P53 Inhibition Diminishes IGSF9 Gene Activity to Promote DNA Repair and Exacerbate the Progression of Colon Cancer

ABSTRACT

Colon cancer (CC) is a malignancy with high global incidence and mortality, and elucidating its underlying molecular mechanisms is critical for improving prognostic assessment and therapeutic strategies. In this study, transcriptomic data from a large cohort of CC samples and a limited number of normal controls from the TCGA database were used to construct a multigene prognostic risk model using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. The expression of key prognostic genes, including immunoglobulin superfamily member 9 (IGSF9), was further validated in CC tissues by PCR and Western blotting. Functional assays were performed in HCT116 cells to investigate the biological effects of IGSF9 overexpression and its regulatory relationship with p53. The prognostic model identified IGSF9 as a gene significantly associated with patient survival. Although IGSF9 expression was reduced at both the mRNA and protein levels in CC tissues, its overexpression in vitro markedly promoted apoptosis, alleviated DNA damage, and suppressed cell migration and invasion. Notably, silencing of p53 partially reversed the tumor‐suppressive effects induced by IGSF9 overexpression, indicating that IGSF9 exerts its biological functions in a p53‐dependent manner. Collectively, these findings demonstrate that IGSF9 acts as a tumor suppressor in colorectal cancer and regulates DNA damage responses and apoptosis through a mechanism partially dependent on p53, highlighting its potential value as a prognostic biomarker and therapeutic target in CC.

Highlights of the Study: “P53 Inhibition Diminishes IGSF9 Gene Activity to Promote DNA Repair and Exacerbate Progression of Colon Cancer”

Summary

• This study elucidates the pivotal role of p53 inhibition in diminishing IGSF9 gene activity, revealing a novel mechanism in colorectal cancer progression.

• We demonstrate how reduced IGSF9 activity, driven by p53 inhibition, leads to enhanced DNA repair capabilities that paradoxically exacerbate tumor progression.

• Using a robust multi‐gene prognostic model based on extensive TCGA database analysis, we identify a significant correlation between IGSF9 downregulation and poor survival outcomes in colorectal cancer patients.

• Our findings suggest potential therapeutic targets, highlighting the importance of the p53/IGSF9 axis in developing treatment strategies that might inhibit colorectal cancer advancement by modulating DNA repair pathways.

1. Background

Colorectal cancer (CC) remains a significant public health challenge and is a primary cause of cancer diagnosis and cancer‐related deaths worldwide [1]. The incidence and mortality rates of CC exhibit substantial geographical variability, potentially attributable to genetic background, dietary habits, lifestyle choices, and environmental factors [2, 3]. Despite recent advancements in diagnostic and treatment modalities, the 5‐year survival rate for patients with CC lags behind expectations, especially for those diagnosed at advanced stages [4]. Therefore, a deep understanding of the molecular mechanisms underlying CC and the identification of effective biomarkers are crucial for early diagnosis, prognosis assessment, and the formulation of therapeutic strategies.

In recent decades, scientists have conducted in‐depth investigations into the molecular mechanisms of CC, revealing a plethora of genes and proteins associated with its onset and progression [5, 6]. Among these, the p53 protein, a well‐studied tumor suppressor factor, plays a pivotal role in maintaining genomic stability and preventing malignant transformation. p53, renowned for its tumor‐suppressing properties, is integral to cell cycle regulation, DNA repair, apoptosis, and cellular senescence. Variations in or loss of p53 function have been implicated in the exacerbation and progression of CC through a spectrum of mechanisms [7]. p53 modulates cell cycle arrest and facilitates DNA repair by activating a range of downstream genes, such as p27, GADD45, and 14‐3‐3σ [8, 9, 10]. In instances of irreparable DNA damage, p53 promotes apoptosis, thwarting the proliferation and dissemination of cells harboring genetic mutations and thereby curbing the invasive and migratory potential of tumor cells. Additionally, p53 inhibits the activity of enzymes such as HDAC1 and SIRT1, affecting chromatin structure and gene expression and thus suppressing the invasiveness and mobility of cancer cells [11, 12]. Furthermore, by downregulating several epithelial–mesenchymal transition (EMT) inducers, including Snail, Slug, and ZEB1/2, as well as other EMT‐associated genes, such as N‐cadherin and vimentin, while upregulating E‐cadherin, p53 impedes the EMT process, diminishing the invasion and migration capabilities of tumor cells [13, 14, 15]. p53 also modulates the invasion and migration of tumor cells by regulating matrix metalloproteinases (MMPs) [16] and adhesion molecules such as ICAM‐1 and VCAM‐1 [17].

