.2. Clinicopathological analysis of H2AC14 in CRC
A comprehensive clinicopathological analysis of H2AC4 in CRC was performed utilizing data from TCGA. Given the characteristics of the data format, the Kruskal–Wallis test was employed for statistical analysis, and the results were visualized using the ggplot2 package. The clinical prognostic information of CRC patients encompassed overall survival (OS) and disease-specific survival (DSS). To evaluate prognosis, both univariate and multivariate Cox regression analyses were conducted, supplemented by Kaplan–Meier (K–M) survival analysis.

2.3. GO/KEGG/GSEA analysis of differentially expressed genes
Initially, CRC patients were stratified according to the expression levels of the H2AC14 gene. Differential gene expression analysis was performed using the R package (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria) with a significance threshold of P < .05 and a fold change criterion of |log FC| ≥ 1.5. The identified differentially expressed genes (DEGs) were subsequently utilized to investigate potential functional disparities between the high and low H2AC14 expression cohorts. Visualization of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses for the DEGs was conducted using the sangberbox platform (http://vip.sangerbox.com/login.html). The GO enrichment analysis delineated 3 principal categories: biological processes (BP), cellular components (CC), and molecular functions (MF). Additional validation was performed using the IOB and GseaVis packages,[14,15] confirming the consistency of our results.

2.4. Immune microenvironment analysis
Utilizing TCGA pan-cancer RNA sequencing data, we employed the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm (GSVA v1.42.0) to quantify the infiltration levels of 24 immune cell types.[15] The Spearman rank correlation analysis was conducted to evaluate the relationship between H2AC14 expression and immune subsets across various cancer types. For the CRC dataset, we further performed Wilcoxon rank sum test to compare the differences in immune cell infiltration between high and low H2AC14 expression groups, with data visualization achieved using the “ggplot2” package (v3.3.6). Additionally, the immune infiltration characteristics were analyzed using the Wilcoxon rank sum test to compare the differences in immune cells between high and low H2AC14 expression groups in the CRC dataset. The visualization of these results was performed using the “ggplot2” package (v3.3.6). The differences in immune infiltration outcomes between the high- and low-expression groups were determined based on the CIBERSORT core algorithm. Specifically, we utilized the CIBERSORTx website (https://cibersortx.stanford.edu/) to calculate the immune infiltration status of the uploaded data using markers for 22 immune cells provided by the CIBERSORT.R script.

2.5. Correlation analysis
Using H2AC14 as the primary variable, correlation analysis was performed to identify genes associated with H2AC14 in both high-expression and low-expression groups, employing the Spearman correlation coefficient. The results of this analysis were visualized using the ggplot package, with co-expression heatmaps and correlation lollipop charts generated to illustrate the relationships.

2.6. Diagnostic analysis
Receiver operating characteristic (ROC) analysis of the dataset was conducted utilizing the pROC package, and the resulting data were graphically represented using the ggplot2 visualization tool.

2.7. Cell culture
CRC cell lines (HCT116, HT29, LOVO, SW480) and normal colon epithelial cells (NCM460) were procured from laboratory supplies. NCM460 and HT29 cells were maintained in RPMI-1640 medium, while HCT116 and SW480 cells were cultured in DMEM medium, and LOVO cells in F-12K medium. All media were supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin solution. Cells were incubated under standard conditions at 37°C with 5% CO2 and 95% humidity.

