CALD1 inhibits invasion of human ovarian cancer cells by affecting cytoskeletal structure and the number of focal adhesion

Introduction

Ovarian cancer (OV) is a prevalent malignant tumor in women, with more than 313,959 new cases and 207,252 deaths reported annually. Based on its histopathological features, OV is classified into several categories: epithelial OV, germ cell tumors, and sex cord-mesenchymal tumors. Epithelial tumors are the most prevalent, accounting for over 90% of all cases. Epithelial OV is further divided into multiple histological subtypes, including serous, mucinous, endometrioid, and clear cell carcinomas. Each subtype exhibits distinct biological behaviors, treatment responses, and prognostic outcomes (1-3).

Diagnosing OV in its early stages is challenging due to the lack of distinct clinical features and diagnostic markers. As a result, around 75% of patients are diagnosed at an advanced stage. The standard first-line treatment regimen for OV involves cytoreductive surgery and platinum-based chemotherapy. However, treatment efficacy can vary significantly among the different subtypes; for instance, high-grade serous carcinoma is often more responsive to chemotherapy compared to clear cell carcinoma, which frequently exhibits resistance to standard treatments. Compared to the 29% 5-year survival rate for patients with advanced OV, those with early-stage cancer have a much higher rate of 92% (4-8). Therefore, it is crucial to prioritize further exploration of the pathogenesis of OV, especially with regard to the unique characteristics of its various subtypes, and to search for new diagnostic markers and therapeutic targets in OV research.

The CALD1 gene on human chromosome 7q33 comprises 17 exons that undergo selective splicing to generate two distinct isoforms: h-caldesmon and l-caldesmon (9,10). H-caldesmon, a heavy-chain calmodulin-binding protein with a molecular weight of 89–93 kDa, is predominantly expressed in smooth muscle cells and is crucial in regulating smooth muscle contraction. In contrast, l-caldesmon, a light-chain calmodulin-binding protein with a molecular weight of 59–63 kDa, is ubiquitously present in all vertebrate cells. This isoform is intimately involved in cytoskeletal regulation, modulating the cytoskeletal structure and consequently affecting cell morphology and motility in response to intracellular and extracellular physiological alterations (10,11).

Recent studies have found that CALD1 is expressed abnormally in various tumor types, including glioblastoma, gastric, colorectal, and bladder cancer. The expression level of CALD1 has been closely linked to the clinical prognosis of patients in these cancer settings (12-16). Regarding OV specifically, previous studies have indicated a relationship between CALD1 expression and disease progression. Li et al. [2024] demonstrated that elevated CALD1 levels correlate with advanced tumor stages and poorer prognosis in OV patients, highlighting its potential as a biomarker for disease progression (17). Conversely, lower expression of CALD1 in OV tissues compared to benign samples has also been reported by Boljevic et al. [2020] (18). Despite these insights, the specific role of CALD1 in OV drug sensitivity and its detailed molecular mechanisms in tumor invasion remain to be fully elucidated.

This study utilized bioinformatics analysis and experimental validation to compare CALD1 expression levels in OV patients and healthy individuals. Furthermore, we investigated the regulatory role of CALD1 in the morphological changes and cell migration of OV cells and examined its possible clinical implications for the diagnosis and treatment of OV. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1375/rc).

Methods

Differential expression analysis

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The messenger RNA (mRNA) expression profiles of OV and normal tissues were obtained from The Cancer Genome Atlas Program (TCGA, https://tcga-data.nci.nih.gov/tcga/) and Genotype-Tissue Expression (GTEx, https://gtexportal.org/home/) databases, respectively. The TCGA database provided tumor samples (n=419) while the GTEx database provided normal tissue samples (n=88). The expression values were transformed using log₂(x+1) and analyzed using unpaired Wilcoxon Rank Sum Tests to determine differential expression between OV and normal tissues.