With the advent of high‐throughput sequencing technologies and the evolution of bioinformatics, researchers have turned their attention to previously underexplored genes in CC, with immunoglobulin superfamily member 9 (IGSF9) being among them. Aberrant expression of IGSF9 has been reported in various neurological disorders; however, studies of IGSF9 in the field of oncology are relatively rare [18, 19, 20]. Recent findings suggest that IGSF9 is significantly differentially expressed in cancers such as nasopharyngeal carcinoma [21], breast cancer [22, 23], and CC [24], suggesting its potential role in tumor development. Nonetheless, reports on the function of IGSF9 in CC are exceedingly limited.

Given the potential significance of IGSF9 and p53 in cancer development and progression, our study was designed to explore the regulatory relationship between IGSF9 and p53 in CC and their impact on tumor progression. By employing an integrative approach that encompasses genomics, molecular biology, and cellular biology, this study extensively analyzed the expression patterns of IGSF9 and p53 in CC, their interrelationship, and their effects on the migratory and invasive capabilities of CC cells.

2. Materials and Methods

2.1. Patient Inclusion and Ethical Statement

This study was approved by the Institutional Review Board of the Affiliated Hospital of Yunnan University (Ethics Number: 2019138), and all patients provided written informed consent. Patients who were diagnosed with CC between June 15, 2021, and November 23, 2022, were recruited. The inclusion criteria were as follows: histologically confirmed CC with tumor and adjacent normal tissue, and no history of other cancers. The exclusion criteria were as follows: metastatic CC, receipt of neoadjuvant therapy, and inability to provide informed consent. All procedures performed in this study were in accordance with the ethical standards of the institutional review board. The inclusion criteria for patients in this study were as follows: (1) histologically confirmed CC; the tissue origin locations were the cecum, ascending colon, transverse colon, descending colon, and sigmoid colon; (2) paired tumor and adjacent nontumor tissue samples; and (3) complete clinical and follow‐up data. Patients who had previously received chemotherapy or radiation therapy were excluded. The ethical standards were consistent with the Declaration of Helsinki (revised 2013).

2.2. Screening for Differential Genes

Patient information for CC was obtained through the TCGA. Differential analysis of CC‐related data was performed using the Limma package in the R language. The differential expression analysis revealed differences in mRNA expression. Volcano plots and heatmaps were constructed based on the results when logFC = 1 and p value < 0.05 were used as the filtering thresholds [25]. The Limma package (version 3.48.3) was used to perform differential gene expression analysis. This software package is distributed as free and open‐source software under the GNU General Public License (GPL) and is available for download at https://bioconductor.org/packages/release/bioc/html/limma.html. The GPL terms can be found in the file COPYING distributed with the software package. We confirm that we have complied with the terms of the GPL when using and modifying the Limma package.