2.8. Quantitative real-time polymerase chain reactions (qRT-PCR)
Total RNA was extracted from cell lines using TRIzol reagent (Vazyme Biotech Co., Ltd.) in accordance with the manufacturer’s instructions. RNA transcription was performed for cDNA synthesis using HiScript RT SuperMix for qPCR (Vazyme Biotech Co., Ltd.) and SYBR Green (Vazyme Biotech Co., Ltd.), and generated products were analyzed via qRT-PCR. The qRT-PCR procedure was performed at 95°C for 10 minutes, followed by 40 cycles at 95°C for 10 seconds and 60°C for 30 seconds. HOMER3 expression levels were normalized to those of GAPDH, and relative expression levels were calculated using the 2-ΔΔCt method. The sequences of primers and siRNAs used for qPCR were as follows: H2AC14 forward primers, 5′-CCGCGACAACAAGAAGACTC-3′; H2AC14 reverse primers, 5′-TCTTGTGGTGGCTCTAGTT-3′; SANIL forward primers, 5′-CCCCAATCGGAACCTAACT-3′; SANIL reverse primers, GACAGAGTCCCAGATGAGCA; VIM forward primers, GAGAACTTTGCCTTGAAGC; VIM reverse primers, TCCAGCAGCTTCCTGTAGGT; N-Cad forward primers, CAGAGAGTCGCCAAATGTCA; N-Cad reverse primers, TTCACAAGTCTCGGCCTCTT; E-Cad forward primers, TTGAGTGTCCTGCACAGAGG; E-Cad reverse primers, GAGGGAGCTGAGTGAACCTG; si-H2AC14 forward primers, 5′-GCACAUGACCAUCCAUGAATT-3′; si-H2AC14 reverse primers, 5′-UGCAUGGAUGGUCAUGUCCTT-3′; GAPDH forward primers, 5′-CTGACTTCAACAGCGACACC-3′; GAPDH reverse primers, 5′-TGCTGTAGCCAAATTCGTTGT-3′.

2.9. Silencing of H2AC14
Silencing experiments targeting the H2AC14 gene were performed using short RNAs (siRNA), and transfection was performed using Lipofectamine 2000 (Invitrogen, USA) reagents. The medium was replaced with complete medium 6 hours after transfection. Cells were either reset or used for RNA extraction in subsequent experiments 48 hours after siRNA transfection. After confirming the impact of effective siRNA interference on the H2AC14 gene in 3 replicated studies, siRNA was employed in additional tests.

2.10. Wound healing test
The transfected cells were resuspended, counted, and re-inoculated into 6-well plates with 1 × 106 cells per well. After 24 hours, the cell monolayer was scratched and photographed. Subsequently, cell migration was observed and recorded under the microscope every 24 hours.

2.11. Transwell test
Matrix glue (BD Biosciences, MA) was added to the upper chamber containing the invasion group. After 30 minutes in the incubator, the excess matrix glue solution was removed. Transfected cells were resuspended in serum-free medium, adjusted to a concentration of 1 × 106 cells/mL, and 200 µL of the cell suspension was added to the upper chamber. Additionally, 600 µL complete medium was added to the lower chamber. After 24 hours, the upper chamber was removed, and excess cells and matrix glue in the upper chamber were wiped off. It was washed twice with PBS, fixed with paraformaldehyde for 10 minutes, stained with 0.1% crystal violet (C8470, Solarbio, China) for 30 minutes, and finally observed and photographed under a microscope.

2.12. Statistical analysis
Our data analysis mainly applied to Xiantao academic website (https://www.xiantaozi.com) and sangerbox website (http://vip.sangerbox.com/login.html). Wilcoxon rank sum test, Chi-square test, Fisher exact test, and logistic regression were used to analyze the relationship between clinical pathologic features and H2AC14. K–M methods were applied to survival analysis and logarithmic ranking tests were used to estimate statistical significance. GraphPad Prism 9 was used to analyze the RT-qPCR data. P < .05 was considered statistically significant (*P < .05; **P < .01; ***P < .001).

3. Results

3.1. H2AC4 is highly expressed in CRC
We initially conducted differential gene expression analysis utilizing the TCGA tumor database. Among the up-regulated genes, H2AC14 (ENSG00000276368.2) was selected for further investigation due to its pronounced distinctiveness (Fig. 1A). Analysis of the TCGA dataset revealed that H2AC14 expression exhibited significant variability across different tumor types. Notably, H2AC14 demonstrated a marked differential expression in colon adenocarcinoma (COAD) compared to corresponding normal tissues (P < .001) (Fig. 1B). Paired sample analysis was performed using the Wilcoxon rank sum test, with data visualization implemented through the ggplot2 package. This analysis confirmed significant differential expression of H2AC14 in COAD (P < .01) (Fig. 1C). Furthermore, we evaluated H2AC14 expression levels in 41 normal colon tissues and 480 CRC tissues, revealing significantly elevated expression in CRC (P < .001) (Fig. 1D). Subsequent application of the Wilcoxon signed rank test to compare H2AC14 expression levels between 41 CRC tissues and their adjacent normal colon tissues corroborated our previous findings, demonstrating significantly higher H2AC14 expression in CRC (P < .01) (Fig. 1E).