Tissue microarray and immunohistochemistry (IHC) staining

OV tissue microarray (50-tumor, 11-normal OV tissue microarray; Cat No.OVC1021, WEIAOBIO, Shanghai, China) slides were dewaxed, rehydrated, and placed in 3% hydrogen peroxide for 10 minutes to eliminate endogenous peroxidase (Table S1). An antigen retrieval step was carried out in citrate buffer (pH =6.0). To prevent nonspecific binding, the slides were incubated with 5% bovine serum albumin (BSA) for 30 minutes. Subsequently, the slides were incubated with rabbit anti-human CALD1 antibody (1:2,000; Cat No. AF6411; Affinity, Jiangsu, China) for 1 hour at 37 ℃, followed by goat anti-rabbit Horseradish Peroxidase (HRP) (1:5,000; Cat No. SA00001-1; Proteintech, Wuhan, China) for 1 hour. Finally, the tissue sections were stained with 3,3'-diaminobenzidine solution and counterstained with hematoxylin for analysis. CaseViewer (3DHISTECH) was used for image acquisition. Histochemistry Score [H-Score = ∑(PI × I) = (percentage of cells of weak intensity × 1) + (percentage of cells of moderate intensity × 2) + (percentage of cells of strong intensity × 3)] was obtained with Quant Center Analysis tool (19).

Drug sensitivity

The ‘oncoPredict’ package is an R language package for predicting tumor cell sensitivity to drugs (20). Predictive models were constructed using the ‘oncoPredict’ package, which utilizes tumor cell gene expression data and drug sensitivity information sourced from the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database. The sensitivity of patients in the CALD1 high and low expression groups to chemotherapeutic drugs was calculated according to the model.

Gene interaction network construction

GeneMANIA, an online website (http://genemania.org/), enables the analysis and prediction of gene interactions, including co-expression, protein interactions, gene interactions, protein shared structural domains and co-localization, and signaling pathways (21). In this study, we utilized GeneMANIA to construct the interaction network for the CALD1 gene.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

Metascape (https://metascape.org) is a popular website in the field of bioinformatics, widely used for pathway enrichment and gene annotation (22). The website integrates almost 40 databases including GO, KEGG, and Uniport. In this study, CALD1-associated genes obtained from GeneMANIA analysis were analyzed using Metascape for GO and KEGG enrichment analysis. The statistical significance was determined using P values calculated based on the cumulative hypergeometric distribution, and q-values were calculated using the Benjamini-Hochberg procedure to account for multiple testing (P<0.01).

Cell culture

The human SK-OV-3 (Cat No. CL-0215Pricella) and 293T (Cat No. CL-0005) cell lines were obtained from Procell (Wuhan, China). SKOV-3 cells were cultured in McCoy’s 5a Medium (Cat No. KGM4892S; Keygen Biotech, Jiangsu, China), while 293T cells were cultured in DMEM (Cat No. PM150210B; Procell, Wuhan, China) high glucose, both supplemented with 10% fetal bovine serum (Cat No. 26140-079; Procell, Wuhan, China) and 1% penicillin/streptomycin. The cells were maintained in a humidified atmosphere of 5% CO₂ at 37 ℃.

Plasmid construction and lentivirus packaging

Three shRNA sequences targeting the CALD1 gene were designed using the BWieBLOCK-iT™ RNAi Designer (http://rnaidesigner.thermofisher.com/rnaiexpress/) and synthesized by Sangon Biotech, Shanghai, China) (Table 1). These shRNA sequences were then individually cloned into the lentiviral vector pHBLV-U6-MCS-CMV-ZsGreen-PGK-PURO (Hanbio Biotechnology, Shanghai, China) to generate the recombinant plasmids pHBLV-U6-MCS-CMV-ZsGreen-PGK-PURO-CALD1-sh1, pHBLV-U6-MCS-CMV-ZsGreen-PGK-PURO-CALD1-sh2, and pHBLV-U6-MCS-CMV-ZsGreen-PGK-PURO-CALD1-sh3. For the sake of brevity, we referred to these constructs as “pHBLV-CALD1-sh1, pHBLV-CALD1-sh2, and pHBLV-CALD1-sh3” in the manuscript. The recombinant plasmids were co-transfected with the pSPAX2 and pMD2G (Hanbio Biotechnology) packaging plasmids into 293T cells using lipofectamine 2000 to produce lentivirus (23). After three days of incubation, the lentivirus was collected, concentrated, and its titer was determined by dilution count assay. The collected viruses were stored at −80 ℃.