2.3. Modeling of Multigene Combinations

We employed several statistical methods in R software to analyze and identify differential genes related to risk prediction. First, we screened significant genes using univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model. We subsequently formed multigene combinations and performed multivariate Cox regression analyses using the “glmnet” and “survival” packages in R. The samples were then divided into high‐ and low‐risk groups based on these multigene combinations. To visualize the risk distribution, we utilized the R packages “cowplot” (version 1.1.1) and “pheatmap” (version 1.0.12) to create and arrange plots and heatmaps, respectively. We adhered to the terms of the GPL‐2 while using and modifying the “cowplot” and “pheatmap” packages. Additionally, we used the “glmnet” package (version 4.1‐2) for regression analysis and the “survival” package (version 3.2‐13) for survival analysis, both distributed under the LGPL (GNU Lesser General Public License). We ensured compliance with the terms of the LGPL while using and modifying these packages.

2.4. Validation of the Multigene Combination Model

Validation of the risk model by survival analysis and ROC curves. The relevance of the risk model to CC was assessed using Kaplan–Meier curves. Finally, a ROC curve was used to assess the validity of the risk model for predicting the 1‐, 3‐ and 5‐year prognosis of patients with CC.

2.5. Cell Culture and Transfection

HCT116 cells (Pricella, Cat. No. CL‐0096) were cultured in an atmosphere of 95% air and 5% CO₂ at a maintained temperature of 37°C. The culture medium used was McCoy's 5A (Pricella, Cat. No. PM150710), supplemented with 10% fetal bovine serum (Pricella, Cat. No. 164210‐50) and 1% penicillin/streptomycin (Pricella, Cat. No. PB180120) to prevent bacterial and fungal contamination, and the cells were passaged at approximately 70% confluence. The full‐length coding sequence of IGSF9 was amplified from a human cDNA library via PCR, with primer designs incorporating restriction enzyme sites suitable for cloning. The forward primer (including a HindIII site) 5′‐AAGCTTATGATGGACCTGCCCCGAGA‐3′ and the reverse primer (including a NotI site) 5′‐GCGGCCGCTCATTGTGGTGGTGGTGGTGT‐3′ were used. The amplified fragment was digested with enzymes and cloned and inserted into an adenoviral expression vector carrying the CMV promoter (CMV‐IGSF9). Additionally, a specific shRNA sequence targeting human p53 mRNA, 5′‐GACTCCAGTGGTAATCTACTTCAAGAGAGTAGATTACCACTGGAGTCTTTTTTC‐3′, was designed, cloned, and inserted into an adenoviral shRNA expression vector with a U6 promoter (shRNA‐p53). The adenoviral vectors for overexpression of IGSF9 and silencing of p53 were packaged and amplified in 293A cells, followed by collection of viral particles, which were then purified through centrifugation and filter sterilization to a titer of 1 × 10^9 PFU/mL. For transfection, HCT116 cells were seeded into culture dishes, and upon reaching 70%–80% confluency, 1 μL of viral solution was added per 1 × 10^5 cells/well. After 48 h post‐transfection, IGSF9 overexpression and the silencing effect of p53 were verified.

2.6. Real‐Time Fluorescence Quantitative PCR (qRT–PCR)

The qPCR mixture contained 10 μL of 2× SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA), 2 μL of cDNA template, 0.5 μL of each primer, and 7 μL of nuclease‐free water in a final volume of 20 μL. The reaction conditions were as follows: initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s and annealing and extension at 60°C for 60 s. A melting curve analysis was performed at the end of each run to confirm the specificity of the amplification products. The expression levels of each gene were normalized to those of GAPDH (mRNA) or U6 (miRNA) using the 2^−ΔΔCt method. All the samples were analyzed in triplicate.

