ALDH1A3's role in controlling cell aging and aging-related secretions in prostate cancer

HEK 293T and PC3 cells were purchased from Wuhan Promoter Life Science & Technology Co., Ltd. (Wuhan, China), and C4-2 cells were obtained from Tianjin Hamosi Technology Development Co., Ltd. (Tianjin, China). All cell lines were cultured at 37 °C in an atmosphere of 5% CO₂ using DMEM or 1640 medium containing 10% fetal bovine serum (FBS) (Gibco, Thermo Fisher Scientific, Shanghai, China) and 1% penicillin/streptomycin. When cell confluence reached 70–80%, they were subjected to ionizing radiation treatment or lentiviral transduction.

The irradiated or non-irradiated PC3 cells were sent to Novogene for sequencing. Each sample had three replicates.

PC3 cells that were non-irradiated, irradiated with 20 Gy for 5 days, and irradiated with 20 Gy for 7 days were lysed with RIPA lysis buffer (Solarbio, Beijing, China) at 4 °C for 20 min. The protein samples were then separated by SDS-PAGE, and the gel strips were sent to Novogene for mass spectrometry analysis. Each sample had three replicates.

Trizol was used to extract RNA from cells. After RNA extraction, the quantity and quality of the RNA were analyzed using the Qnano spectrophotometry method from Yeasen. A reverse transcription reaction was performed using 1 μg of total RNA with Takara’s reverse transcription kit (RR047A). The resulting cDNA was subjected to qPCR analysis using Hieff UNICON^® Universal Blue qPCR SYBR Green Master Mix (Yeasen, Cat #11184ES08, Shanghai, China), and quantification was performed using Yeasen 80520ES03. In all experimental replicates, all expression levels were normalized to GAPDH. The primers used were as follows:

CDKN2A Forward (5′-CTTCCTGGACACGCTGGTGG-3′)

CDKN2A Reverse (5′-TCGGGGATGTCTGAGGGACC-3′)

CDKN1A Forward (5′-TGTCCGTCAGAACCCATGC-3′)

CDKN1A Reverse (5′-AAAGTCGAAGTTCCATCGCTC-3′)

GAPDH Forward (5′-GGAGCGAGATCCCTCCAAAAT-3′)

GAPDH Reverse (5′-GGCTGTTGTCATACTTCTCATGG-3′)

To obtain gene expression and clinical information from patients, we utilized The Cancer Genome Atlas (TCGA) database to collect data from patients with PRAD. Subsequently, we employed the R package “edgeR” to conduct differentially expressed genes (DEGs) analysis on these datasets. We set |logFC| ≥ 1 and adj. p < 0.05 as the selection criteria to define the DEGs in the database.

We explored the predictive value of ALDH1A3 expression in relation to the efficacy of immunotherapy and chemotherapy. Tumor immune dysfunction and exclusion (TIDE) was used to predict patients’ responses to immunotherapy. The “oncoPredict” package in R was applied to estimate drug sensitivity.

Senescent cells were detected using the Senescence β-Galactosidase Staining Kit (Beyotime, Cat# C0602, Shanghai, China) according to the manufacturer’s protocol.

Western blot was performed using total protein extracts. The following antibodies were used: cGAS (Cell Signaling Technology, Cat# 15102, Shanghai, China), STING (Cell Signaling Technology, Cat# 13647, Shanghai, China), Phospho-STING (Cell Signaling Technology, Cat# 19781, Shanghai, China), TBK1 (Cell Signaling Technology, Cat# 3504, Shanghai, China), Phospho-TBK1 (Cell Signaling Technology, Cat# 5483, Shanghai, China), IRF-3 (Cell Signaling Technology, Cat# 4302, Shanghai, China), STAT2 (Invitrogen, Cat# 44-362G, Thermo Fisher Scientific, Shanghai, China), p65 (Cell Signaling Technology, Cat# 8242, Shanghai, China), Beta Actin (Proteintech, Cat# 66009-1-lg, Wuhan, China), GAPDH (Proteintech, Cat# 60004-1-lg, Wuhan, China), ALDH1A3 (Proteintech, Cat# 25167-1-AP, Wuhan, China), and p16 (Santa Cruz, Cat# sc-9968, Santa Cruz, CA, USA).