Construction and screening of a CALD1 knockdown SKOV-3 cell line

Recombinant plasmids (pHBLV-CALD1-NC, pHBLV-CALD1-sh1, pHBLV-CALD1-sh2, and pHBLV-CALD1-sh3) were identified through DNA sequencing. The correctly sequenced recombinant plasmids and helper plasmids (pSPAX2, pMD2G) were cotransfected with 293T cells using Lipofiter™ transfection reagent. Viruses were collected after 72 hours and titrated by dilution counting. Lentivirus was used to infect SK-OV-3 cells at multiplicity of infection (MOI) =5, with 5 µg/mL puromycin added to the cell culture medium. This infection process was conducted in three independent replicates. After three days, green fluorescent signals were observed using fluorescence microscopy. Following three weeks of continuous culture, the effectiveness of CALD1 silencing was assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB).

qRT-PCR analysis

Total RNA was extracted using the TRIzol reagent Ultrapure RNA Kit (Cat No. CW0581M; CWBIO, Beijing, China). To prepare cDNA, HiScript II Q RT SuperMix for qPCR (+gDNA wiper) (Cat No. R223-01; Vazyme, Nanjing, China) was used, and ChamQ Universal SYBR qPCR Master Mix (Cat No. Q711-02; Vazyme, Nanjing, China) was used for RT-PCR. All procedures were carried out following the manufacturer’s instructions. Experiments were performed in triplicate and repeated at least twice. The primers used are as follows: CALD1 forward, 5'-GCCAGAAGATGGCTTGTCAG-3'; CALD1 reverse, 5'-ATTCTGCTCGCTCTTCTATCT TGA-3'; β-actin forward, 5'-TGGCACCCAGCACAATGAA-3'; β-actin reverse, 5'-CTAAGTCATA GTCCGCCTAGAAGCA-3'. The qRT-PCR data were analyzed using 2^−ΔΔCt method. The one-way ANOVA was used to test for differences between the groups.

WB

For western blotting, cells were washed with cold phosphate buffer saline (PBS) and lysed using radioimmunoprecipitation assay (RIPA) Lysis Buffer (Cat No. C1053; APPLYGEN, Beijing, China). Protein concentrations were quantified using the BCA Protein Assay Kit (Cat No. E-BC-K318-M; Elabscience, Wuhan, China). Samples were separated on 10% sodium dodecyl sulfate-polyacrylamide gels (Cat No. A1010; Solarbio, Beijing, China) and transferred onto polyvinylidene difluoride membranes (Cat No. FFP39; Beyotime, Shanghai, China). The membranes were then blocked with 5% skim milk and incubated with Rabbit Anti CALD1 (1:1,000; Cat No. AF6411; Affinity, Jiangsu, China), Mouse Monoclonal Anti-β-actin (1:2,000; Cat No. TA-09; ZSGB-BIO, Beijing, China) antibodies, followed by incubation with HRP-conjugated AffiniPure goat anti-rabbit IgG (H+L) (1:2,000; Cat No. ZB-2301; ZSGB-BIO, Beijing, China) and goat anti-mouse IgG (H+L) (1:2,000; Cat No. ZB-2305; ZSGB-BIO, Beijing, China). The immunoblot signal was quantified using ImageJ software. All WB experiments were conducted with at least three independent replicates.