2.7. Western Blot

Total cellular proteins were extracted using RIPA buffer (Beyotime, Cat. No. P0013B), and the protein concentration was determined using a BCA protein assay kit (Beyotime, Cat. No. P0012). The proteins were then mixed with sample buffer and separated by SDS–PAGE. The proteins were then transferred from the gel onto a polyvinylidene difluoride (PVDF) membrane (Merck Millipore, Cat. No. IPVH00010) and blocked with 5% nonfat milk to prevent nonspecific binding for 1 h. The membranes were incubated overnight at 4°C with primary antibodies against IGSF9 (1:500, Bioss, Cat. No. bs‐9199R), p53 (1:1000, Abcam, Cat. No. ab26), γ‐H2AX (1:1000, Abcam, Cat. No. ab229914), PAR (1:1000, Abcam, Cat. No. ab180953), PARP (1:1000, Abcam, Cat. No. ab191217), and the internal control α‐tubulin (1:5000, Abcam, Cat. No. ab7291). This was followed by a 1 h incubation with the corresponding horseradish peroxidase‐conjugated secondary antibodies: anti‐rabbit IgG (H + L) (1:5000, KPL, 074‐1506) and anti‐mouse IgA + IgG + IgM (H + L) (1:5000, KPL, 074‐1807). Protein expression signals were then detected using a chemiluminescent substrate (Beyotime, Cat. No. P0018S).

2.8. Flow Cytometry

Cells were processed according to the instructions of the Annexin V‐FITC Apoptosis Detection Kit (Biolegend, Cat. No. 640914) and analyzed using a flow cytometer (BECKMAN, CytoFLEX). Apoptotic cells were quantified as the sum of the Annexin V+/PI– (early apoptosis) and Annexin V+/PI+ (late apoptosis) populations. Quantitative analysis of apoptosis was performed using FlowJo software.

2.9. Scratch and Transwell Assays

For the scratch assay, HCT116 cells were seeded in 6‐well plates (Corning, Cat. No. 430166) and grown to 90% confluence, at which point a straight line was gently drawn across the center of the cell layer to create a “scratch”. The cells were then washed with serum‐free DMEM (HyClone, Cat. No. SH30022.01) to remove floating cells and debris, after which complete medium was added. Images of the scratch were captured immediately after the scratch was created (0 h) and at 48 h using an inverted microscope (Nikon) to assess cell migration. For the Transwell assay, treated HCT116 cell suspensions were added to the upper chamber of a Transwell filter membrane (Corning, Cat. No. 3422) with an 8 μm pore size, while the lower chamber was filled with medium containing 10% FBS (Gibco, Cat. No. 42F7180K) to serve as a chemoattractant. After 24 h of incubation, noninvading cells in the upper chamber were removed with a cotton swab. Cells that had invaded through to the lower chamber were fixed and stained, and then counted under an inverted microscope (Nikon).

2.10. TdT‐Mediated dUTP Nick‐End Labeling (TUNEL)

First, the treated cells were fixed onto slides, followed by incubation with 3% hydrogen peroxide at room temperature for 10 min to block endogenous peroxidase activity. DNA strand breaks were subsequently labeled with terminal deoxynucleotidyl transferase (TdT) enzyme and dUTP, as per the guidelines provided by the TUNEL Assay Kit (Servicebio, Cat. No. G1502‐50T). Afterward, the cells were washed with PBS, and anti‐fade reagent was added. Finally, the labeled cells were observed and photographed using a 3DHISTECH scanner (DANJIER, China), and the percentage of positive (labeled) cells was calculated to assess the rate of apoptosis.

2.11. Immunohistochemistry

For immunostaining, xenograft tissue sections were fixed in 4% paraformaldehyde and embedded in paraffin. The sections were incubated with primary antibody against γ‐H2AX (1:500 dilution; Cell Signaling Technology), followed by color development with a DAB Chromogen Kit (AURAGENE; Cat. No. P013IH‐2). The slides were then examined under a BZ‐9000 fluorescence microscope (Keyence).

2.12. Statistical Analysis

All the statistical calculations were performed using R software, and p < 0.05 was considered to indicate statistical significance. PCR data analysis was performed using GraphPad Prism software (version 8.0.2). The PCR data were input into Prism for analysis. Relative expression levels were calculated using the ΔΔCt method. A bar graph was generated using Prism to display the relative expression levels for each sample. Statistical analysis was performed using a t test, and p values and significance levels (p < 0.05) are reported in the legend.