The cells to be tested were collected and washed once with PBS. The cells were then resuspended in PBS and MHC I antibody (Biolegend, San Diego, CA, USA) was added to the suspension at a ratio of 5 μL per 1 million cells. Subsequently, the cell suspension was incubated at 4 °C for 30 min, and then washed twice with PBS. The cells were fixed with 4% paraformaldehyde for further analysis.

The plasmids psPAX2, pMD2.G, and lentiCRISPRv2 were purchased from Addgene (Cambridge, MA, USA). The purchased lentiCRISPRv2 plasmid was digested with the BSMBI restriction enzyme (Yeasen, Cat#15203RS80), and the synthesized sgRNA was ligated into the digested lentiCRISPRv2 using T4 ligase (Takara, Shanghai, China) to obtain the lentiCRISPRv2-sgALDH1A3 plasmid. HEK 293T cells were transfected with psPAX2, pMD2.G, and either lentiCRISPRv2 or lentiCRISPRv2-sgALDH1A3. After 72 h, the supernatant containing lentiviral particles was collected. The lentivirus was directly added to the culture medium and used to transduce PC3 cells with 8 μg/μL polybrene.

sgALDH1A3-1: 5′-TAGTCTGCGGCGCACCGGCT-3′

sgALDH1A3-2: 5′-TTCCACGGCCCCGTTAGCGG-3′

sgALDH1A3-3: 5′-CATATTTGCTCCCGAGTTGA-3′

PC3 cells transduced with the virus were seeded onto transwell permeable supports (Corning, Cat# 3470, New York, NY, USA). When they reached 70–80% confluence, the supports were placed into a 24-well plate seeded with PC3 cells. Cell viability was measured every 24 h using the Cell Counting Kit-8 (CCK-8) (Biosharp, Cat# BS350B, Shanghai, China).

A transwell plate was prepared as described above. PC3 cells were seeded in a 6-well plate and cultured to confluence. A scratch was made using a 200 µL pipette tip, and debris was removed by washing the cells with a culture medium. The transwell plate was then placed into the culture plate, and scratch wound images were captured under the microscope 24 h later.

All statistical analyses and graphical visualizations were performed in R (version 4.4.1). Continuous variables between groups were compared using the Student’s t-test or the Wilcoxon rank-sum test. p-value < 0.05 was considered statistically significant (two-tailed).

In order to study the effect of irradiation on cell gene expression, we used an irradiation dose of 5 Gy in the experiment, which is a common dose to study the effect of cell irradiation. Irradiation with 5 Gy can trigger a significant biological response without causing extensive cell death and is widely used to simulate moderate doses of radiotherapy. Through this dose of irradiation, we can observe a series of reactions such as DNA damage repair, gene expression regulation, and immune response, providing a scientific basis for the study of irradiation resistance, stress response, and potential immune escape mechanism of cancer cells.

In this study, we compared the RNA-seq results of PC3 cells unirradiated (control), 24 h after 5 Gy irradiation, and 96 h after 5 Gy irradiation (Table S1). The results showed that irradiation had a significant effect on gene expression in PC3 cells. Figure 1A shows the overall differences in gene expression among the experimental groups, further confirming the significant influence of short- and long-term effects of irradiation on gene expression. We found different effects of short-term (24 h) and long-term (96 h) effects of irradiation on cellular gene expression in water. Some genes are activated or inhibited rapidly in the short term, while others show delayed expression changes over time. This phenomenon suggests that irradiation not only has immediate effects on cells, but may also induce long-lasting changes in gene expression.