Transwell assay

Cell invasion was assessed using a Transwell chamber assay with 24-well chambers. Cells (1×10⁵) were suspended in serum-free medium and added to the upper chamber, which was pre-coated with Matrigel^® (Cat No. 354234; Coring, New York, USA). The lower chamber was filled with medium containing 10% FBS. After 24 hours, unmigrated cells were removed with a cotton swab, and migrated cells were stained with crystal violet (Cat No. G1061; Solarbio, Beijing, China). To elute the crystal violet, 33% acetic acid was used, and the absorbance value was determined at 570 nm. The migrated cell number was quantified based on the absorbance value, and each experiment was performed three times.

Immunofluorescence assay

Cells were fixed with 4% paraformaldehyde for 20 minutes, permeabilized with 0.5% Triton X-100 for 10 minutes, and then blocked with blocking solution (Cat No. P0231; Beyotime, Shanghai, China) for 30 minutes to reduce non-specific binding. Following three washes with PBS for 5 minutes each, cells were incubated with Vinculin antibody (1:2,000; Cat No. AF6206; Affinity, Jiangsu, China) or TRITC-phalloidin (1:2,000; Cat No. CA1610; Solarbio, Beijing, China) at 37 ℃ for 2 hours. After three washes with TNST, cells were incubated with Cy3 Goat Anti-Rabbit IgG (Cat No. SA00009-2; Proteintech, Wuhan, China) at room temperature for 1 hour in the dark (TRITC-phalloidin group omits this step). Finally, cells were incubated with 4'-6-diamidino-2-phenylindole (Cat No. KGA215-50; KeyGENBio, Jiangsu, China), and the coverslip was sealed with an anti-fluorescent quencher before fluorescence observation under a microscope. The fluorescence intensity of the cytoskeleton and focal adhesion (Vinculin) was quantified using ImageJ software. Each experiment was performed at least three times, and a minimum of 6 images were analyzed for each cell group.

Statistical analysis

The statistical analysis for this study was conducted using GraphPad Prism 8 and R (version 4.12). The data were presented as mean ± standard deviation. The Student t-test was used to analyze statistical differences between two groups, while one-way analysis of variance (ANOVA) was utilized for comparisons involving three or more groups. All statistical tests were two-sided, and a P value less than 0.05 was considered statistically significant.

Results

Differential expression of CALD1 between cancer and normal samples

We present this article analyzing the expression of CALD1 in OV samples obtained from TCGA and normal tissue samples from the GTEx database. The results showed a significant decrease in CALD1 mRNA levels in OV tissues compared to normal tissues (P<0.0001, Figure 1A). To validate these findings, we conducted IHC on an OV tissue microarray consisting of 50 tumor samples and 11 normal samples. The IHC analysis revealed a marked decrease in CALD1 protein expression in OV tissues (P<0.0001, Figure 1B-1F), confirming the reduced expression of CALD1 in OV tissues compared to normal tissues.

Figure 1 Differential analysis of CALD1 expression in tumor tissue and healthy tissue. (A) Analysis of CALD1 expression levels in ovarian cancer and normal tissues from TCGA and GTEx databases. (B) Comparison of the differences in CALD1 expression between ovarian cancer tissues and normal tissues on tissue microarrays. (C,D) Representative image of immunohistochemistry of normal tissue on tissue microarray. (E,F) Representative image of immunohistochemistry of ovarian cancer tissue on tissue microarray. Scale bar: 200 µm. ****, P<0.0001. TPM, transcripts per million; OV, ovarian cancer; H-Score, Histochemistry Score; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression.

