The role of the CDCA gene family in tumor stemness maintenance and drug sensitivity

Introduction

The cell division cycle-associated (CDCA) gene family, including CDCA1, CDCA2, CDCA3, CDCA4, CDCA5, CDCA6 (CBX2), CDCA7, CDCA7L, and CDCA8, plays crucial roles in regulating mitosis and ensuring proper cell cycle progression. Dysregulation of members such as CDCA8, a core component of the chromosome passenger complex, can cause chromosomal instability and promote tumorigenesis (1). CDCA1 (NUF2), an essential kinetochore protein, ensures accurate chromosome-microtubule attachment during mitosis (2). Although CDCAs are critical for mitosis and implicated in cancer, their functions across tumors remain insufficiently studied.

The cell cycle is fundamental to tumor biology, influencing tumor stemness and cancer progression (3). Tumor stemness—associated with tumor initiation, self-renewal, metastasis, and therapy resistance—is closely tied to uncontrolled cell cycle activity (4). Dysregulated checkpoints and key cell cycle regulators, such as CDK4/6 and E2Fs, enhance stem cell-like self-renewal in cancer cells (5,6). Tumor stem cells also exhibit strong resistance to chemotherapy and radiotherapy, contributing to recurrence and treatment failure (7). Thus, understanding molecular mechanisms linking the cell cycle to tumor stemness is of great importance.

Given the potential connection between CDCAs, tumor stemness, and drug resistance, but the lack of systematic studies. This study conducted a comprehensive bioinformatics analysis of the expression patterns of CDCAs in various cancers, and evaluated their association with tumor stem cell characteristics and drug sensitivity. It also explored the impact of one of the CDCAs members, CDCA2, on the malignant proliferation and stemness of lung cancer and colorectal cancer. We present this article in accordance with the MDAR and ARRIVE reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2654/rc).

Methods

CDCAs expression data acquisition and collation

UCSC Xena (https://xena.ucsc.edu) is an integrated genomic data platform that consolidates multiple public bioinformatics datasets, allowing users to download and compare gene expression, variations, clinical information, and more. We obtained the RNA sequencing (RNA-seq) expression matrix and associated clinical data for The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) pan-cancer datasets from the UCSC Xena database. Using R language (R Studio version: 2024.09.0+375, R version: 4.4.1) (https://posit.co/downloads/, https://cran.rstudio.com/), we filtered the entire dataset with the “dplyr” package to remove missing and duplicate results. The “limma” package was used to analyze the differential expression of CDCAs in tumor and non-tumor tissues across 33 cancer types. The “corrplot” package was employed to analyze the correlation of expression between the 9 genes in the family. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Stemness evaluation of tumor

To analyze the characteristics of pan-cancer stemness, we downloaded RNA-based stemness scores (RNAss) and DNA methylation-based stemness scores (DNAss) for various cancers from the UCSC Xena database. These stemness scores were previously calculated using a one-class logistic regression (OCLR) machine learning algorithm trained on stem cell samples, as described by Malta et al. (8). The relationship between the gene family and pan-cancer expression levels was analyzed and visualized using the “limma” and “corrplot” packages in R.

Drug sensitivity analysis

To clarify the relationship between CDCAs and drug sensitivity, we obtained gene expression data and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Since CDCA3 gene expression data are not available in GDSC, we only analyzed the remaining 8 CDCAs. The results were visualized and analyzed using the “limma”, “impute”, “ggplot2”, and “ggpubr” packages in R.

Cell culture

NCI-H1703 (TCH-C436, Haixing Biology, Suzhou, China) lung cancer and DLD1 (TCH-C169, Haixing Biology, China) colorectal cancer cells were cultured in DMEM/1640 (Haixing Biology, China) supplemented with 10% fetal bovine serum (FBS) (Haixing Biology, China) and 1% penicillin-streptomycin at 37 ℃ with 5% CO₂.

Western blotting

Proteins were extracted using Radio-Immunoprecipitation Assay (RIPA) and phenylmethanesulfonyl fluoride (PMSF) (Solarbio, Beijing, China), quantified by bicinchoninic acid (BCA) (Solarbio, China), separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Darmstadt, Germany). After bovine serum albumin (BSA) (Solarbio, China) blocking, membranes were incubated with primary antibodies overnight at 4 ℃, followed by secondary antibody incubation and chemiluminescent detection. Antibodies such as CDCA2 (17701-1-AP), CD133 (18470-1-AP), SOX2 (11064-1-AP), and OCT3/4 (11263-1-AP) are all provided by Proteintech Company (Wuhan, China).