We further compared the differential genes between 96 h and control groups and between 96 h and 24 h after irradiation. |logFC| ≥ 1 and adj. p < 0.05 were used as truncation criteria. The left side of Figure 1B shows the number of up-regulated and down-regulated genes compared to the control group after 96 h of irradiation, while the right side of Figure 1B compares the gene differences between the 96 h and 24 h groups. Compared with the control group, there were 1991 significantly up-regulated genes and 1319 significantly down-regulated genes 96 h after irradiation. Compared with 24 h after irradiation, there were 1842 significantly up-regulated genes and 1467 significantly down-regulated genes after 96 h of irradiation. Figure 1C shows the trend of gene expression levels under different time effects, which reveals a significant difference between short- and long-term effects.

We paid special attention to the long-term effects after irradiation and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for genes that were significantly up-regulated compared to the control group at 96 h after irradiation. GO enrichment results showed that these up-regulated genes were mainly involved in biological processes and molecular functions related to stress response, immune response, cell growth inhibition, and protein metabolism (Figure 1D). This suggests that irradiation may affect cells on multiple levels, including enhancing immune response, regulating protein synthesis, affecting cell repair and regeneration, and regulating cell cycle and metabolic processes.

KEGG enrichment analysis showed that the up-regulated genes induced by irradiation were involved in several key cell signaling pathways, such as immune response and inflammation regulation, cell stress and apoptosis, cancer-related pathways, metabolic abnormalities, cell death, immune escape, cell senescence, and autophagy. Figure 1E shows the top 15 enrichment pathways and their significance levels (p-values). Figure 1F shows the gene enriched to the cellular senescence pathway, with a total of 25 genes enriched in this pathway. These KEGG enrichment results highlight the extensive effects of irradiation on cell function, which not only involves the physiological regulation of cells, but also may affect pathological processes such as cancer, and have a profound impact on long-term tumor control and patient prognosis.

After identifying the short- and long-term gene expression changes induced by medium-dose irradiation, we further investigated the cell response to high-dose irradiation, especially the effects of longer duration on cell biological function. This high-dose irradiation is closer to the conditions used in clinical therapy and can provide clearer evidence to reveal the mechanism of tumor cell tolerance to radiation therapy [20]. A dose of 20 Gy is close to a single high-dose exposure or fractional total dose in some cancer radiation treatments [21]. This dose can simulate the conditions in actual clinical treatment and help study the effects of radiation on tumor cells, thus providing data support for radiotherapy optimization [22,23]. By using high doses of irradiation, it is possible to distinguish more clearly between radio-sensitive and radio-resistant cells.

Mass spectrometry was performed on cell protein samples from the unirradiated (group A), 5 days after 20 Gy irradiation (group B), and 7 days after 20 Gy irradiation (group C) (Table S2). Figure 2A shows the principal component analysis (PCA) results of these three groups. PCA extracted two main components: the first principal component (PC1) explained 83.66% of the variance, and the second principal component (PC2) explained 9.89% of the variance. The samples of Group A are clustered in the area where PC1 and PC2 are close to the origin, indicating that the characteristics of the samples in this group are similar. In contrast, samples from Group B were clearly distributed in the positive region of PC1, indicating significant differences in major features between them and group A. Group C samples were concentrated on the distal end of PC1 and PC2, which formed a clear distinction from the other two groups.

The bottom of Figure 2A shows the cumulative distribution of the coefficients of variation (cv) for the three sets of data (A, B, and C). The cumulative curves of the three groups overlap almost exactly, indicating that they differ little in terms of variability or data volatility. This highly similar accumulation curve of coefficient of variation indicates that the sample data under different treatments have good stability and consistency, which ensures the credibility of the results of mass spectrometry, and can truly reflect the changes in protein expression under different treatment conditions.

Figure 2B shows the protein expression of the three groups of samples. In the unirradiated group (group A), the expression levels of most proteins were lower; in group B and group C, the expression levels of some proteins changed significantly, the expression of some proteins increased, some proteins decreased, and some proteins maintained relatively stable expression levels. Heat maps show that the effects of irradiation on protein expression are time-dependent. Protein expression was relatively stable under unirradiated conditions, and some protein expression levels gradually increased with the extension of irradiation time, suggesting that the cells may initiate large-scale stress response or repair mechanism under continuous external stimulation. These results provide important clues for further investigation of how irradiation regulates cell biological processes.