Chemotherapeutic response analysis

The study utilized the “oncoPredict” software package to predict drug sensitivity for OV treatment. A total of 12 drugs were evaluated, including three clinical routine OV drugs (irinotecan, oxaliplatin, topotecan) and nine development-stage OV drugs (CDK9_5038, entospletinib, AZD5363, AZD5991, LCL161, GNE-317, tozasertib, entinostat, telomerase inhibitor IX). The findings revealed that the high-CALD1 expression group showed greater sensitivity to CDK9_5038, Entospletinib, AZD5363, and GNE-317, while the low-CALD1 group showed greater sensitivity to irinotecan, oxaliplatin, topotecan, AZD5991, LCL161, tozasertib, entinostat, and telomerase inhibitor IX (Figure 2).

Figure 2 Drug sensitivity analysis of high and low CALD1 expression groups. This figure presents a drug sensitivity analysis for groups with high and low CALD1 expression, performed using the ‘oncopredict’ R package. Each boxplot illustrates the sensitivity differences for a specific drug between the high expression group (red) and the low expression group (blue). The y-axis represents drug sensitivity, while the x-axis categorizes the groups based on CALD1 expression levels. The P values above each boxplot indicate the statistical significance of the differences observed between the two groups. Drugs analyzed include irinotecan, oxaliplatin, topotecan, CDK9_5038, entospletinib, AZD5363, AZD5991, LCL161, GNE-317, tozasertib, entinostat, and telomerase inhibitor IX.

Gene network construction and functional enrichment analysis

We utilized the Gene Multiple Association Network Integration Algorithm of the GeneMANIA website to identify twenty genes associated with CALD1 and constructed an interaction network as shown in Figure 3A. Physical interactions made up the largest proportion of the interaction network at 77.64%, followed by co-expression (8.01%), prediction (5.37%), co-localization (3.63%), genetic interactions (2.87%), signaling pathways (1.88%), and shared protein structural domains (0.60%).

Figure 3 Construction and enrichment analysis of CALD1 gene network. (A) Construction of CALD1 gene network using GeneMANIA. If two genes have a relationship, such as co-expression, shared protein domain, physical interactions, co-localization, or pathway, connecting lines are drawn. The thickness of the lines represents the degree to which two genes are similar. (B,C) GO enrichment analysis and KEGG enrichment analysis. Bar chart of GO and KEGG enrichment analysis results sorted by P value. GO, Gene Ontology; GeneMANIA, Gene Multiple Association Network Integration Algorithm; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Subsequently, we conducted GO enrichment analysis on CALD1 and its associated genes. Our findings revealed significant enrichment at the cellular component level, which included actin cytoskeleton, stress fibers, contractile fibers, adhesion patches, intercellular adhesion, and smooth muscle contraction. Furthermore, at the biological process level, significant enrichment was observed in actin fiber assembly and positive regulation of stress fiber assembly, as demonstrated in Figure 3B. Notably, the KEGG signaling pathway analysis suggested that CALD1 and its associated genes were mainly enriched in signaling pathways related to actin cytoskeleton regulation, vascular smooth muscle contraction, and adherens junction, as presented in Figure 3C.

Construction and validation of CALD1 knockdown SKOV-3 cells

Lentiviral constructs were generated with titers as detailed in Table 2. Fluorescence microscopy revealed robust GFP expression in lentivirus-infected cell groups 3 days post-infection (Figure 4A). qRT-PCR and WB analyses demonstrated significant downregulation of CALD1 expression in SK-OV-3-CALD1-sh1 cells compared to controls (P<0.01; Figure 4B,4C). Figure S1 displays the results of the Cell Counting Kit 8 (CCK-8) assay, which assessed cell viability in stable cell lines with CALD1 knockdown and control groups. The data indicated no significant differences in cell viability between the groups.

Figure 4 Screening and construction of cell lines with stable knockdown of CALD1. (A) Fluorescence photomicrographs of SK-OV-3 cells 72 h after lentivirus infection. (B,C) Knockdown of CALD1 in SK-OV-3 cell lines analyzed by qRT-PCR and WB. One-way ANOVA was used for statistical analysis. *, P<0.05; **, P<0.01. NC, negative control group; qRT-PCR, quantitative real-time polymerase chain reaction; WB, western blot; ANOVA, analysis of variance.