3. Results

3.1. Multifactorial Prognostic Model Screening for CC

By utilizing a large cohort of CC samples and a relatively small set of normal samples from the TCGA database, multiple genes associated with CC survival events were identified, and a risk model for predicting survival events was constructed. Unidirectional Cox analysis and LASSO regression analysis were performed on the collected samples to identify genes significantly associated with survival events. A total of 56 genes whose p values were less than 0.05 were selected (Figure 1A), and using the LASSO regression model, the multigene combinations most relevant to survival events were further filtered, resulting in the identification of 23 gene combinations (Figure 1B). After multigene combinations were established, a multigene prognostic model was constructed, and multivariate Cox regression analysis was used to examine the correlation between the expression of these genes and survival events. The results revealed significant associations with survival for genes such as the Fc fragment of IgG binding protein (FCGBP), glutathione S‐transferase Mu 1 (GSTM1), immunoglobulin lambda variant 4‐69 (IGLV4‐69), immunoglobulin superfamily member 9 (IGSF9), and procollagen C‐endopeptidase enhancer 2 (PCOLCE2) (Figure 1C).

Figure 1.

Multifactorial prognostic model screening for CC. (A) LASSO Cox regression analysis of 56 significant genes. (B) Tenfold cross‐validation in the LASSO model for parameter selection. (C) Forest plot illustrating the correlation between prognostic genes and survival events. *p < 0.05, **p < 0.01. These figures were generated using the R package “glmnet” (version 4.1‐2, Bioconductor project).

3.2. Prognostic Modeling

To construct a correct and reliable multifactorial model, we first validated the distribution of risk values for the original sample in this multigene combination model. The risk score calculation method in Figure 2A is as follows: Risk score = ATOH1 * ( − 0.27168159) + GSTM1 * 0.245018500959804 + ASPG * ( − 0.154303569782697) + PCOLCE2 * 0.347788560238481 + IGSF9 * 0.478958535511115 + IGLV7_3 * ( − 0.20359762018598) + ULBP2 * 0.118470023559271 + WDR78 * ( − 0.12886824938988) + SOWAHA * ( − 0.0939020744283498) + CCBE1 * 0.281137360311058 + TMEM220 * ( − 0.234347727574223) + IGLV1_50 * ( − 0.124055456858496) + SGCG * 0.267020389819333 + FCGBp * 0.395645210115894 + MS4A1 * ( − 0.163203116691064) + ZIC5 * 0.0322500258419833 + MS4A2 * ( − 0.247730643621708) + SLC8A3 * ( − 0.220747627760141) + IGKV1D_12 * ( − 0.252862027469404) + IGLV4_69 * 0.529185498710499 + VIT * ( − 0.234827225397136) + ZBTB7C * ( − 0.129050284195718). Based on the median risk score, patients were assigned to low‐ or high‐risk groups. We subsequently calculated the associations between the risk model and certain clinical data (patient age, sex, risk level, and tumor stage) with respect to survival events. The results in Figure 2B indicate that the multifactor model significantly correlated with age, risk level, and tumor stage in patients with CC.

Figure 2.

Risk groups in the prognostic model. (A) Distribution of original samples in the risk model. (B) Forest plot showing survival factors associated with these prognostic genes. *p < 0.05, **p < 0.01, ***p < 0.001. These figures were created using R packages “cowplot” (version 1.1.1, The Comprehensive R Archive Network) and “pheatmap” (version 1.0.12, The Comprehensive R Archive Network).

3.3. Prognostic Validation

To validate the risk model developed in this study, ROC and survival analyses were conducted on the prognostic model. The model predicted survival events with AUC values of 0.77, 0.78, and 0.82 for the 1‐, 2‐, and 3‐year predictions, respectively, with a p value of < 0.0001 in the survival analysis. These results demonstrate that this prognostic model can be effectively utilized as a prognostic tool (Figure 3).

Figure 3.