Figure 2C visually shows the difference in gene expression at different times after high-dose irradiation. It can be observed that the expression of some genes is similar, but the expression of some genes is different. The difference between group C and group B mainly focused on nuclear proteins (35%), cytoplasmic proteins, and mitochondrial proteins (Figure 2D).

Figure 2E shows the top 10 KEGG enrichment pathways of up-regulated genes in groups B and C compared to group A. Compared with group A, up-regulated gene enrichment in group B was found in multiple pathways, including cell death, apoptosis, cell senescence, cell cycle, immune response, and metabolic pathways. The genes up-regulated in group C compared to group A were concentrated in a variety of biological processes related to cell death, immune response, and metabolic regulation. As can be seen from Figure 2E, necroptosis ranks first in the enrichment pathway. Necroptosis is an acute cell death mechanism in which cells are quickly cleared after death, and while it triggers a strong inflammatory response, it may not have the same impact on the long-term cellular environment as the persistence of senescent cells. Therefore, our research focus turned to cellular senescence.

Cell senescence is when cells stop dividing and enter a stable, non-proliferating state after being severely damaged, such as DNA damage caused by radiation, rather than dying immediately [24]. Senescent cells can exist for a long time and affect the surrounding tissue and cell microenvironment by secreting inflammatory factors and matrix remodeling factors, especially in cancer, senescence-related diseases, and tissue repair [25]. Studying cellular senescence can reveal the effects of radiation on long-term tissue function, including tissue senescence, carcinogenesis, and decreased regenerative capacity.

We compared the expression differences of senescence pathway up-regulated genes in group B and group C. Figure 2F shows the expression of senescence pathway genes at different times after high-dose irradiation. As can be seen from the figure, the expression of some genes in group C was significantly higher than that in group B, indicating that the degree of cell senescence in group C may be more significant. This provides new insights into the long-term effects of high doses of radiation.

In radiation therapy studies, exploring the combined effects of different dose and time effects helps to model the mechanism by which tumor cells respond to radiation therapy. By comparing genetic and protein changes at these doses and time points, we can identify radiation-sensitive and resistant cells, as well as potential therapeutic targets and side-effect mechanisms.

We used Venn diagram analysis to identify 110 genes that were affected by long-term effects at different doses (Figure 3A). These 110 genes are enriched into immune-related pathways, cell death and senescence pathways, metabolism and metabolic regulation pathways, etc. Figure 3B shows the top 10 enriched pathways. This suggests that the long-term effects of irradiation involve multiple biological processes such as immune response, cellular senescence, and metabolic regulation, and estimates of these pathways provide important clues to understanding the far-reaching effects of irradiation on cells and tissues.

In order to identify potential cancer markers and study the effects of irradiation on tumors, providing a basis for precise treatment, we then performed an intersection analysis between these 110 genes and the genes highly expressed in the cancer tissues of prostate adenocarcinoma (PRAD) patients in the TCGA database, and identified four genes (Figure 3A, Table S3). Figure 3C shows the expression of these four genes in cancer and normal tissue of PRAD patients. The results showed that these four genes were highly expressed in cancer tissue and less expressed in normal tissue, suggesting that they may serve as potential cancer markers.

Next, we analyzed the influence of these four genes on the biochemical recurrence-free survival (BRFS) of PRAD patients receiving radiotherapy, using the “surv_cutpoint” function, and divided them into two groups based on the expression of ALDH1A3 (Figure 3D, Table S4). We found that patients with high expression of ALDH1A3 had poor BRFS, suggesting that high expression of ALDH1A3 may be associated with disease progression or recurrence in patients (Figure 3E).