The low expression of CALD1 affects the invasive ability of SK-OV-3 cells

We utilized the Transwell assay to evaluate the impact of low CALD1 expression on the invasive potential of SK-OV-3 OV cells. Following staining with crystal violet, images of invading cells were captured (Figure 5A). The cells were subsequently eluted with 33% acetic acid and absorbance values were measured at 570 nm. Statistical analysis was conducted using one-way ANOVA. Our results showed that the cell invasion ability was significantly increased in the SK-OV-3-CALD1-sh1 group compared to SK-OV-3 and SK-OV-3-CALD1-NC (negative control) cells (Figure 5B, P<0.001).

Figure 5 Transwell invasion assay. (A) Representative images of Transwell invasion assays for different SK-OV-3 cell groups. (B) Results analysis of Transwell assay. One-way ANOVA was used for statistical analysis. ***, P<0.001. OD, optical density; NC, negative control group; ANOVA, analysis of variance.

The low expression of CALD1 affects the cytoskeletal structure of SK-OV-3 cells

In order to study the effect of low CALD1 expression on cell structure and morphology during cell migration, we conducted cytoskeleton staining with rhodamine-phalloidin (red) and used ImageJ software to compute the mean fluorescence intensity (Figure 6A). The results showed that the cytoskeletal structure of SK-OV-3-CALD1-sh1 cells displayed disorganization and laxity, with a significant reduction in F-actin stress fibers on the cytoskeleton compared to SK-OV-3 cells and SK-OV-3-CALD1-NC cells (Figure 6B, P<0.01).

Figure 6 F-actin was visualized by staining with rhodamine-phalloidin (red). Nuclei were stained with DAPI (blue). Scale bar =25 µm. (A) Representative images of actin cytoskeleton staining for different SK-OV-3 cell groups. The right-side images are approximately two times magnified compared to the corresponding areas in the left images. (B,C) The fluorescence intensity of the cytoskeleton and focal adhesion (Vinculin) was quantified using ImageJ software. One-way ANOVA was used for statistical analysis. **, P<0.01; ***, P<0.001; ****, P<0.0001. ANOVA, analysis of variance.

The low expression of CALD1 affects the formation of focal adhesion in SK-OV-3 cells

The Vinculin protein plays a crucial role in regulating the formation and disassembly of focal adhesions, which in turn affects cell adhesion and motility. To visualize these focal adhesions, we utilized immunofluorescent staining with Vinculin protein and calculated the mean fluorescence intensity using ImageJ software (24,25). The results demonstrate a significant reduction in the number of focal adhesions in SK-OV-3-CALD1-sh1 cells compared to SK-OV-3 and SK-OV-3-CALD1-NC cells (Figure 6C and Figure 7, P<0.001).

Figure 7 Immunofluorescence staining images of the focal adhesion marker Vinculin (red) in different SK-OV-3 cell groups. Nuclei were stained using DAPI (blue). The right-side images are approximately two times magnified compared to the corresponding areas in the left images.

Discussion

We present this article detailing our findings of a significant decrease in CALD1 expression in OV compared to normal tissue samples. Additionally, we observed a notable difference in chemotherapeutic drug sensitivity between patients with high and low CALD1 expression levels. The high-CALD1 expression group showed greater sensitivity to CDK9_5038, Entospletinib, AZD5363, and GNE-317, while the low-CALD1 group exhibited increased sensitivity to drugs such as Irinotecan, Oxaliplatin, and Topotecan.