Validation of the Study's Risk Model. (A) Time‐dependent ROC curves (AUCs: 0.77, 0.78, and 0.82 for 1‐, 2‐, and 5‐year RFS). (B) Kaplan–Meier curves. Significance was calculated using the log‐rank test, with the red line representing the high‐risk group and the blue line representing the low‐risk group. This figure was produced using the R package “survival” (version 3.2‐13, The Comprehensive R Archive Network).

3.4. Prognostic Genes for This Model Were Detected in CC Samples

In this study, we systematically analyzed the expression differences of key genes within a multigene prognostic model in CC tissues compared with paired normal tissues. By delving into the publicly accessible Cancer Genome Atlas (TCGA) database, we determined that FCGBP, GSTM1, IGSF9, and PCOLCE2 are significantly upregulated in CC tissue samples (Figure 4A), revealing their potential roles in the pathogenesis of CC. Similar expression patterns were observed by qRT–PCR (Figure 4B), further confirming the importance of these genes in the progression of CC. The main baseline demographic and clinical characteristics of the participants are presented in Table 1. Notably, although PCOLCE2 was differentially expressed in the TCGA data set, no significant differences were observed in our experimental samples, suggesting that individual variability might affect the expression fluctuations of single genes. Additionally, Western blot analysis confirmed the expression levels of IGSF9 protein in CC tissues, with a significant decreasing trend in CC samples compared with normal samples (Figure 4C–D).

Figure 4.

Abnormal expression of IGSF9 in patients with CC. (A) Expression of various genes in CC samples from the TCGA database. (B–D) qRT–PCR and Western blot experiments to measure IGSF9 mRNA and protein expression levels. *p < 0.05, **p < 0.01, ***p < 0.001.

Table 1.

Clinical characteristics of patients with CC who underwent genetic testing in this study. TNM stage was determined according to the AJCC Cancer Staging Manual, 8th edition.

ID	Sex	Age	TNM stage	Tumor size (cm)	Lymph node metastasis	Distant metastasis
1	Male	70	T3N1M0 IIIb	4	1 node	None
2	Female	42	T4bN2M1 Ivb	3.5	3 nodes	Uterus
3	Female	59	T2N0M0 Ia	2.5	No nodes	None
4	Male	51	T3N0M0 IIa	3	No nodes	None
5	Female	72	T4aN1M0 IIIb	4.5	2 nodes	None
6	Male	58	T4bN1M0IIIc	7	3 nodes	None
7	Male	74	T4bN0M0IIIc	7	No nodes	None
8	Male	72	T4aN1bM1cIVc	2.5	2 nodes	Peritoneum
9	Female	40	T3N0M1 Ivb	2	No nodes	Liver
10	Male	43	T3N1M1 Ivb	3	2 nodes	Liver, lung
11	Female	51	T4bN1M0IIIc	2.5	3 nodes	None
12	Female	65	T4aN0M0 IIb	3.5	No nodes	None

3.5. Overexpression of IGSF9 Inhibits CC Cell Activity

In this study, we demonstrated the suppressive effects of immunoglobulin superfamily member 9 (IGSF9) on tumor cell function by overexpressing it in the CC cell line HCT116. PCR validation confirmed a significant increase in IGSF9 mRNA levels in overexpressed cells (Figure 5A). Western blot analysis further verified that IGSF9 protein levels were also significantly elevated in these cells (Figure 5B). Additionally, the results of the scratch assay (Figure 5C) indicated that, compared with the control group, the group overexpressing IGSF9 had significantly slower cell migration. The results of the Transwell assay (Figure 5D) supported these findings, revealing a reduction in the invasive ability of cells in a basement membrane model due to IGSF9 overexpression. Finally, flow cytometry (Figure 5E) revealed an increase in apoptosis induced by overexpressed IGSF9 in the HCT116 cell line. These comprehensive results clearly demonstrate the significant role of IGSF9 in regulating the biological characteristics of CC cells, particularly in inhibiting cell invasion and migration and promoting apoptosis, providing a potential molecular target for future CC treatments.