3.2. H2AC14 is associated with clinicopathological progression of CRC
Subsequently, we retrieved 478 CRC cases with gene expression and clinical data from the TCGA database and stratified these cases into 2 groups based on H2AC14 expression levels: a high-expression group (n = 239) and a low-expression group (n = 239). As presented in Table 1, Chi-square and Fisher exact tests revealed significant correlations between H2AC14 expression and pathologic N stage (P = .015), pathologic M stage (P = .022), pathologic stage (P = .017), and DSS event (P = .015).
Univariate and multivariate Cox regression analyses (using the coxph function) were performed on the 478 CRC samples. The primary results of these analyses, incorporating common clinical indicators, are summarized in Tables 2 and 3. The findings demonstrated that H2AC14 expression levels were significantly associated with pathologic T stage (P = .004), N2 stage (P < .001), M1 stage (P < .001), pathologic stage III and IV (P < .001), PD&SD (P < .001), age (P = .028), carcinoembryonic antigen level (P < .001), and R1&R2 (P < .001).
Building upon the correlation analysis of H2AC14 with clinicopathological indicators and the Cox regression results, we employed the Wilcoxon rank sum test to conduct a visual analysis of these clinical case indicators. The results, as depicted in Figure 2, revealed that H2AC14 expression was significantly correlated with the T stage (P < .001) (Fig. 2A), N stage (P < .05) (Fig. 2B), and M stage (P < .001) (Fig. 2C) of CRC.

3.3. H2AC14 expression is associated with poor prognosis in CRC patients
Building upon the results of the previous multivariate Cox regression analysis, and considering the significant association of H2AC14 with CRC staging, we incorporated clinical staging factors of CRC to construct a predictive nomogram (Fig. 3A). Parameters from 100 samples were recalculated, excluding the normal group while retaining clinical information, to predict individual patient survival risk and generate a calibration curve (Fig. 3B). The nomogram calibration curve demonstrated that, across the entire TCGA cohort, the actual probabilities aligned closely with the model-predicted probabilities at 1, 3, and 5 years.
Recent studies have suggested a correlation between H2AC14 expression levels and CRC patient prognosis. To further explore this, we evaluated the association between OS, DSS, and varying H2AC14 expression levels in CRC patients using K–M analysis. The analysis revealed that low H2AC14 expression was significantly associated with poor DSS (Fig. 3C, P = .012) and poor OS (Fig. 3D, P = .047). Additionally, survival analysis stratified by age, sex, and tumor stage demonstrated that low H2AC14 expression was significantly correlated with poor prognosis in Stage II (Fig. 3E, P = .040), N0 stage (Fig. 3F, P = .025), male patients (Fig. 3G, P < .001), and individuals aged over 65 (Fig. 3H, P < .010).
The prognostic survival analysis indicated that patients with high H2AC14 expression exhibited better survival outcomes. However, our earlier findings revealed that H2AC14 expression was elevated in CRC tissues compared to normal tissues, which appears contradictory to the prognostic results. Although this seems counterintuitive, such patterns are observed with numerous genes. Genes highly expressed in tumor tissues may also be significantly expressed in immune cells, as RNA-seq is a bulk sequencing method that aggregates signals from diverse cell types. Consequently, the expression level alone should not be solely relied upon to infer its functional role in tumors; instead, it should be interpreted in conjunction with its intrinsic properties. Without this approach, many functionally significant molecules that could serve as prognostic targets in clinical practice may be overlooked.