Cell protein samples were processed using RIPA lysis buffer (NCM Biotech, Suzhou, China) and PMSF (100 mM) (Solarbio, China). Protein concentration was determined using a BCA assay kit (Solarbio, China) to ensure consistent protein levels across samples. The samples were mixed with 5× protein loading buffer and heated at 95 ℃ for 5–10 minutes. Protein separation was performed by SDS-PAGE, followed by electrotransfer onto a PVDF membrane and blocking with 5% BSA. Primary antibodies used include CDCA2 antibody (Wuhan Sanying, China), CD133 antibody (Wuhan Sanying, China), SOX2 antibody (Wuhan Sanying, China), and OCT3/4 antibody (Wuhan Sanying, China). The primary antibodies were incubated overnight at 4 ℃, and the secondary antibody was incubated at room temperature for 1 hour. Chemiluminescence was used for protein exposure and imaging.

Small interfering RNA (siRNA) transfection

Gene silencing was performed using siRNA from Suzhou GenePharm. CDCA2 siRNAs (CDCA2: siRNA1: sense GAUGAAGAUCCAAAUACAAAU, antisense UUGUAUUUGGAUCUUCAUCAG; siRNA2: sense GCACUGUAUCGAAAUGUUAAC, antisense UAACAUUUCGAUACAGUGCAG) were transfected into cells at 50–70% confluence using Lipofectamine 2000 (Thermo Fisher Scientific, China). Medium was replaced 6–24 hours post-transfection.

Cell Counting Kit-8 (CCK-8) assay

Cells to be tested were seeded in a 6-well plate, and when the cells reached 70–80% confluence, they were transfected with specific siRNA. After incubation at 37 ℃ with 5% CO₂ for 24 hours, the cells were digested and resuspended, then seeded at a density of 5×10³ cells per well in a 96-well plate. The 96-well plate was placed in a 37 ℃, 5% CO₂ incubator. CCK-8 reagent (APExBIO, USA) was added at 0, 24, 48, 72, and 96 hours. The absorbance [optical density (OD) value] of each well was measured at 450 nm using a microplate reader. Cell viability was calculated based on the absorbance values, and the differences in cell viability between different treatment groups were analyzed. The curve graph was plotted using GraphPad Prism 8.

Colony formation experiment

Inoculated 1,000 or 2,000 treated fine cultures in six-well plates for 7–14 days until significant clone formation was observed. After treatment, it is gently rinsed to remove the floating cells, and then stained with crystal violet staining solution. After staining, the clones formed are counted with a microscope to calculate the clone formation rate.

Drug susceptibility

The drug sensitivity experiment was performed by seeding 7×10³ cells per well in a 96-well plate. After the cells adhered, different concentrations of oxaliplatin (Aladdin, Shanghai, China) were added for treatment and incubated for 48 hours. The concentration gradient of oxaliplatin was set as 0, 0.5, 1, 3, 5, 10, 20, 50, 100, and 300 µmol. After the treatment, CCK-8 reagent was added, and the cells were incubated for 1.5 hours. The absorbance (OD value) of each well was measured. Cell viability was calculated based on the OD values using the formula: cell viability (%) = (treatment group OD/control group OD) ×100%. The curve graph was plotted using GraphPad Prism 8.

Subcutaneous tumor xenograft in nude mice

A protocol was prepared before the study without registration. All animal experiments were performed under a project license (No. NCULAE-20250509002) granted by the Institutional Animal Care and Use Committee (IACUC) of Nanchang University, in compliance with institutional guidelines for the care and use of animals. Female BALB/c mice (4–6 weeks old) were purchased from Jimei Pharmaceutical Co., Ltd. (China) and housed under specific pathogen-free (SPF) conditions. Animals were maintained in a controlled environment (temperature: 22±2 ℃; 12/12 h light/dark cycle) with ad libitum access to food and water. After a 1-week acclimatization period, mice were randomly assigned into two experimental groups (n=5 per group): the negative control (NC) group and the CDCA2 knockdown group. Log-phase DLD1 cells (5×10⁷ cells/mL) were subcutaneously injected into the right flank (200 µL). Animal health and behavior were monitored daily. Tumor size was measured weekly using a digital caliper, and tumor volume was calculated using the formula: V= (length × width²)/2. Humane endpoints were strictly observed. Mice were euthanized when the tumor volume reached approximately 1,000 mm³ or if they met predefined ethical criteria indicating significant distress. Following euthanasia, tumors were excised, weighed, and processed for subsequent analysis.

Statistical analysis

Pearson’s correlation (|r|>0.4) was used to assess CDCAs and stemness indices. Experimental data are shown as mean ± standard deviation (SD) and analyzed in SPSS 25.0. Normally distributed data were tested with Student’s t-test or ANOVA; non-normal data with Mann-Whitney U or Kruskal-Wallis tests. P<0.05 was considered significant.