We further analyzed the expression of ALDH1A3 in other types of cancer in TCGA and found that the expression of ALDH1A3 in cancer tissue of PRAD patients was the highest, indicating that the high expression of ALDH1A3 may play an important role in the occurrence, progression, or malignant transformation of PRAD, and it may be a key regulatory gene in prostate cancer (Figure 3F). Because ALDH1A3 is expressed higher in prostate cancer than in other cancers, it suggests that it may have some specificity as a diagnostic or prognostic marker for prostate cancer, this specificity helps identify prostate cancer patients, and may be useful for monitoring disease progression or risk of recurrence.

By analyzing the expression levels of the ALDH1A3 gene in PRAD patients of different ages receiving radiotherapy, the results showed that the expression of ALDH1A3 was higher in patients under 60 years old, while the expression was lower in patients 60 years old and above, with statistically significant differences between the two (Figure 4A). This suggests that the expression of ALDH1A3 may be regulated by age, thus affecting the response of patients of different ages to radiotherapy and immunotherapy.

To further investigate the effect of ALDH1A3 expression level on immune cell infiltration, we used a variety of immune cell infiltration evaluation algorithms, including xCell, CIBERSORT, EPIC, and MCPCOUNTER. The xCell algorithm analysis revealed that the infiltration level of CD4⁺ Th1 cells was higher in the group with high expression of ALDH1A3 (Figure 4B). After analysis using the CIBERSORT algorithm, it was found that the infiltration level of the macrophage M2 subtype was higher in the high-expression group (Figure 4C). The EPIC algorithm showed that the infiltration level of NK cells was higher in the group with low expression of ALDH1A3 (Figure 4D). The analysis results of the MCPCOUNTER algorithm showed that B cells and monocytes were infiltrated more in the high-expression group, while monocytes were infiltrated more in the low-expression group (Figure 4E,F). In addition, we performed tumor immune dysfunction and exclusion (TIDE) prediction, which showed that the TIDE score was higher in the group with low expression of ALDH1A3, suggesting that these patients responded less well to immunotherapy (Figure 4G).

The results of drug sensitivity prediction showed that the high expression group of ALDH1A3 was sensitive to rapamycin, OSI-027, ribociclib, and azd6482, and the low expression group was sensitive to azd5991 (Figure 4H). These results suggest that the expression level of ALDH1A3 is not only related to the infiltration of immune cells, but also may affect the sensitivity of patients to different drugs, thus providing a basis for personalized treatment.

The above studies have shown that the expression level of ALDH1A3 is closely related to immune cell infiltration and drug sensitivity, especially patients with low expression of ALDH1A3 show poor immunotherapy response, and the cells in the low expression group may be closely related to cellular senescence. Senescent cells are often associated with immune escape and chronic inflammation, which is consistent with the observation of high TIDE scores and poor response to immunotherapy.

To better understand the long-term effects of radiation on prostate cancer cells, we further explored the effects of irradiation on cellular senescence. In the long-term effects of irradiation, we found that many up-regulated genes were enriched in the cellular senescence pathway. To verify this phenomenon, the cells were examined using SA-β-gal staining and the results showed that the staining of the cells gradually deepened over time, indicating a significant increase in senescence (Figure 5A). In addition, the expression of senescence marker genes CDKN2A (p16) and CDKN1A (p21) also gradually increased over time, further supporting the irradiation-induced increased senescence (Figure 5B).

During senescence, cells often maintain chronic inflammatory states through the cGAS–STING pathway and affect the surrounding environment through the secretion of SASP [13]. A key feature of cellular senescence is the decline in the integrity of the nuclear membrane, especially the functional damage or dysregulation of nuclear membrane proteins [26]. This leads to increased vulnerability of the nuclear membrane and the escape of DNA fragments into the cytoplasm, a phenomenon common during senescence and closely associated with DNA damage responses. cGAS is a DNA sensor inside cells. When cells are senescent or damaged, DNA in the nucleus will leak into the cytoplasm, and cGAS can recognize and bind these DNA fragments, thereby activating the STING signaling pathway [15,27]. Activated STING can initiate a series of immune responses, induce cells to produce interferon and pro-inflammatory factors, and maintain the chronic inflammatory state of cells [27].