3.4. Correlation analysis and diagnostic analysis
The Spearman correlation coefficient was employed to analyze H2AC14-related gene expression in the dataset stratified by H2AC14 expression levels. Using H2AC14 as the primary variable, the ggplot package was utilized to generate a co-expression heatmap for visualizing the analysis results (Fig. 4A). When H2AC14 was highly expressed, ZNF439 and ZNF440 exhibited elevated expression levels, whereas FAM241B demonstrated reduced expression. Additionally, several other genes associated with H2AC14 were identified, and their correlation coefficients were annotated (Fig. 4B).
To assess the clinical significance of H2AC14, we performed ROC analysis using the pROC package, with the results visualized using ggplot2 (Fig. 4C). The area under the ROC curve (AUC), a commonly used metric for evaluating diagnostic tests, was calculated to be 0.696, indicating a relatively robust clinical diagnostic performance.

3.5. GO analysis of H2AC14
GO analysis was performed across 3 categories: BP, CC, and MF. The top 15 significantly enriched terms in each category were selected to generate a bubble chart for GO analysis (Table 4, Fig. 5). The BPs identified included regulation of nervous system development, negative regulation of immune system processes, regulation of cell–cell adhesion, and cytokine-mediated signaling pathways. Enriched CCs comprised the mitochondrial inner membrane, protein–DNA complex, mitochondrial matrix, and external side of the plasma membrane. MFs highlighted in the analysis encompassed channel activity, passive transmembrane transporter activity, ion channel activity, and gated channel activity.

3.6. Analysis of H2AC14 related pathways based on GSEA
Initially, CRC patients were stratified according to the expression levels of the H2AC14 gene. Differential gene expression analysis was conducted using the R package (version 4.2.1) org, with a significance threshold of P < .05 and |log2FC| ≥ 1.5 as the cutoff criteria. The identified DEGs were employed to evaluate potential functional disparities between groups exhibiting high and low H2AC14 expression. Bubble maps illustrating signaling pathways associated with H2AC14 were generated using the Sangerbox platform, based on the screened DEGs. These pathways included prominent oncogenic pathways such as MAPK and JAK-STAT, underscoring a strong association between H2AC14 and cancer. Six enriched datasets demonstrating the highest significance in H2AC14 high-expression phenotypes were selected for further analysis (Table 5, Fig. 6).

3.7. Immune infiltration analysis of H2AC14 in CRC
To investigate the relationship between H2AC14 expression levels and the tumor immune microenvironment (TIME) in CRC patients, we employed the CIBERSORT method to estimate the proportions of 22 immune cell types and quantitatively analyzed immune infiltration. Differential immune cells were screened based on H2AC14 expression levels, revealing 12 significantly altered immune cell types (Fig. 7A and B), including resting aDC cells, cytotoxic cells, eosinophils, iDC cells, mast cells, NK cells, pDC cells, T helper cells, TFH cells, Tgd cells, Th12 cells, and Th2 cells. Using ssGSE technology, we evaluated the infiltration levels of 24 immune cell types across 33 cancer types (Fig. 7C and D) and explored the correlation between H2AC14 expression and immune cell infiltration in different cancers through Spearman correlation analysis. The results demonstrated that in CRC, H2AC14 expression was significantly associated with multiple immune cell subtypes. Specifically, H2AC14 expression levels showed positive correlations with Tgd cell regulation (R = 0.032, P = .024), memory B cells (R = 0.054, P = .024), Th2 cells (R = 0.07, P = .024), NK CD56bright cells (R = 0.087, P < .05), CD8 T cells (R = 0.089, P < .05), TReg cells (R = 0.103, P < .05), T helper cells (R = 0.111, P < .01), TFH cells (R = 0.170, P < .01), T cells (R = 0.171, P < .01), Th1 cells (R = 0.203, P < .001), cytotoxic cells (R = 0.212, P < .01), and aDC cells (R = 0.320, P < .001). Conversely, H2AC14 expression levels exhibited negative correlations with mast cells (R = -0.377, P = .001), eosinophils (R = -0.364, P = .001), NK cells (R = -0.326, P = .002), Tem cells (R = -0.309, P = .002), pDC cells (R = -0.278, P < .005), neutrophils (R = -0.244, P < .05), Th17 cells (R = -0.174, P < .005), and macrophages (R = -0.155, P < .005). These findings suggest that H2AC14 may play a regulatory role in tumor-infiltrating immune cells within the TIME and influence the composition. To further explore the clinical significance of H2AC14 expression in CRC immunotherapy, we conducted a detailed assessment of the infiltration levels of the 9 immune cell types most strongly correlated with H2AC14, which validated the consistency of our preliminary analysis (Fig. 8A–H). This discovery indicates that H2AC14 could serve as a potential novel target for future CRC immunotherapy.