Results

Expression and prognostic of CDCAs in pan-cancer

Pan-cancer analysis showed that all nine CDCAs were highly expressed across tumor types (Figure 1A). Differential expression analysis across 31 cancers [excluding uveal melanoma (UVM) and mesothelioma (MESO) due to lack of normal controls] revealed that most CDCAs were significantly upregulated, with the exception of CDCA7L, which was elevated only in acute myeloid leukemia (LAML) (Figure 1B). Strong correlations were also observed among CDCAs family members (Figure 1C). Kaplan-Meier and Cox regression analyses indicated that high CDCAs expression was associated with worse overall survival in several cancers, including adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), MESO, and pancreatic adenocarcinoma (PAAD), but predicted better prognosis in thymoma (THYM) (Figure 1D,1E).

Figure 1 Expression and prognosis of CDCAs in pan-cancer. (A) Expression levels of CDCAs across various cancer types. (B) Heatmap showing differential expression of CDCAs in pan-cancer compared to normal tissue. (C) Correlation analysis of CDCA gene expression. (D) Kaplan-Meier analysis. (E) Univariate Cox regression analysis. FC, fold change; HR, hazard ratio; RPKM, reads per kilobase per million mapped reads.

CDCAs and tumor stemness

Most CDCAs showed significant positive correlations with tumor stemness. Based on RNAss, weak negative correlations were observed in thyroid carcinoma (THCA) and sporadically in cancers such as KIRC, KIRP, and pheochromocytoma and paraganglioma (PCPG), whereas positive correlations dominated in other tumor types (Figure 2A). Using DNAss, CDCAs in THYM showed marked negative correlations, while other tumors generally exhibited strong positive associations (Figure 2B). High CDCAs expression (median-based grouping) corresponded to significantly increased RNAss and DNAss scores in nearly all cancers (Figures S1,S2). Further analysis in lung and colorectal cancers showed that RNAss and DNAss were significantly higher in tumor versus normal tissues in LUAD, lung squamous cell carcinoma (LUSC), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) (Figure 2C,2D), with no significant differences across clinical stages (Figure 2E,2F).

Figure 2 Relationship between CDCAs and stemness index in pan-cancer. (A) RNAss. (B) DNAss. (C,D) Differential expression in lung or bowel cancer and paracancer tissues. (E,F) Different stages of lung and bowel cancer. ns, not significant; ***, P<0.001; ****, P<0.0001. COAD, colon adenocarcinoma; DNAss, DNA methylation-based stemness scores; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; READ, rectum adenocarcinoma; RNAss, RNA-based stemness score.

CDCAs and drug sensitivity

Pearson correlation analysis between CDCAs expression and GDSC drug response identified the top compounds associated with CDCAs (Figure 3). CDCA5 expression showed positive correlations with sensitivity to 6-thioguanine, curcumin, and methylprednisolone. CBX2 was negatively correlated with agents such as Melbex, BAY-2402234, BAY-1161909, and OSU-03012. CDCA3 expression was negatively associated with responses to 5-fluoro-deoxy-uridine, Gemcitabine, and LMP-400. CDCA7L high expression markedly reduced sensitivity to multiple inhibitors, including AZD-3147, PQR-620, CC-223, GDC-0349, Pluripotin, and LY-302341.

Figure 3 Top 20 drugs with the strongest correlation between CDCAs expression and drug sensitivity. The x-axis represents gene expression levels, and the y-axis represents drug sensitivity. Cor, correlation.

Effects of CDCAs on proliferation, dryness and drug sensitivity of lung and bowel cancer cells

Given the differential expression of CDCA2 among the CDCA family members in lung and colorectal cancers and its potential roles in tumor stemness and drug sensitivity, CDCA2 was selected for further experimental validation. We selected CDCA2 for experimental validation. CCK-8, colony formation assays, and nude mouse xenografts showed that CDCA2 knockdown significantly inhibited proliferation in lung and colorectal cancer cells (Figure 4A-4F). Western Blot (WB) and quantitative polymerase chain reaction (qPCR) analysis demonstrated reduced SOX2, OCT3/4, and CD133 expression following CDCA2 silencing (Figure 4G,4H), indicating reduced tumor stemness. Drug sensitivity analysis showed that, in lung cancer, CDCA1 and CDCA6 expression correlated with sensitivity or resistance to THZ-1-87, lapatinib, and THZ-2-98-01 (Figure 4I). In colorectal cancer, CDCA1 correlated with IOX2 and tozasertib sensitivity, whereas CDCA6 was associated with IOX2 and compound 150412 (Figure 4J). These findings identified multiple candidate compounds linked to CDCA expression patterns. To validate drug responses experimentally, we tested oxaliplatin and observed increased sensitivity in CDCA2-knockdown cells (Figure 4K,4L), indicating that CDCA2 contributes to oxaliplatin resistance. Together with bioinformatics results, these findings support that CDCAs promote tumor proliferation partly by enhancing tumor stemness.