Figure 5.

Regulation of CC Cell Activity by IGSF9 Expression. (A,B) PCR and Western blot analysis to detect the expression of overexpressed IGSF9 at the gene and protein levels. (C,D) Transwell and scratch assays to assess the impact of overexpressed IGSF9 on cell invasion and migration capabilities. (E) Flow cytometry to measure the impact of overexpressed IGSF9 on apoptosis. **p < 0.01, ***p < 0.001.

3.6. Silencing p53 Inhibits IGSF9 Expression

We further explored the regulatory relationship between the p53 protein and IGSF9, as well as their roles in the progression of CC. Using RNA interference technology, we silenced the p53 protein in the HCT116 CC cell line to examine its impact on IGSF9 expression and cellular behavior. Following p53 silencing, the expression of IGSF9 significantly decreased at both the mRNA and protein levels, suggesting that p53 may positively regulate IGSF9 expression at the transcriptional level (Figure 6A–C). Scratch assays (Figure 6D) and Transwell invasion assays (Figure 6E) revealed that silencing p53 could partially reverse the reduction in cell migration and invasion induced by overexpressed IGSF9, indicating that p53 plays a crucial role in the regulation of IGSF9‐mediated cellular behaviors. Additionally, the flow cytometry results (Figure 6F) demonstrated that silencing p53 expression significantly reduced apoptosis induced by overexpressed IGSF9, further emphasizing the key role of p53 in maintaining cell survival and countering cell death signals.

Figure 6.

Role of p53/IGSF9 in CC progression. (A–C) PCR and Western blot analyses to detect the expression of overexpressed p53/IGSF9 genes and proteins. (D,E) Transwell and scratch assays to assess the impact of overexpressed IGSF9 on cell invasion and migration. (F) Flow cytometry to measure the impact of overexpressed IGSF9 on apoptosis. *p < 0.05, ***p < 0.001.

3.7. IGSF9 Remodels the DDR and Apoptosis in HCT116 Cells, and p53 Silencing Enhances PARP Dependence

We next examined the p53–IGSF9 axis in HCT116 cells and its impact on apoptosis and the DNA‐damage response. IGSF9 overexpression increased apoptosis, as evidenced by more TUNEL‐positive cells, whereas p53 silencing markedly blunted this pro‐apoptotic effect (Figure 7A). Consistent with a shift in damage processing, IGSF9 overexpression decreased the DSB marker γ‐H2AX while increasing PAR and PARP, and p53 silencing further potentiated this γ‐H2AX↓/PAR(P)↑ pattern (Figure 7B). Immunohistochemistry corroborated the reduction of nuclear γ‐H2AX with IGSF9 overexpression and its further decrease upon p53 knockdown (Figure 7C). These data support a model in which IGSF9 alleviates replication/DSB stress and redirects lesions toward rapid PARP‐dependent single‐strand repair, while loss of p53 lowers apoptotic execution and drives greater reliance on PARP‐mediated repair—thereby amplifying PARylation and further suppressing γ‐H2AX.

Figure 7.

p53 inhibition of IGSF9 reduces DNA damage. (A) TUNEL assay to detect apoptosis. (B) Western blot to measure the expression of DNA damage‐related factors. (C) Immunohistochemistry to assess the localization and expression of the DNA damage marker γH2AX.

4. Discussion

In this study, a vast array of CC samples and a relatively small set of normal samples from the TCGA database were used to construct a predictive multigene risk model for CC survival events. Our analysis highlights the critical roles of IGSF9 and p53 in the development of CC, particularly in regulating DNA damage responses and apoptosis processes. These findings align with previous research on the tumor‐suppressive role of p53 and offer new insights into the function of IGSF9 in cancer biology.