3.8. The expression of H2AC14 in colon epithelial cells and CRC cells was verified by RT-qPCR
To assess the expression levels of H2AC14 in normal colon epithelial cells and CRC cells, we analyzed the RNA expression of H2AC14 in normal colon epithelial cells (NCM460) and CRC cell lines (LOVO, HCT116, HT29, SW480). qPCR results revealed that H2AC14 expression was significantly elevated in CRC cells compared to normal colon epithelial cells. The RT-PCR validation results were consistent with our prior statistical findings from the TCGA database, confirming that H2AC14 is highly expressed in colon cancer (Fig. 9).

3.9. H2AC14 silencing inhibited cell growth, migration, and invasion in CRC cell lines
Because this result supported previous research demonstrating that HT29 and SW480 cells expressed H2AC14 at a stable high level, we chose to employ these cells in our subsequent tests. The H2AC14 gene was silenced using short interfering RNA to investigate how gene silencing affects CRC development. Wound healing assays conducted at 0, 24, and 48 hours showed reduced cell migration ability in HT29 and SW480 cells under the microscope when the H2AC14 gene was silenced, indicating that high H2AC14 expression promoted cancer cell migration (Fig. 10A and Fig. 11A). We then performed transwell experiments using HT29 and SW480 cells. Transwell experiments further confirmed these findings, as the results showed decreased HT29 and SW480 cells numbers upon H2AC14 gene silencing in invasion and migration experiments, confirming the role of H2AC14 in promoting cancer cell migration (Fig. 11B).

3.10. H2AC14 gene affects tumor progression by influencing epithelial–mesenchymal transition (EMT) process
We used short interfering RNA to silence the H2AC14 gene in order to examine if H2AC14 gene promotes tumor progression through altering the EMT process. RT-qPCR was used to confirm the H2AC14 gene’s silencing effect (Fig. 12A). Next, we measured the expression levels of N-cad, SNAIL, VIM, and E-cad. The findings indicated that following the silencing of the H2AC14 gene, there was a rise in the RNA and protein levels of E-cad, N-cad, SNAIL, and VIM all had lower amounts of RNA (Figs. 12B–D). These results imply that H2AC14 knockdown slows the growth of tumors. Thus, H2AC14 may facilitate the EMT process in CRC, thereby facilitating tumor invasion and migration.