cGAS–STING pathway maintains SASP secretion and a persistent inflammatory state by activating type I interferon and pro-inflammatory factors [15]. This process plays a key role in the development of inflammatory diseases and cancer progression. In some cases, overactivation of the cGAS–STING pathway can keep the immune system at a low level of activation for a long time, prompting senescent cells to escape immune surveillance and even accelerate tumorigenesis [28]. In addition, the secretion of SASP may promote tumor growth and spread by altering the tumor microenvironment [29].

cGAS–STING pathway induces an immune response by activating type I interferon, and type I interferon activates STAT2 through the JAK–STAT signaling pathway [30]. The activated STAT2 forms a complex with STAT1 and IRF9 to regulate the expression of interferon-stimulated genes and further enhance immune and inflammatory responses [30]. This amplifying effect drives the production of SASP, exacerbating chronic inflammation.

Studies have shown that the cGAS–STING pathway plays a crucial role in maintaining chronic inflammation and regulating cellular senescence [28]. We monitored key proteins in this pathway through Western blot, and the results showed that cGAS, p-STING, TBK1, p-TBK1, IRF3, STAT2, p65, and other proteins were significantly up-regulated in senescent cells. This is consistent with other studies showing that the cGAS–STING pathway is widely active in senescent cells, further confirming the importance of this pathway in regulating cellular inflammatory response and cancer progression [31,32,33,34]. (Figure 5C,D). These results suggest that cGAS senses DNA in the cytoplasm and activates STING, which in turn promotes the production of type I interferon and inflammatory response through downstream signaling molecules such as TBK1 and IRF3. Activation of STAT2 suggests that the interferon signaling pathway is also activated, while up-regulation of p65 suggests that the NF-κB pathway is involved in the expression of pro-inflammatory factors. This is consistent with the characteristics of SASP secretion and chronic inflammation in senescent cells, revealing the important role of the cGAS–STING pathway in senescence-related inflammation and diseases.

In addition, we observed that type I interferons eventually induced up-regulation of MHC I expression in several different prostate cancer cell lines induced by irradiation (Figure 5E), suggesting that type I interferons play a key role in immune regulation after irradiation. This finding further suggests that type I interferons not only play a regulatory role in the senescence process, but also play an important role in immune responses in the tumor microenvironment.

The above studies revealed the key role of the cGAS–STING pathway in senescent cells, especially through the maintenance of SASP secretion and chronic inflammatory state, affecting tumor microenvironment and cancer progression. Considering that ALDH1A3 may play a role in the regulation of cGAS–STING pathway activation and SASP secretion, we speculated that its expression level has a direct impact on the inflammatory response of senescent cells. First, we conducted Spearman’s rho analysis on publicly available prostate cancer clinical samples to evaluate the potential correlation between ALDH1A3 and SASP genes; however, the results did not reveal a significant association (Figure S1).

Therefore, in order to further investigate the mechanism of action of ALDH1A3 in cell senescence, especially its regulation of the cGAS–STING pathway before and after radiation, we designed a series of experiments to verify its function. First, we used CRISPR-Cas9 technology to knock down the ALDH1A3 gene, and the knock-down effect is shown in Figure 6A. Next, cells transfected with sgRNA were subjected to either unirradiation or irradiation treatment, and SA-β-gal staining was performed to detect the senescence phenotype of the cells (Figure 6B). In the unirradiation group, we found that the staining was significantly deepened in cells that knocked down ALDH1A3, indicating that the knocking down of ALDH1A3 has led to the occurrence of cell senescence even in the absence of irradiation (Figure 6C, left).

In the irradiation group, we observed increased staining intensity in both control and knockdown cells, indicating that irradiation induced a distinct senescence phenotype in both groups. However, statistical analysis showed no significant difference in staining depth between the knockdown group and the control group after irradiation (Figure 6C, right). This indicates that the knockdown of ALDH1A3 did not further aggravate the senescence phenotype under irradiation.