Further bioinformatics analysis revealed that CALD1 and related genes are primarily involved in cytoskeletal regulation, cell adhesion development, and other aspects contributing to cellular movement and migration. This suggests that CALD1 expression levels may affect the migration of tumor cells. To test the hypothesis, we utilized lentivirus to construct SK-OV-3-CALD1-sh1 cells with low expression of CALD1 and analyzed the impact on cell migration through examination of cytoskeleton and focal adhesions. The findings revealed that SK-OV-3-CALD1-sh1 cells exhibited decreased F-actin stress fibers, a notably less compact cytoskeleton, diminished focal adhesions, and significantly increased cell invasiveness.

Similar results have been shown in previous studies. For example, Yoshio et al. found that reducing the expression of l-caldesmon in various cancer cell lines, including human colon cancer (HCA7), human breast cancer (MB435s), mouse melanoma (B16F10), and rat breast cancer (MTC), significantly enhanced the invasive activity of cancer cells (26). Similarly, Hou et al. found that l-caldesmon expression levels were significantly lower in metastatic gastric cancer cell lines (MKN7, AZ521) compared to primary gastric cancer cell lines (AGS, FU97). However, overexpression of CALD1 in AGS and FU97 cells effectively reduced cell migration and invasion (27). Dierks et al. demonstrated that knocking down the CaD gene in the prostate cancer cell lines PC-3 and DU145 resulted in a twofold increase in the migratory capacity of both cell types, with a threefold increase in the invasive capacity of PC-3 cells (28).

However, the role of CALD1 in cancer progression and drug response appears to be context-dependent. Wei Li et al. (17) reported that upregulation of CALD1 predicts poor prognosis in platinum-treated OV, contrasting with our observations. Studies in other cancer types have also shown varying correlations between CALD1 expression and cancer progression (13,14).

These discrepancies highlight the complex nature of CALD1’s role in cancer biology and emphasize the need for further investigation. Future studies should focus on reconciling these disparate findings by examining CALD1’s role in different OV subtypes and stages, as well as exploring its potential dual function in tumor suppression and drug resistance.

While our findings provide valuable insights into the role of CALD1 in OV, several limitations must be acknowledged. First, the sample size for our clinical analysis may not fully represent the heterogeneity seen in different OV subtypes and stages, potentially affecting the generalizability of our conclusions. Second, the in vitro results obtained from the SK-OV-3 cell line may not accurately reflect the behavior of CALD1 in other OV types or in the tumor microenvironment. Finally, the mechanisms governing CALD1’s dual roles in cancer progression and drug resistance remain to be elucidated. Future studies should focus on a broader range of OV samples and incorporate in vivo models to provide a more comprehensive understanding of CALD1’s functional context.

Conclusions

In conclusion, this study highlights the significant role of CALD1 in OV, demonstrating its decreased expression in cancerous tissues compared to normal tissues. The research reveals that CALD1 expression levels influence the sensitivity of OV cells to various chemotherapeutic drugs, with distinct responses observed between high and low expression groups. Additionally, CALD1 is implicated in cytoskeletal regulation and cell migration, as evidenced by the increased invasiveness and altered cytoskeletal structure in cells with reduced CALD1 expression. These findings suggest that CALD1 could serve as a potential biomarker for OV prognosis and treatment response. However, further research is necessary to fully elucidate the molecular mechanisms of CALD1 in OV progression and its potential dual role in tumor suppression and drug resistance.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1375/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1375/prf

Funding: This study was supported by Xinjiang Uyghur Autonomous Region Department of Science and Technology and Xinjiang Production and Construction Corps Science and Technology Bureau jointly fund the “Tianshan Talents”—Science and Technology Innovation Leading Talents Training Project (No. 2023TSYCLJ0057), Science and Technology Program Innovation and Development Support Plan for Key Industries in Southern Xinjiang (No. 2021DB023), Key Laboratory of Protection & Utilization of Biological Resources in Tarim Basin Project (No. BRZD1906), and Tarim University Graduate Students Creation Project (No. TDBSCX202209).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1375/coif). The authors have no 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 (as revised in 2013).

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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