4. Discussion
CRC has consistently ranked as the most prevalent gastrointestinal malignancy in terms of both incidence and mortality over the past 5 years. The recent loss of prominent scientists to CRC underscores its persistent impact on society, while the declining age of onset further highlights its growing public health concern.[16] Early detection of CRC remains challenging, emphasizing the critical need for the development of reliable and stable biomarkers to enhance diagnosis, treatment, and prognosis prediction.[17,18] At the bioinformatics level, this study explores the expression of a novel biomarker, H2AC14, in CRC and its potential implications for diagnosis and prognosis.
The H2AC14 gene, located at chromosome 6p22-p21.3, belongs to the histone H2A family. Histones, which bind to DNA in vivo, play a pivotal role in regulating gene expression. Through processes such as methylation and acetylation, histones undergo modifications that, when dysregulated, are associated with various diseases, including cancer. Current research highlights the significance of histone modifications in predicting prostate cancer and modulating the biological clock.[19–21] While the role of histones in cancer has been explored, the histone family-comprising H2A, H2B, H3, and H4-forms the fundamental unit of chromatin, known as the nucleosome, in conjunction with DNA. Among these, H2A holds particular importance in transcriptional regulation and other processes due to its strategic positioning at the DNA entry and exit points. However, as no prior studies have investigated H2AC14 in cancer, this work represents the first bioinformatics-based exploration of its potential role in CRC diagnosis and prognosis.
We initially assessed the expression levels of H2AC14 across various cancers and observed its upregulation in the majority of malignancies. Subsequently, paired and unpaired analyses of H2AC14 in CRC revealed significantly higher expression levels in CRC tissues compared to normal tissues. Furthermore, H2AC14 demonstrated significant relevance in the TNM staging of CRC patients and played a critical role in CRC prognosis. To further elucidate its mechanistic involvement in CRC, we identified 1387 differentially expressed genes. Utilizing these genes, we performed signal pathway enrichment analysis, as well as GO and GSEA. These analyses revealed the association of H2AC14 with classic cancer-related pathways, particularly those involving the cell cycle and cell cycle checkpoints, suggesting its pivotal role in CRC progression.
Despite certain limitations, to our knowledge, this study represents the 1st identification of the role of H2AC14, a member of the histone family, in CRC. Our findings underscore its significant implications for CRC staging and prognosis. One of the most pressing clinical challenges in CRC is the absence of reliable diagnostic markers, often resulting in late-stage detection (stage III or IV). Thus, our study provides a potential diagnostic marker for CRC and a valuable prognostic indicator. Additionally, while we have only examined H2AC14 expression at the RNA level in CRC cell lines and normal colon cell lines, our GO/KEGG/GSEA analyses have identified meaningful pathways and cancer phenotypes, laying the groundwork for further mechanistic investigations. Given the critical role of the histone family in transcriptional regulation, targeting histone-related mechanisms represents a promising direction for future therapeutic research.
As a member of the histone H2A family, H2AC14 plays a pivotal role in the regulation of gene transcription through epigenetic mechanisms. Histone modifications, including acetylation, methylation, and phosphorylation, are crucial for modulating chromatin structure and gene expression. Specifically, H2AC14 is involved in the acetylation of lysine 14 (K14), a modification that dynamically regulates transcriptional activity, DNA repair, and cell cycle progression.[22–25] Recent studies have highlighted the importance of histone modifications in cancer development, particularly in the context of epigenetic dysregulation. For instance, aberrant histone acetylation has been associated with the activation of onc and the silencing of tumor suppressor genes, thereby promoting tumorigenesis.[26,27] In CRC, epigenetic alterations, including changes in histones, are increasingly recognized as critical factors contributing to disease progression and therapeutic resistance.[28,29] Our findings suggest that H2AC14 may influence the development of CRC by regulating the expression of genes involved in cell cycle control and immune response, as evidenced by our GO/KEGG/GSEA analyses. Future research should explore the specific epigenetic mechanisms by which H2AC14 regulates gene transcription, particularly its interactions with histone acetyltransferases and deacetylases, to gain deeper insights into its role in CRC.