To further verify the regulatory role of ALDH1A3 in the cGAS–STING pathway, Western blot analysis was performed on unirradiated cells. The results showed that after knocking down ALDH1A3, the expression levels of key proteins in the CGAS–STING pathway, such as cGAS, STING, and TBK1, were significantly lower than those in the control group. This suggests that ALDH1A3 may play an important role in regulating the activation of the cGAS–STING pathway (Figure 6D). By inhibiting the activation of this pathway, ALDH1A3 knock-down may reduce inflammatory cytokines released by senescent cells.

These results further support the hypothesis that ALDH1A3 influences SASP secretion by regulating the cGAS–STING pathway. Although ALDH1A3 knockdown promoted cell senescence, inflammation, and SASP secretion did not intensify and even decreased due to the inhibition of the cGAS–STING pathway. This suggests that ALDH1A3 not only plays a role in regulating cellular senescence but also participates in the regulation of SASP by influencing the cGAS–STING pathway, which may have a significant impact on the tumor microenvironment.

Although senescent cells themselves do not proliferate, they are theoretically expected to prevent carcinogenesis. However, the SASP factors secreted by senescent cells may promote the growth and spread of cancer cells. Previous experimental results have shown that while the knockout of ALDH1A3 can promote cellular senescence, its inhibition of the cGAS–STING pathway may reduce SASP secretion. Therefore, we investigated whether the senescent cells induced by the knockdown of ALDH1A3 could promote the in vitro proliferation and migration of cancers. By assessing the role of SASP in these functions, we can further elucidate whether ALDH1A3 plays a broader role in cellular senescence through the regulation of SASP.

To verify the impact of ALDH1A3 knockout on the SASP function of senescent cells, we designed and conducted relevant experiments. We used a transwell co-culture system to physically separate senescent cells and cancer cells, while allowing cancer cells to receive the SASP secreted by senescent cells. Figure 7A shows the detailed operational flow of the experiment. First, we used CCK-8 to assess the effects of control and ALDH1A3 knockout on cell proliferation at 0, 24, 48, and 72 h. As expected, the ALDH1A3 knockout group did not significantly promote cell proliferation, and there were no significant differences at any time point compared to the control group (Figure 7B,C). Statistical analysis of the data after 72 h of co-culture revealed no significant differences in cell proliferation capability between the knockout and control groups (Figure 7D). Specifically, the p-values for the comparisons between the sgALDH1A3-1 and sgCTL, sgALDH1A3-2 and sgCTL, and sgALDH1A3-3 and sgCTL groups were 0.3, 0.33, and 0.42, respectively. These results indicate that the senescent cells induced by ALDH1A3 knockout had a limited promoting effect on cancer cell proliferation.

Next, we evaluated the effect of the SASP components secreted by senescent cells induced by ALDH1A3 knockdown on cancer cell migration using a scratch assay (Figure 7E). After 24 h of co-culture, we performed statistical analysis on the healing area. The results showed that there were no statistically significant differences between the ALDH1A3 knockout group and the control group in promoting cancer cell migration (Figure 7F). Specifically, the p-values for the comparisons between the sgALDH1A3-1 and sgCTL, sgALDH1A3-2 and sgCTL, and sgALDH1A3-3 and sgCTL groups were 0.45, 0.38, and 0.65, respectively. This indicates that ALDH1A3 knockout did not significantly alter the influence of SASP on cancer cell migration.

Overall, these results suggest that senescent cells knocked down by ALDH1A3 inhibit the promoting effect of SASP on cancer progression. This result may be due to impaired cGAS–STING pathways. Therefore, ALDH1A3 plays an important role in controlling the harmful behavior of senescent cells, and targeting ALDH1A3 may be a new strategy to combat the diseases induced by SASP.

All data supporting this study are provided in the published article and its. Permission from the corresponding author is required for data usage. Supplementary Information files