In the pathogenesis of CC, dysregulation of DNA damage and repair mechanisms plays a central role. The occurrence of CC is often directly linked to the accumulation of DNA damage within cells [26, 27, 28], which can be induced by various factors, including genetic defects [29], environmental carcinogens [28], microbes [30], inflammatory responses [31], and oxidative stress [32]. When DNA repair mechanisms such as base excision repair (BER) [33], mismatch repair (MMR) [34], and homologous recombination repair (HR) [35] become dysfunctional, increased accumulation of damaged DNA may lead to permanent genetic alterations, triggering cellular carcinogenesis. Known as the “guardian of the genome”, p53 is crucial for maintaining DNA stability and preventing tumor development [36, 37, 38, 39]. Under normal conditions, p53 senses DNA damage and prevents potential malignant transformation by activating the expression of DNA repair genes or by promoting apoptosis in damaged cells [40, 41], activating genes such as p21 to induce cell cycle arrest and thus providing time for DNA repair [42]. If DNA damage is irreparable, p53 also activates proapoptotic genes such as Bax and Noxa, leading cells toward programmed cell death to prevent the transmission of genetic defects [43, 44, 45]. In CC, mutations in the p53 gene are common genetic events, typically resulting in the loss of a normal p53 protein response to DNA damage and impairing its tumor‐suppressive function. Such mutations not only reduce the ability of CC cells to repair DNA damage but also may increase their resistance to cancer treatments, such as chemotherapy and radiotherapy [46, 47]. Therefore, the status of p53 significantly impacts treatment choices and prognosis assessment in patients with CC. Understanding the role of DNA damage and p53 in CC not only helps reveal the molecular mechanisms of tumorigenesis but also may guide the development of new treatment strategies. Our study further explored the impact of p53 on the expression of IGSF9 and revealed that silencing p53 expression significantly suppressed IGSF9 expression, which is consistent with findings in breast cancer, in which p53 can transactivate IGSF9 to inhibit cancer cell migration and invasion [22]. These results suggest that p53/IGSF9 plays a role in maintaining genomic stability and suppressing tumor development.

By constructing a multigene combination‐based prognostic model for CC, this research not only provides a powerful tool for predicting survival events in patients with CC but also emphasizes the importance of multiple genes, including IGSF9, in the development of CC, which is consistent with the findings of previous studies [24]. The discovery of these genes not only enhances our understanding of the molecular mechanisms of CC but also may offer new targets for the early diagnosis and treatment of CC.

5. Conclusions

In summary, molecular screening and bioinformatics analysis were performed to establish a multigene prognostic model and provide new molecular markers for risk assessment in CC. Our research results indicate that IGSF9 plays a potential role in the development of cervical cancer, especially in terms of its expression pattern with p53, as well as the significance of this relationship in the tumor formation process. These findings provide a crucial biological foundation for the development of new therapeutic strategies targeting CC and guide future clinical research directions.

Author Contributions

Huan‐yu Zhang and Dan Tian contributed equally to this work. Huan‐yu Zhang and Dan Tian designed the study, performed bioinformatics analyses, and drafted the manuscript. Wan‐fu Zhang and Ying‐hui Zhang collected clinical samples and clinical data and assisted with data interpretation. Jia‐li Feng conducted the in vitro experiments, including cell culture, transfection, and functional assays. Juan Sheng performed molecular biology experiments and contributed to data acquisition and validation. Xue‐qin Shang conceived and supervised the project, provided critical revisions of the manuscript, and was responsible for overall study coordination. All authors reviewed and approved the final manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Zhang H.‐y., Tian D., Zhang W.‐f., et al., “P53 Inhibition Diminishes IGSF9 Gene Activity to Promote DNA Repair and Exacerbate the Progression of Colon Cancer,” Journal of Biochemical and Molecular Toxicology 40 (2025): e70678, 10.1002/jbt.70678.

Data Availability Statement

The data sets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Additional materials or raw data supporting the findings of this work can also be obtained directly from the authors.