Our study found that H2AC14 is highly expressed in CRC, according to our research. In order to identify changes in certain EMT-related markers and regulators at the RNA and protein levels, we 1st knocked down H2AC14 in CRC cells. According to our research, the H2AC14 gene may aid in the growth of tumors by encouraging the development of EMT in CRC. Further investigations into the specific mechanism by which H2AC14 affects the EMT process and its regulation, are crucial. RNA sequencing of H2AC14-silenced cells must be conducted to identify enriched differential genes, related signaling pathways, or transcription factors. The expression of these differential genes was detected by qPCR. Appropriate genes were selected for further knockdown or overexpression experiments to improve our understanding of the specific mechanism by which H2AC14 affected the EMT process of CRC and its regulation.
The identification of reliable biomarkers for the diagnosis and prognosis of CRC remains a significant challenge in clinical practice. Currently used biomarkers, such as carcinoembryonic antigen and carbohydrate antigen 19-9, although widely applied, exhibit limitations in sensitivity and specificity, particularly in the early stages of CRC.[30] Our study introduces H2AC14 as a novel biomarker with potential diagnostic and prognostic value. While our ROC analysis revealed an AUC of 0.696, indicating moderate diagnostic performance, this value is comparable to or better than the performance of certain existing biomarkers in early-stage CRC.[31] For instance, C demonstrates an AUC of approximately 0.65 to 0.70 in early-stage CRC, while carbohydrate antigen 19-9 exhibits even lower sensitivity.[32] The advantage of H2AC14 lies in its potential to complement existing biomarkers, enhancing overall diagnostic accuracy when used in combination with current markers. Furthermore, H2AC14’s involvement in key cancer-related pathways, such as cell cycle regulation and immune modulation, suggests its potential utility in predicting treatment response and patient prognosis. However, further validation in larger, independent cohorts is necessary to confirm its clinical applicability and define its role in routine clinical practice.
H2AC14 holds promising clinical potential as a diagnostic and prognostic biomarker, yet several challenges remain to be addressed. Our study highlights its potential in CRC staging and prognosis, but it also underscores certain limitations. First, the moderate AUC value of 0.696 suggests that H2AC14 alone may not suffice as an independent diagnostic marker but could be valuable as part of a multi-marker panel. Second, the dynamic regulation of histone modifications, including H2AC14, may complicate its measurement and interpretation in clinical settings. Standardized protocols for detecting histone modifications in patient samples need to be established to ensure reproducibility and reliability. Third, the role of H2AC in the TIME and its interactions with immune cells warrant further investigation. Recent studies have emphasized the importance of histone modifications in shaping the TIME and modulating immune responses, suggesting that targeting H2AC14 could enhance the efficacy of immunotherapy in CRC.[33] Future research should explore the therapeutic potential of H2AC14, particularly its effects when combined with immune checkpoint inhibitors or epigenetic drugs.
Recent advancements in the study of the histone H2A family have revealed its diverse roles in cancer biology, highlighting its potential as a therapeutic target. Variants such as H2A.X, H2A.Z, and H2A.B are implicated in DNA damage response, transcriptional regulation, and chromatin remodeling, making them attractive targets for cancer therapy.[34,35] The integration of multi-omics technologies, including genomics, transcriptomics, and epigenomics, has further accelerated the discovery of histone-related biomarkers and therapeutic targets. For instance, studies combining ChIP-seq and RNA-seq have identified histone modifications associated with drug resistance in CRC, providing new avenues for personalized.[36,37] In the case of H2AC14, multi-omics approaches could elucidate its regulatory networks and identify downstream effectors, thereby enhancing our understanding of its role in CRC. Additionally, the development of small-molecule inhibitors targeting histone modifications, such as histone deacetylases, could further clarify these regulatory networks and deepen our insights their functional contributions in CRC.

5. Conclusion
In summary, a comprehensive bioinformatics analysis of H2AC14 in the TCGA-COAD dataset demonstrated its elevated expression in CRC and its significant association with tumor TNM staging, OS, and other prognostic factors. Furthermore, the ROC curve analysis indicated the potential utility of H2AC14 as a diagnostic marker for CRC. The findings from GO, KEGG, and GSEA analyses also provided insights into the potential mechanisms of H2AC14 in CRC, offering a foundation and valuable clues for further investigation into its functional role.

Author contributions
Conceptualization: Min Luo, Yue Zhang.
Data curation: Min Luo.
Formal analysis: Min Luo.
Funding acquisition: Pei-Feng Li.
Investigation: Min Luo, Yue Zhang.
Methodology: Min Luo, Yue Zhang.
Project administration: Min Luo.
Resources: Min Luo.
Software: Min Luo.
Supervision: Min Luo.
Validation: Min Luo.
Visualization: Min Luo.
Writing – original draft: Min Luo.
Writing – review & editing: Pei-Feng Li.