Figure 4 Effect of knockdown CDCA2 expression on the stemness and drug sensitivity of lung and bowel cancer cells. (A) Cell colony formation assay DLD1 and NCI-H1703. (B) DLD1 and NCI-H1703 CCK-8 experiments. (C) Effects of CDCA2 knockdown on subcutaneous tumor formation in nude mice. (D) Comparison of subcutaneous tumor weights. (E) Immunohistochemistry of CDCA2 in subcutaneous tumors (magnification ×10; DAB stained). (F) Time-volume curve of subcutaneous tumor formation in nude mice. (G) Effect of knockdown of CDCA2 on the stemness of DLD1 and NCI-H1703 cells. (H) Knock-down efficiency of CDCA2 siRNA. (I,J) Correlation analysis with drug sensitivity of lung cancer and colorectal cancer. (K,L) Effect of knockdown of CDCA2 on oxaliplatin sensitivity in DLD1 and NCI-H1703 cells. ns, not significant; **, P<0.01; ***, P<0.001; ****, P<0.0001. IC₅₀, half-maximal drug inhibitory concentration; mRNA, messenger RNA; NC, negative control; OD, optical density; SI, SI CDCA2; siRNA, small interfering RNA.

Discussion

This study systematically characterized CDCAs expression across cancers and examined their associations with stemness and drug sensitivity. Analyses of TCGA and GTEx datasets revealed widespread CDCAs overexpression in malignancies, including lung and colorectal cancer, and experimental validation confirmed their roles in regulating cancer cell proliferation, stemness, and chemoresistance. These findings highlight CDCAs as potential contributors to tumorigenesis and therapeutic resistance and as promising targets for intervention.

Consistent with previous studies (9,10), the CDCAs—key regulators of cell division—were broadly upregulated in cancers and associated with poor prognosis. We also identified strong correlations between CDCAs expression and tumor stemness, with higher RNAss and DNAss scores observed in most tumors. These results underscore the clinical heterogeneity of cancer stemness and its relevance for patient-specific treatment.

Functional assays further supported CDCAs involvement in stemness regulation. CDCA2 knockdown reduced proliferation and clonogenicity and downregulated SOX2, OCT3/4, and CD133. Other CDCAs have been similarly implicated in stemness maintenance, including CDCA6 in cervical cancer and leukemia (11,12), CDCA8 in liver cancer stem-like cells (13), and CDCA5 in breast cancer stem cells (9). Although the role of CDCA2 in stemness remains less defined, prior evidence suggests potential links through cell-cycle-related epigenetic regulation (14). These findings collectively indicate that multiple CDCAs contribute to stemness and tumor progression (15,16).

Drug sensitivity analyses revealed negative correlations between high CDCAs expression and responses to various compounds, suggesting a role in chemoresistance. CDCAs may promote resistance through effects on DNA repair, cell-cycle regulation, and apoptosis evasion. Consistently, CDCA2 knockdown enhanced oxaliplatin sensitivity, aligning with previous findings that CDCA3 and CDCA5 influence platinum drug response in non-small cell lung cancer (NSCLC) and esophageal squamous cell carcinoma (ESCC) (17,18). Positive correlations with several anticancer agents further suggest therapeutic potential in targeting CDCA-associated pathways.

Despite these insights, there are limitations in this study. Bioinformatics results rely on heterogeneous public datasets, and experimental validation was restricted to two cancer types. However, this study was based on a pan-cancer analysis, and differences in the tumor microenvironment among cancer types may lead to functional heterogeneity of the CDCA family. This will be an important focus for future investigations. The precise mechanisms by which CDCAs regulate stemness and drug resistance—such as potential involvement of PI3K/Akt or Wnt/β-catenin signaling (19-21)—remain to be elucidated. Future studies should explore these pathways and expand validation across additional tumor models.

Conclusions

In summary, this study demonstrates that CDCAs are broadly overexpressed across cancers and closely associated with tumor stemness and drug sensitivity. Integrated bioinformatics and experimental analyses indicate that CDCAs contribute to cancer stem cell maintenance and chemoresistance. These findings highlight CDCAs as promising therapeutic targets and provide a foundation for developing strategies to overcome tumor drug resistance.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and ARRIVE reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2654/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2654/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2654/prf

Funding: This study was supported by the National Natural Science Foundation of China (Nos. 82360416 and 82460545).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2654/coif). All authors declared that this study was supported by the National Natural Science Foundation of China (Nos. 82360416 and 82460545). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All animal experiments were performed under a project license (No. NCULAE-20250509002) granted by the Institutional Animal Care and Use Committee (IACUC) of Nanchang University, in compliance with institutional guidelines for the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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